Will the Globe Encounter the Warmest Winter after the Hottest Summer in 2023?

In the boreal summer and autumn of 2023,the globe experienced an extremely hot period across both oceans and continents.The consecutive record-breaking mean surface temperature has caused many to speculate upon how the global temperature will evolve in the coming 2023/24 boreal winter.In this report,as shown in the multi-model ensemble mean(MME)prediction released by the Institute of Atmospheric Physics at the Chinese Academy of Sciences,a medium-to-strong eastern Pacific El Niño event will reach its mature phase in the following 2−3 months,which tends to excite an anomalous anticyclone over the western North Pacific and the Pacific-North American teleconnection,thus serving to modulate the winter climate in East Asia and North America.Despite some uncertainty due to unpredictable internal atmospheric variability,the global mean surface temperature(GMST)in the 2023/24 winter will likely be the warmest in recorded history as a consequence of both the El Niño event and the long-term global warming trend.Specifically,the middle and low latitudes of Eurasia are expected to experience an anomalously warm winter,and the surface air temperature anomaly in China will likely exceed 2.4 standard deviations above climatology and subsequently be recorded as the warmest winter since 1991.Moreover,the necessary early warnings are still reliable in the timely updated mediumterm numerical weather forecasts and sub-seasonal-to-seasonal prediction.

Climate prediction of the seasonal sea-ice early melt onset in the Bering Sea

基于大尺度环流异常对海冰消融的影响过程,本文采用年际增量预测方法研制了白令海季节性海冰早期消融开始日期(EMO)的统计预测模型.预测模型选取了3个具有明确物理意义的预测因子:1月波弗特高压,前期11月东西伯利亚地区海平面气压,以及11月东欧平原积雪覆盖率。1月波弗特高压可以通过海气相互作用影响白令海地区海温异常,该海温异常能够从1月持续到3月,进而影响白令海EMO.11月东西伯利亚地区海平面气压与11月至次年2月北太平洋中纬度东部海温密切相关。伴随着北太平洋中纬度东部冷海温异常的出现,白令海地区会出现暖海温异常,进而导致白令海海冰范围减少,EMO较晚.1月北极偶极子异常是11月东欧平原积雪覆盖率影响次年白令海EMO的桥梁之一.1981-2022年的交叉检验结果表明:统计模型对白令海EMO具有较好的预测能力,预测与观测的EMO之间时间相关系数达到了0.45,超过了99%的置信水平.统计模型对白令海EMO正常年份和异常年份的预测准确率分别为60%和41%.

为什么NCEP-CFSv2模式对11月西伯利亚高压强度的预测性能较好

作为东亚冬季风的关键系统,西伯利亚高压的变化对欧亚大陆冬季天气及气候异常产生重要影响。本文系统地评估了美国国家环境预测中心第二代气候预测系统(NCEP-CFSv2,National Center for Environment Prediction-Climate Forecast System,version 2)对冬半年(11~2月)及逐月西伯利亚高压强度的预测效能。结果表明,NCEP-CFSv2模式仅对11月西伯利亚高压强度的预测效能较好,研究其成因发现11月西伯利亚高压强度主要受该地区热力、动力过程以及西伯利亚地区积雪状况的影响。在热力过程方面,NCEP-CFSv2模式可以较好地再现11月西伯利亚高压强度及其相联的该地区表层土壤温度、对外长波辐射等热力因素;在动力过程方面,模式能较好地再现11月西伯利亚高压强度及其相联的该地区对流层低层辐散环流、中高层下沉运动;同时,模式也能较好地再现11月西伯利亚高压强度与该地区积雪覆盖率之间的相互作用。因此,与11月西伯利亚高压相联的热力、动力过程和该地区积雪状况可能是11月西伯利亚高压强度的可预测来源,且NCEP-CFSv2模式能较好地再现这些可预测来源。

A hybrid forecasting model for depth-averaged current velocities of underwater gliders

In this paper,we propose a hybrid forecasting model to improve the forecasting accuracy for depth-averaged current velocities(DACVs) of underwater gliders.The hybrid model is based on a discrete wavelet transform(DWT),a deep belief network(DBN),and a least squares support vector machine(LSSVM).The original DACV series are first decomposed into several high-and one low-frequency subseries by DWT.Then,DBN is used for high-frequency component forecasting,and the LSSVM model is adopted for low-frequency subseries.The effectiveness of the proposed model is verified by two groups of DACV data from sea trials in the South China Sea.Based on four general error criteria,the forecast performance of the proposed model is demonstrated.The comparison models include some well-recognized single models and some related hybrid models.The performance of the proposed model outperformed those of the other methods indicated above.

Seasonal Climate Prediction Models for the Number of Landfalling Tropical Cyclones in China

Two prediction models are developed to predict the number of landfalling tropical cyclones(LTCs) in China during June–August(JJA). One is a statistical model using preceding predictors from the observation, and the other is a hybrid model using both the aforementioned preceding predictors and concurrent summer large-scale environmental conditions from the NCEP Climate Forecast System version 2(CFSv2).(1) For the statistical model, the year-to-year increment method is adopted to analyze the predictors and their physical processes, and the JJA number of LTCs in China is then predicted by using the previous boreal summer sea surface temperature(SST) in Southwest Indonesia,preceding October South Australia sea level pressure, and winter SST in the Sea of Japan. The temporal correlation coefficient between the observed and predicted number of LTCs during 1983–2017 is 0.63.(2) For the hybrid prediction model, the prediction skill of CFSv2 initiated each month from February to May in capturing the relationships between summer environmental conditions(denoted by seven potential factors: three steering factors and four genesis factors) and the JJA number of LTCs is firstly evaluated. For the 2-and 1-month leads, CFSv2 has successfully reproduced these relationships. For the 4-, 3-, and 2-month leads, the predictor of geopotential height at 500 h Pa over the western North Pacific(WNP) shows the worst forecasting skill among these factors. In general, the summer relative vorticity at 850 h Pa over the WNP is a modest predictor, with stable and good forecasting skills at all lead times.