Better-constrained climate sensitivity when accounting for dataset dependency on pattern effect estimates
-
Published:2023-07-11
Issue:13
Volume:23
Page:7535-7549
-
ISSN:1680-7324
-
Container-title:Atmospheric Chemistry and Physics
-
language:en
-
Short-container-title:Atmos. Chem. Phys.
Author:
Modak AngshumanORCID, Mauritsen Thorsten
Abstract
Abstract. The best estimate of equilibrium climate sensitivity (ECS) constrained based on the instrumental record of historical warming becomes coherent with other lines of evidence when the dependence of radiative feedback on the pattern of surface temperature change (pattern effect) is incorporated. Pattern effect strength is usually estimated with atmosphere-only model simulations forced with observed historical sea-surface temperature (SST) and sea-ice change and constant pre-industrial forcing. However, recent studies indicate that pattern effect estimates depend on the choice of SST boundary condition dataset, due to differences in the measurement sources and the techniques used to merge and construct them. Here, we systematically explore this dataset dependency by applying seven different observed SST datasets to the MPI-ESM1.2-LR model covering 1871–2017. We find that the pattern effect ranges from -0.01±0.09 to 0.42±0.10 W m−2 K−1 (standard error), whereby the commonly used Atmospheric Model Intercomparison Project II (AMIPII) dataset produces by far the largest estimate. When accounting for the generally weaker pattern effect in MPI-ESM1.2-LR compared to other models, as well as dataset dependency and intermodel spread, we obtain a combined pattern effect estimate of 0.37 W m−2 K−1 [−0.14 to 0.88 W m−2 K−1] (5th–95th percentiles) and a resulting instrumental record ECS estimate of 3.2 K [1.8 to 11.0 K], which as a result of the weaker pattern effect is slightly lower and better constrained than in previous studies.
Funder
H2020 European Research Council
Publisher
Copernicus GmbH
Subject
Atmospheric Science
Reference50 articles.
1. Andrews, T. and Webb, M. J.: The Dependence of Global Cloud and Lapse Rate
Feedbacks on the Spatial Structure of Tropical Pacific Warming, J. Climate, 31, 641–654, https://doi.org/10.1175/JCLI-D-17-0087.1, 2018. a, b 2. Andrews, T., Gregory, J. M., and Webb, M. J.: The dependence of radiative
forcing and feedback on evolving patterns of surface temperature change in
climate models, J. Climate, 28, 1630–1648, https://doi.org/10.1175/JCLI-D-14-00545.1, 2015. a 3. Andrews, T., Gregory, J. M., Paynter, D., Silvers, L. G., Zhou, C., Mauritsen, T., Webb, M. J., Armour, K. C., Forster, P. M., and Titchner, H.: Accounting for Changing Temperature Patterns Increases Historical Estimates of Climate Sensitivity, Geophys. Res. Lett., 45, 8490–8499,
https://doi.org/10.1029/2018GL078887, 2018. a, b, c, d, e, f 4. Andrews, T., Gregory, J. M., Dong, Y., Armour, K., Paynter, D., Lin, P., Modak, A., Mauritsen, T., Cole, J., Medeiros, B., and et al.: On the effect of historical SST patterns on radiative feedback, Earth and Space Science Open Archive, p. 48, https://doi.org/10.1002/essoar.10510623.3, 2022. a, b, c, d, e, f, g, h, i, j 5. Armour, K. C.: Energy budget constraints on climate sensitivity in light of
inconstant climate feedbacks, Nat. Clim. Change, 7, 331–335,
https://doi.org/10.1038/nclimate3278, 2017. a, b
|
|