Evaluation of Precipitation from CMIP5 Models for Western Colorado and Development of a Scenario based Method for Regional Climate Change Planning
Thursday, 17 December 2015: 17:00
3008 (Moscone West)
As the latest generation of climate and Earth system models become more complex , the use of model output to project certain impact relevant indicators for regional climate change adaptation planning is increasingly sought. However, barriers due to skill remain when utilizing this model data to project changes in precipitation over mountainous areas at regional and subregional scales. Complex topography, localized meteorological phenomenon, and other factors are still not well represented by global scale models, which can limit the representation of key impact criteria needed for planning.We explore limitations and opportunities of utilizing precipitation data from Coupled Model Intercomparison Project 5(CMIP5) models to provide use relevant projections of future precipitation conditions in Western Colorado, with a focus on applications relevant to climate information needs in the resort community of Aspen. First, a model skill evaluation is conducted by comparing precipitation and temperature values of selected model ensemble from CMIP5 to observations during a historical period. The comparison is conducted for both temporal and spatial scales, on both yearly and seasonal increments. Results indicate that the models are more skillful at representing temperature than precipitation and that the apparent lack of skill for precipitation warrants caution in the use of such data in climate impacts assessment that serve to inform adaptation planning and preparedness decision making. In light of model evaluation, the authors introduce a scenario based method in which individual models within the CMIP5 ensemble are organized into plausible qualitative futures and individual model runs are selected as representative scenarios by which detailed analysis can then be applied. The results from scenario based method are viewed as useful for exploring regional climate futures in instances when it is not appropriate to utilize data directly from global-scale climate models.