Preprints
https://doi.org/10.5194/acp-2022-184
https://doi.org/10.5194/acp-2022-184
 
11 Mar 2022
11 Mar 2022
Status: this preprint is currently under review for the journal ACP.

Machine learning of cloud types shows higher climate sensitivity is associated with lower cloud biases

Peter Kuma1, Frida A.-M. Bender1, Alex Schuddeboom2, Adrian J. McDonald2, and Øyvind Seland3 Peter Kuma et al.
  • 1Department of Meteorology (MISU), Stockholm University, Stockholm, Sweden
  • 2School of Physical and Chemical Sciences, University of Canterbury, Christchurch, Aotearoa/New Zealand
  • 3Norwegian Meteorological Institute, Oslo, Norway

Abstract. Uncertainty in cloud feedback in climate models is a major limitation in projections of future climate. Therefore, to ensure the accuracy of climate models, evaluation and improvement of cloud simulation is essential. 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 (approx. 1°×1°) 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. We train this network on a satellite top of atmosphere radiation observed by the Clouds and the Earth’s Radiant Energy System (CERES), and apply it on the Climate Model Intercomparison Project phase 5 and 6 (CMIP5 and CMIP6) historical and abrupt-4xCO2 experiment model output and the ECMWF Reanalysis version 5 (ERA5) and the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) reanalyses. We compare these with satellite observations, link biases in cloud type occurrence derived from the neural network to change with respect to GMST to climate sensitivity, and compare our cloud types with an existing cloud regime classification based on the Moderate Resolution Imaging Spectroradiometer (MODIS) and International Satellite Cloud Climatology Project (ISCCP) satellite data. We show that there is a significant negative linear relationship between the root mean square error of cloud type occurrence derived from the neural network and model equilibrium climate sensitivity and transient climate response (Bayes factor 22 and 17, respectively). This indicates that models with a better representation of the cloud types globally have higher climate sensitivity. Factoring in results from other studies, there are two possible explanations: either high climate sensitivity models are plausible, contrary to combined assessments of climate sensitivity by previous review studies, or the accuracy of representation of present-day clouds in models is negatively correlated with the accuracy of representation of future projected clouds.

Peter Kuma et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-184', Steven Sherwood, 01 Apr 2022
  • RC2: 'Comment on acp-2022-184', Anonymous Referee #2, 04 Apr 2022
  • AC1: 'Comment on acp-2022-184', Peter Kuma, 22 Jul 2022

Peter Kuma et al.

Peter Kuma et al.

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Short summary
We present a machine learning method of determining cloud types in climate model output and satellite data based on surface observations of cloud genera. We analyse cloud type biases and change with global mean near-surface temperature in a set of climate models, and show that there is a negative linear relationship between the error of cloud type occurrence and model climate sensitivity. This indicates that models with a better representation of the cloud types have higher climate sensitivity.
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