Analysis and quantification of uncertainty for climate change decision aids for energy consumption in the southwestern US

Outputs from current generation General Circulation Models (GCMs) are being downscaled to produce primary adaptation products used to investigate the utility of such products for aiding end-user decisions on enduring energy infrastructure as well as short and long-term production and conservation policy alternatives. Effective decision support products carry representations of both objective and subjective uncertainty through to the end-user, allowing them to appropriately weigh climate change factors in with myriad other relevant elements of their decisions. To that end, our methodology compares GCMs and their corresponding downscales with relevant objective historical measurements, systematically evaluates these products with respect to common baseline datasets, and then applies statistical bias correction and uncertainty analysis to establish objective confidence intervals. These primary products are then used to drive empirical energy consumption models fitted to end-user supplied energy consumption records, and undergo additional data quality control and model parameter uncertainty analysis. Our results show a high degree of fit for natural gas consumption involved in facility heating, a lesser degree for electrical power consumption for both heating and cooling, and illustrate a climate-change signal from the European Center/Hamburg Model (ECHAM5) GCM constrained Weather Research and Forecasting (WRF) runs for a parallel current period of 1999-2009, and a future period for 2029-2039; along with confidence bounds on monthly and cumulative annual changes base on these statistical analyses expressed as changes in millions of cubic feet of natural gas and megawatt-hours of electrical energy. Results indicate that our bias corrected energy products provide the necessary detailed output and uncertainty bounds required by public and private facility planners to support and develop their long term energy strategies.