Dynamical Downscaling of SINTEX-F2v CGCM Seasonal Retrospective Austral Summer Forecasts over Australia

Abstract

AbstractAn ensemble of 1-month-lead seasonal retrospective forecasts generated by the Scale Interaction Experiment (SINTEX)?Frontier Research Center for Global Change (FRCGC), version 2 tuned for performance on a vector supercomputer (SINTEX-F2v), coupled global circulation model (CGCM) were downscaled using the Weather Research and Forecasting (WRF) Model to improve the forecast of the austral summer precipitation and 2-m air temperatures over Australia. A set of four experiments was carried out with the WRF Model to improve the forecasts. The first was to drive the WRF Model with the SINTEX-F2v output, and the second was to bias correct the mean component of the SINTEX-F2v forecast and drive the WRF Model with the corrected fields. The other experiments were to use the SINTEX-F2v forecasts and the mean bias-corrected SINTEX-F2v forecasts to drive the WRF Model coupled to a simple mixed layer ocean model. Evaluation of the forecasts revealed the WRF Model driven by bias-corrected SINTEX-F2v forecasts to have a better spatial and temporal representation of forecast precipitation and 2-m air temperature, compared to SINTEX-F2v forecasts. Using a regional coupled model with the bias-corrected SINTEX-F2v forecast as the driver further improved the skill of the precipitation forecasts. The improvement in the WRF Model forecasts is due to better representation of the variables in the bias-corrected SINTEX-F2v forecasts driving the WRF Model. The study brings out the importance of including air?sea interactions and correcting the global forecasts for systematic biases before downscaling them for societal applications over Australia. These results are important for potentially improving austral summer seasonal forecasts over Australia.