global warming project work methodology

Methodology

Climate security risk methodology overview.

Climate change reverses progress towards sustainable development and peace, posing risks to human security and making peace harder to achieve. Both slow onset changes such as temperature rise, ocean acidification and changes in precipitation patterns, as well as fast onset events such as storms and floods can have an effect on economic and political security, and on the security of a particular community’s food, health and environment.

Policy- and decision-makers in multiple sectors, especially development, diplomacy and defence, have sought ways to predict and respond to these impacts. However, in order to do so effectively and sustainably, the complexity of different factors and interactions has to be unpacked into a granular understanding of the relationship between environmental change and insecurity in a given context . For awareness of the risks to be converted into action that improves lives, concrete entry points need to be identified.

This is where the Weathering Risk methodology comes in.

Developed by adelphi and the Potsdam Institute for Climate Impact Research and supported by partners such as Chatham House, CGIAR, The Economist Intelligence Unit, Institute for Security Studies, Igarapé Institute, UNDP, UNEP, United Nations University and The World Bank, the Weathering Risk methodology provides a framework for our own assessments, while also offering others a flexible toolkit that can be applied across different scales and degrees of complexity.

Based on two years of field testing through over twelve Weathering Risk assessments on the ground from the Pacific to the Levant, this guidance document has been adapted the original approach to make it easier, replicable and usable. Elements of the climate security assessment approach include:

Context factors shaping vulnerability and resilience to climate and security risks, including a variety of cross-cutting topics like gender equality and social inclusion. These context factors normally play a decisive role in all pathways and should be at the centre of the analysis.

Climate changes and their direct impacts, including temperature rise and its impacts on agriculture or flooding and its impact on infrastructure as well as other non-climate related environmental issues such as pollution.

Peace and security context, which comprise the history and state of economic, social and political (in)stability, past and ongoing security risks and conflict dynamics, the drivers and causes of insecurity, and the main actors that have an impact on security and stability. 

The interactions between climate impacts, security and peace or climate-security risk pathways that link certain climatic impacts with specific security risks and conflicts; for example, how more pressure on natural resources such as land and water can increase competition and tensions over access and availability of these resources and show how security risks and conflicts effect resilience, the environment and climate risks; for example, how insecurity can contribute to increased environmental degradation which in turn can undermine the resilience of local communities.

Identification of Responses

The final aspect of the methodology focuses on identifying context-specific response measures and actions to address climate-related security risks. The focus should be on inclusive and integrated responses that build resilience against both climate and security risks and include a special focus on ‘no regret options’ in the face of uncertainty and shifting probabilities of climate-related hazards and future socio-political developments. Although there is no universal set of activities that simultaneously provides climate change adaptation, peacebuilding, and development benefits, evidence and lessons learned from past climate-security programming can help inform responses.

Scenarios - Looking Forward

Climate-security risk assessments are often future oriented as climate impacts and pressures are set to change significantly in the future. Thus, one key approach to deal with this uncertainty and think about future risks is scenario development or planning . The basis of any scenario development is to identify key trends and critical factors that – judging from the status quo – will have a decisive influence in the future. The greater their influence on the subject being investigated and the higher the degree of uncertainty in how they will develop, the more important they are in scenario development. Based on the different pathways and interactions between these key drivers, a number of coherent and plausible narrative scenarios are developed to test responses.

Monitoring and Evaluation

The last part of the guidance introduces monitoring and evaluation of climate-security interventions (M&E) . As with any other intervention, a clear theory of change is not only key for implementation, but also for effective M&E. In addition to a clear theory of change, indicators are the second building block for effective M&E. Responses to climate-security risks and the resilience they build are multidimensional, and therefore require adequate indicators to track progress across different dimensions within an interactive system.

Our approach is openly accessible, free to use and replicable. Selected tools, dashboards, variables, and indicators to inform climate-security assessments are available as a separate download here .

Learn more about how qualitative research can uncover hidden drivers and connectors and generate contextualised and actionable knowledge and the importance of combining qualitative and quantitative approaches in our op-ed The power of real-world qualitative assessments for addressing climate security risks . 

Learn more about our projects .

Weathering Risk Climate Security Risk Assessment Methodology Guide and Tools 2,1 MB | PDF

Weathering Risk Methodology Infographic 499,6 KB | PNG

Weathering Risk Methodology Paper (first edition) 659,0 KB | PDF

Janani Vivekananda

Weathering Risk project co-lead and head of the Climate Diplomacy and Security programme at adelphi. Full biography & contact information .

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MIT unveils a new action plan to tackle the climate crisis

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MIT has released an ambitious new plan for action to address the world’s accelerating climate crisis. The plan, titled “ Fast Forward: MIT’s Climate Action Plan for the Decade ,” includes a broad array of new initiatives and significant expansions of existing programs, to address the needs for new technologies, new policies, and new kinds of outreach to bring the Institute’s expertise to bear on this critical global issue.

As MIT President L. Rafael Reif and other senior leaders have written in a letter to the MIT community announcing the new plan, “Humanity must find affordable, equitable ways to bring every sector of the global economy to net-zero carbon emissions no later than 2050.” And in order to do that, “we must go as far as we can, as fast as we can, with the tools and methods we have now.” But that alone, they stress, will not be enough to meet that essential goal. Significant investments will also be needed to invent and deploy new tools, including technological breakthroughs, policy initiatives, and effective strategies for education and communication about this epochal challenge.

“Our approach is to build on what the MIT community does best — and then aspire for still more. Harnessing MIT’s long record as a leader in innovation, the plan’s driving force is a series of initiatives to ignite research on, and accelerate the deployment of, the technologies and policies that will produce the greatest impact on limiting global climate change,” says Vice President for Research Maria Zuber, who led the creation and implementation of MIT’s first climate action plan and oversaw the development of the new plan alongside Associate Provost Richard Lester and School of Engineering Dean Anantha Chandrakasan.

The new plan includes a commitment to investigate the essential dynamics of global warming and its impacts, increasing efforts toward more precise predictions, and advocating for science-based climate policies and increased funding for climate research. It also aims to foster innovation through new research grants, faculty hiring policies, and student fellowship opportunities.

Decarbonizing the world’s economy in time will require “new ideas, transformed into practical solutions, in record time,” the plan states, and so it includes a push for research focused on key areas such as cement and steel production, heavy transportation, and ways to remove carbon from the air. The plan affirms the imperative for decarbonization efforts to emphasize the need for equity and fairness, and for broad outreach to all segments of society.

Charting a shared course for the future

Having made substantial progress in implementing the Institute’s original five-year Plan for Action on Climate Change , MIT’s new plan outlines measures to build upon and expand that progress over the next decade. The plan consists of five broad areas of action: sparking innovation, educating future generations, informing and leveraging government action, reducing MIT’s own climate impact, and uniting and coordinating all of MIT’s climate efforts.

MIT is already well on its way to reaching the initial target, set in 2015, to reduce the Institute’s net carbon emissions by at least 32 percent from 2014 levels by the year 2030. That goal is being met through a combination of innovative off-campus power purchase agreements that enable the construction of large-scale solar and wind farms, and an array of renewable energy and building efficiency measures on campus. In the new plan, MIT commits to net-zero direct carbon emissions by 2026.

The initial plan focused largely on intensifying efforts to find breakthrough solutions for addressing climate change, through a series of actions including the creation of new low-carbon energy centers for research, and the convening of researchers, industry leaders, and policymakers to facilitate the sharing of best practices and successful measures. The new plan expands upon these actions and incorporates new measures, such as climate-focused faculty positions and student work opportunities to help tackle climate issues from a variety of disciplines and perspectives.

A long-running series of symposia , community forums, and other events and discussions helped shape a set of underlying principles that apply to all of the plan’s many component parts. These themes are:

• The centrality of science, to build on MIT’s pioneering work in understanding the dynamics of global warming and its effects;
• The need to innovate and scale, requiring new ideas to be made into practical solutions quickly;
• The imperative of justice, since many of those who will be most affected by climate change are among those with the least resources to adapt;
• The need for engagement, dealing with government, industry, and society as a whole, reflecting the fact that decarbonizing the world’s economy will require working with leaders in all sectors; and
• The power of coordination, emphasizing the need for the many different parts of the Institute’s climate research, education, and outreach to have clear structures for decision making, action, and accountability.

Bolstering research and innovation

The new plan features a wide array of action items to encourage innovation in critical areas, including new programs as well as the expansions of existing programs. This includes the Climate Grand Challenges , announced last year, which focus on game-changing research advances across disciplines spanning MIT.

“We must, and we do, call for critical self-examination of our own footprint, and aspire to substantial reductions. We also must, and we do, renew and bolster our commitment to the kind of paradigm-shifting research and innovation, across every sector and in every field of human endeavor, that the world expects from MIT,” notes Professor Lester. “An existential challenge like climate change calls for both immediate action and extraordinary long shots. I believe the people of MIT are capable of both.”

The plan also calls for expanding the MIT Climate and Sustainability Consortium, created earlier this year , to foster collaborations among companies and researchers to work for solutions to climate problems. The aim is to greatly accelerate the adoption of large-scale, real-world climate solutions, across different industries around the world, by working with large companies as they work to find ways to meet new net-zero climate targets, in areas ranging from aerospace to packaged food.

Another planned action is to establish a Future Energy Systems Center, which will coalesce the work that has been fostered through MIT’s Low-Carbon Energy Centers , created under the previous climate action plan. The Institute is also committing to devoting at least 20 upcoming faculty positions to climate-focused talent. And, there will be new midcareer ignition grants for faculty to spur work related to climate change and clean energy.

For students, the plan will provide up to 100 new Climate and Sustainability Energy Fellowships, spanning the Institute’s five schools and one college. These will enable work on current or new projects related to climate change. There will also be a new Climate Education Task Force to evaluate current offerings and make recommendations for strengthening research on climate-related topics. And, in-depth climate or clean-energy-related research opportunities will be offered to every undergraduate who wants one. Climate and sustainability topics and examples will be introduced into courses throughout the Institute, especially in the General Institute Requirements that all undergraduates must take.

This emphasis on MIT’s students is reflected in the plan’s introductory cover letter from Reif, Zuber, Lester, Chandrakasan, and Executive Vice President and Treasurer Glen Shor. They write: “In facing this challenge, we have very high expectations for our students; we expect them to help make the impossible possible. And we owe it to them to face this crisis by coming together in a whole-of-MIT effort — deliberately, wholeheartedly, and as fast as we can.”

The plan’s educational components provide “the opportunity to fundamentally change how we have our graduates think in terms of a sustainable future,” Chandrakasan says. “I think the opportunity to embed this notion of sustainability into every class, to think about design for sustainability, is a very important aspect of what we’re doing. And, this plan could significantly increase the faculty focused on this critical area in the next several years. The potential impact of that is tremendous.”

Reaching outward

The plan calls for creating a new Sustainability Policy Hub for undergraduates and graduate students to foster interactions with sustainability policymakers and faculty, including facilitating climate policy internships in Washington. There will be an expansion of the Council on the Uncertain Human Future, which started last year to bring together various groups to consider the climate crisis and its impacts on how people might live now and in the future.

“The proposed new Sustainability Policy Hub, coordinated by the Technology and Policy Program , will help MIT students and researchers engage with decision makers on topics that directly affect people and their well-being today and in the future,” says Noelle Selin, an associate professor in the Institute for Data, Systems, and Society and the Department of Earth, Atmospheric, and Planetary Sciences. “Ensuring sustainability in a changed climate is a collaborative effort, and working with policymakers and communities will be critical to ensure our research leads to action.”

A new series of Climate Action Symposia, similar to a successful series held in 2019-2020, will be convened. These events may include a focus on climate challenges for the developing world. In addition, MIT will develop a science- and fact-based curriculum on climate issues for high school students. These will be aimed at underserved populations and at countering sources of misinformation.

Building on its ongoing efforts to provide reliable, evidence-based information on climate science, technology, and policy solutions to policymakers at all levels of government, MIT is establishing a faculty-led Climate Policy Working Group, which will work with the Institute’s Washington office to help connect faculty members doing relevant research with officials working in those areas.

In the financial arena, MIT will lead more research and discussions aimed at strengthening the financial disclosures relating to climate that corporations need to make, thus making the markets more sensitive to the true risks to investors posed by climate change. In addition, MIT will develop a series of case studies of companies that have made a conversion to decarbonized energy and to sustainable practices, in order to provide useful models for others.

MIT will also expand the reach of its tools for modeling the impacts of various policy decisions on climate outcomes, economics, and energy systems. And, it will continue to send delegations to the major climate policy forums such as the UN’s Conference of the Parties, and to find new audiences for its Climate Portal , web-based Climate Primer , and TILclimate podcast .

“This plan reaffirms MIT’s commitment to developing climate change solutions,” says Christopher Knittel, the George P. Shultz Professor of Applied Economics. “It understands that solving climate change will require not only new technologies but also new climate leaders and new policy. The plan leverages MIT’s strength across all three of these, as well as its most prized resources: its students. I look forward to working with our students and policymakers in using the tools of economics to provide the research needed for evidence-based policymaking.”

Recognizing that the impacts of climate change fall most heavily on some populations that have contributed little to the problem but have limited means to make the needed changes, the plan emphasizes the importance of addressing the socioeconomic challenges posed by major transitions in energy systems, and will focus on job creation and community support in these regions, both domestically and in the developing world. These programs include the Environmental Solutions Initiative’s Natural Climate Solutions Program , and the Climate Resilience Early Warning System Network , which aims to provide fine-grained climate predictions.

“I’m extraordinarily excited about the plan,” says Professor John Fernández, director of the Environmental Solutions Initiative and a professor of building technology. “These are exactly the right things for MIT to be doing, and they align well with an increasing appetite across our community. We have extensive expertise at MIT to contribute to diverse solutions, but our reach should be expanded and I think this plan will help us do that.”

“It’s so encouraging to see environmental justice issues and community collaborations centered in the new climate action plan,” says Amy Moran-Thomas, the Alfred Henry and Jean Morrison Hayes Career Development Associate Professor of Anthropology. “This is a vital step forward. MIT’s policy responses and climate technology design can be so much more significant in their reach with these engagements done in a meaningful way.”

Decarbonizing campus

MIT’s first climate action plan produced mechanisms and actions that have led to significant reductions in net emissions. For example, through an innovative collaborative power purchase agreement , MIT enabled the construction of a large solar farm and the early retirement of a coal plant, and also provided a model that others have since adopted. Because of the existing agreement, MIT has already reduced its net emissions by 24 percent despite a boom in construction of new buildings on campus. This model will be extended moving forward, as MIT explores a variety of possible large-scale collaborative agreements to enable solar energy, wind energy, energy storage, and other emissions-curbing facilities.

Using the campus as a living testbed, the Institute has studied every aspect of its operations to assess their climate impacts, including heating and cooling, electricity, lighting, materials, and transportation. The studies confirm the difficulties inherent in transforming large existing infrastructure, but all feasible reductions in emissions are being pursued. Among them: All new purchases of light vehicles will be zero-emissions if available. The amount of solar generation on campus will increase fivefold, from 100 to 500 kilowatts. Shuttle buses will begin converting to electric power no later than  2026, and the number of car-charging stations will triple, to 360.

Meanwhile, a new working group will study possibilities for further reductions of on-campus emissions, including indirect emissions encompassed in the UN’s Scope 3 category, such as embedded energy in construction materials, as well as possible measures to offset off-campus Institute-sponsored travel. The group will also study goals relating to food, water, and waste systems; develop a campus climate resilience plan; and expand the accounting of greenhouse gas emissions to include MIT’s facilities outside the campus. It will encourage all labs, departments, and centers to develop plans for sustainability and reductions in emissions.

“This is a broad and appropriately ambitious plan that reflects the headway we’ve made building up capacity over the last five years,” says Robert Armstrong, director of the MIT Energy Initiative. “To succeed we’ll need to continually integrate new understanding of climate science, science and technology innovations, and societal engagement from the many elements of this plan, and to be agile in adapting ongoing work accordingly.”

Examining investments

To help bring MIT’s investments in line with these climate goals, MIT has already begun the process of decarbonizing its portfolio, but aims to go further.

Beyond merely declaring an aspirational goal for such reductions, the Institute will take this on as a serious research question, by undertaking an intensive analysis of what it would mean to achieve net-zero carbon by 2050 in a broad investment portfolio.

“I am grateful to MITIMCO for their seriousness in affirming this step,” Zuber says. “We hope the outcome of this analysis will help not just our institution but possibly other institutional managers with a broad portfolio who aspire to a net-zero carbon goal.”

MIT’s investment management company will also review its environmental, social, and governance investment framework and post it online. And, as a member of Climate Action 100+ , MIT will be actively engaging with major companies about their climate-change planning. For the planned development of the Volpe site in Kendall square, MIT will offset the entire carbon footprint and raise the site above the projected 2070 100-year flood level.

Institute-wide participation

A centerpiece of the new plan is the creation of two high-level committees representing all parts of the MIT community. The MIT Climate Steering Committee, a council of faculty and administrative leaders, will oversee and coordinate MIT’s strategies on climate change, from technology to policy. The steering committee will serve as an “orchestra conductor,” coordinating with the heads of the various climate-related departments, labs, and centers, as well as issue-focused working groups, seeking input from across the Institute, setting priorities, committing resources, and communicating regularly on the progress of the climate plan’s implementation.

The second committee, called the Climate Nucleus, will include representatives of climate- and energy-focused departments, labs, and centers that have significant responsibilities under the climate plan. It will have broad responsibility for overseeing the management and implementation of all elements of the plan, including program planning, budgeting and staffing, fundraising, external and internal engagement, and program-level accountability. The Nucleus will make recommendations to the Climate Steering Committee on a regular basis and report annually to the steering committee on progress under the plan.

“We heard loud and clear that MIT needed both a representative voice for all those pursuing research, education, and innovation to achieve our climate and sustainability goals, but also a body that’s nimble enough to move quickly and imbued with enough budgetary oversight and leadership authority to act decisively. With the Climate Steering Committee and Climate Nucleus together, we hope to do both,” Lester says.

The new plan also calls for the creation of three working groups to address specific aspects of climate action. The working groups will include faculty, staff, students, and alumni and give these groups direct input into the ongoing implementation of MIT’s plans. The three groups will focus on climate education, climate policy, and MIT’s own carbon footprint. They will track progress under the plan and make recommendations to the Nucleus on ways of increasing MIT’s effectiveness and impact.

“MIT is in an extraordinary position to make a difference and to set a standard of climate leadership,” the plan’s cover letter says. “With this plan, we commit to a coordinated set of leadership actions to spur innovation, accelerate action, and deliver practical impact.”

“Successfully addressing the challenges posed by climate change will require breakthrough science, daring innovation, and practical solutions, the very trifecta that defines MIT research,” says Raffaele Ferrari, the Cecil and Ida Green Professor of Oceanography. “The MIT climate action plan lays out a comprehensive vision to bring the whole Institute together and address these challenges head on. “Last century, MIT helped put humans on the moon. This century, it is committing to help save humanity and the environment from climate change here on Earth.”

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MIT has announced a new climate action plan aimed at helping the Institute tackle climate change, reports Kristina Chen for  The Tech . The plan offers increased opportunities for student involvement and a new organizational structure. Maria Zuber, MIT’s vice president for research, explains that MIT feels “that it’s our responsibility and duty to try to make a genuine difference, and to do that, we’re going to need the help of everyone in the community.” 

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GLOBAL WARMING METHODOLOGY

HOMEWORK OF METHODOLOGY MEANT TO EXPLAIN THE GLOBAL WARMING AND ITS EFFECTS

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Bharat Raj Singh , Dr Bharat Raj Singh , Sergio Franchito

Global Warming is now becoming a challenge for survival of species on Earth and draws attention of many modern societies, power and energy engineers, academicians, researchers and stakeholders to go for deeper study. Almost all countries are required to act fast and attend to major problems of depletion of fossil fuel resources, poor energy efficiency and environmental pollution and its dire consequences on priority. This book is written to create awareness of the energy engineers, academicians, researchers, industry personnel and society as a whole, and to emphasize current status of global warming and its impact on climate changes. We all know that humanity is at risk due to Greenhouse gases which are the main source of Global Warming . Our beautiful planet is being destroyed, due to excessive exploitation of Earth’s resources from its reservoirs and other serious man-made problems. The main objective of this book is to produce a good documentation from the point of view of knowledge seekers or public readers at large, and also those who are eager to know more about Global Warming and its impact on the Climate Changes, besides those who have raisen their voice for its remedial measures. Present state of environmental damage and continuous occurrence of natural disasters have made the environmentalist and scientists inevitable for their extensive study, and to carry out detailed analysis of the following threats faced by civilization across the entire globe due to global warming: i). Is Global Warming caused by human activity? ii). What are Greenhouse Gases? iii). Fast shrinkage of polar ice may leave us with no ice by summer 2040. iv). Fast rise of the Sea Level. v). Danger for species like polar bear, etc. vi). Ice sheets, where they meet at the Atlantic sea. This area may be affected by cold waves, heavy snow falls and intense storms. vii). Permafrost may create further warming which cannot be reversed. The Global Warming is increasing Earth's average surface temperature, due to the effect of Greenhouse gases such as: Carbon dioxide through emissions produced from burning of fossil fuels or from deforestation, which traps heat that would otherwise escape the Earth. This is a type of Greenhouse effect . The most significant Greenhouse gas is actually Water Vapor , not something produced directly by humankind in significant amounts. However, even slight increase in atmospheric levels of Carbon dioxide (CO2) can cause a substantial increase in Earth’s atmospheric temperature. The ultimate effects, which we are likely to be faced as 21 st century.

The Complete Briefing

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The Earth's thermostat is a complex and delicate mechanism, at the heart of which lie the greenhouse gases in the atmosphere. Carbon dioxide (CO2), a colourless and odourless gas, is the principal well-mixed greenhouse gas. It is through emissions of this gas that human activities exert their greatest influence on climate. Increased concentrations of carbon dioxide disturb the natural radiative balance of the atmosphere and lead to warming of the Earth's surface. The latest report from the Intergovernmental Panel on Climate Change, the Fourth Assessment Report (2007), has confirmed the assertion that “warming of the climate system is unequivocal” and that most of the observed 20th century increase in globally averaged temperatures is “very likely” due to the observed increases in anthropogenic greenhouse gas concentrations. A discernable human influence on the climate system is now apparent and extends to oceanic warming, temperature extremes and wind patterns. Concentration...

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If you want to learn the science related to global warming and its effects (including climate change), this and the document of equal length, "The Pacific Islands and Pro-Poor Adaptation to Global Warming--Organized Research Notes," would be good places to start. I generated this after I agree to replace a book chapter that was originally supposed to be based on an expensive report that an economist had made that nobody (including me) could make sense of. I was paid for a couple of months work, but ended up spending an additional year at my own expense. I ended up completely broke. So I had to sell mattresses for a while. That gave rise to the treatise on mattresses. The work on "climate change" was done for the Asian Development Bank. Please send comments and criticisms (including fact checking and editing problems) to [email protected]. Thanks

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Methodology Underpinning the State of Climate Action Series: 2023 Update

This technical note describes the State of Climate Action 2023 ’s methodology for identifying sectors that must transform, translating these transformations into global mitigation targets primarily for 2030 and 2050 and selecting indicators with datasets to monitor annual change. It also outlines the report’s approach for assessing progress made toward near-term targets and comparing trends over time.

Published under  Systems Change Lab , this Technical Note features analysis from Climate , Energy , Food , Forests , WRI Ross Center for Sustainable Cities and Finance . Reach out to Joel Jaeger for more information.

• Joel Jaeger

State of Climate Action

State of climate action 2023.

• Methodology Underpinning the State of Climate Action Series
• State of Climate Action 2022
• State of Climate Action 2021: Systems Transformations Required to Limit Global Warming to 1.5°C
• State of Climate Action: Assessing Progress toward 2030 and 2050

Limiting global temperature rise to 1.5°C requires transformational change across power, buildings, industry, transport, forests and land and food and agriculture as well as the immediate scale-up of carbon removal technologies and climate finance. The State of Climate Action series provides an overview of the world’s collective efforts to accelerate these far-reaching transitions. We first translate each sectoral transformation into a set of actionable, 1.5°C-aligned targets for 2030 and 2050, with associated indicators and datasets. Annual installments of the report then compare recent progress made toward (or away from) these mitigation goals with the pace of change required to achieve 2030 targets to quantify the global gap in climate action. While a similar effort is warranted to evaluate adaptation efforts, we limit this series’ scope to tracking progress made in reducing greenhouse gas emissions and removing carbon dioxide from the atmosphere.

This technical note accompanies the State of Climate Action 2023 . It describes our methods for identifying sectors that must transform, translating these transformations into global mitigation targets primarily for 2030 and 2050 and selecting indicators with datasets to monitor annual change. It also outlines our approach for assessing the world’s progress made toward near-term targets and categorizing recent efforts as on track, off track, well off track, heading in the wrong direction or insufficient data. Finally, it details how we compare trends over time, as well as limitations to our methodology.

This year’s technical note features several changes to the technical note we published alongside the State of Climate Action 2022 . Key updates include the following: revisions to 15 targets to reflect the best available science, including scenarios from the integrated assessment models included in the Intergovernmental Panel on Climate Change’s Sixth Assessment Report and recently published literature; the inclusion of interim targets for 2035 or 2040 where possible; the addition of four indicators (share of new buildings that are zero-carbon in operation, share of electric vehicles in two- and three-wheeler sales, the GHG emissions intensity of agricultural production and ratio of investment in low-carbon to fossil fuel energy supply); and the removal of two indicators (carbon intensity of land-based passenger transport and GHG emissions from agricultural production). We also refined our methodology for assessing the progress of indicators that we can reasonably expect to follow an S-curve, and added new methods for comparing each indicator’s most recent data point to recent trends. Finally, we removed the discussion of enabling conditions from this technical note, as the series no longer covers them.

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• Published: 19 November 2020

Predicting global patterns of long-term climate change from short-term simulations using machine learning

• L. A. Mansfield   ORCID: orcid.org/0000-0002-6285-6045 1 , 2 ,
• P. J. Nowack   ORCID: orcid.org/0000-0003-4588-7832 1 , 3 , 4 , 5 ,
• M. Kasoar   ORCID: orcid.org/0000-0001-5571-8843 1 , 3 , 6 ,
• R. G. Everitt   ORCID: orcid.org/0000-0002-0822-5648 7 ,
• W. J. Collins   ORCID: orcid.org/0000-0002-7419-0850 8 &
• A. Voulgarakis   ORCID: orcid.org/0000-0002-6656-4437 1 , 6 , 9  

npj Climate and Atmospheric Science volume  3 , Article number:  44 ( 2020 ) Cite this article

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• Atmospheric science
• Climate change
• Climate-change impacts
• Climate-change mitigation
• Projection and prediction

Understanding and estimating regional climate change under different anthropogenic emission scenarios is pivotal for informing societal adaptation and mitigation measures. However, the high computational complexity of state-of-the-art climate models remains a central bottleneck in this endeavour. Here we introduce a machine learning approach, which utilises a unique dataset of existing climate model simulations to learn relationships between short-term and long-term temperature responses to different climate forcing scenarios. This approach not only has the potential to accelerate climate change projections by reducing the costs of scenario computations, but also helps uncover early indicators of modelled long-term climate responses, which is of relevance to climate change detection, predictability, and attribution. Our results highlight challenges and opportunities for data-driven climate modelling, especially concerning the incorporation of even larger model datasets in the future. We therefore encourage extensive data sharing among research institutes to build ever more powerful climate response emulators, and thus to enable faster climate change projections.

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A machine learning approach to rapidly project climate responses under a multitude of net-zero emission pathways

Introduction.

To achieve long-term climate change mitigation and adaptation goals, such as limiting global warming to 1.5 or 2 °C, there must be a global effort to decide and act upon effective but realistic emission pathways 1 . This requires an understanding of the consequences of such pathways, which are often diverse and involve changes in multiple climate forcers 1 , 2 , 3 . In particular, different emission scenarios of, for example, greenhouse gases and aerosols are responsible for diverse changes in regional climate, which are not always well captured by a metric such as global temperature-change potential 4 , 5 , 6 , 7 , 8 , 9 . Exploring more detailed relationships between emissions and multiregional climate responses still requires the application of Global Climate Models (GCMs) that allow the behaviour of the climate to be simulated under various conditions (e.g. different atmospheric greenhouse gas and aerosol concentrations or emissions fields) 10 , 11 , 12 on decadal to multi-centennial timescales (e.g. refs. 5 , 13 , 14 , 15 , 16 ). However, modelling climate at increasingly high spatial resolutions has significantly increased the computational complexity of GCMs 2 , a tendency that has been accelerated by the incorporation and enhancement of a number of new Earth system model components and processes 17 , 18 , 19 , 20 . This high computational cost has driven us to investigate how machine learning methods can help accelerate estimates of global and regional climate change under different climate forcing scenarios.

Our work is further motivated by studies that have suggested links between characteristic short-term and long-term response patterns to different climate forcing agents 5 , 21 , 22 . Here, we seek a fast ‘surrogate model’ 23 to find a mapping from short-term to long-term response patterns within a given GCM (Fig. 1 ). Once learned, this surrogate model can be used to rapidly predict other outputs (long-term responses) given new unseen inputs (short-term responses i.e. the results of easier to perform short-term simulations). While data science methods are increasingly used within climate science (e.g. refs. 24 , 25 , 26 , 27 , 28 , 29 , 30 ), no study has attempted the application we present here, i.e. to predict the magnitude and patterns of long-term climate response to a wide range of global and regional forcing scenarios.

a Global mean surface temperature response of a GCM (HadGEM3) to selected global and regional sudden step perturbations, e.g. to changes in long-lived greenhouse gases (CO 2 , CH 4 ), the solar constant and short-lived aerosols (SO 4 , BC). b Example of the short-term and long-term surface temperature response patterns for 2xCO 2 scenario, defined as an average over the first 10 years and years 70–100, respectively. c Process diagram highlighting the training and prediction stages. In the training stage, a regression function is learned for pairs of short-term and long-term response maps, where the data are obtained from existing HadGEM3 simulations. In the prediction stage, the long-term response for a new unseen scenario is predicted by applying the already learned function to the short-term response to this new scenario, which is cheaper to obtain (here only 10 climate model years).

Building surrogate climate models

To train our learning algorithms, we take advantage of a unique set of GCM simulations performed in recent years using the Hadley Centre Global Environment Model 3 (HadGEM3). In these, step-wise perturbations were applied to various forcing agents to explore characteristic short- and long-term climate responses to them 5 , 7 , 8 , 14 , 16 , 31 , 32 , 33 , 34 . The set of simulations includes global perturbations of long-lived greenhouse gases such as carbon dioxide (CO 2 ) and methane (CH 4 ), as well as global and local perturbations to key short-lived pollutants such as sulfate (SO 4 ) and black carbon (BC) particles, amongst others (Supplementary Table 1 ). A key difference between these two types of perturbations is that long-lived forcers are homogeneously distributed in the atmosphere so that the region of emission is effectively inconsequential for the global temperature response pattern. In contrast, the response pattern does depend on the region of emission for short-lived forcers.

The evolution of the GCM’s global mean temperature response to some example forcing scenarios is highlighted in Fig. 1a . All scenarios show an initial sudden response in the first few years, which we label the ‘short-term response’. The global mean temperature then converges towards a new (approximately) equilibrated steady state, which we label the ‘long-term response’. We are interested in not just the global mean response but, more importantly, in the global response patterns, such as the example shown in Fig. 1b for the 2xCO 2 scenario.

In essence, GCMs map the initial state of the climate system and its boundary conditions, such as emission fields, to a state of the climate at a later time, using complicated functions representing the model physics, chemistry, and biology 17 . Our statistical model approximates the behaviour of the full GCM for a specific target climate variable of interest; here we choose surface temperature at each GCM grid cell, a central variable of interest in climate science and impact studies. This model is trained on simulations from the full global climate model (supervised learning 35 ), in order to predict the long-term surface temperature response of the GCM from the short-term temperature responses to perturbations (Fig. 1c ). Then we can make effectively instantaneous predictions using results from new short-term simulations as input so that repeated long GCM runs can be avoided. Based on the available GCM data, we define the ‘long-term’ as the quasi-equilibrium response after removing the initial transient response (first 70 years) and averaging over the remaining years of the simulations, similarly to previous studies (see Methods) 5 , 14 , 36 . We define ‘short-term’ as the response over the first 10 years of each simulation.

The task is to learn the function \(f({\mathbf{x}})\) that maps these short-term responses ( \({\mathbf{x}}\) ) to the long-term responses ( \({\mathbf{y}}\) ) (‘TRAINING’ in Fig. 1c ). We use an independent regression model of the long-term response for each grid cell. Each one depends on the short-term response at all grid cells, so that predictions are not only based on local information but can also draw predictive capability from any changes in surface temperature worldwide. We present Ridge regression 37 and Gaussian Process Regression (GPR) 38 with a linear kernel (see Methods) as approaches for constructing this mapping. Then, the learned regression functions can be used to predict the long-term response for new, unseen inputs ( \({\mathbf{x}}^ \ast\) ), (‘PREDICTION’ in Fig. 1c ). We choose Ridge regression and GPR, because these two methods handle well the limited sample size (number of simulations available) for training, which also limits how effectively the number of free parameters for other approaches such as deep learning, including convolutional neural networks, could be constrained. Future data collaborations, discussed below, could make the adaptation of our methodology to incorporate deep leaning an option. For the learning process, we use all but one of the available simulations at a time for training and cross-validation. The trained model is then used to make a temperature response prediction for the simulation that was left out each time. Finally, we assess the prediction skill of our machine learning models by comparing the predicted response maps \(f({\mathbf{x}}^ \ast )\) to the results of the complex GCM simulations. This is repeated so that each simulation is predicted once based on the information learned from all other independent simulations (Methods).

Results and discussion

Overall method performance.

We evaluate the performance of the two different machine learning methods (Ridge, GPR) by benchmarking them against a traditional pattern scaling approach 36 , 39 , often used for estimating future patterns of climate change 40 , 41 , 42 . The latter relies on multiplying the long-term response pattern for the 2xCO 2 scenario by the relative magnitude of global mean response for each individual climate forcer. This is approximated as the ratio of global mean effective radiative forcing (ERF) between the forcer and the 2xCO 2 scenario (Methods) 36 . Alternative approaches are discussed in Methods and Supplementary.

We compare the predictions of long-term regional surface temperature changes with those produced by the complex GCM. From analysis at a grid-cell level, both Ridge regression and GPR capture some broad features that pattern scaling is also known to predict effectively, such as enhanced warming over the Northern Hemisphere, particularly over land, and Arctic amplification 43 (Supplementary Figs. 1 and 2 ). However, the key advantage of both machine learning methods is that they capture regional patterns and diversity in the response not predicted by pattern scaling. In particular, aerosol forcing scenarios show highly specific regional imprints on surface temperature due to the spatial heterogeneity of the emissions and their short lifetimes 4 , 7 , 33 . It is the ability to learn these patterns that gives data-driven methods the edge over any pattern scaling method for such predictions. The example in Fig. 2 shows the distribution of predicted temperature responses over all individual grid boxes for one short-lived and one long-lived forcing scenario. For the long-lived forcings all three types of model predictions produce a similar distribution of surface temperature responses to the GCM. However, for short-lived forcing scenarios, the range and variability of responses is highly underestimated in the case of pattern scaling. This is consistent across short-lived forcing scenario predictions (Supplementary Fig. 3 ) and exists because pattern scaling is constrained to the same pattern, regardless of the scaling factor used to estimate the global mean response (Methods, Supplementary Fig. 4 ).

The central vertical boxes indicate the interquartile range shown on a standard box plot, the horizontal line shows the median and the black point shows the mean. The horizontal width shows the distribution of temperature values overall grid points, i.e. the wider regions highlight that more grid points have this value of predicted temperature response. Note the different vertical scales.

In the following, we quantify how well the two machine learning models and pattern scaling perform on different spatial scales. At the grid-scale level, we calculate the Root Mean Squared Error (RMSE) by comparing the prediction and GCM response at every grid point (Methods). We highlight that grid-scale error metrics need to be interpreted with care because they can present misleading results, particularly for higher resolution models. For example, they penalize patterns that—as broad features—are predicted correctly but displaced marginally on the spatial grid 44 . This issue is necessarily more prevalent for the machine learning approaches where smaller scale patterns are more frequently predicted, while pattern scaling predicts more consistently smooth, cautious patterns with reduced spatial variability (Supplementary Fig. 1 ). This consideration is a key reason why predictions for larger scale domains are often selected in impact studies 11 , 12 . We therefore also compare the absolute errors in global mean temperature and in regional mean temperature over ten broad regions (Fig. 3 ); four of which are the main emission regions (North America, Europe, South Asia, and East Asia) and the remaining cover primarily land areas where responses affect the majority of the world’s population. The boxplots in Fig. 3 show how these errors are distributed overall predicted scenarios for each regression method.

RMSE at grid-cell level and global/regional absolute errors in °C for all scenarios, calculated by averaging the predicted response over each region and taking the difference between the GCM output and the prediction using three methods: R = Ridge regression, G = Gaussian Process Regression, and P = Pattern scaling. Boxplots show the distribution of errors across scenario predictions. Boxes show the interquartile range, whiskers show the extrema, lines show the medians and black diamonds show the mean. The dots indicate the errors for each individual scenario. Note the different scale for the Arctic and that points exceed the scale in Arctic (9.5), Northwest Asia (4.7), East Asia (3.7) and the Grid RMSE (3.8).

Both Ridge and GPR generally outperform the pattern scaling approach, but we find that, in most cases, it is GPR errors that are lowest. Note that scenario-specific pattern scaling errors are necessarily dependent on the approach chosen to scale the global CO 2 -response pattern (Methods, Supplementary Fig. 4 ), but all pattern scaling approaches share their fundamental limitation in predicting spatial variability (Fig. 2 ). The large spread in absolute errors in Fig. 3 is due to the large spread in response magnitude for the different scenarios. Specifically, the large errors (e.g. 1–2 °C for the machine learning models and >3 °C for pattern scaling) come mostly from regions/scenarios with a large magnitude of response, which expectedly tend to be for strong forcings (e.g. strong solar or greenhouse gas forcings), but these errors can be small relative to the overall magnitude of scenario response. In contrast, small absolute errors can be large relative to the magnitude of response (Supplementary Fig. 5 ), making prediction more challenging for weakly forced scenarios. This is also consistent with the finding that regional aerosol perturbations, with typically weaker forcings, are more difficult to predict compared to long-lived pollutant perturbations (Fig. 2 ).

Learning early indicators

As well as advancing our predictability skills, the machine learning methods inform us about regions that experience the earliest indicators of long-term climate change in the GCM. By assessing the structure of learned Ridge regression coefficients, we find patterns in the short-term response that consistently indicate the long-term temperature response (Supplementary Fig. 6 ). In some regions (e.g. East Asia) the dominant coefficients appear in regions close to the predicted grid cell, whereas in other regions (e.g. Europe) predictions are strongly influenced by the short-term responses over relatively remote areas, such as sea-ice regions over the Arctic. This highlights the fact that climate model response predictability varies strongly depending on the region of interest, and often involves interactions with regions very far from the region of interest as well as from the emission region.

We also examine which areas are overall the most influential for long-term predictability, by averaging magnitude of coefficients across all grid cells to find a global mean coefficient map (Supplementary Fig. 6c, f ). This coefficient map mimics warming patterns seen in previous studies (enhanced at high latitudes, over land and over the subtropics) 14 but also shows amplified coefficient weights in sea-ice regions, high-altitude regions, primary emission regions and mid-latitude jet stream regions. Arctic and high-altitude regions are known to warm more rapidly due to ice and snow albedo feedbacks 45 and faster upper tropospheric warming 11 , 46 respectively. These regions exhibit accelerated warming in the simulation compared to their surroundings, making them robust harbingers of long-term change within the model. We highlight the implications for future studies that attempt to interpret already observed warming patterns from a climate change perspective.

Data constraints and future directions

We identify more extensive training data (additional simulations and forcing scenarios) as key to further improving the skill of our machine learning methods. In Fig. 4 it is demonstrated that as the number of data training samples increases, the mean prediction accuracy significantly increases and becomes more consistent. We therefore expect significant potential for further improvements in predictions with even more training data. More simulations would better constrain parameters of the statistical models and improve the chances that a predicted scenario contains features previously seen by the statistical model (e.g. refs. 38 , 47 , Methods).

Mean of absolute errors in °C across all predicted scenarios against number of training simulations, with each line representing a different region (Fig. 3 ). RMSE at the grid-scale level is also shown in black with white dots. For a fixed number of training data points, the process of training and predicting is repeated several times over different combinations of training data to obtain multiple prediction errors for each scenario. Full boxplots showing the distribution of errors across scenario predictions given these different combinations of training simulations can be found in Supplementary Fig. 7 .

Since obtaining training data from the GCM is expensive, sensible choices can also be made about how to increase the dataset by choosing which new scenarios will benefit the accuracy of the method the most, e.g. to address some complex regional aspects of the responses to short-lived pollutants. We recommend increasing the dataset to include more short-lived pollutant scenarios, noting that those with large forcings may reduce the noise in the training data so as to better constrain learned relationships (e.g. Supplementary Fig. 5 ). Some regions stand out as particularly challenging for our machine learning approaches, with Europe being a prominent example (Supplementary Fig. 2 ). This is partly due to large variations in the long-term response across the training data over Europe relative to other regions, which means predictions are less well constrained and would benefit more from increased training data. Additionally, the variability in the GCM-predicted temperature time series is generally larger over Europe compared to other regions in both the control and perturbation simulations (Supplementary Fig. 8 ). This gives rise to a weaker signal-to-noise ratio for both short- and long-term responses in this region, increasing the difficulty of learning meaningful predictive relationships. It is also noteworthy that Ridge regression predictions for Europe depend strongly on remote parts of the Arctic where the short-term response is stronger but also highly variable (Supplementary Figs. 5 and 6 ). This points to the issue that internal variability can introduce noise to the inputs and outputs of the regression. This is partially addressed with multidecadal averages in the definitions of the short- and long-term responses, under the limitation that we have only a single realization of each simulation available. If, in future work, we have available an ensemble of simulations for each perturbation, an average over these would more effectively separate the internal variability from the response. The use of several diverse simulations in the training dataset also allows the noise in the inputs and outputs to be treated as random noise in the regression, which would be even better determined with increased training data.

A key challenge of working with the climate model information here is its high dimensionality (27,840 grid cells) given the small scenario sample size of 21 simulations. We note that we tried sensible approaches to dimension reduction for decreasing the number of points in both inputs and outputs, including physical dimension reduction by regional averaging, and statistical dimension reduction with principal component analysis (PCA) 47 . However, the resulting regressions generated larger prediction errors (Supplementary Fig. 9 ). Furthermore, we explored the use of different variables as the short-term predictors, such as air temperature at 500 hPa, geopotential height at 500 hPa (as an indicator of the large-scale dynamical responses), radiative forcing or sea level pressure. Surface temperature consistently outperforms other predictors, although a similar degree of accuracy is achieved with 500 hPa air temperature and geopotential height, suggesting the information encoded by these is similar (Supplementary Fig. 10 ). Throughout, we have selected the first 10 years of the GCM simulations as the inputs to our regression, but we find promising results for even shorter periods, e.g. the first 5 years (Supplementary Fig. 11 ). Finally, we also tested other linear (e.g. LASSO 47 ) and nonlinear (e.g. Random Forest) methods for the same learning task. However, these provided weaker results so that we focused our discussion on Ridge and GPR here. We have explored the use of these methods in the context of predicting temperature responses; however, we leave open the topic of predicting other variables such as precipitation, which we expect to be more challenging due to its spatial and temporal variability 48 , 49 , but for which pattern scaling approaches are well-known to perform particularly poorly 36 , 41 , 43 , 50 .

We also wish to highlight another long-term perspective in which the framework presented here could be useful. ‘Emulators’ that approximate model output given specific inputs, are a popular tool of choice for prediction, sensitivity analysis, uncertainty quantification and calibration and have great potential for climate prediction and impact studies 23 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 . However, long-term, spatially resolved climate prediction for diverse forcings has not yet been addressed due to the cost of training such emulators. A major implication of the approach presented here is that it can catalyse designing long-term climate emulators, by using a combination of the short-term/long-term relationships presented here and trained emulators of the short-term climate response to different forcings (i.e. multilevel emulation 52 , 59 ). Training an emulator that predicts the spatial patterns of long-term response to a range of forcings would be an extremely challenging task, as it would require tens of simulations, all of them multidecadal in length, in order to train the emulator. Our method drastically accelerates this process by reducing the length of such simulations to be of the order of 5–10 years, with subsequent use of the relationships presented here for translating short-term responses to long-term responses.

Our study made use of existing simulations from a single global climate model. However, it opens the door for similar approaches to be taken with datasets from other individual climate models. The same GCMs are typically run by several different research centres across the world so that additional simulation data should be an effort of (inter)national collaboration. We therefore encourage widespread data sharing to test the limits of our approach as an important part of future research efforts in this direction. We hope that our work will catalyse developments for coordinated efforts in which carefully selected perturbation experiments will be performed in a multi-model framework. Increased availability of training datasets through model intercomparison exercises, along with increasing access to powerful computing hardware can only help with this endeavour, leading to further advances in climate model emulation.

Available simulations

To learn the regression models, we use data from long-term simulations from the Hadley Centre Global Environment Model 3 (HadGEM3) HadGEM3, a climate model developed by the UK Met Office 17 . HadGEM3 is a GCM for the atmosphere, land 18 , ocean 19 , and sea-ice 20 . In the configuration used here, the horizontal resolution is 1.875° by 1.25°, giving grid boxes ~140 km wide in the mid-latitudes 17 . The simulations were run in previous academic studies and model intercomparison projects, namely the Precipitation Driver and Response Model Intercomparison Project (PDRMIP) 16 , 31 , 32 , Evaluating the Climate and Air Quality Impacts of Short-lived pollutants (ECLIPSE) 7 , 8 , 33 and Kasoar et al. (2018) 5 , 14 , 34 . There are 21 such simulations for a range of forcings, including long-lived greenhouse gas perturbations (e.g. carbon dioxide (CO 2 ), methane (CH 4 ), CFC-12), short-lived pollutant perturbations (e.g. sulfur dioxide emissions (SO 2 , the precursor to sulfate aerosol (SO 4 )), black carbon (BC), organic carbon (OC)) and a solar forcing perturbation. For the short-term pollutants, regional perturbations exist, to account for the influence of emission region to the response 4 , 60 .

The long-lived greenhouse gas (CO 2 , CH 4 , CFC-12) simulations were performed by altering the atmospheric mixing ratios. The short-lived pollutant experiments were performed by abruptly scaling present-day emission fields in simulations performed by ECLIPSE 7 , 8 , 33 and Kasoar et al. (2018) 5 , 14 , 34 or by scaling multi-model mean concentration fields in PDRMIP 16 , 31 , 32 . The solar forcing experiment was performed by changing the solar irradiance constant 31 . The GCM is run until it converges towards a new climate state, to reach an approximate equilibrium (70–100 years). The response is calculated by differencing this with its corresponding control simulation (independent control simulations were run for each project 5 , 7 , 8 , 14 , 16 , 31 , 32 , 33 , 34 ). For the long-term response, we discard the transient response and average from year 70–100 for PDRMIP and Kasoar et al. (2018) to smooth out internal variability over the 30-year period 36 . For the 5 ECLIPSE simulations, we average from year 70 to year 80, since this is the full temporal extent of ECLIPSE simulations. For the short-term response, we average over the first 10 years of the simulation to reduce the influence of natural variability of the GCM 36 .

The experiments from PDRMIP consist of simulations with a doubling of CO 2 concentration, tripling of CH 4 concentration, a 10× increase in CFC-12 concentration, a 2% increase in total solar irradiance, 5× increase in sulfate concentrations (SO 4 ), a 10× increase in black carbon (BC) concentrations, a 10× increase in SO 4 concentrations over Europe only, a 10× increase in SO 4 concentrations over Asia only, and a reduction to preindustrial SO 4 concentrations 16 , 31 . From ECLIPSE project simulations, we use a 20% reduction in CH 4 emissions, a doubling in CO 2 concentration, a 100% reduction in BC emissions, 100% reduction in SO 2 emissions, and a 100% reduction in carbon monoxide (CO) emissions 7 , 8 , 33 . The simulations performed by Kasoar et al. (2018) consist of a 100% reduction in SO 2 over the Northern Hemisphere mid-latitudes (NHML), a 100% reduction in BC over the NHML, a 100% reduction in SO 2 over China only, a 100% reduction in SO 2 over East Asia, a 100% reduction in SO 2 over Europe and a 100% reduction in SO 2 over US 5 , 14 , 34 . Additional simulations had also been performed by the groups, but we only consider simulations where the global mean response exceeds natural variability, calculated as the standard deviation among the control simulations. This is because we want to limit the noise in the small dataset we have. Scenarios that we did not use for this reason were the global removals of organic carbon, volatile organic compounds and nitrogen oxides (ECLIPSE 7 , 8 , 33 ) and the removal of SO 2 over India (Kasoar et al. (2018) 5 , 14 , 34 ).

Regression methods

We construct the mapping between short-term temperature response ( \(x\) ) and long-term temperature response ( \(y\) ) described in Fig. 1b using Ridge regression 37 and Gaussian Process Regression (GPR) 38 . These were found to be strongest from a range of machine learning methods tested, including Random Forest and Lasso.

Ridge regression

Given output variable \(y\) and input variable \(x\) , linear regression uses the mapping

where there are \(p\) predictors, indexed by \(j = 1, \cdots ,p\) . The parameters to fit are the intercept, \(\beta _0\) , and the coefficients, \(\beta _j\) , associated with each predictor \(x_j\) . The method of least squares is used to fit the parameters by minimising the sum of the residual squared error for the training data pairs \((x_i,y_i)\) for grid points \(i = 1, \cdots ,N\) :

When the number of samples exactly equals the number of parameters, \(N = p + 1\) , this can be minimised to give a unique solution. When \(N\, > \,p + 1\) the parameters are overdetermined and this is an optimisation problem in \(\beta _j\) . In contrast, when \(N\, < \,p + 1\) , there are more free parameters, \(\beta _j\) , than there are observed data points to constrain them 47 . There are many possible values of \(\beta _j\) that satisfy (2) equal to zero, making this an under-determined problem. Our problem falls under this regime since we have many predictors (one for each grid point, i.e. \(p = 27,840\) ) but few training simulations \((N = 20)\) . This is why we introduce a regularisation constraint which penalises large values of \(\beta _j\) . Thus, we minimise 47 , 61 :

The last term shrinks many of the \(\beta _j\) coefficients close to zero, so that the remaining large coefficients can be viewed as stronger predictors of \(y\) . This introduces a bias but lowers the variance 5 . The regularisation parameter λ controls the amount of shrinkage and is chosen through cross-validation, described below. Once \(\beta _0\) and \(\beta _j\) have been learned, we can use (1) to make predictions. We carried out the regression with and without inputs \(x\) normalised to zero mean and unit variance with very little difference in results. We use Python package scikit-learn to implement Ridge regression and cross-validation 62 .

Cross-validation

Cross-validation is used here to estimate the best value of λ for prediction based on the available training data. First, we split the training dataset (of size \(N\) ) into a chosen number of subsets of size \(N_{CV}\) . We use three subsets so \(N_{CV}\) is around 6–7. Then, we iterate through a list of possible values of \(\lambda\) , and for each one, the following steps are taken.

Set \(\lambda\) from list.

Set aside one of the smaller datasets as the validation data (size \(N_{CV}\) ).

Train the regression model with the remaining data \((N - N_{CV})\) by minimising (3).

Use the inputs of the validation dataset on the trained model to make predictions on the outputs using (1) and call this \({\boldsymbol{y}} \ast\) .

Compare these predictions with the true outputs of the validation dataset using an error metric such as root-mean-squared error (RMSE), accounting for all grid cells \(i = 1, \ldots ,p\) and weighting by the grid-cell area, \(w_i\) ,

Repeat steps a-d for other subsets of validation data (we use 3 in total).

Calculate the cross-validation score as the mean RMSE for this value of \(\lambda\) for all three subsets.

This process is repeated for all values of λ in the list. The value of λ that produces the lowest \(RMSE_\lambda\) is selected as the parameter for use in the final stage of training of the model, where all training data is used.

Gaussian Process Regression

Rather than learning the parameters \(\beta _0\) and \(\beta _j\) , Gaussian Process Regression is a non-parametric approach, where we seek a distribution over possible functions that fit the data. This is done from a Bayesian perspective, where we define a prior distribution over the possible functions. Then after observing the data, we use Bayes’ theorem to obtain a posterior distribution over possible functions. The prior distribution is a Gaussian process,

where \(\mu _0\) is the prior mean function, which we assume to be linear with slope \(\beta\) , \(\mu _0\left( x \right) = \beta x\) , and \(C_0\left( {x,x^{\prime}} \right)\) is the prior covariance function, which describes the covariance between two points, \(x\) and \(x^{\prime}\) 38 . We choose the following squared exponential covariance function,

where \(\sigma ^2\) and \(l\) are the output variance and lengthscale, respectively, which reflect the sensitivity of the outputs to changes in inputs 38 .

The prior Gaussian process is combined with the data using Bayes’ Theorem to obtain a posterior distribution over functions. This is another Gaussian process, with an updated mean function, \(\mu ^ \ast (x)\) , and covariance function, \(C^ \ast (x,x^{\prime})\) ,

The details can be found in relevant textbooks 38 . Predictions of the output can then be made at unseen values of \(x\) , where the Gaussian process provides both an expected value and the variance around this value. Since the prediction is effectively built on correlations between the new inputs and the training data inputs, this variance will be lower for predictions at values of \(x\) that are closer to values already seen in training data. We follow these steps with the framework provided by GPy in Python. The values of \(\beta\) , \(\sigma ^2\) , and \(l\) are learned through optimisation (the L-BGFS optimiser) in GPy 63 .

Pattern scaling

We benchmark our machine learning models against pattern scaling, a traditional method for obtaining spatial response patterns to forcings without running a full GCM 36 , 39 . It has been widely used for conducting regional climate change projections 40 , 41 , 42 in impact studies 64 and to extend simplified models to predict spatial outputs 58 , 65 . Pattern scaling requires one previous GCM run to obtain the long-term response of the variable of interest for a reference scenario. Typically, a strong greenhouse gas perturbation, such as a doubling of CO 2 is used as this reference response pattern on the longitude-latitude grid, \(V_{{\mathrm{ref}}}\left( {{\mathrm{lat}},{\mathrm{lon}}} \right)\) . We use the 2xCO 2 scenario from PDRMIP (since more than half of the simulations are from PDRMIP we expect this to be a more valid reference pattern than the 2xCO 2 ECLIPSE scenario) 16 , 31 , 32 . Then, the variable of interest is estimated at each grid point for a new scenario, \(V^ \ast \left( {{\mathrm{lat}},{\mathrm{lon}}} \right)\) by multiplying the reference pattern by scaler value s , i.e.

The scaler value s is the ratio of long-term global mean temperature response between the prediction and reference scenario. This can be derived from either a simplified climate model, such as a global energy balance model 43 , 66 ; a statistical model 58 ; or a mathematical relationship, such as the assumed linear relationship between long-term temperature response and effective radiative forcing (ERF) 64 , 67 . We take the latter approach due to the availability of variables required to calculate ERF for the relevant perturbations studied here.

ERF is defined as the energy imbalance between the surface and the top of the atmosphere in a GCM run in which the atmosphere is allowed to respond, while sea-surface temperatures are kept fixed (i.e. no ocean coupling) 1 , 5 , 8 , 33 . These simulations were run for 5 years in previous studies 5 , 7 , 8 , 14 , 16 , 31 , 32 , 33 , 34 and therefore we average over the first 5 years of the simulations to reduce noise in the estimate of global mean ERFs.

Pattern scaling is generally considered as a fair approximation 36 , 43 , 66 but it assumes that the magnitude of the response scales linearly with the amount of radiative forcing, which is not necessarily true, particularly for climate forcings of a different type to the reference scenario 36 . Furthermore, it cannot necessarily predict the highly inhomogeneous effects of certain types of climate forcings such as from aerosol emissions.

There are alternative approaches for obtaining a sensible scaler value s such as using the ratio of short-term temperature response between the predicted and reference scenarios (see Supplementary Fig. 4 ). We note that such a method can sometimes achieve a higher performance in predicting the mean response in some regions than our machine learning approach. However, it suffers the same limitations as the method presented here, in that the spatial variability in the response is not captured, particularly for short-lived pollutants (Supplementary Fig. 3 ). This limitation will be true regardless of the choice of scaler value, since the spatial variability is fixed based on the reference pattern.

Prediction errors

We predict long-term climate response, \({\boldsymbol{y}}^ \ast\) for each scenario following the three methods described above. We calculate the Root Mean Squared Error (RMSE) at the grid-cell level with

where subscript \({i} = {1}, \ldots , {p}\) indexes the grid cell and \({w}_{i}\) is the normalised weight of grid cell \({{i}}\) . We note that measuring errors at these scales can introduce unintended biases in the evaluation of our methods. For example, even small spatial offsets in climate response patterns can lead to large, nonphysical quantitative errors 44 . We also show the absolute error in mean response over ten world regions that cover a broader spatial scale (Fig. 3 ). These are the four main emission regions; North America, Europe, South Asia and East Asia, as defined in the Hemispheric Transport of Air Pollution experiments 68 ; and six remaining regions; the Arctic, Northwest Asia, Northern Africa, Southern Africa, South America and Australia. These cover the land regions where climate responses are of interest due to societal relevance. Here we defined the prediction error as the absolute difference between the predicted response in each region, \({\boldsymbol{y}}_{r}^ \ast\) , and the response from the complex GCM in the same region, \({\boldsymbol{y}}_{{r}}\) :

where subscript \(r\) indicates the mean response overall grid boxes in that region, weighted by the grid box area. We also calculate the absolute error for the global mean response in the same way. These RMSE, regional and global error metrics are presented in Fig. 3 for all prediction methods.

Data availability

Data used in this manuscript were originally produced in previous studies 5 , 7 , 8 , 14 , 16 , 31 , 32 , 33 , 34 . Postprocessed data used to produce results in this study is available at 10.5281/zenodo.3971024.

Code availability

Code to produce results is publicly available on github.com/lm2612/Ridge_3 and github.com/lm2612/GPRegression. Use of the HadGEM3-GA4 climate model was provided by the Met Office through the Joint Weather and Climate Research Programme, and the model source code is not generally available. For more information on accessing the model, see http://www.metoffice.gov.uk/research/collaboration/um-collaboration .

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Acknowledgements

L.A.M.’s work was funded through EPSRC grant EP/L016613/1. P.J.N. is supported through an Imperial College Research Fellowship. A.V. is partially funded by the Leverhulme Centre for Wildfires, Environment and Society through the Leverhulme Trust, grant RC-2018-023. Simulations with HadGEM3-GA4 were performed using the MONSooN system, a collaborative facility supplied under the Joint Weather and Climate Research Programme, which is a strategic partnership between the Met Office and the Natural Environment Research Council.

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This work was initiated by A.V. L.A.M. carried out the analyses and wrote the manuscript. A.V. and P.J.N. supervised and contributed to writing. P.J.N. and R.G.E. advised on statistical methods. M.K. and B.C. performed the simulations.

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Mansfield, L.A., Nowack, P.J., Kasoar, M. et al. Predicting global patterns of long-term climate change from short-term simulations using machine learning. npj Clim Atmos Sci 3 , 44 (2020). https://doi.org/10.1038/s41612-020-00148-5

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The scientific method and climate change: How scientists know

By Holly Shaftel, NASA's Jet Propulsion Laboratory

The scientific method is the gold standard for exploring our natural world. You might have learned about it in grade school, but here’s a quick reminder: It’s the process that scientists use to understand everything from animal behavior to the forces that shape our planet—including climate change.

“The way science works is that I go out and study something, and maybe I collect data or write equations, or I run a big computer program,” said Josh Willis, principal investigator of NASA’s Oceans Melting Greenland (OMG) mission and oceanographer at NASA’s Jet Propulsion Laboratory. “And I use it to learn something about how the world works.”

Using the scientific method, scientists have shown that humans are extremely likely the dominant cause of today’s climate change. The story goes back to the late 1800s, but in 1958, for example, Charles Keeling of the Mauna Loa Observatory in Waimea, Hawaii, started taking meticulous measurements of carbon dioxide (CO 2 ) in the atmosphere, showing the first significant evidence of rapidly rising CO 2 levels and producing the Keeling Curve climate scientists know today.

“The way science works is that I go out and study something, and maybe I collect data or write equations, or I run a big computer program, and I use it to learn something about how the world works.”- Josh Willis, NASA oceanographer and Oceans Melting Greenland principal investigator

Since then, thousands of peer-reviewed scientific papers have come to the same conclusion about climate change, telling us that human activities emit greenhouse gases into the atmosphere, raising Earth’s average temperature and bringing a range of consequences to our ecosystems.

“The weight of all of this information taken together points to the single consistent fact that humans and our activity are warming the planet,” Willis said.

The scientific method’s steps

The exact steps of the scientific method can vary by discipline, but since we have only one Earth (and no “test” Earth), climate scientists follow a few general guidelines to better understand carbon dioxide levels, sea level rise, global temperature and more.

• Form a hypothesis (a statement that an experiment can test)
• Make observations (conduct experiments and gather data)
• Analyze and interpret the data
• Draw conclusions
• Publish results that can be validated with further experiments (rinse and repeat)

As you can see, the scientific method is iterative (repetitive), meaning that climate scientists are constantly making new discoveries about the world based on the building blocks of scientific knowledge.

“The weight of all of this information taken together points to the single consistent fact that humans and our activity are warming the planet." - Josh Willis, NASA oceanographer and Oceans Melting Greenland principal investigator

The scientific method at work.

How does the scientific method work in the real world of climate science? Let’s take NASA’s Oceans Melting Greenland (OMG) campaign, a multi-year survey of Greenland’s ice melt that’s paving the way for improved sea level rise estimates, as an example.

• Form a hypothesis OMG hypothesizes that the oceans are playing a major role in Greenland ice loss.
• Make observations Over a five-year period, OMG will survey Greenland by air and ship to collect ocean temperature and salinity (saltiness) data and take ice thinning measurements to help climate scientists better understand how the ice and warming ocean interact with each other. OMG will also collect data on the sea floor’s shape and depth, which determines how much warm water can reach any given glacier.
• Analyze and interpret data As the OMG crew and scientists collect data around 27,000 miles (over 43,000 kilometers) of Greenland coastline over that five-year period, each year scientists will analyze the data to see how much the oceans warmed or cooled and how the ice changed in response.
• Draw conclusions In one OMG study , scientists discovered that many Greenland glaciers extend deeper (some around 1,000 feet, or about 300 meters) beneath the ocean’s surface than once thought, making them quite vulnerable to the warming ocean. They also discovered that Greenland’s west coast is generally more vulnerable than its east coast.
• Publish results Scientists like Willis write up the results, send in the paper for peer review (a process in which other experts in the field anonymously critique the submission), and then those peers determine whether the information is correct and valuable enough to be published in an academic journal, such as Nature or Earth and Planetary Science Letters . Then it becomes another contribution to the well-substantiated body of climate change knowledge, which evolves and grows stronger as scientists gather and confirm more evidence. Other scientists can take that information further by conducting their own studies to better understand sea level rise.

All in all, the scientific method is “a way of going from observations to answers,” NASA terrestrial ecosystem scientist Erika Podest, based at JPL, said. It adds clarity to our way of thinking and shows that scientific knowledge is always evolving.

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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

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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.

Not applicable.

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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Methodology for the evaluation of global warming impact on soil moisture and runoff

Research output : Chapter in Book/Report/Conference proceeding › Conference contribution

Global warming is expected to increase the intensity of the global hydrologic cycle. Precipitation and temperature patterns, soil moisture requirements, and the physical structure of the vegetation canopy play important roles in the hydrologic system of drainage basins. In this work a methodology for the evaluation of impact on soil moisture concentration and direct surface runoff is presented.

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• General Engineering

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• Runoff Engineering & Materials Science 100%
• Soil moisture Engineering & Materials Science 95%
• Global warming Engineering & Materials Science 78%
• Catchments Engineering & Materials Science 52%
• Temperature Engineering & Materials Science 13%

T1 - Methodology for the evaluation of global warming impact on soil moisture and runoff

AU - Valdes, Juan B.

AU - Seoane, Rafael S.

AU - North, Gerald R.

N2 - Global warming is expected to increase the intensity of the global hydrologic cycle. Precipitation and temperature patterns, soil moisture requirements, and the physical structure of the vegetation canopy play important roles in the hydrologic system of drainage basins. In this work a methodology for the evaluation of impact on soil moisture concentration and direct surface runoff is presented.

AB - Global warming is expected to increase the intensity of the global hydrologic cycle. Precipitation and temperature patterns, soil moisture requirements, and the physical structure of the vegetation canopy play important roles in the hydrologic system of drainage basins. In this work a methodology for the evaluation of impact on soil moisture concentration and direct surface runoff is presented.

UR - http://www.scopus.com/inward/record.url?scp=0027149601&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0027149601&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0027149601

SN - 0872629120

T3 - Water Resources Planning and Management and Urban Water Resources

BT - Water Resources Planning and Management and Urban Water Resources

PB - Publ by ASCE

T2 - Proceedings of the 20th Anniversary Conference on Water Management in the '90s

Y2 - 1 May 1993 through 5 May 1993

ENCYCLOPEDIC ENTRY

Global warming.

The causes, effects, and complexities of global warming are important to understand so that we can fight for the health of our planet.

Earth Science, Climatology

Tennessee Power Plant

Ash spews from a coal-fueled power plant in New Johnsonville, Tennessee, United States.

Photograph by Emory Kristof/ National Geographic

Global warming is the long-term warming of the planet’s overall temperature. Though this warming trend has been going on for a long time, its pace has significantly increased in the last hundred years due to the burning of fossil fuels . As the human population has increased, so has the volume of fossil fuels burned. Fossil fuels include coal, oil, and natural gas, and burning them causes what is known as the “greenhouse effect” in Earth’s atmosphere.

The greenhouse effect is when the sun’s rays penetrate the atmosphere, but when that heat is reflected off the surface cannot escape back into space. Gases produced by the burning of fossil fuels prevent the heat from leaving the atmosphere. These greenhouse gasses are carbon dioxide , chlorofluorocarbons, water vapor , methane , and nitrous oxide . The excess heat in the atmosphere has caused the average global temperature to rise overtime, otherwise known as global warming.

Global warming has presented another issue called climate change. Sometimes these phrases are used interchangeably, however, they are different. Climate change refers to changes in weather patterns and growing seasons around the world. It also refers to sea level rise caused by the expansion of warmer seas and melting ice sheets and glaciers . Global warming causes climate change, which poses a serious threat to life on Earth in the forms of widespread flooding and extreme weather. Scientists continue to study global warming and its impact on Earth.

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Climeworks switches on world’s largest direct air capture plant

Key take-aways.

Climeworks starts operations of its to-date largest direct air capture and storage (DAC+S) plant, Mammoth, in Iceland. It is the second commercial DAC+S facility of Climeworks and is about ten times bigger than its predecessor plant, Orca.

The plant is designed for a nameplate capture capacity of up to 36,000 tons of CO₂ per year once in full swing by filtering CO₂ from the air and storing it permanently underground. The plant has successfully started to capture its first CO₂, with twelve of its total 72 collector containers installed onsite.

Mammoth is another milestone in Climeworks journey to reach megaton carbon removal capacity by 2030 and gigaton scale by 2050, which is needed to fight global warming.

Beyond Iceland, Climeworks is developing multiple megaton hubs in the U.S. with operational and testing experience derived from its now two commercial plants in Iceland.

Hellisheiði, Iceland, 8 May 2024 – The largest direct air capture and storage plant, named Mammoth, starts operations in Iceland. It is the second commercial facility of Climeworks in Iceland and is about ten times bigger than its predecessor, Orca. Mammoth will bring new high-quality carbon removal capacity to the market for Climeworks to provide to its customers.

Mammoth – the facts in a nutshell

Climeworks broke ground on Mammoth in June 2022. The plant is built in a modular design, with twelve of its total 72 collector containers currently installed onsite. The plant will be completed throughout 2024. It is designed for a nameplate capture capacity of up to 36,000 tons of CO₂ per year.

Mammoth has successfully started to capture its first CO₂. Climeworks uses renewable energy to power its direct air capture process, which requires low-temperature heat like boiling water. The geothermal energy partner ON Power in Iceland provides the energy necessary for this process. Once the CO₂ is released from the filters, storage partner  Carbfix  transports the CO₂ underground, where it reacts with basaltic rock through a natural process, which transforms into stone, and remains permanently stored. Climeworks verifies and certifies the whole process by independent third parties.

Learn and improve continuously

Jan Wurzbacher, Co-founder and Co-CEO of Climeworks

Climeworks looks back on seven years of field experience. Its engineers process close to 200 million data points daily. The derived learnings were applied to Mammoth which increases plant performance, efficiency, recovery and ensure better availability to maximize CO₂ capture through the year. With Mammoth, Climeworks will gain further operational field experience, and its 180 science and R&D experts will continue large-scale testing and development.

100x scale-up through 2030

The operational and testing learnings will be deployed in the next direct air capture projects. Until 2030, Climeworks’ roadmap focuses on megaton hub roll-out. Climeworks is part of three megaton direct air capture hub proposals in the U.S., all of which were selected by the US Department of Energy for public funding for a total of more than 600 million USD. The largest one, Project Cypress in Louisiana, was granted an initial 50 million US dollars in March to kickstart the project. Climeworks will replicate its megaton hubs worldwide to reach a global scale. The company actively develops projects in Norway , Kenya , and Canada and explores further potential direct air capture and storage sites.

Learn more about Mammoth

Climeworks' mammoth taking final shape in iceland.

The core infrastructure of Climeworks' newest and largest direct air capture plant is in place

Mammoth: manufacturing direct air capture plants at new scales

The construction of Mammoth is well underway,, 2023 kicked off with the start of the CO₂ collector container production.

Mammoth: innovating the DAC+S process with a CO₂ absorption tower

Mammoth, Climeworks' DAC+S plant currently under construction, is making steady progress in Iceland with a CO₂ absorption tower developed by Carbfix.

Lead the race toward net zero

High-quality carbon removal for your climate strategy.

Monthly industry updates from Climeworks

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