Spatial Analog Models of Snow

Empirical modeling of physical processes is essential to the advancement of our scientific understanding and to the improvement of more complex physically based and conceptual models. In the face of climate change, these models must not only be grounded in strong knowledge of the natural world; they must also be transferable to conditions not observed in the historical record. Here we develop empirical spatial analog models of April 1 SWE and Snow Residence Time at SNOTEL sites based on winter temperature and precipitation. We find that spatial analog models capture the nonlinear relationship between the independent and dependent variables, provide very strong fits to the data, and provide additional insight into physical mechanisms.  A non-random cross-validation test indicates that the models transfer well to warmer conditions. Finally, we provide an example of how this simple empirical model can be leveraged to obtain information from more complex global climate models in a straightforward and computationally efficient manner.