Statistically downscaled precipitation sensitivity to gridded observation data and downscaling technique

• Published in: International journal of climatology 2021-02, Vol.41 (2), p.980-1001
• Main Authors: Wootten, Adrienne M., Dixon, Keith W., Adams‐Smith, Dennis J., McPherson, Renee A.
• Format: Article
• Language: English
• Subjects: Assessments; Climate change; climate model evaluation; Climate models; climate projections; Climate system; Daily precipitation; Data; downscaling; Dry periods; Environmental assessment; Extreme weather; Future climates; Global climate; Global climate models; Hydrologic data; Illustrations; impact assessments; Precipitation; Sensitivity analysis; Statistical analysis; Symptoms
• ISSN: 0899-8418; 1097-0088
• DOI: 10.1002/joc.6716

Future climate projections illuminate our understanding of the climate system and generate data products often used in climate impact assessments. Statistical downscaling (SD) is commonly used to address biases in global climate models (GCM) and to translate large‐scale projected changes to the higher spatial resolutions desired for regional and local scale studies. However, downscaled climate projections are sensitive to method configuration and input data source choices made during the downscaling process that can affect a projection's ultimate suitability for particular impact assessments. Quantifying how changes in inputs or parameters affect SD‐generated projections of precipitation is critical for improving these datasets and their use by impacts researchers. Through analysis of a systematically designed set of 18 statistically downscaled future daily precipitation projections for the south‐central United States, this study aims to improve the guidance available to impacts researchers. Two statistical processing techniques are examined: a ratio delta downscaling technique and an equi‐ratio quantile mapping method. The projections are generated using as input results from three GCMs forced with representative concentration pathway (RCP) 8.5 and three gridded observation‐based data products. Sensitivity analyses identify differences in the values of precipitation variables among the projections and the underlying reasons for the differences.Results indicate that differences in how observational station data are converted to gridded daily observational products can markedly affect statistically downscaled future projections of wet‐day frequency, intensity of precipitation extremes, and the length of multi‐day wet and dry periods. The choice of downscaling technique also can affect the climate change signal for variables of interest, in some cases causing change signals to reverse sign. Hence, this study provides illustrations and explanations for some downscaled precipitation projection differences that users may encounter, as well as evidence of symptoms that can affect user decisions. Statistical downscaling techniques are frequently used to translate changes from global climate models to local scales. However, there is no standard practice with respect to downscaling technique or creating the gridded observations used for training. This study demonstrates that multiple precipitation variables are sensitive to the training observations and the downsca