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500m gridded historical and future climate surfaces for Risaralda, Colombia

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title 500m gridded historical and future climate surfaces for Risaralda, Colombia
 
Identifier https://doi.org/10.7910/DVN/GIQMCI
 
Creator Navarro Racines, Carlos E.
Vallejo Arango, Eliana
Ramirez Villegas, Julian
 
Publisher Harvard Dataverse
 
Description The gridded yearly climate data (5-year moving averages) were developed from weather station observations (from IDEAM and Cenicafe) for the period 1980 to 2010, at 500 m spatial resolution, for monthly precipitation, monthly minimum temperature and maximum temperature, following the method of Hijmans et al. (2005). In areas with low station density and low interpolaton quality, weather observations were complemented with pseudo-stations from AgMERRA for temperature (Ruane et al., 2015) and CHIRPS (Funk et al., 2015) for precipitation. The results were aggregated into decadal and 30-yr climatology data. Future climate data was developed by downscaling CMIP5 projections from 14 General Circulation Models (GCMs) for all four RCPs (RCP 2.6, 4.5, 6.0 and 8.5; IPCC, 2013) following the method of Ramirez-Villegas and Jarvis (2010). Data were for 2020s and 2030s, which reflect the 10 and 15-year coffee planning cycles. Using the monthly data, we developed climate indicators for modelling following expert knowledge (from Cenicafe) and literature (CENICAFE, 2016) for specific seasons (DJF, MAM, and SON) associated with phenological events: mean temperature range (thermal amplitude), thermal time, mean temperature, temperature seasonality, precipitation seasonality and reference evapotranspiration.
 
Subject Agricultural Sciences
Earth and Environmental Sciences
Downscaling
Agroclimatic indices
Weather stations
Climate change
Precipitation
Maximum temperature
Minimum temperature
Latin America and the Caribbean
Decision and Policy Analysis - DAPA
 
Date 2016
 
Contributor Garcia, Carolina
 
Type Interpolated Data
Aggregate Data
Climate Data