Probabilistic U.S. county-level climate projections: A new data set for local climate risk analysis

Abstract:

Quantitative estimates of local climate change risk necessitate the development of probabilistic time series of physical climate variables at high spatial resolution. To address this issue, we develop two methods, Surrogate/Model Mixed Ensemble (SMME) and Monte Carlo Pattern/Residual (MCPR), and apply them to construct joint probability density functions (PDFs) of temperature and precipitation change over the 21st century for every county in the United States. Both methods produce likely (67% probability) temperature and precipitation projections consistent with the Intergovernmental Panel on Climate Change's interpretation of an equal-weighted Coupled Model Intercomparison Project 5 (CMIP5) ensemble and also include extreme temperature pathways that, while not present in general circulation model (GCM) ensembles, cannot be ruled out according to climate sensitivity uncertainty estimates. For example, both probabilistic methods indicate that, under representative concentration pathway (RCP) 8.5, there is a 5% chance that Washington, D.C. could warm by at least 7 degrees Celsius by the end of the century, roughly 1 degree warmer that the hottest CMIP5 model projection. Joint PDFs of temperature and precipitation change that attempt to represent uncertainties embedded in modeling future climate at local scales, such as these, are ripe for input into impacts models (e.g. crop, energy, human health) that are used for studying strategies for adaptation and decision making under uncertainty.