Uncertainty in the Representation of Climate Extremes Across Downscaled and Bias-Corrected CMIP Model Ensembles David Lafferty & Ryan Sriver University of Illinois
D. Lafferty (University of Illinois) 1 Managing local climate risks requires high-resolution, accurate climate information. Downscaled and bias-corrected model outputs are often used for this purpose. Downscaling & bias-correction can contribute considerable uncertainty to local climate projections.
How much uncertainty in local climate projections arises from downscaling & bias-correction? Downscaling and bias-correction are important sources of uncertainty: • in the near-term (early-to-mid 21st century) • in projections of precipitation • in projections of extremes 2 D. Lafferty (University of Illinois)
D. Lafferty (University of Illinois) 3 We include all* global, publicly available, downscaled and bias-corrected CMIP6 outputs Ensemble # GCMs Spatial resolution Algorithm Training dataset Reference NEX-GDDP 22 1/4° BCSD GMFD (1960-2014) Thrasher, B., Wang, W., Michaelis, A. et al. NASA Global Daily Downscaled Projections, CMIP6. Sci Data 9, 262 (2022). CIL-GDPCIR 17 1/4° QDM + QPLAD ERA5 (1995-2014) Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo ISIMIP3b 10 1/2° ISIMIP3BASD W5E5 v2.0 (1979-2019) Lange, S.: Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0), Geoscientific Model Development, 12, 3055–3070,. carbonplan GARD-SV 4 1/4° Generalized Analog Regression ERA5 (1981-2010) O Chegwidden, R Hagen, K Martin, M Jones, A Banihirwe, C Chiao, S Frank, J Freeman, J Hamman (2022) “Open data and tools for multiple methods of global climate downscaling" CarbonPlan. https://carbonplan.org/research/cmip6-downscaling- explainer carbonplan DeepSD-BC 2 1/4° SRCNN *that we are aware of
D. Lafferty (University of Illinois) 4 Parent CMIP6 models Downscaling & bias-correction methods SSP scenarios Model uncertainty: variance across models, averaged over SSPs and downscaling methods We employ a simple variance-decomposition approach to partition uncertainty
D. Lafferty (University of Illinois) 5 Downscaling & bias-correction methods SSP scenarios Model uncertainty: variance across models, averaged over SSPs and downscaling methods We employ a simple variance-decomposition approach to partition uncertainty
D. Lafferty (University of Illinois) 6 Model uncertainty: variance across models, averaged over SSPs and downscaling methods We employ a simple variance-decomposition approach to partition uncertainty
We employ a simple variance-decomposition approach to partition uncertainty D. Lafferty (University of Illinois) 7 Parent CMIP6 models Downscaling & bias-correction methods SSP scenarios Model uncertainty: variance across models, averaged over SSPs and downscaling methods Downscaling uncertainty: variance across downscaling methods, averaged over SSPs and models Scenario uncertainty: variance across SSPs of the multi-model, multi-downscaling method mean
We separate inter-annual variability from the forced response • Forced response is extracted via a 4th order polynomial fit • Inter-annual variability is characterized as the average magnitude of the residuals D. Lafferty (University of Illinois) 8 o Hawkins, E. & Sutton, R. The Potential to Narrow Uncertainty in Regional Climate Predictions. B Am Meteorol Soc 90, 1095–1107 (2009). o Hawkins, E. & Sutton, R. The potential to narrow uncertainty in projections of regional precipitation change. Clim Dyn 37, 407–418 (2011). o Lehner, F. et al. Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6. Earth Syst Dynam 11, 491–508 (2020).
Downscaling and bias-correction are important sources of uncertainty: • in the near-term (early-to-mid 21st century) • in projections of precipitation • in projections of extremes 9 D. Lafferty (University of Illinois)
D. Lafferty (University of Illinois) 10 Downscaling and bias-correction are important in the near term Near-term (2020s) Medium-term (2050s) Long-term (2080s) Annual average temperature Scenario uncertainty Model uncertainty Downscaling uncertainty Inter-annual variability
Downscaling and bias-correction are important in projections of precipitation D. Lafferty (University of Illinois) 11 Near-term (2020s) Medium-term (2050s) Long-term (2080s) Annual average precipitation Scenario uncertainty Model uncertainty Downscaling uncertainty Inter-annual variability
Downscaling and bias-correction are important in projections of extremes D. Lafferty (University of Illinois) 12 Near-term (2020s) Medium-term (2050s) Long-term (2080s) Annual maximum temperature (tasmax) Scenario uncertainty Model uncertainty Downscaling uncertainty Inter-annual variability
Downscaling and bias-correction are important in projections of extremes D. Lafferty (University of Illinois) 13 Near-term (2020s) Medium-term (2050s) Long-term (2080s) Annual maximum 1-day precipitation Scenario uncertainty Model uncertainty Downscaling uncertainty Inter-annual variability
D. Lafferty (University of Illinois) 14 Downscaling and bias-correction are important sources of uncertainty: • in the near-term (early-to-mid 21st century) • in projections of precipitation • in projections of extremes In ongoing work, we are analyzing: • multivariate extremes • spatially-compounding extremes • spatial patterns of the uncertainty decomposition [email protected] @DavidCLafferty
Downscaling and bias-correction are important in regions of complex terrain D. Lafferty (University of Illinois) 19 Iturbide, M. et al. An update of IPCC climate reference regions for subcontinental analysis of climate model data: definition and aggregated datasets. Earth Syst Sci Data 12, 2959–2970 (2020).
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