High-Resolution Climate Change Projections Capture the Elevation Dependence of Warming and Snow Cover Loss in California’s Sierra Nevada

Abstract:

High-resolution projections of warming and snow cover change are made for California’s Sierra Nevada mountain range for the period 2081-2100 using hybrid dynamical-statistical downscaling. First, future climate change projections from five global climate models (GCMs) are downscaled dynamically. The warming signal exhibits a strong elevation dependence that is not captured by common statistical downscaling methods. Variations in the warming are attributed to snow albedo feedback and the blocking effect of the Sierra Nevada, which creates a sharp warming gradient between marine and continental air masses. These two physical processes are incorporated into a simple statistical model that mimics the dynamical model’s warming patterns given GCM input. This statistical model is used to produce warming and snow cover loss projections for an ensemble of 35 GCMs. Capturing the elevation dependence is important for many applications of climate change, including surface hydrology, water resources, and ecosystems.