Estimating daily meteorological data and downscaling climate models over landscapes. (October 2018)

• Record Type: Journal Article
• Authors: De Cáceres, Miquel; Martin-StPaul, Nicolas; Turco, Marco; Cabon, Antoine; Granda, Victor
• Is Part Of: Environmental modelling & software. Volume 108(2018)
• Page Start: 186
• Page End: 196
• Publication Date: 2018-10
• Subjects: Bias correction -- Climate change -- Drought stress -- Statistical downscaling -- Regional climate model -- Weather interpolation; CDF Cumulative distribution function -- GCM Global climate model -- MAE Mean average error -- MAP Mean annual precipitation -- RCM Regional climate model; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.08.003
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: High-resolution meteorological data are necessary to understand and predict climate-driven impacts on the structure and function of terrestrial ecosystems. However, the spatial resolution of climate reanalysis data and climate model outputs is often too coarse for studies at local/landscape scales. Additionally, climate model projections usually contain important biases, requiring the application of statistical corrections. Here we present 'meteoland', an R package that integrates several tools to facilitate the estimation of daily weather over landscapes, both under current and future conditions. The package contains functions: (1) to interpolate daily weather including topographic effects; and (2) to correct the biases of a given weather series (e.g., climate model outputs). We illustrate and validate the functions of the package using weather station data from Catalonia (NE Spain), re-analysis data and climate model outputs for a specific county. We conclude with a discussion of current limitations and potential improvements of the package. Highlights: High-resolution under current and future conditions data are needed to predict climate-driven impacts on ecosystems. We present 'meteoland', an R package to estimate daily weather over landscapes. The package provides an integrated interface to obtain suitable fine-scale inputs for climate impact studies. We illustrate the use of the package for interpolation and correction/downscaling of climate model outputs.