TY - JOUR
T1 - Sea-surface temperature pattern effects have slowed global warming and biased warming-based constraints on climate sensitivity
AU - Armour, Kyle C.
AU - Proistosescu, Cristian
AU - Dong, Yue
AU - Hahn, Lily C.
AU - Blanchard-Wrigglesworth, Edward
AU - Pauling, Andrew G.
AU - Jnglin Wills, Robert C.
AU - Andrews, Timothy
AU - Stuecker, Malte F.
AU - Po-Chedley, Stephen
AU - Mitevski, Ivan
AU - Forster, Piers M.
AU - Gregory, Jonathan M.
N1 - K.C.A., C.P., and L.C.H. were supported by Department ofEnergy(DOE)AwardDE-SC0022110,NSFAwardAGS-1752796,andNational OceanicandAtmosphericAdministration(NOAA)Modeling,Analysis,Predictions and Projections Program Award NA20OAR4310391. K.C.A. was supported by an Alfred P. Sloan Research Fellowship (Award FG-2020-13568) and a Calvin ProfessorshipinOceanography.L.C.H.wassupportedbytheTamakiFoundation. T.A. was supported by the Met Office Hadley Centre Climate Programme funded by the UK Department of Business, Energy and Industrial Strategy and the European Union's Horizon 2020 research and innovation programme under grant agreement 820829. M.F.S. was supported by NSF Award AGS-2141728. E.B.-W. was supported by NSF Award OPP-2213988. R.C.J.W. was supported by the Swiss NSF Award PCEFP2_203376 and NSF Award AGS-2203543. I.M. was supported by Future Investigators in NASA Earth and Space Science and Technology grant 80NSSC20K1657. Y.D. was supported by the NOAA Climate and Global Change Postdoctoral Fellowship Program, administered by University Corporation for Atmospheric Research's Cooperative Programs for the Advancement of Earth System Science under award NA210AR4310383. Work by S.P.-C. was supported by the Regional and Global Model Analysis Program of the Office of Science and was performed under the auspices of the U.S. DOE by Lawrence Livermore National Laboratory under Contract DEAC52-07NA27344. We also acknowledge high-performance computing support from Cheyenne (https://doi.org/10.5065/D6RX99HX) provided by the National Center for Atmospheric Research's Computational and Information Systems Laboratory, sponsored by the NSF. This is International Pacific Research Center (IPRC) publication 1614 and School of Ocean and Earth Science and Technology (SOEST) contribution 11755.
ACKNOWLEDGMENTS. K.C.A., C.P., and L.C.H. were supported by Department of Energy (DOE) Award DE-SC0022110, NSF Award AGS-1752796, and National OceanicandAtmosphericAdministration(NOAA)Modeling,Analysis,Predictions and Projections Program Award NA20OAR4310391. K.C.A. was supported by an Alfred P. Sloan Research Fellowship (Award FG-2020-13568) and a Calvin Professorship in Oceanography. L.C.H. was supported by the Tamaki Foundation.
T.A. was supported by the Met Office Hadley Centre Climate Programme funded by the UK Department of Business, Energy and Industrial Strategy and the European Union\u2019s Horizon 2020 research and innovation programme under grant agreement 820829. M.F.S. was supported by NSF Award AGS-2141728. E.B.-W. was supported by NSF Award OPP-2213988. R.C.J.W. was supported by the Swiss NSF Award PCEFP2_203376 and NSF Award AGS-2203543. I.M. was supported by Future Investigators in NASA Earth and Space Science and Technology grant 80NSSC20K1657. Y.D. was supported by the NOAA Climate and Global Change Postdoctoral Fellowship Program, administered by University Corporation for Atmospheric Research\u2019s Cooperative Programs for the Advancement of Earth System Science under award NA210AR4310383. Work by S.P.-C. was supported by the Regional and Global Model Analysis Program of the Office of Science and was performed under the auspices of the U.S. DOE by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. We also acknowledge high-performance computing support from Cheyenne (https://doi.org/10.5065/D6RX99HX) provided by the National Center for Atmospheric Research\u2019s Computational and Information Systems Laboratory, sponsored by the NSF. This is International Pacific Research Center
PY - 2024/3/19
Y1 - 2024/3/19
N2 - The observed rate of global warming since the 1970s has been proposed as a strong constraint on equilibrium climate sensitivity (ECS) and transient climate response (TCR)-key metrics of the global climate response to greenhouse-gas forcing. Using CMIP5/6 models, we show that the inter-model relationship between warming and these climate sensitivity metrics (the basis for the constraint) arises from a similarity in transient and equilibrium warming patterns within the models, producing an effective climate sensitivity (EffCS) governing recent warming that is comparable to the value of ECS governing long-term warming under CO2 forcing. However, CMIP5/6 historical simulations do not reproduce observed warming patterns. When driven by observed patterns, even high ECS models produce low EffCS values consistent with the observed global warming rate. The inability of CMIP5/6 models to reproduce observed warming patterns thus results in a bias in the modeled relationship between recent global warming and climate sensitivity. Correcting for this bias means that observed warming is consistent with wide ranges of ECS and TCR extending to higher values than previously recognized. These findings are corroborated by energy balance model simulations and coupled model (CESM1-CAM5) simulations that better replicate observed patterns via tropospheric wind nudging or Antarctic meltwater fluxes. Because CMIP5/6 models fail to simulate observed warming patterns, proposed warming-based constraints on ECS, TCR, and projected global warming are biased low. The results reinforce recent findings that the unique pattern of observed warming has slowed global-mean warming over recent decades and that how the pattern will evolve in the future represents a major source of uncertainty in climate projections.
AB - The observed rate of global warming since the 1970s has been proposed as a strong constraint on equilibrium climate sensitivity (ECS) and transient climate response (TCR)-key metrics of the global climate response to greenhouse-gas forcing. Using CMIP5/6 models, we show that the inter-model relationship between warming and these climate sensitivity metrics (the basis for the constraint) arises from a similarity in transient and equilibrium warming patterns within the models, producing an effective climate sensitivity (EffCS) governing recent warming that is comparable to the value of ECS governing long-term warming under CO2 forcing. However, CMIP5/6 historical simulations do not reproduce observed warming patterns. When driven by observed patterns, even high ECS models produce low EffCS values consistent with the observed global warming rate. The inability of CMIP5/6 models to reproduce observed warming patterns thus results in a bias in the modeled relationship between recent global warming and climate sensitivity. Correcting for this bias means that observed warming is consistent with wide ranges of ECS and TCR extending to higher values than previously recognized. These findings are corroborated by energy balance model simulations and coupled model (CESM1-CAM5) simulations that better replicate observed patterns via tropospheric wind nudging or Antarctic meltwater fluxes. Because CMIP5/6 models fail to simulate observed warming patterns, proposed warming-based constraints on ECS, TCR, and projected global warming are biased low. The results reinforce recent findings that the unique pattern of observed warming has slowed global-mean warming over recent decades and that how the pattern will evolve in the future represents a major source of uncertainty in climate projections.
KW - climate dynamics
KW - climate sensitivity
KW - global warming
UR - http://www.scopus.com/inward/record.url?scp=85187758967&partnerID=8YFLogxK
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U2 - 10.1073/pnas.2312093121
DO - 10.1073/pnas.2312093121
M3 - Article
C2 - 38466843
AN - SCOPUS:85187758967
SN - 0027-8424
VL - 121
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 12
M1 - e2312093121
ER -