INSTITUTO ARGENTINO DE NIVOLOGIA, GLACIOLOGIA Y CIENCIAS AMBIENTALES
Unidad Ejecutora - UE
New precipitation and temperature grids for northern Patagonia: Advances in relation to global climate grids
VIALE MAXIMILIANO; BIANCHI EMILIO; COUVREUX FLEUR; VILLALBA RICARDO; MARTICORENA ROCIO
Journal of Meteorological
Año: 2016 vol. 30 p. 38 - 38
Climate data of mean monthly temperature and total monthly precipitation compiled from different sources in northern Patagonia were interpolated to 20-km resolution grids over the period 1997?2010. This northern Patagonian climate grid (NPCG) improves upon previous gridded products in terms of its spatial resolution and number of contributing stations, since it incorporates 218 and 114 precipitation and temperature records, respectively. A geostatistical method using surface elevation from a Digital Elevation Model (DEM) as the ancillary variable was used to interpolate station data into even spaced points. The maps provided by NPCG are consistent with the broad spatial and temporal patterns of the northern Patagonian climate, showing a comprehensive representation of the latitudinal and altitudinal gradients in temperature and precipitation, as well as their related patterns of seasonality and continentality. We compared the performance of NPCG and various other datasets available to the climate community for northern Patagonia. The grids used for the comparison included those of the Global Precipitation Climatology Project, ERAInterim, Climate Research Unit (University of East Anglia), and University of Delaware. Based on three statistics that quantitatively assess the spatial coherence of gridded data against available observations (bias, MAE, and RMSE), NPCG outperforms other global grids. NPCG represents a useful tool for understanding climate variability in northern Patagonia and a valuable input for regional models of hydrological and ecological processes. Its resolution is optimal for validating data from the general circulation models and working with raster data derived from remote sensing, such as vegetation indices.