Machine learning of cloud types in satellite observations and climate models
نویسندگان
چکیده
Abstract. Uncertainty in cloud feedbacks climate models is a major limitation projections of future climate. Therefore, evaluation and improvement simulation are essential to ensure the accuracy models. We analyse biases change with respect global mean near-surface temperature (GMST) relative satellite observations relate them equilibrium sensitivity, transient response feedback. For this purpose, we develop supervised deep convolutional artificial neural network for determination types from low-resolution (2.5∘×2.5∘) daily top-of-atmosphere shortwave longwave radiation fields, corresponding World Meteorological Organization (WMO) genera recorded by human observers Global Telecommunication System (GTS). train on retrieved Clouds Earth’s Radiant Energy (CERES) GTS apply it Coupled Model Intercomparison Project Phase 5 6 (CMIP5 CMIP6) model output European Centre Medium-Range Weather Forecasts (ECMWF) Reanalysis version (ERA5) Modern-Era Retrospective Analysis Research Applications 2 (MERRA-2) reanalyses. compare between observations. link sensitivity identify negative linear relationship root square error type occurrence derived (ECS), (TCR) This statistical ensemble favours higher ECS, TCR However, could be due relatively small size used or decoupling present-day projected change. Using abrupt-4×CO2 CMIP5 CMIP6 experiments, show that simulating decreasing stratiform increasing cumuliform clouds tend have ECS than clouds, also partially explain association ECS.
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ژورنال
عنوان ژورنال: Atmospheric Chemistry and Physics
سال: 2023
ISSN: ['1680-7316', '1680-7324']
DOI: https://doi.org/10.5194/acp-23-523-2023