Jianghua Wan

China Meteorological Administration

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Co-Authors

Hong‐Li Ren

Chinese Academy of Meteorological Sciences

Bo Lü

China Meteorological Administration

Jie Wu

China Meteorological Administration

Ying Liu

China Meteorological Administration

Ben Tian

China Meteorological Administration

Chongbo Zhao

Affiliated Hospital of Youjiang Medical University for Nationalities

Qing Bao

Gansu Academy of Agricultural Sciences

Jinqing Zuo

China Meteorological Administration

Changzheng Liu

Shihezi University

Yu Nie

University of Science and Technology of China

Cooperative Institutions

China Meteorological Administration

Chinese Academy of Sciences

Institute of Atmospheric Physics

Nanjing University of Information Science and Technology

National Center for Climate Change Strategy and International Cooperation

University of Chinese Academy of Sciences

Met Office

China University of Geosciences

Chinese Academy of Meteorological Sciences

University of Exeter

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An extreme negative Indian Ocean Dipole event in 2016: dynamics and predictability

Climate Dynamics (2017)

Bo Lü Hong‐Li Ren Adam A. Scaife Jie Wu Nick Dunstone

During 2016 boreal summer and fall, a strong negative Indian Ocean Dipole (IOD) event occurred, which led to large climate impacts such as the drought over East Africa. In this study, efforts are made to understand the dynamics of this IOD event and to evaluate real-time IOD predictions from current operational seasonal forecast systems. We show that both the wind-evaporation-SST and thermocline feedback lead to fast IOD growth in boreal summer 2016. Anomalous westerlies over the tropical Indian Ocean warmed the sea surface temperature (SST) over the tropical southeastern Indian Ocean (TSEIO) by reducing local evaporation; and wind induced thermocline deepening increased TSEIO SST by vertical advection. The intraseasonal disturbances in May induced the early subsurface warming and initiated the 2016 IOD. Due to negative cloud-radiation-SST feedback, the 2016 IOD event decayed quickly after October. We also demonstrate the successful real-time IOD predictions by the operational Hadley Center Global seasonal forecasting system version 5 (GloSea5) and the Beijing Climate Center Climate System Model (BCC-CSM1.1m). Resulting from the realistic representation of observed air–sea interactions, both models successfully predicted the evolution of the 2016 IOD up to 2 seasons ahead. The skillful prediction is also due to the precursor of the early subsurface warming in the eastern Indian Ocean, which increases intrinsic predictability of the 2016 IOD event. It is also demonstrated that IOD amplitude biases can be reduced by the joint-model prediction. The successful prediction of the 2016 IOD event allowed the East African drought to be predicted 4–6 months ahead. Our study reveals that current operational climate models can give useful warning of impending IOD events and impending climate extremes.

Predictability

Hindcast

10.1007/s00382-017-3908-2

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Citations (97)

The China Multi-Model Ensemble Prediction System and Its Application to Flood-Season Prediction in 2018

Journal of Meteorological Research (2019)

Hong‐Li Ren Yujie Wu Qing Bao Jiehua Ma Changzheng Liu

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 Niño-Southern Oscillation (EN-SO) 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.

Subtropical ridge

10.1007/s13351-019-8154-6

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Citations (42)

Prediction of primary climate variability modes at the Beijing Climate Center

Journal of Meteorological Research (2017)

Hong‐Li Ren Fei‐Fei Jin Lianchun Song Bo Lü Ben Tian

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.

Predictability

Hindcast

Madden–Julian oscillation

Subtropical ridge

Arctic oscillation

10.1007/s13351-017-6097-3

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Citations (56)

Representation of the ENSO Combination Mode and its Asymmetric SST Response in Different Resolutions of HadGEM3

Advances in Atmospheric Sciences (2018)

Jianghua Wan Hong‐Li Ren Peili Wu

Mode (computer interface)

Atmospheric models

10.1007/s00376-018-7285-5

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Citations (4)

The observational influence of the North Atlantic SST tripole on the early spring atmospheric circulation

Geophysical Research Letters (2016)

Zhe Han Feifei Luo Jianghua Wan

Abstract This study investigated the forcing of the North Atlantic sea surface temperature (SST) tripole on the North Atlantic Oscillation (NAO)‐like circulation in early spring (February–April) in observations. Corresponding to an SST tripole forcing in early spring, the atmospheric circulation is very weak and insignificant. However, further analyses indicate that the observational effect of the SST anomalies on the NAO‐like circulation is disturbed by the concomitant sea ice anomalies. With the linear effects of sea ice anomalies removed, there is an equivalent barotropic NAO‐like circulation in early spring related to a North Atlantic SST tripole.

Forcing (mathematics)

Barotropic fluid

Atmospheric Circulation

Circulation (fluid dynamics)

10.1002/2016gl068099

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Citations (32)

Identification Standard for ENSO Events and Its Application to Climate Monitoring and Prediction in China

Journal of Meteorological Research (2018)

Hong‐Li Ren Bo Lü Jianghua Wan Ben Tian Peiqun Zhang

The El Niño–Southern Oscillation (ENSO) reflects anomalous variations in the sea surface temperature (SST) and atmospheric circulation over the tropical central–eastern Pacific. It remarkably impacts on weather and climate worldwide, so monitoring and prediction of ENSO draw intensive research. However, there is not yet a unique standard internationally for identifying the timing, intensity, and type of ENSO events. The National Climate Center of China Meteorological Administration (NCC/CMA) has led the effort to establish a national identification standard of ENSO events, which was officially endorsed by the National Standardization Administration of China and implemented operationally in NCC/CMA in 2017. In this paper, two key aspects of this standard are introduced. First, the Niño3.4 SST anomaly index, which is well-recognized in the international ENSO research community and used operationally in the US, has replaced the previous Niño Z index and been used to identify the start, end, and peak times, and intensity of ENSO events. Second, two new indices—the eastern Pacific ENSO (EP) index and the central Pacific ENSO (CP) index, based on the SST conditions in Niño3 and Niño4 region respectively, are calculated to first determine the ENSO type before monitoring and assessing the impacts of ENSO on China's climate. With this standard, all historical ENSO events since 1950 are consistently re-identified; their distinct properties are diagnosed and presented; and the impacts of ENSO events under different types on China's climate are re-assessed. This standard is also employed to validate the intensity, grade, and type of the ENSO events predicted by the NCC/CMA operational ENSO prediction system. The new standard and the thus derived unified set of re-analyzed historical ENSO events and associated information provide a good reference for better monitoring and prediction of future ENSO events.

Multivariate ENSO index

Anomaly (physics)

10.1007/s13351-018-8078-6

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Citations (78)

Verification of Seasonal Prediction by the Upgraded China Multi-Model Ensemble Prediction System (CMMEv2.0)

Journal of Meteorological Research (2024)

Jie Wu Hong‐Li Ren Jianghua Wan Jingpeng Liu Jinqing Zuo

10.1007/s13351-024-4001-5

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Citations (1)

The warmest year 2015 in the instrumental record and its comparison with year 1998

Atmospheric and Oceanic Science Letters (2016)

Chao Zhang Shuanglin Li Jianghua Wan

The global annual averaged Surface Air Temperature Anomaly (SATA) in 2015 and its rank in the historical instrumental records are analyzed using the CRU, NASA, and NOAA datasets. All datasets indicate that 2015 is the warmest year, which is 0.74 °C warmer than normal years from 1961 to 1990 in the HadCRUT4 data-set. The most evident warm anomaly occurs over land, especially at high latitudes. The averaged SATA over land is 1.13 °C, which is 0.54 °C warmer than that over oceans (0.59 °C). Because an El Niño event occurred in 2015 and 1998 and 1998 is also the warmest year in the twentieth century, these two years are compared to explain the formation of the warmest climate. A statistical approach that is known as the Ensemble Empirical Mode Decomposition (EEMD) is employed to isolate the components with different timescales, which range from interannual to centennial and a long-term trend. In 2015 the developing El Niño may have contributed an anomaly of 0.10 °C, while this value is 0.18 °C for 1998. The contribution of the decadal-multidecadal variability and beyond to 2015 is 0.64 °C, which is significantly larger than that of the interannual anomaly components (0.10 °C). This indicates that the warmest climate in 2015 occurred in the context of the timescales beyond the interannual.

Anomaly (physics)

Cru

10.1080/16742834.2016.1237255

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Citations (4)