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

Previous studies have revealed a combination mode(C-mode) occurring in the Indo-Pacific region,arising from nonlinear interactions between ENSO and the western Pacific warm pool annual cycle.This paper evaluates the simulation of this C-mode and its asymmetric SST response in Had GEM3 and its resolution sensitivity using three sets of simulations at horizontal resolutions of N96,N216 and N512.The results show that Had GEM3 can capture well the spatial pattern of the C-mode associated surface wind anomalies,as well as the asymmetric response of SST in the tropical Pacific,but it strongly overestimates the explained variability of the C-mode compared to the ENSO mode.The model with the three resolutions is able to reproduce the distinct spectral peaks of the C-mode at the near annual combination frequencies,but the performance in simulating the longer periods is not satisfactory,presumably due to the unrealistic simulation of the ENSO mode.Increasing the horizontal resolution can improve the consistency between atmospheric and oceanic representations of the C-mode,but not necessarily enhance the accuracy of C-mode simulation compared with observation.

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

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.

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

The El Ni?o–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 Ni?o3.4 SST anomaly index,which is well-recognized in the international ENSO research community and used operationally in the US,has replaced the previous Ni?o 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 Ni?o3 and Ni?o4 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.

Prediction of Primary Climate Variability Modes at the Beijing Climate Center

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?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.