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The Influence of Climate Model Biases on Projections of Aridity and Drought

Compiler(s): Darren L. Ficklin, John T. Abatzoglou, Scott M. Robeson, Anna Dufficy
Year Published: 2016

Global climate models (GCMs) have biases when simulating historical climate conditions, which in turn have implications for estimating the hydrological impacts of climate change. This study examines the differences in projected changes of aridity [defined as the ratio of precipitation (P) over potential evapotranspiration (PET), or P/PET] and the Palmer drought severity index (PDSI) between raw and bias-corrected GCM output for the continental United States (CONUS). For historical simulations (1950–79) the raw GCM ensemble median has a positive precipitation bias (+24%) and negative PET bias (−7%) compared to the bias-corrected output when averaged over CONUS with the most acute biases over the interior western United States. While both raw and bias-corrected GCM ensembles project more aridity (lower P/PET) for CONUS in the late twenty-first century (2070–99), relative enhancements in aridity were found for bias-corrected data compared to the raw GCM ensemble owing to positive precipitation and negative PET biases in the raw GCM ensemble. However, the bias-corrected GCM ensemble projects less acute decreases in summer PDSI for the southwestern United States compared to the raw GCM ensemble (from 1 to 2 PDSI units higher), stemming from biases in precipitation amount and seasonality in the raw GCM ensemble. Compared to the raw GCM ensemble, bias-corrected GCM inputs not only correct for systematic errors but also can produce high-resolution projections that are useful for impact analyses. Therefore, changes in hydroclimate metrics often appear considerably different in bias-corrected output compared to raw GCM output.

Citation: Ficklin DL, Abatzoglou JT, Robeson SM, Dufficy A. 2016. The Influence of Climate Model Biases on Projections of Aridity and Drought. Journal of Climate, Online article https://doi.org/10.1175/JCLI-D-15-0439.1
Topic(s): Fire Behavior, Data Evaluation or Data Analysis for Fire Modeling, Weather
Ecosystem(s): None
Document Type: Book or Chapter or Journal Article
NRFSN number: 15624
Record updated: Jun 14, 2018