Machine learning of cloud types in satellite observations and climate models

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.

Re-appraisal of the global climatic role of natural forests for improved climate projections and policies

“…Today, climate science faces a new challenge. Global climate models with an improved representation of clouds display a higher sensitivity of the Earth's climate to CO 2 doubling than models with a poorer representation of clouds (Zelinka et al, 2020;Kuma et al, 2023). This implies more dire projections for future climate…”

Natural forests for a safe climate: Enhancing ecosystem integrity, biodiversity and adaptive capacity for climate mitigation

“…The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/ffgc. 2023…”

“…The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/ffgc. 2023 Along with the accumulation of atmospheric greenhouse gases, particularly carbon dioxide, the loss of primary forests and other natural ecosystems is a major disruption of the Earth's system and is causing global concern. Quantifying planetary warming from carbon emissions, global climate models highlight natural forests' high carbon storage potential supporting conservation policies.…”

Climate Model Code Genealogy and Its Relation to Climate Feedbacks and Sensitivity

“…For example, the New Zealand Earth System Model (NZESM) (in practice developed alongside HadGEM/UKESM) was developed to reduce Southern Ocean biases (Williams et al., 2016); the Indian Institute of Tropical Meteorology ESM (IITM ESM) has a special focus on the South Asian monsoon (Krishnan et al., 2021); the Australian Community Climate and Earth System Simulator coupled model (ACCESS‐CM) has a focus on reducing uncertainties over the Australian region (Bi et al., 2013); and the Energy Exascale Earth System Model (E3SM) aims to support the U.S. energy sector decisions (Golaz et al., 2019). Weighting models by errors relative to observations (performance weighting) is complicated by the fact that there can be a decoupling between a climate model's accuracy in representing present‐day and historical climate variables and its accuracy in representing the projected change (or trend) of the variables under a climate scenario (Jun et al., 2008a; Kuma et al., 2022; Zelinka, 2022). Thus, a model's performance in future climate projections cannot be fully inferred from its performance in present‐day and historical climate.…”