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This study investigates the dominant characteristics of winter Arctic tropospheric thickness (1000–200 hPa), the variations of winter atmospheric circulation in the Northern Hemisphere, and the related winter North Pacific storm track (NPST) variabilities during 1979–2018 under the combined effects of the La Niña events with different periods of Arctic tropospheric thermal conditions. Results show that the leading mode (42.7%) exhibits prominent warm anomalies centered on Greenland and Baffin Bay. The winter Arctic tropospheric thickness experienced a phase shift from a cold period of the Arctic tropospheric temperature in 1979–1999 to the warm period after 2000. During the La Niña events with Arctic tropospheric warm anomalies, a wave train is shown in the mid-high latitudes with alternative anticyclonic, cyclonic, and anticyclonic anomalies over the Ural Mountains, Lake Baikal, and North Pacific, respectively. This atmospheric circulation pattern not only intensifies the linkage between the Arctic and mid-low latitudes but also induces the winter NPST shifting poleward. The possible physical mechanism is attributed to the large-scale circulation change and the local baroclinic energy conversion (BCEC). The enhanced anticyclonic anomaly in the North Pacific alters the climatological mean flow, further influencing the local BCEC through the interaction between the mean flow and eddies. The significantly robust BCEC over the North Pacific possibly induces the poleward shift of winter NPST during the La Niña events under the warm period.

The variability of boreal spring Hadley circulation (HC) over the Asian monsoon domain over the last four decades is explored. The climatological distribution of the regional HC is symmetric of the equator, with the ascending branch around the equator and sinking branch around the subtropics in each hemisphere. The first dominant mode (EOF1) of the regional HC is equatorial asymmetric, with the main body in the Southern Hemisphere (SH) and the ascending branch to the north of the equator. This mode is mainly characterized by interannual variation and is related to El Niño-Southern Oscillation (ENSO). Significant negative sea surface temperature (SST) anomalies are observed over the tropical Indian Ocean (TIO) along with the development of La Niña events; however, the magnitude of SST anomalies in the southern Indian Ocean is greater than that in the northern counterpart, contributing to EOF1 formation. The spatial distribution of the second dominant mode (EOF2) is with the main body lying in the Northern Hemisphere (NH) and the ascending branch located to the south of the equator. The temporal variation of this mode is connected to the warming of the TIO. The warming rate of the southern TIO SST is faster than that in the northern counterpart, resulting in the southward migration of the rising branch. The above result indicates the critical role of the meridional distribution of SST on the variability of the regional HC.

The “spring predictability barrier” (SPB) is a well-known characteristic of ENSO prediction, which has been widely studied for El Niño events. However, due to the nonlinearity of the coupled ocean–atmosphere system and the asymmetries between El Niño and La Niña, it is worthy to investigate the SPB for La Niña events and reveal their differences with El Niño. This study investigates the season-dependent predictability of sea surface temperature (SST) for La Niña events by exploring initial error growth in a perfect model scenario within the Community Earth System Model. The results show that for the prediction through the spring season, the prediction errors caused by initial errors have a season-dependent evolution and induce an SPB for La Niña events. Two types of initial errors that often yield the SPB phenomenon are identified: the first are type-1 initial errors showing positive SST errors in the central-eastern equatorial Pacific accompanied by a large positive error in the upper layers of the eastern equatorial Pacific. The second are type-2 errors presenting an SST pattern with positive errors in the southeastern equatorial Pacific and a west–east dipole pattern in the subsurface ocean. The type-1 errors exhibit an evolving mode similar to the growth phase of an El Niño-like event, while the type-2 initially experience a La Niña-like decay and then a transition to the growth phase of an El Niño-like event. Both types of initial errors cause positive prediction errors for Niño3 SST and under-predict the corresponding La Niña events. The resultant prediction errors of type-1 errors are owing to the growth of the initial errors in the upper layers of the eastern equatorial Pacific. For the type-2 errors, the prediction errors originate from the initial errors in the subsurface layers of the western equatorial Pacific. These two regions may represent the sensitive areas of targeted observation for La Niña prediction. In addition, the type-2 errors in the equatorial regions are enlarged by the recharge process from 10°N in the central Pacific during the eastward propagation. Therefore, the off-equatorial regions around 10°N in the central Pacific may represent another sensitive area of La Niña prediction. Additional observations may be prioritized in these identified sensitive areas to better predict La Niña events.

Most El Niño events occur sporadically and peak in a single winter 1 – 3 , whereas La Niña tends to develop after an El Niño and last for two years or longer 4 – 7 . Relative to single-year La Niña, consecutive La Niña features meridionally broader easterly winds and hence a slower heat recharge of the equatorial Pacific 6 , 7 , enabling the cold anomalies to persist, exerting prolonged impacts on global climate, ecosystems and agriculture 8 – 13 . Future changes to multi-year-long La Niña events remain unknown. Here, using climate models under future greenhouse-gas forcings 14 , we find an increased frequency of consecutive La Niña ranging from 19 ± 11% in a low-emission scenario to 33 ± 13% in a high-emission scenario, supported by an inter-model consensus stronger in higher-emission scenarios. Under greenhouse warming, a mean-state warming maximum in the subtropical northeastern Pacific enhances the regional thermodynamic response to perturbations, generating anomalous easterlies that are further northward than in the twentieth century in response to El Niño warm anomalies. The sensitivity of the northward-broadened anomaly pattern is further increased by a warming maximum in the equatorial eastern Pacific. The slower heat recharge associated with the northward-broadened easterly anomalies facilitates the cold anomalies of the first-year La Niña to persist into a second-year La Niña. Thus, climate extremes as seen during historical consecutive La Niña episodes probably occur more frequently in the twenty-first century. Analysis of climate models under future greenhouse-gas forcings shows that the frequency of consecutive La Niña events will increase, driven by ocean–atmosphere feedbacks that slow the heat recharge of the equatorial Pacific.

There is high demand and a growing expectation for predictions of environmental conditions that go beyond 0–14-day weather forecasts with outlooks extending to one or more seasons and beyond. This is driven by the needs of the energy, water management, and agriculture sectors, to name a few. There is an increasing realization that, unlike weather forecasts, prediction skill on longer time scales can leverage specific climate phenomena or conditions for a predictable signal above the weather noise. Currently, it is understood that these conditions are intermittent in time and have spatially heterogeneous impacts on skill, hence providing strategic windows of opportunity for skillful forecasts. Research points to such windows of opportunity, including El Niño or La Niña events, active periods of the Madden–Julian oscillation, disruptions of the stratospheric polar vortex, when certain large-scale atmospheric regimes are in place, or when persistent anomalies occur in the ocean or land surface. Gains could be obtained by increasingly developing prediction tools and metrics that strategically target these specific windows of opportunity. Across the globe, reevaluating forecasts in this manner could find value in forecasts previously discarded as not skillful. Users’ expectations for prediction skill could be more adequately met, as they are better aware of when and where to expect skill and if the prediction is actionable. Given that there is still untapped potential, in terms of process understanding and prediction methodologies, it is safe to expect that in the future forecast opportunities will expand. Process research and the development of innovative methodologies will aid such progress.

Five out of six La Niña events since 1998 have lasted two to three years. Why so many long-lasting multiyear La Niña events have emerged recently and whether they will become more common remains unknown. Here we show that ten multiyear La Niña events over the past century had an accelerated trend, with eight of these occurring after 1970. The two types of multiyear La Niña events over this time period followed either a super El Niño or a central Pacific El Niño. We find that multiyear La Niña events differ from single-year La Niñas by a prominent onset rate, which is rooted in the western Pacific warming-enhanced zonal advective feedback for the central Pacific multiyear La Niña events type and thermocline feedback for the super El Niño multiyear La Niña events type. The results from large ensemble climate simulations support the observed multiyear La Niña events–western Pacific warming link. More multiyear La Niña events will exacerbate adverse socioeconomic impacts if the western Pacific continues to warm relative to the central Pacific.Recent decades have seen the increasing frequency of multiyear La Niña events. Here the authors find that there are two different types of multiyear La Niña that are each linked to different mechanisms related to warming in the western equatorial Pacific.

The causes of the California drought during November–April winters of 2011/12–2013/14 are analyzed using observations and ensemble simulations with seven atmosphere models forced by observed SSTs. Historically, dry California winters are most commonly associated with a ridge off the west coast but no obvious SST forcing. Wet winters are most commonly associated with a trough off the west coast and an El Niño event. These attributes of dry and wet winters are captured by many of the seven models. According to the models, SST forcing can explain up to a third of California winter precipitation variance. SST forcing was key to sustaining a high pressure ridge over the west coast and suppressing precipitation during the three winters. In 2011/12 this was a response to a La Niña event, whereas in 2012/13 and 2013/14 it appears related to a warm west–cool east tropical Pacific SST pattern. All models contain a mode of variability linking such tropical Pacific SST anomalies to a wave train with a ridge off the North American west coast. This mode explains less variance than ENSO and Pacific decadal variability, and its importance in 2012/13 and 2013/14 was unusual. The models from phase 5 of CMIP (CMIP5) project rising greenhouse gases to cause changes in California all-winter precipitation that are very small compared to recent drought anomalies. However, a long-term warming trend likely contributed to surface moisture deficits during the drought. As such, the precipitation deficit during the drought was dominated by natural variability, a conclusion framed by discussion of differences between observed and modeled tropical SST trends.

The Pacific Meridional Mode (PMM) plays a critical role in affecting El Niño‐Southern Oscillation (ENSO). This study examines the phase asymmetry of PMM events triggered by tropical and extratropical forcings, namely successive and stochastic events, respectively. It is shown that successive events exhibit negative asymmetry due to stronger trigger in the negative phase, while stochastic events display positive asymmetry due to stronger growth in the positive phase. The opposite phase asymmetry of two types of events respectively results in more frequent persistent La Niña events than El Niño events and more frequent episodic El Niño events than La Niña events, which increase ENSO transition complexity. This research provides a comprehensive understanding of PMM asymmetry and reconciles conflicting perspectives from previous studies. Additionally, the newly proposed contribution of positively asymmetric stochastic PMM events to more frequent episodic El Niño events in this study may enhance our comprehension of ENSO transition complexity. Plain Language Summary In this study, we investigated the influences of the Pacific Meridional Mode (PMM), an important modulator of El Niño‐Southern Oscillation (ENSO), on the diverse transition preference of ENSO events (ENSO transition complexity). We first divided PMM events into two types that are respectively triggered by tropical and extratropical forcing, and named them as successive and stochastic PMM events. Successive PMM events tend to enhance persistency of ENSO events, whereas stochastic PMM events incline to initiate ENSO events from a neutral state. We found that the successive PMM events are negatively asymmetric because the triggering effect of tropical forcing is stronger in the negative phase. This negative asymmetry leads to longer persistency of La Niña events than El Niño events. On the contrary, the stochastic PMM events are positively asymmetric because the growth rate of PMM is stronger in the positive phase. This positive phase asymmetry of stochastic PMM events results in more frequent episodic El Niño events than La Niña events. This study is the first to show the contribution of positively asymmetric stochastic PMM events to higher frequency of episodic El Niño events, which may supplement our understanding of ENSO transition complexity. Key Points Pacific Meridional Mode (PMM) events can be divided into tropical‐forced successive events and extratropical‐forced stochastic events Successive PMM events are negatively asymmetric, whereas stochastic ones are positively asymmetric Opposite asymmetry of two types of PMM events, respectively, is conducive to more frequent persistent La Niñas and episodic El Niños

In the Pacific Basin, the El Niño/Southern Oscillation (ENSO) is the dominant mode of interannual climate variability, driving substantial changes in oceanographic forcing and impacting Pacific coastlines. Yet, how sandy coasts respond to these basin-scale changes has to date been limited to a few long-term beach monitoring sites, predominantly on developed coasts. Here we use 38 years of Landsat imagery to map shoreline variability around the Pacific Rim and identify coherent patterns of beach erosion and accretion controlled by ENSO. On the basis of more than 83,000 beach transects covering 8,300 km of sandy coastline, we find that approximately one-third of all transects experience significant erosion during El Niño phases. The Eastern Pacific is particularly vulnerable to widespread erosion, most notably during the large 1997/1998 El Niño event. By contrast, La Niña events coincide with significant accretion for approximately one-quarter of all transects, although substantial erosion is observed in southeast Australia and other localized regions. The observed regional variability in the coastal response to ENSO has important implications for coastal planning and adaptation measures across the Pacific, particularly in light of projected future changes in ENSO amplitude and flavour.The El Niño/Southern Oscillation drives coherent patterns of beach erosion and accretion around the Pacific Rim, according to analysis of satellite imagery covering over 8,300 km of sandy coastline.

NOAA Daily Optimum Interpolation Sea Surface Temperature (DOISST) and other similar sea surface temperature (SST) products indicate that the globally averaged SST set a new daily record in March 2023. The record‐high SST in March was immediately broken in April, and new daily records were set again in July and August 2023. The SST anomaly (SSTA) persisted at a record high from mid‐March to the remainder of 2023. Our analysis indicates that the record‐high SSTs, and associated marine heatwaves (MHWs) and even super‐MHWs, are attributed to three factors: (a) a long‐term warming trend, (b) a shift to the warm phase of the multi‐decadal Pacific‐Atlantic‐Arctic (PAA) mode, and (c) the transition from the triple‐dip succession of La Niña events to the 2023–24 El Niño event. Plain Language Summary Observation‐based analyses such as the NOAA DOISST show that global mean SST reached a record high in April 2023, breaking the previous record of global mean SST set in March 2016, and the April 2023 record of global mean SST was broken again in July and August 2023. Our study indicates that these record‐breaking SSTs in 2023 resulted from record‐high SSTs over much of the global oceans and associated with widespread marine heatwaves (MHWs). Further analyses show that the record‐high SSTs are attributed to a long‐term‐warming trend associated with increasing greenhouse gases, a shift to the warm phase of a multidecadal Pacific‐Atlantic‐Arctic (PAA) mode, and a warming associated with the transition from 2020–23 La Niña events to the 2023–24 El Niño event. Key Points Global mean sea surface temperature (SST) in 2023 set a record high Record‐high SST was associated with widespread marine heatwaves (MHWs) and super‐MHWs throughout the oceans Record‐high SST was attributed to the multi‐decadal warming trend, warm phase of Pacific‐Atlantic‐Arctic mode, and 2023–24 El Niño event