ALBERT

Description: ABSTRACT Monthly total precipitation and mean temperature climate surfaces, gridded to 30-arcseconds (≈1 km at the equator) and available for all global land areas, are presented. These datasets are generated with a Delta downscaling method, using the 30-arcsecond WorldClim climatologies to scale monthly anomaly grids. For monthly mean temperature, the anomalies are constructed from both the Climate Research Unit (CRU) and Willmott & Matsuura (W&M) 0.5 degree time-series datasets, whereas for monthly precipitation Global Precipitation Climatology Centre (GPCC) data are also used. The 0.5 degree anomalies are then interpolated to the 30-arcsecond resolution. Use of piecewise cubic Hermite interpolating polynomials (PCHIP) to interpolate the anomaly grids results in more physically representative Delta downscaled surfaces, compared to bilinear and cubic spline interpolation. The Delta downscaled products are compared to Global Historical Climatology Network (GHCN) station records for six test regions distributed globally. In this analysis, the Delta grids produced using the W&M time-series dataset perform better than grids produced using GPCC or CRU. Using Oregon, USA as a test region, the Delta downscaled datasets are compared to the Parameter-elevation Regressions on Independent Slopes Model (PRISM) datasets. For monthly precipitation, PRISM performs better than each of the three Delta downscaled datasets, but for mean temperature both Delta downscaled datasets outperform PRISM. Through computing the Pearson product–moment correlation coefficient between GHCN station delineated errors in the WorldClim climatologies and the Delta downscaled W&M data, it is shown that performance of the Delta grids corresponds strongly to performance of the reference climatologies. Therefore, future improvement of the 30-arcsecond Delta grids described in this article is strongly tied to advances in the high-resolution climatological data for all global land surfaces. The Delta downscaled datasets discussed herein are open-source and freely distributed at http://www.globalclimatedata.org .