%0 Journal Article %1 liu2018evaluation %A Liu, Ying %A Ren, Hong‐Li %A Scaife, Adam A. %A Li, Chaofan %D 2018 %J Quarterly Journal of the Royal Meteorological Society %K MyYangtzeWork colleagues eastasianmonsoon seasonal skill %N 717 %P 2798-2811 %R 10.1002/qj.3405 %T Evaluation and Statistical Downscaling of East Asian Summer Monsoon Forecasting in BCC and MOHC Seasonal Prediction Systems %V 144 %X In seasonal climate predictions, the East Asian summer monsoon (EASM) is still a challenge, in spite of the wide usage of coupled climate models. Therefore, in this paper the predictability of the monsoon including the atmospheric circulation and precipitation anomalies is investigated, based on the reforecast data during 1992‐2011 in two coupled prediction systems: GloSea5 from the Met Office Hadley Center (MOHC) and BCC_CSM1.1m from the Beijing Climate Center (BCC). The results show that, the interannual variability of 850hPa zonal wind over East Asia and the northwest Pacific can be well reproduced, and the prediction skill is significant in both systems. The Philippine anticyclone is highly predictable over the tropical northwest Pacific, and it transfers the predictable signals from the winter El Niño‐South Oscillation (ENSO) into the climate prediction in East Asia and rainfall prediction in the Yangtze River basin. Two dynamical indexes of the EASM show that the prediction skill in these two systems are comparable. BCC_CSM1.1m tends to overestimate the coupling between the monsoon and ENSO, while GloSea5 shows a similar magnitude to the observations. Besides, a simple statistical downscaling method is adopted in this paper based on the predictable circulation data, which provides an efficient tool to improve the raw rainfall forecast skill over China.

@article{liu2018evaluation, abstract = {In seasonal climate predictions, the East Asian summer monsoon (EASM) is still a challenge, in spite of the wide usage of coupled climate models. Therefore, in this paper the predictability of the monsoon including the atmospheric circulation and precipitation anomalies is investigated, based on the reforecast data during 1992‐2011 in two coupled prediction systems: GloSea5 from the Met Office Hadley Center (MOHC) and BCC_CSM1.1m from the Beijing Climate Center (BCC). The results show that, the interannual variability of 850hPa zonal wind over East Asia and the northwest Pacific can be well reproduced, and the prediction skill is significant in both systems. The Philippine anticyclone is highly predictable over the tropical northwest Pacific, and it transfers the predictable signals from the winter El Niño‐South Oscillation (ENSO) into the climate prediction in East Asia and rainfall prediction in the Yangtze River basin. Two dynamical indexes of the EASM show that the prediction skill in these two systems are comparable. BCC_CSM1.1m tends to overestimate the coupling between the monsoon and ENSO, while GloSea5 shows a similar magnitude to the observations. Besides, a simple statistical downscaling method is adopted in this paper based on the predictable circulation data, which provides an efficient tool to improve the raw rainfall forecast skill over China.}, added-at = {2018-09-21T10:23:45.000+0200}, author = {Liu, Ying and Ren, Hong‐Li and Scaife, Adam A. and Li, Chaofan}, biburl = {https://www.bibsonomy.org/bibtex/20e75acaede5d5d58594532d020e1faa7/pbett}, description = {Evaluation and Statistical Downscaling of East Asian Summer Monsoon Forecasting in BCC and MOHC Seasonal Prediction Systems - Liu - - Quarterly Journal of the Royal Meteorological Society - Wiley Online Library}, doi = {10.1002/qj.3405}, interhash = {5d6ecae11160cfc3f21ecc015e550f7f}, intrahash = {0e75acaede5d5d58594532d020e1faa7}, journal = {Quarterly Journal of the Royal Meteorological Society}, keywords = {MyYangtzeWork colleagues eastasianmonsoon seasonal skill}, month = oct, number = 717, pages = {2798-2811}, timestamp = {2018-12-18T17:59:51.000+0100}, title = {Evaluation and Statistical Downscaling of East Asian Summer Monsoon Forecasting in BCC and MOHC Seasonal Prediction Systems}, volume = 144, year = 2018 }