Pareto-optimal estimates of California precipitation change.
Baird Langenbrunner and J. D. Neelin, 2017
Geophys. Res. Lett., 44, 12,436-12,446.
Paper (5.5 MB).
Abstract
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n seeking constraints on global climate model projections under global warming,
one commonly finds that different subsets of models perform well under different
objective functions, and these trade-offs are difficult to weigh. Here a
multiobjective approach is applied to a large set of subensembles generated from
the Climate Model Intercomparison Project phase 5 ensemble. We use observations
and reanalyses to constrain tropical Pacific sea surface temperatures, upper
level zonal winds in the midlatitude Pacific, and California precipitation. An
evolutionary algorithm identifies the set of Pareto-optimal subensembles across
these three measures, and these subensembles are used to constrain end-of-century
California wet season precipitation change. This methodology narrows the range of
projections throughout California, increasing confidence in estimates of positive
mean precipitation change. Finally, we show how this technique complements and
generalizes emergent constraint approaches for restricting uncertainty in
end-of-century projections within multimodel ensembles using multiple criteria
for observational constraints.
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Citation
Langengrunner, B. and J. D. Neelin, 2017:
Pareto-optimal estimates of California precipitation change.
Geophys. Res. Lett., 44, 12,436-12,446.
Acknowledgments.
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We thank two anonymous reviewers for their comments and feedback on this manuscript.
This work was supported in part by the United States National Science Foundation (NSF)
grant AGS-1540518 and the United States National Oceanic and Atmospheric Administration
(NOAA) grant NA14OAR4310274. We acknowledge the World Climate Research Programme and
thank the climate modeling groups responsible for producing and making available their
output. Original climate model and observational/reanalysis data sets can be downloaded,
respectively, from the Earth System Grid Federation portal (https://esgf-node.llnl.gov/projects/cmip5/)
and the NOAA Earth System Research Laboratory gridded data website (https://www.esrl.noaa.gov/psd/data/gridded/).
Python scripts used for figures/analysis will be made available via the MultiObjective
Optimization of SubEnsembles (MOOSE) repository at https://dept.atmos.ucla.edu/csi/software.
The NCAR Command Language version 6.3.0 (https://doi.org/10.5065/D6WD3XH5) was used to
interpolate all data sets to a common grid. Plots were generated using Matplotlib, and
maps were created using the Basemap Toolkit.
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An edited version of this paper was published by AGU. © Copyright (2017) American Geophysical Union.
To view the published open abstract, go to http://onlinelibrary.wiley.com/doi/10.1002/2017GL075226/full.