This study uses two methods for constructing probabilistic time series of changes in physical climate parameters for quantitative assessment of climate change risk, applied to construct joint probability density functions (PDFs) of temperature and precipitation change over the twenty-first century for every county in the United States.
Abstract
Quantitative assessment of climate change risk requires a method for constructing probabilistic time series of changes in physical climate parameters. Here, two such methods, surrogate/model mixed ensemble (SMME) and Monte Carlo pattern/residual (MCPR), are developed and then are applied to construct joint probability density functions (PDFs) of temperature and precipitation change over the twenty-first century for every county in the United States. Both methods produce likely (67% probability) temperature and precipitation projections that are consistent with the Intergovernmental Panel on Climate Change’s interpretation of an equal-weighted Coupled Model Intercomparison Project phase 5 (CMIP5) ensemble but also provide full PDFs that include tail estimates. For example, both methods indicate that, under “Representative Concentration Pathway” 8.5, there is a 5% chance that the contiguous United States could warm by at least 8°C between 1981–2010 and 2080–99. Variance decomposition of SMME and MCPR projections indicates that background variability dominates uncertainty in the early twenty-first century whereas forcing-driven changes emerge in the second half of the twenty-first 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.