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Su T, Li Z, Henao NR, Luan Q, Yu F. Constraining effects of aerosol-cloud interaction by accounting for coupling between cloud and land surface. SCIENCE ADVANCES 2024; 10:eadl5044. [PMID: 38781324 PMCID: PMC11114194 DOI: 10.1126/sciadv.adl5044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Accepted: 04/17/2024] [Indexed: 05/25/2024]
Abstract
Aerosol-cloud interactions (ACIs) are vital for regulating Earth's climate by influencing energy and water cycles. Yet, effects of ACI bear large uncertainties, evidenced by systematic discrepancies between observed and modeled estimates. This study quantifies a major bias in ACI determinations, stemming from conventional surface or space measurements that fail to capture aerosol at the cloud level unless the cloud is coupled with land surface. We introduce an advanced approach to determine radiative forcing of ACI by accounting for cloud-surface coupling. By integrating field observations, satellite data, and model simulations, this approach reveals a drastic alteration in aerosol vertical transport and ACI effects caused by cloud coupling. In coupled regimes, aerosols enhance cloud droplet number concentration across the boundary layer more homogeneously than in decoupled conditions, under which aerosols from the free atmosphere predominantly affect cloud properties, leading to marked cooling effects. Our findings spotlight cloud-surface coupling as a key factor for ACI quantification, hinting at potential underassessments in traditional estimates.
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Affiliation(s)
- Tianning Su
- Earth System Science Interdisciplinary Center & AOSC, University of Maryland, College Park, MD, USA
| | - Zhanqing Li
- Earth System Science Interdisciplinary Center & AOSC, University of Maryland, College Park, MD, USA
| | - Natalia Roldan Henao
- Earth System Science Interdisciplinary Center & AOSC, University of Maryland, College Park, MD, USA
| | - Qingzu Luan
- Earth System Science Interdisciplinary Center & AOSC, University of Maryland, College Park, MD, USA
| | - Fangqun Yu
- Atmospheric Sciences Research Center, University at Albany, Albany, NY, USA
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2
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Jiang X, Su H, Jiang JH, Neelin JD, Wu L, Tsushima Y, Elsaesser G. Muted extratropical low cloud seasonal cycle is closely linked to underestimated climate sensitivity in models. Nat Commun 2023; 14:5586. [PMID: 37696809 PMCID: PMC10495370 DOI: 10.1038/s41467-023-41360-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 08/31/2023] [Indexed: 09/13/2023] Open
Abstract
A large spread in model estimates of the equilibrium climate sensitivity (ECS), defined as the global mean near-surface air-temperature increase following a doubling of atmospheric CO2 concentration, leaves us greatly disadvantaged in guiding policy-making for climate change adaptation and mitigation. In this study, we show that the projected ECS in the latest generation of climate models is highly related to seasonal variations of extratropical low-cloud fraction (LCF) in historical simulations. Marked reduction of extratropical LCF from winter to summer is found in models with ECS > 4.75 K, in accordance with the significant reduction of extratropical LCF under a warming climate in these models. In contrast, a pronounced seasonal cycle of extratropical LCF, as supported by satellite observations, is largely absent in models with ECS < 3.3 K. The distinct seasonality in extratropical LCF in climate models is ascribed to their different prevailing cloud regimes governing the extratropical LCF variability.
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Affiliation(s)
- Xianan Jiang
- Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, Los Angeles, CA, USA.
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.
| | - Hui Su
- Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology, Hong Kong, China
- Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jonathan H Jiang
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - J David Neelin
- Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - Longtao Wu
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | | | - Gregory Elsaesser
- NASA Goddard Institute for Space Studies, and Department of Applied Physics and Mathematics, Columbia University, New York, NY, USA
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3
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Machine Learning-Based Small Hydropower Potential Prediction under Climate Change. ENERGIES 2021. [DOI: 10.3390/en14123643] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
As the effects of climate change are becoming severe, countries need to substantially reduce carbon emissions. Small hydropower (SHP) can be a useful renewable energy source with a high energy density for the reduction of carbon emission. Therefore, it is necessary to revitalize the development of SHP to expand the use of renewable energy. To efficiently plan and utilize this energy source, there is a need to assess the future SHP potential based on an accurate runoff prediction. In this study, the future SHP potential was predicted using a climate change scenario and an artificial neural network model. The runoff was simulated accurately, and the applicability of an artificial neural network to the runoff prediction was confirmed. The results showed that the total amount of SHP potential in the future will generally a decrease compared to the past. This result is applicable as base data for planning future energy supplies and carbon emission reductions.
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Analysis of Small Hydropower Generation Potential: (2) Future Prospect of the Potential under Climate Change. ENERGIES 2021. [DOI: 10.3390/en14113001] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The interest in renewable energy to replace fossil fuel is increasing as the problem caused by climate change has become more severe. In this study, small hydropower (SHP) was evaluated as a resource with high development value because of its high energy density compared to other renewable energy sources. SHP may be an attractive and sustainable power generation environmental perspective because of its potential to be found in small rivers and streams. The power generation potential could be estimated based on the discharge in the river basin. Since the river discharge depends on the climate conditions, the hydropower generation potential changes sensitively according to climate variability. Therefore, it is necessary to analyze the SHP potential in consideration of future climate change. In this study, the future prospect of SHP potential is simulated for the period of 2021 to 2100 considering the climate change in three hydropower plants of Deoksong, Hanseok, and Socheon stations, Korea. The results show that SHP potential for the near future (2021 to 2040) shows a tendency to be increased, and the highest increase is 23.4% at the Deoksong SPH plant. Through the result of future prospect, we have shown that hydroelectric power generation capacity or SHP potential will be increased in the future. Therefore, we believe that it is necessary to revitalize the development of SHP to expand the use of renewable energy. In addition, a methodology presented in this study could be used for the future prospect of the SHP potential.
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Sherwood SC, Webb MJ, Annan JD, Armour KC, Forster PM, Hargreaves JC, Hegerl G, Klein SA, Marvel KD, Rohling EJ, Watanabe M, Andrews T, Braconnot P, Bretherton CS, Foster GL, Hausfather Z, von der Heydt AS, Knutti R, Mauritsen T, Norris JR, Proistosescu C, Rugenstein M, Schmidt GA, Tokarska KB, Zelinka MD. An Assessment of Earth's Climate Sensitivity Using Multiple Lines of Evidence. REVIEWS OF GEOPHYSICS (WASHINGTON, D.C. : 1985) 2020; 58:e2019RG000678. [PMID: 33015673 PMCID: PMC7524012 DOI: 10.1029/2019rg000678] [Citation(s) in RCA: 87] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 04/22/2020] [Accepted: 06/24/2020] [Indexed: 05/10/2023]
Abstract
We assess evidence relevant to Earth's equilibrium climate sensitivity per doubling of atmospheric CO2, characterized by an effective sensitivity S. This evidence includes feedback process understanding, the historical climate record, and the paleoclimate record. An S value lower than 2 K is difficult to reconcile with any of the three lines of evidence. The amount of cooling during the Last Glacial Maximum provides strong evidence against values of S greater than 4.5 K. Other lines of evidence in combination also show that this is relatively unlikely. We use a Bayesian approach to produce a probability density function (PDF) for S given all the evidence, including tests of robustness to difficult-to-quantify uncertainties and different priors. The 66% range is 2.6-3.9 K for our Baseline calculation and remains within 2.3-4.5 K under the robustness tests; corresponding 5-95% ranges are 2.3-4.7 K, bounded by 2.0-5.7 K (although such high-confidence ranges should be regarded more cautiously). This indicates a stronger constraint on S than reported in past assessments, by lifting the low end of the range. This narrowing occurs because the three lines of evidence agree and are judged to be largely independent and because of greater confidence in understanding feedback processes and in combining evidence. We identify promising avenues for further narrowing the range in S, in particular using comprehensive models and process understanding to address limitations in the traditional forcing-feedback paradigm for interpreting past changes.
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Affiliation(s)
- S C Sherwood
- Climate Change Research Centre and ARC Centre of Excellence for Climate Extremes University of New South Wales Sydney Sydney New South Wales Australia
| | - M J Webb
- Met Office Hadley Centre Exeter UK
| | | | | | - P M Forster
- Priestley International Centre for Climate University of Leeds Leeds UK
| | | | - G Hegerl
- School of Geosciences University of Edinburgh Edinburgh UK
| | | | - K D Marvel
- Department of Applied Physics and Applied Math Columbia University New York NY USA
- NASA Goddard Institute for Space Studies New York NY USA
| | - E J Rohling
- Research School of Earth Sciences Australian National University Canberra ACT Australia
- Ocean and Earth Science, National Oceanography Centre University of Southampton Southampton UK
| | - M Watanabe
- Atmosphere and Ocean Research Institute The University of Tokyo Tokyo Japan
| | | | - P Braconnot
- Laboratoire des Sciences du Climat et de l'Environnement, unité mixte CEA-CNRS-UVSQ Université Paris-Saclay Gif sur Yvette France
| | | | - G L Foster
- Ocean and Earth Science, National Oceanography Centre University of Southampton Southampton UK
| | | | - A S von der Heydt
- Institute for Marine and Atmospheric Research, and Centre for Complex Systems Science Utrecht University Utrecht The Netherlands
| | - R Knutti
- Institute for Atmospheric and Climate Science Zurich Switzerland
| | - T Mauritsen
- Department of Meteorology Stockholm University Stockholm Sweden
| | - J R Norris
- Scripps Institution of Oceanography La Jolla CA USA
| | - C Proistosescu
- Department of Atmospheric Sciences and Department of Geology University of Illinois at Urbana-Champaign Urbana IL USA
| | - M Rugenstein
- Max Planck Institute for Meteorology Hamburg Germany
| | - G A Schmidt
- NASA Goddard Institute for Space Studies New York NY USA
| | - K B Tokarska
- School of Geosciences University of Edinburgh Edinburgh UK
- Institute for Atmospheric and Climate Science Zurich Switzerland
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Senior CA, Jones CG, Wood RA, Sellar A, Belcher S, Klein‐Tank A, Sutton R, Walton J, Lawrence B, Andrews T, Mulcahy JP. U.K. Community Earth System Modeling for CMIP6. JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS 2020; 12:e2019MS002004. [PMID: 33042388 PMCID: PMC7539988 DOI: 10.1029/2019ms002004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 03/31/2020] [Accepted: 04/01/2020] [Indexed: 06/01/2023]
Abstract
We describe the approach taken to develop the United Kingdom's first community Earth system model, UKESM1. This is a joint effort involving the Met Office and the Natural Environment Research Council (NERC), representing the U.K. academic community. We document our model development procedure and the subsequent U.K. submission to CMIP6, based on a traceable hierarchy of coupled physical and Earth system models. UKESM1 builds on the well-established, world-leading HadGEM models of the physical climate system and incorporates cutting-edge new representations of aerosols, atmospheric chemistry, terrestrial carbon, and nitrogen cycles and an advanced model of ocean biogeochemistry. A high-level metric of overall performance shows that both models, HadGEM3-GC3.1 and UKESM1, perform better than most other CMIP6 models so far submitted for a broad range of variables. We point to much more extensive evaluation performed in other papers in this special issue. The merits of not using any forced climate change simulations within our model development process are discussed. First results from HadGEM3-GC3.1 and UKESM1 include the emergent climate sensitivity (5.5 and 5.4 K, respectively) which is high relative to the current range of CMIP5 models. The role of cloud microphysics and cloud-aerosol interactions in driving the climate sensitivity, and the systematic approach taken to understand this role, is highlighted in other papers in this special issue. We place our findings within the broader modeling landscape indicating how our understanding of key processes driving higher sensitivity in the two U.K. models seems to align with results from a number of other CMIP6 models.
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Affiliation(s)
| | - Colin G. Jones
- NCAS, School of Earth and the EnvironmentUniversity of LeedsLeedsUK
| | | | | | | | | | - Rowan Sutton
- NCAS, Department of MeteorologyUniversity of ReadingReadingUK
| | | | - Bryan Lawrence
- NCAS, Department of MeteorologyUniversity of ReadingReadingUK
- Department of Computer ScienceUniversity of ReadingReadingUK
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McCoy IL, McCoy DT, Wood R, Regayre L, Watson-Parris D, Grosvenor DP, Mulcahy JP, Hu Y, Bender FAM, Field PR, Carslaw KS, Gordon H. The hemispheric contrast in cloud microphysical properties constrains aerosol forcing. Proc Natl Acad Sci U S A 2020; 117:18998-19006. [PMID: 32719114 PMCID: PMC7431023 DOI: 10.1073/pnas.1922502117] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The change in planetary albedo due to aerosol-cloud interactions during the industrial era is the leading source of uncertainty in inferring Earth's climate sensitivity to increased greenhouse gases from the historical record. The variable that controls aerosol-cloud interactions in warm clouds is droplet number concentration. Global climate models demonstrate that the present-day hemispheric contrast in cloud droplet number concentration between the pristine Southern Hemisphere and the polluted Northern Hemisphere oceans can be used as a proxy for anthropogenically driven change in cloud droplet number concentration. Remotely sensed estimates constrain this change in droplet number concentration to be between 8 cm-3 and 24 cm-3 By extension, the radiative forcing since 1850 from aerosol-cloud interactions is constrained to be -1.2 W⋅m-2 to -0.6 W⋅m-2 The robustness of this constraint depends upon the assumption that pristine Southern Ocean droplet number concentration is a suitable proxy for preindustrial concentrations. Droplet number concentrations calculated from satellite data over the Southern Ocean are high in austral summer. Near Antarctica, they reach values typical of Northern Hemisphere polluted outflows. These concentrations are found to agree with several in situ datasets. In contrast, climate models show systematic underpredictions of cloud droplet number concentration across the Southern Ocean. Near Antarctica, where precipitation sinks of aerosol are small, the underestimation by climate models is particularly large. This motivates the need for detailed process studies of aerosol production and aerosol-cloud interactions in pristine environments. The hemispheric difference in satellite estimated cloud droplet number concentration implies preindustrial aerosol concentrations were higher than estimated by most models.
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Affiliation(s)
- Isabel L McCoy
- Atmospheric Sciences Department, University of Washington, Seattle, WA 98105;
| | - Daniel T McCoy
- Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, LS2 9JT Leeds, United Kingdom
| | - Robert Wood
- Atmospheric Sciences Department, University of Washington, Seattle, WA 98105
| | - Leighton Regayre
- Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, LS2 9JT Leeds, United Kingdom
| | | | - Daniel P Grosvenor
- Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, LS2 9JT Leeds, United Kingdom
- National Center for Atmospheric Science, University of Leeds, LS2 9JT Leeds, United Kingdom
| | | | - Yongxiang Hu
- Atmospheric Composition Branch, NASA Langley Research Center, Hampton, VA 23681
| | - Frida A-M Bender
- Department of Meteorology, Stockholm University, SE-106 91 Stockholm, Sweden
- Bolin Centre for Climate Research, Stockholm University, SE-106 91 Stockholm, Sweden
| | - Paul R Field
- Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, LS2 9JT Leeds, United Kingdom
- Met Office, Exeter EX1 3PB, United Kingdom
| | - Kenneth S Carslaw
- Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, LS2 9JT Leeds, United Kingdom
| | - Hamish Gordon
- Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, LS2 9JT Leeds, United Kingdom
- College of Engineering, Carnegie-Mellon University, Pittsburgh, PA 15213
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Morrison H, van Lier‐Walqui M, Fridlind AM, Grabowski WW, Harrington JY, Hoose C, Korolev A, Kumjian MR, Milbrandt JA, Pawlowska H, Posselt DJ, Prat OP, Reimel KJ, Shima S, van Diedenhoven B, Xue L. Confronting the Challenge of Modeling Cloud and Precipitation Microphysics. JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS 2020; 12:e2019MS001689. [PMID: 32999700 PMCID: PMC7507216 DOI: 10.1029/2019ms001689] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 05/03/2020] [Accepted: 05/05/2020] [Indexed: 06/11/2023]
Abstract
In the atmosphere, microphysics refers to the microscale processes that affect cloud and precipitation particles and is a key linkage among the various components of Earth's atmospheric water and energy cycles. The representation of microphysical processes in models continues to pose a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models is divided into two parts: (i) how to represent the population of cloud and precipitation particles, given the impossibility of simulating all particles individually within a cloud, and (ii) uncertainties in the microphysical process rates owing to fundamental gaps in knowledge of cloud physics. The recently developed Lagrangian particle-based method is advocated as a way to address several conceptual and practical challenges of representing particle populations using traditional bulk and bin microphysics parameterization schemes. For addressing critical gaps in cloud physics knowledge, sustained investment for observational advances from laboratory experiments, new probe development, and next-generation instruments in space is needed. Greater emphasis on laboratory work, which has apparently declined over the past several decades relative to other areas of cloud physics research, is argued to be an essential ingredient for improving process-level understanding. More systematic use of natural cloud and precipitation observations to constrain microphysics schemes is also advocated. Because it is generally difficult to quantify individual microphysical process rates from these observations directly, this presents an inverse problem that can be viewed from the standpoint of Bayesian statistics. Following this idea, a probabilistic framework is proposed that combines elements from statistical and physical modeling. Besides providing rigorous constraint of schemes, there is an added benefit of quantifying uncertainty systematically. Finally, a broader hierarchical approach is proposed to accelerate improvements in microphysics schemes, leveraging the advances described in this paper related to process modeling (using Lagrangian particle-based schemes), laboratory experimentation, cloud and precipitation observations, and statistical methods.
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Affiliation(s)
- Hugh Morrison
- National Center for Atmospheric ResearchBoulderCOUSA
| | - Marcus van Lier‐Walqui
- NASA Goddard Institute for Space Studies and Center for Climate Systems ResearchColumbia UniversityNew YorkNYUSA
| | | | | | - Jerry Y. Harrington
- Department of Meteorology and Atmospheric ScienceThe Pennsylvania State UniversityUniversity ParkPAUSA
| | - Corinna Hoose
- Institute of Meteorology and Climate ResearchKarlsruhe Institute of TechnologyKarlsruheGermany
| | - Alexei Korolev
- Observation Based Research SectionEnvironment and Climate Change CanadaTorontoOntarioCanada
| | - Matthew R. Kumjian
- Department of Meteorology and Atmospheric ScienceThe Pennsylvania State UniversityUniversity ParkPAUSA
| | - Jason A. Milbrandt
- Atmospheric Numerical Prediction ResearchEnvironment and Climate Change CanadaDorvalQuebecCanada
| | - Hanna Pawlowska
- Institute of Geophysics, Faculty of PhysicsUniversity of WarsawWarsawPoland
| | - Derek J. Posselt
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | - Olivier P. Prat
- North Carolina Institute for Climate StudiesNorth Carolina State UniversityAshevilleNCUSA
| | - Karly J. Reimel
- Department of Meteorology and Atmospheric ScienceThe Pennsylvania State UniversityUniversity ParkPAUSA
| | - Shin‐Ichiro Shima
- University of Hyogo and RIKEN Center for Computational ScienceKobeJapan
| | - Bastiaan van Diedenhoven
- NASA Goddard Institute for Space Studies and Center for Climate Systems ResearchColumbia UniversityNew YorkNYUSA
| | - Lulin Xue
- National Center for Atmospheric ResearchBoulderCOUSA
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Meehl GA, Senior CA, Eyring V, Flato G, Lamarque JF, Stouffer RJ, Taylor KE, Schlund M. Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. SCIENCE ADVANCES 2020; 6:eaba1981. [PMID: 32637602 PMCID: PMC7314520 DOI: 10.1126/sciadv.aba1981] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 05/11/2020] [Indexed: 05/08/2023]
Abstract
For the current generation of earth system models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6), the range of equilibrium climate sensitivity (ECS, a hypothetical value of global warming at equilibrium for a doubling of CO2) is 1.8°C to 5.6°C, the largest of any generation of models dating to the 1990s. Meanwhile, the range of transient climate response (TCR, the surface temperature warming around the time of CO2 doubling in a 1% per year CO2 increase simulation) for the CMIP6 models of 1.7°C (1.3°C to 3.0°C) is only slightly larger than for the CMIP3 and CMIP5 models. Here we review and synthesize the latest developments in ECS and TCR values in CMIP, compile possible reasons for the current values as supplied by the modeling groups, and highlight future directions. Cloud feedbacks and cloud-aerosol interactions are the most likely contributors to the high values and increased range of ECS in CMIP6.
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Affiliation(s)
- Gerald A. Meehl
- National Center for Atmospheric Research, Boulder, CO, USA
- Corresponding author.
| | | | - Veronika Eyring
- Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
- University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany
| | - Gregory Flato
- Canadian Centre for Modelling and Analysis, Environment and Climate Change Canada, Victoria, Canada
| | | | | | | | - Manuel Schlund
- Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
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