Improving long-lead seasonal forecasts of precipitation over Southern China based on statistical downscaling using BCC_CSM1.1m |
| |
| Institution: | 1. Laboratory for Climate Studies & CMA-NJU Joint Laboratory for Climate Prediction Studies, National Climate Center, China Meteorological Administration, Beijing, China;2. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China;3. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, China;4. National Centre for Atmospheric Science and Department of Meteorology, University of Reading, Reading, UK;1. Department of Geography, Tokyo Metropolitan University, Hachioji-shi, Tokyo, Japan;2. Centre for Electromagnetics, National Aerospace Laboratories, Council of Scientific and Industrial Research, Kodihalli, Old Airport Road, Bengaluru, Karnataka, India;1. K. Banerjee Centre of Atmospheric and Ocean Studies (KBCAOS), Institute of Interdisciplinary Studies, Nehru Science Centre, University of Allahabad, Prayagraj, 211 002, India;2. Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, 411 008, India |
| |
| Abstract: | Long-lead precipitation forecasts for 1–4 seasons ahead are usually difficult in dynamical climate models due to the model deficiencies and the limited persistence of initial signals. But, these forecasts could be empirically improved by statistical approaches. In this study, to improve the seasonal precipitation forecast over the southern China (SC), the statistical downscaling (SD) models are built by using the predictors of atmospheric circulation and sea surface temperature (SST) simulated by the Beijing Climate Center Climate System Model version 1.1 m (BCC_CSM1.1 m). The different predictors involved in each SD model is selected based on both its close relationship with the target seasonal precipitation and its reasonable prediction skill in the BCC_CSM1.1 m. Cross and independent validations show the superior performance of the SD models, relative to the BCC_CSM1.1 m. The temporal correlation coefficient of SD models could reach > 0.4, exceeding the 95 % confidence level. The SC precipitation index can be much better forecasted by the SD models than by the BCC_CSM1.1 m in terms of the interannual variability. In addition, the errors of the precipitation forecast in all four seasons are significantly reduced over most of SC in the SD models. For the 2015/2016 strong El Niño event, the SD models outperform the dynamical BCC_CSM1.1 m model on the spatial and regional-average precipitation anomalies, mostly due to the effective SST predictor in the SD models and the weak response of the SC precipitation to El Niño-related SST anomalies in the BCC_CSM1.1 m. |
| |
| Keywords: | Four-season precipitation forecast Southern China BCC_CSM1 1m Statistical downscaling |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
