Accounting for Changing Temperature Patterns Increases Historical Estimates of Climate Sensitivity

Eight atmospheric general circulation models (AGCMs) are forced with observed historical (1871–2010) monthly sea surface temperature and sea ice variations using the Atmospheric Model Intercomparison Project II data set. The AGCMs therefore have a similar temperature pattern and trend to that of observed historical climate change. The AGCMs simulate a spread in climate feedback similar to that seen in coupled simulations of the response to CO2 quadrupling. However, the feedbacks are robustly more stabilizing and the effective climate sensitivity (EffCS) smaller. This is due to a pattern effect, whereby the pattern of observed historical sea surface temperature change gives rise to more negative cloud and longwave clear‐sky feedbacks. Assuming the patterns of long‐term temperature change simulated by models, and the radiative response to them, are credible; this implies that existing constraints on EffCS from historical energy budget variations give values that are too low and overly constrained, particularly at the upper end. For example, the pattern effect increases the long‐term Otto et al. (2013, https://doi.org/10.1038/ngeo1836) EffCS median and 5–95% confidence interval from 1.9 K (0.9–5.0 K) to 3.2 K (1.5–8.1 K).

[1]  Nick Rayner,et al.  The Met Office Hadley Centre sea ice and sea surface temperature data set, version 2: 1. Sea ice concentrations , 2014 .

[2]  K. Taylor,et al.  Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere‐ocean climate models , 2012 .

[3]  Erik Asphaug Rise and fall of the Martian moons , 2016 .

[4]  A. Pier Siebesma,et al.  The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6. , 2016 .

[5]  Michael Schulz,et al.  Information from paleoclimate archives , 2013 .

[6]  Alexander J. Winkler,et al.  Developments in the MPI‐M Earth System Model version 1.2 (MPI‐ESM1.2) and Its Response to Increasing CO2 , 2019, Journal of advances in modeling earth systems.

[7]  John F. B. Mitchell,et al.  The time‐dependence of climate sensitivity , 2000 .

[8]  T. Andrews,et al.  Variation in climate sensitivity and feedback parameters during the historical period , 2016 .

[9]  S. Klein,et al.  Analyzing the dependence of global cloud feedback on the spatial pattern of sea surface temperature change with a Green's function approach , 2017 .

[10]  M. Watanabe,et al.  Pacific trade winds accelerated by aerosol forcing over the past two decades , 2016 .

[11]  Karl E. Taylor,et al.  An overview of CMIP5 and the experiment design , 2012 .

[12]  T. Andrews Using an AGCM to Diagnose Historical Effective Radiative Forcing and Mechanisms of Recent Decadal Climate Change , 2014 .

[13]  J. Curry,et al.  The Impact of Recent Forcing and Ocean Heat Uptake Data on Estimates of Climate Sensitivity , 2018, Journal of Climate.

[14]  M. Webb,et al.  The Dependence of Radiative Forcing and Feedback on Evolving Patterns of Surface Temperature Change in Climate Models , 2015 .

[15]  S. Xie,et al.  Distinct energy budgets for anthropogenic and natural changes during global warming hiatus , 2016 .

[16]  K. Lawrence,et al.  Tightly linked zonal and meridional sea surface temperature gradients over the past five million years , 2015 .

[17]  James J. Hack,et al.  A New Sea Surface Temperature and Sea Ice Boundary Dataset for the Community Atmosphere Model , 2008 .

[18]  K. Armour Energy budget constraints on climate sensitivity in light of inconstant climate feedbacks , 2017 .

[19]  R. Seager,et al.  An Ocean Dynamical Thermostat , 1996 .

[20]  M. Webb,et al.  Global‐mean radiative feedbacks and forcing in atmosphere‐only and coupled atmosphere‐ocean climate change experiments , 2014 .

[21]  D. Battisti,et al.  The dependence of transient climate sensitivity and radiative feedbacks on the spatial pattern of ocean heat uptake , 2014 .

[22]  T. Delworth,et al.  Probing the Fast and Slow Components of Global Warming by Returning Abruptly to Preindustrial Forcing , 2010 .

[23]  G. Schmidt,et al.  Internal Variability and Disequilibrium Confound Estimates of Climate Sensitivity From Observations , 2018 .

[24]  Peter A. Stott,et al.  Attribution of observed historical near‒surface temperature variations to anthropogenic and natural causes using CMIP5 simulations , 2013 .

[25]  S. Raper,et al.  An Observationally Based Estimate of the Climate Sensitivity , 2002 .

[26]  G. Roe,et al.  Feedbacks, Timescales, and Seeing Red , 2009 .

[27]  S. Bony,et al.  Prospects for narrowing bounds on Earth's equilibrium climate sensitivity , 2016, Earth's future.

[28]  Raymond T. Pierrehumbert,et al.  Feedback temperature dependence determines the risk of high warming , 2015 .

[29]  S. M. Marlais,et al.  An Overview of the Results of the Atmospheric Model Intercomparison Project (AMIP I) , 1999 .

[30]  T. Frölicher,et al.  Sensitivity of radiative forcing, ocean heat uptake, and climate feedback to changes in anthropogenic greenhouse gases and aerosols , 2015 .

[31]  P. Jones,et al.  Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set , 2012 .

[32]  S. Klein,et al.  Impact of decadal cloud variations on the Earth/'s energy budget , 2016 .

[33]  L. Horowitz,et al.  Equilibrium Climate Sensitivity Obtained From Multimillennial Runs of Two GFDL Climate Models , 2018 .

[34]  J. Gregory,et al.  Relationship of tropospheric stability to climate sensitivity and Earth’s observed radiation budget , 2017, Proceedings of the National Academy of Sciences.

[35]  Reto Knutti,et al.  Energy budget constraints on climate response , 2013 .

[36]  Ming Zhao,et al.  The Diversity of Cloud Responses to Twentieth Century Sea Surface Temperatures , 2017 .

[37]  Cecilia M. Bitz,et al.  Time-Varying Climate Sensitivity from Regional Feedbacks , 2012 .

[38]  A. P. Siebesma,et al.  The Cloud Feedback Model Intercomparison Project (CFMIP) , 2016 .

[39]  Adam A. Scaife,et al.  Role of volcanic and anthropogenic aerosols in the recent global surface warming slowdown , 2016 .

[40]  D. Battisti,et al.  Relative roles of surface temperature and climate forcing patterns in the inconstancy of radiative feedbacks , 2017 .

[41]  Piers M. Forster,et al.  Inference of Climate Sensitivity from Analysis of Earth's Energy Budget , 2016 .

[42]  T. Mauritsen Global warming: Clouds cooled the Earth , 2016 .

[43]  P. Huybers,et al.  Slow climate mode reconciles historical and model-based estimates of climate sensitivity , 2017, Science Advances.

[44]  Jeffery R. Scott,et al.  Southern Ocean warming delayed by circumpolar upwelling and equatorward transport , 2016 .

[45]  M. Webb,et al.  The Dependence of Global Cloud and Lapse Rate Feedbacks on the Spatial Structure of Tropical Pacific Warming , 2018 .