Preprints
https://doi.org/10.5194/egusphere-2024-226
https://doi.org/10.5194/egusphere-2024-226
05 Feb 2024
 | 05 Feb 2024
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

A systematic evaluation of high-cloud controlling factors

Sarah Wilson Kemsley, Paulo Ceppi, Hendrik Andersen, Jan Cermak, Philip Stier, and Peer Nowack

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. All combinations of tested CCFs predict historical, monthly variability well for most locations at grid-cell scales. Differences between configurations for predicting globally-aggregated radiative anomalies are more pronounced, where configurations including SUT outperform others. We show that for predicting local, historical anomalies, spatial domain size is more important than the selection of CCFs, finding an important discrepancy between optimal domain sizes for local 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 thus deduce that 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.

Sarah Wilson Kemsley, Paulo Ceppi, Hendrik Andersen, Jan Cermak, Philip Stier, and Peer Nowack

Status: open (until 18 Mar 2024)

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  • RC1: 'Comment on egusphere-2024-226', Anonymous Referee #1, 28 Feb 2024 reply
Sarah Wilson Kemsley, Paulo Ceppi, Hendrik Andersen, Jan Cermak, Philip Stier, and Peer Nowack
Sarah Wilson Kemsley, Paulo Ceppi, Hendrik Andersen, Jan Cermak, Philip Stier, and Peer Nowack

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Short summary
Aiming to inform parameter selection for future observational constraint analyses, we incorporate five candidate meteorological drivers specifically targeting high clouds, into a cloud controlling factor framework within a range of spatial domain sizes. We find a discrepancy between optimal domain size for predicting local and globally-aggregated cloud radiative anomalies, and identify upper tropospheric static stability as an important high-cloud controlling factor.