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| 1 | Performance of the Seasonal Forecasting of the Asian Summer Monsoon by BCC_CSM1.1(m)显示文摘This paper provides a comprehensive assessment of Asian summer monsoon prediction skill as a function of lead time and its relationship to sea surface temperature prediction using the seasonal hindcasts of the Beijing Climate Center Climate System Model, BCC CSM1.1(m). For the South and Southeast Asian summer monsoon, reasonable skill is found in the model's forecasting of certain aspects of monsoon climatology and spatiotemporal variability. Nevertheless, deficiencies such as significant forecast errors over the tropical western North Pacific and the eastern equatorial Indian Ocean are also found. In particular, overestimation of the connections of some dynamical monsoon indices with large-scale circulation and precipitation patterns exists in most ensemble mean forecasts, even for short lead-time forecasts.Variations of SST, measured by the first mode over the tropical Pacific and Indian oceans, as well as the spatiotemporal features over the Ni no3.4 region, are overall well predicted. However, this does not necessarily translate into successful forecasts of the Asian summer monsoon by the model. Diagnostics of the relationships between monsoon and SST show that difficulties in predicting the South Asian monsoon can be mainly attributed to the limited regional response of monsoon in observations but the extensive and exaggerated response in predictions due partially to the application of ensemble average forecasting methods. In contrast, in spite of a similar deficiency, the Southeast Asian monsoon can still be forecasted reasonably, probably because of its closer relationship with large-scale circulation patterns and El Nino–Southern Oscillation. | LIU Xiangwen WU Tongwen YANG Song JIE Weihua NIE Suping LI Qiaoping CHENG Yanjie LIANG Xiaoyun | 2015 | Advances in Atmospheric Sciences2015，32，8： | 25 |
| 2 | The China Multi-Model Ensemble Prediction System and Its Application to Flood-Season Prediction in 2018显示文摘Multi-model ensemble prediction is an effective approach for improving the prediction skill short-term climate prediction and evaluating related uncertainties. Based on a combination of localized operation outputs of Chinese climate models and imported forecast data of some international operational models, the National Climate Center of the China Meteorological Administration has established the China multi-model ensemble prediction system version 1.0 (CMMEv1.0) for monthly-seasonal prediction of primary climate variability modes and climate elements. We verified the real-time forecasts of CMMEv1.0 for the 2018 flood season (June-August) starting from March 2018 and evaluated the 1991-2016 hindcasts of CMMEv1.0. The results show that CMMEv1.0 has a significantly high prediction skill for global sea surface temperature (SST) anomalies, especially for the El Nino-Southern Oscillation (ENSO) in the tropical central-eastern Pacific. Additionally, its prediction skill for the North Atlantic SST triple (NAST) mode is high, but is relatively low for the Indian Ocean Dipole (IOD) mode. Moreover, CMMEv1.0 has high skills in predicting the western Pacific subtropical high (WPSH) and East Asian summer monsoon (EASM) in the June-July-August (JJA) season. The JJA air temperature in the CMMEv1.0 is predicted with a fairly high skill in most regions of China, while the JJA precipitation exhibits some skills only in northwestern and eastern China. For real-time forecasts in March-August 2018, CMMEv1.0 has accurately predicted the ENSO phase transition from cold to neutral in the tropical central-eastern Pacific and captures evolutions of the NAST and IOD indices in general. The system has also captured the main features of the summer WPSH and EASM indices in 2018, except that the predicted EASM is slightly weaker than the observed. Furthermore, CMMEv1.0 has also successfully predicted warmer air temperatures in northern China and captured the primary rainbelt over northern China, except that it predicted much more precipitation in the middle and lower reaches of the Yangtze River than observation. | Hong-Li REN Yujie WU Qing BAO Jiehua MA Changzheng LIU Jianghua WAN Qiaoping LI Xiaofei WU Ying LIU Ben TIAN Joshua-Xiouhua FU Jianqi SUN | 2019 | Journal of Meteorological Research2019，33，3： | 16 |
| 3 | Relationships between Interannual and Intraseasonal Variations of the Asian–Western Pacific Summer Monsoon Hindcasted by BCC CSM 1.1(m)显示文摘Using hindcasts of the Beijing Climate Center Climate System Model, the relationships between interannual variability(IAV) and intraseasonal variability(ISV) of the Asian–western Pacific summer monsoon are diagnosed. Predictions show reasonable skill with respect to some basic characteristics of the ISV and IAV of the western North Pacific summer monsoon(WNPSM) and the Indian summer monsoon(ISM). However, the links between the seasonally averaged ISV(SAISV) and seasonal mean of ISM are overestimated by the model. This deficiency may be partially attributable to the overestimated frequency of long breaks and underestimated frequency of long active spells of ISV in normal ISM years, although the model is capable of capturing the impact of ISV on the seasonal mean by its shift in the probability of phases.Furthermore, the interannual relationships of seasonal mean, SAISV, and seasonally averaged long-wave variability(SALWV; i.e., the part with periods longer than the intraseasonal scale) of the WNPSM and ISM with SST and low-level circulation are examined. The observed seasonal mean, SAISV, and SALWV show similar correlation patterns with SST and atmospheric circulation, but with different details. However, the model presents these correlation distributions with unrealistically small differences among different scales, and it somewhat overestimates the teleconnection between monsoon and tropical central-eastern Pacific SST for the ISM, but underestimates it for the WNPSM, the latter of which is partially related to the too-rapid decrease in the impact of El Nino–Southern Oscillation with forecast time in the model. | LIU Xiangwen WU Tongwen YANG Song LI Qiaoping CHENG Yanjie LIANG Xiaoyun FANG Yongjie JIE Weihua NIE Suping | 2014 | Advances in Atmospheric Sciences2014，31，5： | 9 |
| 4 | Thuja sutchuensis:A rediscovered species of the Cupressaceae显示文摘 | XIAO QIAOPING FAJON A LI ZHENYU | 2002 | Botanical Journal of the Linnean2002，139，： | 1 |
| 5 | A climatology of extratropical cyclones over East Asia during 1958-2001显示文摘 | Zhang Yingxian Ding Yihui Li Qiaoping | 2011 | Acta Meteor Sinica2011，，： | 1 |
| 6 | Sliding mode control for uncertain chaotic systems with input nonlinearity 显示文摘 | Li Juntao Li Wenlin Li Qiaoping | 2012 | Communications in Nonlinear Science and Numerical Simulation2012，17，1： | 1 |
| 7 | Investigating the ENSO prediction skills of the Beijing Climate Center climate prediction system version 2显示文摘The El Niño-Southern Oscillation(ENSO)ensemble prediction skills of the Beijing Climate Center(BCC)climate prediction system version 2(BCC-CPS2)are examined for the period from 1991 to 2018.The upper-limit ENSO predictability of this system is quantified by measuring its“potential”predictability using information-based metrics,whereas the actual prediction skill is evaluated using deterministic and probabilistic skill measures.Results show that:(1)In general,the current operational BCC model achieves an effective 10-month lead predictability for ENSO.Moreover,prediction skills are up to 10–11 months for the warm and cold ENSO phases,while the normal phase has a prediction skill of just 6 months.(2)Similar to previous results of the intermediate coupled models,the relative entropy(RE)with a dominating ENSO signal component can more effectively quantify correlation-based prediction skills compared to the predictive information(PI)and the predictive power(PP).(3)An evaluation of the signal-dependent feature of the prediction skill scores suggests the relationship between the“Spring predictability barrier(SPB)”of ENSO prediction and the weak ENSO signal phase during boreal spring and early summer. | Yanjie Cheng Youmin Tang Tongwen Wu Xiaoge Xin Xiangwen Liu Jianglong Li Xiaoyun Liang Qiaoping Li Junchen Yao Jinghui Yan | 2022 | Acta Oceanologica Sinica2022，41，5： | 0 |
| 8 | Evolution of the serum resistance-associated SRA gene in African trypanosomes显示文摘Serum resistance-associated (SRA) protein, a protein unique for Trypanosoma brucei rhodesiense, is responsible for resistance of this parasite to the lysis by normal human serum (NHS) and is a vital molecular marker to distinguish this species from other African trypanosomes. We cloned and sequenced the SRA basic copy (SRAbc) gene from T. b. rhodesiense and related species and found that this gene is confined to the subgenus Trypanozoon. The average 82% identity among the sequenced SRAbc genes indicates that they may have a common origin and are highly conserved. Since SRAbc coexists in the T. b. rhodesiense genome with SRA, we propose that SRAbc might be the 'donor VSG', which after duplication became inserted into the expression site by recombination. Under natural selection, SRAbc could reform into SRA following mosaic formation. | LAI DeHua WANG QiaoPing LI Zhi JULIUS Lukes LUN ZhaoRong | 2009 | Chinese Science Bulletin2009，54，7： | 0 |
| 9 | Progress of MJO Prediction at CMA from Phase I to Phase II of the Sub-Seasonal to Seasonal Prediction Project显示文摘As one of the participants in the Subseasonal to Seasonal(S2S)Prediction Project,the China Meteorological Administration(CMA)has adopted several model versions to participate in the S2S Project.This study evaluates the models’capability to simulate and predict the Madden-Julian Oscillation(MJO).Three versions of the Beijing Climate Center Climate System Model(BCC-CSM)are used to conduct historical simulations and re-forecast experiments(referred to as EXP1,EXP1-M,and EXP2,respectively).In simulating MJO characteristics,the newly-developed high-resolution BCC-CSM outperforms its predecessors.In terms of MJO prediction,the useful prediction skill of the MJO index is enhanced from 15 days in EXP1 to 22 days in EXP1-M,and further to 24 days in EXP2.Within the first forecast week,the better initial condition in EXP2 largely contributes to the enhancement of MJO prediction skill.However,during forecast weeks 2–3,EXP2 shows little advantage compared with EXP1-M because the increased skill at MJO initial phases 6–7 is largely offset by the degraded skill at MJO initial phases 2–3.Particularly at initial phases 2–3,EXP1-M skillfully captures the wind field and Kelvin-wave response to MJO convection,leading to the highest prediction skill of the MJO.Our results reveal that,during the participation of the CMA models in the S2S Project,both the improved model initialization and updated model physics played positive roles in improving MJO prediction.Future efforts should focus on improving the model physics to better simulate MJO convection over the Maritime Continent and further improve MJO prediction at long lead times. | Junchen YAO Xiangwen LIU Tongwen WU Jinghui YAN Qiaoping LI Weihua JIE | 2023 | Advances in Atmospheric Sciences2023，40，10： | 0 |
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