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Abstract This study examines December-January-February (DJF) soil moisture responses to multi-year (MY) and single-year (SY) La Niñas using a 2200-year CESM1 simulation, AGCM experiments, and observational data. Four regions where MY La Niñas amplify SY La Niñas’ impacts on soil moisture were identified: North America, Australia, the Middle East, and the Sahel. SY La Niñas typically cause soil moisture drying in the Middle East and North America and wetting in Australia and the Sahel. MY La Niñas enhance these effects in the second DJF due to the strengthening of precipitation anomalies or the accumulation of precipitation-induced soil moisture anomalies, except in the Sahel where wetting is driven in part by evapotranspiration anomalies. Soil moisture variations are linked to La Niña-induced sea surface temperature changes in the Indian Ocean (for Australia and the Middle East) and the Pacific Ocean (for North America). These amplified effects are largely supported by the observed MY La Niña events from 1948 to 2022. These findings emphasize the need to integrate MY La Niñas into regional agriculture and water resource management strategies to better anticipate and mitigate their impacts. 

Abstract The 2023/24 El Niño commenced with an exceptionally large warm water volume in the equatorial western Pacific, comparable to the extreme 1997/98 and 2015/16 events, but did not develop into a super El Niño. This study highlights the critical role of contrasting Northern Pacific Meridional Mode (NPMM) conditions in this divergence. Warm NPMM conditions during the 1997/98 and 2015/16 events created a positive zonal sea surface temperature (SST) gradient in the equatorial western-central Pacific and enhanced Madden-Julian Oscillation (MJO) propagation, driving sustained westerly wind bursts (WWBs) and downwelling Kelvin waves that intensified both events. In contrast, the cold NPMM during 2023/24 induced a negative SST gradient and suppressed MJO activity, resulting in weaker WWBs and limited eastward wave activity, preventing the event from reaching super El Niño intensity. A 2,200-year CESM1 pre-industrial simulation corroborates these observational findings, underscoring the importance of NPMM interference in improving El Niño intensity predictions. 

Abstract Multi-year El Niño-Southern Oscillation (ENSO) events, where the warming (El Niño) or cooling (La Niña) extends beyond a single year, have become increasingly prominent in recent decades. Using observations and climate model simulations, we show that the South Pacific Oscillation (SPO) plays a crucial, previously unrecognized role in determining whether ENSO evolves into a multi-year event. Specifically, when an El Niño (La Niña) triggers a positive (negative) SPO in the extratropical Southern Hemisphere during its decaying phase, the SPO feedbacks onto the tropical Pacific through the wind-evaporation-sea surface temperature mechanism, helping sustain ENSO into a multi-year event. This SPO–ENSO interaction is absent in single-year ENSO events. Furthermore, whether ENSO can trigger the SPO depends systematically on the central SST anomaly location for El Niños and the anomaly intensity for La Niñas, with interference from atmospheric internal variability. These findings highlight the importance of including off-equatorial processes from the Southern Hemisphere in studies of ENSO complexity dynamics. 

Abstract Multi-year marine heatwaves (MHWs) in the Gulf of Alaska (GOA) are major climate events with lasting ecological and economic effects. Though often seen as local Pacific phenomena, our study shows their persistence depends on trans-basin interactions between the North Pacific and North Atlantic. Using observational data and climate model experiments, we find that prolonged MHWs occur as sequential warming episodes triggered by atmospheric wave trains crossing ocean basins. These wave trains alter surface heat flux, initiating MHWs in the GOA and changing North Atlantic sea surface temperatures (SSTs). In turn, Atlantic SST anomalies reinforce wave activity, fueling subsequent MHW episodes in a feedback loop. This mechanism appears in historical events (1949–52, 1962–65, 2013–16, and 2018–22), highlighting MHWs as a trans-basin phenomenon. Our findings link GOA MHWs to trans-basin atmospheric wave dynamics and identify North Atlantic SSTs as a potential predictor of their duration. 

Abstract This study explores the Antarctic sea ice concentration (SIC) response to multiyear (MY) and single-year (SY) El Niños using a 2200-yr CESM1 preindustrial simulation. During the first austral winter, MY El Niño weakens the amplitude of the typical SIC anomaly pattern induced by SY El Niño but maintains the same impact pattern. During the second winter, MY El Niños not only intensify the amplitude but also shift the typical impact pattern of SY El Niños eastward. The amplitude variation effect on SIC is caused by an Indian Ocean memory mechanism, while the zonal shifting effect on SIC pattern is caused by an Atlantic Ocean memory mechanism. These mechanisms result from the different responses of the two oceans to different locations and intensities between SY and MY El Niños. Observed MY El Niños during 1979–2020 confirm the distinct impacts during the second austral winter revealed by the CESM1 simulation. These results demonstrate that SIC in the Ross and Amundsen–Bellingshausen–Weddell Seas is sensitive to the SY or MY types of El Niño. 

Abstract This study investigates boreal spring events of Pacific Meridional Mode (PMM) from 1950 to 2022, revealing that cold PMM is more effective in triggering subsequent La Niña compared to warm PMM's induction of following El Niño. This asymmetry stems from the varying origins and sub‐efficacies of PMM groups. The cold PMM is primarily initiated by pre‐existing La Niña, while the warm PMM is comparably activated by pre‐existing El Niño and internal atmospheric dynamics. PMMs initiated by pre‐existing El Niño or La Niña play a crucial role in determining the efficacies of PMMs in triggering subsequent El Niño‐Southern Oscillation (ENSO). The strong discharge of pre‐existing El Niño hampers warm PMM's induction of subsequent El Niño, whereas weak recharge from pre‐existing La Niña enhances the efficacy of cold PMM in inducing subsequent La Niña. Comprehending not only the PMM phase but also its origin is crucial for ENSO research and prediction. 

Abstract In around 1990, significant shifts occurred in the spatial pattern and temporal evolution of the El Niño‐Southern Oscillation (ENSO), with these shifts showing asymmetry between El Niño and La Niña phases. El Niño transitioned from the Eastern Pacific (EP) to the Central Pacific (CP) type, while La Niña's multi‐year (MY) events increased. These changes correlated with shifts in ENSO dynamics. Before 1990, El Niño was influenced by the Tropical Pacific (TP) ENSO dynamic, shifting to the Subtropical Pacific (SP) ENSO dynamic afterward, altering its spatial pattern. La Niña was influenced by the SP ENSO dynamic both before and after 1990 and has maintained the CP type. The strengthened SP ENSO dynamic since 1990, accompanied by enhanced precipitation efficiency during La Niña, make it easier for La Niña to transition into MY events. In contrast, there is no observed increase in precipitation efficiency during El Niño. 

Abstract This study illustrates the considerable improvement in accuracy achievable for long‐lead forecasts (18 months) of the Ocean Niño Index (ONI) through the utilization of a long short‐term memory (LSTM) machine learning algorithm. The research assesses the predictive potential of eight predictors from both tropical and extratropical regions constructed based on sea surface temperature, outgoing longwave radiation, sea surface height and zonal and meridional wind anomalies. In comparison to linear regression model forecasts, the LSTM model outperforms them for both the tropical and extratropical predictor sets. Among all the predictors, the western North Pacific (WNP) index demonstrates the highest prediction skill in ONI forecasts, followed by the North Tropical Atlantic (NTA) index and then the sea surface height index. While other predictors help the LSTM model to forecast either the phase variation of the amplitude variation of the observed ONI, the extratropical WNP predictor enables the LSTM model to forecast both variations. This superiority can be attributed to the involvement of SST anomalies in the WNP region in both tropical and extratropical El Niño–Southern Oscillation (ENSO) dynamics, allowing for the utilization of predictive potential from both components of ENSO dynamics. The study also concludes that the extratropical ENSO dynamics provide a robust source of predictability for long‐lead ENSO forecasts, which can be effectively harnessed using the LSTM model. 

Abstract Utilizing a 2200-yr CESM1 preindustrial simulation, this study examines the influence of property distinctions between single-year (SY) and multiyear (MY) La Niñas on their respective impacts on winter surface air temperatures across mid–high-latitude continents in the model, focusing on specific teleconnection mechanisms. Distinct impacts were identified in four continent sectors: North America, Europe, Western Siberia (W-Siberia), and Eastern Siberia (E-Siberia). The typical impacts of simulated SY La Niña events are featured with anomalous warming over Europe and W&E-Siberia and anomalous cooling over North America. Simulated MY La Niña events reduce the typical anomalous cooling over North America and the typical anomalous warming over W&E-Siberia but intensify the typical anomalous warming over Europe. The distinct impacts of simulated MY La Niñas are more prominent during their first winter than during the second winter, except over W-Siberia, where the distinct impact is more pronounced during the second winter. These overall distinct impacts in the CESM1 simulation can be attributed to the varying sensitivities of these continent sectors to the differences between MY and SY La Niñas in their intensity, location, and induced sea surface temperature anomalies in the Atlantic Ocean. These property differences were linked to the distinct climate impacts through the Pacific North America, North Atlantic Oscillation, Indian Ocean–induced wave train, and tropical North Atlantic–induced wave train mechanisms. The modeling results are then validated against observations from 1900 to 2022 to identify disparities in the CESM1 simulation.