A systematic evaluation of high-cloud controlling factors

Wilson Kemsley, Sarah, Ceppi, Paulo, Andersen, Hendrik, Cermak, Jan, Stier, Philip and Nowack, Peer ORCID: https://orcid.org/0000-0003-4588-7832 (2024) A systematic evaluation of high-cloud controlling factors. Atmospheric Chemistry and Physics, 24 (14). 8295–8316. ISSN 1680-7375

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Abstract

Clouds strongly modulate the top-of-the-atmosphere energy budget and are a major source of uncertainty in climate projections. “Cloud controlling factor” (CCF) analysis derives relationships between large-scale meteorological drivers and cloud radiative anomalies, which can be used to constrain cloud feedback. However, the choice of meteorological CCFs is crucial for a meaningful constraint. While there is rich literature investigating ideal CCF setups for low-level clouds, there is a lack of analogous research explicitly targeting high clouds. Here, we use ridge regression to systematically evaluate the addition of five candidate CCFs to previously established core CCFs within large spatial domains to predict longwave high-cloud radiative anomalies: upper-tropospheric static stability (SUT), sub-cloud moist static energy, convective available potential energy, convective inhibition, and upper-tropospheric wind shear (ΔU300). We identify an optimal configuration for predicting high-cloud radiative anomalies that includes SUT and ΔU300 and show that spatial domain size is more important than the selection of CCFs for predictive skill. We also find an important discrepancy between the optimal domain sizes required for predicting locally and globally aggregated radiative anomalies. Finally, we scientifically interpret the ridge regression coefficients, where we show that SUT captures physical drivers of known high-cloud feedbacks and deduce that the inclusion of SUT into observational constraint frameworks may reduce uncertainty associated with changes in anvil cloud amount as a function of climate change. Therefore, we highlight SUT as an important CCF for high clouds and longwave cloud feedback.

Item Type:	Article
Additional Information:	Data availability: ERA5 meteorological reanalysis data are freely available from the Copernicus Climate Change Service (C3S) Climate Data Store (https://doi.org/10.24381/cds.f17050d7, Hersbach et al., 2023a; https://doi.org/10.24381/cds.6860a573, Hersbach et al., 2023b; and https://doi.org/10.24381/cds.bd0915c6, Hersbach et al., 2023c). Combined MODIS Aqua–Terra data are also freely available and downloaded monthly (https://doi.org/10.5067/MODIS/MCD06COSP_M3_MODIS.062, Hubanks et al., 2022). All CMIP5/6 data were obtained from the UK Center for Environmental Data Analysis portal (https://esgf-ui.ceda.ac.uk/cog/projects/esgf-ceda/, CEDA, 2024). Funding information: This research has been supported by the Natural Environment Research Council (grant no. NE/V012045/1), the European Horizon 2020 (grant no. 821205), and the Deutsche Forschungsgemeinschaft (grant no. 440521482).
Faculty \ School:	Faculty of Science > School of Environmental Sciences University of East Anglia Research Groups/Centres > Theme - ClimateUEA
UEA Research Groups:	Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences Faculty of Science > Research Groups > Climatic Research Unit
Depositing User:	LivePure Connector
Date Deposited:	13 Jun 2024 13:30
Last Modified:	09 Oct 2024 19:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/95600
DOI:	10.5194/acp-24-8295-2024

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