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A Model Validation Strategy to Reduce the Persistent Spread in Projections of Future Climate

A Model Validation Strategy to Reduce the Persistent Spread in Projections of Future Climate

Alex Hall

UCLA

Divergence in simulations of climate feedbacks are sources of significant spread in climate models' temperature response to anthropogenic forcing. Here we map out a strategy for targeted observation of the climate system to reduce divergence in simulations of climate feedbacks, relying on the example of snow albedo feedback. The strength of this feedback in current models exhibits nearly a factor-of-three spread, which in turn accounts for much of the spread in the models' annual-mean temperature response in heavily-populated northern hemisphere land masses. 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, indicating 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. We also discuss the possibility of observational strategies to reduce the spread in other feedbacks contributing to climate sensitivity.