A meteorological forcing data set for global crop modeling: Development, evaluation, and intercomparison

Abstract

AbstractThe Global Risk Assessment toward Stable Production of Food (GRASP) project uses global crop models to evaluate the impacts on global food security by changes in climate extremes, water resources, and land use. Such models require meteorological forcing data. This study presents the development of the GRASP forcing data that is a hybrid of the reanalyses (ERA‐40 and JRA‐25) and observations. The GRASP data offer daily mean, maximum, and minimum 2 m air temperatures as well as precipitation, solar radiation, vapor pressure, and 10 m wind speed over global land areas, excluding Antarctica, for the period 1961–2010 at a grid size of 1.125°. The monthly climatologies of the variables of the GRASP data were forced to be close to those of the observations for the baseline period (1961–1990 or 1983–2005) through bias corrections. The GRASP data are intercompared with other forcing data for land surface modeling (the S06, WATCH Forcing Data, and WATCH Forcing Data Methodology Applied to ERA‐Interim data). The results demonstrate that the daily minimum temperature, diurnal temperature range, vapor pressure, solar radiation, and wind speed from the GRASP data are more valuable for crop modeling than the reanalyses and other forcing data. For remaining variables, the reliability of the GRASP data is higher than that of the reanalyses and on a similar level with that of the other forcing data. The GRASP data offer accurate estimates of daily weather as the inputs for crop models, providing unique opportunities to link historical changes in climate with crop production over the last half century.