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A framework for investigating large-scale patterns as an alternative to precipitation for downscaling to local drought

Towler, Erin, PaiMazumder, Debasish, Holland, Greg
Climate dynamics 2017 v.48 no.3-4 pp. 881-892
case studies, climate, climate models, drought, drying, prediction, statistical analysis, statistical models, temperature, variance, Oklahoma
Global Climate Model (GCM) projections suggest that drought will increase across large areas of the globe, but lack skill at simulating climate variations at local-scales where adaptation decisions are made. As such, GCMs are often downscaled using statistical methods. This study develops a 3-step framework to assess the use of large-scale environmental patterns to assess local precipitation in statistically downscaling to local drought. In Step 1, two statistical downscaling models are developed: one based on temperature and precipitation and another based on temperature and a large-scale predictor that serves as a proxy for precipitation. A key component is identifying the large-scale predictor, which is customized for the location of interest. In Step 2, the statistical models are evaluated using NCEP/NCAR Reanalysis data. In Step 3, we apply a large ensemble of future GCM projections to the statistical models. The technique is demonstrated for predicting drought, as measured by the Palmer Drought Severity Index, in South-central Oklahoma, but the framework is general and applicable to other locations. Case study results using the Reanalysis show that the large-scale predictor explains slightly more variance than precipitation when predicting local drought. Applying future GCM projections to both statistical models indicates similar drying trends, but demonstrates notable internal variability. The case study demonstrates: (1) where a large-scale predictor performs comparably (or better) than precipitation directly, then it is an appealing predictor choice to use with future projections, (2) when statistically downscaling to local scales, it is critical to consider internal variability, as it may be more important than predictor selection.