基于ERA5的逐小时100m风场数据,利用时间序列K-means聚类方法,将中国沿海冬季风能年际变化划分为四个区域,分别为北中国海(NorthChina Sea,NCS)、东海(East China Sea,ECS)、南海北部(Northern South China Sea,NSCS)及南海南部(Souther...基于ERA5的逐小时100m风场数据,利用时间序列K-means聚类方法,将中国沿海冬季风能年际变化划分为四个区域,分别为北中国海(NorthChina Sea,NCS)、东海(East China Sea,ECS)、南海北部(Northern South China Sea,NSCS)及南海南部(SouthernSouthChinaSea,SSCS)。四个区域风能的年际变化受不同气候模态的影响,其中NCS风能的年际变化与北极涛动(ArcticOscillation,AO)有关;ECS风能的年际变化与中部型ENSO及西伯利亚高压有关;SSCS和NSCS的年际变化则和东部型ENSO及大陆高压的南北位置存在联系。鉴于影响各区域风能年际变化的气候模态具有较高的可预测性,进一步评估了多个气候模式对中国沿海风能年际变化的预测技巧。结果表明,气候模式对南中国海的风能年际变化预测技巧更高,这与气候模式对ENSO的高预测技巧有关。气候模式对北方海域风能年际变化的预测技巧较差,这和气候模式不能较好地预测AO和西伯利亚高压有关。展开更多
Driven by the global model,Beijing Climate Center Climate System Model version 1.1(BCC_CSM1.1),climate change over China in the 21st century is simulated by a regional climate model(RegCM4.0)under the new emission sce...Driven by the global model,Beijing Climate Center Climate System Model version 1.1(BCC_CSM1.1),climate change over China in the 21st century is simulated by a regional climate model(RegCM4.0)under the new emission scenarios of the Representative Concentration Pathways—RCP4.5 and RCP8.5.This is based on a period of transient simulations from 1950 to2099,with a grid spacing of 50 km.The present paper focuses on the annual mean temperature and precipitation in China over this period,with emphasis on their future changes.Validation of model performance reveals marked improvement of the RegCM4.0 model in reproducing present day temperature and precipitation relative to the driving BCC_CSM1.1 model.Significant warming is simulated by both BCC_CSM1.1 and RegCM4.0,however,spatial distribution and magnitude differ between the simulations.The high emission scenario RCP8.5 results in greater warming compared to RCP4.5.The two models project different precipitation changes,characterized by a general increase in the BCC_CSM1.1,and broader areas with decrease in the RegCM4.0 simulations.展开更多
Model initialization is a key process of climate predictions using dynamical models. In this study, the authors evaluated the performances of two distinct initialization approaches--anomaly and full-field initializati...Model initialization is a key process of climate predictions using dynamical models. In this study, the authors evaluated the performances of two distinct initialization approaches--anomaly and full-field initializations--in ENSO predictions conducted using the IAP-DecPreS near-term climate prediction system developed by the Institute of Atmospheric Physics （lAP）. IAP-DecPreS is composed of the FGOALS-s2 coupled general circulation model and a newly developed ocean data assimilation scheme called＇ensemble optimal interpolation-incremental analysis update＇ （EnOI-IAU）. It was found that, for IAP-DecPreS, the hindcast runs using the anomaly initialization have higher predictive skills for both conventional ENSO and El Nino Modoki, as compared to using the full-field initialization. The anomaly hindcasts can predict super El Nino/La Nina 10 months in advance and have good skill for most moderate and weak ENSO events about 4-7 months in advance.The predictive skill of the anomaly hindcasts for El Nino Modoki is close to that for conventional ENSO. On the other hand, the anomaly hindcasts at 1- and 4-month lead time can reproduce the major features of large-scale patterns of sea surface temperature, precipitation and atmospheric circulation anomalies during conventional ENSO and El Nino Modoki winter.展开更多
The Madden–Julian Oscillation（MJO）is a dominant mode of tropical intraseasonal variability（ISV）and has prominent impacts on the climate of the tropics and extratropics.Predicting the MJO using fully coupled clima...The Madden–Julian Oscillation（MJO）is a dominant mode of tropical intraseasonal variability（ISV）and has prominent impacts on the climate of the tropics and extratropics.Predicting the MJO using fully coupled climate system models is an interesting and important topic.This paper reports upon a recent progress in MJO ensemble prediction using the climate system model of the Beijing Climate Center,BCC-CSM1.1（m）;specifically,the development of three different initialization schemes in the BCC ISV/MJO prediction system,IMPRESS.Three sets of 10-yr hindcasts were separately conducted with the three initialization schemes.The results showed that the IMPRESS is able to usefully predict the MJO,but is sensitive to the initialization scheme used and becomes better with the initialization of moisture.In addition,a new ensemble approach was developed by averaging the predictions generated from the different initialization schemes,helping to address the uncertainty in the initial values of the MJO.The ensemble-mean MJO prediction showed significant improvement,with a valid prediction length of about 20 days in terms of the different criteria,i.e.,a correlation score beyond 0.5,a RMSE lower than 1.414,or a mean square skill score beyond 0.This study indicates that utilizing the different initialization schemes of this climate model may be an efficient approach when forming ensemble predictions of the MJO.展开更多
In recent decades,the damage and economic losses caused by climate change and extreme climate events have been increasing rapidly.Although scientists all over the world have made great efforts to understand and predic...In recent decades,the damage and economic losses caused by climate change and extreme climate events have been increasing rapidly.Although scientists all over the world have made great efforts to understand and predict climatic variations,there are still several major problems for improving climate prediction.In 2020,the Center for Climate System Prediction Research(CCSP) was established with support from the National Natural Science Foundation of China.CCSP aims to tackle three scientific problems related to climate prediction—namely,El Ni?o-Southern Oscillation(ENSO) prediction,extended-range weather forecasting,and interannual-to-decadal climate prediction—and hence provide a solid scientific basis for more reliable climate predictions and disaster prevention.In this paper,the major objectives and scientific challenges of CCSP are reported,along with related achievements of its research groups in monsoon dynamics,land-atmosphere interaction and model development,ENSO variability,intraseasonal oscillation,and climate prediction.CCSP will endeavor to tackle key scientific problems in these areas.展开更多
文摘基于ERA5的逐小时100m风场数据,利用时间序列K-means聚类方法,将中国沿海冬季风能年际变化划分为四个区域,分别为北中国海(NorthChina Sea,NCS)、东海(East China Sea,ECS)、南海北部(Northern South China Sea,NSCS)及南海南部(SouthernSouthChinaSea,SSCS)。四个区域风能的年际变化受不同气候模态的影响,其中NCS风能的年际变化与北极涛动(ArcticOscillation,AO)有关;ECS风能的年际变化与中部型ENSO及西伯利亚高压有关;SSCS和NSCS的年际变化则和东部型ENSO及大陆高压的南北位置存在联系。鉴于影响各区域风能年际变化的气候模态具有较高的可预测性,进一步评估了多个气候模式对中国沿海风能年际变化的预测技巧。结果表明,气候模式对南中国海的风能年际变化预测技巧更高,这与气候模式对ENSO的高预测技巧有关。气候模式对北方海域风能年际变化的预测技巧较差,这和气候模式不能较好地预测AO和西伯利亚高压有关。
基金supported by the National Basic Research Program of China (Grant No. 2010CB 950903)the China-UK-Swiss Adapting to Climate Change in China Project (ACCC)-Climate Science
文摘Driven by the global model,Beijing Climate Center Climate System Model version 1.1(BCC_CSM1.1),climate change over China in the 21st century is simulated by a regional climate model(RegCM4.0)under the new emission scenarios of the Representative Concentration Pathways—RCP4.5 and RCP8.5.This is based on a period of transient simulations from 1950 to2099,with a grid spacing of 50 km.The present paper focuses on the annual mean temperature and precipitation in China over this period,with emphasis on their future changes.Validation of model performance reveals marked improvement of the RegCM4.0 model in reproducing present day temperature and precipitation relative to the driving BCC_CSM1.1 model.Significant warming is simulated by both BCC_CSM1.1 and RegCM4.0,however,spatial distribution and magnitude differ between the simulations.The high emission scenario RCP8.5 results in greater warming compared to RCP4.5.The two models project different precipitation changes,characterized by a general increase in the BCC_CSM1.1,and broader areas with decrease in the RegCM4.0 simulations.
基金jointly supported by the National Key Research and Development Program of China(grant number2017YFA0604201)the National Natural Science Foundation of China(grant numbers.41661144009 and 41675089)the R&D Special Fund for Public Welfare Industry(meteorology)(grant number GYHY201506012)
文摘Model initialization is a key process of climate predictions using dynamical models. In this study, the authors evaluated the performances of two distinct initialization approaches--anomaly and full-field initializations--in ENSO predictions conducted using the IAP-DecPreS near-term climate prediction system developed by the Institute of Atmospheric Physics （lAP）. IAP-DecPreS is composed of the FGOALS-s2 coupled general circulation model and a newly developed ocean data assimilation scheme called＇ensemble optimal interpolation-incremental analysis update＇ （EnOI-IAU）. It was found that, for IAP-DecPreS, the hindcast runs using the anomaly initialization have higher predictive skills for both conventional ENSO and El Nino Modoki, as compared to using the full-field initialization. The anomaly hindcasts can predict super El Nino/La Nina 10 months in advance and have good skill for most moderate and weak ENSO events about 4-7 months in advance.The predictive skill of the anomaly hindcasts for El Nino Modoki is close to that for conventional ENSO. On the other hand, the anomaly hindcasts at 1- and 4-month lead time can reproduce the major features of large-scale patterns of sea surface temperature, precipitation and atmospheric circulation anomalies during conventional ENSO and El Nino Modoki winter.
基金jointly supported by the National Basic Research Program of China(973 Program,Grant No.2015CB453203)the China Meteorological Special Project(Grant No.GYHY201406022)the LCS/CMA Open Funds for Young Scholars(2014)
文摘The Madden–Julian Oscillation（MJO）is a dominant mode of tropical intraseasonal variability（ISV）and has prominent impacts on the climate of the tropics and extratropics.Predicting the MJO using fully coupled climate system models is an interesting and important topic.This paper reports upon a recent progress in MJO ensemble prediction using the climate system model of the Beijing Climate Center,BCC-CSM1.1（m）;specifically,the development of three different initialization schemes in the BCC ISV/MJO prediction system,IMPRESS.Three sets of 10-yr hindcasts were separately conducted with the three initialization schemes.The results showed that the IMPRESS is able to usefully predict the MJO,but is sensitive to the initialization scheme used and becomes better with the initialization of moisture.In addition,a new ensemble approach was developed by averaging the predictions generated from the different initialization schemes,helping to address the uncertainty in the initial values of the MJO.The ensemble-mean MJO prediction showed significant improvement,with a valid prediction length of about 20 days in terms of the different criteria,i.e.,a correlation score beyond 0.5,a RMSE lower than 1.414,or a mean square skill score beyond 0.This study indicates that utilizing the different initialization schemes of this climate model may be an efficient approach when forming ensemble predictions of the MJO.
基金supported by the National Natural Science Foundation of China [grant number 42088101]。
文摘In recent decades,the damage and economic losses caused by climate change and extreme climate events have been increasing rapidly.Although scientists all over the world have made great efforts to understand and predict climatic variations,there are still several major problems for improving climate prediction.In 2020,the Center for Climate System Prediction Research(CCSP) was established with support from the National Natural Science Foundation of China.CCSP aims to tackle three scientific problems related to climate prediction—namely,El Ni?o-Southern Oscillation(ENSO) prediction,extended-range weather forecasting,and interannual-to-decadal climate prediction—and hence provide a solid scientific basis for more reliable climate predictions and disaster prevention.In this paper,the major objectives and scientific challenges of CCSP are reported,along with related achievements of its research groups in monsoon dynamics,land-atmosphere interaction and model development,ENSO variability,intraseasonal oscillation,and climate prediction.CCSP will endeavor to tackle key scientific problems in these areas.
