Composite Meteorological Forcing for Agricultural Adaptation and Sustainability Studies (CFAS) in Global Climate Change

Meteorological datasets are of paramount importance in agricultural adaptation and sustainability studies, particularly when employing global crop models. The seamless integration of historical and forecasting datasets is essential for accurately assessing meteorological extremes and understanding the extensive impacts of global climate change on agriculture. We have meticulously assembled a comprehensive dataset, CFAS, for global crop modeling, featuring daily, 0.5° global reanalysis-forecast data spanning from 1991 to 2019. This dataset harmonizes the Japanese Reanalysis for Three Quarters of a Century (JRA-3Q) with the Japan Meteorological Agency/Meteorological Research Institute Coupled Prediction System Version 3 (CPS3), providing both reanalysis and forecast data. To address initially absent variables—such as radiation, humidity, and wind speed—in CPS3 for the global crop model, we thoughtfully incorporated these by correlating CPS3 variables at the 850 hPa isobaric surface with those from JRA-3Q at the surface level. Additionally, CFAS biases in key components—temperature, precipitation, humidity, radiation, pressure, and wind speed—were carefully adjusted using the bias-adjusted ECMWF Reanalysis Version 5 (WFDE5). In this presentation, we will introduce the distinctive features of CFAS and illustrate its effectiveness through in-depth case studies.