Probability-weighted ensembles of U.S. county-level climate projections for climate risk analysis: Monte Carlo Pattern/Residual (MCPR) data set RCP45

Quantitative 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. ## Citation 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 climaterisk 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