Machine learning of cloud types in satellite observations and climate models
-
Published:2023-01-13
Issue:1
Volume:23
Page:523-549
-
ISSN:1680-7324
-
Container-title:Atmospheric Chemistry and Physics
-
language:en
-
Short-container-title:Atmos. Chem. Phys.
Author:
Kuma PeterORCID, Bender Frida A.-M.ORCID, Schuddeboom AlexORCID, McDonald Adrian J.ORCID, Seland ØyvindORCID
Abstract
Abstract. Uncertainty in cloud feedbacks in climate models is a major limitation in projections of future climate. Therefore, evaluation and improvement of cloud simulation are essential to ensure the accuracy of climate models. We analyse cloud biases and cloud change with respect to global mean near-surface temperature (GMST) in climate models relative to satellite observations and relate them to equilibrium climate sensitivity, transient climate response and cloud feedback. For this purpose, we develop a supervised deep convolutional artificial neural network for determination of cloud types from low-resolution (2.5∘×2.5∘) daily mean top-of-atmosphere shortwave and longwave radiation fields, corresponding to the World Meteorological Organization (WMO) cloud genera recorded by human observers in the Global Telecommunication System (GTS). We train this network on top-of-atmosphere radiation retrieved by the Clouds and the Earth’s Radiant Energy System (CERES) and GTS and apply it to the Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) model output and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5) and the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) reanalyses. We compare the cloud types between models and satellite observations. We link biases to climate sensitivity and identify a negative linear relationship between the root mean square error of cloud type occurrence derived from the neural network and model equilibrium climate sensitivity (ECS), transient climate response (TCR) and cloud feedback. This statistical relationship in the model ensemble favours models with higher ECS, TCR and cloud feedback. However, this relationship could be due to the relatively small size of the ensemble used or decoupling between present-day biases and future projected cloud change. Using the abrupt-4×CO2 CMIP5 and CMIP6 experiments, we show that models simulating decreasing stratiform and increasing cumuliform clouds tend to have higher ECS than models simulating increasing stratiform and decreasing cumuliform clouds, and this could also partially explain the association between the model cloud type occurrence error and model ECS.
Funder
Horizon 2020 Framework Programme Swedish e-Science Research Centre
Publisher
Copernicus GmbH
Subject
Atmospheric Science
Reference101 articles.
1. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M.,
Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R.,
Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P.,
Wicke, M., Yu, Y., and Zheng, X.: TensorFlow: A System for Large-Scale
Machine Learning, in: Proceedings of the 12th USENIX Conference on Operating
Systems Design and Implementation, OSDI'16, [code], USENIX Association,
USA, 265–283, 2016. a, b 2. Behnel, S., Bradshaw, R., Citro, C., Dalcin, L., Seljebotn, D. S., and Smith,
K.: Cython: The Best of Both Worlds, Comput. Sci. Eng., 13,
31–39, https://doi.org/10.1109/MCSE.2010.118, 2011. a 3. Bender, F. A.-M., Engström, A., Wood, R., and Charlson, R. J.: Evaluation of
Hemispheric Asymmetries in Marine Cloud Radiative Properties, J. Climate, 30, 4131–4147, https://doi.org/10.1175/JCLI-D-16-0263.1, 2017. a 4. Bjordal, J., Storelvmo, T., Alterskjær, K., and Carlsen, T.: Equilibrium
climate sensitivity above 5 ∘C plausible due to
state-dependent cloud feedback, Nat. Geosci., 13, 718–721,
https://doi.org/10.1038/s41561-020-00649-1, 2020. a 5. Bretherton, C. S. and Caldwell, P. M.: Combining Emergent Constraints for
Climate Sensitivity, J. Climate, 33, 7413–7430,
https://doi.org/10.1175/JCLI-D-19-0911.1, 2020. a
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|