Snow-albedo feedback in future climate change

We quantify the two factors controlling Northern Hemisphere springtime snow-albedo feedback in transient climate change based on scenario runs of 18 climate models used in the Intergovernmental Panel of Climate Change 4th Assessment. The first factor is the dependence of planetary albedo on surface albedo. We find in all simulations surface albedo anomalies are attenuated by approximately half in Northern Hemisphere land areas as they are transformed into planetary albedo anomalies. The intermodel standard deviation in this factor is surprisingly small. Moreover, when we calculate an observational estimate of this factor using the satellite-based International Satellite Cloud Climatology Project data, we find most simulations agree with ISCCP values to within about 10%.; The second factor, related exclusively to surface processes, is the change in surface albedo associated with an anthropogenically-induced temperature change in Northern Hemisphere land areas. It exhibits much more intermodel variability. This large intermodel spread is attributable mostly to a correspondingly large spread in mean effective snow albedo. Models without explicit treatment of the vegetation canopy in their surface albedo calculations typically have high effective snow albedos and strong SAF, often stronger than observed. In models with explicit canopy treatment, completely snow-covered surfaces typically have lower albedos and the simulations have weaker SAF, generally weaker than observed.; These large intermodel variations in feedback strength in climate change are nearly perfectly correlated with comparably large intermodel variations in feedback strength in the context of the seasonal cycle. Moreover, the feedback strength in the real seasonal cycle can be measured and compared to simulated values. These mostly fall outside the range of the observed estimate. Because of the tight correlation between simulated feedback strength in the seasonal cycle and climate change, eliminating the model errors in the seasonal cycle will lead directly to a reduction in the spread of feedback strength in climate change.