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A review of the global climate change impacts, adaptation, and sustainable mitigation measures

Kashif abbass.

1 School of Economics and Management, Nanjing University of Science and Technology, Nanjing, 210094 People’s Republic of China

Muhammad Zeeshan Qasim

2 Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, 210094 People’s Republic of China

Huaming Song

Muntasir murshed.

3 School of Business and Economics, North South University, Dhaka, 1229 Bangladesh

4 Department of Journalism, Media and Communications, Daffodil International University, Dhaka, Bangladesh

Haider Mahmood

5 Department of Finance, College of Business Administration, Prince Sattam Bin Abdulaziz University, 173, Alkharj, 11942 Saudi Arabia

Ijaz Younis

Associated data.

Data sources and relevant links are provided in the paper to access data.

Climate change is a long-lasting change in the weather arrays across tropics to polls. It is a global threat that has embarked on to put stress on various sectors. This study is aimed to conceptually engineer how climate variability is deteriorating the sustainability of diverse sectors worldwide. Specifically, the agricultural sector’s vulnerability is a globally concerning scenario, as sufficient production and food supplies are threatened due to irreversible weather fluctuations. In turn, it is challenging the global feeding patterns, particularly in countries with agriculture as an integral part of their economy and total productivity. Climate change has also put the integrity and survival of many species at stake due to shifts in optimum temperature ranges, thereby accelerating biodiversity loss by progressively changing the ecosystem structures. Climate variations increase the likelihood of particular food and waterborne and vector-borne diseases, and a recent example is a coronavirus pandemic. Climate change also accelerates the enigma of antimicrobial resistance, another threat to human health due to the increasing incidence of resistant pathogenic infections. Besides, the global tourism industry is devastated as climate change impacts unfavorable tourism spots. The methodology investigates hypothetical scenarios of climate variability and attempts to describe the quality of evidence to facilitate readers’ careful, critical engagement. Secondary data is used to identify sustainability issues such as environmental, social, and economic viability. To better understand the problem, gathered the information in this report from various media outlets, research agencies, policy papers, newspapers, and other sources. This review is a sectorial assessment of climate change mitigation and adaptation approaches worldwide in the aforementioned sectors and the associated economic costs. According to the findings, government involvement is necessary for the country’s long-term development through strict accountability of resources and regulations implemented in the past to generate cutting-edge climate policy. Therefore, mitigating the impacts of climate change must be of the utmost importance, and hence, this global threat requires global commitment to address its dreadful implications to ensure global sustenance.

Introduction

Worldwide observed and anticipated climatic changes for the twenty-first century and global warming are significant global changes that have been encountered during the past 65 years. Climate change (CC) is an inter-governmental complex challenge globally with its influence over various components of the ecological, environmental, socio-political, and socio-economic disciplines (Adger et al.  2005 ; Leal Filho et al.  2021 ; Feliciano et al.  2022 ). Climate change involves heightened temperatures across numerous worlds (Battisti and Naylor  2009 ; Schuurmans  2021 ; Weisheimer and Palmer  2005 ; Yadav et al.  2015 ). With the onset of the industrial revolution, the problem of earth climate was amplified manifold (Leppänen et al.  2014 ). It is reported that the immediate attention and due steps might increase the probability of overcoming its devastating impacts. It is not plausible to interpret the exact consequences of climate change (CC) on a sectoral basis (Izaguirre et al.  2021 ; Jurgilevich et al.  2017 ), which is evident by the emerging level of recognition plus the inclusion of climatic uncertainties at both local and national level of policymaking (Ayers et al.  2014 ).

Climate change is characterized based on the comprehensive long-haul temperature and precipitation trends and other components such as pressure and humidity level in the surrounding environment. Besides, the irregular weather patterns, retreating of global ice sheets, and the corresponding elevated sea level rise are among the most renowned international and domestic effects of climate change (Lipczynska-Kochany  2018 ; Michel et al.  2021 ; Murshed and Dao 2020 ). Before the industrial revolution, natural sources, including volcanoes, forest fires, and seismic activities, were regarded as the distinct sources of greenhouse gases (GHGs) such as CO 2 , CH 4 , N 2 O, and H 2 O into the atmosphere (Murshed et al. 2020 ; Hussain et al.  2020 ; Sovacool et al.  2021 ; Usman and Balsalobre-Lorente 2022 ; Murshed 2022 ). United Nations Framework Convention on Climate Change (UNFCCC) struck a major agreement to tackle climate change and accelerate and intensify the actions and investments required for a sustainable low-carbon future at Conference of the Parties (COP-21) in Paris on December 12, 2015. The Paris Agreement expands on the Convention by bringing all nations together for the first time in a single cause to undertake ambitious measures to prevent climate change and adapt to its impacts, with increased funding to assist developing countries in doing so. As so, it marks a turning point in the global climate fight. The core goal of the Paris Agreement is to improve the global response to the threat of climate change by keeping the global temperature rise this century well below 2 °C over pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5° C (Sharma et al. 2020 ; Sharif et al. 2020 ; Chien et al. 2021 .

Furthermore, the agreement aspires to strengthen nations’ ability to deal with the effects of climate change and align financing flows with low GHG emissions and climate-resilient paths (Shahbaz et al. 2019 ; Anwar et al. 2021 ; Usman et al. 2022a ). To achieve these lofty goals, adequate financial resources must be mobilized and provided, as well as a new technology framework and expanded capacity building, allowing developing countries and the most vulnerable countries to act under their respective national objectives. The agreement also establishes a more transparent action and support mechanism. All Parties are required by the Paris Agreement to do their best through “nationally determined contributions” (NDCs) and to strengthen these efforts in the coming years (Balsalobre-Lorente et al. 2020 ). It includes obligations that all Parties regularly report on their emissions and implementation activities. A global stock-take will be conducted every five years to review collective progress toward the agreement’s goal and inform the Parties’ future individual actions. The Paris Agreement became available for signature on April 22, 2016, Earth Day, at the United Nations Headquarters in New York. On November 4, 2016, it went into effect 30 days after the so-called double threshold was met (ratification by 55 nations accounting for at least 55% of world emissions). More countries have ratified and continue to ratify the agreement since then, bringing 125 Parties in early 2017. To fully operationalize the Paris Agreement, a work program was initiated in Paris to define mechanisms, processes, and recommendations on a wide range of concerns (Murshed et al. 2021 ). Since 2016, Parties have collaborated in subsidiary bodies (APA, SBSTA, and SBI) and numerous formed entities. The Conference of the Parties functioning as the meeting of the Parties to the Paris Agreement (CMA) convened for the first time in November 2016 in Marrakesh in conjunction with COP22 and made its first two resolutions. The work plan is scheduled to be finished by 2018. Some mitigation and adaptation strategies to reduce the emission in the prospective of Paris agreement are following firstly, a long-term goal of keeping the increase in global average temperature to well below 2 °C above pre-industrial levels, secondly, to aim to limit the rise to 1.5 °C, since this would significantly reduce risks and the impacts of climate change, thirdly, on the need for global emissions to peak as soon as possible, recognizing that this will take longer for developing countries, lastly, to undertake rapid reductions after that under the best available science, to achieve a balance between emissions and removals in the second half of the century. On the other side, some adaptation strategies are; strengthening societies’ ability to deal with the effects of climate change and to continue & expand international assistance for developing nations’ adaptation.

However, anthropogenic activities are currently regarded as most accountable for CC (Murshed et al. 2022 ). Apart from the industrial revolution, other anthropogenic activities include excessive agricultural operations, which further involve the high use of fuel-based mechanization, burning of agricultural residues, burning fossil fuels, deforestation, national and domestic transportation sectors, etc. (Huang et al.  2016 ). Consequently, these anthropogenic activities lead to climatic catastrophes, damaging local and global infrastructure, human health, and total productivity. Energy consumption has mounted GHGs levels concerning warming temperatures as most of the energy production in developing countries comes from fossil fuels (Balsalobre-Lorente et al. 2022 ; Usman et al. 2022b ; Abbass et al. 2021a ; Ishikawa-Ishiwata and Furuya  2022 ).

This review aims to highlight the effects of climate change in a socio-scientific aspect by analyzing the existing literature on various sectorial pieces of evidence globally that influence the environment. Although this review provides a thorough examination of climate change and its severe affected sectors that pose a grave danger for global agriculture, biodiversity, health, economy, forestry, and tourism, and to purpose some practical prophylactic measures and mitigation strategies to be adapted as sound substitutes to survive from climate change (CC) impacts. The societal implications of irregular weather patterns and other effects of climate changes are discussed in detail. Some numerous sustainable mitigation measures and adaptation practices and techniques at the global level are discussed in this review with an in-depth focus on its economic, social, and environmental aspects. Methods of data collection section are included in the supplementary information.

Review methodology

Related study and its objectives.

Today, we live an ordinary life in the beautiful digital, globalized world where climate change has a decisive role. What happens in one country has a massive influence on geographically far apart countries, which points to the current crisis known as COVID-19 (Sarkar et al.  2021 ). The most dangerous disease like COVID-19 has affected the world’s climate changes and economic conditions (Abbass et al. 2022 ; Pirasteh-Anosheh et al.  2021 ). The purpose of the present study is to review the status of research on the subject, which is based on “Global Climate Change Impacts, adaptation, and sustainable mitigation measures” by systematically reviewing past published and unpublished research work. Furthermore, the current study seeks to comment on research on the same topic and suggest future research on the same topic. Specifically, the present study aims: The first one is, organize publications to make them easy and quick to find. Secondly, to explore issues in this area, propose an outline of research for future work. The third aim of the study is to synthesize the previous literature on climate change, various sectors, and their mitigation measurement. Lastly , classify the articles according to the different methods and procedures that have been adopted.

Review methodology for reviewers

This review-based article followed systematic literature review techniques that have proved the literature review as a rigorous framework (Benita  2021 ; Tranfield et al.  2003 ). Moreover, we illustrate in Fig.  1 the search method that we have started for this research. First, finalized the research theme to search literature (Cooper et al.  2018 ). Second, used numerous research databases to search related articles and download from the database (Web of Science, Google Scholar, Scopus Index Journals, Emerald, Elsevier Science Direct, Springer, and Sciverse). We focused on various articles, with research articles, feedback pieces, short notes, debates, and review articles published in scholarly journals. Reports used to search for multiple keywords such as “Climate Change,” “Mitigation and Adaptation,” “Department of Agriculture and Human Health,” “Department of Biodiversity and Forestry,” etc.; in summary, keyword list and full text have been made. Initially, the search for keywords yielded a large amount of literature.

Methodology search for finalized articles for investigations.

Source : constructed by authors

Since 2020, it has been impossible to review all the articles found; some restrictions have been set for the literature exhibition. The study searched 95 articles on a different database mentioned above based on the nature of the study. It excluded 40 irrelevant papers due to copied from a previous search after readings tiles, abstract and full pieces. The criteria for inclusion were: (i) articles focused on “Global Climate Change Impacts, adaptation, and sustainable mitigation measures,” and (ii) the search key terms related to study requirements. The complete procedure yielded 55 articles for our study. We repeat our search on the “Web of Science and Google Scholars” database to enhance the search results and check the referenced articles.

In this study, 55 articles are reviewed systematically and analyzed for research topics and other aspects, such as the methods, contexts, and theories used in these studies. Furthermore, this study analyzes closely related areas to provide unique research opportunities in the future. The study also discussed future direction opportunities and research questions by understanding the research findings climate changes and other affected sectors. The reviewed paper framework analysis process is outlined in Fig.  2 .

Framework of the analysis Process.

Natural disasters and climate change’s socio-economic consequences

Natural and environmental disasters can be highly variable from year to year; some years pass with very few deaths before a significant disaster event claims many lives (Symanski et al.  2021 ). Approximately 60,000 people globally died from natural disasters each year on average over the past decade (Ritchie and Roser  2014 ; Wiranata and Simbolon  2021 ). So, according to the report, around 0.1% of global deaths. Annual variability in the number and share of deaths from natural disasters in recent decades are shown in Fig.  3 . The number of fatalities can be meager—sometimes less than 10,000, and as few as 0.01% of all deaths. But shock events have a devastating impact: the 1983–1985 famine and drought in Ethiopia; the 2004 Indian Ocean earthquake and tsunami; Cyclone Nargis, which struck Myanmar in 2008; and the 2010 Port-au-Prince earthquake in Haiti and now recent example is COVID-19 pandemic (Erman et al.  2021 ). These events pushed global disaster deaths to over 200,000—more than 0.4% of deaths in these years. Low-frequency, high-impact events such as earthquakes and tsunamis are not preventable, but such high losses of human life are. Historical evidence shows that earlier disaster detection, more robust infrastructure, emergency preparedness, and response programmers have substantially reduced disaster deaths worldwide. Low-income is also the most vulnerable to disasters; improving living conditions, facilities, and response services in these areas would be critical in reducing natural disaster deaths in the coming decades.

Global deaths from natural disasters, 1978 to 2020.

Source EMDAT ( 2020 )

The interior regions of the continent are likely to be impacted by rising temperatures (Dimri et al.  2018 ; Goes et al.  2020 ; Mannig et al.  2018 ; Schuurmans  2021 ). Weather patterns change due to the shortage of natural resources (water), increase in glacier melting, and rising mercury are likely to cause extinction to many planted species (Gampe et al.  2016 ; Mihiretu et al.  2021 ; Shaffril et al.  2018 ).On the other hand, the coastal ecosystem is on the verge of devastation (Perera et al.  2018 ; Phillips  2018 ). The temperature rises, insect disease outbreaks, health-related problems, and seasonal and lifestyle changes are persistent, with a strong probability of these patterns continuing in the future (Abbass et al. 2021c ; Hussain et al.  2018 ). At the global level, a shortage of good infrastructure and insufficient adaptive capacity are hammering the most (IPCC  2013 ). In addition to the above concerns, a lack of environmental education and knowledge, outdated consumer behavior, a scarcity of incentives, a lack of legislation, and the government’s lack of commitment to climate change contribute to the general public’s concerns. By 2050, a 2 to 3% rise in mercury and a drastic shift in rainfall patterns may have serious consequences (Huang et al. 2022 ; Gorst et al.  2018 ). Natural and environmental calamities caused huge losses globally, such as decreased agriculture outputs, rehabilitation of the system, and rebuilding necessary technologies (Ali and Erenstein  2017 ; Ramankutty et al.  2018 ; Yu et al.  2021 ) (Table ​ (Table1). 1 ). Furthermore, in the last 3 or 4 years, the world has been plagued by smog-related eye and skin diseases, as well as a rise in road accidents due to poor visibility.

Main natural danger statistics for 1985–2020 at the global level

Source: EM-DAT ( 2020 )

Climate change and agriculture

Global agriculture is the ultimate sector responsible for 30–40% of all greenhouse emissions, which makes it a leading industry predominantly contributing to climate warming and significantly impacted by it (Grieg; Mishra et al.  2021 ; Ortiz et al.  2021 ; Thornton and Lipper  2014 ). Numerous agro-environmental and climatic factors that have a dominant influence on agriculture productivity (Pautasso et al.  2012 ) are significantly impacted in response to precipitation extremes including floods, forest fires, and droughts (Huang  2004 ). Besides, the immense dependency on exhaustible resources also fuels the fire and leads global agriculture to become prone to devastation. Godfray et al. ( 2010 ) mentioned that decline in agriculture challenges the farmer’s quality of life and thus a significant factor to poverty as the food and water supplies are critically impacted by CC (Ortiz et al.  2021 ; Rosenzweig et al.  2014 ). As an essential part of the economic systems, especially in developing countries, agricultural systems affect the overall economy and potentially the well-being of households (Schlenker and Roberts  2009 ). According to the report published by the Intergovernmental Panel on Climate Change (IPCC), atmospheric concentrations of greenhouse gases, i.e., CH 4, CO 2 , and N 2 O, are increased in the air to extraordinary levels over the last few centuries (Usman and Makhdum 2021 ; Stocker et al.  2013 ). Climate change is the composite outcome of two different factors. The first is the natural causes, and the second is the anthropogenic actions (Karami 2012 ). It is also forecasted that the world may experience a typical rise in temperature stretching from 1 to 3.7 °C at the end of this century (Pachauri et al. 2014 ). The world’s crop production is also highly vulnerable to these global temperature-changing trends as raised temperatures will pose severe negative impacts on crop growth (Reidsma et al. 2009 ). Some of the recent modeling about the fate of global agriculture is briefly described below.

Decline in cereal productivity

Crop productivity will also be affected dramatically in the next few decades due to variations in integral abiotic factors such as temperature, solar radiation, precipitation, and CO 2 . These all factors are included in various regulatory instruments like progress and growth, weather-tempted changes, pest invasions (Cammell and Knight 1992 ), accompanying disease snags (Fand et al. 2012 ), water supplies (Panda et al. 2003 ), high prices of agro-products in world’s agriculture industry, and preeminent quantity of fertilizer consumption. Lobell and field ( 2007 ) claimed that from 1962 to 2002, wheat crop output had condensed significantly due to rising temperatures. Therefore, during 1980–2011, the common wheat productivity trends endorsed extreme temperature events confirmed by Gourdji et al. ( 2013 ) around South Asia, South America, and Central Asia. Various other studies (Asseng, Cao, Zhang, and Ludwig 2009 ; Asseng et al. 2013 ; García et al. 2015 ; Ortiz et al. 2021 ) also proved that wheat output is negatively affected by the rising temperatures and also caused adverse effects on biomass productivity (Calderini et al. 1999 ; Sadras and Slafer 2012 ). Hereafter, the rice crop is also influenced by the high temperatures at night. These difficulties will worsen because the temperature will be rising further in the future owing to CC (Tebaldi et al. 2006 ). Another research conducted in China revealed that a 4.6% of rice production per 1 °C has happened connected with the advancement in night temperatures (Tao et al. 2006 ). Moreover, the average night temperature growth also affected rice indicia cultivar’s output pragmatically during 25 years in the Philippines (Peng et al. 2004 ). It is anticipated that the increase in world average temperature will also cause a substantial reduction in yield (Hatfield et al. 2011 ; Lobell and Gourdji 2012 ). In the southern hemisphere, Parry et al. ( 2007 ) noted a rise of 1–4 °C in average daily temperatures at the end of spring season unti the middle of summers, and this raised temperature reduced crop output by cutting down the time length for phenophases eventually reduce the yield (Hatfield and Prueger 2015 ; R. Ortiz 2008 ). Also, world climate models have recommended that humid and subtropical regions expect to be plentiful prey to the upcoming heat strokes (Battisti and Naylor 2009 ). Grain production is the amalgamation of two constituents: the average weight and the grain output/m 2 , however, in crop production. Crop output is mainly accredited to the grain quantity (Araus et al. 2008 ; Gambín and Borrás 2010 ). In the times of grain set, yield resources are mainly strewn between hitherto defined components, i.e., grain usual weight and grain output, which presents a trade-off between them (Gambín and Borrás 2010 ) beside disparities in per grain integration (B. L. Gambín et al. 2006 ). In addition to this, the maize crop is also susceptible to raised temperatures, principally in the flowering stage (Edreira and Otegui 2013 ). In reality, the lower grain number is associated with insufficient acclimatization due to intense photosynthesis and higher respiration and the high-temperature effect on the reproduction phenomena (Edreira and Otegui 2013 ). During the flowering phase, maize visible to heat (30–36 °C) seemed less anthesis-silking intermissions (Edreira et al. 2011 ). Another research by Dupuis and Dumas ( 1990 ) proved that a drop in spikelet when directly visible to high temperatures above 35 °C in vitro pollination. Abnormalities in kernel number claimed by Vega et al. ( 2001 ) is related to conceded plant development during a flowering phase that is linked with the active ear growth phase and categorized as a critical phase for approximation of kernel number during silking (Otegui and Bonhomme 1998 ).

The retort of rice output to high temperature presents disparities in flowering patterns, and seed set lessens and lessens grain weight (Qasim et al. 2020 ; Qasim, Hammad, Maqsood, Tariq, & Chawla). During the daytime, heat directly impacts flowers which lessens the thesis period and quickens the earlier peak flowering (Tao et al. 2006 ). Antagonistic effect of higher daytime temperature d on pollen sprouting proposed seed set decay, whereas, seed set was lengthily reduced than could be explicated by pollen growing at high temperatures 40◦C (Matsui et al. 2001 ).

The decline in wheat output is linked with higher temperatures, confirmed in numerous studies (Semenov 2009 ; Stone and Nicolas 1994 ). High temperatures fast-track the arrangements of plant expansion (Blum et al. 2001 ), diminution photosynthetic process (Salvucci and Crafts‐Brandner 2004 ), and also considerably affect the reproductive operations (Farooq et al. 2011 ).

The destructive impacts of CC induced weather extremes to deteriorate the integrity of crops (Chaudhary et al. 2011 ), e.g., Spartan cold and extreme fog cause falling and discoloration of betel leaves (Rosenzweig et al. 2001 ), giving them a somehow reddish appearance, squeezing of lemon leaves (Pautasso et al. 2012 ), as well as root rot of pineapple, have reported (Vedwan and Rhoades 2001 ). Henceforth, in tackling the disruptive effects of CC, several short-term and long-term management approaches are the crucial need of time (Fig.  4 ). Moreover, various studies (Chaudhary et al. 2011 ; Patz et al. 2005 ; Pautasso et al. 2012 ) have demonstrated adapting trends such as ameliorating crop diversity can yield better adaptability towards CC.

Schematic description of potential impacts of climate change on the agriculture sector and the appropriate mitigation and adaptation measures to overcome its impact.

Climate change impacts on biodiversity

Global biodiversity is among the severe victims of CC because it is the fastest emerging cause of species loss. Studies demonstrated that the massive scale species dynamics are considerably associated with diverse climatic events (Abraham and Chain 1988 ; Manes et al. 2021 ; A. M. D. Ortiz et al. 2021 ). Both the pace and magnitude of CC are altering the compatible habitat ranges for living entities of marine, freshwater, and terrestrial regions. Alterations in general climate regimes influence the integrity of ecosystems in numerous ways, such as variation in the relative abundance of species, range shifts, changes in activity timing, and microhabitat use (Bates et al. 2014 ). The geographic distribution of any species often depends upon its ability to tolerate environmental stresses, biological interactions, and dispersal constraints. Hence, instead of the CC, the local species must only accept, adapt, move, or face extinction (Berg et al. 2010 ). So, the best performer species have a better survival capacity for adjusting to new ecosystems or a decreased perseverance to survive where they are already situated (Bates et al. 2014 ). An important aspect here is the inadequate habitat connectivity and access to microclimates, also crucial in raising the exposure to climate warming and extreme heatwave episodes. For example, the carbon sequestration rates are undergoing fluctuations due to climate-driven expansion in the range of global mangroves (Cavanaugh et al. 2014 ).

Similarly, the loss of kelp-forest ecosystems in various regions and its occupancy by the seaweed turfs has set the track for elevated herbivory by the high influx of tropical fish populations. Not only this, the increased water temperatures have exacerbated the conditions far away from the physiological tolerance level of the kelp communities (Vergés et al. 2016 ; Wernberg et al. 2016 ). Another pertinent danger is the devastation of keystone species, which even has more pervasive effects on the entire communities in that habitat (Zarnetske et al. 2012 ). It is particularly important as CC does not specify specific populations or communities. Eventually, this CC-induced redistribution of species may deteriorate carbon storage and the net ecosystem productivity (Weed et al. 2013 ). Among the typical disruptions, the prominent ones include impacts on marine and terrestrial productivity, marine community assembly, and the extended invasion of toxic cyanobacteria bloom (Fossheim et al. 2015 ).

The CC-impacted species extinction is widely reported in the literature (Beesley et al. 2019 ; Urban 2015 ), and the predictions of demise until the twenty-first century are dreadful (Abbass et al. 2019 ; Pereira et al. 2013 ). In a few cases, northward shifting of species may not be formidable as it allows mountain-dwelling species to find optimum climates. However, the migrant species may be trapped in isolated and incompatible habitats due to losing topography and range (Dullinger et al. 2012 ). For example, a study indicated that the American pika has been extirpated or intensely diminished in some regions, primarily attributed to the CC-impacted extinction or at least local extirpation (Stewart et al. 2015 ). Besides, the anticipation of persistent responses to the impacts of CC often requires data records of several decades to rigorously analyze the critical pre and post CC patterns at species and ecosystem levels (Manes et al. 2021 ; Testa et al. 2018 ).

Nonetheless, the availability of such long-term data records is rare; hence, attempts are needed to focus on these profound aspects. Biodiversity is also vulnerable to the other associated impacts of CC, such as rising temperatures, droughts, and certain invasive pest species. For instance, a study revealed the changes in the composition of plankton communities attributed to rising temperatures. Henceforth, alterations in such aquatic producer communities, i.e., diatoms and calcareous plants, can ultimately lead to variation in the recycling of biological carbon. Moreover, such changes are characterized as a potential contributor to CO 2 differences between the Pleistocene glacial and interglacial periods (Kohfeld et al. 2005 ).

Climate change implications on human health

It is an understood corporality that human health is a significant victim of CC (Costello et al. 2009 ). According to the WHO, CC might be responsible for 250,000 additional deaths per year during 2030–2050 (Watts et al. 2015 ). These deaths are attributed to extreme weather-induced mortality and morbidity and the global expansion of vector-borne diseases (Lemery et al. 2021; Yang and Usman 2021 ; Meierrieks 2021 ; UNEP 2017 ). Here, some of the emerging health issues pertinent to this global problem are briefly described.

Climate change and antimicrobial resistance with corresponding economic costs

Antimicrobial resistance (AMR) is an up-surging complex global health challenge (Garner et al. 2019 ; Lemery et al. 2021 ). Health professionals across the globe are extremely worried due to this phenomenon that has critical potential to reverse almost all the progress that has been achieved so far in the health discipline (Gosling and Arnell 2016 ). A massive amount of antibiotics is produced by many pharmaceutical industries worldwide, and the pathogenic microorganisms are gradually developing resistance to them, which can be comprehended how strongly this aspect can shake the foundations of national and global economies (UNEP 2017 ). This statement is supported by the fact that AMR is not developing in a particular region or country. Instead, it is flourishing in every continent of the world (WHO 2018 ). This plague is heavily pushing humanity to the post-antibiotic era, in which currently antibiotic-susceptible pathogens will once again lead to certain endemics and pandemics after being resistant(WHO 2018 ). Undesirably, if this statement would become a factuality, there might emerge certain risks in undertaking sophisticated interventions such as chemotherapy, joint replacement cases, and organ transplantation (Su et al. 2018 ). Presently, the amplification of drug resistance cases has made common illnesses like pneumonia, post-surgical infections, HIV/AIDS, tuberculosis, malaria, etc., too difficult and costly to be treated or cure well (WHO 2018 ). From a simple example, it can be assumed how easily antibiotic-resistant strains can be transmitted from one person to another and ultimately travel across the boundaries (Berendonk et al. 2015 ). Talking about the second- and third-generation classes of antibiotics, e.g., most renowned generations of cephalosporin antibiotics that are more expensive, broad-spectrum, more toxic, and usually require more extended periods whenever prescribed to patients (Lemery et al. 2021 ; Pärnänen et al. 2019 ). This scenario has also revealed that the abundance of resistant strains of pathogens was also higher in the Southern part (WHO 2018 ). As southern parts are generally warmer than their counterparts, it is evident from this example how CC-induced global warming can augment the spread of antibiotic-resistant strains within the biosphere, eventually putting additional economic burden in the face of developing new and costlier antibiotics. The ARG exchange to susceptible bacteria through one of the potential mechanisms, transformation, transduction, and conjugation; Selection pressure can be caused by certain antibiotics, metals or pesticides, etc., as shown in Fig.  5 .

A typical interaction between the susceptible and resistant strains.

Source: Elsayed et al. ( 2021 ); Karkman et al. ( 2018 )

Certain studies highlighted that conventional urban wastewater treatment plants are typical hotspots where most bacterial strains exchange genetic material through horizontal gene transfer (Fig.  5 ). Although at present, the extent of risks associated with the antibiotic resistance found in wastewater is complicated; environmental scientists and engineers have particular concerns about the potential impacts of these antibiotic resistance genes on human health (Ashbolt 2015 ). At most undesirable and worst case, these antibiotic-resistant genes containing bacteria can make their way to enter into the environment (Pruden et al. 2013 ), irrigation water used for crops and public water supplies and ultimately become a part of food chains and food webs (Ma et al. 2019 ; D. Wu et al. 2019 ). This problem has been reported manifold in several countries (Hendriksen et al. 2019 ), where wastewater as a means of irrigated water is quite common.

Climate change and vector borne-diseases

Temperature is a fundamental factor for the sustenance of living entities regardless of an ecosystem. So, a specific living being, especially a pathogen, requires a sophisticated temperature range to exist on earth. The second essential component of CC is precipitation, which also impacts numerous infectious agents’ transport and dissemination patterns. Global rising temperature is a significant cause of many species extinction. On the one hand, this changing environmental temperature may be causing species extinction, and on the other, this warming temperature might favor the thriving of some new organisms. Here, it was evident that some pathogens may also upraise once non-evident or reported (Patz et al. 2000 ). This concept can be exemplified through certain pathogenic strains of microorganisms that how the likelihood of various diseases increases in response to climate warming-induced environmental changes (Table ​ (Table2 2 ).

Examples of how various environmental changes affect various infectious diseases in humans

Source: Aron and Patz ( 2001 )

A recent example is an outburst of coronavirus (COVID-19) in the Republic of China, causing pneumonia and severe acute respiratory complications (Cui et al. 2021 ; Song et al. 2021 ). The large family of viruses is harbored in numerous animals, bats, and snakes in particular (livescience.com) with the subsequent transfer into human beings. Hence, it is worth noting that the thriving of numerous vectors involved in spreading various diseases is influenced by Climate change (Ogden 2018 ; Santos et al. 2021 ).

Psychological impacts of climate change

Climate change (CC) is responsible for the rapid dissemination and exaggeration of certain epidemics and pandemics. In addition to the vast apparent impacts of climate change on health, forestry, agriculture, etc., it may also have psychological implications on vulnerable societies. It can be exemplified through the recent outburst of (COVID-19) in various countries around the world (Pal 2021 ). Besides, the victims of this viral infection have made healthy beings scarier and terrified. In the wake of such epidemics, people with common colds or fever are also frightened and must pass specific regulatory protocols. Living in such situations continuously terrifies the public and makes the stress familiar, which eventually makes them psychologically weak (npr.org).

CC boosts the extent of anxiety, distress, and other issues in public, pushing them to develop various mental-related problems. Besides, frequent exposure to extreme climatic catastrophes such as geological disasters also imprints post-traumatic disorder, and their ubiquitous occurrence paves the way to developing chronic psychological dysfunction. Moreover, repetitive listening from media also causes an increase in the person’s stress level (Association 2020 ). Similarly, communities living in flood-prone areas constantly live in extreme fear of drowning and die by floods. In addition to human lives, the flood-induced destruction of physical infrastructure is a specific reason for putting pressure on these communities (Ogden 2018 ). For instance, Ogden ( 2018 ) comprehensively denoted that Katrina’s Hurricane augmented the mental health issues in the victim communities.

Climate change impacts on the forestry sector

Forests are the global regulators of the world’s climate (FAO 2018 ) and have an indispensable role in regulating global carbon and nitrogen cycles (Rehman et al. 2021 ; Reichstein and Carvalhais 2019 ). Hence, disturbances in forest ecology affect the micro and macro-climates (Ellison et al. 2017 ). Climate warming, in return, has profound impacts on the growth and productivity of transboundary forests by influencing the temperature and precipitation patterns, etc. As CC induces specific changes in the typical structure and functions of ecosystems (Zhang et al. 2017 ) as well impacts forest health, climate change also has several devastating consequences such as forest fires, droughts, pest outbreaks (EPA 2018 ), and last but not the least is the livelihoods of forest-dependent communities. The rising frequency and intensity of another CC product, i.e., droughts, pose plenty of challenges to the well-being of global forests (Diffenbaugh et al. 2017 ), which is further projected to increase soon (Hartmann et al. 2018 ; Lehner et al. 2017 ; Rehman et al. 2021 ). Hence, CC induces storms, with more significant impacts also put extra pressure on the survival of the global forests (Martínez-Alvarado et al. 2018 ), significantly since their influences are augmented during higher winter precipitations with corresponding wetter soils causing weak root anchorage of trees (Brázdil et al. 2018 ). Surging temperature regimes causes alterations in usual precipitation patterns, which is a significant hurdle for the survival of temperate forests (Allen et al. 2010 ; Flannigan et al. 2013 ), letting them encounter severe stress and disturbances which adversely affects the local tree species (Hubbart et al. 2016 ; Millar and Stephenson 2015 ; Rehman et al. 2021 ).

Climate change impacts on forest-dependent communities

Forests are the fundamental livelihood resource for about 1.6 billion people worldwide; out of them, 350 million are distinguished with relatively higher reliance (Bank 2008 ). Agro-forestry-dependent communities comprise 1.2 billion, and 60 million indigenous people solely rely on forests and their products to sustain their lives (Sunderlin et al. 2005 ). For example, in the entire African continent, more than 2/3rd of inhabitants depend on forest resources and woodlands for their alimonies, e.g., food, fuelwood and grazing (Wasiq and Ahmad 2004 ). The livings of these people are more intensely affected by the climatic disruptions making their lives harder (Brown et al. 2014 ). On the one hand, forest communities are incredibly vulnerable to CC due to their livelihoods, cultural and spiritual ties as well as socio-ecological connections, and on the other, they are not familiar with the term “climate change.” (Rahman and Alam 2016 ). Among the destructive impacts of temperature and rainfall, disruption of the agroforestry crops with resultant downscale growth and yield (Macchi et al. 2008 ). Cruz ( 2015 ) ascribed that forest-dependent smallholder farmers in the Philippines face the enigma of delayed fruiting, more severe damages by insect and pest incidences due to unfavorable temperature regimes, and changed rainfall patterns.

Among these series of challenges to forest communities, their well-being is also distinctly vulnerable to CC. Though the detailed climate change impacts on human health have been comprehensively mentioned in the previous section, some studies have listed a few more devastating effects on the prosperity of forest-dependent communities. For instance, the Himalayan people have been experiencing frequent skin-borne diseases such as malaria and other skin diseases due to increasing mosquitoes, wild boar as well, and new wasps species, particularly in higher altitudes that were almost non-existent before last 5–10 years (Xu et al. 2008 ). Similarly, people living at high altitudes in Bangladesh have experienced frequent mosquito-borne calamities (Fardous; Sharma 2012 ). In addition, the pace of other waterborne diseases such as infectious diarrhea, cholera, pathogenic induced abdominal complications and dengue has also been boosted in other distinguished regions of Bangladesh (Cell 2009 ; Gunter et al. 2008 ).

Pest outbreak

Upscaling hotter climate may positively affect the mobile organisms with shorter generation times because they can scurry from harsh conditions than the immobile species (Fettig et al. 2013 ; Schoene and Bernier 2012 ) and are also relatively more capable of adapting to new environments (Jactel et al. 2019 ). It reveals that insects adapt quickly to global warming due to their mobility advantages. Due to past outbreaks, the trees (forests) are relatively more susceptible victims (Kurz et al. 2008 ). Before CC, the influence of factors mentioned earlier, i.e., droughts and storms, was existent and made the forests susceptible to insect pest interventions; however, the global forests remain steadfast, assiduous, and green (Jactel et al. 2019 ). The typical reasons could be the insect herbivores were regulated by several tree defenses and pressures of predation (Wilkinson and Sherratt 2016 ). As climate greatly influences these phenomena, the global forests cannot be so sedulous against such challenges (Jactel et al. 2019 ). Table ​ Table3 3 demonstrates some of the particular considerations with practical examples that are essential while mitigating the impacts of CC in the forestry sector.

Essential considerations while mitigating the climate change impacts on the forestry sector

Source : Fischer ( 2019 )

Climate change impacts on tourism

Tourism is a commercial activity that has roots in multi-dimensions and an efficient tool with adequate job generation potential, revenue creation, earning of spectacular foreign exchange, enhancement in cross-cultural promulgation and cooperation, a business tool for entrepreneurs and eventually for the country’s national development (Arshad et al. 2018 ; Scott 2021 ). Among a plethora of other disciplines, the tourism industry is also a distinct victim of climate warming (Gössling et al. 2012 ; Hall et al. 2015 ) as the climate is among the essential resources that enable tourism in particular regions as most preferred locations. Different places at different times of the year attract tourists both within and across the countries depending upon the feasibility and compatibility of particular weather patterns. Hence, the massive variations in these weather patterns resulting from CC will eventually lead to monumental challenges to the local economy in that specific area’s particular and national economy (Bujosa et al. 2015 ). For instance, the Intergovernmental Panel on Climate Change (IPCC) report demonstrated that the global tourism industry had faced a considerable decline in the duration of ski season, including the loss of some ski areas and the dramatic shifts in tourist destinations’ climate warming.

Furthermore, different studies (Neuvonen et al. 2015 ; Scott et al. 2004 ) indicated that various currently perfect tourist spots, e.g., coastal areas, splendid islands, and ski resorts, will suffer consequences of CC. It is also worth noting that the quality and potential of administrative management potential to cope with the influence of CC on the tourism industry is of crucial significance, which renders specific strengths of resiliency to numerous destinations to withstand against it (Füssel and Hildén 2014 ). Similarly, in the partial or complete absence of adequate socio-economic and socio-political capital, the high-demanding tourist sites scurry towards the verge of vulnerability. The susceptibility of tourism is based on different components such as the extent of exposure, sensitivity, life-supporting sectors, and capacity assessment factors (Füssel and Hildén 2014 ). It is obvious corporality that sectors such as health, food, ecosystems, human habitat, infrastructure, water availability, and the accessibility of a particular region are prone to CC. Henceforth, the sensitivity of these critical sectors to CC and, in return, the adaptive measures are a hallmark in determining the composite vulnerability of climate warming (Ionescu et al. 2009 ).

Moreover, the dependence on imported food items, poor hygienic conditions, and inadequate health professionals are dominant aspects affecting the local terrestrial and aquatic biodiversity. Meanwhile, the greater dependency on ecosystem services and its products also makes a destination more fragile to become a prey of CC (Rizvi et al. 2015 ). Some significant non-climatic factors are important indicators of a particular ecosystem’s typical health and functioning, e.g., resource richness and abundance portray the picture of ecosystem stability. Similarly, the species abundance is also a productive tool that ensures that the ecosystem has a higher buffering capacity, which is terrific in terms of resiliency (Roscher et al. 2013 ).

Climate change impacts on the economic sector

Climate plays a significant role in overall productivity and economic growth. Due to its increasingly global existence and its effect on economic growth, CC has become one of the major concerns of both local and international environmental policymakers (Ferreira et al. 2020 ; Gleditsch 2021 ; Abbass et al. 2021b ; Lamperti et al. 2021 ). The adverse effects of CC on the overall productivity factor of the agricultural sector are therefore significant for understanding the creation of local adaptation policies and the composition of productive climate policy contracts. Previous studies on CC in the world have already forecasted its effects on the agricultural sector. Researchers have found that global CC will impact the agricultural sector in different world regions. The study of the impacts of CC on various agrarian activities in other demographic areas and the development of relative strategies to respond to effects has become a focal point for researchers (Chandioet al. 2020 ; Gleditsch 2021 ; Mosavi et al. 2020 ).

With the rapid growth of global warming since the 1980s, the temperature has started increasing globally, which resulted in the incredible transformation of rain and evaporation in the countries. The agricultural development of many countries has been reliant, delicate, and susceptible to CC for a long time, and it is on the development of agriculture total factor productivity (ATFP) influence different crops and yields of farmers (Alhassan 2021 ; Wu  2020 ).

Food security and natural disasters are increasing rapidly in the world. Several major climatic/natural disasters have impacted local crop production in the countries concerned. The effects of these natural disasters have been poorly controlled by the development of the economies and populations and may affect human life as well. One example is China, which is among the world’s most affected countries, vulnerable to natural disasters due to its large population, harsh environmental conditions, rapid CC, low environmental stability, and disaster power. According to the January 2016 statistical survey, China experienced an economic loss of 298.3 billion Yuan, and about 137 million Chinese people were severely affected by various natural disasters (Xie et al. 2018 ).

Mitigation and adaptation strategies of climate changes

Adaptation and mitigation are the crucial factors to address the response to CC (Jahanzad et al. 2020 ). Researchers define mitigation on climate changes, and on the other hand, adaptation directly impacts climate changes like floods. To some extent, mitigation reduces or moderates greenhouse gas emission, and it becomes a critical issue both economically and environmentally (Botzen et al. 2021 ; Jahanzad et al. 2020 ; Kongsager 2018 ; Smit et al. 2000 ; Vale et al. 2021 ; Usman et al. 2021 ; Verheyen 2005 ).

Researchers have deep concern about the adaptation and mitigation methodologies in sectoral and geographical contexts. Agriculture, industry, forestry, transport, and land use are the main sectors to adapt and mitigate policies(Kärkkäinen et al. 2020 ; Waheed et al. 2021 ). Adaptation and mitigation require particular concern both at the national and international levels. The world has faced a significant problem of climate change in the last decades, and adaptation to these effects is compulsory for economic and social development. To adapt and mitigate against CC, one should develop policies and strategies at the international level (Hussain et al. 2020 ). Figure  6 depicts the list of current studies on sectoral impacts of CC with adaptation and mitigation measures globally.

Sectoral impacts of climate change with adaptation and mitigation measures.

Conclusion and future perspectives

Specific socio-agricultural, socio-economic, and physical systems are the cornerstone of psychological well-being, and the alteration in these systems by CC will have disastrous impacts. Climate variability, alongside other anthropogenic and natural stressors, influences human and environmental health sustainability. Food security is another concerning scenario that may lead to compromised food quality, higher food prices, and inadequate food distribution systems. Global forests are challenged by different climatic factors such as storms, droughts, flash floods, and intense precipitation. On the other hand, their anthropogenic wiping is aggrandizing their existence. Undoubtedly, the vulnerability scale of the world’s regions differs; however, appropriate mitigation and adaptation measures can aid the decision-making bodies in developing effective policies to tackle its impacts. Presently, modern life on earth has tailored to consistent climatic patterns, and accordingly, adapting to such considerable variations is of paramount importance. Because the faster changes in climate will make it harder to survive and adjust, this globally-raising enigma calls for immediate attention at every scale ranging from elementary community level to international level. Still, much effort, research, and dedication are required, which is the most critical time. Some policy implications can help us to mitigate the consequences of climate change, especially the most affected sectors like the agriculture sector;

Warming might lengthen the season in frost-prone growing regions (temperate and arctic zones), allowing for longer-maturing seasonal cultivars with better yields (Pfadenhauer 2020 ; Bonacci 2019 ). Extending the planting season may allow additional crops each year; when warming leads to frequent warmer months highs over critical thresholds, a split season with a brief summer fallow may be conceivable for short-period crops such as wheat barley, cereals, and many other vegetable crops. The capacity to prolong the planting season in tropical and subtropical places where the harvest season is constrained by precipitation or agriculture farming occurs after the year may be more limited and dependent on how precipitation patterns vary (Wu et al. 2017 ).

The genetic component is comprehensive for many yields, but it is restricted like kiwi fruit for a few. Ali et al. ( 2017 ) investigated how new crops will react to climatic changes (also stated in Mall et al. 2017 ). Hot temperature, drought, insect resistance; salt tolerance; and overall crop production and product quality increases would all be advantageous (Akkari 2016 ). Genetic mapping and engineering can introduce a greater spectrum of features. The adoption of genetically altered cultivars has been slowed, particularly in the early forecasts owing to the complexity in ensuring features are expediently expressed throughout the entire plant, customer concerns, economic profitability, and regulatory impediments (Wirehn 2018 ; Davidson et al. 2016 ).

To get the full benefit of the CO 2 would certainly require additional nitrogen and other fertilizers. Nitrogen not consumed by the plants may be excreted into groundwater, discharged into water surface, or emitted from the land, soil nitrous oxide when large doses of fertilizer are sprayed. Increased nitrogen levels in groundwater sources have been related to human chronic illnesses and impact marine ecosystems. Cultivation, grain drying, and other field activities have all been examined in depth in the studies (Barua et al. 2018 ).

• The technological and socio-economic adaptation

The policy consequence of the causative conclusion is that as a source of alternative energy, biofuel production is one of the routes that explain oil price volatility separate from international macroeconomic factors. Even though biofuel production has just begun in a few sample nations, there is still a tremendous worldwide need for feedstock to satisfy industrial expansion in China and the USA, which explains the food price relationship to the global oil price. Essentially, oil-exporting countries may create incentives in their economies to increase food production. It may accomplish by giving farmers financing, seedlings, fertilizers, and farming equipment. Because of the declining global oil price and, as a result, their earnings from oil export, oil-producing nations may be unable to subsidize food imports even in the near term. As a result, these countries can boost the agricultural value chain for export. It may be accomplished through R&D and adding value to their food products to increase income by correcting exchange rate misalignment and adverse trade terms. These nations may also diversify their economies away from oil, as dependence on oil exports alone is no longer economically viable given the extreme volatility of global oil prices. Finally, resource-rich and oil-exporting countries can convert to non-food renewable energy sources such as solar, hydro, coal, wind, wave, and tidal energy. By doing so, both world food and oil supplies would be maintained rather than harmed.

IRENA’s modeling work shows that, if a comprehensive policy framework is in place, efforts toward decarbonizing the energy future will benefit economic activity, jobs (outweighing losses in the fossil fuel industry), and welfare. Countries with weak domestic supply chains and a large reliance on fossil fuel income, in particular, must undertake structural reforms to capitalize on the opportunities inherent in the energy transition. Governments continue to give major policy assistance to extract fossil fuels, including tax incentives, financing, direct infrastructure expenditures, exemptions from environmental regulations, and other measures. The majority of major oil and gas producing countries intend to increase output. Some countries intend to cut coal output, while others plan to maintain or expand it. While some nations are beginning to explore and execute policies aimed at a just and equitable transition away from fossil fuel production, these efforts have yet to impact major producing countries’ plans and goals. Verifiable and comparable data on fossil fuel output and assistance from governments and industries are critical to closing the production gap. Governments could increase openness by declaring their production intentions in their climate obligations under the Paris Agreement.

It is firmly believed that achieving the Paris Agreement commitments is doubtlful without undergoing renewable energy transition across the globe (Murshed 2020 ; Zhao et al. 2022 ). Policy instruments play the most important role in determining the degree of investment in renewable energy technology. This study examines the efficacy of various policy strategies in the renewable energy industry of multiple nations. Although its impact is more visible in established renewable energy markets, a renewable portfolio standard is also a useful policy instrument. The cost of producing renewable energy is still greater than other traditional energy sources. Furthermore, government incentives in the R&D sector can foster innovation in this field, resulting in cost reductions in the renewable energy industry. These nations may export their technologies and share their policy experiences by forming networks among their renewable energy-focused organizations. All policy measures aim to reduce production costs while increasing the proportion of renewables to a country’s energy system. Meanwhile, long-term contracts with renewable energy providers, government commitment and control, and the establishment of long-term goals can assist developing nations in deploying renewable energy technology in their energy sector.

Author contribution

KA: Writing the original manuscript, data collection, data analysis, Study design, Formal analysis, Visualization, Revised draft, Writing-review, and editing. MZQ: Writing the original manuscript, data collection, data analysis, Writing-review, and editing. HS: Contribution to the contextualization of the theme, Conceptualization, Validation, Supervision, literature review, Revised drapt, and writing review and editing. MM: Writing review and editing, compiling the literature review, language editing. HM: Writing review and editing, compiling the literature review, language editing. IY: Contribution to the contextualization of the theme, literature review, and writing review and editing.

Availability of data and material

Declarations.

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The authors declare no competing interests.

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Contributor Information

Kashif Abbass, Email: nc.ude.tsujn@ssabbafihsak .

Muhammad Zeeshan Qasim, Email: moc.kooltuo@888misaqnahseez .

Huaming Song, Email: nc.ude.tsujn@gnimauh .

Muntasir Murshed, Email: [email protected] .

Haider Mahmood, Email: moc.liamtoh@doomhamrediah .

Ijaz Younis, Email: nc.ude.tsujn@sinuoyzaji .

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ENCYCLOPEDIC ENTRY

Climate change.

Climate change is a long-term shift in global or regional climate patterns. Often climate change refers specifically to the rise in global temperatures from the mid-20th century to present.

Earth Science, Climatology

Fracking tower

Fracking is a controversial form of drilling that uses high-pressure liquid to create cracks in underground shale to extract natural gas and petroleum. Carbon emissions from fossils fuels like these have been linked to global warming and climate change.

Photograph by Mark Thiessen / National Geographic

Climate is sometimes mistaken for weather. But climate is different from weather because it is measured over a long period of time, whereas weather can change from day to day, or from year to year. The climate of an area includes seasonal temperature and rainfall averages, and wind patterns. Different places have different climates. A desert, for example, is referred to as an arid climate because little water falls, as rain or snow, during the year. Other types of climate include tropical climates, which are hot and humid , and temperate climates, which have warm summers and cooler winters.

Climate change is the long-term alteration of temperature and typical weather patterns in a place. Climate change could refer to a particular location or the planet as a whole. Climate change may cause weather patterns to be less predictable. These unexpected weather patterns can make it difficult to maintain and grow crops in regions that rely on farming because expected temperature and rainfall levels can no longer be relied on. Climate change has also been connected with other damaging weather events such as more frequent and more intense hurricanes, floods, downpours, and winter storms.

In polar regions, the warming global temperatures associated with climate change have meant ice sheets and glaciers are melting at an accelerated rate from season to season. This contributes to sea levels rising in different regions of the planet. Together with expanding ocean waters due to rising temperatures, the resulting rise in sea level has begun to damage coastlines as a result of increased flooding and erosion.

The cause of current climate change is largely human activity, like burning fossil fuels , like natural gas, oil, and coal. Burning these materials releases what are called greenhouse gases into Earth’s atmosphere . There, these gases trap heat from the sun’s rays inside the atmosphere causing Earth’s average temperature to rise. This rise in the planet's temperature is called global warming. The warming of the planet impacts local and regional climates. Throughout Earth's history, climate has continually changed. When occuring naturally, this is a slow process that has taken place over hundreds and thousands of years. The human influenced climate change that is happening now is occuring at a much faster rate.

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Human activity affects global surface temperatures by changing Earth ’s radiative balance—the “give and take” between what comes in during the day and what Earth emits at night. Increases in greenhouse gases —i.e., trace gases such as carbon dioxide and methane that absorb heat energy emitted from Earth’s surface and reradiate it back—generated by industry and transportation cause the atmosphere to retain more heat, which increases temperatures and alters precipitation patterns.

Global warming, the phenomenon of increasing average air temperatures near Earth’s surface over the past one to two centuries, happens mostly in the troposphere , the lowest level of the atmosphere, which extends from Earth’s surface up to a height of 6–11 miles. This layer contains most of Earth’s clouds and is where living things and their habitats and weather primarily occur.

Continued global warming is expected to impact everything from energy use to water availability to crop productivity throughout the world. Poor countries and communities with limited abilities to adapt to these changes are expected to suffer disproportionately. Global warming is already being associated with increases in the incidence of severe and extreme weather, heavy flooding , and wildfires —phenomena that threaten homes, dams, transportation networks, and other facets of human infrastructure. Learn more about how the IPCC’s Sixth Assessment Report, released in 2021, describes the social impacts of global warming.

Polar bears live in the Arctic , where they use the region’s ice floes as they hunt seals and other marine mammals . Temperature increases related to global warming have been the most pronounced at the poles, where they often make the difference between frozen and melted ice. Polar bears rely on small gaps in the ice to hunt their prey. As these gaps widen because of continued melting, prey capture has become more challenging for these animals.

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global warming , the phenomenon of increasing average air temperatures near the surface of Earth over the past one to two centuries. Climate scientists have since the mid-20th century gathered detailed observations of various weather phenomena (such as temperatures, precipitation , and storms) and of related influences on climate (such as ocean currents and the atmosphere’s chemical composition). These data indicate that Earth’s climate has changed over almost every conceivable timescale since the beginning of geologic time and that human activities since at least the beginning of the Industrial Revolution have a growing influence over the pace and extent of present-day climate change .

Giving voice to a growing conviction of most of the scientific community , the Intergovernmental Panel on Climate Change (IPCC) was formed in 1988 by the World Meteorological Organization (WMO) and the United Nations Environment Program (UNEP). The IPCC’s Sixth Assessment Report (AR6), published in 2021, noted that the best estimate of the increase in global average surface temperature between 1850 and 2019 was 1.07 °C (1.9 °F). An IPCC special report produced in 2018 noted that human beings and their activities have been responsible for a worldwide average temperature increase between 0.8 and 1.2 °C (1.4 and 2.2 °F) since preindustrial times, and most of the warming over the second half of the 20th century could be attributed to human activities.

AR6 produced a series of global climate predictions based on modeling five greenhouse gas emission scenarios that accounted for future emissions, mitigation (severity reduction) measures, and uncertainties in the model projections. Some of the main uncertainties include the precise role of feedback processes and the impacts of industrial pollutants known as aerosols , which may offset some warming. The lowest-emissions scenario, which assumed steep cuts in greenhouse gas emissions beginning in 2015, predicted that the global mean surface temperature would increase between 1.0 and 1.8 °C (1.8 and 3.2 °F) by 2100 relative to the 1850–1900 average. This range stood in stark contrast to the highest-emissions scenario, which predicted that the mean surface temperature would rise between 3.3 and 5.7 °C (5.9 and 10.2 °F) by 2100 based on the assumption that greenhouse gas emissions would continue to increase throughout the 21st century. The intermediate-emissions scenario, which assumed that emissions would stabilize by 2050 before declining gradually, projected an increase of between 2.1 and 3.5 °C (3.8 and 6.3 °F) by 2100.

Many climate scientists agree that significant societal, economic, and ecological damage would result if the global average temperature rose by more than 2 °C (3.6 °F) in such a short time. Such damage would include increased extinction of many plant and animal species, shifts in patterns of agriculture , and rising sea levels. By 2015 all but a few national governments had begun the process of instituting carbon reduction plans as part of the Paris Agreement , a treaty designed to help countries keep global warming to 1.5 °C (2.7 °F) above preindustrial levels in order to avoid the worst of the predicted effects. Whereas authors of the 2018 special report noted that should carbon emissions continue at their present rate, the increase in average near-surface air temperature would reach 1.5 °C sometime between 2030 and 2052, authors of the AR6 report suggested that this threshold would be reached by 2041 at the latest.

The AR6 report also noted that the global average sea level had risen by some 20 cm (7.9 inches) between 1901 and 2018 and that sea level rose faster in the second half of the 20th century than in the first half. It also predicted, again depending on a wide range of scenarios, that the global average sea level would rise by different amounts by 2100 relative to the 1995–2014 average. Under the report’s lowest-emission scenario, sea level would rise by 28–55 cm (11–21.7 inches), whereas, under the intermediate emissions scenario, sea level would rise by 44–76 cm (17.3–29.9 inches). The highest-emissions scenario suggested that sea level would rise by 63–101 cm (24.8–39.8 inches) by 2100.

The scenarios referred to above depend mainly on future concentrations of certain trace gases, called greenhouse gases , that have been injected into the lower atmosphere in increasing amounts through the burning of fossil fuels for industry, transportation , and residential uses. Modern global warming is the result of an increase in magnitude of the so-called greenhouse effect , a warming of Earth’s surface and lower atmosphere caused by the presence of water vapour , carbon dioxide , methane , nitrous oxides , and other greenhouse gases. In 2014 the IPCC first reported that concentrations of carbon dioxide, methane, and nitrous oxides in the atmosphere surpassed those found in ice cores dating back 800,000 years.

Of all these gases, carbon dioxide is the most important, both for its role in the greenhouse effect and for its role in the human economy. It has been estimated that, at the beginning of the industrial age in the mid-18th century, carbon dioxide concentrations in the atmosphere were roughly 280 parts per million (ppm). By the end of 2022 they had risen to 419 ppm, and, if fossil fuels continue to be burned at current rates, they are projected to reach 550 ppm by the mid-21st century—essentially, a doubling of carbon dioxide concentrations in 300 years.

A vigorous debate is in progress over the extent and seriousness of rising surface temperatures, the effects of past and future warming on human life, and the need for action to reduce future warming and deal with its consequences. This article provides an overview of the scientific background related to the subject of global warming. It considers the causes of rising near-surface air temperatures, the influencing factors, the process of climate research and forecasting, and the possible ecological and social impacts of rising temperatures. For an overview of the public policy developments related to global warming occurring since the mid-20th century, see global warming policy . For a detailed description of Earth’s climate, its processes, and the responses of living things to its changing nature, see climate . For additional background on how Earth’s climate has changed throughout geologic time , see climatic variation and change . For a full description of Earth’s gaseous envelope, within which climate change and global warming occur, see atmosphere .

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• > Climate Change
• > An introduction to climate change

Book contents

• Frontmatter
• Introduction
• Acknowledgements
• 1 An introduction to climate change
• 2 Principal indicators of past climates
• 3 Past climate change
• 4 The Oligocene to the Quaternary: climate and biology
• 5 Present climate and biological change
• 6 Current warming and likely future impacts
• 7 The human ecology of climate change
• 8 Sustainability and policy
• Appendix 1 Glossary and abbreviations
• Appendix 2 Bio-geological chronology
• Appendix 3 Calculations of energy demand/supply and orders of magnitude
• Appendix 4 The IPCC 2007 report

1 - An introduction to climate change

Published online by Cambridge University Press:  17 December 2010

In most places on this planet's terrestrial surface there are the signs of life. Even in those places where there is not much life today, there are frequently signs of past life, be it fossils, coal or chalk. Further, it is almost a rule of thumb that if you do discover signs of past life, either tens of thousands or millions of years ago, then such signs will most likely point to different species to those found there today. Why? Here there are a number of answers, not least of which is evolution. Yet a key feature of why broad types of species (be they broad-leaved tree species as opposed to narrow needle-leaved ones) live in one place and not another is to do with climate. Climate is a fundamental factor influencing biology. Consequently a key factor (among others) as to why different species existed in a particular place 5000, 50 000, 500 000 or even 5 000 000 years ago (to take some arbitrary snapshots in time) is due to different climatic regimens existing at that place in those times.

It is also possible to turn this truism on its head and use biology to ascertain aspects of the climate, and biological remains are aspects of past climates. Furthermore, biology can influence climate: for example, an expanse of rainforest transpires such a quantity of water, and influences the flow of water through a catchment area, that it can modify the climate from what it otherwise would have been in the absence of living species.

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• An introduction to climate change
• Jonathan Cowie
• Book: Climate Change
• Online publication: 17 December 2010
• Chapter DOI: https://doi.org/10.1017/CBO9780511803826.002

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Abrupt Impacts of Climate Change: Anticipating Surprises (2013)

Chapter: 1 introduction.

CHAPTER ONE Introduction

T he idea that Earth’s climate could abruptly change in a drastic manner has been around for several decades. Early studies of ice cores showed that very large changes in climate could happen in a matter of a few decades or even years, for example, local to regional temperature changes of a dozen degrees or more, doubling or halving of precipitation rates, and dust concentrations changing by orders of magnitude (Dansgaard et al., 1989; Alley et al., 1993). In the last few decades, scientific research has advanced our understanding of abrupt climate change significantly. Some original fears have been allayed or now seem less ominous, but new ones have sprung up. Fresh reminders occur regularly that thresholds and tipping points exist not only in the climate system, but in other parts of the Earth system ( Box 1.1 ).

What has become clearer recently is that the issue of abrupt change cannot be confined to a geophysical discussion of the climate system alone. The key concerns are not limited to large and abrupt shifts in temperature or rainfall, for example, but also extend to other systems that can exhibit abrupt or threshold-like behavior even in response to a gradually changing climate. The fundamental concerns with abrupt change include those of speed—faster changes leave less time for adaptation, either economically or ecologically—and of magnitude—larger changes require more adaptation and generally have greater impact. This report offers an updated look at the issue of abrupt climate change and its potential impacts, and takes the added step of considering not only abrupt changes to the climate system itself, but also abrupt impacts and tipping points that can be triggered by gradual changes in climate.

This examination of the impacts of abrupt change brings the discussion into the human realm, raising questions such as: Are there potential thresholds in society’s ability to grow sufficient food? Or to obtain sufficient clean water? Are there thresholds in the risk to coastal infrastructure as sea levels rise? The spectrum of possibilities here is very wide, too wide to be fully covered in any single report. In practice, little is known about these and other possible abrupt changes. As such, this report lays out what is currently known about the risks, raises flags to point out potential threats, and proposes improved monitoring and warning schemes to help prepare us for both known and unknown abrupt changes. This report can be viewed as the current frame in an ongoing movie in which we grasp the basic plot, but we are not sure what plot twists lie ahead or even how the various characters are related. As scientific research and monitoring progresses, i.e., as we watch the movie and learn more about the key characters and

BOX 1.1 EXAMPLES OF RECENT ABRUPT CHANGE IN THE EARTH SYSTEM

Stratospheric Ozone Depletion

During the early 1970s, concerns arose in the scientific community that inputs of nitrogen oxides (known as “NOx”) from a proposed fleet of supersonic aircraft flying in the stratosphere and of industrially produced halocarbon gases containing chlorine and bromine (CFCs or chlorofluorocarbons and chlorofluorobromocarbons) had the potential to deplete the amount of ozone in the stratosphere. Halogen oxide radicals were predicted to form from the degradation of halocarbons in the stratosphere. Intensive study of the stratosphere, extending more than a decade, confirmed the rising concentrations of CFCs and halons in the atmosphere, and of halogen oxide radicals in the stratosphere. International negotiations led to the signing of the Montreal Protocol in 1987, requiring a 50 percent reduction in CFCs and a 100 percent reduction in halon production by 2000 by the developed countries.

However, two years prior to the treaty, scientists learned that the column amount of ozone over Antarctica in the austral spring had been declining since the late 1960s, and it had been reduced by almost a factor of two by the mid-1980s (Farman et al., 1985); See Figure A . The continuous record of column ozone abundances measured at Halley Bay, Antarctica, showed

FIGURE A Total column ozone in Antarctica, at the Halley Bay station of the British Antarctic Survey (black) and averaged over the whole polar region of Antarctica (blue, from satellite data). (Adapted from WMO/ UNEP [2010] plus data from the British Antarctic Survey [ http://www.antarctica.ac.uk/met/jds/ozone/data/ZOZ5699.DAT , downloaded 26 April 2013].)

that October ozone column amounts started to drift lower in the late 1960s and 1970s. Satellite records and measurements from other stations confirmed that this change was occurring on the continental scale of Antarctica. This was an abrupt change in the timescale of human activities, the scale of the whole polar region, but lack of continuity and rejection of data perceived to be anomalous prevented the detection of the change from space observations.

The Montreal Protocol was amended to require complete phase-out of most ozone-depleting CFCs by 1996 in developed countries and by 2010 in rest of the world. In addition, the Protocol was amended or adjusted multiple times to reduce emissions of all ozone-depleting substances. As a result of the Montreal Protocol and its amendments, stratospheric ozone is expected to return to its pre-1980 values as the atmospheric abundances of ozone-depleting substances decline in the coming decades. Column global ozone amounts prevalent in the early1970s are expected to be restored by the mid-21st century, although stratospheric cooling associated with changes in greenhouse gases will alter the trajectory of the restoration. The Antarctic ozone hole is expected to no longer occur towards the late 21st century, and this recovery is not expected to be influenced as much by climate change as the global ozone amounts (WMO/UNEP, 2010).

The Antarctic ozone hole represents an abrupt change to the Earth system. Although it is not specifically an abrupt climate change, for the purposes of this report, it is a recent example of the type an unforeseen global threshold event. The Antarctic ozone hole appeared within a few years after a threshold was crossed—when the concentrations of inorganic chlorine exceeded the concentration of nitrogen oxides in the lower altitudes of the polar stratosphere—and it affected a large portion of the globe. Thus, it exemplifies the scope and magnitude of the types of impacts that abrupt changes from human activities can have on the planet.

Bark Beetle Outbreaks

Bark beetles are a natural part of forested ecosystems, and infestations are a regular force of natural change. In the last two decades, though, the bark beetle infestations that have occurred across large areas of North America have been the largest and most severe in recorded history, killing millions of trees across millions of hectares of forest from Alaska to southern California (Bentz, 2008); see Figure B . Bark beetle outbreak dynamics are complex, and a variety of circumstances must coincide and thresholds must be surpassed for an outbreak to occur on a large scale. Climate change is thought to have played a significant role in these recent outbreaks by maintaining temperatures above a threshold that would normally lead to cold-induced mortality. In general, elevated temperatures in a warmer climate, particularly when there are consecutive warm years, can speed up reproductive cycles and increase the likelihood of outbreaks (Bentz et al., 2010). Similar to many of the issues described in this report, climate change is only one contributing factor to these types of abrupt climate impacts, with other human actions such as forest history and management also playing a role. There are also feedbacks to the climate system from these outbreaks, which represent an important mechanism by which climate change may undermine the ability of northern forests to take up and store atmospheric carbon (Kurz et al., 2008).

FIGURE B Photographs of a pine bark beetle and of a beetle-killed forest in the Colorado Rocky Mountains. Source: Top: Photo by Dion Manastyrski; Bottom: Photo from Anthony Barnosky.

how they interact, it is hoped that scientists and policymakers will learn to anticipate abrupt plot changes and surprises so that societies can be better prepared to handle them.

PREVIOUS DEFINITIONS OF ABRUPT CLIMATE CHANGE

As recently as the 1980s, the typical view of major climate change was one of slow shifts, paced by the changes in solar energy that accompany predictable variations in Earth’s orbit around the sun over thousands to tens of thousands of years (Hays et al., 1976). While some early studies of rates of climate change, particularly during the last glacial period and the transition from glacial to interglacial climates, found large changes in apparently short periods of time (e.g., Coope et al., 1971), most of the paleoclimate records reaching back tens of thousands of years lacked the temporal resolution to resolve yearly to decadal changes. This situation began to change in the late 1980s as scientists began to examine events such as the climate transition that occurred at the end of the Younger Dryas about 12,000 years ago (e.g., Dansgaard et al., 1989) and the large swings in climate during the glacial period that have come to be termed “Dansgaard-Oescher events” (“D-O events;” named after two of the ice core scientists who first studied these phenomena using ice cores). At first these variations seemed to many to be too large and fast to be climatic changes, and it was only after they were found in several ice cores (e.g., Anklin et al., 1993; Grootes et al., 1993), 1 and in many properties (e.g., Alley et al., 1993), including greenhouse gases (e.g., Severinghaus and Brook, 1999) that they became widely accepted as real.

This perspective is important, as first definitions of abrupt climate change were tied directly to these D-O events, which themselves are defined by changes in temperature, precipitation rates, dust fallout, and concentrations of certain greenhouse gases. For this reason, previous reviews of abrupt change have tended to focus on the physical climate system, and the potential for abrupt changes and threshold behavior has been expressed primarily in climatic terms (key references listed in Box 1.2 ).

The first systematic review of abrupt climate change was by the National Research Council ( Abrupt Climate Change: Inevitable Surprises; NRC, 2002). This study defined abrupt climate change as follows:

“Technically, an abrupt climate change occurs when the climate system is forced to cross some threshold, triggering a transition to a new state at a rate determined by the

____________________

1 http://www.gisp2.sr.unh.edu/ .

BOX 1.2 PREVIOUS REPORTS ON ABRUPT CLIMATE CHANGE

Key references on the subject of abrupt climate change:

• Abrupt Climate Change: Inevitable Surprises (NRC, 2002)
• IPCC Fourth Assessment Report: Climate Change 2007 (IPCC, 2007c)
• Synthesis and Assessment Product 3.4: Abrupt Climate Change (USCCSP, 2008)
• Tipping Elements in the Earth’s Climate System (Lenton et al., 2008)
• Climate and Social Stress: Implications for Security Analysis (NRC, 2012a)
• 2013 National Climate Assessment (National Climate Assessment and Development Advisory Committee, 2013)

climate system itself and faster than the cause. Chaotic processes in the climate system may allow the cause of such an abrupt climate change to be undetectably small.”

This early definition is critically important in two regards. First, it focuses on the climate system itself, a focus that remains widely used today. Second, it raises the possibility of thresholds or tipping points being forced or pushed by an undetectably small change in the cause of the shift. The 2002 report goes on to expand on its definition by placing abrupt climate change into a social context:

“To use this definition in a policy setting or public discussion requires some additional context, … because while many scientists measure time on geological scales, most people are concerned with changes and their potential impacts on societal and ecological time scales. From this point of view, an abrupt change is one that takes place so rapidly and unexpectedly that human or natural systems have difficulty adapting to it. Abrupt changes in climate are most likely to be significant, from a human perspective, if they persist over years or longer, are larger than typical climate variability, and affect sub-continental or larger regions. Change in any measure of climate or its variability can be abrupt, including change in the intensity, duration, or frequency of extreme events.”

This expanded definition raised the issues of persistence, of changes being so large that they stand out above typical variability, and that changes in extremes, not just baselines, were considered to be abrupt climate changes. It also placed climate change into the context of impacts of those changes, and the change being considered abrupt if it exceeds the system’s capacity to adapt.

In the subsequent years many papers were published on abrupt climate change, some with definitions more focused on time (e.g., Clark et al., 2002), and others on the relative speed of the causes and reactions. Overpeck and Cole (2006), for example, defined abrupt climate change as “a transition in the climate system whose duration is

fast relative to the duration of the preceding or subsequent state.” Lenton et al. (2008) formally introduced the concept of tipping point, defining abrupt climate change as:

“We offer a formal definition, introducing the term “tipping element” to describe subsystems of the Earth system that are at least subcontinental in scale and can be switched—under certain circumstances—into a qualitatively different state by small perturbations. The tipping point is the corresponding critical point—in forcing and a feature of the system—at which the future state of the system is qualitatively altered.”

In 2007, the Intergovernmental Panel on Climate Change Fourth Assessment report defined abrupt climate change as:

“forced or unforced climatic change that involves crossing a threshold to a new climate regime (e.g., new mean state or character of variability), often where the transition time to the new regime is short relative to the duration of the regime.”

In late 2008 a report of the U.S. Climate Change Science Program (USCCSP) was dedicated to the topic of abrupt climate change. The Synthesis and Assessment Product 3.4: Abrupt Climate Change (USCCSP, 2008) defined abrupt climate change as:

“A large-scale change in the climate system that takes place over a few decades or less, persists (or is anticipated to persist) for at least a few decades, and causes substantial disruptions in human and natural systems.”

This simple definition directly focuses attention on the impacts of change on natural and human systems and is important in that it directly combines the physical climate system with human impacts. As an increasingly interdisciplinary approach was taken to studying abrupt climate change, there was an accompanying evolution in thinking, expanding from abrupt changes in the physical climate system to include abrupt impacts from climate change.

More recently, Climate and Social Stress: Implications for Security Analysis (NRC, 2012b) examined the topic of climate change in the context of national security and briefly addressed the issue of abrupt climate change. They noted that events that did not meet the common criterion of a semi-permanent change in state could still force other systems into a permanent change, and thus qualify as an abrupt change. For example, a mega-drought may be followed by the return of normal precipitation rates, such that no baseline change occurred, but if that drought caused the collapse of a civilization, a permanent, abrupt change occurred in the system impacted by climate.

The 2002 NRC study introduced the important issue of gradual climate change causing abrupt responses in human or natural systems, noting “Abrupt impacts therefore have the potential to occur when gradual climatic changes push societies or ecosys-

tems across thresholds and lead to profound and potentially irreversible impacts.” The 2002 report also noted that “…the more rapid the forcing, the more likely it is that the resulting change will be abrupt on the time scale of human economies or global ecosystems” and “The major impacts of abrupt climate change are most likely to occur when economic or ecological systems cross important thresholds” (NRC, 2002). The 2012 NRC study embraced this issue more fully and expanded on the concept. The first part of their definition is straightforward:

“Abrupt climate change is generally defined as occurring when some part of the climate system passes a threshold or tipping point resulting in a rapid change that produces a new state lasting decades or longer (Alley et al., 2003). In this case “rapid” refers to timelines of a few years to decades.”

The second part of their definition echoes the 2002 report in emphasizing the role of abrupt responses to gradually changing forcing (emphasis added):

“Abrupt climate change can occur on a regional, continental, hemispheric, or even global basis. Even a gradual forcing of a system with naturally occurring and chaotic variability can cause some part of the system to cross a threshold, triggering an abrupt change . Therefore, it is likely that gradual or monotonic forcings increase the probability of an abrupt change occurring.”

DEFINITION OF ABRUPT CLIMATE CHANGE FOR THIS REPORT

The committee embraces the broader concept of abrupt climate change described in the 2002 NRC report and the definition from the 2012 Climate and Social Stress report, while expanding the scope of the definition further by considering abrupt climate impacts , as well as abrupt climate changes ( Box 1.3 ). This distinction is critical, and represents a broadening of the focus from just the physical climate system itself to also encompass abrupt changes in the natural and human-built world that may be triggered by gradual changes in the physical climate system. Thus, the committee begins by defining that, for this report, the term “abrupt climate change” as being abrupt changes in the physical climate system, and the related term, “abrupt climate impacts,” as being abrupt impacts resulting from climate change, even if the climate change itself is gradual (but reaches a threshold value that triggers an abrupt impact in a related system)

This definition of abrupt climate change also helps to set a time frame for what kinds of phenomena are considered in this report. Environmental changes occurring over timescales exceeding 100 years are not frequently considered in decision-making by the general public, private sector, or the government. For some, projected changes oc-

BOX 1.3 DEFINITION OF ABRUPT CLIMATE CHANGE FOR THIS REPORT

The subject of this report includes both the abrupt changes in the physical climate system (hereafter called “abrupt climate change”) and abrupt impacts in the physical, biological, or human systems triggered by a gradually changing climate (hereafter called “abrupt climate impacts”. These abrupt changes can affect natural or human systems, or both. The primary timescale of concern is years to decades. A key characteristic of these changes is that they can unfold faster than expected, planned for, or budgeted for, forcing a reactive, rather than proactive mode of behavior. These changes can propagate systemically, rapidly affecting multiple interconnected areas of concern.

curring over less than 100 years begin to raise questions related to inter-generational equity and can be viewed as a relevant time frame for certain policy settings. Changes occurring over a few decades, i.e., a generation or two, begin to capture the interest of most people because it is a time frame that is considered in many personal decisions and relates to personal memories. Also, at this time scale, changes and impacts can occur faster than the expected, stable lifetime of systems about which society cares. For example, the sizing of a new air conditioning system may not take into consideration the potential that climate change could make the system inadequate and unusable before the end of its useful lifetime (often 30 years or more). The same concept applies to other infrastructure, such as airport runways, subway systems, and rail lines. Thus, even if a change is occurring over several decades, and therefore might not at first glance seem “abrupt,” if that change affects systems that are expected to function for an even longer period of time, the impact can indeed be abrupt when a threshold is crossed. “Abrupt” then, is relative to our “expectations,” which for the most part come from a simple linear extrapolation of recent history, and “expectations” invoke notions of risk and uncertainty. In such cases, it is the cost associated with unfulfilled expectations that motivates discussion of abrupt change. Finally, changes occurring over one to a few years are abrupt, and for most people, would also be alarming if sufficiently large and impactful. In this report, the committee adopts the time frame for “abrupt” climate changes as years to decades.

The committee chose to focus their discussions of abrupt climate changes to those relevant to human society, including changes in the physical climate itself, and resulting changes to human expectations. Given our reliance on natural systems for ecosystem services, impacts to natural systems are of great concern to society as well. This consideration of unexpected impacts to societies and ecosystems broadens the discussion beyond the physics and chemistry of the climate system to include effects on humans and biota on local, regional, national, and international scales occurring

over years to decades. This is a broad definition that could easily encompass too many topics to cover in one report, and in this report, the committee has attempted to steer clear of the temptation to craft a laundry list of topics. As the climate science community is in the early stages of examining many potential socioeconomic impacts, the discussion is thus necessarily limited in this report to those impacts for which there is good reason to suspect they are both abrupt and could actually occur.

There is a nascent but rapidly growing literature on the theory behind how abrupt transitions occur ( Box 1.4 ). This research is also beginning to tackle the even harder question of how to anticipate abrupt transitions, across many disciplines and systems, a topic that this report returns to in more depth in Chapter 4 .

BOX 1.4 MECHANISMS OF ABRUPT CHANGE

Shocks or sudden events in the environment have often been classified into categories based on duration: (1) large temporary disturbances (e.g., earthquakes, hurricanes, tsunamis); and (2) shifts in long-term behavior (e.g., El Niño events, glacial cycles) (Lenton, 2013). However, both of these categories are really different aspects of the same fundamental phenomenon, a change in the system dynamics from the “normal” behavior.

Although much is unknown about the mechanisms that can result in abrupt changes, some examples where there has been progress include positive feedbacks and bifurcations. Positive feedbacks occur when the system’s own dynamics enhance the effect of a perturbation, leading to an instability. If these positive feedbacks are not controlled via damping mechanisms or negative feedbacks, the system can pass through a “tipping point” into a new domain (Scheffer et al., 2012a). Bifurcations occur when changes in a parameter of the system result in qualitatively different behavior (e.g., stable points become unstable, one stable point becomes multiple stable points). The presence of bifurcations can easily result in abrupt changes. For instance, random fluctuations from within a system (stochastic endogenous fluctuations) can cause the system to depart from an equilibrium or quasi-equilibrium state (e.g., fast, weather time-scale phenomena forcing changes on the longer time-scale climate). Rate-dependent shifts can also occur: a rapid change to an input or parameter of the system may cause it to fail to track changes, and thus tip (Ashwin et al., 2012).

More generally, however, a key characteristic required for abrupt changes to occur is the property of state dependence (aka nonlinearity or nonseperability), where the dynamics (i.e., behavior) of the system are dependent on the system’s current state, which may also include its history (time-lagged manifolds). Generically, it is not correct to study these systems using linear methods or by examining variables in isolation (Sugihara et. al. 2012). Overall, research on abrupt changes and tipping points is moving from examining simple systems to investigations of highly connected networks (Scheffer et al., 2012a). The literature on downstream consequences of climate change has not arrived at a clear, common framework analytically as is the case for the physical aspects.

HISTORICAL PERSPECTIVE—PREVIOUS REPORTS ON ABRUPT CHANGE

Here the committee summarizes several previous reports on the topic of abrupt climate change and their recommendations, with the purpose of placing the present study and its recommendations within the context of this previous work. It is particularly instructive to report where progress has been made, and where previous recommendations continue to be echoed but not acted upon.

2002 NRC Report on Abrupt Climate Change

As mentioned above, the first NRC report to comprehensively address abrupt climate change was entitled Abrupt Climate Change: Inevitable Surprises (NRC, 2002). This study remains one of the most comprehensive investigations of abrupt climate change to date, addressing the evidence, the potential causes, the potential for the current greenhouse-gas-induced warming to trigger abrupt change, and the potential impacts, ranging from economic to ecological to hydrological to agricultural. One of their key findings was captured in the title of the report, and is summarized by the following quotation:

“Abrupt climate changes were especially common when the climate system was being forced to change most rapidly. Thus, greenhouse warming and other human alterations of the Earth system may increase the possibility of large, abrupt, and unwelcome regional or global climatic events. The abrupt changes of the past are not fully explained yet, and climate models typically underestimate the size, speed, and extent of those changes. Hence, future abrupt changes cannot be predicted with confidence, and climate surprises are to be expected.”

The report made five recommendations in two general categories: implementation of targeted areas of research to expand observations of the present and the past, and implementation of focused modeling efforts.

The first recommendation called for research programs to “collect data to improve understanding of thresholds and non-linearities in geophysical, ecological, and economic systems.” The report particularly called out for more work on modes of coupled atmosphere-ocean behavior, oceanic deep-water processes, hydrology, and ice. In the intervening decade, progress has been made in some of these areas. Since 2004 the ocean’s meridional overturning circulation has been monitored at 26°N in the North Atlantic (Cunningham et al., 2007). Progress has been made in ice sheet observations from satellites (e.g., Pritchard et al., 2010; Joughin et al., 2010) and in a better understanding of modes of ocean-atmosphere behavior. Nonetheless, as detailed in this

report, additional improvements in monitoring for abrupt climate change could be undertaken. For example, the interface between ocean and ice sheet is known to be critical part of ice sheet functioning, yet there are few observations, and no systematic monitoring of the changing conditions at this interface. Also, satellite observations of ice sheets are tenuous as satellites age and funding to replace them, let alone expand their capabilities, is uncertain.

The 2002 report also called for economic and ecological research, a comprehensive land-use census, and development of integrated economic and ecological data sets. Again, some improvements have been made in these areas, notably the National Ecological Observing Network (NEON) to monitor key ecosystem variables in the United States. Other areas, such as a comprehensive land-use census, remain largely unaddressed.

In its second recommendation, the 2002 report called for new, interdisciplinary modeling efforts that would bring together the physical climate system with ecosystems and human systems in an effort to better predict the impacts of abrupt climate change on humans and natural systems, and to better understand the potential for abrupt climate change during warm climate regimes. During the last decade, considerable improvements have been made in many aspects of coupled climate models. Although biases remain, simulation quality has improved for the models in the Coupled Model Intercomparison Project 5 (CMIP5) compared to earlier models (e.g., Knutti et al., 2013). In addition, many climate models have transitioned to include Earth system modeling capabilities (e.g., Hurrell et al., 2013; Collins et al., 2012) in that they incorporate biogeochemical cycles and/or other aspects beyond the standard physical climate model components (atmosphere, ocean, land, sea ice). These new capabilities allow for prognostic simulation of the carbon cycle and the assessment of biogeochemical feedbacks (e.g., Long et al., 2013). In some cases, models now also include the ability to simulate atmospheric chemistry (e.g., Lamarque et al., 2013; Shindell et al., 2013) and large ice sheets (e.g., Lipscomb et al., 2013). This has resulted in more complete system interactions within the model and the ability to investigate additional feedbacks and climate-relevant processes. The inclusion of isotopes into some models (e.g., Sturm et al., 2010; Tindall et al., 2010) is also allowing for a more direct comparison with paleoclimate proxies of relevance to past abrupt change, and a more comprehensive evaluation of the sources and sinks of the atmospheric water cycle that is critical in assessing the risk of future abrupt change and its impacts (e.g., Risi et al., 2010).

Efforts are also underway to more directly link human system interactions into Earth system models. This includes the incorporation of new elements such as agricultural crops (Levis et al., 2012) and urban components (Oleson, 2012). It also includes new efforts to link Integrated Assessment Models to Earth System models (e.g., van Vuuren

et al., 2012; Schneider, 1997 Goodess et al., 2003; Bouwman et al., 2006; Warren et al., 2008; Sokolov et al., 2005). Enhancements in model resolution are also enabling the simulation of high-impact weather events of societal relevance (such as tropical cyclones) within climate models (e.g., Jung et al., 2012; Zhao et al., 2009; Bacmeister et al., 2013; Manganello et al., 2012). However, computational resources, while increasing, still remain an obstacle for climate-scale high-resolution simulations. Additionally, model parameterizations and processes need to be reconsidered for simulations at these scales, a task that remains an active research area.

The third recommendation of the 2002 report called for more and better observational data on how our planet and climate system have behaved in the past, with a focus on the high temporal-resolution paleoclimate records required to assess abrupt climate changes. The past decade has witnessed a number of advances in this area, notably terrestrial records from temperate latitudes from cave deposits (e.g., Wang et al., 2008, and references therein), more and better resolved records from ocean sediments (NRC, 2011b), and expanded reconstructions of regional scale hydrological data—including mega-droughts—and changes to the monsoons from records including tree rings and pollen (e.g., Cook et al., 2010c). However, although scientists clearly have an improved understanding of past abrupt climate changes today compared with a decade ago, in many cases the data still remain too sparse spatially to test mechanisms of change using models. Multi-proxy data sets, in which a number of aspects of the climatic and environmental systems are simultaneously reconstructed, remain sparse as well.

The fourth recommendation of the 2002 report focused on improving incorporation of low-probability but high-impact events into societal thinking about climate change. The tendency is to assume a simple distribution of outcomes, and focus on the most probable ones. This approach underestimates the likelihood of extreme events, even ones that would have high impact. If one views risk as the product of likelihood and consequence, then highly consequential, “extreme” events, even if they are unlikely, may pose an equal risk to common events that are not as consequential. The damages resulting from recent extreme weather events (e.g., Hurricane Katrina, Superstorm Sandy, etc.) suggest that there is still a need to better plan for low-probability, highconsequence events, regardless of whether or not their cause is statistically rooted in observed climate trends. That most model predictions for future climate change include more frequent extreme events only heightens the need to take this recommendation seriously.

The fifth and final recommendation of the 2002 report dealt with “no regrets” measures and their application to the potential for abrupt climate change. The report called for taking low-cost steps such as slowing climate change, improving climate

forecasts, slowing biodiversity loss, and developing new technologies to increase the adaptability and resiliency of markets, ecological systems, and infrastructure. While there has been some progress in this regard over the past decade, progress has been slow, and remains inadequate to match the scope and scale of the problem. The scientific community has worked to improve climate models, for example, but little has been done to limit greenhouse gas emissions. In fact, the rate of greenhouse gas addition to the atmosphere continues to increase, with many policies in place to accelerate rising greenhouse gases (IMF, 2013). It is sobering to consider that about one-fifth of all fossil fuels ever burned were burned since the 2002 report was released. 2 If indeed, as the 2002 report states, “… greenhouse warming and other human alterations of the Earth system may increase the possibility of large, abrupt, and unwelcome regional or global climatic events”, then the danger that existed in 2002 is even higher now, a decade later.

2007 IPCC Fourth Assessment Report

The next major report on climate change following the 2002 report was the 2007 Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC, 2007c). 3 The AR4 did not specifically call out abrupt climate change and address it separately, but abrupt climate change was discussed in both the physical science context and in the context of mitigation and adaptation. The Working Group I report, the Physical Science Basis (IPCC, 2007b), acknowledged that our understanding of abrupt climate change was notably incomplete and that this limited the ability to model abrupt change, stating that “Mechanisms of onset and evolution of past abrupt climate change and associated climate thresholds are not well understood. This limits confidence in the ability of climate models to simulate realistic abrupt change.” However, the Working Group I report did specifically address the issue of a shutdown of the formation of North Atlantic Deep Water and concluded from modeling studies that although it was very likely (>90 percent chance) that the deep water formation would slow in the coming century, it was very unlikely (<10 percent chance) that this process would undergo a large abrupt transition, at least in the coming decades to century. This was an important advancement in the understanding of the potential threats of

2 Sum of global emissions from 1751 through 2009 inclusive is 355,676 million metric tons of carbon; sum of global emissions from 2002 through 2009 inclusive is 64,788 million metric tons of carbon (Boden et al., 2011). Total carbon emissions for 2002-2009 compared to the total 1751-2009 is thus greater than 18%.

3 The Working Group I report of the Fifth Assessment Report (AR5) of the IPCC was released after this report had been submitted for peer-review. The Committee drew their conclusions from the broader scientific literature, which is also the basis for IPCC AR5. Although this report only references the IPCC AR5 in a few instances, the broader conclusions of this report are consistent with the IPCC AR5.

abrupt climate change, and an example of a threat that has been categorized as less likely due to improved understanding of the process.

The AR4 Working Group 2 (WG2) report, Impacts, Adaptation and Vulnerability (IPCC, 2007a), addresses abrupt climate change throughout the report, and summarizes the impacts of extreme events and key vulnerabilities including topics such as coastal inundation, food supply disruption, and drought. The AR4 WG2 report repeatedly calls for more research to be done on the impacts of abrupt change, particularly a collapse of the North Atlantic Deep Water formation (which was not considered likely) and a relatively rapid sea level rise of many meters due to rapid (century-scale) loss of ice from Greenland and/or West Antarctica, noting that without a better scientific understanding of the potential impacts, it was impossible to carry out impact assessments. That report also notes that there has been “little advance” on the topic of “proximity to thresholds and tipping points.”

The AR4 Working Group 3 report on Mitigation of Climate Change (Metz et al., 2007) mentions abrupt climate change, but does not consider the topic in detail. It acknowledges that abrupt climate changes are not well incorporated into conventional decision-making analysis, which tends to enable substantial vulnerability to high-impact, low-probability events. This potentially increases the damages from any such events that could occur—and perhaps even the probability of such events—through lack of mitigation and adaptation. Similarly, abrupt climate change can challenge assumptions made in economic cost-benefit analyses, for example the cost of a lost species versus the savings realized in not acting to save that species.

2008 USCCSP Synthesis and Assessment Product 3.4: Abrupt Climate Change

The next major report to address abrupt climate change was the 2008 United States Climate Change Strategic Plan Synthesis and Assessment Product 3.4, Abrupt Climate Change report (USCCSP, 2008). This report (also known as SAP 3.4) was focused solely on abrupt climate change, but took a different approach from the 2002 NRC report by focusing on four key areas of interest:

• Rapid Changes in Glaciers and Ice Sheets and their Impacts on Sea Level;
• Hydrological Variability and Change;
• Potential for Abrupt Change in the Atlantic Meridional Overturning Circulation (AMOC); and
• Potential for Abrupt Changes in Atmospheric Methane.

As stated in their introduction, “This SAP picks up where the NRC report and the IPCC AR4 leave off, updating the state and strength of existing knowledge, both from the paleoclimate and historical records, as well as from model predictions for future change.” Their findings are woven into the present report, but are too extensive to repeat in this Introduction. A few key findings are discussed briefly, however.

“Although no ice-sheet model is currently capable of capturing the glacier speedups in Antarctica or Greenland that have been observed over the last decade, including these processes in models will very likely show that IPCC AR4 projected sea level rises for the end of the 21st century are too low.” This finding re-states the caveat expressed in the AR4 concerning the lack of understanding about glacial dynamics, particularly fast-flowing, large glaciers such as parts of Greenland and West Antarctica. As detailed in the present report, the scientific community has not yet formed a consensus regarding the rate with which large glaciers can shed ice, and thus uncertainty remains about the speed and eventual magnitude of sea level rise, both over this coming century, and beyond.

The SAP 3.4 raised two questions concerning tipping points in droughts. The first is the model predicted expansion of aridity into the U.S. Southwest accompanying the general warming of the ocean and atmosphere. As they state, “If the model results are correct, then this drying may have already begun, but currently cannot be definitively identified amidst the considerable natural variability of hydroclimate in Southwestern North America.” This remains a key area of concern, and one that is addressed in this report. The SAP 3.4 also raised the issue of monitoring for tipping point behavior in the hydrological cycle ( Chapter 4 of that report), including the potential for megadroughts in a world warmed by greenhouse gases. Physical understanding suggests that mega-droughts are more likely to be triggered by interior reorganization of the ocean-atmosphere system rather than by overall warming of Earth’s surface, although overall warming can cause interior reorganization and thus can be responsible indirectly. The SAP 3.4 report states that it is unclear whether current climate models are capable of predicting the onset of mega-droughts: “… systematic biases within current coupled atmosphere-ocean models raise concerns as to whether they correctly represent the response of the tropical climate system to radiative forcing and whether greenhouse forcing will actually induce El Niño/Southern Oscillation-like patterns of tropical SST change that will create impacts on global hydroclimate…”. Research done since SAP 3.4 suggests that the drying from human-caused climate change (radiatively forced reduction of the net surface water flux, i.e., the precipitation minus evapotranspiration) appears to be comparable to the drying induced by the impacts of La Nina over the Southwestern North America since 1979 (Seager and Naik, 2012). In the future, drying forced by the addition of anthropogenic greenhouse gases

to the atmosphere is expected to increase along with earlier melting and reduced storage of mountain snow packs, although whether changes of climate variability would intensify or mitigate such drying remains uncertain (Seager and Vecchi, 2010). In addition, as increasing anthropogenic forcing shifts the surface temperature distribution (Trenberth et al., 2007; Meehl et al., 2007b), extreme warm temperatures and soil moisture loss would increase. Thus, the “climate dice”, mainly controlled by random climate variability, would become more “loaded” with the risk of mega-drought even if a particular drought is simply the result of natural climate variability (Hansen et al., 2012, 2013a).

The SAP 3.4 also addressed the potential for tipping points in the North Atlantic Deep Water formation; and in the release of methane, a potent greenhouse gas, to the atmosphere. As with the IPCC AR4 report, the SAP 3.4 report concluded that deepwater formation was not likely to “tip,” although it is likely to decrease, with impacts on precipitation patterns that could be tipped on regional scales. The potential for catastrophic methane release, from decomposition of terrestrial carbon stocks in permafrost, or methane ice in clathrates, was considered small. However, the potential for gradually increasing methane and CO 2 release from thawing permafrost was considered important, and would accelerate the loading of these greenhouse gases into the atmosphere over many decades to centuries. The report recommended that the United States should “Prioritize the monitoring of atmospheric methane abundance and its isotopic composition with spatial density sufficient to allow detection of any change in net emissions from northern and tropical wetland regions.” Such a prioritization has not occurred; in fact, the primary monitoring network for greenhouse gases globally, the NOAA network, 4 has faced funding cuts of over 30 percent in the past several years.

2012 NRC Report on Climate and Social Stress

The NRC report Climate and Social Stress: Implications for Security Analysis (2012b) is the most recent report to address abrupt climate change. It dedicates a section to a general discussion of abrupt climate change, with an additional section allocated to the topic of extreme events. The report focuses on the coming decade, and as such they conclude that there is little expectation in the scientific community for an abrupt change on that timescale. It makes several recommendations including enhanced monitoring, such as enhanced drought metrics to assess if a region is entering a new mega-drought. These include social factors as well, for example:

4 http://www.esrl.noaa.gov/gmd/ccgg/flask.html .

“changes in the social, economic, and political factors that affect the size of the exposed populations, their susceptibility to harm, the ability of the populations to cope, and the ability of their governments to respond. Where potentially affected areas are important producers of key global commodities such as food grains, it would also be important to assess the effects of climate-induced supply reductions on global markets and vulnerable populations.”

The NRC 2012 report also called for enhanced monitoring of such factors, and noted that society is, in general, rather blind to what is at risk to abrupt climate change, for example, having only limited understanding about the risks posed by sea level rise to coastal infrastructure, toxic materials in landfills, or drinking water aquifers.

THIS REPORT

Looking back across these previous reports, it can be seen that while a great deal of progress on the topic of abrupt climate change has been made, there is still a long way to go to achieve an understanding of these issues with enough fidelity to be able to anticipate their occurrence. This report takes on that challenge, as per the committee’s statement of task, given in Box 1.5 .

Organization of the Report

The committee recognized that discussions of abrupt climate changes and impacts of abrupt climate changes may have different audiences. As such, the report is organized so that one can seek information on the processes as well as information on the impacts.

Chapter 2 gives examples of abrupt climate changes, specifically those examples that the committee believes are worthy of highlighting either because they are currently believed to be the most likely and the most impactful, because they are predicted to potentially cause severe impacts but with uncertain likelihood, or because they are now considered to be unlikely to occur but have been widely discussed in the literature or media. This section includes processes such as the changing chemistry of the oceans and the melting of ice sheets leading to sea level rise. Many of these processes have been discussed in the recent reports ( Box 1.2 ), and the committee provides an updated discussion building on those previous reports.

Chapter 3 discusses abrupt climate impacts from the perspective of how they affect humans, building on many of the same processes discussed in Chapter 2 . Examples include abrupt changes in food availability, water availability, and ecosystem services.

BOX 1.5 STATEMENT OF TASK

This study will address the likelihood of various physical components of the Earth system to undergo major and rapid changes (i.e., abrupt climate change) and, as time allows, examine some of the most important potential associated impacts and risks. This study will explore how to monitor climate change for warnings of abrupt changes and emerging impacts. The study will summarize the current state of scientific understanding on questions such as:

1. What is known about the likelihood and timing of abrupt changes in the climate system over decadal timescales? Are any of the phenomena considered by the committee currently embodied in computational climate models? The committee could consider relevant physical and biological phenomena such as:

• large, abrupt changes in ocean circulation and regional climate;

• reduced ice in the Arctic Ocean and permafrost regions;

• large-scale clathrate release;

• changes in ice sheets;

• large, rapid global sea-level rise;

• growing frequency and length of heat waves and droughts;

• effects on biological systems of permafrost/ground thawing (carbon cycle effects);

• phase changes such as cloud formation processes; and

• changes in weather patterns, such as changes in snowpack, increased frequency and magnitude of heavy rainfall events and floods, or changes in monsoon patterns and modes of interannual or decadal variability.

2. For the abrupt climate changes and resulting impacts identified by the committee, what are the prospects for developing an early warning system and at what lead time scales? What can be monitored to provide such warnings? What monitoring capabilities are already in place? The committee will consider monitoring capabilities that include both direct observations and the use of models in conjunction with observations.

3. What are the gaps in our scientific understanding and current monitoring capabilities? What are the highest priority needs for future research directions and monitoring capabilities to fill those gaps?

Chapter 4 examines the way forward in terms of both research on abrupt changes and their impacts, and monitoring to detect and potentially predict abrupt changes. This chapter examines priorities and capabilities for addressing research knowledge gaps. It also addresses the question of what to monitor to observe that an abrupt change is coming, and how to identify tipping points in various systems.

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Climate is changing, forced out of the range of the past million years by levels of carbon dioxide and other greenhouse gases not seen in the Earth's atmosphere for a very, very long time. Lacking action by the world's nations, it is clear that the planet will be warmer, sea level will rise, and patterns of rainfall will change. But the future is also partly uncertain—there is considerable uncertainty about how we will arrive at that different climate. Will the changes be gradual, allowing natural systems and societal infrastructure to adjust in a timely fashion? Or will some of the changes be more abrupt, crossing some threshold or "tipping point" to change so fast that the time between when a problem is recognized and when action is required shrinks to the point where orderly adaptation is not possible?

Abrupt Impacts of Climate Change is an updated look at the issue of abrupt climate change and its potential impacts. This study differs from previous treatments of abrupt changes by focusing on abrupt climate changes and also abrupt climate impacts that have the potential to severely affect the physical climate system, natural systems, or human systems, often affecting multiple interconnected areas of concern. The primary timescale of concern is years to decades. A key characteristic of these changes is that they can come faster than expected, planned, or budgeted for, forcing more reactive, rather than proactive, modes of behavior.

Abrupt Impacts of Climate Change summarizes the state of our knowledge about potential abrupt changes and abrupt climate impacts and categorizes changes that are already occurring, have a high probability of occurrence, or are unlikely to occur. Because of the substantial risks to society and nature posed by abrupt changes, this report recommends the development of an Abrupt Change Early Warning System that would allow for the prediction and possible mitigation of such changes before their societal impacts are severe. Identifying key vulnerabilities can help guide efforts to increase resiliency and avoid large damages from abrupt change in the climate system, or in abrupt impacts of gradual changes in the climate system, and facilitate more informed decisions on the proper balance between mitigation and adaptation. Although there is still much to learn about abrupt climate change and abrupt climate impacts, to willfully ignore the threat of abrupt change could lead to more costs, loss of life, suffering, and environmental degradation. Abrupt Impacts of Climate Change makes the case that the time is here to be serious about the threat of tipping points so as to better anticipate and prepare ourselves for the inevitable surprises.

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• Published: 29 July 2024

Critically examining research funding patterns for climate change and human health

• Benjamin K. Sovacool   ORCID: orcid.org/0000-0002-4794-9403 1 , 2 , 3 , 4 ,
• Heather Clifford 1 ,
• Rebecca Pearl-Martinez 1 ,
• Emma Gause 5 , 6 ,
• Danielle Braun 7 , 8 ,
• Leila Kamareddine 7 ,
• Amruta Nori-Sarma 5 , 6 &
• Gregory A. Wellenius   ORCID: orcid.org/0000-0003-0427-7376 5 , 6  

npj Climate Action volume  3 , Article number:  64 ( 2024 ) Cite this article

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• Climate change

Many actors have recently launched significant new initiatives in the domain of climate change and health. Given this important nexus, we undertook a review of funding patterns from 1985 – 2022, using the NIH RePORTER database for the United States and the Dimensions database globally. This includes an assessment of more than 9 million publicly funded projects across both databases with a collective budget of more than $3 trillion. We estimate that between 1985 and 2022 only 0.26% of research funding awarded by the NIH related to climate change, and only 0.70% of funded projects in the Dimensions database related to climate change and human health. Moreover, we thematically map funding patterns according to four thematic areas: changes in climate, the effects of climate change, health impacts, and interventions and strategies. More funding is needed to better anticipate and prepare for the projected health impacts of climate change.

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Justice considerations in climate research

Introduction.

Climate change is a significant and systemic threat to human health. At COP28 in the United Arab Emirates in December 2023, 124 countries endorsed “the Declaration of Climate and Health” and pledged $ 1 billion in new funding efforts 1 . Such an endorsement may be long overdue, given that the World Health Organization estimates that climate change will cause 250,000 additional deaths per year—or 5 million excess deaths every 20 years—through factors such as increased heat stress, exacerbated malnutrition, or the spread of malaria through new flooding, and at least $ 2–$ 4 billion a year in additional direct healthcare burdens 2 . The total toll from climate change since 2000 will surpass 4 million in 2024 3 , a figure greater than the population of Berlin, Germany or Los Angeles, California. The World Health Organization also reports that 3.6 billion people live in areas extremely vulnerable to climate change impacts, and that the death rate from extreme weather events in vulnerable regions such as low-income countries or small island developing states is 15 times higher than less vulnerable ones over the past decade 2 .

Responses to climate change via investments in mitigation or adaptation also have significant intersections with public health. Shindell and colleagues 4 examine the human health implications of achieving a low-carbon pathway aimed at reducing carbon dioxide emissions by about 180 Gigatons of carbon, and estimated that the resulting decreased air pollution would result in ~153 million fewer premature deaths worldwide. Similar findings showcasing the large health co-benefits from climate action have been confirmed by Pearce and Prehoda 5 , Lelieveld and colleagues 6 , Sovacool and Monyei 7 , and Kharecha and Hansen 8 . Pearce and Parncutt 9 calculate that one future premature death is caused every time 1000 tons of fossilized carbon is burned, concluding that if climate change exceeds a 2 °C temperature change, one billion human lives could be lost by the end of this century 9 , 10 .

The enormity of these estimations makes climate change one of the most pressing health concerns of the modern era, rivaling or even surpassing that of global pandemics such as COVID-19, which resulted in an estimated 3.4 million excess deaths 11 . Previous evaluations suggest that funding research at the intersection of climate change and human health has been insufficient to meet the challenges of the problem, but funding patterns and research trends continue to evolve rapidly 12 , 13 suggesting the need for an updated assessment. Moreover, comparison across prior studies is hampered by the lack of a standard definition of how to identify funding or research in climate change and health 14 .

Accordingly, in this Perspective we aim to use a standardized approach to provide a comprehensive assessment of trends in funding from 1985–2022 for research relevant to climate change and human health, in the United States and globally. Our objective is to provide potential funders and practitioners insights into patterns of past and present funding, and a justification for the greater funding needed to minimize the adverse health impacts of continued climate change.

In doing so, we make multiple contributions. First, we move beyond bibliometric assessments of the published literature 15 , 16 , 17 to examine research funding patterns. Second, we offer a more complete and recent assessment than earlier studies looking at only non-federal funding 18 , or subsets of NIH funding 19 , 20 . Instead, we consider all funding from NIH as well as funding patterns globally, leveraging a database that also includes funding from other government agencies, national research councils, and foundations. Third, we utilize a more exhaustive set of search strings, coupled with a deeper analysis of the thematic categories of funded projects (see Supplementary Tables 1 and 2 for more details). We aim to offer timely insights into global funding for climate change and human health and have salient implications for setting future priorities for public and private investments in research.

Climate change and human health: compelling impacts and complex vulnerabilities

To make the case that climate change deserves sustained and prioritized funding from the health sciences, health economics, implementation sciences, environmental health, and public health communities, consider that climate change can affect human health in ways as sobering as they are complex. Climate change can result in exposure to extreme heat of increasing severity and frequency, aggravated exposure to floods and droughts, and changes in the distribution of vector-borne diseases 21 . Risks and causal pathways between climate change and health can be direct and primary, such as temperature-enhanced levels of urban air pollutants, but also secondary and less direct, such as changes in water availability or food yields, or even tertiary and diffuse, such as mental health problems among suffering farming communities or refugees displaced by rising sea levels 22 .

Communicable and noncommunicable diseases associated with climate exposures represent a growing proportion of the overall morbidity and mortality risks 23 . A systematic review of the global impacts of ten climatic hazards linked to both greenhouse gas emissions and known human pathogenic diseases suggested that 58% (218 out of 375) of infectious diseases confronted by humanity worldwide have been at some point aggravated by climatic hazards 24 . Soberingly, the empirical cases analyzed by the review revealed 1,006 unique pathways in which climatic hazards, via different transmission types, led to pathogenic diseases. It concluded that “the human pathogenic diseases and transmission pathways aggravated by climatic hazards are too numerous for comprehensive societal adaptations,” 24 which in simple terms implies that the spread of climate change-induced diseases will far exceed our capacity to adapt or respond to them.

Recent analysis from the National Institutes of Health (NIH), an agency of the U.S. Department of Health and Human Services with 27 institutes and centers, similarly confirms the multidimensional relationship of climate change to human health, concluding in its Strategic Framework that “climate drivers affect health outcomes directly through weather events such as extreme heat, wildfires, droughts, storm surges, and floods, but also indirectly through a series of exposure pathways such as air and water quality, food quality, infectious diseases, and massive population displacement events.” 25 The NIH cautions that climate change significantly aggravates the risk of a long list of illnesses or injuries from asthma and allergies to waterborne and zoonotic diseases and that particular populations are disproportionately harmed, notably children, older adults, women and pregnant women, and persons with disabilities, as well as communities of color and rural populations 25 . Similarly, the most recent Assessment Report from the Intergovernmental Panel on Climate Change (IPCC) featured multiple chapters detailing the links between climate change and human health and wellbeing 26 .

NIH Funding Patterns

As Fig. 1 summarizes, from 1985 to 2022, 0.26% of grant funding reported to NIH contained keywords related to climate change. During this period, the NIH funded more than two million projects (2,361,684), of which only 6,130 were related to climate change. Total project funding from the NIH from a more recent sample of projects from 2000 to 2022 involved about $ 620 billion ($619,691,645,384), about $ 2.2 billion ($2,209,643,448) or 0.36% of which went to climate change projects. In 2022, the most recent year in our analyses, climate change and health projects received about $ 281 million in total funding or 0.67% of total annual funding.

Further data is provided in Supplementary Table 3 . CH climate change and health, available for reuse under a CC-BY license. A shows total number of projects as a % of all projects by fiscal year. B shows cumulative project budgets and costs by fiscal year. C shows total number of counted projects per year. D shows project budgets and costs as a % of all budgets and costs by fiscal year.

The above estimate for 2022 is similar to NIH’s own estimate of $ 293 million in funding for that year reported under the category of “Climate-Related Exposures and Conditions” 27 . However, examination of the grants included suggests that this definition of which funds are allocated to climate and health may overestimate the agency’s investments in this area. For comparison, the NIH also reports funding allocated in 2022 to “Climate Change” using a much more specific definition (albeit, less sensitive) of only $33 million. Although the estimated level of funding varies substantially depending on the definition of climate and health used, even the largest estimates still represent a tiny fraction of total NIH extramural research funding.

Dimensions Funding Patterns

As depicted in Fig. 2 , between 1985 and 2022 only 0.70% of funded projects in the Dimensions database related to climate change and human health—or 43,146 out of a total of 6,265,486 projects. In the more recent period from 2000 to 2022, Dimensions covered $ 1.943 trillion in funded projects, $ 20.928 billion or 1.1% of which was disbursed for projects identified as related to climate change and health. For 2022, the most recent year for which we have data, 1% of the annual count of grants and 1.83% of the total budgets of projects in the Dimensions database involved climate change and human health.

Additional data is provided in Supplementary Table 4 . CC&H climate change and health. A shows cumulative project budgets and costs by fiscal year. B Project budgets and costs as a % of all budgets and costs by fiscal year. C shows total number of counted projects per year. D shows number of projects as a % of all projects by fiscal year.

Thematic Areas of Climate and Health Research

Figure 3 presents a graphic developed by the National Institute of Environmental Health Sciences (NIEHS, an institute within NIH) to illustrate some of the many interrelated issues with relevance to climate change and health research. The figure illustrates that changes in climate encompass aspects such as rising temperatures, sea level rise, or extreme weather events. Effects of climate change include, for example, exposure to air and water pollution, heat stress, or changing disease vectors, exposures which in turn may lead to higher rates of heat related illnesses, injuries, and death. The category of “Interventions and Strategies” highlights the importance of human responses such as preparedness, community awareness, or education.

Figure courtesy of NIEHS, NIH, available for use without copyright.

Figure 4 maps the funding patterns within the NIH RePORTER and Dimensions databases from 2000 to 2022 according to these four general thematic areas and 20 specific subcategories, with specific keywords described in Table 1 and Supplementary Table 5 . Mapping is not mutually exclusive; projects can fall across multiple categories. Moreover, the use of this schematic and its categories is meant to provide an illustrative view of funding patterns across broad themes; we encourage researchers to consider other categorization rubrics building on this or other frameworks. Using this open approach, within NIH projects (Fig. 4A ), the interventions and strategies area represent almost half of the funded projects within the sample, followed by health impacts and the effects of climate change, with the least funding disbursed to the areas of changes in climate. Interestingly, the same trend holds true for Dimensions projects (Fig. 4B ).

Depicts funding patterns from the NIH Reporter and Dimensions Databases coded by the topical areas depicted by the National Institute of Environmental Health Sciences. Methods are described in Section 3 and corresponding data presented in Supplementary Tables 3 – 5 . Panel A refers to NIH funding, Panel B Dimensions funding. The terminology query for the four thematic areas within the Climate and Health projects has limitations due to the number of search terms selected and not being able to utilize a truncation proximity search.

Table 1 presents data on funding disbursements across both the NIH and Dimensions databases across the four thematic areas of climate change and human health. As expected, the Dimensions database reflects more grants and greater total funding versus what is reflected the NIH RePORTER database. And across both databases, the total funding for climate and health is relatively low compared to total funding budgets. But interestingly, this analysis reveals that NIH allocates a larger percent of its budget to research on climate and health in each theme, apart from Theme 1: changes in climate.

Previous analyses have concluded that climate change research has historically been underfunded compared to the scope and scale of the challenge at hand, especially with respect to specific health effects of climatic exposures. For instance, Ebi and colleagues estimated that in 2009, the U.S. NIH committed only 0.025% of its annual research budget to climate change and health 28 . Ebi and colleagues also calculated that the European Union’s Seventh Framework Programme committed 0.08% of its total budget to climate change and health and that the amount committed under the European Commission’s Horizon 2020 Research Program was 0.04% of the budget 28 . They surmised that these funding “mismatches” were occurring because climate change was viewed primarily as an environmental rather than a health problem (and thus the responsibility of other funders), and much research was focused narrowly on managing the health risks of climate variability (rather than a broader research agenda involving climate adaptation and human resilience).

However, the funding patterns reported within the NIH and Dimensions databases also illustrate more recent improvements in the funding landscape. For example, the NIH and Dimensions databases reveal some substantive funding patterns of climate change and health projects, with the NIH disbursing about $ 2.2 billion on such projects from 2000 to 2022, and Dimensions tracking another $ 20.9 billion. Although still low in both relative and absolute terms, Figs. 2 and 3 also show an accelerating growth trajectory for funding for climate change and health research, which has been further bolstered by the recent focus on the NIH Climate Change and Health Initiative and awards (including to the authors of this piece) made under this umbrella. Indeed, NIH’s own tabulation of funding for “climate change” indicates that funding in this area increased sharply in 2023 compared to prior years 27 .

Although recent trends in funding are improving and promising, both the international community and the United States may need to allocate more funding to climate change and health topics to better anticipate and prepare for the projected health impacts. Given limitations in our research design, it seems likely we are overestimating the amount of funding dedicated to climate change and health research. Regardless of the exact amount, it is clear that climate change and health still receives much less funding than is needed. Moreover, the agencies providing that funding are not always structured or have the diversity of expertise needed to optimally support this field. Some have even called for the creation of a “National Institute of Climate Change and Health” as well as establishing international collaborations to better pool or coordinate resources and expertise 20 . The community of practice remains highly fractured and siloed by discipline and funding agency, perhaps both a cause and effect of historic underinvestment.

The recent launch of the NIH Climate Change and Health Initiative is an encouraging sign, efforts that have been accompanied by synergistic initiatives at other US federal government agencies including the Environmental Protection Agency (EPA), the National Science Foundation (NSF), the National Oceanic and Atmospheric Agency (NOAA) and other agencies specifically to increase research and translation in climate change and health. Various cost benefit analyses continually highlight the long-term economic advantages of investing in both climate mitigation and adaptation. These are welcome trends and findings, and they justify continued and greater investment and synergistic research across the thematic areas of changes in climate, the effects of climate change, health impacts, and interventions and strategies that should be mirrored on a global scale.

Additional funding may be necessary, but insufficient by itself, to systematically embed climate change and health research efforts together. It is likely that such increases in funding need to be complemented with capacity building efforts that also grow a robust climate change and health workforce. Worryingly, pending health threats could very well overwhelm the adaptive capacity of the public health sector 29 . Responding to these threats adequately demands an increased emphasis on coupling new funding streams to institutional learning, innovative management strategies, and new training regimes for health and climate change practitioners.

Given the magnitude of the problem and starkness of the evidence linking climate change to human health, we set out to assess funding patterns definitively and comprehensively through two separate reviews: a national assessment of NIH funding, and a global assessment of projects covered by the Dimensions database.

The NIH RePORTER database

We chose to focus part of our analyses on funding from the US NIH because it is the largest public funder of research related to health in the world 19 . Begun in 1887, the NIH is “the focal point for health research” and the “steward of medical and behavioral research for the Nation.” 30 The NIH invests ~$ 45 billion in medical research annually, primarily through about 50,000 competitive grants, disbursed to >300,000 researchers at >2500 universities, medical schools, and research institutions 31 . We searched the NIH RePORTER database for climate change and health research projects from 1985–2022, with the specific search terms described in Supplementary Table 1 . Our extensive search string included more than fifty words related to climate change, global warming, heat exposure, wildfires, droughts, weather events and flooding. This list also covered all Climate Change terms, and Climate Change Proxy terms from the NIH Climate Change and Health Literature Portal Search Strategy terms (see Supplementary Table 1 ). One advantage to our approach is that we utilized more comprehensive search strings and a greater number of years and projects than previous work, such as Ghosh and colleagues who examined six terms to identify projects funded over the past 10 years 19 .

We searched for the presence of climate change keywords in abstracts across intramural (on NIH campus) and extramural (not on NIH campus) research grants denoted as series R, U, P, K, and T funded by any NIH institutes and centers, as well as funding mechanisms for fellowships, instrumentation, facilities construction, and small businesses, or C, F, G, and S series. The RePORT (Research Portfolio Online Reporting Tools) website provides access to a variety of reporting tools, reports, data, and analyses of NIH research activities, and the RePORTER (RePORT Expenditures and Results) portal includes funding levels and results.

The RePORTER tool contains a repository for users to access information on research projects, publications, and patents resulting from NIH funding. Since we assume the NIH RePORTER repository only contains projects relevant to human health, health-related terminology is excluded from the query to not limit the intake of projects we observed. Our analysis of this data includes collecting the number and total cost of funded projects per year to compare with the total collection of NIH-funded grants annually.

The dimensions database of Global Research Projects

The Dimensions database claims to be “the most comprehensive research grants database which links grants to millions of resulting publications, clinical trials and patents.” 32 The Dimensions Database is managed by a technology company known as Digital Science, which claims that the database has been developed in close collaboration with >100 leading research organizations. As of October 2023, the database included information on 7 million grants and 9.9 million researchers, who in aggregate have secured >$ 2.4 trillion from 686 funders worldwide. The biomedical and clinical health sciences represent the largest single source of funded projects (see Fig. 5 Left), and unlike the NIH RePORTER, the database involves an extensive list of projects outside of the United States (see Fig. 5 Right) 33 .

Distribution of funded projects by discipline (left) and country (right) across the Dimensions Database, 2023.

We searched the Dimensions database for climate change and health grants (“records” within Dimensions) using similar search strings to the NIH RePORTER with the addition of General Disease terms, with Supplementary Table 2 offering a summary of our approach. Climate keywords used to query the Dimensions database were also matched to the Climate Change terms, and Climate Change Proxy terms from the NIH Climate Change and Health Literature Portal Search Strategy terms. The Climate and Health keywords were searched within the “full text” of each document (including title, abstract, full grant text when available, as well as other data fields included in the documentation) using the start year of the grant, between 1985 to 2022. Our analysis consists of collecting the annual grant count and funding to compare with the total collection of grants located in the Dimensions database.

Limitations

Our approach has several important limitations. We searched only for information and projects available in English, meaning we likely overrepresented projects in the Global North in our dataset, although the Dimensions database does include projects in some country specific languages. We built our dataset by searching only the NIH RePORTER and Dimensions databases. We would not capture the extent of all research or funding connecting climate change with health, only those reported to the NIH or Dimensions Databases. In simpler words: we would have missed any projects not captured by these databases. Moreover, the NIH RePORTER database does not always reflect synergistic initiatives with other US federal agencies such as the EPA, NSF, CDC, NOAA, and NASA, which is why we coupled our analysis with the Dimensions database, which does cover those sources. Lastly, given that we harvested information via investigator reported abstracts (for NIH) and official research council or foundation records (for Dimensions), it is possible that certain terms or key words that were perceived to be controversial were removed or avoided (e.g., in Canada 34 and the United States 35 , 36 ). In those university cultures, some scientists across disciplines might feel compelled to minimize the visibility of the fact that their work very much informs climate questions, for all sorts of political or other reasons.

Furthermore, we may overestimate the volume of climate change and human health funding given that some of the titles or abstracts we searched may use words like “climate” or “heat”, but not always in the context of a project about climate change. Similarly, we included many terms for “weather” in our search strings (see Supplementary Tables 1 - 4 ) meaning we would capture some projects that deal only with weather events, but not necessarily those caused by or correlated with a changing climate.

Additionally, the NIH RePORTER query search is constrained by the generalization of climate terms used and the assumption all projects are related to human health, therefore animal studies may be included. The NIH RePORTER database did not allow us to search for truncations of the terms selected, limiting our detection of modified terminology. Our initial results for this perspective allow for a generalized conclusion about Climate and Health projects within the NIH RePORTER database. While the search query for the Dimensions database did allow for a truncation search, our scope was similarly limited due to the number of generalized health terms we included in our query, which did not include detailed exposure or disease terminology.

Lastly, in Fig. 4 and Table 1 we map funding patterns and results thematically using a framework developed by the National Institute of Environmental Health Sciences (NIEHS, an institute within NIH) to illustrate some of the many interrelated issues with relevance to climate change and health research. We acknowledge that this framework, although convenient and widely cited, was developed primarily as an illustrative tool rather than a prescriptive catalog of terms, does not provide an exhaustive list of disease or health impacts, combines education and awareness, and is limited to only a few categories.

We therefore fully acknowledge these results might be approximate due to the limited search terms used and the initial assortment of Climate and Health projects detected within the databases. We plan to execute a more in-depth query and analysis for a future publication (and encourage others to do so) that will expand on search terminology for both Climate and Health. We also call on future research to better differentiate between climate and weather and to discuss and analyze results that go beyond NIH’s Climate Change and Health Initiative framework.

Data availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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Acknowledgements

The research reported in this publication was supported by the National Institute Of Environmental Health Sciences of the National Institutes of Health under Award Number U24ES035309. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Sovacool, B.K., Clifford, H., Pearl-Martinez, R. et al. Critically examining research funding patterns for climate change and human health. npj Clim. Action 3 , 64 (2024). https://doi.org/10.1038/s44168-024-00142-0

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Landmark new research shows how global warming is messing with our rainfall

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The past century of human-induced warming has increased rainfall variability over 75% of the Earth’s land area – particularly over Australia, Europe and eastern North America, new research shows.

The findings, by Chinese researchers and the UK Met Office, were published overnight in the journal Science. They provide the first systematic observational evidence that climate change is making global rainfall patterns more volatile.

Climate models had predicted this variability would worsen under climate change. But these new findings show rainfall variability has already worsened over the past 100 years – especially in Australia.

Past studies of the observational record either focused on long-term average rain, which is not systematically changing globally, or rainfall extremes where changes are hard to measure accurately. This study looks solely at variability, which refers to uneven timing and amount of rainfall.

The results are consistent with previous research, including ours. This means dry periods are drier than in the past, and rainy periods are wetter.

Alarmingly, the problem will worsen as global warming continues. This raises the risk of droughts and floods – a pertinent issue for Australia.

What the study found

The research shows a systematic increase in rainfall variability since the 1900s. Day-to-day rainfall variability increased by 1.2% per decade, globally. The trend was more pronounced in the latter half of the century, after 1950.

The increase in variability means rain is more unevenly distributed over time. It might mean a year’s worth of rain at a given location now falls in fewer days. It can also mean long, dry periods are interspersed by torrential downpours, or drought and flooding in quick succession.

The researchers examined observational data and found since the 1900s, rainfall variability has increased over 75% of the land areas studied. Europe, Australia and eastern North America were particularly affected. These are areas for which detailed and long-running observations are available.

In other regions, the long-term trend in rainfall variability was less prominent. The authors said that may be due to random changes in variability, or errors in the datasets.

The increase in daily rainfall variability occurred in all four seasons worldwide, although seasonal differences emerged at smaller, regional scales.

The authors say the increase is largely the result of human-caused greenhouse gas emissions, which have created a hotter and more humid atmosphere, more intense rain events and greater swings between them.

They say the findings pose new challenges for weather and climate predictions, as well as for resilience and adaptation by societies and ecosystems.

How global warming affects rainfall

To come to grips with these findings, it helps to understand the factors that determine how much heavy rain a storm produces – and how these factors are being affected by global warming.

The first factor is how much water vapour is present in the air. Warm air can contain more moisture. Every degree of global warming creates a 7% increase in the average amount of water vapour over a given patch of the surface.

Scientists have known about this problem for a long time. Earth has warmed 1.5°C since the industrial revolution – equating to a 10% increase in water vapour in the lower atmosphere. So this is driving storms to become rainier.

Second is how strong the storm winds can get, and third is how easily large raindrops form from smaller cloud particles. More research is needed to understand how these factors are affected by climate change, but the current evidence is that together they further amplify increases in rainfall over short time intervals and for very extreme storms, while reducing the increases for weaker storms.

How does this fit in with Australian research?

The findings released overnight confirm research by us and others into rainfall variability in Australia.

Analysis of daily extreme rainfall totals across Australia in present and future simulations revealed future increases were likely to exceed expectations from many past studies. Rainfall is likely to increase more sharply in the most extreme events, and appears to do this nearly everywhere on the continent.

In 2022, we looked at rainfall hour-by-hour in Sydney using radar data. We found the maximum hourly rainfall increased by 40% in Sydney over the past two decades.

Our findings have major implications for Sydney’s preparedness for flash flooding. More intense downpours are likely to overwhelm stormwater systems designed for past conditions. But it is not clear how much of this remarkable regional increase in severe rains is due to climate change, or how widespread it is.

Increasing variability also means a greater risk of drought. Climate models suggest rainfall variability in many parts of Australia will keep increasing , unless greenhouse gas emissions are rapidly reduced.

A change in only a handful of heavy rainfall days can make or break a drought in Australia. This means even small changes in variability can bring more devastating droughts in the future as dry periods become drier.

Heeding the warning

Policymakers can often be overly focused on whether their part of the world is becoming wetter or drier overall. But as this new research shows, it’s variability they should be worried about.

This volatility might come in the form of worse droughts. Or it might mean much bigger increases in extreme rainfall and flooding.

The variability will challenge governments and communities in many ways, from managing scarce water resources to coping with natural disasters. We should start preparing for these future challenges now.

And as this dire global problem worsens, the need to reduce greenhouse gas emissions and limit global warming, becomes ever more pressing.

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ORIGINAL RESEARCH article

An experimental analysis and deep learning model to assess the cooling performance of green walls in humid climates.

• 1 School of Built Environment, Massey University, Auckland, New Zealand
• 2 Faculty of Architecture, Silesian University of Technology, Gliwice, Poland
• 3 School of Design and the Built Environment, Curtin University, Perth, WA, Australia
• 4 Department of Architectural Engineering, Rahbord Shomal University, Rasht, Iran
• 5 Department of Engineering, La Trobe University, Melbourne, VIC, Australia
• 6 Department of Urban and Regional Planning, Universidad Politécnica de Madrid, Madrid, Spain
• 7 Department of Civil and Environmental Engineering and Architecture (DICAAR), University of Cagliari, Cagliari, Italy
• 8 Department of Architecture, Tabriz Branch, Islamic Azad University, Tabriz, Iran
• 9 Center for Peaceful and Sustainable Futures (CEPEAS), The IDEC Institute, Hiroshima University, Higashi Hiroshima, Japan
• 10 School of Architecture and Design, Lebanese American University, Beirut, Lebanon

Introduction: Amidst escalating global temperatures, increasing climate change, and rapid urbanization, addressing urban heat islands and improving outdoor thermal comfort is paramount for sustainable urban development. Green walls offer a promising strategy by effectively lowering ambient air temperatures in urban environments. While previous studies have explored their impact in various climates, their effectiveness in humid climates remains underexplored.

Methods: This research investigates the cooling effect of a green wall during summer in a humid climate, employing two approaches: Field Measurement-Based Analysis (SC 1: FMA) and Deep Learning Model (SC 2: DLM). In SC 1: FMA, experiments utilized data loggers at varying distances from the green wall to capture real-time conditions. SC 2: DLM utilized a deep learning model to predict the green wall’s performance over time.

Results: Results indicate a significant reduction in air temperature, with a 1.5°C (6%) decrease compared to real-time conditions. Long-term analysis identified specific distances (A, B, C, and D) contributing to temperature reductions ranging from 1.5°C to 2.5°C, highlighting optimal distances for green wall efficacy.

Discussion: This study contributes novel insights by determining effective distances for green wall systems to mitigate ambient temperatures, addressing a critical gap in current literature. The integration of a deep learning model enhances analytical precision and forecasts future outcomes. Despite limitations related to a single case study and limited timeframe, this research offers practical benefits in urban heat island mitigation, enhancing outdoor comfort, and fostering sustainable and climate-resilient urban environments.

1 Introduction

In recent decades, overpopulation and economic growth have significantly accelerated global warming, resulting in numerous serious issues for urban areas and their residents ( Buhaug & Urdal, 2013 ; Chowdhury et al., 2021 ). The release of greenhouse gases and chemical pollutants into the atmosphere ( Hashim et al., 2020 ) intensifies climate change ( Filho et al., 2023 ; Perera et al., 2020 ), exacerbates the urban heat island (UHI) effect, and increases heat stress ( Chew et al., 2021 ; Mahdavi Estalkhsari et al., 2023 ; Mohammad Harmay & Choi, 2022 ), leading to higher energy demands ( Khotbehsara et al., 2018 ; Mutschler et al., 2021 ). The construction industry, which addresses essential human needs such as housing, is a significant energy consumer, with building and construction sectors accounting for nearly 40% of total energy usage. This energy is primarily consumed by heating and cooling systems throughout the year ( Ghahramani et al., 2018 ; Rafsanjani et al., 2020 )

Various scientific reports have concluded that multiple urban elements, including exterior walls (building envelopes), roofs, terrain, pavement and roads, and building materials, can play an essential role in urban design. This design aims to create thermally pleasant outdoor spaces where these elements can influence the environment by controlling heat transfer ( Convertino et al., 2019 ), solar radiation ( Vox et al., 2018 ), albedo ( Salata et al., 2015 ), and airflow ( Perini et al., 2011 ). Materials have notably different heat capacities, thermal conductivity (thermal bulk properties), and surface radiative attributes such as albedo and emissivity ( Mohajerani et al., 2017 ; Mohammad et al., 2021 ).

Using dark surfaces and materials, such as asphalt, cement, concrete, composite, and metal, can lead to higher air temperatures and manifest UHI effects ( Stempihar et al., 2012 ; Zhou et al., 2021 ). The UHI phenomenon emerges as cities replace natural landscapes with dense arrangements of heat-absorbing surfaces, such as pavement, buildings, and others, resulting in elevated air temperatures ( Kardinal Jusuf et al., 2007 ). Moreover, as noted earlier, the escalating impact of global warming exacerbates the frequency of hot days and nights, heightening the risk of heat stress and associated health concerns. While everyone can be negatively affected by heat waves and extreme heat events, some people, like the elderly, those with chronic illnesses, children, and pregnant women, are at risk of suffering harm during such hot spells ( McElroy et al., 2020 ; Folkerts et al., 2022 ).

There has been extensive research on various aspects of environmental design in different contexts. A broad overlap and cooperation for expected outcomes emerge by carefully studying these strategies. The use of green walls supports processes such as air purification ( Abdo and Huynh, 2021 ), rainwater collection ( Prenner et al., 2021 ), reduction of ambient noise levels ( Cardinali et al., 2023 ), increase in biodiversity ( Chen et al., 2020 ), and enhancement of a building’s fire resistance ( Kotzen et al., 2023 ). Green walls are also highly recommended due to their significant impact on ambient air temperature ( Susorova et al., 2013 ; Olivieri et al., 2014 ; Afshari, 2017 ; Shafiee et al., 2020 ).

For instance, Alexandri and Jones (2008) found that implementing green wall systems decreased outdoor air temperature by 8°C. Additionally, east-west oriented vegetated walls were shown to lower the average daytime temperature by approximately 2°C. Furthermore, Wong et al. (2010) noted that 0.15 m from the plant surface resulted in a temperature reduction of roughly 3°C. In a broader context, Kontoleon and Eumorfopoulou (2010) reported that the cooling potential of a green wall on a north-oriented facade is insignificant and negligible. Additionally, they concluded that the reduction is more significant for west-oriented facades at about 3.50°C, while for east- and south-oriented facades, the temperature drop reaches about 2°C and 1°C, respectively. Chen et al. (2013) concluded that the cooling merits of the VGS are more pronounced on the exterior surface than in the indoor environment, which is relatively weaker. This reduction was recorded at about 1.5°C.

This study has examined the impact of green walls during warm conditions in Rasht, the largest city along Iran’s Caspian Sea. Rasht is classified under Köppen’s subtropical humid climate (Cfa) and is a representative sample of similar Cfa climate zones globally. This unique climate makes Rasht particularly susceptible to the UHI effect compared to other large cities. While studies on green walls have been explored in various research articles over the decades, only a limited number have investigated the thermal performance of green walls (specifically, the reduction of outdoor temperature and mitigation of UHI) in humid conditions across the globe (e.g., Ref ( Alexandri and Jones, 2008 ; Karimi Zarchi and Shahhoseini, 2019 )).

In line with that, several research investigations have addressed UHI concerns in the humid climate of Rasht. These studies have elucidated temperature differentials ranging from 5°C to 6.4°C during the cold season and 3°C–5.6°C during the warm season, differentiating between the central part of the city and its surrounding areas ( Karimi Zarchi and Shahhoseini, 2019 ). In research conducted by Bahman and Dokhat Mohammad (2010) , UHI in Rasht underwent evaluation by deploying nine environmental sensors for field measurements. The findings indicated 5–6.4°C temperature variances at Sabzeh Meydan Square and Shahrdari stations. The fact that green walls can reduce ambient temperature in humid climates is now evident. However, the extent to which distance matters and how effectively these systems can reduce ambient temperature are still underexplored.

This research seeks to assess the influence of SCP on temperature reduction across distinct cardinal directions. By delving into the intricate dynamics of SCP placement, our study aims to provide in-depth insights into the optimal distances that yield the most effective temperature reduction in a humid climate. We used different analysis techniques, such as field measurements and advanced modeling approaches, to quantify the impact of varying SCP distances on temperature mitigation.

To achieve the research objectives, the research team has established two scenarios for the study’s orientation, including Scenario 1) Field measurement-based analysis (SC 1: FMA), and Scenario 2) Deep Learning Model (SC 2: DLM). As previously described in the literature, this study investigates the impact of SCP in a humid climate, evaluating the thermal performance of SCP and the surrounding conditions when green walls are in use. The present study also offers a novel approach by utilizing a deep learning model to enhance the accuracy of the analysis, utilizing data generated from the experimental scenario.

This study provides new knowledge on the impact of green walls in humid climates, a relatively unexplored area with only a few existing studies. This research is also among the first to assess the cooling performance of green walls at different points from the wall surface in a humid climate, offering a detailed analysis of how proximity to the wall affects temperature reduction. By highlighting the benefits of integrating green wall systems, the study supports the development of more effective and sustainable urban design practices.

2 Material and methods

2.1 climate attributes of the study area.

The city of Rasht is situated in the northern region of Iran and falls under the humid subtropical climate category (Cfa) according to the Köppen climate classification. With an average annual rainfall of 1,359 mm, Rasht holds the top position for rainfall among the provincial centers in Iran, earning it the nickname “the city of rain.” Rasht experiences an average annual temperature of 15.9°C, with the average annual maximum air temperature at 20.6°C and the average annual minimum at 11.3°C. The annual temperature range is reported to be 9.3°C. Figure 1 depicts the geographical location of the study area, while Figure 2 illustrates its climatic attributes.

Figure 1 . The location of the study area on the map.

Figure 2 . Climatic attributes of Rasht include dry-bulb temperature, wind speed and direction, and relative humidity (Plotted by Dview).

The latitude of Rasht is 37.280,834, and the longitude is 49.583,057 which is located in Iran country in the cities place category with the GPS coordinates of 37° 16′ 51.0024″ N and 49° 34′ 59.0052″ E. According to the Köppen climate classification, Rasht has a humid subtropical climate (Cfa). The city experiences an average annual rainfall of 1,359 mm. The city of Rasht receives an average annual rainfall of 1,359 mm, making it the leading city in rainfall among the provincial capitals of Iran, and earning it the nickname “the city of rain.” The average annual temperature in Rasht is 15.9°C, with the average annual maximum temperature being 20.6°C and the average annual minimum temperature at 11.3°C. This results in an annual temperature range of 9.3°C between the maximum and minimum temperatures. Figure 1 depicts the geographical location of the study area, and Figure 2 illustrates the climatic features.

2.2 Case study selection criteria

Based on the findings reported by Oji et al (2021) , the green spaces in Rasht city underwent significant changes between 1964 and 2013. The total green areas decreased from 7,255 hectares to 5,990 hectares, marking a 17% reduction. Dense green spaces saw an even more dramatic decline, shrinking from 2,855 hectares to 788 hectares, which represents a 72% decrease. The majority of these reductions were observed in the central region of Rasht (see Figure 3 ). On this basis, SC 1: FMA includes the selection of 4 case studies, all of which are situated in the central region of Rasht with the same properties as SCP in a dense urban area. These cases have the same leaf area index (LAI) (see Table 1 ).

Figure 3 . (A) Changing process of green spaces in Rasht ( Oji et al., 2021 ), and (B) the location of the candidate cases on the map.

Table 1 . This is a table. Tables should be placed in the main text near the first time they are cited.

Moreover, in selecting the samples, extensive care has been taken to ensure consistency in all cases regarding the parameters influencing the UHI effect. These parameters include the number, density, and height of buildings; the window-to-wall ratio; floor height; building function; and pavement characteristics such as material type, albedo, thermal conductivity, thickness, and volumetric heat capacity. Additionally, the level of traffic has been considered to maintain uniformity. SC 1: FMA aims to evaluate whether the SCP orientation effectively reduces UHI. The SCP used in the study features self-clinging plants that attach directly to the wall through adhesive suckers with adventitious roots. These climbers form a self-supporting layer of vegetation on solid surfaces, creating a traditional green wall.

In the process of selecting our case study, we placed significant emphasis on feasibility, ethical considerations, and contextual understanding. Ensuring the feasibility of the study was paramount, considering available resources, time constraints, and logistical factors. Ethical considerations played a crucial role in guiding our choices to guarantee the privacy and confidentiality of individuals involved. To uphold ethical standards, we sought consent from the building owner, engaging in a detailed conversation to obtain approval to experiment. Additionally, contextual understanding was a key criterion, ensuring that the chosen case study provided a rich and comprehensive understanding of the specific environment in which the experiment was conducted. Therefore, we selected C1 among the four candidates as the primary case study to measure the effects of SCP on dropping ambient temperature. We chose this case because of the building’s favorable location for implementing data loggers. The specific situation of the building facilitated this choice. Additionally, we could redo the analysis when the data was not recorded well. The orientation of the SCP is on the North façade.

2.3 Outline of the experimental measurement

To experimentally characterize the effect of SCP on outdoor cooling performance, among four cases across Rasht city, the research team chose a potential SCP Case 1 as a case study for field measurements. The essential reason is that the selected Case 1 is situated in an urban area close to the pedestrian level where everyone walks through the street. Secondly, the green wall is situated on a sufficiently broad street, which prevents shadows from opposite buildings from falling on its surface, thereby eliminating shadowing as a potential confounding variable. Additionally, this case study features an adequate density of plant cover. The green wall is composed of self-clinging plants that attach directly to the wall through their adhesive sucker’s adventitious roots, without requiring external support ( Steinbrecher et al., 2010 ). These climbers could form a self-supporting vegetation (traditional) layer on a solid surface.

The Solar Chimney Power (SCP) system was positioned facing north, aligning the canyon’s axis parallel to the east-west direction. Outdoor temperature measurements were recorded at 15-minute intervals. The SCP was covered with Hedera helix, a commonly used ivy species. Data loggers, placed 1.5 meters above ground near the wall, were used to collect real-time outdoor temperature data ( Griffith and McKee, 2000 ). This height and location were selected to capture temperature readings that accurately represent the immediate vicinity ambient conditions. People can distinctly feel temperature differences at a height of approximately 1.5 m above the ground, which coincides with pedestrian dynamics. This height is significant for environmental monitoring as it captures conditions that are directly perceived and experienced by individuals in outdoor settings. The dataloggers were placed in front of the SCP. Table 1 indicates the data loggers’ technical data. The data loggers are secured on customized stands and placed at 0.15 m, 0.30 m, 0.60 m, and 0.90 m away from the substrate surface (see Figure 3 ). Table 2 provides the details of experimental devices.

Table 2 . The outline of the measurement devices ( Daemei et al., 2021 ).

The data loggers collected temperature data at 15-min intervals throughout the study period. Environmental parameters consist of real-time outdoor air temperatures, measured for 5 days from 1 August 2020, to 6 August 2020, in front of the green wall surface. Before any record, the temperature near the wall was measured by two devices to ensure that the devices were calibrated. The experimental analysis was carried out during summertime to assess the cooling effects of the SCP in reducing the ambient temperature. Afterward, the developed dataset was generated to predict the SCP’s cooling effect for the near future. Besides, the generated dataset was validated against experimental results to ensure that the new dataset was accurate.

When studying the impact of plants on outdoor air temperature, several factors come into play. While the direct equations can be complex and depend on various environmental variables, we provided the concepts and equations for our study. Evapotranspiration is the combined process of water evaporation from soil and plant surfaces and transpiration from plant leaves. It can be a significant cooling factor in green walls. The Penman-Monteith equation is often used to calculate evapotranspiration:

E = Evapotranspiration ( mm / day )

Δ = Slope vapor pressure curve ( kPa /° C )

R n = Net radiation at the crop surface ( MJ / m 2 / day )

G = Soil heat flux density ( MJ / m 2 / day )

γ = Psychrometric constant ( kPa /° C )

T = Air temperature ( °C )

u = Wind speed at 2 m above the surface ( m / s )

e s = Saturation vapor pressure ( kPa )

e a = Actual vapor pressure ( kPa )

The effectiveness of green walls in cooling outdoor air temperature can also be related to heat transfer equations. The heat transfer equation can be applied to calculate the cooling effect based on the rate of heat exchange and the surface area covered by green walls.

Q = Heat transfer rate ( W )

ℎ = Heat transfer coefficient ( W /( m 2 ° C ))

A = Surface area covered by green walls ( m 2 )

T g r e e n = Temperature of the green wall surface ( °C )

T a i r = Outdoor air temperature ( °C )

2.4 Employing deep learning for data augmentation

The research team encountered limitations in obtaining field measurements in this work due to various practical constraints. These limitations include limited data availability, difficulties in data collection, and insufficient data coverage. By employing machine learning techniques, specifically ANNs, we could generate synthetic data that complement the available measurements. This allowed us to overcome the data scarcity and perform more comprehensive analyses. Machine learning techniques, such as ANNs, have demonstrated remarkable capabilities in modeling complex patterns and relationships within data ( Graupe, 2013 ). By training ANNs on the available field measurements, we were able to develop a model that captured the intricate dynamics between the distance for temperature reduction by SCP and the humid climate. Data augmentation using ANNs is a strategy to enhance the performance of models, especially when faced with limited datasets ( Gibson et al., 2022 ).

The trained ANN could then be used to make predictions and estimate the temperature reduction at various distances, providing valuable insights into the system’s behavior. By incorporating machine learning techniques into the study, we have introduced a novel approach to addressing data limitations in temperature reduction by SCP under a humid climate. In the following, six columns of data collected over 5 days, starting from 1 August 2020, at 00:00:00 and ending on 5 August 2020, at 23:45:00 with a 15-min resolution used to train the deep learning model, including a timestamp, the real air temperature, and air temperatures recorded at distances of 0.15 m, 0.30 m, 0.60 m, and 0.90 m away from the SCP. 80% of the data collected over 5 days (every 15 min): real air temp. - air temp at 0.15 m - air temp at 0.30 m - air temp at 0.60 m - air temp at 0.90 m.

The real-time air temperature (ambient) and air temperature (in front of the four points) datasets were recorded at distances of 0.15 m, 0.30 m, 0.60 m, and 0.90 m from the green wall. Eighty percent of this dataset constituted the training group for the ANN, aimed at discerning patterns and relationships to accurately forecast temperatures across different SCP distances. The remaining 20% of the dataset formed the prediction group, evaluating the model’s ability to generalize to new, unseen data points. The ANN model architecture included two input layers for timestamps and air temperature and four output layers for predicting temperatures at specified distances from the SCP. The Mean Squared Error (MSE) function was employed as the model’s loss function to optimize predictions. The structure of the dataset is shown in Table 3 .

Table 3 . Timestamp.

This paper presents the use of a Multilayer Perceptron (MLP) Artificial Neural Network (ANN) as the Deep Learning model. MLP ANN consists of a network of interconnected simple processors, called artificial neurons, that produce real-valued activations to perform a specific task. The learning process of the ANN involves adjusting the weights to achieve the desired behavior, which may be a sequence of computational stages depending on the problem being addressed. The activations are transformed non-linearly at each step, allowing the network to accurately assign credit across multiple stages, a critical property in deep learning. The MLP ANN used in this research has four hidden layers. The architecture of our MLP ANN model is illustrated in Figure 4 . The input layer with two neurons is fed with the timestamp and real air temperature, the hidden layers try to find the relationship between input data and the desired outputs, and the output layer consists of four neurons that represent the air temperature at four different distances from the green wall, form the neural network.

Figure 4 . The Deep Learning MLP architecture graph.

The optimizer used in our Artificial Neural Network (ANN) is the Adam algorithm, a Stochastic Optimization technique ( Kingma and Adam, 2014 ). The model’s performance was evaluated by calculating the Mean Squared Error (MSE) as the loss function, described by equation 9.

In this context, the number of samples is represented by “n,” the true value by “Y_i,” and the predicted value by “Ŷ_i .” We divided our dataset into two sets using random data point selection to train and evaluate the model. The authors considered 80% to train our model and used the remaining 20% to test its performance. For this purpose, we used two well-known metrics for regression models’ evaluation, MSE and Mean Absolute Error (MAE), as shown in Table 4 .

Table 4 . The deep learning model’s accuracy.

3 Results and discussion

3.1 sc 1: fma.

Based on the primary aim of this study, the research implemented four data loggers at four points near the SCP. The collected real-time indoor temperature and humidity data for the green wall were compared to those for a bare wall. The plotted data illustrate the indoor temperature and relative humidity variations, highlighting the differences between the green wall and the bare wall ( Figure 5 ). In fact, building blocks surrounding the SCP, airspeed was reported to negligible by the anemometer (0 m/s). Figure 6 shows the outputs of the data. At this phase of the evaluation, the dependent variable and independent variable are the temperature variation and SCP, respectively. As aforementioned, the specific points (point A: 0.15 m, point B: 0.30 m, point C: 0.60 m, and point D: 0.90 m away from the substrate surface) were specified, and the temperatures were recorded through the data loggers.

Figure 5 . Experimental data: Real temperature versus SCP temperature.

Figure 6 . Layout of the study area and positions of data loggers for measuring ambient air temperature.

Figure 5 indicates the real-time and SCP temperatures gained during the field measurement in the summer season. Based on the data, the temperature record differs for all four points, so there is a significant temperature reduction and differences between 0.15 m and 0.90 m. To better understand the temperature reduction, Figure 7 provides the performance of each point. Temperature fluctuations are observed in the same range for each day. On the other hand, on the second day, the amplitude of thermal fluctuations increased for all four points, which could occur due to the increase in the real-time temperature.

Figure 7 . Experimental data: Real temperature versus SCP temperature (daily average).

According to Figure 7 , the ambient temperature may be affected by air circulation. Though SCP is covered by well-distributed greenery, it has thicker greenery near the temperature data logger, which may block air circulation and trap heat. Hence, it can be inferred that at point A, 0.15 m away from the substrate (plant), the air temperature is most affected by the presence of the SCP. The higher and lower temperature data for the real-time are recorded at 26.5°C and 25.6°C. The lowest and highest temperature range of the recorded points are shown in Table 5 .

Table 5 . Summary of air temperature for each point.

3.2 SC 2: DLM

The trained model is used to estimate air temperature at the distances from the SCP (points A to D), which is what our model is trained for. The result is based on real air temperature from 2020 to 08-06 to 2020-08-10, depicted in Figure 8 .

Figure 8 . Deep Learning results based on real air temperature.

Figure 8 shows the data generated by deep learning, which demonstrates normal and uniform fluctuations every day for every point at the highest temperature and lowest temperature, including information about the real-time air temperature and the recorded temperature of each point over the first 5 days, which indicates the performance of the SCP cooling effect in the next few days. Also, the data obtained from the field measurement for real-time and the specific points and the data generated by the deep learning for predicting real-time conditions are compared with the base model data ( Figure 9 ).

Figure 9 . Validation: (Left) Real-time experiment measurements were compared with the recorded data for each point (daily average); (Right) experiment measurement records were compared with the deep learning generated data (daily average). Dl: deep learning; Exp. Mes.: Experiential measurement.

By considering the deep learning data and observing the overall cooling performance of the SCP, it can be said that the SCP was able to drop the air temperature in a suitable amount compared with real-time. The average reduction was recorded by about 24.6°C. The real-time average air temperature was about 26°C, which proves that the amount of temperature reduction in the data obtained from the experiments is equal to the amount of the overall data generated by the deep learning (about 1.5°C), indicates the validation of the research process ( Figure 10 ).

Figure 10 . The SCP air temperature long-term reduction from 08/01/2020 to 08/11/2020 (Daily average).

4 Discussion

The current study, which was carried out on an urban scale, investigated the cooling impact of the SCP in a humid climate during the summer season. The performance of the SCP was measured at four points located at varying distances from the plant’s surface. The measurements were conducted in two stages. In the first stage, experimental measurements were performed using temperature data loggers for both specific distances and real-time conditions over 5 days. Subsequently, the research team employed a deep learning model to predict the SCP’s future performance and augment data through the ANN model. While the dataset may appear limited in this work, the deep learning model’s ability to generalize from this data to broader conditions was an essential aspect of our approach.

Deep learning is a subset of machine learning that involves the use of neural networks with many layers to analyze various kinds of data ( Ahmed et al., 2023 ). Deep learning applications are quickly replacing traditional systems in numerous facets of our everyday lives ( LeCun et al., 2015 ). Traditionally, research has relied on costly and time-consuming trial and error, guided by expert intuition. The vast number of material combinations makes experimental study impractical, necessitating empirical and computational methods. Although computational approaches are faster, cheaper, and somehow more accurate. They are constrained by length and time scales, limiting their applicability ( Goodfellow et al., 2016 ; Choudhary et al., 2022 ). ANN is a computational model inspired by the human brain’s neural structure and functioning. ANN learns from labeled datasets to perform tasks such as classification, regression, pattern recognition, and more ( Hardesty, 2017 ; Pantic et al., 2023 ).

During the data collection phase of our experimental model, we encountered several measurement errors primarily due to unpredictable weather conditions. These included cloudy periods and unexpected wind, which affected the accuracy and consistency of our temperature readings. Such variations in weather conditions introduced anomalies in the data, making it challenging to maintain uniformity across all measurements. These factors highlight the inherent difficulties in field experiments, where environmental conditions can vary unpredictably and impact the data collection process. Therefore, we excluded the last day of data collection due to these unpredictable weather conditions to maintain the integrity and accuracy of our measurements.

Relying on the 5-day field measurements, the average air temperature reductions for points A, B, C, and D were 23.08°C, 24.4°C, 25°C, and 25.66°C, respectively. The highest and lowest ambient air temperatures were 26.7°C and 25.4°C, respectively. At this stage, considering the performance of each point, the results indicated that each distance had a significant temperature reduction effect compared to real-time data. The findings of the first stage demonstrated that the SCP had a considerable impact on reducing nearby temperatures. The maximum average reduction across the four points was 25°C, compared to the real-time temperature of 26.5°C. This implies that the SCP’s best performance in lowering air temperature was approximately 6% against real-time conditions, resulting in a drop of ambient air temperature by 1.5°C.

The air temperature reductions from 1 August 2020, to 11 August 2020, for points A, B, C, and D were approximately 23.2°C, 24.5°C, 25.2°C, and 25.6°C, respectively. The long-term real-time daily average air temperature was 26°C. Therefore, the points could reduce the ambient air temperature by about 11% for point A, 6% for point B, 3% for point C, and 1.5% for point D. Point A exhibited better performance than point D by about 9%. For further validation of the present study, some research studies have been conducted under the same climate condition (Cfa), providing people with outdoor thermal comfort (OTC). Spagnolo and de Dear (2003) investigated that the preferred temperature for the Cfa climate zone of Sydney during summertime is 23.4°C, and for the entire year is 25°C. Similarly, Binarti et al. (2020) concluded that the suitable OTC is 23.9°C based on the PET neutral range. Consequently, the appropriate temperature ranges during summertime are between 23°C and 24°C, suggesting that the distances of 0.15 m and 0.30 m could reduce the air temperature to within the outdoor thermal comfort range.

While confirming the outcomes of previous literature regarding the mitigating effect of trees, shrubs, and green walls on the creation of UHI ( Abdulateef & A. S. Al-Alwan, 2022 ; Chun and Guldmann, 2018 ; Edmondson et al., 2016 ; Koch et al., 2020 ), our study takes a step forward and argues for the most effective distance for these systems to reduce ambient temperatures. This finding represents a novel contribution to our research within the existing body of literature. Moreover, our paper presents an innovative methodology employing a deep learning model to improve the precision of our analysis. This strategy involves utilizing data derived from experimental scenarios, facilitating the anticipation of precise future results. Integrating machine learning methodologies into our investigation also provides an original avenue for mitigating data constraints within the realm of temperature reduction through SCP in a humid climate.

Numerous analyses and experiments remain pending for the future, primarily due to the absence of stringent policies for buildings, particularly their envelopes and facades. This gap hinders effective measures against urban heat islands and climate change mitigation at the regional level. According to the literature, the current materials are unsustainable, posing environmental and human wellbeing challenges. Future investigations could explore various ideas, including experiments on the thermal performance of SCPs in both winter and summer conditions. Moreover, considering different plant types is essential when evaluating the reduction in ambient air temperature.

In this study, we investigate the impact of green walls in the reduction of ambient air temperature. However, previous studies have shown the influence of green walls on indoor ambient temperature and human comfort. An instance is the study done by Widiastuti et al. (2020) , which demonstrated that the green wall could significantly reduce wall temperatures and maintain indoor temperature. Daemei et al. (2021) investigated the thermal performance of green walls on indoor thermal comfort. Their study showed that green walls can decrease indoor temperature and humidity. Zhang et al. (2019) conducted a study focusing on the thermal performance of green walls compared to bare walls on the northern facade of a two-story residential building in the humid climate of Rasht during the summer. The results demonstrated that the green wall significantly reduced both indoor temperature and relative humidity. Specifically, the green wall reduced indoor temperature by 9% and relative humidity by 32%.

Lastly, the study faced certain limitations. Firstly, due to building and plant constraints and COVID-19 restrictions, real-time data collection for the entire summer was not possible. Secondly, the research focused on only one orientation (North) of the building where the green wall was attached. These limitations indicate areas for further research and consideration. In conclusion, while this research demonstrates the self-cooling plants’ significant potential in lowering ambient temperatures and enhancing urban comfort, these findings should be understood within the context of these limitations. Moving forward, addressing these constraints, and conducting more comprehensive studies will be essential in refining our understanding of innovative solutions like self-cooling plants and their applicability in creating sustainable, comfortable, and resilient urban environments. For future studies, we recommend expanding upon the findings of this research in several key areas. Firstly, additional field measurements across diverse urban environments are needed to validate the findings regarding the cooling performance of green walls. Secondly, it is important to investigate the impact of different orientations of green walls on their cooling effectiveness. Also, we recommend future studies focusing on the impact of such systems on indoor thermal comfort.

5 Conclusion

This study aims to measure green walls’ cooling effects in a humid summertime climate. To do this measurement, this research chose two scenarios, such as experimental measurement, to collect real-time data of temperature and humidity in four different points in front of the green wall, including 0.15, 0.30, 0.60, and 0.90 m. Secondly, an Artificial Neural Network (ANN) model was utilized for data augmentation to expand the dataset. The type of the plant was self-cooling plants. This research focused on the cooling performance of green walls in the north orientation of the building. The findings show a relatively significant reduction in ambient air temperatures at various distances from the plant surface. The results indicate that the green wall effectively lowers air temperature, achieving a 1.5°C (6%) reduction compared to real-time conditions. Over a long-term investigation, specific distances (A, B, C, and D) were found to contribute to temperature decreases of approximately 23.2°C (11%), 24.5°C (6%), 25.2°C (3%), and 25.6°C (1.5%), respectively.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Author contributions

TB: Supervision, Writing–review and editing. AP: Supervision, Writing–review and editing. AR: Resources, Visualization, Writing–original draft. AJ: Resources, Validation, Visualization, Writing–original draft. SA: Resources, Visualization, Writing–original draft. RA: Supervision, Writing–review and editing. MK: Data curation, Resources, Visualization, Writing–original draft. AS: Supervision, Writing–review and editing, Amir Aslan Darvish: Data curation, Formal Analysis, Writing–original draft.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Publisher’s note

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Keywords: green walls, experimental measurement, humid climate, cooling performance, ambient air temperature, urban heat island, deep learning model, artificial neural network

Citation: Daemei AB, Bradecki T, Pancewicz A, Razzaghipour A, Jamali A, Abbaszadegan SM, Askarizad R, Kazemi M and Sharifi A (2024) An experimental analysis and deep learning model to assess the cooling performance of green walls in humid climates. Front. Energy Res. 12:1447655. doi: 10.3389/fenrg.2024.1447655

Received: 11 June 2024; Accepted: 19 July 2024; Published: 05 August 2024.

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Copyright © 2024 Daemei, Bradecki, Pancewicz, Razzaghipour, Jamali, Abbaszadegan, Askarizad, Kazemi and Sharifi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Abdollah Baghaei Daemei, [email protected]

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State of the Art of Coupled Thermo–hydro-Mechanical–Chemical Modelling for Frozen Soils

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• Published: 29 July 2024

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Numerous studies have investigated the coupled multi-field processes in frozen soils, focusing on the variation in frozen soils and addressing the influences of climate change, hydrological processes, and ecosystems in cold regions. The investigation of coupled multi-physics field processes in frozen soils has emerged as a prominent research area, leading to significant advancements in coupling models and simulation solvers. However, substantial differences remain among various coupled models due to the insufficient observations and in-depth understanding of multi-field coupling processes. Therefore, this study comprehensively reviews the latest research process on multi-field models and numerical simulation methods, including thermo-hydraulic (TH) coupling, thermo-mechanical (TM) coupling, hydro-mechanical (HM) coupling, thermo–hydro-mechanical (THM) coupling, thermo–hydro-chemical (THC) coupling and thermo–hydro-mechanical–chemical (THMC) coupling. Furthermore, the primary simulation methods are summarised, including the continuum mechanics method, discrete or discontinuous mechanics method, and simulators specifically designed for heat and mass transfer modelling. Finally, this study outlines critical findings and proposes future research directions on multi-physical field modelling of frozen soils. This study provides the theoretical basis for in-depth mechanism analyses and practical engineering applications, contributing to the advancement of understanding and management of frozen soils.

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1 Introduction

Permafrost and seasonally frozen soil cover approximately 50% of the exposed land surfaces in the Northern Hemisphere [ 59 , 364 ]. In cold regions, these frozen soils are often subjected to complex multi-filed coupling processes in varying temperatures, pressures, and intricate hydraulic–chemical environments. These multi-field coupling processes govern numerous phenomena observed in frozen soils, e.g., frost heave, thaw settlement, moisture migration, phase transition, and ice lens growth [ 40 , 154 , 188 , 189 ]. For example, seasonally frozen regions experience frost heaving due to the volume expansion induced by water/ice phase changes, resulting in uneven deformation. These coupling interactions in frozen soils are critical for construction safety in cold regions since the multi-field coupling process can cause significant deformation even without external loads. Therefore, developing robust and efficient multiphysics models is necessary to comprehensively understand the underlying mechanisms and obtain reliable simulation results for practical applications. Such coupled multi-physics models are theoretically superior to approaches that address individual processes in isolation.

The coupled multi-physics fields play significant roles in various engineering domains, such as railways and highway construction and management (e.g., [ 290 , 347 ]), energy pipeline/water main projects in cold regions (e.g., [ 107 , 108 , 227 , 269 ]), methane hydrates extraction under seabed (e.g., [ 72 , 252 ]), and underground constructions involving artificial ground freezing (AGF, e.g., [ 162 , 203 , 213 ]). Furthermore, the intricate coupling processes exert a profound influence on the behaviour of frozen soils, giving rise to engineering and environmental challenges such as slope instability, climate change impacts, carbon emissions, subgrade settlements, and infrastructure damage [ 98 , 255 , 264 , 268 ]. These coupled multi-physical modelling have direct relevance to numerous research fields, including hydrology and hydrogeology (e.g., [ 244 , 270 ]), nuclear waste storage (e.g., [ 83 , 101 ]), fluvial geomorphology (e.g., [ 136 , 288 ]), Mars studies (e.g., [ 202 ]), climate modelling (e.g., [ 340 ]), and acid mine drainage in cold regions (e.g. [ 36 ]).

Various methods, including experimental and numerical methods, have been employed to investigate the fundamental behaviours of frozen soils. Some investigations focused on elucidating specific properties of frozen soils, such as strength and relationships between different parameters (e.g., soil freezing characteristic curve, SFCC describing the relationship between temperature and unfrozen water content) [ 174 ]. In addition, efforts have been made to explore the coupling mechanisms in frozen soils and develop governing equations for each field. The development of multi-physics modelling for frozen soils has benefited from the advancements in multiphysics investigations of other geomaterials and the development of simulation platforms. For insurance, multiple studies on the coupled model for rocks and soils have been conducted. However, these models have been derived from non-isothermal consolidation of deformable porous media or an extension of Biot's phenomenological model, which fails to consider the phase change in freezing soils. Furthermore, these models are often solved by numerical methods, such as the finite element method (FEM), finite volume method (FVM), and finite difference method (FDM), owing to the highly nonlinear governing equations and complex boundary conditions. On the other hand, field tests are challenging and expensive to perform, resulting in limited available data. Therefore, it is necessary to establish reliable and efficient coupling models for simulating the multi-physical fields of frozen soils, which is crucial for evaluating the risks of engineering where conducting experiments are risky, such as the application of AGF for pollutant dispersion retardation.

The coupled thermo–hydro-mechanical–chemical (THMC) process involves the intricate interactions of thermal, hydraulic, mechanical, and chemical fields. Among these four fields, each pairing of two fields can be intricately interconnected through a coupling process, as illustrated in Fig.  1 . The primary contents of each physical field are described as follows.

Coupled THMC (thermo–hydro-mechanical–chemical) interactions in frozen soils

(1) The thermal ( T ) field is associated with temperature distribution and three heat transfer modes, i.e., conduction, convection, and radiation. The geothermal, solar energy, and local heat sink induced by human/engineering activity (e.g., thermal disturbances) and climate change (e.g., snow conduction and rainfall infiltration) can serve as heat sources. Besides, the temperature gradient acts as the driving force for water migration and phase transition. It is worth noting that the primary type of frozen soil contributing to disasters is warm frozen soil (− 0.5 to 1.5°C).

(2) The hydraulic ( H ) field, referring to Darcy or non-Darcy flows in soils, is an unstable factor affecting frozen soil stability. Moisture migration and water/ice phase transition can exacerbate this deterioration effect. The water–ice phase change occurs when the soil freezes, increasing volume by 1.09 times and generating frost heave from 10 to thousands of kilopascals [ 39 ]. Furthermore, the directional growth of ice lenses during the freezing process can induce anisotropy in hydraulic conductivities after thawing [ 340 ], thereby contributing to the inherent variability in the properties of frozen soils. During thawing, the increase in moisture can increase pore water pressure and reduce effective stress, consequently decreasing the shear strength of frozen soils.

(3) The mechanical ( M ) field mainly refers to stress, strain, strength, damage, failure, and generation and propagation of fractures. The variations of properties and internal structures can significantly influence frozen soils' stress state and mechanical behaviours. During freezing, the soil experiences physical changes induced by the interaction between water and heat, weakening and damaging microstructure in soils, and macroscopic deformation and failure.

(4) The chemical ( C ) field presents the transport of reactive or nonreactive particles or solutes and chemical reactions (dissolution and precipitation), which mainly determines the variation of material and chemical composition in soils, such as leakage and discharge of chemical waste liquids, minerals transformation, and salinisation.

Numerous studies have been conducted on multi-field coupling models for frozen soils, with significant attention given to the numerical implementations of these coupled models. However, these multi-physical field methods for frozen soils have not yet been adequately summarised. Accordingly, this study comprehensively reviews the current investigations on multi-filed coupling models for frozen soils, categorising them into six groups: thermo-hydraulic (TH) coupling, thermo-mechanical (TM) coupling, hydro-mechanical (HM) coupling, thermo–hydro-mechanical (THM) coupling, thermo–hydro-chemical (THC) coupling and thermo–hydro-mechanical–chemical (THMC) coupling. It is worth noting that although the studies on chemical coupling are relatively limited, models related to chemical fields coupled with THM fields (i.e., THMC model) are also considered. In addition to summarising the fruitful investigations on coupled multi-physical modelling, this review also provides a comprehensive overview of the numerical implementations of these coupled models and sheds light on the current research directions of coupled modelling for frozen soils. This comprehensive analysis can facilitate the development of new coupled models in closely related fields and drive advancements in the understanding and simulation of coupled multi-physical processes in frozen soils.

2 Multi-field Coupling Models

2.1 thermo-hydraulic (th) coupling approach.

The heat transfer equation demonstrates that the rate of change of internal energy within a representative volume element (RVE) is contributed to heat flux resulting from thermal conduction, the release of latent heat due to phase change, and the convective heat associated with liquid water seepage per unit time. The governing equation for thermal modelling based on energy conservation can be expressed as:

where C is volumetric heat capacity, T is temperature, λ is thermal conductivity. ρ i and ρ w are density of ice and water; s i is ice saturation, n is porosity, v w is velocity of liquid water; c w is the specific heat of ice.

Based on the principle of mass conservation, the disparity between the inflow and outflow rates of water per unit time should be equal to the rate of change of total water mass within RVE. Therefore, the mathematical representation can be formulated as follows:

where s i is water saturation, k is hydraulic conductivity, H is water head, Q is internal sources or sinks. The left term represents the variation rate of liquid water mass and ice mass, and the right term are the flux of RVE and sources or sinks.

2.1.1 Coupled TH Models

The soils in the seasonal frozen zone experience freeze–thaw cycles annually. Previous soil freezing/thawing investigations primarily concentrate on heat transfer. Since the 1970s, it has been recognised that both thermal flow and mass transfer should be included in the analysis of soils' freezing and thawing process. Since the physical processes in frozen soils are complex, it is difficult to derive a solution to accurately predict the temperature and moisture variation in the freeze–thaw process. Accordingly, numerous numerical models have been developed to simulate the coupled thermo-hydraulic (TH) process within the freezing soils, which is significant for engineers to estimate the frost heave and for soil scientists to predict the temperature and moisture content profiles. Therefore, it is necessary to address the mechanisms of water and heat transfer processes in frozen soils, which is beneficial for revealing the interaction mechanisms of frozen soils and climate change and their influences on the environment and engineering.

Table 1 presents a summary of typical investigations on coupled TH models for frozen soils. The pioneering work by Harlan [ 89 ] established the first TH coupling model for partially frozen soils, providing a numerical finite difference solution to a one-dimensional (1D) coupled TH problem for a homogeneous porous medium under freezing/thawing conditions. Subsequently, numerous TH coupling models were developed to simulate frozen soils' water and heat transfer processes, employing various governing equations and model parameterisations. Some critical conclusions from Table  1 are as follows:

(1) The majority of models account for the variations of unfrozen water content with temperature, especially when the temperature drops below subzero temperature. However, only a few studies consider the migration of unfrozen water. Besides, the water and vapour transfer within frozen soils can affect the water infiltration process and thermal properties during the freezing and thawing, as well as heat transfer by releasing/absorbing a large amount of latent heat [ 253 , 307 ].

(2) Most models used the soil temperature as a prognostic parameter (i.e., a threshold freezing point) to determine the phase change of water, which might lead to numerical instability in simulations [ 51 , 87 ].

(3) The freezing and thawing of frozen soils are accompanied by heat exchanges involving three processes: (i) conductive and convective heat transfer induced by temperature gradient and water migration, (ii) heat release/sink due to the freezing of liquid water and thaw of ice, and (iii) heat exchange between the soil and external environment [ 248 ]. The heat transfer resulting from the latent heat during the phase change of permafrost near 0 °C is considerably larger than that caused by heat conduction or convection resulting from water and vapour flow during freeze–thaw processes. However, as for unsaturated freezing soils, vaporisation is not considered, which would induce errors since the phase change between liquid water and vapour is 7.4 times more energy than that between ice and liquid water [ 251 ].

(4) Many TH coupling models have been validated by comparing field observations, such as variations in temperature and water content within frozen soils at the soil surface or around freezing pipes in AGF and buried oil pipelines. However, it is crucial for TH models to incorporate a sufficiently deep soil profile to enable realistic simulations of temperature profiles over time, particularly for permafrost regions [ 254 ]. For example, to accurately simulate century-long permafrost changes, it is advisable to consider a steady geothermal heat flow as the lower boundary condition, particularly at depths exceeding 30 m [ 283 ]. Besides, numerous studies have shown the significance of enhancing the simulated ground depth and incorporating a greater number of ground layers to capture the diminishing impact of multi-decadal variability with increasing depth more precisely [ 3 , 6 ].

Therefore, it is imperative to investigate the energy distribution state at different depths and transfer features within deeper frozen soils. Given the potential extension of calculation memories with deeper soil configurations, it is crucial to meticulously determine the appropriate soil depth and geothermal flux for surface modelling in cold regions. It is essential to consider the soil properties, commonly derived from lookup tables using available soil maps, as they exhibit variations across diverse areas and subsoil layers [ 217 ].

(5) The majority of TH models commonly fail to address the snow process and surface features that are crucial inputs for accurate TH modelling. Nevertheless, Kelleners [ 132 ] and Lan et al. [ 157 ] have made notable contributions in this regard. Kelleners [ 132 ] developed a numerical coupled TH model for seasonally frozen soils with snow cover, aiming to explore mass and energy exchange among soil, plant, and atmosphere. Snow cover plays a significant role in the land surface, influencing the outcomes of TH model simulations and the energy exchange between the soil and atmosphere. Its impact is primarily attributed to factors such as low thermal conductivity, high surface albedo, and energy absorption resulting from latent heat during snowmelt [ 69 , 90 ]. However, the blowing snow and snow melt are not considered, which causes the underestimation of snow height prediction and overestimation of water content in shallow soils. Accurately simulating the snow process is essential to comprehend frozen soils' thermal and energy balance and better understand coupled TH processes.

In a related study, Lan et al. [ 157 ] established a coupled TH model to analyse the reciprocal relationship between desertification and permafrost degradation, highlighting the crucial role of permafrost in maintaining environmental stability on the Qinghai–Tibet Plateau. Surface parameters, such as surface albedo, emissivity, roughness, sand accumulation, and vegetation coverage, also serve as significant inputs for TH models [ 337 ]. Besides, some researchers [ 42 , 362 ] have emphasised the importance of considering the impacts of freeze–thaw processes on surface parameters to avoid significant errors in simulating water and heat processes in permafrost regions of the Qinghai–Tibetan Plateau. Thus, when exploring coupled TH processes in cold areas, it is crucial to incorporate surface parameters that account for freeze–thaw impacts.

(6) These existing TH models are fully coupled to simulate the interaction between the thermal and hydraulic fields of frozen soils. The thermal transfer within the soil can significantly impact the hydraulic field through phase change phenomena. As freezing progresses, the ice content increases, leading to changes in the hydraulic properties of the soil. To account for this, many studies have incorporated empirical functions to represent the effect of temperature on unfrozen water content. Additionally, some researchers have assumed that the fluid's viscosity coefficient or the frozen soil's hydraulic properties, such as permeability and saturation, depend on temperature. Besides, the impedance impact of ice lenses was also involved in some coupled TH models, and its detailed discussion is depicted in Sect. 2.2.2. Regarding the influence of the hydraulic field on heat transfer, it is crucial to consider the movement of moisture within the frozen soil, which is strongly influenced by temperature gradients. The variation in ice and water content within the soil also affects the effective heat capacity and thermal conductivity values. By accounting for these influences, the coupled TH models can accurately represent the impact of the hydraulic field on heat transfer processes within the frozen soil.

(7) Existing TH models were developed from one-dimensional to two- and three-dimensional configurations, with most models adopting a macroscopic perspective. However, several studies have also been conducted from meso/micro perspectives (e.g., [ 63 , 316 ]). Dong and Yu [ 63 ] specifically developed a microstructure-based four-phase model for clay, employing finite element software (i.e., COMSOL Multiphysics, hereafter referred to as COMSOL) to simulate the coupling TH process. Moreover, Wang et al. [ 316 ] established a multiphase pseudo-potential model with an enthalpy-based model, and employed the lattice Boltzmann method (LBM) to predict the spatial distributions of temperature and water content during the thawing process of frozen soil.

(8) Various solvers have simulated the complex heat and mass transfer processes within frozen soils. Notable examples of coupled TH models include Coupled Heat and Mass Transfer (CoupModel) [ 117 , 169 ], Hydrus-1D model [ 87 , 367 ], and Heatflow model [ 121 ]. These models provide a comprehensive understanding of the combined effects of water flow and heat transfer in frozen soils. Additionally, advanced methods, such as the lattice Boltzmann method, LBM [ 316 ] and bond-based peridynamics [ 229 ], have been employed to analyse the coupled TH process. Numerical methods, including the finite volume method (FVM), finite difference method (FDM), and finite element method (FEM), are alternative approaches for simulating the TH coupling process. These methods are often implemented using popular software packages such as COMSOL and OpenFOAM. A comprehensive discussion of the different solvers will be presented in Sect.  3 .

2.1.2 Parameterisation of TH Model

2.1.2.1 phase change.

Phase change significantly impacts not only the thermal characteristics of soils but also the heat and mass transfer processes within frozen soils. During phase change, a significant release or absorption of latent heat occurs, which impedes rapid cooling or warming of the soil and causes temperature disparities between the air and ground [ 15 , 65 ].

In general, two methods can be employed to account for phase change [ 28 ]. The commonly used method for phase-change porous media problems is apparent heat capacity that adds the latent energy associated with the phase change to the heat conduction equation, which could be defined as [ 27 ]

where C a is apparent heat capacity, C is heat capacity, T is temperature, and L is latent heat during water/ice phase change. The subscripts w and i correspond to the water and ice phases, respectively. However, a notable feature of heat transfer in frozen soils that is different from the conventional process is the presence of unfrozen water, which is not accounted for in the apparent heat capacity method. Another approach to consider the phase change is the excess energy method [ 206 ]. The temperature of soil undergoing phase change remains constant at the freezing point until the heat gain or loss equals the latent heat of the soil. The excess energy method offers a practical approximation of modelling phase change phenomena and enables the coexistence of multiple phases of water within the soil [ 231 ]. Nonetheless, this method does not allow for supercooled water (i.e., liquid water coexists with ice over a range of temperatures below the freezing point), which can be compensated by the relationship between temperature ( T ) and unfrozen water content [ 48 , 170 ]. By comparing the two methods with analytical solutions from Jumikis [ 124 ] and Lunardini [ 206 ], Bonan [ 28 ] indicated that frost penetration depths from the two methods align with the analytical solution, while results from excess energy produce some fluctuations. Furthermore, incorporating phase change improves the accuracy of the simulation.

2.1.2.2 Thermal Conductivity

As a multi-phase medium, the thermal conductivity ( λ ) of soils can be influenced by the content and properties of each component [ 172 , 180 , 181 ]. Moreover, soils with smaller particle sizes, possessing smaller unfrozen water content and lower saturation, tend to have a higher thermal conductivity value in the warm season than that in the cold season [ 173 ]. Many scholars have proposed prediction models for thermal conductivity where λ is a function of soil mineral composition (e.g., [ 46 , 123 , 160 , 171 ]). He et al. [ 95 ] evaluated 39 models for λ and suggested the need for further research to develop a more accurate and generalised model for λ . Besides, some researchers have proposed data-driven models to aid the development of a theory that can better estimate soil thermal conductivity [ 166 , 167 , 177 , 183 ].

2.1.2.3 Hydraulic Conductivity

Hydraulic conductivity ( k ) is another important factor affecting water and heat transfer. Previous studies assume that the soil water content is zero and hydraulic conductivity is directly set to 0 when the temperature is subzero [ 28 , 50 ]. Considering fluid flow in porous soils, two methods have been developed to address the variation of k as water freezes into ice, i.e., k is a function of T , and k is a function of ice and water content.

Previous numerical studies have indicated the accumulation of a significant amount of ice behind the frost front. Jame and Norum [ 115 ] introduced an impedance factor ( I ) to illustrate the resistance imposed by ice on the porous medium's water flow to elucidate the disruption of the presence of ice on frozen soils.

where k f and k u are the hydraulic conductivity of frozen soils and unfrozen soils; a is an empirical constant depending on soil type, which can be obtained by fitting a diffusivity versus water content function in the laboratory experiments; and θ i is the volumetric ice content. I is the impedance factor, which indicates that a larger value of I can promote a lower value in the conductivity of liquid as the ice content increases. However, there is no consensus regarding the specific value of the impedance factor ( I ) in existing studies. For example, Jame and Norum [ 115 ] reported that the impedance factor increases exponentially from 1 for ice-free conditions to 1000 when ice contents exceed 20%. Gosink et al. [ 81 ] suggested values of 8 for fine sand and silts and 20–30 for coarse gravel soils. In contrast, Black and Hardenberg [ 23 ] considered the impedance factor method as a “potent and wholly arbitrary correction function” for determining hydraulic conductivity. Generally, the determination of I relies on calibration using measurements, which restricts its application and integration within numerical models. Besides, the numerical results from Newman and Wilson [ 228 ] demonstrated that the application of impedance factors remarkably affects the prediction accuracy of ice content and suggested that the adoption of I becomes unnecessary if soil water characteristic curve data is available.

Another approach employed to quantify the hydraulic conductivity ( k ) of soils under freezing conditions is incorporating a scalar parameter, i.e., relative permeability ( K r ) ranging from 0 to 1 [ 196 ]. The most commonly used equation for K r is relevant to saturation degree as follows.

where b is material constant, and S r is saturation. In addition, the saturation degree is considered to be a function of temperature. Two widely used formulas of S r are presented in Eqs. ( 6 )–( 7 ) (Nishimura et al. [ 230 ], Marwan et al. [ 213 ], Li et al. [ 184 ], Lunardini [ 206 ]; McKenzie et al. [ 216 ]).

where T f is the freezing point; T is temperature; χ and η are material constants; S r,res is residual saturation that equals the minimum value of S r . It is worth noting that the process history can affect K r , i.e., K r during the thawing process differs from K r during the freezing process. As reported by Kaviany [ 130 ], the hysteresis phenomenon can lead to various values of S r at the same saturation. Previous studies have achieved reasonable results using the concept of K r , such as in studies on AGF [ 184 , 196 ]. However, it remains challenging to obtain accurate values of hydraulic conductivity over a wide range of T [ 323 ]. Therefore, further research should be dedicated to addressing the effect of ice blocking on the hydraulic properties of soils.

2.2 Hydro-mechanical (HM) Coupling Approach

The hydro-mechanical (HM) coupling models explore the interactions of hydraulic and mechanical fields. Terzaghi [ 292 ] initially proposed the concept of effective stress ( σ′ ) and linked the pore pressure and medium deformation through the stress balance equation. The specific consolidation equation was derived by Tarzaghi [ 292 ] as follows.

where u w is excess pore pressure, which reflects the variation of pore pressure due to stress; C vx , C vy , and C vz are consolidation coefficients in x -, y -, and z -direction, respectively. Different from the one-dimensional (1D) consolidation model proposed by Tarzaghi [ 292 ], Biot [ 20 , 21 ] extended the consolidation mechanism to a three-dimensional (3D) condition with consideration of the interaction between solids and fluids [see Eqs. ( 9 )–( 12 )], whose model describes the relations between pore pressure dissipation and medium skeleton deformation.

where p is pore pressure; u , v and w are displacements along x -, y -, and z -direction, respectively. G is shear modulus; ν is Poisson ratio; γ is solid density; k is hydraulic conductivity of soils.

Based on Terzaghi and Biot models, various coupled HM models have been developed. After the introduction of the mixture theory concept [ 297 ], the macro homogenization and superposition assumptions have been applied to multi-phase media materials. Bowen [ 29 , 30 ] further advanced the coupling HM mechanics equations based on the mixture theory and proposed a comprehensive elastic stress–hydraulic mixture constitutive theory. Subsequently, Borja and his co-authors [ 351 , 365 ] used coupled HM finite element models to calculate the stress and deformation fields in steep hillsides impacted by rainfall infiltration. They have derived the analytical expression for the Biot tensor, effective tensor, and total Cauchy stress tensor:

where σ and σ' are total Cauchy stress tensor and effective stress tensor, respectively; b is Biot tensor.

It is worth noting that since the freezing and thawing processes are significantly related to temperature, it seems impossible to simulate solely the coupled HM process in frozen soils without considering the thermal aspect.

2.3 Thermo-mechanical (TM) Coupling Approach

The phenomena of frost heave and thaw settlement in cold regions are closely related to the THM process within frozen soils, which can be attributed to the movements of soils affected by strength enhancing during freezing and strength weakening during the thawing process. To gain insights into the underlying mechanisms of frost heave and thaw settlement, a reliable computational coupled THM model is required to be developed based on TM or TH models. However, early studies primarily focused on studying the thermo-mechanical (TM) coupling models due to the greater complexity and challenges associated with solving THM models. Table 2 provides a summary of notable investigations on TM models for frozen soils. It is evident from Table  2 that various TM models have been proposed to simulate the TM process of frozen soils and address engineering issues such as frost heave, thawing settlements, crack formation, and pipeline settlements. These models have been validated by test results or numerical simulations. The majority of TM models predominantly focus on 3D macro-scale analyses employing FEM, in addition to the work of Sun et al. [ 281 ], where the TM behaviours of frozen soils were modelled by the discrete element method (DEM).

As shown in Table  2 , these TM models for frozen soils are typically derived from energy conservation and linear momentum equations without directly considering cryogenic suction and water migration into the frozen zone (e.g., neglecting the heat convection due to water migration). In addition, they simplified the coupling TM process by considering a partially unidirectional coupling relationship, i.e., only considering heat transfer's influence on mechanical aspects, such as assuming mechanical properties (e.g., elastic modulus, Poisson ratio, friction angle and cohesion) are functions of temperature, involving thermal strain/damage. In terms of the mechanical aspects, most models employed relatively simple representations of the mechanical behaviours of frozen soils, such as the linear elastic model [ 311 ], Mohr–Coulomb failure criterion [ 54 , 60 , 259 ], modified Cam Clay model [ 205 ] and so on.

Regarding the thermal aspect of the coupled TM model, TM models typically employ heat conduction and energy conservation to describe the thermal behaviours of frozen soils, allowing the models to account for the heat transfer and energy exchange processes within the frozen soil [ 37 , 60 , 205 , 290 , 311 , 373 ]. Furthermore, some TM models incorporate specific mechanisms to account for water migration within the frozen soil as the temperature varies. For instance, Dayarathne et al. [ 54 ] utilised the Konrad–Morgenstern segregation potential model [ 144 ] to determine the velocity of water migration towards the ice lens. Shan et al. [ 259 ] proposed a novel coupled TM model incorporating a damage mechanism. Based on the strain equivalent theory of damage mechanisms, their study adopted the initial elastic modulus as a temperature damage parameter and introduced a composite damage factor to reflect the interdependence between mechanical and thermal damage.

Therefore, advancements in TM models for frozen soils deserve to be explored, including the development of constitutive models capable of capturing complex mechanical behaviours (e.g., anisotropic and rheological behaviour of ice lenses, nonlinearity of strength envelope, and potential pressure melting phenomena) and establishment of fully coupled models. Furthermore, the parameterisation of TM models should be refined to allow for more accurate representations of the TM processes in frozen soils. These advancements offer opportunities to improve the parameterisation process and enhance the reliability of TM models in capturing the complex TM response of frozen soils.

2.4 Thermo–hydro-mechanical (THM) Coupling Approach

The THM coupling phenomena during freezing and thawing processes play a pivotal role in soil frost heave and thaw settlement. Figure  2 illustrates the THM interactions in frozen soils involving governing equations and auxiliary relationships. To solve these models effectively, numerical methods such as the finite difference method (FDM), finite element method (FEM), or finite volume method (FVM) are commonly employed, which are validated independently through comparisons with experimental data, i.e., in situ monitoring data and laboratory measurements. Accurate simulations of this intricate THM process within frozen soils are imperative for comprehending the fundamental mechanisms underlying frost heave and thaw settlements in cold regions. Consequently, this section offers a comprehensive synthesis and classification of the technical underpinnings of various THM models, aiming to identify apparent disparities and commonalities by integrating contributions from diverse disciplines.

Schematic diagram of coupled THM model of frozen soils ( h total water head, T temperature, u displacement, θ volumetric water content, θ i volumetric ice content)

2.4.1 Coupled THM Models

Table 3 presents a compilation of typical investigations on THM models for frozen soils, including the governing equations for each field and detailed model information (i.e., suitable soil types, validations, applications, dimensions, and corresponding solvers).

2.4.1.1 Partially or Fully Coupled Model

As depicted in Table  3 , one of the earliest studies on the coupled THM model for frozen soils was proposed by Mu and Ladanyi [ 225 ], who derived a simplified model for solving the frost heave issues in practice based on some simplifying assumptions, such as a constant volume of soil skeleton during the freezing process, neglecting the effect of consolidation and stress on heat transport, considering elastic unfrozen soils, and assuming that the elastic modulus and yield points are independent of strain rate and confining pressure. Specifically, the frozen soils were treated as isotropic Mises materials, and creep was assumed to follow the Prandtl–Reuss law. However, these oversimplifying assumptions limited its application.

Selvadurai et al. [ 257 ] derived a numerical model for the heave of soil–pipeline interaction, calibrating the model with unidirectional freezing of saturated soil [ 242 ]. Besides, Selvadurai et al. [ 258 ] extended their 3D model to simulate the interaction between the buried pipeline and soil region. Lai et al. [ 152 , 153 ] conducted numerical investigations on the behaviour of existing tunnels and retaining walls in cold regions by proposing a THM model according to thermal transfer theory, seepage theory, and frozen soil mechanics. However, their coupling THM analyses did not account for water migration. Subsequently, Lai et al. [ 155 ] developed a novel THM model that incorporated the water migration theory and explored the frost-heaving process of land bridges in the Qinghai–Tibetan railway. Yang et al. [ 338 ] analysed the frost heave in AGF via semi-coupled THM models with consideration of the effect of water migration on the temperature field and the impacts of stresses and temperature on the frost heave strain by introducing some empirical equation to simply the hydraulic aspect.

In contrast, fully coupled THM models are more reasonable and capable of accurately reproducing the deformation and coupled heat and mass transfer in frozen soils. To achieve a comprehensive integration, the mechanical constitutive model of frozen soils should maintain consistency with the effective-stress constitutive models of unfrozen soils. Most boundary value problems include both states and transient moving boundaries among them. However, the framework for such models does not seem to be well-established. Accordingly, given the similarity between behaviours of unsaturated soil and frozen soil, Nishimura et al. [ 230 ] developed a fully coupled THM framework (i.e., critical state elastoplastic model) for freezing and thawing soils by involving two sets of stress variables, i.e., net stress and suction-equivalent stress. This model possesses a similar form to the Barcelona Basic Model (BBM) for unfrozen, unsaturated soils [ 4 ] and was validated by in situ tests with buried large chilled gas pipelines [ 269 ]. However, some essential behaviours of frozen soils, such as strain-rate-dependent features and cumulative response to the freeze–thaw cycles, are excluded from their model. Besides, Shastri and Sanchez [ 262 ] employed the THM model of Nishimura et al. [ 4 ] and validated it by comparing numerical results calculated by the finite element program CODE_BRIGHT and results from unconfined and triaxial tests from Parameswaran [ 237 ] and Parameswaran and Jones [ 238 ]. The comparison demonstrated that the coupled model behaves well in confined tests, but the differences increase with increasing confining pressure, which might be attributed to the fact that the model of Nishimura et al. [ 230 ] ignored the ice melting caused by higher confining stress. Subsequently, some scholars also employed the THM model of Nishimura et al. [ 230 ] to analyse other geotechnical issues, such as slope stability and AGF in underground construction [ 35 , 148 ]. Some scholars also employed the concept of effective stress and developed various expressions to calculate effective stress for modelling the THM process within frozen soils. Furthermore, Qi et al. (2024) have comprehensively examined the expressions of effective stress for frozen soils and classified them into two categories. Based on extensive analysis, they concluded that developing a mechanism-based principle of effective stress for geotechnical engineering in cold regions is highly challenging, which arises from the presence of new substances in ice and the dynamic occurrence of phase change. A more detailed discussion on the effective stress applied in cold regions geotechnical engineering can be found in Qi et al. [ 245 ].

2.4.1.2 Coupling Modes

As shown in Table  3 , the interactions between thermal, hydraulic, and mechanical fields in frozen soil can be simulated by multiple governing and complementary equations. The specific manifestations can be summarised as follows:

(i) Thermal aspect heat conduction and heat convection (due to water migration) are generally considered in THM models accompanied by the phase change phenomena. The thermal properties (i.e., thermal conductivity, heat capacity) of soil mixtures are calculated by the fraction of each phase, which can reflect the effect of the hydraulic field on the temperature field. Similarly, the impact of porosity or void ratio variations on soil mixtures' thermal properties (i.e., thermal conductivity, heat capacity) is also considered to describe the TM interactions. Moreover, deformation energy is often included in the energy conservation equation as a strategy to capture the influence of the mechanical field on the thermal field.

(ii) Hydraulic aspect water movement can be caused by temperature gradients, hydraulic gradients and pressure variations; therefore, the corresponding items are involved in the mass conservation equation. Darcy's law for saturated flow or Richards' equation for unsaturated flow are used to model water flow. The permeability of frozen soil is typically temperature/pore pressure-dependent, as freezing and thawing affect pore structure and water flow paths.

(iii) Mechanical aspect the stress caused by thermal expansion and volumetric expansion due to ice are taken into account to reflect the influences of the thermal and hydraulic fields on the mechanical field. In addition, elastic parameters are often assumed to be related to temperature, saturation and porosity, with their relationships capturing the effects of the thermal and hydraulic processes on the mechanical field. A more detailed discussion of the Coupling modes for the THM process can be found in Sect.  2.4.2 .

2.4.1.3 Freezing and Thawing Soils

It can be noted from Table  3 that various models have been proposed to predict the frost heave in frozen soils. Because of the complexity of these problems, many studies independently developed the coupled THM model of frozen soils subject to freezing conditions, which merely focused on the investigations of frost heave of frozen soils while often neglecting the thawing issue caused by temperature rise and seasonal changes (e.g., [ 56 , 197 , 265 , 334 , 342 , 361 ]). Existing investigations have demonstrated that the thawing soils suffer from strength reduction and settle deformation, negatively affecting construction safety [ 93 , 357 ]. A general method for simultaneously simulating the frost heave and thaw settlements is to establish a unified constitutive model for both frozen soils and unfrozen soils [ 195 , 226 , 374 ]. Therefore, some scholars have devoted themselves to developing more unified THM models for both freezing and thawing soils (e.g., [ 9 , 17 , 192 , 207 , 208 , 226 , 239 , 246 , 331 , 345 , 348 , 350 , 353 , 354 , 363 ]).

One notable work is proposed by Zhou and Meschke [ 374 ] who presented a three-phase model considering solid particles, liquid water, and ice crystals as separate phases and regulated temperature, fluid pressure, and solid displacement as primary field variables. Their model was developed within the framework of Coussy's linear poro-elasticity [ 47 ] and premelting dynamics of Wettlaufer and Worster [ 325 ], essentially derived using the entropy concept. Being validated by an in-situ frost test, the THM model of Zhou and Meschke [ 374 ] demonstrated its ability to capture the volume expansion caused by change changes, water migration, and mechanical deformation. Na and Sun [ 226 ] introduced a novel generalised theory that incorporates all critical aspects of THM mechanisms into balance equations within the finite deformation range to simulate the complex responses of freezing and thawing soils. Unlike the single-physics solid mechanics problem, the generalised hardening rule proposed by Na and Sun [ 226 ], explicitly incorporating thermal and cryo-suction effects, enables the evolution of the yield surface with the variation of pore ice content and temperature.

2.4.1.4 Saturated and Unsaturated Frozen Soils

It can be noted from Table  3 that the number of THM models under saturated conditions exceeds those for partially saturated conditions. The frost heave can occur when the saturation exceeds 80–90% rather than reaching 100% [ 58 ]. Furthermore, most in situ frozen soils are unsaturated, and vapour plays a significant role in the water and energy balance, especially when the temperature gradient is large and the initial water content is low. Accordingly, some scholars have emphasised the influence of vapour on THM modelling of frozen soils (e.g., [ 105 , 131 , 164 , 178 , 192 , 200 , 207 , 227 , 246 , 265 , 273 , 294 , 306 , 326 , 344 , 348 , 370 ]). For example, Liu and Yu [ 200 ] combined Fourier’s law, generalised Richards’ equation, and mechanical relation (i.e., Navier’s equation) to model the THM process in unsaturated frozen soils. Considering the condensation and congelation of vapour in unsaturated freezing soil, a novel THM model was proposed by Yin et al. [ 344 ] by involving three variables (i.e., temperature, overburden pressure, and saturation), but their simulated results were not validated by laboratory or filed tests. Huang et al. [ 109 ] developed a fully coupled THM model for unsaturated freezing soils and conducted a numerical simulation to replicate one-side freezing tests. Wang et al. [ 306 ] proposed a THM model of a single pile in frozen soil to simulate the ground heave and pile uplift under one-dimensional freezing conditions. Soltanpour and Foriero [ 273 ] derived a THM model for predicting the frost heave in unsaturated freezing fine sands compared with full-scale freeze–thaw tests at CRREL. Based on the modified Cam Clay model, Li et al. [ 192 ] developed a thermo–elastoplastic model for unsaturated freezing soils to address the soil hardening caused by temperature and compression during thawing, whose model was applied to assess the long-term freezing and thawing behaviours of Railway subgrade.

2.4.1.5 Impact of Ice Lens

In addition, some studies proposed novel THM models that consider the formation and evolution of ice lenses to reproduce frozen soil behaviours more accurately. Table 4 summarises the criterion of ice lens formation, indicating that ice lens generation is influenced not only by temperature and overburden pressure but also by the separation strength. The ice lenses can induce volume expansion, alter the yield condition and strength variations, and block water migration within freezing soils. The growth of ice lenses tends to be anisotropic due to the directional formation of ice lenses perpendicular to the heat transfer direction. Therefore, accurately determining the occurrence moment and position of ice lenses is crucial for capturing the THM process in frozen soils.

2.4.2 Coupling Modes for THM Fields

It can be noted from Table  3 that the coupling interactions among THM fields can be achieved in three manners. The first is directly incorporating the relevant actions into the governing partial differential equations (PDEs), i.e., the impact of water on the temperature field can be addressed by including corresponding terms in Fourier's equation [ 200 ]. The second is establishing explicit relations, i.e., connections among the state variables can be regarded as independent variables within the governing equations. The last method is to develop the implicit relationships, referring to the dependence of material properties on the state variables and other parameters.

2.4.2.1 Basic Mechanisms

For the first coupling mode, the fundamental governing equations of THM models (i.e., mass conservation, energy conservation, and momentum conservation equations) describe the basic mechanisms and serve as critical components of the THM models. The basic framework of THM models is to define the governing balance equations based on different assumptions and to propose a mechanical constitutive model. Three primary governing laws form the foundation for describing the THM process, which are: (i) mass conservation for the hydraulic field. The mass conservation can be formulated in two ways, i.e., considering the bulk mixture body as a whole or accounting for the mass balance for each component while applying the superposition of mass balance. (ii) Momentum conservation for the mechanical field. The momentum conservation indicates that the time derivation of momentum equals the summation of external forces. (iii) Energy conservation for the thermal field. Energy conservation refers to the first law of thermodynamics, which represents that the sum of time derivatives of internal and kinetic energies is equal to the rates of mechanical work rate and heat. Generally, phenomenological thermodynamics, energy conservation, and Fourier’s law serve as the fundamental theories for temperature fields, while mass conservation, Darcy’s law, and Richards' equation form the basis of moisture fields. However, the approaches to mechanical constitutive models vary considerably in the literature. Therefore, a comprehensive review of the mechanical constitutive models for frozen soils was summarised by the authors [ 185 ] who categorised the constitutive models into different groups based on their underlying theories.

It can be noted from Table  3 that existing THM models primarily rely on stress fields governed by Navier's equation, effective stress theory, poromechanics theory, and elastic/elastoplasticity theory. It is worth noting that treating the frozen soils as temperature-dependent elastic materials during freezing is generally reasonable, but the responses of thawing soils under freeze–thaw cycles exhibit non-linear elastoplastic behaviours. Therefore, more advanced mechanical constitutive models capable of describing multiple mechanical responses of frozen soils should be incorporated into the THM models to provide a more precise understanding of the complex coupling process related to frozen soils.

2.4.2.2 Explicit Relations

Among these explicit relations, the state variables (i.e., T , ω and displacement) are independent variables and do not directly influence the coupling process, but the PDEs’ solutions are sensitive to these explicit relationships. The soil water characteristic curve (SWCC) and the Clapeyron equation are two typical explicit relations.

The SWCC depicts the relationship between water content and suction, which depends on the soil type and is employed to model drying and wetting processes in soils [ 186 ]. Due to the similarity between the drying–wetting process and freezing–thawing process, the relationship described by SWCC is extensively utilised for freezing processes in frozen soils. Various empirical equations are proposed for SWCC (e.g., [ 31 , 73 , 298 , 301 ]), and van Genuchten's [ 298 ] model and Fredlund and Xing [ 73 ] model are widely employed in existing THM models (van Genuchten [ 298 ], Fredlund and Xing [ 73 ]).

where s is suction (kPa); θ is the volumetric water content and θ s and θ r are the saturated and residual volumetric water contents, respectively. a , m and n are fitting parameters. s r is suction corresponding to the residual water content. However, directly applying the SWCC equation to frozen soils remains questionable [ 22 , 147 ], as it is only suitable when the suction in frozen soils exceeds 50 kPa [ 275 , 276 ]. Besides, the SWCC displays noticeable hysteresis, whereas these commonly used SWCC equations do not account for hysteresis effects.

The Clapeyron equation depicts the relationships between pressure and temperature, which can be expressed in various ways and notations. Based on equilibrium assumptions of the Clapeyron equation, the soil water potential is influenced by ice and water pressures [ 149 ]. Table 5 lists the typical Clapeyron equation for frozen soils. The original derivation of the Clapeyron equation was formulated by combining the thermodynamic concept of Gibbs free energy and the Gibbs–Duhem relationship for each phase [ 84 ]. Equation ( 2 ) in Table  5 is often used in THM models due to its convenience in implementation. It accurately describes the behaviour of an ice crystal using ice pressure once the temperature and water pressure values are available. However, it is essential to note that the strict validity of its application in frozen soils is questionable since the equation assumes a closed system, whereas a porous medium represents an open system. Besides, equilibrium in the quasi-static sense can only be confidently ensured near interfaces. Thus, caution is necessary when applying the Clapeyron equation across the entire region, especially for rapid transient transport processes.

It is worth noting that the primary state variables in existing THM models include displacement, pore pressure, and temperature, which are suitable for slow freezing rate scenarios. However, in cases of high freezing rates, such as AGF, the selection of state variables should be carefully selected to avoid the occurrence of spurious oscillations unless they are appropriately treated. For example, Suh and Sun [ 280 ] alleviated this issue by implementing a stabilization procedure in the weighted residuals of the heat and mass balance equation. Arzanfudi and Al-Khoury [ 9 ] also treated cryogenic suction as a primary state variable to address this problem.

2.4.2.3 Implicit Relations

The implicit relations refer to the variations of soil properties (e.g., thermal conductivity, hydraulic conductivity/permeability, heat capacity) with the change of state variables (e.g., T , ω and displacement). In addition, other soil properties are also functions of state variables, such as the hydraulic conductivity of the vapour phase, coefficient of convective/conduction, and various moduli. These implicit relations can remarkably influence the coupled THM process and introduce high nonlinearity into PDEs governing frozen soils. When unknown parameters exceed the number of PDEs, supplementary equations should be added. Typically, empirical equations and the concept of ice–water ratio are utilised to solve the PDEs. The coefficients in these supplementary equations are derived from experimental measurements, which are highly influenced by testing conditions and the type and location of soil samples.

Solving the PDEs requires the incorporation of explicit relations, such as the soil water characteristic curve (SWCC) and the Clapeyron equation, as well as implicit relations. However, the inclusion of these relations can lead to computational challenges. Implementing numerical calculations becomes difficult due to the PDEs' highly nonlinear and interdependent nature. Existing THM models have primarily focused on the coupling interactions between the mechanical field and the other two fields, as these couplings typically exert weaker effects, particularly the coupling from the mechanical field to the thermal or hydraulic fields. In most THM models, simplified methods based on mixture theories, poromechanics, or direct coupling using experimental relationships are employed, which can capture the complicated THM interactions while reducing computational complexity. However, they may overlook critical behaviours of frozen soils under freezing/thawing conditions, such as pressure melting phenomena, freezing point depression, and time-dependent behaviours. Therefore, further research and development are necessary to enhance the understanding and modelling of the interdependencies among the mechanical, thermal, and hydraulic fields within frozen soils.

2.5 Thermo–hydro-chemical (THC) Coupling Approach

As outlined in Sect.  2.1 , numerous modelling studies have focused on the coupled mechanisms of heat and water transport in frozen soils, which often neglects the freezing point depression. The freezing point of pure water occurs at 0°C, but in a soil–water system, it appears below 0°C. The depression of the freezing point can be neglected in coarse soils with a small specific surface area (SSA). However, fine-grained soils, such as silts and clays, with a high SSA and the ability to retain unfrozen water content, experience a temperature depression of up to 5°C [ 8 ]. Water content, overburden pressures, and the presence of solutes could induce lower values of freezing/melting point [ 11 ]. Therefore, it is necessary to consider the influence of freezing point depression in subsequent research and extension of THM models. In other words, the chemical aspect should be incorporated into the coupled modelling of frozen soils.

Existing studies demonstrate that neglecting the effect of salt in the simulation of frozen soils can introduce significant uncertainties in modelling the freezing and thawing processes [ 232 , 327 ]. Generally, salt exists in two phases within frozen soils: dissolved salt and salt crystals. Recent findings reveal that dissolved salt tends to be expelled into the unfrozen water during the freezing process. Furthermore, diffusion and migration of salt lead to the formation of a zone with a higher concentration of salt crystals near the freezing fringe [ 13 , 92 ]. The effect of salt on frozen soils is complicated, the concrete manifestation of which can be concluded into two aspects: one is the freezing point depression of soils due to the existence of salt; another is the salt dynamics of soils under freezing/thawing is influenced by the processes of diffusion and repulsion [ 315 , 329 ].

Accordingly, some scholars have attempted to explore the influences of salt on the frozen sols during freezing/thawing, as well as the interactions between freezing/thawing and salination in cold regions. Considering the heat flux during salt crystallization, Koniorczyk [ 141 ] developed a fully coupled THC model using the kinetics of salt phase change but without accounting for the influence of crystallization pressure on the stress field. Wu et al. [ 330 ] analysed the salt dynamics and soil freezing/thawing over three winter periods based on CoupModel, and their model was verified by comparing simulated results and observed data (i.e., temperature, water content, and groundwater table depth). Their model mainly focused on the coupled water and heat transfer by considering the effect of temperature on hydraulic conductivity, freezing on thermal properties and heat convection due to water flux, and the freezing point depression caused by salt. Wan et al. [ 303 ] also employed the CoupModel as a coupled THC model to investigate the effect of climate change on water, heat, and salt migration of unsaturated frozen soils, and their model was validated by the comparison between the meteorological data (i.e., temperature, precipitation, evaporation) from filed observations and simulated data. In their model, temperature gradient served as the driving force for water migration and salt transport, while salt dispersion or diffusion was not considered. Liu et al. [ 194 ] modified the simultaneous heat and water (SHAW) model by considering soil deformation and its impact on hydrothermal properties during the freeze and thaw process. When simulating water, heat and salt transport processes, the heat convection due to water flux, blocking impact of ice and solute absorption (i.e., diffusion, convection and dispersion processes) are involved in this coupled model. Besides, their model was compared with in situ water content and temperature observations, demonstrating its ability to capture water–heat–salt dynamics in frozen soils. However, since neglecting the lateral groundwater exchange, ground surface albedo, and salt expulsion, their model yielded underestimations of water content in deep soil layers and mispredicted the temperature during the thawing period. Hence, THC models can be further improved by considering these essential factors.

Furthermore, the presence of salinity can alter the evolution of the freezing front and freezing points, which, in turn, affects the formation of frozen walls in artificial ground freezing (AGF) and poses safety concerns for AGF construction [ 175 , 199 ]. Therefore, investigations on coupled THC models are relatively scarce, and more effort should be dedicated to analysing the complex interactions among the fluid field, thermal field, and chemical field in frozen soils, which is crucial for understanding the mechanisms of THC processes in frozen soils and to identify the salinisation for better water management and construction safety in cold regions.

2.6 Thermo–hydro-Mechanical–Chemical (THMC) Coupling Approach

The coupled thermo–hydro-mechanical–chemical (THMC) process is a widely researched topic that significantly impacts frozen soils' mechanical behaviour and failure mechanisms. Investigating the mechanical behaviour of frozen soils under multi-physics coupled processes is crucial for ensuring construction safety in cold regions, such as railway construction on the Qinghai–Tibet Plateau and tunnel constructions related to AGF. Salinity significantly alters the freezing behaviour of frozen soils, leading to freezing point depression [ 19 , 263 ]. However, the effect of salt in the fluid on the performance of frozen soils has been rarely investigated, despite its relevance to construction safety and potential harm to adjacent structures and infrastructure foundations caused by frost heave in sulfate saline soils [ 141 , 328 ]. It is worth noting that methane hydrates are ice-like materials comprising methane gas and water, wherein the methane gas is confined within cage-like structures in solid form due to high-pressure and low-temperature conditions [ 247 , 317 ]. The methane hydrates occur naturally in permafrost regions and beneath the deep marine bed. Table 6 presents an overview of existing investigations on coupled THMC models for frozen soils, revealing that most studies have explored the effect of salt on the THM process in frozen soils, while some investigations have focused on the THMC process during natural gas hydrates.

As for the THMC models for frozen soils, Zhang et al. [ 356 ] established a coupled THM model for freezing saturated saline soils and explored the effect of salt by involving mass conservation, Darcy’s law, and energy conservation. However, their models did not account for salt expansion. Tounsi et al. [ 296 ] derived a fully coupled THM model considering salinity influence to explore the THM behaviours of AGF, but they neglected salt crystallisation and its impact on ground deformation. Accordingly, Zhang et al. [ 352 ] proposed a coupled THMC model to simulate the one-side freezing process of saturated frozen sulfate saline soil by employing the crystallisation kinetics theory to elaborate the ice and salt crystallisation mechanism. However, many studies assumed soil saturation and neglected the vapour phase in freezing soils. To enhance the modelling of dynamic processes within frozen soils, including heat transfer, vapour flow, water migration, deformation, and solute transport, Zhang et al. [ 359 ] enhanced the THMC model, while their model neglected the salt effect on the mechanical aspect. Additionally, Huang and Rudolph [ 106 ] developed a 2D THMC model for variably saturated soils under freezing/thawing conditions, considering the influence of freeze–thaw cycles. However, their models' configuration (i.e., shallow model and short simulation duration) and assumptions (i.e., ignoring subsurface lateral flow, surface runoff, rainfalls, snow, irrigation, and other mass sinks) may introduce errors when modelling the coupled processes in the subsurface during freeze–thaw cycles. Furthermore, they neglected the interaction between the mechanical field and chemical field, the hysteresis of freeze–thaw cycles and the occurrence of ice lenses.

In recent years, some THMC models have been developed to simulate the dissociation of methane hydrates and the mechanical responses of reservoirs using numerical simulations. Kimoto et al. [ 137 , 138 ] established 1D and 2D THMC models for simulating the hydrate dissociation and indicated that the ground deformation is remarkably influenced by water and gas generation and dissipation, as well as soil strength reduction due to hydrate loss. However, their model ignored the mechanical–chemical interaction, and its effectiveness was limited by the lack of validation through experimental data. Based on thermodynamics theory and critical state concept, Sun et al. [ 282 ] also developed novel THMC models for analysing the mechanical responses of methane hydrate-bearing sediment and showed that decoupling the mechanical and hydraulic fields (i.e., keeping porosity and volume strain constant) would result in an overestimation of the depressurization rate. Furthermore, Wan et al. [ 304 ] developed a THMC model to simulate fluid flow within hydrate sediments, dividing it into fluid and solid subsystems and solving it using a hybrid control volume finite element method (CVFEM)–finite element method (FEM). They validated the model with two classical experimental cases, demonstrating its capability to capture coupled THMC behaviours.

Therefore, it can be concluded that most existing THMC models for frozen soils are formulated based on a set of assumptions aimed at simplifying the governing equations that govern the complex coupling processes. Although these assumptions are physically reasonable, they have the potential to introduce inaccuracies in the numerical results. Table 6 demonstrates how these models simplify the coupling of THMC processes by tailoring them to specific problem characteristics, typically focusing on partial bidirectional coupling relationships. For example, the interactions between the mechanical field and chemical field (e.g., salt expansion and crystallisation) were rarely taken into account. Neglecting these critical factors or phenomena can lead to incomplete or inaccurate representations of the actual behaviour of frozen soils. Furthermore, loading/unloading actions often occur on the ground surface in cold regions. Therefore, enhancements should be made to incorporate a more comprehensive understanding of the underlying processes and phenomena to simulate the non-elastic deformation and develop complete coupled models to fully reflect the coupling relationship between multiple physical fields.

3 Numerical Simulation Methods

The coupling of multi-physical fields (i.e., thermal, hydraulic, mechanical, and chemical fields) can be realised by variables that interactively transfer information. Although the effective stress principle and consolidation model of the HM coupling model were proposed in the early 1920s, along with the establishment of the corresponding differential equations, their solutions required modifications due to the limitations in computational power. Besides, analytical solutions were derived for some simple cases, such as axisymmetric and plane strain issues [ 156 , 240 , 319 ]. However, obtaining analytical or exact solutions for more general cases with slightly complex boundary conditions is not feasible. To address this issue, computational techniques, specifically advancements in numerical methods for discretely solving the coupled model's differential equations, become necessary.

This section mainly summarises the typical numerical methods that can solve the coupled multi-physical models according to their underlying principles, which include two types of methods, i.e., continuum mechanics method (CMM) and discrete or discontinuous mechanics method (DMM). The continuum mechanics methods include the finite element method (FEM) and its modified versions (XFEM), finite volume method (FVM), finite difference method (FDM), and phase-field modelling (PFM). The discrete element method (DEM), lattice Boltzmann model (LBM), and peridynamics (PD) are the typical discrete or discontinuous mechanics methods. Section  3.3 presents the primary advantages and disadvantages of each numerical method for reference on the choice of suitable numerical solvers for coupled models of frozen soils. As summarised in Sect.  2.1 , the coupled TH models are different from other models related to the mechanical field. Therefore, some typical models that are tailored for TH coupling issues are introduced in Sect.  3.4 .

3.1 Continuum Mechanics Method (CMM)

The continuum mechanics method treats frozen soils as a continuous medium, where they are considered to be occupied by fluid or solid substances that can be modelled as a continuous medium composed of particles without pores. The macroscopic physical quantities of these particles adhere to fundamental physical laws, including mass, energy and momentum conservation, thermal dynamics, diffusion, and heat conduction [ 10 , 91 , 165 , 190 ]. The governing differential equations representing these physical laws can be solved using various numerical methods, i.e., FEM and modified FEM (e.g., XFEM, RFEM), FVM, FDM, and PFM methods.

3.1.1 Finite Element Method (FEM)

The finite element method (FEM) is one of the most popular numerical techniques used to solve differential equations that arise in engineering and mathematical modelling. The foundational works of Zienkiewicz [ 380 ] and Strang and Fix [ 279 ] have laid the groundwork for future advancements in FEM. FEM discretises a continuous object into finite elements, each possessing a set of nodes, thereby representing the continuum as a series of interconnected elements [ 44 , 67 , 122 , 165 ]. The value of each node in the field function serves as primary unknowns, and an approximate interpolation function is assumed in each element. Accordingly, the simple equations of these finite elements can be subsequently assembled into a more extensive system of equations modelling the entire problem.

Based on the review of coupled models in Sect.  2 , it can be noted that FEM has been extensively employed to simulate complex multi-physical processes in frozen soils, especially for coupled models involving mechanical aspects. When combined with phenomenological constitutive models, this continuum-based numerical method (i.e., FEM) has demonstrated robustness and efficiency in multi-physical modelling. Various commercial software packages, such as COMSOL, ABAQUS, and ANSYS, have been widely adopted for finite element analysis in coupled modelling. COMSOL offers a user-friendly graphical user interface (GUI), enabling researchers to build and modify governing equations through secondary development, which is helpful for improving modelling efficiency and different hypothesis testing. COMSOL has proven to be an effective tool for simulating complex multi-physical coupled issues [ 105 ].

FEM is capable of addressing nonlinear and heterogeneous issues and complex boundary conditions. However, the applications of FEM in modelling coupled processes in frozen soils are limited when dealing with specific issues such as severe discontinuity problems and regional scale systems. These limitations can be attributed to several factors. (i) Internal flaws (e.g., fractures) introduce discontinuities in the research object, necessitating the refinement of local meshes surrounding these flaws, which increases the computational burden. (ii) Large deformations can distort the mesh, leading to calculation deviations and computational challenges. Additionally, the remeshing process at each step introduces a significant workload. (iii) The generation and propagation of ice lenses are difficult to solve since the ice lenses should be attached to nodes so that the formation of ice lenses becomes a dynamic internal boundary, which also introduces additional complications due to remeshing. The classical FEM discretization for such issues often results in unstable solutions and requires extremely small-time steps; (iv) when solving large-scale problems, FEM requires multiple parameters and involves a vast number of elements and nodes that demand substantial computational resources and time. Hence, approximate upscaling schemes should be employed to improve computational efficiency [ 160 ].

Accordingly, modified versions of FEM have been developed to remedy the weakness of FEM in solving special issues, such as extended finite element model (XFEM) and random finite element model (RFEM). The classic FEM is limited in its ability to handle discontinuities within an element due to the continuity requirements of the shape functions. As for coupled models of frozen soils, FEM struggles to accurately capture the sharp interface between ice and water, which involves a weak discontinuity in the temperature field and then induces a discontinuity in its gradient field. To overcome this limitation, the XFEM method has been effectively developed to model such discontinuities and high gradient fields [ 134 ], which introduces an additional field to the standard interpolation field. Recently, some scholars have employed XFEM to reproduce frozen soils' freezing and thawing behaviours. For example, Amiri et al. [ 5 ] proposed a TH model via XFEM to model the temperature discontinuity of the ice/water interface. Arzanfudi and Al-Khoury [ 9 ] focused on issues involving relatively high freezing–thawing rates, such as AGF, and employed the partition of unity within the framework of XFEM to discrete cryo-suction.

Another modified version of FEM is RFEM, which accounts for the randomness in materials components and properties. Dong and Yu [ 61 ] employed XFEM to explore slope stability based on a coupled TM model. In addition, Dong and Yu [ 62 ] developed a microstructure-based THM model using RFEM to model frost heave in frozen soils, which was validated by laboratory-scale experiments.

3.1.2 Finite Difference Method (FDM) and Finite Volume Method (FVM)

The finite difference method (FDM) was one of the earliest discretization schemes, primarily favoured for its simplicity and ease of implementation on structured grids. However, the limitations of FDM become apparent when dealing with complex geometries, particularly in multiple dimensions. This drawback has motivated the development of more advanced integral-based discretization techniques, such as the FVM and FEM [ 241 ], which offer greater flexibility and accuracy in handling complex geometries.

The finite volume method (FVM) is well-suited for handling geometrically complex regions without the need for variable transformations due to the flexible utilization of grids (e.g., unstructured grids). In FVM, the computational domain is divided into a collection of control volumes, and the PDEs are integrated over the control volume and solved [ 66 , 159 ]. FVM has been extensively utilised as a numerical technique for modelling fluid flow and heat transfer. Its popularity arises from its inherent conservation properties, ensuring that the discretised equations preserve physical quantities and clear physical interpretations of coefficients in the FVM equations. Recently, FVM has been extended into solid mechanic analysis [ 360 , 379 ] and proved to be a promising method for THM coupling issues [ 55 , 272 ]. However, numerical diffusion in FVM is likely to induce the smoothing of sharp gradients and loss of fine-scale details, which might limit its accuracy in capturing the transport process. Besides, FVM might encounter difficulties when handling complex equations involving non-linearities or coupling different physical processes.

The choice of the appropriate numerical discretization method is critical for multi-physics modelling for frozen soils, such as FEM, FDM, and FVM. FEM was initially developed for static stress analysis and then extended to various fields, which has been the most widely utilised method in computational mechanics and solving material and geometric nonlinearities due to its ability to handle complex, highly heterogeneous domains with irregular boundaries [ 215 , 277 ]. However, when it comes to flow modelling, FVM is considered a superior choice compared to FEM due to its maintenance of local conservation properties at the discrete level and accurate representation of flow behaviour [ 111 ]. FVM combines the advantages of FDM in terms of simplicity of implementation and the flexibility of FEM in handling complex geometries.

3.1.3 Phase-Field Modelling (PFM)

Phase-field modelling (PFM) has recently emerged as a robust computational tool for simulating and modelling the mesoscale development of morphological and microstructure in materials [ 126 , 182 , 187 , 278 ], which introduces phase-filed variables to track the dynamic evolution of interfaces. The temporal evolution of the phase field variables is governed by a system of PDEs, which is typically solved using numerical methods. PFM possesses two key characteristics: (i) a continuous phase field used to distinguish different microstructure domains; (ii) a diffuse interface where physical properties smoothly transition between phases [ 299 ]. Depending on the problem being addressed, the phase field can serve as an auxiliary variable or a physical parameter [ 32 , 45 ]. In both scenarios, the diffuse interface is characterised by excess free energy, typically expressed as a function of the spatial gradient of the phase field. For a comprehensive understanding and further information on PFM, relevant details and references can be found in some publications (e.g., [ 100 , 127 , 278 ]).

Recently, researchers have formulated the framework of PFM coupled with the continuum theory of porous media (TPM) to address coupled issues and developed interface models to describe the ice–water interface. Sweidan et al. [ 286 ] derived a unified model to simulate frost action in saturated frozen soils, extending TPM with PFM to describe the macroscopic phase-change process in saturated frozen soils. Sweidan et al. [ 287 ] combined TPM and PFM to develop a unified kinematics approach for modelling the coupled thermal, hydraulic, and mechanical (THM) processes in freezing soils with different frost penetration directions. Suh and Sun [ 280 ] formulated a THM model to simulate freezing-induced fracture caused by the growth of ice lenses, introducing two-phase field variables.

The PFM method lays a solid foundation for future research in the coupled modelling of frozen soils. However, some improvements can be addressed in future work to provide a more realistic description of soil freezing/thawing processes. This can be achieved by extending the framework for unsaturated soils and considering the hysteresis effect in freeze–thaw cycles. Furthermore, the initiation and formation of ice lenses will be modelled with the aid of fracture-based PFM.

3.2 Discrete or Discontinuous Mechanics Method (DMM)

3.2.1 discrete element method (dem).

The discrete element method (DEM) is a well-known micromechanics-based approach that captures the inherent discrete characteristics of particles. Specifically, heat transfer in frozen soils can be regarded as heat flows at the grain scale, where the thermally-induced inter-particle force can be described by the contact force model in DEM [ 14 , 75 , 309 ]. Furthermore, combining DEM with computational fluid dynamics (CFD) has been developed to account for heat convection through granular materials [ 309 , 318 , 335 ]. However, conducting large-scale DEM simulations (involving billions of particles) is computationally expensive when solving actual engineering problems, particularly when coupled with CFD, although speed-up techniques (e.g., parallelization with multi-core CPUs and distinguishing GPU acceleration techniques) can be conducted [ 274 ].

3.2.2 Lattice Boltzmann Method (LBM)

The lattice Boltzmann method (LBM) was initially proposed by Hardy et al. [ 88 ] as a micromechanics-based approach. In LBM, variables are typically obtained from particle interactions [ 125 , 220 ], which have higher parallel computation efficiencies than traditional macroscopic methods due to the improved functional form and quantities [ 110 ]. Another advantage of LBM is that the equations used are dimensionless, expanding its applicability to various scenarios. With these advantages, LBM has been widely used in several fields, including multi-component flow (e.g., multiphase and thermal flow), chemical reaction, mass transfer in fuel cells, and flow in porous media, and so on [ 77 , 224 , 332 , 336 ].

Overall, investigations on coupled models of frozen soils via LBM are relatively scarce since the studies on frozen soils initially focused on macroscopic relations. Wang et al. [ 316 ] established a multiphase model based on LBM to simulate heat and mass transfer in frozen soils and evaluate the intricate temperature and water content distribution during the thawing process. However, one limitation of LBM is the relatively small model dimension, which poses challenges when dealing with macroscopic issues that involve enormous calculation quantities. Additionally, obtaining parameters for small particles is relatively difficult. It remains challenging to transform the parameters obtained from in situ observations into the micro/mesoscale parameters required in LBM [ 162 ].

3.2.3 Peridynamics (PD)

Peridynamics (PD) was initially developed to describe the mechanical behaviours of solids [ 266 , 267 ] and subsequently extended to address various diffusion phenomena [ 26 , 212 ]. Unlike classical local theories that employ partial differential equations (PDEs), such as those used in FEM, PD utilises a set of integral–differential equations. This approach ensures a mathematically consistent formulation that remains valid despite significant non-linearities and discontinuities.

Initially introduced as a bond-based approach, PD has evolved into a state-based PD, presenting two variants: ordinary and non-ordinary [ 2 ]. Recent developments have introduced an element-based PD formulation [ 193 ]. The connection between PD and continuum formulations is established using the concept of peridynamics differential operator [ 210 ]. Previous studies have demonstrated the successful applications of PD in solving problems such as heat conduction [ 25 , 38 , 78 ], phase change [ 211 ] and water flow in porous media [ 113 , 129 ], which shows the simplicity and universality of this theory in addressing coupled issues. Nikolaev et al. [ 229 ] developed a non-local approach based on bond-based PD to analyse the heat and water transfer with phase change in saturated frozen soils under freezing and thawing conditions. To validate the accuracy of their model, they compared the calculated temperature distribution from PD with the analytical solutions and FEM results. More importantly, their model was successfully applied for convention-dominated heat transfer simulations in frozen soils with high-pressure gradients, which poses a challenge for other methods such as FEM. Therefore, the PD-based method can be extended further for THM and THMC models, which is beneficial for modelling the hydrological behaviours of permafrost soils and frost heave that remarkably influences construction safety and increases the risk of geological disasters.

3.3 Comparison Between CMM and DMM

The selection of an appropriate numerical method is of utmost importance when addressing various coupling problems associated with frozen soils. Different numerical methods possess distinct characteristics and application domains. Table 7 summarises the advantages and disadvantages of relevant numerical methods. Based on Table  7 , researchers and practitioners can adopt the most suitable approach for solving their specific coupling problems in frozen soil applications.

3.4 Heat and Mass Transfer Simulators

Various coupled water and heat process models for frozen soils, without considering the mechanical aspect, are depicted in this section, such as simultaneous heat and water (SHAW), coupled heat and mass transfer (CoupModel), and Hydrus-1D models. Table 8 presents a compilation of typical simulators for modelling water and mass transfer processes related to frozen soils, providing a brief overview of their distinct characteristics.

4 Conclusions and Future Prospects

The multi-physical field modelling of frozen soil plays a crucial role in the design of structures, oil pipelines, and engineering constructions in cold regions. However, coupling these multi-physical fields presents a complex and interdisciplinary challenge. Further efforts are needed to improve the accuracy of predicting the soil freezing process, understand the coupling mechanisms, refine coupling methods, and develop effective solving techniques. Accordingly, this study provides a comprehensive state-of-the-art review of coupled models for frozen soils.

The advancements in coupled multi-field models and corresponding numerical solvers for frozen soils are extensively summarised. Firstly, studies on coupled multi-physical field models for frozen soils were thoroughly examined and discussed, which provides insights into the various approaches and methodologies used to model frozen soils' coupled behaviours under the freezing and thawing process. Secondly, this review explored existing numerical simulations employed in frozen soil coupling modelling. Each numerical method's key advantages and disadvantages are also listed to provide guidelines for choosing appropriate solvers for coupled models of frozen soils. However, due to the complexity of the interaction process occurring in frozen soils, some critical issues in coupled multi-physics field modelling and numerical methods require further research. Based on the critical discussion in this review, the primary conclusion and challenges in simulating the multi-field coupling process on frozen soils are summarised as follows.

(1) The coupled models of frozen soils can be categorised into six types, i.e., TH models, TM models, HM models, THM models, THC models, and THMC models. In general, the TH and THM models have been extensively investigated, while the other coupled models, especially models incorporating chemical effects, are worthy of further development. TH models primarily concentrated on the interaction mechanisms of frozen soils and external environments (e.g., climate change) and their influences on the environment and engineering. In contrast, THM models addressing the mechanical effect have been developed to analyse the freeze–thaw action of frozen soils, which sheds light on the frost and settlement mechanisms of interaction processes in frozen soils.

(2) Further establishment of 3D models and numerical simulators for coupled multi-physics fields with different scales is essential. It is crucial to comprehensively consider the realistic conditions of frozen soils at various scales when modelling complex multi-field interactions. By simulating the physical environment of frozen soils across different scales, the coupling mechanisms of multi-physics can be investigated from micro, meso, and macro/multi-scales. This approach provides valuable insights for solving practical engineering problems in various domains.

(3) It is essential to conduct large-scale and long-term in situ tests to investigate multi-physics coupling in frozen soils. To establish accurate multi-physics coupling models, it is crucial to understand the interactions among hydraulic, mechanical, thermal, solute transport, and other fields in frozen soils. However, current coupling models often rely on small-scale laboratory tests at the centimetre or meter level. These tests simplify the actual conditions using similarity criteria and neglect secondary factors. To overcome these limitations, it is necessary to minimise the size effect in testing by conducting large-scale in situ tests that encompass multi-physics coupling and long-term monitoring.

(4) A more comprehensive, fully coupled model for frozen soils needs to be developed by incorporating additional factors to simulate multi-physics field interactions more accurately. The prediction errors in existing models might be attributed to the oversimplification of complex boundary conditions (e.g., groundwater exchange, change in soil surface albedo, and salt expulsion) and neglecting critical behaviours of frozen soils (e.g., time/temperature dependence, pressure melting, freezing point depression, hysteresis of the freeze–thaw cycle, and vapour effects). Furthermore, the heterogeneity of structures (e.g., ice lenses) in frozen soils will be of interest in subsequent work to derive more general coupled models.

(5) Concerning the numerical simulations of multi-physics field processes in frozen soils, a balance needs to be struck between simulation accuracy, simulation efficiency, calculation complexity, and ease of implementation. Several challenges exist in the numerical implementation of coupled models for frozen soils. (i) Efficient and unified software systems for large-scale and long-term computations with coupled multi-field processes should be further developed. (ii) Numerical simulators should contain core moduli, such as user-defined material modes, adaptive inputs for boundary conditions, engineering/environmental procedures (e.g., AGF), and functions that evaluate model uncertainty/sensibility. (iii) Special attention can be given to techniques that can enhance calculation efficiency (e.g., parallel computing), interface with visualisation techniques (e.g., BIM or digital twin), artificial intelligence, and other emerging technologies. Advancements in these areas will strengthen the foundation of simulation models and contribute to a comprehensive and holistic simulation platform for frozen soils subjected to freezing/thawing actions.

Data Availability

All data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This research was financially supported by the Research Grants Council (RGC) of Hong Kong Special Administrative Region Government (HKSARG) of China (NSFC/RGC Joint Research Scheme, Grant No. N_PolyU534/20; General Research Fund, Grant No. 15220221, 15229223).

Open access funding provided by The Hong Kong Polytechnic University.

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Kai-Qi Li & Zhen-Yu Yin

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Kai-Qi Li: Conceptualization; Methodology; Software; Validation; Formal Analysis; Investigation; Data Curation; Writing—Original Draft; Writing—Review & Editing. Zhen-Yu Yin: Writing—Review & Editing; Supervision; Project Administration; Funding Acquisition.

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Li, KQ., Yin, ZY. State of the Art of Coupled Thermo–hydro-Mechanical–Chemical Modelling for Frozen Soils. Arch Computat Methods Eng (2024). https://doi.org/10.1007/s11831-024-10164-w

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Received : 06 February 2024

Accepted : 04 July 2024

Published : 29 July 2024

DOI : https://doi.org/10.1007/s11831-024-10164-w

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