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Probability-weighted ensembles of U.S. county-level climate projections for climate risk analysis: Surrogate/Model Mixed Ensemble (SMME) data set RCP45
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Probability-weighted ensembles of U.S. county-level climate projections for climate risk analysis: Surrogate/Model Mixed Ensemble (SMME) data set RCP45. Retrieved from https://doi.org/doi:10.7282/T3RX9FFV

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TitleProbability-weighted ensembles of U.S. county-level climate projections for climate risk analysis: Surrogate/Model Mixed Ensemble (SMME) data set RCP45
Research genreResearch data
Type of itemDataset
Creator(s)Rasmussen, D. J.; Meinshausen, Malte; Kopp, Robert E.
Date(s) of creation2016
Extent798 files
Abstract or summaryQuantitative assessment of climate change risk requires a method for constructing probabilistic time series of changes in physical climate parameters. Here, we develop two such methods, Surrogate/Model Mixed Ensemble (SMME) and Monte Carlo Pattern/Residual (MCPR), and apply them to construct joint probability density functions (PDFs) of temperature and precipitation change over the 21st century for every county in the United States. Both methods produce likely (67% probability) temperature and precipitation projections consistent with the Intergovernmental Panel on Climate Change's interpretation of an equal-weighted Coupled Model Intercomparison Project 5 (CMIP5) ensemble, but also provide full PDFs that include tail estimates. For example, both methods indicate that, under representative concentration pathway (RCP) 8.5, there is a 5% chance that the contiguous United States could warm by at least 8°C. Variance decomposition of SMME and MCPR projections indicate that background variability dominates uncertainty in the early 21st century, while forcing-driven changes emerge in the second half of the 21st century. By separating CMIP5 projections into unforced and forced components using linear regression, these methods generate estimates of unforced variability from existing CMIP5 projections without requiring the computationally expensive use of multiple realizations of a single GCM.


This data set is intended to accompany these studies:

(1) T. Houser, R.E. Kopp, S.M. Hsiang, M. Delgado, A.S. Jina, K. Larsen,
M. Mastrandrea, S. Mohan, R. Muir-Wood, D.J. Rasmussen, J. Rising,
and P. Wilson. (2015). American Climate Prospectus: Economic Risks
in the United States. Columbia University Press. ISBN: 978-0231174565

(2) D. J. Rasmussen, M. Meinshausen, and R. E. Kopp. (2016). Probability-
weighted ensembles of U.S. county-level climate projections for climate
risk analysis. Journal of Applied Meteorology and Climatology. DOI: 10.1175/JAMC-D-15-0302.1

Please cite these works when using any results generated with these projections.

Copyright (C) 2016 by ROBERT E. KOPP AND RHODIUM GROUP LLC

This dataset is made available under a non-Commercial Creative Commons License.
https://creativecommons.org/licenses/by-nc/3.0/us/

You are free to:
1. Share: copy and redistribute the material in any medium or format
2. Remix, transform, and build upon the material

You must:
1. Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
2. You may not use the material for commercial purposes.

No warranties are given.
Funder or SponsorRutgers University
Research DomainScience: Earth and Planetary sciences
SubjectsClimatic changes--United States: Ocean-atmosphere interaction--Mathematical models: Climatic changes--Risk assessment: Atmospheric temperature--Mathematical models: Precipitation (Meteorology)--Mathematical models: Humidity--Mathematical models
Rights statementCopyright for research resources published in RUcore is retained by the copyright holder. By virtue of its appearance in this open access medium, you are free to use this resource, with proper attribution, in educational and other non-commercial settings. Other uses, such as reproduction or republication, may require the permission of the copyright holder.
Citation URLhttps://doi.org/doi:10.7282/T3RX9FFV
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