An Alternative Method for Evaluating Climate Forecast

Values added by a regional climate model, compared to a global climate model, are explicitly unveiled by using non-traditional skill evaluation statistics. The conventional model evaluation methods, such as temporal correlation of seasonal average rainfall, cannot explain the values of dynamically downscaled data. One of our primary metrics for evaluating the downscaling methodologies is comparing the variation of crop yields simulated using the downscaled data, assuming non-irrigated conditions, to yields simulated using observations. The hidden values of the regional model are better uncovered by using a crop model as a forecast evaluation tool because the crop yield data include the high-frequency variability of seasonal climate (e.g., dry/wet spell sequences). In addition, the values of the regional model were also exposed by high frequency statistics, e.g., the time series of accumulated rainfall and Lawn-and-Garden Moisture Index, which are derived from rainfall data only.