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Study On The Seasonal Division Of China And Its Prediction Of Summer Precipitation

Posted on:2020-05-04Degree:DoctorType:Dissertation
Country:ChinaCandidate:Z WangFull Text:PDF
GTID:1360330620951685Subject:Atmospheric Science
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The division of natural weather seasons is very important for weather forecast and climate prediction,especially for medium and long-term weather forecast and short-term climate prediction.It is an important topic in the field of climate monitoring,diagnosis,analysis and prediction to identify and classify the seasonal change by comprehensively considering various meteorological elements.The development of relevant research will help to better understand the new characteristics of seasonal change in the context of global warming.In recent years,the multi-element atmospheric condition similarity season division method is a newly developed objective season division method,which has been widely used in climate change research,climate monitoring,short-term climate prediction and other scientific research and business.The key of this method lies in the fusion of multi-element and the selection of typical field,which refers to the atmospheric condition anomaly field selected in the multi-element atmospheric condition similarity method that can represent the average climate characteristics in winter and summer.Based on NCEP / NCAR reanalysis data,GPCP reanalysis precipitation data and monthly precipitation data of China stations,on the basis of improving the multi-element atmospheric condition similarity season division method,using the multi-element atmospheric condition similarity season division method and statistical diagnosis methods,this paper discusses the characteristics of season transition in China,the relationship between the starting time of the pre-winter and summer precipitation,and the mechanism of precipitation prediction.The innovation of this paper is to link the research of seasonal change with short-term climate prediction,and to translate the research results of seasonal change into the research of short-term climate prediction.The main research contents and conclusions are as follows:(1)Study on the seasonal division method of multi-element atmospheric condition similarityThe accuracy of typical field is very important to the research results of season division.In this paper,we first take 1998 and 2013 in Central China as an example,and find that the calculation of typical field based on single year atmospheric condition can effectively reduce the error effect of climate change and seasonal change stage on seasonal division results.The results of seasonal division based on the new typical field can accurately reflect the regional atmospheric condition and the seasonal variation of atmospheric circulation.It is also found that there are interdecadal differences between the typical fields based on the single-year atmospheric condition and the typical fields based on the multi-year average atmospheric condition,especially in the transition stage of climate change.(2)Study on the characteristics of seasonal change in the South China SeaThe seasonal division method of multi-element atmospheric condition similarity is extended to the study of seasonal transition in the South China Sea.The results show that the 850 hPa seasonal division results in the South China Sea are in good agreement with the seasonal change time of the overall atmospheric condition composed of meteorological elements.All meteorological elements have obvious seasonal change characteristics,and the atmospheric circulation and the surface upwardly long wave radiation also change significantly with the seasonal change,which further verifies the effectiveness of multi-element atmospheric condition similarity method in the subtropical zone.In the South China Sea,each meteorological element presents different changing characteristics during the seasonal change.The change from winter to summer is dominated by the change of thermal elements,while the change from summer to winter is dominated by the change of dynamic elements.The spatial distribution of the beginning and ending time of 850 hPa summer and winter in the South China Sea can also accurately reflect the spatial evolution characteristics of the seasonal changes of atmospheric circulation and atmospheric condition.Summer in the South China Sea began first in the northwest of the South China Sea and the latest in the southeast of the South China Sea;summer ended first in the northwest of the South China Sea and finally in the southwest of the South China Sea.Winter began in the southwest and gradually expanded to the northeast,and its ending time contracted from the West and south to the central and northeastern regions.(3)Study on the characteristics of seasonal change in ChinaFrom the perspective of the interdecadal evolution characteristics of Chinese seasons,it is found that the relationship between the duration of different seasons and its main influencing factors is obviously different in longitude and latitude,and there is a significant complementary relationship between this elimination(shortened duration)and that of the long(increased duration).There is a complementary relationship among the length of seasons in spring-summer,autumn-winter,and cold-warm season.The complementary relationship between the length of spring and summer season is better,and the complementary regions are widespread.The complementary regions of the length of autumn and winter season are mainly concentrated in the western region.The complementary relationship of length in cold and warm seasons is widely distributed,but the whole complementary relationship is weak.Compared with the two stages before and after 1980,the interdecadal variation of seasonal duration shows the difference between East and west.The interdecadal variation in spring and summer is concentrated in the north and West regions,while that in autumn and winter is concentrated in the West regions.Further analysis of the key factors affecting the seasonal change shows that the significant regions of the interdecadal change of the seasonal duration are consistent with the significant regions of the interdecadal change of the key factors,which are concentrated in the northern and western regions of China.(4)Study on the prediction of summer precipitation by the characteristics of the pre-winterThe relationship between the onset time of the pre-winter season(i.e.the beginning time of last winter)and the precipitation in summer in eastern China is discussed.The statistical relationship and physical conceptual model of the onset time and the precipitation are established,and the prediction results of the precipitation in summer are provided accordingly.There is a weak positive correlation between the onset time of pre-winter and the intensity of East Asian winter monsoon and East Asian summer monsoon.The onset time of pre-winter is earlier,the onset time of pre-winter is weaker,and the onset time of pre-winter is later,and the intensity of winter monsoon is stronger.In the early beginning of the pre-winter season,the precipitation distribution in summer of the following year in China is characterized by-+-.The main rain belt is located in the Huaihe River Basin,which means the precipitation characteristics of type II rain pattern appear.In the years of late onset of the pre-winter season,the characteristics of type I(the main rain belt is located in the Yellow River Basin and its North region)and type III(the main rain belt is located in the middle and lower reaches of the Yangtze River and its south region)are generally shown.The return forecast and test of summer precipitation in China from 2012 to 2018 are carried out.It is found that the statistical model established in this paper has high prediction skills and stable prediction results.The research of this paper shows that the result of season division is not only a time node,but also a good starting point for the study of climate change and climate prediction.
Keywords/Search Tags:multi-element atmospheric condition similarity, seasonal division, interdecadal variation, summer precipitation prediction
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