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High-resolution monthly precipitation and temperature time series from 2006 to 2100

Dirk Nikolaus Karger, Dirk R. Schmatz, Gabriel Dettling, Niklaus E. Zimmermann

2020Scientific Data162 citationsDOIOpen Access PDF

Abstract

Predicting future climatic conditions at high spatial resolution is essential for many applications and impact studies in science. Here, we present monthly time series data on precipitation, minimum- and maximum temperature for four downscaled global circulation models. We used model output statistics in combination with mechanistic downscaling (the CHELSA algorithm) to calculate mean monthly maximum and minimum temperatures, as well as monthly precipitation at ~5 km spatial resolution globally for the years 2006-2100. We validated the performance of the downscaling algorithm by comparing model output with the observed climate of the historical period 1950-1969.

Topics & Concepts

DownscalingPrecipitationEnvironmental scienceClimatologySeries (stratigraphy)General Circulation ModelClimate changeMean radiant temperatureTime seriesClimate modelSpatial ecologyTemporal resolutionMeteorologyRepresentative Concentration PathwaysAtmospheric sciencesSpatial variabilityQuantitative precipitation estimationPeriod (music)Maximum temperatureCoupled model intercomparison projectHigh resolutionTrend analysisModel output statisticsImage resolutionBaseline (sea)Climate variability and modelsCryospheric studies and observationsMeteorological Phenomena and Simulations
High-resolution monthly precipitation and temperature time series from 2006 to 2100 | Litcius