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
Validated satellite-derived sea surface temperatures (SSTs) are widely used for climate monitoring and ocean data assimilation systems. In this study, the Fengyun-3A (FY-3A) SST experimental product is evaluated using...Validated satellite-derived sea surface temperatures (SSTs) are widely used for climate monitoring and ocean data assimilation systems. In this study, the Fengyun-3A (FY-3A) SST experimental product is evaluated using Advanced Very High Resolution Radiometer (AVHRR)-merged and in situ SSTs. A comparison of AVHRR-merged SSTs reveals a negative bias of more than 2K in FY-3A SSTs in most of the tropical Pacific and low-latitude Indian and Atlantic Oceans. The error variance of FY-3A SSTs is estimated using three-way error analysis. FY-3A SSTs show regional error variance in global oceans with a maximum error variance of 2.2 K in the Pacific Ocean. In addition, a significant seasonal variation of error variance is present in FY-3A SSTs, which indicates that the quality of FY-3A SST could be improved by adjusting the parameters in the SST retrieval algorithm and by applying regional and seasonal algorithms, particularly in key areas such as the tropical Pacific Ocean. An objective analysis method is used to merge FY-3A SSTs with the drifter buoy data. The errors of FY-3A SSTs are decreased to-0.45K comparing with SST observations from GTSPP.展开更多
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 Basic Research Program of China(973 Program,Grant Nos.2010CB951902 and 2011CB403505)the National Key Technologies R&D Program of China(Grant No.2009BAC51B03)the National Natural Science Foundation of China(Grant No.41106003)
文摘Validated satellite-derived sea surface temperatures (SSTs) are widely used for climate monitoring and ocean data assimilation systems. In this study, the Fengyun-3A (FY-3A) SST experimental product is evaluated using Advanced Very High Resolution Radiometer (AVHRR)-merged and in situ SSTs. A comparison of AVHRR-merged SSTs reveals a negative bias of more than 2K in FY-3A SSTs in most of the tropical Pacific and low-latitude Indian and Atlantic Oceans. The error variance of FY-3A SSTs is estimated using three-way error analysis. FY-3A SSTs show regional error variance in global oceans with a maximum error variance of 2.2 K in the Pacific Ocean. In addition, a significant seasonal variation of error variance is present in FY-3A SSTs, which indicates that the quality of FY-3A SST could be improved by adjusting the parameters in the SST retrieval algorithm and by applying regional and seasonal algorithms, particularly in key areas such as the tropical Pacific Ocean. An objective analysis method is used to merge FY-3A SSTs with the drifter buoy data. The errors of FY-3A SSTs are decreased to-0.45K comparing with SST observations from GTSPP.
