Seasonal Prediction Skill and Biases in GloSea5 Relating to the East Asia Winter Monsoon

The simulation and prediction of the climatology and interannual variability of the East Asia winter monsoon(EAWM),as well as the associated atmospheric circulation,was investigated using the hindcast data from Global Seasonal Forecast System version 5(GloSea5),with a focus on the evolution of model bias among different forecast lead times.While GloSea5 reproduces the climatological means of large-scale circulation systems related to the EAWM well,systematic biases exist,including a cold bias for most of China’s mainland,especially for North and Northeast China.GloSea5 shows robust skill in predicting the EAWM intensity index two months ahead,which can be attributed to the performance in representing the leading modes of surface air temperature and associated background circulation.GloSea5 realistically reproduces the synergistic effect of El Niño–Southern Oscillation(ENSO)and the Arctic Oscillation(AO)on the EAWM,especially for the western North Pacific anticyclone(WNPAC).Compared with the North Pacific and North America,the representation of circulation anomalies over Eurasia is poor,especially for sea level pressure(SLP),which limits the prediction skill for surface air temperature over East Asia.The representation of SLP anomalies might be associated with the model performance in simulating the interaction between atmospheric circulations and underlying surface conditions.

Dynamical Predictability of Leading Interannual Variability Modes of the Asian-Australian Monsoon in Climate Models

The dynamical prediction of the Asian-Australian monsoon(AAM)has been an important and long-standing issue in climate science.In this study,the predictability of the first two leading modes of the AAM is studied using retrospective prediction datasets from the seasonal forecasting models in four operational centers worldwide.Results show that the model predictability of the leading AAM modes is sensitive to how they are defined in different seasonal sequences,especially for the second mode.The first AAM mode,from various seasonal sequences,coincides with the El Niño phase transition in the eastern-central Pacific.The second mode,initialized from boreal summer and autumn,leads El Niño by about one year but can exist during the decay phase of El Niño when initialized from boreal winter and spring.Our findings hint that ENSO,as an early signal,is conducive to better performance of model predictions in capturing the spatiotemporal variations of the leading AAM modes.Still,the persistence barrier of ENSO in spring leads to poor forecasting skills of spatial features.The multimodel ensemble(MME)mean shows some advantage in capturing the spatiotemporal variations of the AAM modes but does not provide a significant improvement in predicting its temporal features compared to the best individual models in predicting its temporal features.The BCC_CSM1.1M shows promising skill in predicting the two AAM indices associated with two leading AAM modes.The predictability demonstrated in this study is potentially useful for AAM prediction in operational and climate services.

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.

Shades of Chaos: Lessons Learned About Lessons Learned About Forecasting El Nio and Its Impacts

The troubled forecast of El Nio’s onset in2014 requires an explanation as well as an open dialogue with the user community that depends on such an important forecast. A review of the forecasts on the Internet reflects two different perceptions about what transpired. The forecast community suggests they got it right, while the popular media suggests forecasters got it wrong. Why such a gap? The major El Nio that was alluded to by several organizations did not materialize when or even as expected. A science-fed media frenzy took place about an event considered in retrospect to have been an unusual borderline(weak) El Nio event, 'trickiest ever to forecast.'That is understandable, as the science of El Nio is still on a learning curve. But it suggests that the forecasting of El Nio’s onset is still in an experimental phase and not yet operational. Forecasting its onset(as a specific event)should be separated from forecasting its behavior and impacts(as a process) once the onset has been assured.Whenever a forecast is made, someone is responding to it.Therefore, such a distinction is necessary for the benefit of those societies and economic sectors affected by El Nio.

Understanding the El Nio Costero of 2017: The Definition Problem and Challenges of Climate Forecasting and Disaster Responses

This preliminary study examines the definition problem and challenges of climate forecasting and disaster responses associated with the El Nio costero(coastal) of2017, which developed rapidly with no warning and had catastrophic effects in Peru. Such a localized El Nio was not documented since 1925. An initial review suggests that in addition to the characteristics of the event(surprise),government responses may have been inadequate(as media reported) because of conflicting forecast reports(U.S. and Peru), which provoked a hydrometeorological debate and stifled decision making. Partly to blame was the El Nio definition problem, which can cause uncertainty and affect perception of risk, depending on which region of the equatorial Pacific one uses to identify an event. Responses were further complicated by the fact that some regions within Peru were experiencing drought prior to the El Nio costero's onset and impacts from the El Nio 2015–2016 were less than expected. Furthermore, a new government was in place, which may have hindered action. Thus, El Nio costero provides lessons to heed, not only with respect to the forecast information, but also with reference to the context of the forecast and disaster setting, which can influence disaster responses to hydrometeorological threats.