The need for carbon-emissions-driven climate projections in CMIP7

Abstract. Previous phases of the Coupled Model Intercomparison Project (CMIP) have primarily focused on simulations driven by atmospheric concentrations of greenhouse gases (GHGs), for both idealized model experiments and climate projections of different emissions scenarios. We argue that although this approach was practical to allow parallel development of Earth system model simulations and detailed socioeconomic futures, carbon cycle uncertainty as represented by diverse, process-resolving Earth system models (ESMs) is not manifested in the scenario outcomes, thus omitting a dominant source of uncertainty in meeting the Paris Agreement. Mitigation policy is defined in terms of human activity (including emissions), with strategies varying in their timing of net-zero emissions, the balance of mitigation effort between short-lived and long-lived climate forcers, their reliance on land use strategy, and the extent and timing of carbon removals. To explore the response to these drivers, ESMs need to explicitly represent complete cycles of major GHGs, including natural processes and anthropogenic influences. Carbon removal and sequestration strategies, which rely on proposed human management of natural systems, are currently calculated in integrated assessment models (IAMs) during scenario development with only the net carbon emissions passed to the ESM. However, proper accounting of the coupled system impacts of and feedback on such interventions requires explicit process representation in ESMs to build self-consistent physical representations of their potential effectiveness and risks under climate change. We propose that CMIP7 efforts prioritize simulations driven by CO2 emissions from fossil fuel use and projected deployment of carbon dioxide removal technologies, as well as land use and management, using the process resolution allowed by state-of-the-art ESMs to resolve carbon–climate feedbacks. Post-CMIP7 ambitions should aim to incorporate modeling of non-CO2 GHGs (in particular, sources and sinks of methane and nitrous oxide) and process-based representation of carbon removal options. These developments will allow three primary benefits: (1) resources to be allocated to policy-relevant climate projections and better real-time information related to the detectability and verification of emissions reductions and their relationship to expected near-term climate impacts, (2) scenario modeling of the range of possible future climate states including Earth system processes and feedbacks that are increasingly well-represented in ESMs, and (3) optimal utilization of the strengths of ESMs in the wider context of climate modeling infrastructure (which includes simple climate models, machine learning approaches and kilometer-scale climate models).

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Publication Details

Journal
Geoscientific model development
Published
2024-11-19
DOI
https://doi.org/10.5194/gmd-17-8141-2024
Citations
34
Primary Topic
Atmospheric and Environmental Gas Dynamics
Type
article
Field-Weighted Citation Impact
9.61

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article

The need for carbon-emissions-driven climate projections in CMIP7

B. H. Samset, Pierre Friedlingstein, Rosie A. Fisher, Isla Ruth Simpson et al.
34 citations
Geoscientific model development
Atmospheric and Environmental Gas Dynamics
9.61
article

The need for carbon-emissions-driven climate projections in CMIP7

B. H. Samset, Pierre Friedlingstein, Rosie A. Fisher, Isla Ruth Simpson, Matthew Gidden, Joeri Rogelj, Ben Booth, Benjamin Mark Sanderson, Roland Séférian, Chris Smith, Sönke Zaehle, Chris Jones, Tomohiro Hajima, Carl‐Friedrich Schleussner, Veronika Eyring, Claudia Tebaldi, Abigail Snyder, Tatiana Ilyina, Glen P. Peters, Jason Lowe, John P. Dunne, David M. Lawrence, Charles D. Koven, Andrew David King, Abigail L. S. Swann, Nadine Mengis, Detlef Peter van Vuuren, Colin Jones
article en
34 citations

Abstract

Abstract. Previous phases of the Coupled Model Intercomparison Project (CMIP) have primarily focused on simulations driven by atmospheric concentrations of greenhouse gases (GHGs), for both idealized model experiments and climate projections of different emissions scenarios. We argue that although this approach was practical to allow parallel development of Earth system model simulations and detailed socioeconomic futures, carbon cycle uncertainty as represented by diverse, process-resolving Earth system models (ESMs) is not manifested in the scenario outcomes, thus omitting a dominant source of uncertainty in meeting the Paris Agreement. Mitigation policy is defined in terms of human activity (including emissions), with strategies varying in their timing of net-zero emissions, the balance of mitigation effort between short-lived and long-lived climate forcers, their reliance on land use strategy, and the extent and timing of carbon removals. To explore the response to these drivers, ESMs need to explicitly represent complete cycles of major GHGs, including natural processes and anthropogenic influences. Carbon removal and sequestration strategies, which rely on proposed human management of natural systems, are currently calculated in integrated assessment models (IAMs) during scenario development with only the net carbon emissions passed to the ESM. However, proper accounting of the coupled system impacts of and feedback on such interventions requires explicit process representation in ESMs to build self-consistent physical representations of their potential effectiveness and risks under climate change. We propose that CMIP7 efforts prioritize simulations driven by CO2 emissions from fossil fuel use and projected deployment of carbon dioxide removal technologies, as well as land use and management, using the process resolution allowed by state-of-the-art ESMs to resolve carbon–climate feedbacks. Post-CMIP7 ambitions should aim to incorporate modeling of non-CO2 GHGs (in particular, sources and sinks of methane and nitrous oxide) and process-based representation of carbon removal options. These developments will allow three primary benefits: (1) resources to be allocated to policy-relevant climate projections and better real-time information related to the detectability and verification of emissions reductions and their relationship to expected near-term climate impacts, (2) scenario modeling of the range of possible future climate states including Earth system processes and feedbacks that are increasingly well-represented in ESMs, and (3) optimal utilization of the strengths of ESMs in the wider context of climate modeling infrastructure (which includes simple climate models, machine learning approaches and kilometer-scale climate models).

Geoscientific model developmentVol. 17(22)
NSF National Center for Atmospheric Research (US), Joint Global Change Research Institute (US), Met Office (GB), Centre National de la Recherche Scientifique (FR), University of Leeds (GB), Japan Agency for Marine-Earth Science and Technology (JP), International Institute for Applied Systems Analysis (AT), Netherlands Environmental Assessment Agency (NL), Lawrence Berkeley National Laboratory (US), The University of Melbourne (AU), Université Fédérale de Toulouse Midi-Pyrénées (FR), University of Bremen (DE), NOAA Geophysical Fluid Dynamics Laboratory (US), University of Washington (US), University of Exeter (GB), Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE), GEOMAR Helmholtz Centre for Ocean Research Kiel (DE), University of Bristol (GB), Humboldt-Universität zu Berlin (DE), CICERO Center for International Climate Research (NO), National Centre for Atmospheric Science (GB), Météo-France (FR), Climate Analytics (DE), Helmholtz-Zentrum Hereon (DE), Max Planck Institute for Biogeochemistry (DE), Max Planck Institute for Meteorology (DE), Imperial College London (GB)
U.S. Department of Energy, University of Washington, Department for Environment, Food and Rural Affairs, UK Government, Sight Research UK, European Commission, Met Office, Deutsche Forschungsgemeinschaft, Office of Science, Horizon 2020 Framework Programme, HORIZON EUROPE Framework Programme, Natural Environment Research Council, Horizon 2020, Biological and Environmental Research
Climate action
Openalex Percentile: Top 1%
Atmospheric and Environmental Gas Dynamics
9.61
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