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Journal Article

Exploiting large ensembles for a better yet simpler climate model evaluation

MPS-Authors
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Suarez-Gutierrez,  Laura
Director’s Research Group OES, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

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Maher,  Nicola
Director’s Research Group OES, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

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Milinski,  Sebastian
Director’s Research Group OES, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

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Supplementary Material (public)

Better-Eval-Scripts-2e.zip
(Supplementary material), 644KB

382_2021_5821_MOESM1_ESM.pdf
(Supplementary material), 18MB

382_2021_5821_MOESM2_ESM.pdf
(Supplementary material), 875KB

382_2021_5821_MOESM3_ESM.pdf
(Supplementary material), 2MB

Citation

Suarez-Gutierrez, L., Maher, N., & Milinski, S. (2021). Exploiting large ensembles for a better yet simpler climate model evaluation. Climate Dynamics, available online. doi:10.1007/s00382-021-05821-w.


Cite as: http://hdl.handle.net/21.11116/0000-0007-7A38-2
Abstract
We use a methodological framework exploiting the power of large ensembles to evaluate how well ten coupled climate models represent the internal variability and response to external forcings in observed historical surface temperatures. This evaluation framework allows us to directly attribute discrepancies between models and observations to biases in the simulated internal variability or forced response, without relying on assumptions to separate these signals in observations. The largest discrepancies result from the overestimated forced warming in some models during recent decades. In contrast, models do not systematically over- or underestimate internal variability in global mean temperature. On regional scales, all models misrepresent surface temperature variability over the Southern Ocean, while overestimating variability over land-surface areas, such as the Amazon and South Asia, and high-latitude oceans. Our evaluation shows that MPI-GE, followed by GFDL-ESM2M and CESM-LE offer the best global and regional representation of both the internal variability and forced response in observed historical temperatures.