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Publication Date
30 September 2023

Downscaling and bias-correction contribute considerable uncertainty to local climate projections in CMIP6



Efforts to diagnose the risks of a changing climate often rely on downscaled and bias-corrected climate information, making it important to understand the uncertainties and potential biases of this approach. Here, we perform a variance decomposition to partition uncertainty in global climate projections and quantify the relative importance of downscaling and bias-correction. We analyze simple climate metrics such as annual temperature and precipitation averages, as well as several indices of climate extremes. We find that downscaling and bias-correction often contribute substantial uncertainty to local decision-relevant climate outcomes, though our results are strongly heterogeneous across space, time, and climate metrics. Our results can provide guidance to impact modelers and decision-makers regarding the uncertainties associated with downscaling and bias-correction when performing local-scale analyses, as neglecting to account for these uncertainties may risk overconfidence relative to the full range of possible climate futures. 

Lafferty, David C., and Ryan L. Sriver. 2023. “Downscaling And Bias-Correction Contribute Considerable Uncertainty To Local Climate Projections In Cmip6”. Npj Climate And Atmospheric Science 6 (1). Springer Science and Business Media LLC. doi:10.1038/s41612-023-00486-0.
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