The ensemble of CMIP6 daily predictor variables for statistical downscaling (FR) (French, HTML)

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2022-02-14

One of the ways of obtaining local-scale climate change scenarios is to use regression-based statistical downscaling of GCMs. In this approach, an empirical relationship between GCM predictors (i.e., near-surface and upper-level atmosphere circulation variables) and surface predictands (such as observed temperature or precipitation from a station) is derived by linear or non-linear transfer functions. For this purpose, an ensemble of daily predictor variables are produced from CanESM5, MPI-ESM1.2-HR, NorESM2-MM, and two reanalysis datasets. A total of 26 predictor variables are included in each ensemble, composed of both raw and derived variables, with multiple atmospheric variables available at three different pressure levels. Predictor variables are available at the daily scale on a 64 by 128 latitude-longitude global Gaussian grid with T42 spectral truncation. The historical simulation for 1979-2014 as well as the four Tier 1 Shared Socioeconomic Pathways (SSPs) prioritized by the Intergovernmental Panel on Climate Change (IPCC) and Scenario Model Intercomparison Project (ScenarioMIP) (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) and SSP1-1.9 (due to its relevance for the Paris…

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