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      Tackling Climate Change with Machine Learning

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          Abstract

          Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.

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          Most cited references857

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          An Overview of CMIP5 and the Experiment Design

          Karl E. Taylor, Ronald J Stouffer, Gerald Meehl (2012)
          The fifth phase of the Coupled Model Intercomparison Project (CMIP5) will produce a state-of-the- art multimodel dataset designed to advance our knowledge of climate variability and climate change. Researchers worldwide are analyzing the model output and will produce results likely to underlie the forthcoming Fifth Assessment Report by the Intergovernmental Panel on Climate Change. Unprecedented in scale and attracting interest from all major climate modeling groups, CMIP5 includes “long term” simulations of twentieth-century climate and projections for the twenty-first century and beyond. Conventional atmosphere–ocean global climate models and Earth system models of intermediate complexity are for the first time being joined by more recently developed Earth system models under an experiment design that allows both types of models to be compared to observations on an equal footing. Besides the longterm experiments, CMIP5 calls for an entirely new suite of “near term” simulations focusing on recent decades and the future to year 2035. These “decadal predictions” are initialized based on observations and will be used to explore the predictability of climate and to assess the forecast system's predictive skill. The CMIP5 experiment design also allows for participation of stand-alone atmospheric models and includes a variety of idealized experiments that will improve understanding of the range of model responses found in the more complex and realistic simulations. An exceptionally comprehensive set of model output is being collected and made freely available to researchers through an integrated but distributed data archive. For researchers unfamiliar with climate models, the limitations of the models and experiment design are described.
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            Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization

            Karl E. Taylor, Ronald J Stouffer, Bjorn Stevens … (2016)
            By coordinating the design and distribution of global climate model simulations of the past, current, and future climate, the Coupled Model Intercomparison Project (CMIP) has become one of the foundational elements of climate science. However, the need to address an ever-expanding range of scientific questions arising from more and more research communities has made it necessary to revise the organization of CMIP. After a long and wide community consultation, a new and more federated structure has been put in place. It consists of three major elements: (1) a handful of common experiments, the DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations (1850–near present) that will maintain continuity and help document basic characteristics of models across different phases of CMIP; (2) common standards, coordination, infrastructure, and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble; and (3) an ensemble of CMIP-Endorsed Model Intercomparison Projects (MIPs) that will be specific to a particular phase of CMIP (now CMIP6) and that will build on the DECK and CMIP historical simulations to address a large range of specific questions and fill the scientific gaps of the previous CMIP phases. The DECK and CMIP historical simulations, together with the use of CMIP data standards, will be the entry cards for models participating in CMIP. Participation in CMIP6-Endorsed MIPs by individual modelling groups will be at their own discretion and will depend on their scientific interests and priorities. With the Grand Science Challenges of the World Climate Research Programme (WCRP) as its scientific backdrop, CMIP6 will address three broad questions: – How does the Earth system respond to forcing? – What are the origins and consequences of systematic model biases? – How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? This CMIP6 overview paper presents the background and rationale for the new structure of CMIP, provides a detailed description of the DECK and CMIP6 historical simulations, and includes a brief introduction to the 21 CMIP6-Endorsed MIPs.
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              High-resolution global maps of 21st-century forest cover change.

              M. Hansen, P. Potapov, R. Moore … (2013)
              Quantification of global forest change has been lacking despite the recognized importance of forest ecosystem services. In this study, Earth observation satellite data were used to map global forest loss (2.3 million square kilometers) and gain (0.8 million square kilometers) from 2000 to 2012 at a spatial resolution of 30 meters. The tropics were the only climate domain to exhibit a trend, with forest loss increasing by 2101 square kilometers per year. Brazil's well-documented reduction in deforestation was offset by increasing forest loss in Indonesia, Malaysia, Paraguay, Bolivia, Zambia, Angola, and elsewhere. Intensive forestry practiced within subtropical forests resulted in the highest rates of forest change globally. Boreal forest loss due largely to fire and forestry was second to that in the tropics in absolute and proportional terms. These results depict a globally consistent and locally relevant record of forest change.
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                Author and article information

                Contributors
                David Rolnick: (View ORCID Profile)
                Journal
                Title: ACM Computing Surveys
                Abbreviated Title: ACM Comput. Surv.
                Publisher: Association for Computing Machinery (ACM)
                ISSN (Print): 0360-0300
                ISSN (Electronic): 1557-7341
                Publication date Created: March 31 2023
                Publication date (Print): March 31 2023
                Volume: 55
                Issue: 2
                Pages: 1-96
                Affiliations
                [1 ]McGill University and Mila - Quebec AI Institute
                [2 ]Carnegie Mellon University
                [3 ]Hertie School and ETH Zürich
                [4 ]University of Colorado Boulder
                [5 ]Element AI/Service Now
                [6 ]University of Wisconsin - Madison and Université de Montréal
                [7 ]New York University and Harvard University
                [8 ]Mercator Research Institute on Global Commonsand Climate Change and Technische Universität Berlin
                [9 ]Google Brain and UC Berkeley
                [10 ]Massachusetts Institute of Technology
                [11 ]Mila - Quebec AI Institute and Université de Montréal
                [12 ]Mila - Quebec AI Institute and Polytechnique Montréal
                [13 ]Stanford University
                [14 ]University of California and Lawrence Berkeley National Lab
                [15 ]University of Pennsylvania
                [16 ]Cornell University
                [17 ]DeepMind
                [18 ]Google AI
                [19 ]University of California, Berkeley
                Article
                DOI: 10.1145/3485128
                SO-VID: 7841fd64-306e-47c1-8ddd-fb08fe479767
                Copyright © © 2023
                License:

                http://www.acm.org/publications/policies/copyright_policy#Background

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