%0 Journal Article %1 Ren2017Prediction %A Ren, Hong-Li %A Jin, Fei-Fei %A Song, Lianchun %A Lu, Bo %A Tian, Ben %A Zuo, Jinqing %A Liu, Ying %A Wu, Jie %A Zhao, Chongbo %A Nie, Yu %A Zhang, Peiqun %A Ba, Jin %A Wu, Yujie %A Wan, Jianghua %A Yan, Yuping %A Zhou, Fang %B Journal of Meteorological Research %D 2017 %I The Chinese Meteorological Society %K teleconnections colleagues decadal eastasianmonsoon skill seasonal monsoon monthly ArcticOscillation ENSO China %N 1 %P 204--223 %R 10.1007/s13351-017-6097-3 %T Prediction of primary climate variability modes at the Beijing Climate Center %U http://dx.doi.org/10.1007/s13351-017-6097-3 %V 31 %X Climate variability modes, usually known as primary climate phenomena, are well recognized as the most important predictability sources in subseasonal–interannual climate prediction. This paper begins by reviewing the research and development carried out, and the recent progress made, at the Beijing Climate Center (BCC) in predicting some primary climate variability modes. These include the El Niño–Southern Oscillation (ENSO), Madden–Julian Oscillation (MJO), and Arctic Oscillation (AO), on global scales, as well as the sea surface temperature (SST) modes in the Indian Ocean and North Atlantic, western Pacific subtropical high (WPSH), and the East Asian winter and summer monsoons (EAWM and EASM, respectively), on regional scales. Based on its latest climate and statistical models, the BCC has established a climate phenomenon prediction system (CPPS) and completed a hindcast experiment for the period 1991–2014. The performance of the CPPS in predicting such climate variability modes is systematically evaluated. The results show that skillful predictions have been made for ENSO, MJO, the Indian Ocean basin mode, the WPSH, and partly for the EASM, whereas less skillful predictions were made for the Indian Ocean Dipole (IOD) and North Atlantic SST Tripole, and no clear skill at all for the AO, subtropical IOD, and EAWM. Improvements in the prediction of these climate variability modes with low skill need to be achieved by improving the BCC's climate models, developing physically based statistical models as well as correction methods for model predictions. Some of the monitoring/prediction products of the BCC-CPPS are also introduced in this paper.

@article{Ren2017Prediction, abstract = {Climate variability modes, usually known as primary climate phenomena, are well recognized as the most important predictability sources in subseasonal–interannual climate prediction. This paper begins by reviewing the research and development carried out, and the recent progress made, at the Beijing Climate Center (BCC) in predicting some primary climate variability modes. These include the El Ni\~{n}o–Southern Oscillation (ENSO), Madden–Julian Oscillation (MJO), and Arctic Oscillation (AO), on global scales, as well as the sea surface temperature (SST) modes in the Indian Ocean and North Atlantic, western Pacific subtropical high (WPSH), and the East Asian winter and summer monsoons (EAWM and EASM, respectively), on regional scales. Based on its latest climate and statistical models, the BCC has established a climate phenomenon prediction system (CPPS) and completed a hindcast experiment for the period 1991–2014. The performance of the CPPS in predicting such climate variability modes is systematically evaluated. The results show that skillful predictions have been made for ENSO, MJO, the Indian Ocean basin mode, the WPSH, and partly for the EASM, whereas less skillful predictions were made for the Indian Ocean Dipole (IOD) and North Atlantic SST Tripole, and no clear skill at all for the AO, subtropical IOD, and EAWM. Improvements in the prediction of these climate variability modes with low skill need to be achieved by improving the BCC's climate models, developing physically based statistical models as well as correction methods for model predictions. Some of the monitoring/prediction products of the BCC-CPPS are also introduced in this paper.}, added-at = {2018-06-18T21:23:34.000+0200}, author = {Ren, Hong-Li and Jin, Fei-Fei and Song, Lianchun and Lu, Bo and Tian, Ben and Zuo, Jinqing and Liu, Ying and Wu, Jie and Zhao, Chongbo and Nie, Yu and Zhang, Peiqun and Ba, Jin and Wu, Yujie and Wan, Jianghua and Yan, Yuping and Zhou, Fang}, biburl = {https://www.bibsonomy.org/bibtex/26dc10e509fbd67e13a6af628acd28434/pbett}, booktitle = {Journal of Meteorological Research}, citeulike-article-id = {14314368}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/s13351-017-6097-3}, citeulike-linkout-1 = {http://link.springer.com/article/10.1007/s13351-017-6097-3}, doi = {10.1007/s13351-017-6097-3}, interhash = {d72feef482f94cf581ad2f17bb1bbb5d}, intrahash = {6dc10e509fbd67e13a6af628acd28434}, keywords = {teleconnections colleagues decadal eastasianmonsoon skill seasonal monsoon monthly ArcticOscillation ENSO China}, number = 1, pages = {204--223}, posted-at = {2017-03-20 07:43:00}, priority = {2}, publisher = {The Chinese Meteorological Society}, timestamp = {2020-04-15T09:29:49.000+0200}, title = {Prediction of primary climate variability modes at the Beijing Climate Center}, url = {http://dx.doi.org/10.1007/s13351-017-6097-3}, volume = 31, year = 2017 }