A Brief Comparison Between North America Regional Climate Change Assessment Program (NARCCAP) Output and Oklahoma Mesonet Observations: Temperature Comparisons

Previous studies show the importance of downscaled model temperature data from the global level to the regional level. For example, people in the agricultural and climate-related decision-making fields need this type of data to make well-informed decisions. However, many of the users are concerned that the downscaled temperature data are not of high enough quality at the regional level to perform the required decision-making. This particular study was motivated by the concern of these users.

The research involved focuses on comparing regional climate model output from the North American Regional Climate Change Assessment Program (NARCCAP) with observations from the Oklahoma Mesonet. The Oklahoma Climatological Survey along with its partners created the Oklahoma Mesonet, which now provides high-quality, county-level data from 1994 to the present. Global models used for this particular study included the National Centers for Environmental Prediction (NCEP) and the Global Forecast System (GFS). Regional models used for this particular study included the Weather Research and Forecasting (WRF) and the Community Climate System Model (CCSM).

The purpose of this study is to better understand how well different combinations of global and regional climate models in NARCCAP represent the observed gradients and extremes in temperature. Seasonal, monthly, and hourly comparisons were performed among the datasets. No one model was found to completely represent Oklahoma's climate on all time scales; however, each model did have their own strong and weak attributes. Although many previous studies have compared NARCCAP model output to surface observations, this research is the first that compares the regional climate model output with observations from such a rich source of quality assured surface observations. The authors will discuss the key results of this ongoing research.