Striking stationarity of large-scale climate model bias patterns under strong climate change

Because all climate models exhibit biases, their use for assessing future climate change requires implicitly assuming or explicitly postulating that the biases are stationary or vary predictably. This hypothesis, however, has not been, and cannot be, tested directly. This work shows that under very...

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Published inProceedings of the National Academy of Sciences - PNAS Vol. 115; no. 38; pp. 9462 - 9466
Main Authors Krinner, Gerhard, Flanner, Mark G.
Format Journal Article
LanguageEnglish
Published United States National Academy of Sciences 18.09.2018
Subjects
Bias
Climate
Climate change
Climate models
Environmental assessment
Ocean, Atmosphere
Perturbation methods
Physical Sciences
Sciences of the Universe
climate modeling
model biases
climate change
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Summary:Because all climate models exhibit biases, their use for assessing future climate change requires implicitly assuming or explicitly postulating that the biases are stationary or vary predictably. This hypothesis, however, has not been, and cannot be, tested directly. This work shows that under very large climate change the bias patterns of key climate variables exhibit a striking degree of stationarity. Using only correlation with a model’s preindustrial bias pattern, a model’s 4xCO₂ bias pattern is objectively and correctly identified among a large model ensemble in almost all cases. This outcome would be exceedingly improbable if bias patterns were independent of climate state. A similar result is also found for bias patterns in two historical periods. This provides compelling and heretofore missing justification for using such models to quantify climate perturbation patterns and for selecting well-performing models for regional downscaling. Furthermore, it opens the way to extending bias corrections to perturbed states, substantially broadening the range of justified applications of climate models.
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PMCID: PMC6156650
Edited by Dennis L. Hartmann, University of Washington, Seattle, WA, and approved July 20, 2018 (received for review May 7, 2018)
Author contributions: G.K. designed research; G.K. and M.G.F. performed research; G.K. and M.G.F. analyzed data; and G.K. and M.G.F. wrote the paper.
2Present addresses: Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, BC V8W 2Y2, Canada; and School of Earth and Ocean Sciences, University of Victoria, Victoria, BC V8W 2Y2, Canada.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1807912115