Climate Feedbacks

Using the current seasonal cycle to constrain snow albedo feedback in future climate change

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 exhibits a factor-of-three spread in transient climate change experiments with 17 models used in the U.N. IPCC 4th Assessment report. These large intermodel variations in feedback strength in climate change are nearly perfectly correlated with comparably large intermodel variations in feedback strength in the seasonal cycle. This is demonstrated by the accompanying figure, a scatterplot of simulated springtime snow albedo feedback parameters in climate change (ordinate) vs. simulated springtime feedback parameters in the seasonal cycle (abscissa). The snow albedo feedback parameter is defined as the change in surface albedo in the northern hemisphere extratropical land masses divided by the associated change in surface air temperature in the region. The focus here is on the surface component of snow albedo feedback because the component relating to the atmosphere does not vary significantly among models. See another of our studies for more details on separating the surface and atmospheric components of snow albedo feedback. The numbers of the 17 experiments are used as plotting symbols.

Snow albedo feedback strength in the real seasonal cycle can be measured and compared to simulated values. An estimate of the observed springtime snow albedo feedback parameter based on satellite and reanalysis data is plotted on the figure as a dashed vertical line. The grey bar gives an estimate of statistical error. The probability the actual observed value lies outside the grey bar is 5% if statistical error only is taken into account. The simulated snow albedo feedback parameters 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.

Download the publication (Hall and Qu 2006) describing these results in more detail.

Alex Hall and Xin Qu make up the team that performed this research. We are grateful to the PCMDI IPCC data archive, which facilitated access to model data.