IMPACT OF SUMMER WARMING ON DYNAMICS-STATISTICS-COMBINED METHOD TO PREDICT THE SUMMER TEMPERATURE IN CHINA

• Published in: Journal of Tropical Meteorology Vol. 23; no. 4; pp. 440 - 449
• Main Author: 苏海晶 乔少博 杨杰 王晓娟
• Format: Journal Article
• Language: English
• Published: Guangzhou Guangzhou Institute of Tropical & Marine Meteorology 01.12.2017
• Subjects: Business; Climate; Climate change; Climate models; Climate trends; Dynamics; Error correction & detection; Global climate; Global climate models; Global warming; Impact analysis; Independent sample; Post-production processing; Precipitation; Statistical analysis; Statistical methods; Statistics; Summer; Temperature; Temperature effects; Trends; Weather forecasting; China
• ISSN: 1006-8775
• DOI: 10.16555/j.1006-8775.2017.04.009

Based on NCEP/NCAR daily reanalysis data, climate trend rate and other methods are used to quantitatively analyze the change trend of China’s summer observed temperature in 1983—2012. Moreover, a dynamics-statistics-combined seasonal forecast method with optimal multi-factor portfolio is applied to analyze the impact of this trend on summer temperature forecast. The results show that: in the three decades, the summer temperature shows a clear upward trend under the condition of global warming, especially over South China, East China, Northeast China and Xinjiang Region, and the trend rate of national average summer temperature was 0.27℃ per decade. However, it is found that the current business model forecast(Coupled Global Climate Model) of National Climate Centre is unable to forecast summer warming trends in China, so that the post-processing forecast effect of dynamics-statistics-combined method is relatively poor. In this study, observed temperatures are processed first by removing linear fitting trend, and then adding it after forecast to offset the deficiency of model forecast indirectly. After test, ACC average value in the latest decade was 0.44 through dynamics-statistics-combined independent sample return forecast. The temporal correlation(TCC) between forecast and observed temperature was significantly improved compared with direct forecast results in most regions, and effectively improved the skill of the dynamics-statistics-combined forecast method in seasonal temperature forecast.