The ultimate goal of climate research is to produce climate predictions on various time scales. In China, efforts to predict the climate started in the 1930 s. Experimental operational climate forecasts have been perf...The ultimate goal of climate research is to produce climate predictions on various time scales. In China, efforts to predict the climate started in the 1930 s. Experimental operational climate forecasts have been performed since the late 1950 s,based on historical analog circulation patterns. However, due to the inherent complexity of climate variability, the forecasts produced at that time were fairly inaccurate. Only from the late 1980 s has seasonal climate prediction experienced substantial progress, when the Tropical Ocean and Global Atmosphere project of the World Climate Research program（WCRP） was launched. This paper, following a brief description of the history of seasonal climate prediction research, provides an overview of these studies in China. Processes and factors associated with the climate variability and predictability are discussed based on the literature published by Chinese scientists. These studies in China mirror aspects of the climate research effort made in other parts of the world over the past several decades, and are particularly associated with monsoon research in East Asia. As the climate warms, climate extremes, their frequency, and intensity are projected to change, with a large possibility that they will increase. Thus, seasonal climate prediction is even more important for China in order to effectively mitigate disasters produced by climate extremes, such as frequent floods, droughts, and the heavy frozen rain events of South China.展开更多
Based on a 153-year (1948-2100) transient simulation of East Asian climate performed by a high resolution regional climate model (RegCM3) under the Intergovernmental Panel on Climate Change (IPCC) Special Report on Em...Based on a 153-year (1948-2100) transient simulation of East Asian climate performed by a high resolution regional climate model (RegCM3) under the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B scenario, the potential future changes in mean and extreme climates over China in association with a global warming of 2℃ with respect to pre-industrial times are assessed in this study. Results show that annual temperature rises over the whole of China, with a greater magnitude of around 0.6℃ compared to the global mean increase, at the time of a 2℃ global warming. Large-scale surface warming gets stronger towards the high latitudes and on the Qinghai-Tibetan Plateau, while it is similar in magnitude but somewhat different in spatial pattern between seasons. Annual precipitation increases by 5.2%, and seasonal precipitation increases by 4.2%-8.5% with respect to the 1986-2005 climatology. At the large scale, apart from in boreal winter when precipitation increases in northern China but decreases in southern China, annual and seasonal precipitation increases in western and southeastern China but decreases over the rest of the country. Nationwide extreme warm (cold) temperature events increase (decrease). With respect to the 1986-2005 climatology, the country-averaged annual extreme precipitation events R5d, SDII, R95T, and R10 increase by 5.1 mm, 0.28 mm d -1 , 6.6%, and 0.4 d respectively, and CDD decreases by 0.5 d. There is a large spatial variability in R10 and CDD changes.展开更多
A new composite index called the yearly tropical cyclone potential impact(YTCPI)is introduced.The relationship between YTCPI and activities of tropical cyclones(TCs)in China,disaster loss,and main ambient fields are i...A new composite index called the yearly tropical cyclone potential impact(YTCPI)is introduced.The relationship between YTCPI and activities of tropical cyclones(TCs)in China,disaster loss,and main ambient fields are investigated to show the potential of YTCPI as a new tool for short-term climate prediction of TCs.YTCPI can indicate TC activity and potential disaster loss.As correlation coefficients between YTCPI and frequency of landfalling TCs,the frequency of TCs traversing or forming inside a 24 h warning line in China from 1971 to 2010 are 0.58 and 0.56,respectively(both are at a statistically significant level,aboveα=0.001).Furthermore,three simple indexes are used to compare with YTCPI.They all have very close relationships with it,with correlation coefficients 0.75,0.82 and 0.78.For economic loss and YTCPI,the correlation coefficient is 0.57 for 1994–2009.Information on principal ambient fields(sea surface temperature,850 and 500 hPa geopotential heights)during the previous winter is reflected in the relationship with YTCPI.Spatial and temporal variabilities of ambient fields are extracted through empirical orthogonal function(EOF)analysis.Spatial distributions of correlation coefficient between YTCPI and ambient fields match the EOF main mode.Correlation coefficients between YTCPI and the EOF time array for the three ambient fields are 0.46,0.44 and 0.4,respectively,all statistically significant,aboveα=0.01.The YTCPI has the overall potential to be an improved prediction tool.展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos. 41130103 and 41210007)
文摘The ultimate goal of climate research is to produce climate predictions on various time scales. In China, efforts to predict the climate started in the 1930 s. Experimental operational climate forecasts have been performed since the late 1950 s,based on historical analog circulation patterns. However, due to the inherent complexity of climate variability, the forecasts produced at that time were fairly inaccurate. Only from the late 1980 s has seasonal climate prediction experienced substantial progress, when the Tropical Ocean and Global Atmosphere project of the World Climate Research program（WCRP） was launched. This paper, following a brief description of the history of seasonal climate prediction research, provides an overview of these studies in China. Processes and factors associated with the climate variability and predictability are discussed based on the literature published by Chinese scientists. These studies in China mirror aspects of the climate research effort made in other parts of the world over the past several decades, and are particularly associated with monsoon research in East Asia. As the climate warms, climate extremes, their frequency, and intensity are projected to change, with a large possibility that they will increase. Thus, seasonal climate prediction is even more important for China in order to effectively mitigate disasters produced by climate extremes, such as frequent floods, droughts, and the heavy frozen rain events of South China.
基金supported by the National Basic Research Program of China (2012CB955401)the National Natural Science Foundation of China (41175072)
文摘Based on a 153-year (1948-2100) transient simulation of East Asian climate performed by a high resolution regional climate model (RegCM3) under the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B scenario, the potential future changes in mean and extreme climates over China in association with a global warming of 2℃ with respect to pre-industrial times are assessed in this study. Results show that annual temperature rises over the whole of China, with a greater magnitude of around 0.6℃ compared to the global mean increase, at the time of a 2℃ global warming. Large-scale surface warming gets stronger towards the high latitudes and on the Qinghai-Tibetan Plateau, while it is similar in magnitude but somewhat different in spatial pattern between seasons. Annual precipitation increases by 5.2%, and seasonal precipitation increases by 4.2%-8.5% with respect to the 1986-2005 climatology. At the large scale, apart from in boreal winter when precipitation increases in northern China but decreases in southern China, annual and seasonal precipitation increases in western and southeastern China but decreases over the rest of the country. Nationwide extreme warm (cold) temperature events increase (decrease). With respect to the 1986-2005 climatology, the country-averaged annual extreme precipitation events R5d, SDII, R95T, and R10 increase by 5.1 mm, 0.28 mm d -1 , 6.6%, and 0.4 d respectively, and CDD decreases by 0.5 d. There is a large spatial variability in R10 and CDD changes.
基金supported by the National Science & Technology Pillar Program during the 11th Five-Year Plan Period(Grant No.2007BAC29B05)the Knowledge Innovation Project of the Chinese Academy of Sciences(Grant No.KZCX2-YW-Q03-3)the National Natural Science Foundation of China(Grant No.41001021)
文摘A new composite index called the yearly tropical cyclone potential impact(YTCPI)is introduced.The relationship between YTCPI and activities of tropical cyclones(TCs)in China,disaster loss,and main ambient fields are investigated to show the potential of YTCPI as a new tool for short-term climate prediction of TCs.YTCPI can indicate TC activity and potential disaster loss.As correlation coefficients between YTCPI and frequency of landfalling TCs,the frequency of TCs traversing or forming inside a 24 h warning line in China from 1971 to 2010 are 0.58 and 0.56,respectively(both are at a statistically significant level,aboveα=0.001).Furthermore,three simple indexes are used to compare with YTCPI.They all have very close relationships with it,with correlation coefficients 0.75,0.82 and 0.78.For economic loss and YTCPI,the correlation coefficient is 0.57 for 1994–2009.Information on principal ambient fields(sea surface temperature,850 and 500 hPa geopotential heights)during the previous winter is reflected in the relationship with YTCPI.Spatial and temporal variabilities of ambient fields are extracted through empirical orthogonal function(EOF)analysis.Spatial distributions of correlation coefficient between YTCPI and ambient fields match the EOF main mode.Correlation coefficients between YTCPI and the EOF time array for the three ambient fields are 0.46,0.44 and 0.4,respectively,all statistically significant,aboveα=0.01.The YTCPI has the overall potential to be an improved prediction tool.
