TY - JOUR
T1 - Downscaling and bias-correction contribute considerable uncertainty to local climate projections in CMIP6
AU - Lafferty, David C.
AU - Sriver, Ryan L.
N1 - This work was supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research Program, Earth and Environmental Systems Modeling, MultiSector Dynamics under Cooperative Agreement DE-SC0022141. Computations for this research were performed on the Pennsylvania State University’s Institute for Computational and Data Sciences’ Roar supercomputer. Computations for this research were performed on Microsoft Planetary Computer. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies. The authors are grateful to the participants of the Climate Discussion Group at the University of Illinois for useful feedback. The authors thank Carina Lansing and Casey Burleyson for assistance in developing and deploying the interactive dashboard associated with this paper. The dashboard leverages the capabilities of the MultiSector Dynamics—Living, Intuitive, Value-adding, Environment (MSD-LIVE) platform funded by the U.S. Department of Energy, Office of Science. MSD-LIVE is developed and maintained by Pacific Northwest National Laboratory. The authors are grateful to three anonymous reviewers for their helpful comments and to the editor for handling this manuscript.
This work was supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research Program, Earth and Environmental Systems Modeling, MultiSector Dynamics under Cooperative Agreement DE-SC0022141. Computations for this research were performed on the Pennsylvania State University’s Institute for Computational and Data Sciences’ Roar supercomputer. Computations for this research were performed on Microsoft Planetary Computer92. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies. The authors are grateful to the participants of the Climate Discussion Group at the University of Illinois for useful feedback. The authors thank Carina Lansing and Casey Burleyson for assistance in developing and deploying the interactive dashboard associated with this paper. The dashboard leverages the capabilities of the MultiSector Dynamics—Living, Intuitive, Value-adding, Environment (MSD-LIVE) platform funded by the U.S. Department of Energy, Office of Science. MSD-LIVE is developed and maintained by Pacific Northwest National Laboratory. The authors are grateful to three anonymous reviewers for their helpful comments and to the editor for handling this manuscript.
PY - 2023/12
Y1 - 2023/12
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85173476171
UR - https://www.scopus.com/pages/publications/85173476171#tab=citedBy
U2 - 10.1038/s41612-023-00486-0
DO - 10.1038/s41612-023-00486-0
M3 - Article
AN - SCOPUS:85173476171
SN - 2397-3722
VL - 6
JO - npj Climate and Atmospheric Science
JF - npj Climate and Atmospheric Science
IS - 1
M1 - 158
ER -