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Article Dans Une Revue Journal of Geophysical Research: Atmospheres Année : 2022

Should Multivariate Bias Corrections of Climate Simulations Account for Changes of Rank Correlation Over Time?

Résumé

To investigate the manifold impacts of future climate changes, numerical simulations from Global and Regional Climate Models (GCM and RCM, respectively) remain essential (IPCC WGI, 2021). However, it is now well-known that these simulations can have statistical biases with respect to observational references (e.g., from measurements at weather stations, or reanalyzes). Therefore, using such simulations directly as input in an impact model (e.g., in hydrology or agronomy) is not always relevant without "correcting" those biases (Doblas-Reyes et al., 2021). Various "bias correction" (BC) methods have thus been developed and extensively applied over the last few decades. Such methods transform the initial simulations to make the corrected data more similar to a reference data set in terms of specific statistical criteria such as mean, standard deviation or in terms of probability distribution. The transformation is defined based on calibration data-usually corresponding to references and simulations over a historical period-and is supposed valid for a different period (e.g., the future). It can then be applied to climate projections for a period of interest. Due to its coding facility, its speed of calculation and the fact that it globally preserves the main trends of the simulations (e.g., A.
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Dates et versions

hal-03738510 , version 1 (26-07-2022)

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M. Vrac, S. Thao, Pascal Yiou. Should Multivariate Bias Corrections of Climate Simulations Account for Changes of Rank Correlation Over Time?. Journal of Geophysical Research: Atmospheres, 2022, 127 (14), ⟨10.1029/2022jd036562⟩. ⟨hal-03738510⟩
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