2015/10/01 by D. J. Rasmussen, Malte Meinshausen, Rasmussen, D. J. +3 · 1 citation
Earth and Planetary Sciences · Environmental Science · Mathematics · Physics and Astronomy · #Algorithm #Atmospheric and Oceanic Physics (physics.ao-ph) #Climate change #Climate model #Climate variability and models #Climatology #Coupled model intercomparison project #Econometrics #Environmental science #FOS: Physical sciences #Forcing (mathematics) #GCM transcription factors #General Circulation Model #Geography #Geology #Hydrology and Drought Analysis #Mathematics #Meteorological Phenomena and Simulations #Meteorology #Monte Carlo method #Precipitation #Probabilistic logic #Probability density function #Representative Concentration Pathways #Residual #Statistics #Variance (accounting) #physics.ao-ph
paper · pdf · doi:10.48550/arxiv.1510.00313
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2015/10/01 · arxiv created 2015/10/05 · arxiv updated 2015/10/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.