CCAFS-CMIP5 Delta Method Downscaling for monthly averages and bioclimatic indices of four RCPs

We developed a global dataset of downscaled future projections developed by applying a statistical method for climate model downscaling and bias correction. To develop the dataset, we applied the delta method, which comprises the sum of interpolated anomalies of each GCM to the WorldClim 1-km spatia...

Full description

Bibliographic Details
Main Authors: Navarro Racines, Carlos Eduardo, Tarapues Montenegro, Jaime Eduardo, Thornton, Philip K., Jarvis, Andy, Ramírez Villegas, Julián Armando
Format: Conjunto de datos
Language:Inglés
Published: World Data Center for Climate (WDCC) at DKRZ 2019
Online Access:https://hdl.handle.net/10568/100682
Description
Summary:We developed a global dataset of downscaled future projections developed by applying a statistical method for climate model downscaling and bias correction. To develop the dataset, we applied the delta method, which comprises the sum of interpolated anomalies of each GCM to the WorldClim 1-km spatial resolution dataset. The GCMs were the 35 Coupled Model Intercomparison Project Phase 5 (CMIP5) models, for four representative concentrations pathways (RCPs). For each of these, we used the 30-year future periods named as 2030s (mean of 2020-2049), 2050s (2040-2069), 2070s (2060-2089) and 2080s (2070-2099) with three climate variables (mean monthly maximum and minimum temperatures and monthly rainfall). From these, we also derive a set of bioclimatic indices.