Climate Feedbacks

What controls the strength of snow-albedo feedback?

The two factors which together control Northern Hemisphere springtime snow-albedo feedback (SAF) in transient climate change simulations used in the IPCC 4th Assessment were quantified. The first factor is the dependence of planetary albedo on surface albedo, representing the atmosphere' s attenuation effect on surface albedo anomalies. 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, less than 10% of the mean. Moreover, when we calculate an observational estimate of this factor using the satellite-based ISCCP data set, we find most simulations agree with ISCCP values to within about 10%, in spite of disagreements between observed and simulated cloud fields.

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. Its standard deviation is about 1/3 of the mean, with the largest value being approximately three times larger than the smallest. The magnitude of this factor is generally controlled by two feedback loops: snow cover feedback and snowpack metamorphosis feedback. To understand what causes it to vary so much from model to model, one need to separate and quantify contributions of the two loops. In this study, we developed methods to do so for the current AR4 models.

We found that the strength of SAF in the models is determined primarily by the surface albedo decrease associated with loss of snow cover rather than the reduction in snow albedo due to snow metamorphosis in a warming climate. The three-fold intermodel spread in SAF strength is likewise attributable mostly to the snow cover component. The spread in the strength of this component is in turn mostly attributable to a more than two-fold spread in mean effective snow albedo. Models with large effective snow albedos have a large surface albedo contrast between snow-covered and snow-free regions, and exhibit a correspondingly large surface albedo decrease when snow cover decreases.

There is a further relationship between effective snow albedo and SAF strength, and surface albedo parameterization. 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 snow albedo feedback. SAF in these models is generally weaker than observed. We speculate that in these models either snow albedos or canopy albedos when snow is present are too low, or vegetation shields snow-covered surfaces excessively. Detailed observations of surface albedo in a representative sampling of snow-covered surfaces would therefore be extremely useful in constraining these parameterizations and reducing SAF spread in the next generation of models.

Download the publication describing these results in more detail.

Xin Qu and Alex Hall make up the team that performed this research.