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Spatial, Temporal, and Multivariate Bias in Regional Climate Model Simulations
Author(s) -
Kim Youngil,
Evans Jason P.,
Sharma Ashish,
Rocheta Eytan
Publication year - 2021
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2020gl092058
Subject(s) - downscaling , univariate , multivariate statistics , climate model , general circulation model , spatial dependence , climatology , environmental science , climate change , variable (mathematics) , gcm transcription factors , boundary (topology) , statistics , mathematics , geology , oceanography , mathematical analysis
Correction of atmospheric variables to remove systematic biases in global climate model (GCM) simulations before downscaling offers a means of improving climate simulation accuracy in climate change impact assessments. Various mathematical approaches have been used to correct the lateral and lower boundary conditions of regional climate models (RCMs). Most of these techniques correct only the magnitude of each variable individually over time without regard to spatial and multivariate bias. Here, we investigate how well an RCM is able to reproduce the dependence of an observed variable based on three aspects: temporal, spatial, and multivariate. Results show that the RCM simulations with univariate bias‐corrected GCM boundary conditions perform well in capturing both temporal and spatial dependence. However, all RCM simulations do not show improvement in the representation of dependence between variables, indicating the need for alternatives that correct systematic biases in multivariate dependence in both lateral and lower boundary conditions.