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Origins of differences in climate sensitivity, forcing and feedback in climate models

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Abstract

We diagnose climate feedback parameters and CO2 forcing including rapid adjustment in twelve atmosphere/mixed-layer-ocean (“slab”) climate models from the CMIP3/CFMIP-1 project (the AR4 ensemble) and fifteen parameter-perturbed versions of the HadSM3 slab model (the PPE). In both ensembles, differences in climate feedbacks can account for approximately twice as much of the range in climate sensitivity as differences in CO2 forcing. In the AR4 ensemble, cloud effects can explain the full range of climate sensitivities, and cloud feedback components contribute four times as much as cloud components of CO2 forcing to the range. Non-cloud feedbacks are required to fully account for the high sensitivities of some models however. The largest contribution to the high sensitivity of HadGEM1 is from a high latitude clear-sky shortwave feedback, and clear-sky longwave feedbacks contribute substantially to the highest sensitivity members of the PPE. Differences in low latitude ocean regions (30°N/S) contribute more to the range than those in mid-latitude oceans (30–55°N/S), low/mid latitude land (55°N/S) or high latitude ocean/land (55–90°N/S), but contributions from these other regions are required to account fully for the higher model sensitivities, for example from land areas in IPSL CM4. Net cloud feedback components over the low latitude oceans sorted into percentile ranges of lower tropospheric stability (LTS) show largest differences among models in stable regions, mainly due to their shortwave components, most of which are positive in spite of increasing LTS. Differences in the mid-stability range are smaller, but cover a larger area, contributing a comparable amount to the range in climate sensitivity. These are strongly anti-correlated with changes in subsidence. Cloud components of CO2 forcing also show the largest differences in stable regions, and are strongly anticorrelated with changes in estimated inversion strength (EIS). This is qualitatively consistent with what would be expected from observed relationships between EIS and low-level cloud fraction. We identify a number of cases where individual models show unusually strong forcings and feedbacks compared to other members of the ensemble. We encourage modelling groups to investigate unusual model behaviours further with sensitivity experiments. Most of the models fail to correctly reproduce the observed relationships between stability and cloud radiative effect in the subtropics, indicating that there remains considerable room for model improvements in the future.

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Notes

  1. See http://www.clivar.org/publications/exchanges/exchanges.php.

  2. See http://www.gewex.org.

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Acknowledgments

We would like to acknowledge Rob Wood for providing code to calculate the EIS, and Tim Andrews, Alejandro Bodas-Salcedo, Ben Booth, Chris Bretherton, Philip Brohan, Leo Donner, William Ingram, Manoj Joshi, Adrian Lock, Tomoo Ogura, Mark Ringer, David Sexton, Yoko Tsushima, Keith Williams, Tokuta Yokohata and the anonymous reviewers for their helpful comments and suggestions. We acknowledge the modelling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 and CFMIP multi-model datasets. Support of these datasets is provided by the Office of Science, US Department of Energy. This work was supported by the Joint DECC/Defra Met Office Hadley Centre Climate Programme (GA01101).

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  1. Hadley Centre, Met Office, FitzRoy Road, Exeter, EX1 3PB, UK

    Mark J. Webb & Jonathan M. Gregory

  2. College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK

    F. Hugo Lambert

  3. National Centre for Atmospheric Science, Reading University, Reading, UK

    Jonathan M. Gregory

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  1. Mark J. Webb
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Correspondence to Mark J. Webb.

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Webb, M.J., Lambert, F.H. & Gregory, J.M. Origins of differences in climate sensitivity, forcing and feedback in climate models. Clim Dyn 40, 677–707 (2013). https://doi.org/10.1007/s00382-012-1336-x

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