Insights into cloud biases over high-latitude oceans from a cloud-controlling factor framework

A long-standing and pervasive problem within the modelling community is the proper representation of cloud albedo over the Southern Hemisphere (SH) oceanic region. Errors persist despite the extensive evidence that these are related to the unique microphysical characteristics of the austral clouds. In this study we investigate additional causes of cloud albedo biases over the 50˚–65˚ oceanic band using CMIP6 simulations and a cloud-controlling factor (CCF) approach on daily timescales. We gain further insight by replicating our method over the equivalent oceanic region in the Northern Hemisphere (NH).

Cloud albedo, computed from upwelling and downwelling shortwave radiation at surface and top of the atmosphere, is averaged into bins of vertical velocity, surface wind, and sea-surface temperature. The performance of fifteen models in both atmospheric-only and ocean-coupled configurations is evaluated against CERES satellite retrievals in combination with ERA5 reanalysis for the 2000–2014 period.

When averaging cloud albedo by vertical velocity bins, we find that shallow boundary-layer (deep convective) clouds are consistently underpredicted (overpredicted) over the high-latitude oceans of the SH. We repeat the method for the 50˚–65˚ band in the North Atlantic and Pacific oceans and find that similar compensating errors exist.

Another important result is that the SH cloud biases occur for sea-surface temperatures below 4°C. We show that a connection exists between this empirical finding and the biases as determined from microphysical effects, i.e.: a deficit of cloud albedo is due to models producing glaciated rather than supercooled liquid water clouds. Our CCF method allow us to see that in such cases, models tend to simulate NH clouds for the SH.

We also find that the positive sign of the cloud albedo hemispheric asymmetry (SH-NH difference over the 50°–65° band) is consistently predicted by nearly all models, many of which also predict a similar magnitude to observations. However, this is a consequence of compensating errors as individually most models tend to either overpredict or underpredict cloud albedo in both hemispheres.