A statistical downscaling approach based on multiple-linear-regression(MLR) for the prediction of summer precipitation anomaly in southeastern China was established,which was based on the outputs of seven operational ...A statistical downscaling approach based on multiple-linear-regression(MLR) for the prediction of summer precipitation anomaly in southeastern China was established,which was based on the outputs of seven operational dynamical models of Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction(DEMETER) and observed data.It was found that the anomaly correlation coefficients(ACCs) spatial pattern of June-July-August(JJA) precipitation over southeastern China between the seven models and the observation were increased significantly;especially in the central and the northeastern areas,the ACCs were all larger than 0.42(above 95% level) and 0.53(above 99% level).Meanwhile,the root-mean-square errors(RMSE) were reduced in each model along with the multi-model ensemble(MME) for some of the stations in the northeastern area;additionally,the value of RMSE difference between before and after downscaling at some stations were larger than 1 mm d-1.Regionally averaged JJA rainfall anomaly temporal series of the downscaling scheme can capture the main characteristics of observation,while the correlation coefficients(CCs) between the temporal variations of the observation and downscaling results varied from 0.52 to 0.69 with corresponding variations from-0.27 to 0.22 for CCs between the observation and outputs of the models.展开更多
A combination of the optimal subset regression (OSR) approach,the coupled general circulation model of the National Climate Center (NCC-CGCM) and precipitation observations from 160 stations over China is used to cons...A combination of the optimal subset regression (OSR) approach,the coupled general circulation model of the National Climate Center (NCC-CGCM) and precipitation observations from 160 stations over China is used to construct a statistical downscaling forecast model for precipitation in summer.Retroactive forecasts are performed to assess the skill of statistical downscaling during the period from 2003 to 2009.The results show a poor simulation for summer precipitation by the NCCCGCM for China,and the average spatial anomaly correlation coefficient (ACC) is 0.01 in the forecast period.The forecast skill can be improved by OSR statistical downscaling,and the OSR forecast performs better than the NCC-CGCM in most years except 2003.The spatial ACC is more than 0.2 in the years 2008 and 2009,which proves to be relatively skillful.Moreover,the statistical downscaling forecast performs relatively well for the main rain belt of the summer precipitation in some years,including 2005,2006,2008,and 2009.However,the forecast skill of statistical downscaling is restricted to some extent by the relatively low skill of the NCCCGCM.展开更多
An effective statistical downscaling scheme was developed on the basis of singular value decomposition to predict boreal winter(December-January-February)precipitation over China.The variable geopotential height at 50...An effective statistical downscaling scheme was developed on the basis of singular value decomposition to predict boreal winter(December-January-February)precipitation over China.The variable geopotential height at 500 hPa(GH5)over East Asia,which was obtained from National Centers for Environmental Prediction’s Coupled Forecast System(NCEP CFS),was used as one predictor for the scheme.The preceding sea ice concentration(SIC)signal obtained from observed data over high latitudes of the Northern Hemisphere was chosen as an additional predictor.This downscaling scheme showed significantly improvement in predictability over the original CFS general circulation model(GCM)output in cross validation.The multi-year average spatial anomaly correlation coefficient increased from–0.03 to 0.31,and the downscaling temporal root-mean-square-error(RMSE)decreased significantly over that of the original CFS GCM for most China stations.Furthermore,large precipitation anomaly centers were reproduced with greater accuracy in the downscaling scheme than those in the original CFS GCM,and the anomaly correlation coefficient between the observation and downscaling results reached～0.6 in the winter of 2008.展开更多
By using 1958-2001 NOAA extended reconstructed sea surface temperature(SST) data, ERA40 reanalysis soil moisture data and precipitation data of 444 stations in China(east of 100°E), the possible relationships amo...By using 1958-2001 NOAA extended reconstructed sea surface temperature(SST) data, ERA40 reanalysis soil moisture data and precipitation data of 444 stations in China(east of 100°E), the possible relationships among South China Sea(SCS) SST anomaly(SSTA), soil moisture anomalies(SMA) and summer precipitation in eastern China as well as their possible physical processes are investigated. Results show that the SSTA of SCS bears an evidently negative correlation with spring soil moisture in the east part of Southwest China. More(less) precipitation happens in the Yangtze River basin and less(more) in the Southeast China in summer when the SSTA of SCS is higher(lower) than normal and the soil in the east part of Southwest China is dry(wet) in spring. Further analysis shows that when the SSTA of SCS is high(low), the southwesterly wind at low level is weak(strong), decreasing(increasing) the water vapor transport in South China, resulting in reduced(increased) spring precipitation in the east part of Southwest China and more(less) soil moisture in spring. Through the evaporation feedback mechanism, the dry(wet) soil makes the surface temperature higher(lower) in summer, causing the westward extension(eastward retreat) of the West Pacific Subtropical High, eventually leading to the summer precipitation anomalies.展开更多
The 21-yr ensemble predictions of model precipitation and circulation in the East Asian and western North Pacific （Asia-Pacific） summer monsoon region （0°-50°N, 100° 150°E） were evaluated in ni...The 21-yr ensemble predictions of model precipitation and circulation in the East Asian and western North Pacific （Asia-Pacific） summer monsoon region （0°-50°N, 100° 150°E） were evaluated in nine different AGCM, used in the Asia-Pacific Economic Cooperation Climate Center （APCC） multi-model ensemble seasonal prediction system. The analysis indicates that the precipitation anomaly patterns of model ensemble predictions are substantially different from the observed counterparts in this region, but the summer monsoon circulations are reasonably predicted. For example, all models can well produce the interannual variability of the western North Pacific monsoon index （WNPMI） defined by 850 hPa winds, but they failed to predict the relationship between WNPMI and precipitation anomalies. The interannual variability of the 500 hPa geopotential height （GPH） can be well predicted by the models in contrast to precipitation anomalies. On the basis of such model performances and the relationship between the interannual variations of 500 hPa GPH and precipitation anomalies, we developed a statistical scheme used to downscale the summer monsoon precipitation anomaly on the basis of EOF and singular value decomposition （SVD）. In this scheme, the three leading EOF modes of 500 hPa GPH anomaly fields predicted by the models are firstly corrected by the linear regression between the principal components in each model and observation, respectively. Then, the corrected model GPH is chosen as the predictor to downscale the precipitation anomaly field, which is assembled by the forecasted expansion coefficients of model 500 hPa GPH and the three leading SVD modes of observed precipitation anomaly corresponding to the prediction of model 500 hPa GPH during a 19-year training period. The cross-validated forecasts suggest that this downscaling scheme may have a potential to improve the forecast skill of the precipitation anomaly in the South China Sea, western North Pacific and the East Asia Pacific regions, where the anomaly correlation coefficient （ACC） has been improved by 0.14, corresponding to the reduced RMSE of 10.4% in the conventional multi-model ensemble （MME） forecast.展开更多
By using observational daily precipitation data over the Yangtze-Huaihe River basin, ERA-40 data, and the data from eight CMIP5 climate models, statistical downscaling models are constructed based on BP-CCA （combinat...By using observational daily precipitation data over the Yangtze-Huaihe River basin, ERA-40 data, and the data from eight CMIP5 climate models, statistical downscaling models are constructed based on BP-CCA （combination of empirical orthogonal function and canonical correlation analysis） to project future changes of precipitation. The results show that the absolute values of domain-averaged precipitation relative errors of most models are reduced from 8%-46% to 1% 7% after statistical downscaling. The spatial correlations are all improved from less than 0.40 to more than 0.60. As a result of the statistical downscaling multi- model ensemble （SDMME）, the relative error is improved from -15.8% to -1.3%, and the spatial correlation increases significantly from 0.46 to 0.88. These results demonstrate that the simulation skill of SDMME is relatively better than that of the multimodel ensemble （MME） and the downscaling of most individual models. The projections of SDMME reveal that under the RCP （Representative Concentration Pathway） 4.5 scenario, the projected domain-averaged precipitation changes for the early （2016-2035）, middle （2046 2065）, and late （2081-2100） 21st century are 1.8%, 6.1%, and 9.9%, respectively. For the early period, the increasing trends of precipitation in the western region are relatively weak, while the precipitation in the east shows a decreasing trend. Furthermore, the reliability of the projected changes over the area east of l15°E is higher than that in the west. The stations with significant increasing trends are primarily located over the western region in both the middle and late periods~ with larger magnitude for the latter. Stations with high reliability mainly appear in the region north of 28.5°N for both periods.展开更多
利用BP-CCA方法并结合当前国际先进气候预测模式结果,探讨了如何建立对西南夏季降水具有较高预测技巧的统计降尺度模型及其可预报性来源。结果表明,将热带区域海表温度作为预测因子的降尺度模型的预测能力优于亚洲区域和热带区域500 h P...利用BP-CCA方法并结合当前国际先进气候预测模式结果,探讨了如何建立对西南夏季降水具有较高预测技巧的统计降尺度模型及其可预报性来源。结果表明,将热带区域海表温度作为预测因子的降尺度模型的预测能力优于亚洲区域和热带区域500 h Pa位势高度作为预测因子的模型。对模型可预报性来源的分析表明,热带区域海表温度作为预测因子的降尺度模型的预测能力年与年之间的差异主要受热带海表温度EOF第二模态的影响。该模态表现为在热带东南印度洋及西太平洋区域有正载荷值,而在热带中东太平洋区域有负载荷中心,其与影响西南夏季降水的菲律宾和海洋大陆西部对流有较好的相关,并且ECMWF和NCEP业务气候预测模式对其有较好的预测能力。展开更多
基金supported by the special Fund for Public Welfare Industry (Meteorology) (Grant No. GYHY200906018)the National Basic Research Program of China (Grant Nos. 2010CB950304 and 2009CB421406)the Knowl-edge Innovation Program of the Chinese Academy of Sciences (Grant No. KZCX2-YW-QN202)
文摘A statistical downscaling approach based on multiple-linear-regression(MLR) for the prediction of summer precipitation anomaly in southeastern China was established,which was based on the outputs of seven operational dynamical models of Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction(DEMETER) and observed data.It was found that the anomaly correlation coefficients(ACCs) spatial pattern of June-July-August(JJA) precipitation over southeastern China between the seven models and the observation were increased significantly;especially in the central and the northeastern areas,the ACCs were all larger than 0.42(above 95% level) and 0.53(above 99% level).Meanwhile,the root-mean-square errors(RMSE) were reduced in each model along with the multi-model ensemble(MME) for some of the stations in the northeastern area;additionally,the value of RMSE difference between before and after downscaling at some stations were larger than 1 mm d-1.Regionally averaged JJA rainfall anomaly temporal series of the downscaling scheme can capture the main characteristics of observation,while the correlation coefficients(CCs) between the temporal variations of the observation and downscaling results varied from 0.52 to 0.69 with corresponding variations from-0.27 to 0.22 for CCs between the observation and outputs of the models.
基金supported by China Meteorological Administration R & D Special Fund for Public Welfare (Meteorology) (Grant Nos. GYHY200906018 and GYHY200906015)the National Natural Science Foundation of China (Grant No.41005051)the National Key Technologies R & D Program of China (Grant No. 2009BAC51B05)
文摘A combination of the optimal subset regression (OSR) approach,the coupled general circulation model of the National Climate Center (NCC-CGCM) and precipitation observations from 160 stations over China is used to construct a statistical downscaling forecast model for precipitation in summer.Retroactive forecasts are performed to assess the skill of statistical downscaling during the period from 2003 to 2009.The results show a poor simulation for summer precipitation by the NCCCGCM for China,and the average spatial anomaly correlation coefficient (ACC) is 0.01 in the forecast period.The forecast skill can be improved by OSR statistical downscaling,and the OSR forecast performs better than the NCC-CGCM in most years except 2003.The spatial ACC is more than 0.2 in the years 2008 and 2009,which proves to be relatively skillful.Moreover,the statistical downscaling forecast performs relatively well for the main rain belt of the summer precipitation in some years,including 2005,2006,2008,and 2009.However,the forecast skill of statistical downscaling is restricted to some extent by the relatively low skill of the NCCCGCM.
基金supported by the China Meteorological Special Project(GYHY201206016)the National Basic Research Program of China(2010CB950304)the Innovation Key Program of the Chinese Academy of Sciences(KZCX2-YW-QN202)
文摘An effective statistical downscaling scheme was developed on the basis of singular value decomposition to predict boreal winter(December-January-February)precipitation over China.The variable geopotential height at 500 hPa(GH5)over East Asia,which was obtained from National Centers for Environmental Prediction’s Coupled Forecast System(NCEP CFS),was used as one predictor for the scheme.The preceding sea ice concentration(SIC)signal obtained from observed data over high latitudes of the Northern Hemisphere was chosen as an additional predictor.This downscaling scheme showed significantly improvement in predictability over the original CFS general circulation model(GCM)output in cross validation.The multi-year average spatial anomaly correlation coefficient increased from–0.03 to 0.31,and the downscaling temporal root-mean-square-error(RMSE)decreased significantly over that of the original CFS GCM for most China stations.Furthermore,large precipitation anomaly centers were reproduced with greater accuracy in the downscaling scheme than those in the original CFS GCM,and the anomaly correlation coefficient between the observation and downscaling results reached～0.6 in the winter of 2008.
基金National Science Foundation of China(41230422)Special Funds for Public Welfare of China(GYHY 201206017)+3 种基金NCET ProgramNatural Science Foundation of Jiangsu Province of China(BK2004001)Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)Research Innovation Program for College Graduates of Jiangsu Province(CXZZ13_0499)
文摘By using 1958-2001 NOAA extended reconstructed sea surface temperature(SST) data, ERA40 reanalysis soil moisture data and precipitation data of 444 stations in China(east of 100°E), the possible relationships among South China Sea(SCS) SST anomaly(SSTA), soil moisture anomalies(SMA) and summer precipitation in eastern China as well as their possible physical processes are investigated. Results show that the SSTA of SCS bears an evidently negative correlation with spring soil moisture in the east part of Southwest China. More(less) precipitation happens in the Yangtze River basin and less(more) in the Southeast China in summer when the SSTA of SCS is higher(lower) than normal and the soil in the east part of Southwest China is dry(wet) in spring. Further analysis shows that when the SSTA of SCS is high(low), the southwesterly wind at low level is weak(strong), decreasing(increasing) the water vapor transport in South China, resulting in reduced(increased) spring precipitation in the east part of Southwest China and more(less) soil moisture in spring. Through the evaporation feedback mechanism, the dry(wet) soil makes the surface temperature higher(lower) in summer, causing the westward extension(eastward retreat) of the West Pacific Subtropical High, eventually leading to the summer precipitation anomalies.
基金The National Nat-ural Science Foundation of China (NSFC), Grant Nos.90711003, 40375014the program of GYHY200706005, and the APCC Visiting Scientist Program jointly supportedthis work.
文摘The 21-yr ensemble predictions of model precipitation and circulation in the East Asian and western North Pacific （Asia-Pacific） summer monsoon region （0°-50°N, 100° 150°E） were evaluated in nine different AGCM, used in the Asia-Pacific Economic Cooperation Climate Center （APCC） multi-model ensemble seasonal prediction system. The analysis indicates that the precipitation anomaly patterns of model ensemble predictions are substantially different from the observed counterparts in this region, but the summer monsoon circulations are reasonably predicted. For example, all models can well produce the interannual variability of the western North Pacific monsoon index （WNPMI） defined by 850 hPa winds, but they failed to predict the relationship between WNPMI and precipitation anomalies. The interannual variability of the 500 hPa geopotential height （GPH） can be well predicted by the models in contrast to precipitation anomalies. On the basis of such model performances and the relationship between the interannual variations of 500 hPa GPH and precipitation anomalies, we developed a statistical scheme used to downscale the summer monsoon precipitation anomaly on the basis of EOF and singular value decomposition （SVD）. In this scheme, the three leading EOF modes of 500 hPa GPH anomaly fields predicted by the models are firstly corrected by the linear regression between the principal components in each model and observation, respectively. Then, the corrected model GPH is chosen as the predictor to downscale the precipitation anomaly field, which is assembled by the forecasted expansion coefficients of model 500 hPa GPH and the three leading SVD modes of observed precipitation anomaly corresponding to the prediction of model 500 hPa GPH during a 19-year training period. The cross-validated forecasts suggest that this downscaling scheme may have a potential to improve the forecast skill of the precipitation anomaly in the South China Sea, western North Pacific and the East Asia Pacific regions, where the anomaly correlation coefficient （ACC） has been improved by 0.14, corresponding to the reduced RMSE of 10.4% in the conventional multi-model ensemble （MME） forecast.
基金Supported by the National Natural Science Foundation of China(41230528)Priority Academic Program Development(PAPD)of Jiangsu Higher Education InstitutionsNational Key Pesearch and Development Program of China(2016YFA0600402)
文摘By using observational daily precipitation data over the Yangtze-Huaihe River basin, ERA-40 data, and the data from eight CMIP5 climate models, statistical downscaling models are constructed based on BP-CCA （combination of empirical orthogonal function and canonical correlation analysis） to project future changes of precipitation. The results show that the absolute values of domain-averaged precipitation relative errors of most models are reduced from 8%-46% to 1% 7% after statistical downscaling. The spatial correlations are all improved from less than 0.40 to more than 0.60. As a result of the statistical downscaling multi- model ensemble （SDMME）, the relative error is improved from -15.8% to -1.3%, and the spatial correlation increases significantly from 0.46 to 0.88. These results demonstrate that the simulation skill of SDMME is relatively better than that of the multimodel ensemble （MME） and the downscaling of most individual models. The projections of SDMME reveal that under the RCP （Representative Concentration Pathway） 4.5 scenario, the projected domain-averaged precipitation changes for the early （2016-2035）, middle （2046 2065）, and late （2081-2100） 21st century are 1.8%, 6.1%, and 9.9%, respectively. For the early period, the increasing trends of precipitation in the western region are relatively weak, while the precipitation in the east shows a decreasing trend. Furthermore, the reliability of the projected changes over the area east of l15°E is higher than that in the west. The stations with significant increasing trends are primarily located over the western region in both the middle and late periods~ with larger magnitude for the latter. Stations with high reliability mainly appear in the region north of 28.5°N for both periods.
文摘利用BP-CCA方法并结合当前国际先进气候预测模式结果,探讨了如何建立对西南夏季降水具有较高预测技巧的统计降尺度模型及其可预报性来源。结果表明,将热带区域海表温度作为预测因子的降尺度模型的预测能力优于亚洲区域和热带区域500 h Pa位势高度作为预测因子的模型。对模型可预报性来源的分析表明,热带区域海表温度作为预测因子的降尺度模型的预测能力年与年之间的差异主要受热带海表温度EOF第二模态的影响。该模态表现为在热带东南印度洋及西太平洋区域有正载荷值,而在热带中东太平洋区域有负载荷中心,其与影响西南夏季降水的菲律宾和海洋大陆西部对流有较好的相关,并且ECMWF和NCEP业务气候预测模式对其有较好的预测能力。
基金“十三五”国家重点研发计划项目(2016YFA0601501)公益性行业专项(GYHY201406018)+1 种基金Basic Scientific Research and Operation Foundation of CAMS(2018Z006,2018KJ029,2018KJ030)国家重点基础研究发展计划973项目(2013CB430203)共同资助
