The China Multi-Model Ensemble Prediction System and Its Application to Flood-Season Prediction in 2018
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摘要
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 Ni?o–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.
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 Ni?o–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.
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 Ni?o–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.
引文
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