Clouds cover on average nearly 70% of Earth’s surface and regulate the global albedo. The magnitude of the shortwave reflection by clouds depends on their location, optical properties, and three-dimensional (3D) structure. Due to computational limitations, Earth system models are unable to perform 3D radiative transfer calculations. Instead they make assumptions, including the independent column approximation (ICA), that neglect effects of 3D cloud morphology on albedo. We show how the resulting radiative flux bias (ICA-3D) depends on cloud morphology and solar zenith angle. Using large-eddy simulations to produce 3D cloud fields, a Monte Carlo code for 3D radiative transfer, and observations of cloud climatology, we estimate the effect of this flux bias on global climate. The flux bias is largest at small zenith angles and for deeper clouds, while the negative albedo bias is most prominent for large zenith angles. In the tropics, the radiative flux bias from neglecting 3D radiative transfer is estimated to be 4.0 +/- 2.4 Wm-2 in the mean and locally as large as 9 Wm-2.

The uncertainty in polar cloud feedbacks calls for process understanding of the cloud response to climate warming. As an initial step toward improved process understanding, we investigate the seasonal cycle of polar clouds in the current climate by adopting a novel modeling framework using large eddy simulations (LES), which explicitly resolve cloud dynamics. Resolved horizontal and vertical advection of heat and moisture from an idealized general circulation model (GCM) are prescribed as forcing in the LES. The LES are also forced with prescribed sea ice thickness, but surface temperature, atmospheric temperature, and moisture evolve freely without nudging. A semigray radiative transfer scheme without water vapor and cloud feedbacks allows the GCM and LES to achieve closed energy budgets more easily than would be possible with more complex schemes. This enables the mean states in the two models to be consistently compared, without the added complications from interaction with more comprehensive radiation. We show that the LES closely follow the GCM seasonal cycle, and the seasonal cycle of low-level clouds in the LES resembles observations: maximum cloud liquid occurs in late summer and early autumn, and winter clouds are dominated by ice in the upper troposphere. Large-scale advection of moisture provides the main source of water vapor for the liquid-containing clouds in summer, while a temperature advection peak in winter makes the atmosphere relatively dry and reduces cloud condensate. The framework we develop and employ can be used broadly for studying cloud processes and the response of polar clouds to climate warming.

Despite global warming, SSTs in the Southern Ocean (SO) have cooled in recent decades largely as a result of internal variability. The global impact of this cooling is assessed by nudging evolving SO SST anomalies to observations in an ensemble of coupled climate model simulations under historical radiative forcing, and comparing against a control ensemble. The most significant remote response to observed SO cooling is found in the tropical South Atlantic, where increased clouds and strengthened trade winds cool the sea surface, partially offsetting the radiatively-forced warming trend. The SO ensemble produces a more realistic tropical South Atlantic SST trend, and exhibits a higher pattern correlation with observed SST trends in the greater Atlantic basin, compared to the control ensemble. SO cooling also produces a significant increase in Antarctic sea ice, but not enough to offset radiatively-induced ice loss; thus, the SO ensemble remains biased in its sea ice trends.