A systematic evaluation of high-cloud controlling factors

Clouds strongly modulate the top-of-the-atmosphere energy budget and are a major source of uncer-
tainty 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 inves-
tigating 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 previ-
ously 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 ($\Delta$U$_{300}$). We identify an optimal configuration for pre-
dicting high-cloud radiative anomalies that includes S$_{UT}$ and $\Delta$U$_{300}$ and show that spatial domain size is more
important than the selection of CCFs for predictive skill. ... mehrWe 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 S$_{UT}$ captures physical drivers of
known high-cloud feedbacks and deduce that the 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.