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

Based on a combination of six Chinese climate models and three international operational models,the China multimodel ensemble(CMME)prediction system has been upgraded into its version 2(CMMEv2.0)at the National Climate Centre(NCC)of the China Meteorological Administration(CMA)by including new model members and expanding prediction products.A comprehensive assessment of the performance of the upgraded CMME during its hindcast(1993–2016)and real-time prediction(2021–present)periods is conducted in this study.The results demonstrate that CMMEv2.0 outperforms all the individual models by capturing more realistic equatorial sea surface temperature(SST)variability.It exhibits better prediction skills for precipitation and 2-m temperature anomalies,and the improvements in prediction skill of CMMEv2.0 are significant over East Asia.The superiority of CMMEv2.0 can be attributed to its better projection of El Niño–Southern Oscillation(ENSO;with the temporal correlation coefficient score for Niño3.4 index reaching 0.87 at 6-month lead)and ENSO-related teleconnections.As for the real-time prediction in recent three years,CMMEv2.0 has also yielded relatively stable skills;it successfully predicted the primary rainbelt over northern China in summers of 2021–2023 and the warm conditions in winters of 2022/2023.Beyond that,ensemble sampling experiments indicate that the CMMEv2.0 skills become saturated after the ensemble model number increased to 5–6,indicating that selection of only an optimal subgroup of ensemble models could benefit the prediction performance,especially over the extratropics,yet the underlying reasons await future investigation.

Predictability and Risk of Extreme Winter PM_(2.5)Concentration in Beijing

Air pollution remains a serious environmental and social problem in many big cities in the world.How to predict and estimate the risk of extreme air pollution is unsettled yet.This study tries to provide a solution to this challenge by examining the winter PM_(2.5)concentration in Beijing based on the UNprecedented Simulation of Extremes with ENsembles(UNSEEN)method.The PM_(2.5)concentration observations in Beijing,Japanese 55-yr reanalysis data,and the Met Office near term climate prediction system(DePreSys3a)large ensemble simulations are used,and 10,000proxy series are generated with the model fidelity test.It is found that in Beijing,the main meteorological driver of PM_(2.5)concentration is monthly 850-hPa meridional wind(V850).Although the skill in prediction of V850 is low on seasonal and longer timescales,based on the UNSEEN,we use large ensemble of initialized climate simulations of V850 to estimate the current chance and risk of unprecedented PM_(2.5)concentration in Beijing.We unravel that there is a 3%(2.1%–3.9%)chance of unprecedented low monthly V850 corresponding to high PM_(2.5)in each winter,within the 95%range,calculated by bootstrap resampling of the data.Moreover,we use the relationship between air quality and winds to remove the meridional wind influence from the observed record,and find that anthropogenic intervention appears to have reduced the risk of extreme PM_(2.5)in Beijing in recent years.

Spatiotemporal distribution and decadal change of the monthly temperature predictability limit in China

Based on the nonlinear Lyapunov exponent and nonlinear error growth dynamics, the spatiotemporal distribution and decadal change of the monthly temperature predictability limit(MTPL) in China is quantitatively analyzed. Data used are daily temperature of 518 stations from 1960 to 2011 in China. The results are summarized as follows:(1) The spatial distribution of MTPL varies regionally. MTPL is higher in most areas of Northeast China, southwest Yunnan Province, and the eastern part of Northwest China. MTPL is lower in the middle and lower reaches of the Yangtze River and Huang-huai Basin.(2)The spatial distribution of MTPL varies distinctly with seasons. MTPL is higher in boreal summer than in boreal winter.(3) MTPL has had distinct decadal changes in China, with increase since the 1970 s and decrease since2000. Especially in the northeast part of the country, MTPL has significantly increased since 1986. Decadal change of MTPL in Northwest China, Northeast China and the Huang-huai Basin may have a close relationship with the persistence of temperature anomaly. Since the beginning of the 21 st century, MTPL has decreased slowly in most of the country, except for the south. The research provides a scientific foundation to understand the mechanism of monthly temperature anomalies and an important reference for improvement of monthly temperature prediction.