Advanced Statistical-Dynamical Downscaling Methods and Products for California Electricity System Climate Planning
Optimizing physical models of climate to inform climate impacts across California.
The Regents of California, San Diego
Recipient
La Jolla, CA
Recipient Location
38th
Senate District
77th
Assembly District
$1,399,650
Amount Spent
Completed
Project Status
Project Result
The project has ended and the Final Report has been published. The research team has run dynamic regional climate models and explored the use of statistical models for hourly simulations. In the past, climate scenarios for CA only included projections with daily resolution. The research team has made significant progress on key areas such as: development of dataset variables that focus on low clouds, fire weather, and wind generation, with input from other CEC project(s); comprehensive verification of downscaled clouds, wind, and near-surface temperature; integrated hydrologic modeling using machine learning for building statistical models of hydrologic quantities through watersheds; and, merging multiple precipitation products to improve simulated hydrologic fluxes. The project also completed model simulations to replicate historical conditions such as coastal clouds, wind, and humidity.
View Final ReportThe Issue
There are two basic ways to produce climate scenarios for California. One of them involves the use of dynamic regional climate models. These "weather forecast models" are very costly (computationally intensive and computer-resource expensive) to run. The second option is to use statistical methods that use historical relationships with outputs from global climate models to create high resolution climate scenarios for California. This approach is far less expensive than running an entire weather forecast model, but it is unclear if the historical statistical relationships will be valid under future conditions. The researchers are developing and testing a hybrid downscaling technique that merge the benefits of statistical and dynamic models.
Project Innovation
This project develops new and better ways of merging two modeling approaches, using both weather forecast models (more generally called dynamical models) and inferences from past history (statistical models). The combined method is called a hybrid dynamical-statistical approach for inferring fine-resolution climate information from the coarse-resolution global climate models. Ideally, the hybrid approach will be able to capture many of the physical processes simulated by the costly weather forecast models, but with the reduced expense of statistical models. The hybrid approach will be applied to three key areas of California's climate that have important implications for the state's ratepayers: wind, clouds, and hydrology.
Project Goals
Project Benefits
The project includes an extensive quantification (model validation) effort based on data from observed meteorological stations, satellite records of cloudiness compiled by project members, and USGS streamflow and groundwater observations (for the hydrologic modeling). The method under development could be used for the California's Fifth Climate Change Assessment and future energy planning.
Affordability
Knowing how the climate is likely to change provides a sound scientific basis for minimizing economic impacts on the electricity system.
Reliability
The methods to produce high-resolution projections of climate parameters are of great importance for managing the electricity system, in particular for managing peak demand and shifting to a grid reliant on renewable resources.
Safety
This research supports predictive modeling, providing information on how the climate is likely to change, which can be used to limit impacts to residents, infrastructure, and the economy.
Key Project Members
Daniel Cayan
David Pierce
Subrecipients
The Regents of the University of California on behalf of the Riverside campus
Portland State University
Regents of the University of California, Davis
