• Energy Research

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.

The gridded climate surfaces for Honduras (30-year average) were developed from weather station observations from different official sources (local, national and regional institutions) for the period 1981 to 2010 (latest period defined by WMO to calculate the climatological standard normal), at 30-seconds (1 Km2) spatial resolution, for monthly precipitation (prec), monthly minimum temperature (tmin), maximum temperature (tmax), mean temperature (tmean) and diurnal temperature range (dtr). In addition, the seasonal and annual surfaces were derived based on the monthly datasets. For the spatialization (interpolation), we followed the method described by Hijmans et al. (Hijmans et al., 2005),using as inputs the climatic normal for all weather stations after the quality control data and fill gaps processes. In areas with low station density weather observations were complemented with pseudo-stations from TerraClimate (Abatzoglou et al., 2018) for temperature and CHIRPS (Funk et al., 2015) for precipitation. The dataset is part of work carried out by CIAT in the generation of the climate change scenarios for Honduras for the Third National Communication to the UNFCCC.

In order to characterize the historical climate for the Western Honduras region, it was developed monthly surfaces by years through spatial interpolation and available records of weather stations. The interpolated surfaces were generated at 1-km of spatial resolution (30 arc-seconds) for monthly precipitation (1981-2015), and minimum and maximum temperature (1990-2014). It was followed the method described by Hijmans et al. (2005), using data from: (1) the DGRH (General Direction of Water Resources of the Honduran Ministry of Natural Resources); (2) the National Oceanic and Atmospheric Administration (NOAA), including data from the Global Historical Climatology Network (GHCN) and the Global Surface Summary of the Day (GSOD); and (3) the ENEE (National Electric Power Company of Honduras). In some areas with low weather station density, it was added pseudo-stations from CFSR (Climate Forecast System Reanalysis) for temperature (Ruane et al., 2015) and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station; Funk et al., 2015) for precipitation. <br> For future climates, it was performed a statistical downscaling (delta method or change factor) process based on the sum of the anomalies of GCMs (General Circulation Models), to the high resolution baseline surface (the 20-yr normal) at monthly scale (Ramirez & Jarvis, 2010). It was used data from ~20 GCMs from the IPCC AR5 (CMIP5 Archive) run across two Representative Concentration Pathways (RCP 2.6 and 8.5), for the reported IPCC future 20-year periods (IPCC, 2013): 2026-2045 (2030s) and 2046-2065 (2050s).

Future climate change scenarios for Honduras were developed by downscaling CMIP5 projections from 18 General Circulation Models (GCMs) for four Representative Concentration Pathways (RCP’s; RCP 2.6, 4.5, 6.0, and 8.5; IPCC, 2013) and three future periods named as 2030s (Climatic normal –CN- for 2021 to 2050), 2050s (CN for 2041-2070) and 2080s (CN for 2071 to 2100). <br> <br> The future periods were selected by PNUD and MiAmbiente in Honduras in order to get climatic information for the decision making processes around the climate change in the short, medium and large terms. We follow the delta method downscaling described in Ramírez-Villegas and Jarvis (2010). We developed surfaces at 30-seconds (1 Km2) of spatial resolution, for monthly precipitation (prec), monthly minimum temperature (tmin), maximum temperature (tmax), mean temperature (tmean), diurnal temperature range (dtr), solar radiation (rsds) and wind speed mean (wsmean). <br> <br> We make available three types of data: <br> <br> <ul> <li> Downscaled future scenarios for Honduras for each of the 18 GCM. </li> <li> Downscaled future scenarios for Honduras for the ensemble (average) of all GCM. </li> <li> Anomalies or climatic changes for future for Honduras for an ensemble (average) of all GCMs available. </li> </ul> The data is part of work carried out by CIAT in the generation of the climate change scenarios for Honduras for the Third National Communication to the UNFCCC.

Significance Coffee production supports the livelihoods of millions of smallholder farmers around the world, and bees provide coffee farms with pollination. Climate change will modify coffee and bee distributions, and thus coffee production. We modeled impacts for the largest coffee-growing region, Latin America, under global warming scenarios. Although we found reduced coffee suitability and bee species diversity for more than one-third of the future coffee-suitable areas, all future coffee-suitable areas will potentially host at least five bee species, indicating continued pollination services. Bee diversity also can be expected to offset farmers’ losses from reduced coffee suitability. In other areas, bee diversity losses offset increased coffee suitability. Our results highlight the need for responsive management strategies tailored to bee pollination, coffee suitability, and potential coupled effects.

AbstractProjections of climate change are available at coarse scales (70–400 km). But agricultural and species models typically require finer scale climate data to model climate change impacts. Here, we present a global database of future climates developed by applying the delta method –a method for climate model bias correction. We performed a technical evaluation of the bias-correction method using a ‘perfect sibling’ framework and show that it reduces climate model bias by 50–70%. The data include monthly maximum and minimum temperatures and monthly total precipitation, and a set of bioclimatic indices, and can be used for assessing impacts of climate change on agriculture and biodiversity. The data are publicly available in the World Data Center for Climate (WDCC; cera-www.dkrz.de), as well as in the CCAFS-Climate data portal (http://ccafs-climate.org). The database has been used up to date in more than 350 studies of ecosystem and agricultural impact assessment.

We used 500m gridded historical and future climate surfaces for Risaralda, Colombia and coffee presences and absences to train species distribution models (suitability). Five methods were used: Generalized Boosting Model (GBM) (Friedman, 2001), Random Forest (RF) (Breiman, 2001), Maxent (Phillips et al., 2006), Generalized Linear Model (GLM) and Generalized Additive Model (GAM) (Guisan et al., 2002).

CASCADE project “Ecosystem-based Adaptation for smallholder Subsistence and Coffee Farming Communities in Central America”