An Assessment of the Predictability of High-Resolution Seasonal Forecast to Be Applicable for Agricultural Management Over North-East Asia.

Agriculture is highly sensitive to climate variability and climate extremes caused by climate change, such as droughts, floods and heat wave. Recent studies indicate that such climate extremes will be more frequent and severe along with mean climate change and their adverse impact on the crop yields and livestock will be worse than the impact of mean climate change. Therefore, it is important to produce high-quality regional forecast with a focus on crop production region to prevent considerable damage on the crop yields.

For the reason, we examine the Predictability of a high-resolution nested climate model, Regional Climate Model 4 (RegCM4), to capture the statistics of daily-scale temperature and precipitation events over the North-East Asia region, using observation and reanalysis data for comparison. Our analyses show that RegCM4 captures the pattern of mean state, interannual variability, climate extreme events, and trend in the tails of the daily temperature and precipitation distributions. However, consistent biases do exist, including cold and wet bias in the topographically-complex regions of eastern Tibetan Plateau. The biases in heavy precipitation, low temperature over the eastern Tibetan Plateau are associated with complex topography. Even though the RegCM4 shows consistent biases, regional forecast produced by regcm4 would be in agreement with observed daily-scale climate variability over the North-East Asia region.

And we also produce Growing degree days (GDD) base on regional forecast daily-temperature. The GDD has been estimating as one of important heat unit in agricultural field because it is made possible to predict growth stage of a plant and to manage a manure and a pest control in farming. The RegCM4 captures well the pattern and variability of GDD over North-East Asia region.