Advanced Statistical-Dynamical Downscaling Methods and Products for California Electricity System Climate Planning

Global climate models provide important information regarding projected climate change, but they do not provide the necessary fine-scale data needed to fully evaluate and prepare for extreme or impactful events. Therefore, climate projections from the global climate models need to be translated to time-scales, and regional or local spatial scales, relevant to California’s electricity sector. California’s complex topography and hydrological features necessitate higher resolution depictions to illuminate local and regional trends and ultimately to support adaptation planning. This report describes the development of modeling techniques applied to generate high-resolution projections of California’s historical and future weather, climate, and hydrologic phenomena. The report addresses the following elements: statistical and dynamical downscaling of meteorological variables; hybrid downscaling techniques, using both statistical and dynamical methods to obtain more accurate downscaling results than would otherwise be possible, hydrological modeling, including surface and groundwater components, and more detailed groundwater modeling of selected aquifers. The report also describes complementary elements that either inform or stem from these downscaling and hydrologic modeling approaches. These include observed datasets that are used for model validation and analysis, and an assessment of variables of interest to California decision makers that can be expected from dynamical and statistical downscaling. Finally, lessons learned, including opportunities and challenges, are also summarized.