[1] Differences in simulations of climate feedbacks are sources of significant divergence in climate models' temperature response to anthropogenic forcing. Snow albedo feedback is particularly critical for climate change prediction in heavily-populated northern hemisphere land masses. Here we show its strength in current models exhibits a factor-of-three spread. 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, suggesting many models have an unrealistic snow albedo feedback in the seasonal cycle context. 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. Though this comparison to observations may put the models in an unduly harsh light because of uncertainties in the observed estimate that are difficult to quantify, our results map out a clear strategy for targeted observation of the seasonal cycle to reduce divergence in simulations of climate sensitivity.Citation: Hall, A., and X. Qu (2006), Using the current seasonal cycle to constrain snow albedo feedback in future climate change, Geophys. Res. Lett., 33, L03502,

The strength of snow-albedo feedback (SAF) in transient climate change simulations of the Fourth Assessment of the Intergovernmental Panel on Climate Change is generally determined by the surfacealbedo decrease associated with a loss of snow cover rather than the reduction in snow albedo due to snow metamorphosis in a warming climate. The large 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 correspondingly large 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. 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. The authors 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.

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