Fine-Scale Seasonal Climate Prediction


Figure 1. The framework for fine-scale seasonal climate prediction
Figure 1. The framework for fine-scale seasonal climate prediction

Global seasonal climate predictions at about 100~200 km resolution issued by national climate centers provide reliable perspectives of the general circulation about six months in advance. Such forecasts, however, lack of the fine scale details that are critical to regional and local climate-sensitive business and decision-makers. To fill that need, we are developing a fine-scale seasonal climate prediction capability through dynamical downscaling. A framework for fine-scale seasonal climate prediction has been set up. In this framework, the global large-scale seasonal forecasts issued by the Weather Service’s Climate Forecast System (CFS) are applied to force the Weather Research and Forecasting (WRF) model. The version of the WRF model we use has been specially customized and configured for climate purpose.  Both deterministic and ensemble predictions can be performed. Techniques from Artificial Intelligence such as Principal Component Analysis and Self Organizing Map analysis are used to extract the relevant climate information.

Recent Accomplishments

The fine-scale seasonal climate prediction framework has been applied to the Jack Rabbit II field Campaign at Dugway Proving Ground in Utah. A 3-month global climate forecast was downscaled down to the kilometer and subsequently verified with the local observations collected during the field campaign. The verification results show some predictive skills in the method.


An algorithm to correct biases in the global climate forecasts will be devised. Bias-reduced global forecasts are expected to dramatically improve the fine-scale prediction. In addition, we plan to evaluate the benefit of downscaling the other 8 members of the CFS ensemble system. The downscaled ensemble has the potential to provide probabilistic prediction and, thus, a characterization of the uncertainty associated with the produced fine scale climate information.