A systematic evaluation of high-cloud controlling factors

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 (S _UT), 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 S _UT 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 S _UT captures physical drivers of known high-cloud feedbacks, and thus deduce that inclusion of S _UT into observational constraint frameworks may reduce uncertainty associated with changes in anvil cloud amount as a function of climate change. Therefore, we highlight S _UT as an important CCF for high clouds and longwave cloud feedback.