A systematic evaluation of high-cloud controlling factors

Clouds strongly modulate the top-of-the-atmosphere (TOA) energy budget. While most evidence indicates that changes in cloud-induced radiative anomalies at the TOA likely amplifies warming, the magnitude of this global cloud feedback remains highly uncertain. “Cloud Controlling Factor” (CCF) analysis is an approach that can be used to tackle this uncertainty, deriving relationships between large-scale meteorological drivers and cloud-radiative anomalies which can subsequently be used to constrain cloud feedback. However, the choice of meteorological controlling factors is crucial for a meaningful constraint, and while there is rich literature investigating ideal CCF setups for low-level clouds, there is a distinct lack of analogous research that explicitly targets high clouds.

Here, we use ridge regression to systematically evaluate CCFs that specifically target high cloud formation and cessation using historical data. We 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. We identify an optimal configuration including S_UT, and show that the spatial distribution of the  S_UT  ridge regression coefficients are congruent with the physical drivers of known high-cloud feedbacks. We further 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. These results highlight upper-tropospheric static stability as an important CCF for high clouds and longwave cloud feedback, which we begin to explore using modelled data under an abrupt quadrupling of CO₂ (abrupt-4xCO₂).