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The oceanographic conditions of the Southern California Bight (SCB) dictate the distribution and abundance of prey resources and therefore the presence of mobile predators, such as goose-beaked whales (Ziphius cavirostris). Goose-beaked whales are deep-diving odontocetes that spend a majority of their time foraging at depth. Due to their cryptic behavior, little is known about how they respond to seasonal and interannual changes in their environment. This study utilizes passive acoustic data recorded from two sites within the SCB to explore the oceanographic conditions that goose-beaked whales appear to favor. Utilizing optimum multiparameter analysis, modeled temperature and salinity data are used to identify and quantify these source waters: Pacific Subarctic Upper Water (PSUW), Pacific Equatorial Water (PEW), and Eastern North Pacific Central Water (ENPCW). The interannual and seasonal variability in goose-beaked whale presence was related to the variability in El Niño Southern Oscillation events and the fraction and vertical distribution of the three source waters. Goose-beaked whale acoustic presence was highest during the winter and spring and decreased during the late summer and early fall. These seasonal increases occurred at times of increased fractions of PEW in the California Undercurrent and decreased fractions of ENPCW in surface waters. Interannual increases in goose-beaked whale presence occurred during El Niño events. These results establish a baseline understanding of the oceanographic characteristics that correlate with goose-beaked whale presence in the SCB. Furthering our knowledge of this elusive species is key to understanding how anthropogenic activities impact goose-beaked whales.

Future projections indicate that Indian Summer Monsoon Rainfall (ISMR) faces a "wetter and more variable" climate. However, the reasons remain uncertain. The Last Interglacial (LIG) climate provides a potential analog for future warming. Investigating ISMR responses to these two warming scenarios could help understand the causes of ISMR changes. Using PMIP4 simulations, we find that ISMR became "wetter and more stable" during the LIG, contrasting the future climate. The opposing changes in ISMR variability are related to divergent changes in the El Niño‐Southern Oscillation (ENSO) amplitudes, ENSO‐ISMR relationships, and ENSO‐induced large‐scale atmospheric circulation anomalies. During the LIG, orbital forcing weakened ENSO variability and its impacts on ISMR. A westward positioning of ENSO shifted the atmospheric circulation anomalies westward, suppressing extreme ISMR anomalies. These processes are supported by atmospheric model simulations. Our results suggest that different warming patterns (dynamic effects) are more critical than moisture‐increasing effects in controlling regional climate variability. Plain Language Summary: The Last Interglacial (LIG), approximately 129,000 to 116,000 years before the present, is a potential analog for future warming. We found that the variability of Indian Summer Monsoon Rainfall (ISMR) decreased while its mean state increased during the LIG, which is a "wetter and more stable" climate. This contrasts with the simultaneous increase in both the mean state and variability of ISMR projected in future warming scenarios. The opposing changes in ISMR variability during these two warm periods can be attributed to reverse changes in El Niño‐Southern Oscillation (ENSO) variability and its associated large‐scale circulation. During the LIG, reduced ENSO variability weakened the ENSO‐ISMR relationship. Sea surface temperature anomalies associated with ENSO extended westward in LIG, shifting precipitation and associated heating‐induced atmospheric circulation anomalies westward, which weakened the extreme ISMR anomalies, thus making the ISMR variability stable. This process is further supported by atmospheric general circulation model (CAM5) experiments. Our findings suggest that different external forcing‐induced warming patterns (dynamic effects) can be more critical than moisture‐increasing effects in contributing to regional climate variability change. Key Points: Indian Summer Monsoon Rainfall (ISMR) experienced a more stable climate during the LIG opposite to the change under anthropogenic warmingRelationship between ISMR and ENSO significantly weakened due to the waning ENSO variability induced by orbital forcingA westward positioning of ENSO during the LIG shifted the anomalous large‐scale circulation westward, reducing the extreme ISMR anomalies [ABSTRACT FROM AUTHOR]

The impact of the Madden‐Julian oscillation (MJO) on the intraseasonal PNA (ISPNA) was investigated and was found to be modulated by the El Niño‐Southern Oscillation (ENSO), which reasonably explains the skewness of the ISPNA during El Niño and La Niña winters. It was shown that the intensity and periodicity of the ISPNA was much stronger and slightly longer in La Niña winters than in the El Niño winters. The phase‐locked association between the ISPNA and MJO indicate that this skewness was controlled by the MJO. The northward Rossby wave activities derived from the tropics associated with the MJO to the subtropical Pacific sector of the ISPNA clarified that the stronger intensity of the MJO convection in the western Pacific during the La Niña winters, as well as the slower eastward propagation of the MJO, led to the asymmetric intensity and period of the ISPNA in the two ENSO phases. Plain Language Summary: While prior studies have revealed that the El Niño‐Southern Oscillation (ENSO) can impact global climate, how ENSO affects the intraseasonal variability of the Pacific‐North American pattern (ISPNA) remains unclear. It was found that the ISPNA was skewed toward a higher intensity and a longer period in the La Niña winters than in the El Niño winters after removing the interfere of winter‐mean PNA. This is quite different from the conventional understanding that the PNA is often stronger during El Niño winters than during La Niña winters because of the asymmetric heating effort of the ENSO. The phase‐locked association between the ISPNA and the Madden‐Julian Oscillation (MJO) indicate that this skewness was controlled by the MJO. The northward Rossby wave activities derived from the tropics to the subtropical Pacific sector of the ISPNA clarified the cause‐and‐effect relationship between the ISPNA and the MJO. The stronger intensity of the MJO convection in the tropical Western Pacific to oceans east of Australia during the La Niña winters, as well as the slower eastward propagation of the MJO, led to the skewness of the stronger intensity and longer period of the ISPNA in the La Niña winters than in the El Niño winters. Key Points: The intraseasonal PNA (ISPNA) was skewed toward a higher intensity and a longer period in the La Niña winters than in the El Niño wintersThe Madden‐Julian Oscillation (MJO) and the ISPNA were found to have a stable phase‐locked relationshipThe differences in the intensity and propagation of the MJO between La Niña and El Niño winters dominantly led to the skewed ISPNA [ABSTRACT FROM AUTHOR]

The ultimate driver of the end-Permian mass extinction is a topic of much debate. Here, we used a multiproxy and paleoclimate modeling approach to establish a unifying theory elucidating the heightened susceptibility of the Pangean world to the prolonged and intensified El Niño events leading to an extinction state. As atmospheric partial pressure of carbon dioxide doubled from about 410 to about 860 ppm (parts per million) in the latest Permian, the meridional overturning circulation collapsed, the Hadley cell contracted, and El Niños intensified. The resultant deforestation, reef demise, and plankton crisis marked the start of a cascading environmental disaster. Reduced carbon sequestration initiated positive feedback, producing a warmer hothouse and, consequently, stronger El Niños. The compounding effects of elevated climate variability and mean state warming led to catastrophic but diachronous terrestrial and marine losses. [ABSTRACT FROM AUTHOR]

Background: Seasonal fluctuations in weather are recognized as factors that affect both Aedes (Ae.) aegypti mosquitoes and the diseases they carry, such as dengue fever. The El Niño-Southern Oscillation (ENSO) is widely regarded as one of the most impactful atmospheric phenomena on Earth, characterized by the interplay of shifting ocean temperatures, trade wind intensity, and atmospheric pressure, resulting in extensive alterations in climate conditions. In this study, we investigate the influence of ENSO and local weather conditions on the spatio-temporal variability of Ae. aegypti infestation index. Methods: We collected seasonal entomological survey data of immature forms of Ae. aegypti mosquitoes (Breteau index), as well as data on temperature, rainfall and the Oceanic Niño Index (ONI) for the period 2008–2018 over the 645 municipalities of the subtropical State of São Paulo (Brazil). We grounded our analytical approach on a Bayesian framework and we used a hierarchical spatio-temporal model to study the relationship between ENSO tracked by ONI, seasonal weather fluctuations and the larval index, while adjusting for population density and wealth inequalities. Results: Our results showed a relevant positive effect for El Niño on the Ae. aegypti larval index. In particular, we found that the number of positive containers would be expected to increase by 1.30-unit (95% Credible Intervals (CI): 1.23 to 1.37) with El Niño events (i.e., ≥ 1°C, moderate to strong) respect to neutral (and weak) events. We also found that seasonal rainfall exceeding 153.12 mm appears to have a notable impact on vector index, leading potentially to the accumulation of ample water in outdoor discarded receptacles, supporting the aquatic phase of mosquito development. Additionally, seasonal temperature above 23.30°C was found positively associated to the larval index. Although the State of São Paulo as a whole has characteristics favourable to proliferation of the vector, there were specific areas with a greater tendency for mosquito infestation, since the most vulnerable areas are predominantly situated in the central and northern regions of the state, with hot spots of abundance in the south, especially during El Niño events. Our findings also indicate that social disparities present in the municipalities contributes to Ae. aegypti proliferation. Conclusions: Considering the anticipated rise in both the frequency and intensity of El Niño events in the forthcoming decades as a consequence of climate change, the urgency to enhance our ability to track and diminish arbovirus outbreaks is crucial. Author summary: Dengue fever is a dramatically expanding disease transmitted by mosquitoes. Earlier research has demonstrated the susceptibility of dengue-carrying mosquitoes to weather conditions, with rainfall and temperature significantly impacting dengue transmission. While there is evidence of a connection between weather factors, the El Niño-Southern Oscillation (ENSO) and the risk of dengue, their impact on the behaviour of Aedes (Ae.) aegypti mosquitoes, which is the main disease vectors, remains poorly comprehended. In this study we analyse municipality-specific time series of a larval index of Ae. aegypti population in the State of São Paulo (southern Brazil) from 2008 to 2018. The main goal was to explore how intra- and inter-annual changes in weather conditions and ENSO periodic fluctuations affected mosquito abundance. We found a strong and significant coherence between the temporal pattern of mosquito abundance and El Niño phases, rainfall and temperature, showing the role that climate plays in driving infestation periodicity. Specifically, we identified a relationship between Ae. aegypti infestation and the strength of the ENSO phenomenon: periods characterized by El Niño phase (moderate to strong) were associated with a higher abundance of Ae. aegypti mosquito larvae. Additionally, we found that wealth inequalities increase opportunities for Ae. aegypti proliferation. Our discoveries carry significant implications for vector control strategies, as they offer valuable insights for categorizing areas based on spatial risk, enabling the prioritization of intensified vector control measures. Given the anticipated rise in the frequency of El Niño events due to climate change, these findings suggest that arbovirus outbreaks could also see an increase, and that El Niño occurrences might serve as a predictive tool for such outbreaks. [ABSTRACT FROM AUTHOR]

While deep learning models have shown promising capabilities in ENSO prediction, their inherent black-box nature often leads to a lack of physical consistency and interpretability. Here, we introduce ENSO-PhyNet, a Transformer-based model for ENSO prediction, which incorporates heat budget dynamical processes through self-attention computations. The model predicts sea surface temperature (SST) in the equatorial Pacific and achieves skillful predictions of the Niño 3.4 index with a lead time of up to 22 months. The self-attention maps reveal how the model makes predictions by focusing on specific processes in certain regions. Case analyses of recent El Niño and La Niña events underscore the impact of thermocline feedback and zonal advection feedback on the warming of the 2015 event, as well as the crucial role of anomalous easterlies in the emergence of the second-year La Niña in 2021. These findings demonstrate the model's interpretability and its ability to identify signals that are physically consistent with the development of ENSO events. [ABSTRACT FROM AUTHOR]

Significant strides have been made in understanding El Niño-Southern Oscillation (ENSO) dynamics, yet its long-lead prediction remains challenging, especially for the El Niño events after 2000. Sea surface salinity (SSS) is known to affect ENSO development and intensity by influencing ocean stratification and heat redistribution and therefore, when combined with sea surface temperature (SST) data, can potentially enhance ENSO forecast skill. In this study, we develop a deep learning (DL) model that incorporates a multiscale-pyramid structure and spatiotemporal feature extraction blocks, and the model successfully extends effective ENSO forecast lead time to 24 months for 2000–2021 with reduced effect of the spring predictability barrier (SPB). Interpretable methods are then applied to reveal the time-dependent roles of SST and SSS in ENSO forecast. More specifically, SST is critical for short-medium lead forecasts (<1 year), while SSS is important for medium-long lead forecasts (>6 months). Furthermore, we track global SST and SSS spatiotemporal shifts related to subsequent ENSO development, highlighting the importance of ocean inter-basin and tropics-extratropics interactions. With increasing availability of satellite SSS observations, our findings unveil unprecedented potential for advancing ENSO long-lead forecast skills. [ABSTRACT FROM AUTHOR]

The continued threat from climate change and human impacts on water resources demands high-resolution and continuous hydrological data accessibility for predicting trends and availability. This study proposes a novel threefold downscaling method based on machine learning (ML) which integrates: data normalization; interaction of hydrometeorological variables; and the application of a time series split for cross-validation that produces a high spatial resolution groundwater storage anomaly (GWSA) dataset from the Gravity Recovery and Climate Experiment (GRACE) and its successor mission, GRACE Follow-On (GRACE-FO). In the study, the relationship between the terrestrial water storage anomaly (TWSA) from GRACE and other land surface and hydrometeorological variables (e.g., vegetation coverage, land surface temperature, precipitation, and in situ groundwater level data) is leveraged to downscale the GWSA. The predicted downscaled GWSA datasets were tested using monthly in situ groundwater level observations, and the results showed that the model satisfactorily reproduced the spatial and temporal variations in the GWSA in the study area, with Nash-Sutcliffe efficiency (NSE) correlation coefficient values of 0.8674 (random forest) and 0.7909 (XGBoost), respectively. Evapotranspiration was the most influential predictor variable in the random forest model, whereas it was rainfall in the XGBoost model. In particular, the random forest model excelled in aligning closely with the observed groundwater storage patterns, as evidenced by its high positive correlations and lower error metrics (Mean Absolute Error (MAE) of 54.78 mm; R-squared (R²) of 0.8674). The downscaled 5 km GWSA data (based on random forest) showed a decreasing trend in storage associated with variability in the rainfall pattern. An increase in drought severity during El Niño lengthened the full recovery time of groundwater based on historical storage trends. Furthermore, the time lag between the occurrence of precipitation and recharge was likely controlled by the drought intensity and the spatial recharge characteristics of the aquifer. Projected increases in drought severity could further increase groundwater recovery times in response to droughts in a changing climate, resetting storage to a new tipping condition. Therefore, climate change adaptation strategies must recognise that less groundwater will be available to supplement the surface water supply during droughts. [ABSTRACT FROM AUTHOR]

Estimating tropical cyclone (TC) intensity is crucial for disaster reduction and risk management. This study aims to estimate TC intensity using machine learning (ML) models. We utilized eight ML models to predict TC intensity, incorporating factors such as TC location, central pressure, distance to land, landfall in the next six hours, storm speed, storm direction, date, and number from the International Best Track Archive for Climate Stewardship Version 4 (IBTrACS V4). The dataset was divided into four sub-datasets based on the El Niño–Southern Oscillation (ENSO) phases (Neutral, El Niño, and La Niña). Our results highlight that central pressure has the greatest effect on TC intensity estimation, with a maximum root mean square error (RMSE) of 1.289 knots (equivalent to 0.663 m/s). Cubist and Random Forest (RF) models consistently outperformed others, with Cubist showing superior performance in both training and testing datasets. The highest bias was observed in SVM models. Temporal analysis revealed the highest mean error in January and November, and the lowest in February. Errors during the Warm phase of ENSO were notably higher, especially in the South China Sea. Central pressure was identified as the most influential factor for TC intensity estimation, with further exploration of environmental features recommended for model robustness. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is a crucial physical parameter in meteorology and oceanography. This study demonstrates that the influence of earth incidence angle (EIA) on the SST retrieved from the microwave radiation imager (MWRI) onboard FengYun-3 (FY-3) meteorological satellites should not be ignored. Compared with algorithms that do not consider the influence of EIA in the regression, those that integrate the EIA into the regression can enhance the accuracy of SST retrievals. Subsequently, based on the recalibrated Level 1B data from the FY-3/MWRI, a long-term SST dataset was reprocessed by employing the algorithm that integrates the EIA into the regression. The reprocessed SST data, including FY-3B/MWRI SST during 2010-2019, FY-3C/MWRI SST during 2013-2019, and FY-3D/MWRI SST during 2018-2020, were compared with the in-situ SST and the SST dataset from the Operational Sea Surface Temperature and Ice Analysis (OSTIA). The results show that the FY-3/MWRI SST data were consistent with both the in-situ SST and the OSTIA SST dataset. Compared with the Copernicus Climate Change Service V2.0 SST, the absolute deviation of the reprocessed SST, with a quality flag of 50, was less than 1.5°C. The root mean square errors of the FY-3/MWRI orbital, daily, and monthly SSTs, with a quality flag of 50, were approximately 0.82°C, 0.69°C, and 0.37°C, respectively. The primary discrepancies between the FY-3/MWRI SST and the OSTIA SST were found mainly in the regions of the western boundary current and the Antarctic Circumpolar Current. Overall, this reprocessed SST product is recommended for El Niño and La Niña events monitoring. [ABSTRACT FROM AUTHOR]

The 2023 Atlantic hurricane season was above normal, producing 20 named storms, 7 hurricanes, 3 major hurricanes, and seasonal accumulated cyclone energy that exceeded the 1991–2020 average. Hurricane Idalia was the most damaging hurricane of the year, making landfall as a Category 3 hurricane in Florida, resulting in eight direct fatalities and 3.6 billion U.S. dollars in damage. The above-normal 2023 hurricane season occurred during a strong El Niño event. El Niño events tend to be associated with increased vertical wind shear across the Caribbean and tropical Atlantic, yet vertical wind shear during the peak hurricane season months of August–October was well below normal. The primary driver of the above-normal season was likely record warm tropical Atlantic sea surface temperatures (SSTs), which effectively counteracted some of the canonical impacts of El Niño. The extremely warm tropical Atlantic and Caribbean were associated with weaker-than-normal trade winds driven by an anomalously weak subtropical ridge, resulting in a positive wind–evaporation–SST feedback. We tested atmospheric circulation sensitivity to SSTs in both the tropical and subtropical Pacific and the Atlantic using the atmospheric component of the Community Earth System Model, version 2.3. We found that the extremely warm Atlantic was the primary driver of the reduced vertical wind shear relative to other moderate/strong El Niño events. The concentrated warmth in the eastern tropical Pacific in August–October may have contributed to increased levels of vertical wind shear than if the warming had been more evenly spread across the eastern and central tropical Pacific. SIGNIFICANCE STATEMENT: The 2023 Atlantic hurricane season produced above-normal activity despite strong El Niño conditions. The season had 20 named storms, along with 7 hurricanes and 3 major hurricanes. Normally, El Niño decreases Atlantic hurricane activity due to increases in vertical wind shear. In 2023, vertical wind shear was below average, likely driven by the record warm tropical Atlantic and Caribbean Sea surface temperatures which led to tropical circulation patterns that were considerably different from the atmospheric flow typically observed during El Niño events. This manuscript also uses a state-of-the-art climate model to investigate the impacts of Atlantic and Pacific SST configurations on Atlantic vertical wind shear patterns. [ABSTRACT FROM AUTHOR]

In order to quantify the influence of external forcings on the predictability limit using observational data, the author introduced an algorithm of the conditional nonlinear local Lyapunov exponent (CNLLE) method. The effectiveness of this algorithm is validated and compared with the nonlinear local Lyapunov exponent (NLLE) and signal-to-noise ratio methods using a coupled Lorenz model. The results show that the CNLLE method is able to capture the slow error growth constrained by external forcings, therefore, it can quantify the predictability limit induced by the external forcings. On this basis, a preliminary attempt was made to apply this method to measure the influence of ENSO on the predictability limit for both atmospheric and oceanic variable fields. The spatial distribution of the predictability limit induced by ENSO is similar to that arising from the initial conditions calculated by the NLLE method. This similarity supports ENSO as the major predictable signal for weather and climate prediction. In addition, a ratio of predictability limit (RPL) calculated by the CNLLE method to that calculated by the NLLE method was proposed. The RPL larger than 1 indicates that the external forcings can significantly benefit the long-term predictability limit. For instance, ENSO can effectively extend the predictability limit arising from the initial conditions of sea surface temperature over the tropical Indian Ocean by approximately four months, as well as the predictability limit of sea level pressure over the eastern and western Pacific Ocean. Moreover, the impact of ENSO on the geopotential height predictability limit is primarily confined to the troposphere. [ABSTRACT FROM AUTHOR]

Winter precipitation over eastern China displays remarkable interannual variability, which has been suggested to be closely related to El Niño–Southern Oscillation (ENSO). This study finds that ENSO impacts on eastern China precipitation patterns exhibit obvious differences in early (November–December) and late (January–February) winter. In early winter, precipitation anomalies associated with ENSO are characterized by a monopole spatial distribution over eastern China. In contrast, the precipitation anomaly pattern in late winter remarkably changes, manifesting as a dipole spatial distribution. The noteworthy change in precipitation responses from early to late winter can be largely attributed to the seasonally varying Kuroshio anticyclonic anomalies. During the early winter of El Niño years, anticyclonic circulation anomalies appear both over the Philippine Sea and Kuroshio region, enhancing water vapor transport to the entirety of eastern China, thus contributing to more precipitation there. During the late winter of El Niño years, the anticyclone over the Philippine Sea is further strengthened, while the one over the Kuroshio dissipates, which could result in differing water vapor transport between northern and southern parts of eastern China and thus a dipole precipitation distribution. Roughly the opposite anomalies of circulation and precipitation are displayed during La Niña winters. Further analysis suggests that the seasonally-varying Kuroshio anticyclonic anomalies are possibly related to the enhancement of ENSO-related tropical central-eastern Pacific convection from early to late winter. These results have important implications for the seasonal-to-interannual predictability of winter precipitation over eastern China. [ABSTRACT FROM AUTHOR]

Panama's new president, José Raúl Mulino, has close ties to former president Ricardo Martinelli, who was disqualified from the presidential race due to a prison sentence for corruption. Mulino's right-wing government is expected to prioritize projects such as pension system reform and public healthcare, which may further exacerbate inequality in the country. Mulino's cabinet is largely composed of Martinelli allies, and his administration is likely to be business-friendly. There are concerns about Mulino's stance on mining and water issues, as he is seen as an ally of the mining sector and has expressed a centralized approach to addressing water shortages. [Extracted from the article]

Aim: This study investigates the biogeographic patterns of Pacific white‐sided dolphins (Lagenorhynchus obliquidens) in the Eastern North Pacific based on long‐term passive acoustic records. We aim to elucidate the ecological and behavioural significance of distinct echolocation click types and their implications for population delineation, geographic distribution, environmental adaptation and management. Location: Eastern North Pacific Ocean. Time Period: 2005–2021. Major Taxa Studied: Pacific white‐sided dolphin. Methods: Over 50 cumulative years of passive acoustic monitoring (PAM) data from 14 locations were analyzed using a deep neural network to classify two distinct Pacific white‐sided dolphin echolocation click types. The study assessed spatial, diel, seasonal and interannual patterns of the two click types, correlating them with major environmental drivers such as the El Niño Southern Oscillation and the North Pacific Gyre Oscillation, and modeling long‐term spatial‐seasonal patterns. Results: Distinct spatial, diel and seasonal patterns were observed for each click type. Significant biogeographical shifts in presence were observed following the 2014–2016 marine heatwave event. Main Conclusions: Distinct spatial distributions of the two click types support the hypothesis that Pacific white‐sided dolphins produce population‐specific echolocation clicks. Seasonal and diel patterns suggest spatiotemporal niche partitioning between the populations in Southern California. Interannual changes, notably initiated during the 2014–2016 marine heatwave, indicate climate‐driven range expansions and contractions related to gradual tropicalization of the Southern California Bight. [ABSTRACT FROM AUTHOR]

The East African Coastal Current (EACC) is an important western boundary current of the tropical south Indian Ocean and plays an important role in the ocean circulation and biogeochemical cycles in the Indian Ocean. This study investigates the interannual variability of the EACC and its dynamical mechanisms. The result shows that the EACC has interannual variability associated with El Niño–Southern Oscillation (ENSO) during 1993–2017. The EACC shows a significantly positive correlation with the Niño-3.4 index with a correlation coefficient of 0.65, lagging the Niño-3.4 index by 18 months. During the decaying phases of El Niño (La Niña) events, the negative (positive) sea level anomaly (SLA) propagates westward as upwelling (downwelling) Rossby waves from the southeast Indian Ocean to the southwest Indian Ocean and then strengthens (weakens) the EACC due to zonal SLA gradient off the East African coast under geostrophic equilibrium. The SLA gradually weakens in the southeast Indian Ocean during its westward propagation but strengthens in the southwest Indian Ocean promoted by local wind stress curl anomaly. This study can improve our understanding of the relationship between the western boundary current of the tropical south Indian Ocean and large-scale ENSO air–sea processes and is important for managing marine fisheries and ecosystems on the East African coast. [ABSTRACT FROM AUTHOR]

Coupled ocean and prescribed sea surface temperature (SST) experiments are performed to investigate the drivers of Northern Hemisphere (NH) midlatitude winter circulation and blocking changes in warmer climates. In coupled experiments, a historical simulation is compared to a simulation following an end of the twenty-first-century shared socioeconomic pathway (SSP5-8.5) emission scenario. The SSP5-8.5 simulation yields poleward-shifted jets and an enhanced stationary wave pattern compared to the historical simulation. In terms of blocking, a reduction is found across North America and over the Pacific Ocean with the suggestion of more blocking over parts of Eurasia. Separately, prescribed SST experiments are performed decomposing the SSP5-8.5 SST response into a uniform warming component plus a spatially dependent change in SST pattern. SSP5-8.5 changes in circulation are primarily driven by a uniform warming of SST. Uniform warming is also found to account for most of the SSP5-8.5 blocking reduction over North America and the Pacific Ocean, but not over Eurasia. El Niño–like changes to the SST pattern also yield less blocking over the Pacific and North America. However, adding the responses of uniform and pattern experiments yields a nonlinear overreduction of blocking compared to the SSP5-8.5 experiment. Regional analyses of block energetics suggest that much of the reductions in blocking in warming simulations are driven by decreased baroclinic conversion in some regions and enhanced dissipation from diabatic sources in others. Significance Statement: Atmospheric blocks are persistent anticyclones that can cause severe weather such as heat waves and cold spells. Climate models generally project that on a warmer Earth, blocking frequency is poised to decrease in the Northern Hemisphere by the end of the twenty-first century. The cause, however, remains unclear. In this study, we investigate the response of mean atmospheric circulation and atmospheric blocking when separately considering the warming of sea surface temperatures (SST) and changing the SST pattern. We find that most of the reduction in blocking can be explained by a uniform warming of SST. Energetics analyses suggest that this reduction is driven by blocks' inhibited extraction of mean flow potential energy in some regions and by enhanced diabatic dissipation in others. [ABSTRACT FROM AUTHOR]

2019 witnessed one of the strongest positive Indian Ocean Dipole. A very strong positive subsurface mode (pSSM) co-evolved in the Tropical Indian Ocean (TIO) during September–October–November 2019, which strengthened further during December–January–February (DJF) 2019–2020. This is the first occurrence of such a very strong pSSM in the recent decades, which strengthened during DJF without any favourable forcing from the Pacific. The TIO further displayed anomalous basin-wide surface warming from winter 2019 to summer 2020. It is found from both observations and model experiments that ocean dynamics associated with pSSM played a major role in the TIO basin-wide warming during 2020. The subsurface–surface interaction along the downwelling Rossby wave path from boreal winter to spring, the reflected Kelvin waves and surface currents have contributed to the basin-wide surface warming of the TIO from DJF (2019–2020) onwards. The mixed layer heat budget analysis reveals that the surface heat fluxes were not favourable for the basin-wide surface warming, thereby undermining the role of any Pacific forcing through atmospheric pathways. The ocean model sensitivity experiments further highlight the importance of Indian Ocean dynamics in the co-evolution of subsurface temperature and sea surface temperature over TIO, especially during the 2019–2020 event. Research highlights: A strong subsurface dipole mode evolved in the Tropical Indian Ocean temperature during 2019–2020. Indian Ocean basin-wide surface warming persisted up to summer 2020, highlighting the role of ocean dynamics. Indian Ocean dynamics associated with the subsurface mode is responsible for the evolution of basin-wide surface warming. Ocean model experiments support the role of ocean dynamics in the evolution of basin-wide surface warming. [ABSTRACT FROM AUTHOR]

Despite common background La Niña conditions, Australia was very dry in November 2020 and wet in November 2021. This paper aims to provide an explanation for this difference. Large-scale drivers of Australian rainfall, including El Niño–Southern Oscillation, Indian Ocean dipole, Southern Annular Mode, and Madden–Julian oscillation, were examined but did not provide obvious clues for the differences. We found that the absence (in 2020) or presence (in 2021) of an enhanced thermal wind and subtropical jet over the Australian continent contributed to the rainfall anomalies. In general, La Niña sets up warm sea surface temperatures around northern Australia, which enhances the meridional temperature gradient over the continent and hence thermal wind and subtropical jet. In November 2021, these warm sea surface temperatures, coupled with a persistent midlatitude trough, which advected cold air over the Australian continent, led to an enhanced meridional temperature gradient and subtropical jet over Australia. The enhanced jet provided favorable conditions for the development of rain-bearing weather systems across Australia. In 2020, the continent was warm, displacing the latitude of maximum meridional temperature gradient south of the continent, resulting in fewer instances of the subtropical jet over Australia, and little development of weather systems over the continent. We highlight that although La Niña tilts the odds to wetter conditions for Australia, in any given month, variability in temperatures over the continent can contribute to subtropical jet variability and resulting rainfall in ways which confound the normal expectation from La Niña. Significance Statement: Forecasts of El Niño–Southern Oscillation are eagerly awaited, as the state of this climate driver has profound impacts on the likelihood of rainfall in regions around the world. While El Niño and La Niña do change rainfall likelihoods, the actual outcomes of these events are sometimes counter to expectation. This work explores one of the confounding factors to those expectations in the Australian context—the role of the meridional temperature gradient over the continent in modifying the storm track over Australia, which can disrupt the expected El Niño and La Niña teleconnections. We present case studies for two La Niña springs, highlighting that the Australian continent can help shape its own weather toward wetter or drier outcomes. [ABSTRACT FROM AUTHOR]

The Nile Basin has long been the subject of extensive research, reflecting its importance, which spans from its historical role in the development of ancient civilizations to its current significance in supporting rapidly changing socioeconomic conditions of the basin countries. This review synthesizes studies focusing on the past and future climate, hydrologic, and drought outlooks of the basin, and explores the roles played by large-scale atmospheric phenomena and water infrastructure on the basin's climate and hydrology. Overall, the studies underscore the complexity of the Nile hydrological system and the necessity for improved modeling and data integration. This review serves as a guide to areas warranting further research by highlighting the uncertainties and inconsistencies among the different studies. It underscores the interconnectedness of climatic and hydrological processes in the basin and encourages the use of diverse data sources to address the data scarcity issue and ensemble models to reduce modeling uncertainty in future research. By summarizing the data and modeling resources employed in these studies, this review also provides a valuable resource for future modeling efforts to understand and explore of the basin's complex climatic and hydrological dynamics. [ABSTRACT FROM AUTHOR]

Floods are highly destructive natural disasters. Climate change and urbanization greatly impact their severity and frequency. Understanding flood causes in urban areas is essential due to significant economic and social impacts. Hydrological data and satellite imagery are critical for assessing and managing flood effects. This study uses satellite images, climate anomalies, reservoir data, and cyclonic activity to examine the 2015 and 2023 floods in Chennai, Kanchipuram, and Thiruvallur districts, Tamil Nadu. Synthetic-aperture radar (SAR) satellite data were used to delineate flood extents, and this information was integrated with reservoir data to understand the hydrological dynamics of floods. The classification and regression tree (CART) model delineates flood zones in Chennai, Kanchipuram, and Thiruvallur during the flood years. The study region is highly susceptible to climatic events such as monsoons and cyclones, leading to recurrent flooding. The region's reservoirs discharged floodwaters exceeding 35,000 cubic meters per second in 2015 and 15,000 cubic meters per second in 2023. Further, the study examines the roles of the Indian Ocean Dipole (IOD), which reached its peak values of 0.33 and 3.96 (positive IOD), and El Niño in causing floods here. The complex network of waterways and large reservoirs poses challenges for flood management. This research offers valuable insights for improving the region's flood preparedness, response strategies, and overall disaster management. [ABSTRACT FROM AUTHOR]

Observational and modeling studies have elucidated the influential role played by the southern and northern extratropical Pacific (SEP and NEP) forcing in shaping dynamics of tropical Pacific climate variability. However, the relative importance of the NEP and SEP and the timescale on which they impact the tropics remain unclear. Using a linear inverse model (LIM) that selectively incorporates or excludes tropical‐extratropical coupling, we find a reduction in tropical interannual variability (∼40%) and low‐frequency (sub‐decadal to decadal) variability in the southeastern tropical Pacific region (∼70%) in the absence of SEP. Conversely, the absence of NEP yields no significant impact on tropical interannual variability but markedly diminishes low‐frequency variability in the central tropical Pacific region (∼70%). LIM and statistic diagnostics on CMIP6 models show the low‐frequency to total variability ratio in the tropical Pacific depending on their NEP and SEP representation. Models with more (less) low‐frequency power tend to show stronger NEP (SEP) dynamics. Plain Language Summary: The tropical Pacific climate variability exerts a strong impact on global climate, regional weather, and marine ecosystems. The tropical and extratropical Pacific are closely coupled with each other through oceanic and atmospheric processes. Previous studies have shown that the southern and northern extratropical Pacific (SEP and NEP) forcing greatly impact the tropical Pacific climate variability. To understand and predict tropical Pacific variability, it is necessary to study the relative importance and the timescale on which the SEP and NEP exert their influence. In this study, we use an empirical dynamical model to exclude the impacts of SEP or NEP on the tropical Pacific based on the observational data. We find that the absence of SEP leads to a significant reduction of interannual variance (∼40%) and the low‐frequency (sub‐decadal to decadal) variance in the southeastern tropical Pacific region (∼70%), while the absence of the NEP does not change the interannual variance but significantly reduces the low‐frequency variance in the central tropical Pacific region (∼70%). In observations, the ratio of low‐frequency to total tropical variability is 0.36, while CMIP6 models exhibit a wider range, with enhanced low (high) frequency power associated with stronger NEP (SEP) dynamics. Key Points: The SEP dynamics contribute ∼40% of the tropical interannual variance and ∼70% of the southeastern tropical low‐frequency varianceThe NEP dynamics do not impact the tropical interannual variance but lead to ∼70% of the central tropical low‐frequency variabilityCMIP6 models show stronger NEP (SEP) dynamics tend to manifest ENSO with more (less) low‐frequency (>6 years) power [ABSTRACT FROM AUTHOR]

We evaluate the skill and sources of skill in initialized seasonal climate forecasts of monthly global mean temperature from the North American Multi‐Model Ensemble (NMME) during the period 1991–2024. The forecasts demonstrate skill in addition to that from the long‐term trend, and that skill is primarily attributable to ENSO. However, the skill varies seasonally, with skill being lowest for target periods during Northern Hemisphere summer. Single model ensembles show underdispersion at short leads, while the multi‐model ensemble is overdispersed, suggesting initial condition errors and highlighting the importance of model initialization for quantification of forecast uncertainty. Lead‐time dependent errors in global mean temperature trends appear related to Pacific trend errors. The multi‐model mean captured the overall trend but underestimated the record‐breaking temperatures of 2023. Forecasts for the remainder of 2024 indicate cooling by the end of the year. Plain Language Summary: Our study looked at how well current climate forecast models can predict the global average temperature up to a year in advance. We found that these models can predict temperature changes better than just looking at long‐term trends, and that their ability to do so was related to the climate phenomenon called ENSO (El Niño‐Southern Oscillation). However, the accuracy of these predictions depends on the time of year, being less accurate when predicting temperatures during the Northern Hemisphere's summer months. We also found that individual models often have too narrow a range of predictions, while combining multiple models results in a range that is too wide. Despite these deficiencies, the combined model predictions generally followed the observed temperatures but failed to predict the extreme high temperatures of 2023. Looking ahead, the models suggest a cooling trend by the end of 2024. This research improves our understanding of what current climate forecast models can tell us about global mean temperature in the short term and highlights areas where improvement is needed. Key Points: Initialized seasonal climate forecasts of global mean temperature show skill beyond the trend and that skill is largely related to ENSOForecast skill is lowest for target months during northern hemisphere summer due to forecast amplitudes that are too largeAt short leads single model ensembles are underdispersed and the multi‐model ensemble is overdispersed, which suggests initialization errors [ABSTRACT FROM AUTHOR]

Earlier proxy‐observational studies, and a sole modeling study, suggest that the Indian Ocean Dipole (IOD), an important global climate driver, exhibited multi‐scale temporal variability during the Last Millennium (LM; CE 0851–1849, with relatively high number of strong positive IOD events during the Little Ice Age (LIA; CE 1550–1749), and strong negative IOD events during the Medieval Warm Period (MWP; CE 1000–1199). Using nine model simulations from the PMIP3, we study the IOD variability during the LM after due validation of the simulated current day (CE 1850–2005) IOD variability. Majority of the models simulate relatively higher number of positive IOD events during the MWP, and negative IOD events in the LIA, commensurate with simulated background conditions. However, higher number of strong positive IOD events are simulated relative to the negative IODs during the LIA, in agreement with proxy‐observations, apparently owing to increased coupled feedback during positive IODs. Plain Language Summary: The Indian Ocean Dipole (IOD) is a natural climate phenomenon in the tropical Indian Ocean with significant global impacts. Positive IOD (pIOD) events are apparently occurring more frequently in recent decades, which may also be due to under‐sampling associated with limited observations span. Analyzing outputs for last millennium (CE 850–1850) from climate models, validated for historical period, helps in generating relatively longer‐period the paleo‐IOD records. Our analysis of simulations of the last thousand years from multiple models indicates relatively more positive (negative) IOD events in medieval warm period—CE 1000–1200 (Little Ice Age—CE 1550–1749). While during the ICA, background conditions similar to a negative IOD were simulated, models also simulate an increase in relatively‐stronger positive IOD events in its latter part, in agreement with a proxy‐climate record. The simulated centennial changes in positive and negative IOD frequencies are associated with changes in coupled ocean‐atmospheric feedback mechanisms. Key Points: Change in the Indian Ocean mean state from the medieval warm period (MWP) to the Little Ice Age (LIA)Despite negative IOD‐like background conditions in the LIA, models and paleo‐data show more stronger positive IODs thenThere are significant changes in feedback mechanisms of IODs from the MWP to LIA [ABSTRACT FROM AUTHOR]

This study investigates the relationship between sea surface temperature (SST) anomalies in the subtropical Atlantic Ocean, as represented by the Southern Atlantic subtropical dipole (SASD), and SST anomalies in the tropical Pacific Ocean, identified by the El Niño‐Southern Oscillation (ENSO). Contrary to the previously held notion of a weak relationship between SASD and ENSO as suggested by earlier literature, our analysis reveals a substantial inverse correlation between the two. This correlation exhibits significant multi‐decadal variability, which has notably intensified over the most recent two decades compared with the preceding two decades. This intensification in the SASD–ENSO inverse correlation may be attributed to the shift in ENSO regime from predominance of eastern Pacific El Niño to central Pacific El Niño events around the turn of the century. This transition triggers wavetrains that propagate along different paths, consequently influencing the South Atlantic subtropical high and inducing alterations in anomalous SST patterns in the subtropical Atlantic Ocean. These findings advance our comprehension of the interactions between South Atlantic and Pacific SST variations, which strongly influence rainfall patterns, particularly in South America and southern Africa. Understanding such teleconnection holds promise for improving sub‐seasonal to seasonal precipitation predictions in these regions. [ABSTRACT FROM AUTHOR]

The North Pacific Meridional Mode (PMM) is the strongest interannual air‐sea coupled system in the subtropical northeastern Pacific, which can significantly impact the development of El Niño and Southern Oscillation (ENSO). This study examines performance of the second version of the Chinese Academy of Sciences Earth System Model (CAS‐ESM2), developed primarily at the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP/CAS), in simulating the PMM, ENSO, and their relationship. It reveals that CAS‐ESM2 can well reproduce the tropical climate mean states, including sea surface temperature (SST), surface winds, and precipitation. Furthermore, the model shows a good ability in reproducing the seasonal evolutions of the PMM and ENSO. Moreover, CAS‐ESM2 effectively simulates the influence of the PMM on subsequent ENSO and the underlying physical mechanisms, including the wind‐evaporation‐SST feedback process, the trade wind charging mechanism and summer deep convection mechanism. However, some improvements are still needed, particularly in representing the periodicity of the PMM, an overestimation of the ENSO intensity and westward extension of ENSO‐related SST anomalies in the tropical Pacific. The results obtained from the CAS‐ESM2 showcase significant progress in understanding the interaction between air‐sea interaction systems over the tropics and subtropics. Plain Language Summary: El Niño and Southern Oscillation (ENSO) is the strongest interannual atmosphere‐ocean coupling variability in the tropical Pacific. ENSO can trigger extreme weather and climate events worldwide, and lead to substantial economic losses. While tropical processes are acknowledged to influence ENSO, many recent studies have emphasized the important role of extratropical air‐sea variabilities in modulating the occurrence and development of ENSO events. The Pacific Meridional Mode (PMM) has been identified as a key channel in transmitting the impact of extratropical forcing on ENSO. Therefore, it is crucial to evaluate the performance of current climate models in representing the PMM and its relationship with the ENSO. This evaluation holds significant scientific value as it can enhance our understanding of the dynamics underlying the ENSO and help improve the prediction of ENSO. The second version of the Chinese Academy of Sciences Earth System Model (CAS‐ESM2), developed primarily at the Institute of Atmospheric Physics, Chinese Academy of Sciences, represents a significant milestone in advancing atmospheric science in China. This study aims to assess the capabilities of CAS‐ESM2 in simulating the ENSO, PMM, and the impact of PMM on ENSO. Our findings indicate that CAS‐ESM2 well reproduces these two air‐sea coupling systems and captures their relation. Key Points: Chinese Academy of Sciences Earth System Model (CAS‐ESM2) has eliminated the double Intertropical Convergence Zone bias to some extentCAS‐ESM2 can reasonably well simulate the spatial‐temporal features of Pacific Meridional Mode (PMM) and El Niño and Southern Oscillation (ENSO)The influence of the spring PMM on ENSO and the underlying mechanism can be captured by the CAS‐ESM2 [ABSTRACT FROM AUTHOR]

Long-lasting La Niña events (including double-year and triple-year La Niña events) have become more frequent in recent years. How the multi-year La Niña events affect tropical cyclone (TC) activities in the western North Pacific (WNP) and whether they differ from single-year La Niña events are unknown. Here we show that TCs are more active over the far-WNP (FWNP, 110°–150°E), leading to marked high risks at China coasts during the second decaying summer of double-year La Niña events. The anomalous TC activities are directly related to the enhanced cyclonic anomaly over the FWNP, possibly a result of large-scale remote forcing initiated by the tropical North Atlantic (TNA) cooling. The persistent TNA cooling from the decaying winter to summer of double-year La Niña events drives westerlies over the Indo-western Pacific through Kelvin waves, which induce the cooling over the north Indian Ocean via the wind-evaporation-sea surface temperature effect, favoring the asymmetric heat distribution pattern and stimulating an anomalous vertical circulation over the eastern Indian Ocean to FWNP. The cooling over the north Indian Ocean also excites Gill responses, magnifying the TNA-induced westerlies and boosting the anomalous vertical circulation, and thus gives rise to the strong cyclonic circulation anomaly over the FWNP in summer. We suggest that the key point of the process is the strong TNA cooling related to the persistent negative Pacific-North American pattern (PNA) and positive North Atlantic Oscillation (NAO) while double-year La Niña events decay, distinct from the rapid decline of PNA and NAO during single-year La Niña events. The work provides a unique perspective on understanding TC activities over the WNP related to the El Niño-Southern Oscillation. [ABSTRACT FROM AUTHOR]

This study derives the climatology of surface currents in the Arabian Gulf using the current velocities obtained from the Copernicus Marine Service (CMEMS) for the period 1993–2019. It reveals distinct temporal and spatial variability in the surface current speeds induced by the variability in surface winds, bathymetry and the changes in the lateral gradients in density. The mean speed of the Iranian Coastal Current (ICC) during summer reaches up to 0.33 m·s−1 along the coast of Iran, while the mean speed of Arabian Coastal Current (ACC) reaches up to 0.26 m·s−1 along the coast of Saudi Arabia. We found the occurrence of 2 major and 1 minor cyclonic eddies in the annual, seasonal and monthly climatology, while these eddies are more prevalent during summer. The major cyclonic eddy in the central Gulf develops in May and persists till November with varying patterns, and decays in December. The climatological mean current speeds are higher during summer compared to winter, due to the seasonal changes in thickness of the surface layer by the stratification/destratification processes. The highest mean current speeds along the coast of Qatar are found in June and the lowest in winter months. The highest annual, monthly and seasonal mean current speeds are observed along the north and northeast coast of Qatar, while the lowest are observed along the west coast and southeast coast of Qatar. Interannual variability in surface current speeds is evident, with notable links with the El Niño–Southern Oscillations (ENSO) and Indian Ocean Dipole (IOD). The annual mean current speeds show positive trends, of the order of 0.06–0.14 cm·s−1·year−1 in the offshore regions and 0.05–0.24 cm·s−1·year−1 in the nearshore regions, wherein the highest positive trend is observed off Ras Laffan and the lowest off Dukhan. [ABSTRACT FROM AUTHOR]

This study analyses the spatio‐temporal variability of heatwave characteristics and their association with large‐scale climate drivers across seven climatic sub‐regions in Vietnam, including the Northwest (R1), Northeast (R2), Red River Delta (R3), North Central (R4), South Central (R5), Central Highlands (R6) and the South (R7). The analysis is based on observed daily maximum temperatures from 102 meteorological stations, spanning the period 1980–2020. The obtained results reveal diverse heatwave patterns across the country. Amongst the seven climatic sub‐regions of Vietnam, the R3 and R4 sub‐regions experienced more frequent heatwaves and a higher number of heatwave days, but shorter durations. In contrast, other sub‐regions had fewer heatwave events and heatwave days but experienced longer‐lasting heatwaves. The intensity of heatwave events varies amongst sub‐regions, with the highest value in the R4 sub‐region, and the lowest in R7. Notably, the R1–R5 sub‐regions are affected by heatwaves over larger areas, compared to others. Additionally, the findings confirm that the lagged influence of El Niño–Southern Oscillation (ENSO) is the primary climatic driver of heatwave characteristics in Vietnam. Generally, heatwaves tend to occur more frequently in the years following El Niño events than after La Niña events. This observation provides opportunities for developing a system of seasonal predictions of heatwaves in Vietnam. The impact of ENSO on the number of heatwave events and heatwave days is evident in five out of seven sub‐regions, with less impact in the R2 and R7 sub‐regions. However, it does not significantly affect heatwave intensity. [ABSTRACT FROM AUTHOR]

Monsoon precipitation variability over the Indo‐China Peninsula (ICP) has become more complicated affected by global warming. In this study, the modulation of Pacific decadal oscillation (PDO) on the relationship between El Niño–Southern Oscillation (ENSO) and the rainy season onset over the ICP were investigated. The results showed that the ICP rainy season onset were predominantly correlated with winter sea surface temperature anomalies (SSTAs) in the East Pacific Ocean, with late and early onsets following El Niño and La Niña events, respectively. During the warm and cold PDO phase, the correlations tended to be substantially strengthened and weakened, respectively. Further analysis indicates that PDO significantly influenced the effects of ENSO on the ICP rainy season onset by modulating SSTAs and low‐level wind fields. During the El Niño events, abnormal easterlies over the Bay of Bengal (BoB) and southern ICP suppressed water vapour transporting to the ICP, which may be related to the zonal SST anomaly gradient between the Indian Ocean and the Northwest Pacific Ocean. When the El Niño occurred during the warm PDO phase, the rainy season onsets were later. The anomalous easterlies became stronger corresponds to the increasing zonal sea surface temperature anomaly (SSTA) gradient between the Indian Ocean and the Northwest Pacific Ocean. There was no significant anomaly on the rainy season onset during the cold PDO phase. During the La Niña events, the abnormal westerlies in BOB accelerated water vapour transport, and the rainy season onset were earlier during the warm and cold PDO phase. The modulating effects of PDO on La Niña were less than those on El Niño. These results suggest that the predictability of rainy season onset over the ICP can be improved through PDO and thus help agricultural planning and water resources management. [ABSTRACT FROM AUTHOR]

Spring Central Asian precipitation (SCAP) holds significant implications for local agriculture and ecosystems, with its variability mainly modulated by El Niño–Southern Oscillation (ENSO). The ENSO–SCAP relationship has experienced pronounced interdecadal shifts, though mechanisms remain elusive. Based on observations and climate model simulations, these shifts may result from transitions in ENSO-induced meridional circulation and Rossby wave trains triggered by North Atlantic (NA) sea surface temperature (SST) anomalies. During high (low) correlation periods, ENSO induces strong (weak) vertical motion anomalies over Central Asia, while NA SST anomalies exert a weak (strong) counteracting effect, modulated by the Pacific decadal oscillation (PDO). In the positive (negative) phase of PDO, a slow (fast) decaying ENSO triggers a strong (weak) NA horseshoe-like SST anomaly in the post-ENSO spring, affecting the ENSO–SCAP relationship. Our study identifies a strengthening trend in the ENSO–SCAP relationship since the 2000s, indicating improved predictability for SCAP in recent decades. [ABSTRACT FROM AUTHOR]

In this study, we analyze the spatiotemporal patterns of propagation of pre‐monsoon heatwaves and their drivers in India. Using complex networks, we find that heatwaves originate most frequently in northwest India and propagate in the northeast or southeast direction. Heatwaves propagating in the northeast direction have a higher intensity, lower moving distance, smaller areal coverage, and shorter duration than heatwaves moving in the southeast. We find that the larger area and duration of heatwaves propagating southeast are a result of development of larger and more persistent high‐pressure systems extending over entire northern and eastern India, which are influenced by El Niño Southern Oscillation. On the other hand, higher radiative fluxes and larger heat entrainment in the boundary layer in the heatwaves propagating northeast contribute to their higher intensities. Plain Language Summary: We analyze the spatial patterns of where heatwaves originate and how they spread in the pre‐monsoon period in India. We find that most heatwaves originate in northwest India. Some of these heatwaves spread to north and central India while some propagate to south India. Heatwaves in northern and central India have higher intensity and last for a shorter period of time as compared to those which spread to south India. The area and duration of heatwaves moving toward south India are influenced by El Nino events. On the other hand, the intensity of heatwaves of central and north India is higher due to greater shortwave radiation and heat entrainment in the boundary layer. Key Points: Regions of origin and termination of pre‐monsoon heatwaves in India are identified using complex networks and event synchronizationMajority of heatwaves originate from northwest India and propagate in the northeast or southeast directionHeatwaves propagating southwards have larger duration and areal extent and are influenced by El Niño Southern Oscillation [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) anomalies over the North Tropical Atlantic (NTA) during the early boreal spring can trigger El Niño‐Southern Oscillation (ENSO) events in the following boreal winter. However, the future changes in the impact of the NTA on ENSO remain controversial. Here, we show distinct changes in the strength of the NTA−ENSO relationship due to global warming by comparing models from the Coupled Model Intercomparison Project (CMIP) 5 and CMIP6. The impact of the NTA on ENSO under greenhouse warming is notably enhanced in CMIP6 compared to CMIP5. A wetter mean state over the subtropical eastern North Pacific and increased oceanic sensitivity over the equatorial central Pacific are key factors that enhance the impact of the NTA SST on ENSO. Therefore, differences in the mean state under greenhouse warming between the CMIP5 and CMIP6 models can modulate the strength of the NTA−ENSO relationship. Plain Language Summary: The North Tropical Atlantic (NTA) region can affect the development of El Niño events, significantly impacting global weather systems. Our study compares two generations of climate models, CMIP5 and CMIP6, to understand how the influence of the NTA sea surface temperature (SST) on El Niño might change due to global warming. We found that the influence of the NTA SST on El Niño is stronger in the CMIP6 models than in the CMIP5 models. This stronger impact is due to changes in the climatological background states, including a wetter mean state over the subtropical eastern North Pacific and increased oceanic sensitivity over the equatorial central Pacific. Understanding these changes is crucial for improving predictions of future climate variabilities and their potential impacts on weather, ecosystems, and food production worldwide. Key Points: The relationship between the North Tropical Atlantic (NTA) and El Niño‐Southern Oscillation (ENSO) is significantly enhanced by global warming in CMIP6 compared to CMIP5Mean state change under greenhouse warming is responsible for the strengthening of the NTA‐ENSO relationship [ABSTRACT FROM AUTHOR]

The wintertime North Atlantic Oscillation (NAO) and East Atlantic Pattern (EA) are the two leading modes of North Atlantic pressure variability and have a substantial impact on winter weather in Europe. The year‐to‐year contributions to multi‐model seasonal forecast skill in the Copernicus C3S ensemble of seven prediction systems are assessed for the wintertime NAO and EA, and well‐forecast and poorly‐forecast years are identified. Years with high NAO predictability are associated with substantial tropical forcing, generally from the El Niño Southern Oscillation (ENSO), while poor forecasts of the NAO occur when ENSO forcing is weak. Well‐forecast EA winters also generally occurred when there was substantial tropical forcing, although the relationship was less robust than for the NAO. These results support previous findings of the impacts of tropical forcing on the North Atlantic and show this is important from a multi‐model seasonal forecasting perspective. Plain Language Summary: The wintertime North Atlantic Oscillation (NAO) and East Atlantic Pattern (EA) are two important indicators of atmospheric variability in the North Atlantic. They can have a substantial impact on European winter weather. The ability of seasonal forecast models to forecast the NAO and EA varies from year to year. This intermittency of forecast skill is investigated in seven different seasonal forecast systems from the Copernicus C3S database, by focusing on the most well‐forecast and poorly‐forecast years. Years where the NAO is well‐forecast are associated with substantial tropical forcing, generally from the El Niño Southern Oscillation (ENSO), while poor forecasts of the NAO occur when ENSO forcing is weak. Similar but weaker results hold for the EA. These results are valuable for increasing the usability of seasonal forecasts by identifying conditions under which forecasts are more likely to be skillful. Key Points: Seasonal forecast skill of the wintertime North Atlantic Oscillation (NAO) and East Atlantic Pattern (EA) is intermittentWell‐forecast NAO/EA winters generally occur when there is substantial tropical forcing [ABSTRACT FROM AUTHOR]

The temporal variability of fish habitat utilization is poorly understood in tropical deltaic systems due to high water turbidity, which limits visual censuses, and to the lack of long-term data incorporating climate variability events. We aimed to assess the influence of body size and El Niño Southern Oscillation (ENSO) variability on the cross-habitat utilization rate of 14 fish species of commercial relevance in the Ciénaga Grande de Santa Marta (CGSM). We estimated the utilization of mangroves and coastal lagoons based on relative catch frequencies from encircling gillnets used within a long-term catch monitoring program, and then tested for significant changes in each species' habitat utilization as a function of body size and climate variability. Six species showed a high dependence on mangroves and four on coastal lagoons for most body size classes (including juveniles) and ENSO conditions. One species (Elops smithi) showed a high utilization of mangroves in some ENSO phases and body size classes, while three species showed a high utilization of both mangroves and coastal lagoons. Mangrove utilization by six species (Megalops atlanticus, E. smithi, Centropomus undecimalis, Mugil incilis, Mugil liza, and Ariopsis canteri) increased in larger body sizes at low depths, which usually occurs under dry ENSO conditions, when predatory risk is higher in coastal lagoons. Another species (Caquetaia kraussi) increased its mangrove utilization from the body size at which its feeding habits change. Mangroves and coastal lagoons are important nurseries and habitats for adults of the main commercial fish species in the CGSM. Seascape habitats and fringe/riverine mangroves must be conserved in tropical deltas to promote not only nurseries but also fish lifecycles. [ABSTRACT FROM AUTHOR]

El Niño Southern Oscillation (ENSO) has been shown to relate to the epidemiology of childhood infectious diseases, but evidence for whether they increase child deaths is limited. Here, we investigate the impact of mothers' ENSO exposure during and prior to delivery on child mortality by constructing a retrospective cohort study in 38 low- and middle-income countries. We find that high levels of ENSO indices cumulated over 0–12 lagged months before delivery are associated with significant increases in risks of under-five mortality; with the hazard ratio ranging from 1.33 (95% confidence interval [CI], 1.26, 1.40) to 1.89 (95% CI, 1.78, 2.00). Child mortality risks are particularly related to maternal exposure to El Niño-like conditions in the 0th–1st and 6th–12th lagged months. The El Niño effects are larger in rural populations and those with unsafe sources of drinking water and less education. Thus, preventive interventions are particularly warranted for the socio-economically disadvantaged. El Niño Southern Oscillation (ENSO) has been shown to relate to the epidemiology of child infectious diseases, but evidence for child deaths is limited. Here, the authors show that maternal exposure to high levels of ENSO before conception and giving birth may increase under-five mortality. [ABSTRACT FROM AUTHOR]

To better understand and improve the prediction of the seasonal surface temperature (TS) across the United States, we employed convolutional neural network (CNN) models trained on the Community Earth System Model Version 2 Large Ensemble (CESM2 LENS). We used lagged sea surface temperatures (SST) over the tropical Pacific region, containing the information of the El Niño Southern Oscillation (ENSO), as input for the CNN models. ENSO is the principal driver of variability in seasonal US surface temperatures (TSUS) and employing CNN models allows for spatiotemporal aspects of ENSO to be analyzed to make seasonal TSUS predictions. For predicting TSUS, the CNN models exhibited significantly improved skill over standard statistical multilinear regression (MLR) models and dynamical forecasts across most regions in the US, for lead times ranging from 1 to 6 months. Furthermore, we employed the CNN models to predict seasonal TSUS during extreme ENSO events. For these events, the CNN models outperformed the MLR models in predicting the effects on seasonal TSUS, suggesting that the CNN models are able to capture the ENSO‐TSUS teleconnection more effectively. Results from a heatmap analysis demonstrate that the CNN models utilize spatial features of ENSO rather than solely the magnitude of the ENSO events, indicating that the improved skill of seasonal TSUS is due to analyzing spatial variation in ENSO events. The proposed CNN model demonstrates a promising improvement in prediction skill compared to existing methods, suggesting a potential path forward for enhancing TSUS forecast skill from subseasonal to seasonal timescales. Plain Language Summary: While seasonal temperature forecasting is crucial for decision makers, it is generally a difficult task. A major setback to statistical machine learning methods for forecasting is the limited observational record. In our study, to bypass this, we used simulated climate model data, not historical data, to train machine learning (ML) models for seasonal surface temperature forecasts. We applied this method to train convolutional neural networks (CNN), a type of ML model that can analyze spatial patterns in data. We found that using CNN models greatly improves seasonal temperature predictions compared to standard linear models and that the CNN models effectively analyze spatial patterns in ENSO to make more accurate predictions. We concluded that using ML models is a promising method to improve seasonal temperature forecasts over the US. Key Points: Seasonal predictability of US temperature is compared between CNN, statistical, and dynamical modelsTraining CNN models on CESM2 LENS improves the seasonal predictability of US temperatureConsidering the spatiotemporal variability of tropical Pacific SST contributes to seasonal predictability improvement [ABSTRACT FROM AUTHOR]

In this study, we employ the Conformal Cubic Atmospheric Model (CCAM), a variable‐resolution global atmospheric model, driven by two distinct sea surface temperature (SST) data sets: the 0.25° Optimum Interpolation Sea Surface Temperature (CCAM_OISST) version 2.1 and the 2° Extended Reconstruction SSTs Version 5 (CCAM_ERSST5). Model performance is assessed using a benchmarking framework, revealing good agreement between both simulations and the climatological rainfall spatial pattern, seasonality, and annual trends obtained from the Australian Gridded Climate Data (AGCD). Notably, wet biases are identified in both simulations, with CCAM_OISST displaying a more pronounced bias. Furthermore, we have examined CCAM's ability to capture El Niño‐Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) correlations with rainfall during Austral spring (SON) utilizing a novel hit rate metric. Results indicate that only CCAM_OISST successfully replicates observed SON ENSO‐ and IOD‐rainfall correlations, achieving hit rates of 86.6% and 87.5%, respectively, compared to 52.7% and 41.8% for CCAM_ERSST5. Large SST differences are found surrounding the Australian coastline between OISST and ERSST5 (termed the "Coastal Effect"). Differences can be induced by the spatial interpolation error due to the discrepancy between model and driving SST. An additional CCAM experiment, employing OISST with SST masked by ERSST5 in 5° proximity to the Australian continent, underscores the "Coastal Effect" has a significant impact on IOD‐Australian rainfall simulations. In contrast, its influence on ENSO‐Australian rainfall is limited. Therefore, simulations of IOD‐Australian rainfall teleconnection are sensitive to local SST representation along coastlines, probably dependent on the spatial resolution of driving SST. Plain Language Summary: In this research, the Conformal Cubic Atmospheric Model (CCAM), a global atmospheric model, is used to study the impact of different driving sea surface temperature (SST) data sets on Australian rainfall simulations. Two SST data sets, one with high resolution (OISST) and another at lower resolution (ERSST5), are employed to drive CCAM (CCAM_OISST and CCAM_ERSST5). Model performance is evaluated using a benchmarking approach, indicating that both SST‐driven experiments are in good agreement with observed rainfall patterns in Australia. However, both simulations exhibit wet biases, with CCAM_OISST having a more noticeable bias. The study assesses CCAM's ability to capture the correlation between El Niño‐Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) with rainfall during Austral spring. Results reveal that CCAM_OISST performs better, replicating observed correlations more accurately than CCAM_ERSST5. The research identifies strong SST differences found between OISST and ERSST5 around the Australian coastline. An additional experiment underscores that this "Coastal Effect" plays an important role in simulating IOD‐Australian rainfall correlations, while its impact on ENSO‐Australian rainfall is limited. In conclusion, robust simulations of IOD‐Australian rainfall teleconnection require an accurate representation of local SST, which is related to the spatial resolution of SST products driving the model. Key Points: Novel hit rate metrics are proposed to evaluate El Niño‐Southern Oscillation and Indian Ocean Dipole‐rainfall teleconnectionsCCAM driven by different SST performs well in mean rainfall but ENSO and IOD related rainfall varies substantially over AustraliaResolution of the driving sea surface temperature is important to simulate IOD‐rainfall variability over Australia [ABSTRACT FROM AUTHOR]

A monsoon climate covers a large portion of the global land and exhibits notable interannual variations. However, the effect of monsoon variability on the terrestrial carbon cycle has been investigated less due to the gap between physical and biogeochemical climate research communities. Here, using the FLUXCOM dataset, we show that the Northern Hemisphere land monsoon (NHLM) regions account for 40.24% (±3.40%) [±one standard deviation (SD)] of the climatological summer mean net ecosystem production (NEP) over the NH land from 1981 to 2010 and contribute 21.35% (±7.57%) to the interannual variability (IAV) in NH NEP. Using singular value decomposition (SVD), we find that the leading modes of NEP anomalies are associated with El Niño development and decay phases. During the El Niño–developing summer, a higher sea surface temperature in the tropical central-eastern Pacific results in reduced photosynthetic productivity and weaker carbon uptake because of the reduced precipitation associated with monsoon circulation changes. Precipitation is the predominant driver of NEP anomalies relative to temperature and radiation, contributing 65.20% in the NHLM regions. During the El Niño–decaying summer, abundant rainfall along the Yangtze River valley in China results in increased photosynthetic productivity and ecosystem respiration, leading to less change in carbon uptake. In other monsoon regions, photosynthesis anomalies are driven by higher temperatures, and respiration anomalies are driven by enhanced precipitation. Both of these conditions are favorable for the release of CO2 into the atmosphere. Our study highlights the impact of monsoon variability on the carbon cycle in monsoon regions during different phases of El Niño–Southern Oscillation (ENSO) evolution. [ABSTRACT FROM AUTHOR]

Turbidity serves as a crucial indicator of coastal water health and productivity. Twenty years of remote sensing data (2003–2022) from the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) satellite were used to analyze the spatial and temporal variations in turbidity, as measured by total suspended matter (TSM), in the Berau Coastal Shelf (BCS), East Kalimantan, Indonesia. The BCS encompasses the estuary of the Berau River and is an integral part of the Coral Triangle, renowned for its rich marine and coastal habitats, including coral reefs, mangroves, and seagrasses. The aim of this research is to comprehend the seasonal and interannual patterns of turbidity and their associations with met-ocean parameters, such as wind, rainfall, and climate variations like the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). The research findings indicate that the seasonal spatial pattern of turbidity is strongly influenced by monsoon winds, while its temporal patterns are closely related to river discharge and rainfall. The ENSO and IOD climate cycles exert an influence on the interannual turbidity variations, with turbidity values decreasing during La Niña and negative IOD events and conversely increasing during El Niño and positive IOD events. Furthermore, the elevated turbidity during negative IOD and La Niña coincides with rising temperatures, potentially acting as a compound stressor on marine habitats. These findings significantly enhance our understanding of turbidity dynamics in the BCS, thereby supporting the management of marine and coastal ecosystems in the face of changing climatic and environmental conditions. [ABSTRACT FROM AUTHOR]

Flash droughts, characterized by a rapid onset and severe intensity, pose a serious threat to water resource management. Extensive research has indicated that drought has lagged impacts on streamflow. Nevertheless, the hydrometeorological conditions by which streamflow dynamics respond to drought within the São Francisco River Basin (SFRB) remain ambiguous. To bridge this gap, we conducted a study on long-term streamflow responses to flash drought in the SFRB from 1991 to 2020, combining the Standardized Antecedent Precipitation Evapotranspiration Index (SAPEI) and quantile streamflow with a trend analysis. This study employed the SAPEI, a daily drought index, to identify flash droughts and assess the response of streamflow to the identified events across the SFRB. Our findings reveal insights into the direct response of streamflow to flash drought conditions, stimulated by the application of the SAPEI for analysis of flash drought events. The interannual flash droughts fluctuated over the years, with the middle part of the SFRB experiencing frequent, longer flash droughts, while the south part encountered shorter but less frequent events. About 55% of the study area is trended towards drying conditions. A comparative analysis of the SAPEI and streamflow identified a synchronized response to the onset of flash drought events, but the recovery timescale for the SAPEI and streamflow varied among the events. This study enhances understanding of the flash-drought–streamflow relationship in the SFRB and provides theoretical support for addressing drought risks under climate change. [ABSTRACT FROM AUTHOR]

September–November (e.g., autumn) 2023 produced the fewest western North Pacific (WNP) tropical cyclones (TCs) since 1951, likely as a joint response to El Niño and a warm phase of the North Pacific Mode (NPM). Decreases in both TC genesis frequency and two genesis potential indices (GPIs) over the western WNP were likely the result of El Niño-induced and warm NPM-induced negative low-level relative vorticity anomalies. Over the eastern WNP, TC genesis and GPI reductions were also associated with vorticity decreases over the eastern WNP, where the TC-suppressing effect of the warm NPM surpassed the TC-favoring effect of El Niño. The changes in vorticity were further linked to anomalous anticyclones centered over the South China Sea and the midlatitude central North Pacific. A linear combination of the responses to El Niño and a warm NPM can explain the changes in TC genesis and low-level circulation over most of the WNP in 2023, except east of 160°E where other climate modes may have played more of a role. [ABSTRACT FROM AUTHOR]

The Antarctic ozone hole exerts a substantial impact on the climate of the Southern Hemisphere, yet research exploring its potential influence on the Northern Hemisphere climate is limited. This study unveils a significant positive relationship between interannual variations of Antarctic total column ozone (TCO) during September–October and East Asian precipitation in the subsequent boreal winter. Specifically, ~10% of the East Asian winter precipitation variability is attributed to Antarctic TCO during September–October. Both observational data and model results indicate that the increased Antarctic TCO during September–October triggers a simultaneous meridional southern Indian Ocean tripole sea surface temperature anomalies (SSTA) through the negative phase of the Southern Annular Mode. This SSTA pattern persists from September–November through the boreal winter, subsequently weakening the local-scale zonal-vertical circulation anomalies in the Indian Ocean. The process leads to positive outgoing longwave radiation (OLR) anomalies over the southern Marine Continent. As a result, the linear response of wind anomalies at 850 hPa over East Asia to the OLR-induced diabatic heating anomalies exhibits southwesterlies, as demonstrated by a linear baroclinic model. These anomalous winds facilitate the transport of abundant moisture from the tropics to East Asia, favoring the formation of winter precipitation. We employ the Specified-Chemistry version of the Whole Atmosphere Community Climate Model to validate that the increase of September–October Antarctic ozone substantially enhances East Asian precipitation during boreal winter. Importantly, the relationship between Antarctic ozone and East Asian winter precipitation is found to be independent of El Niño-Southern Oscillation and the Indian Ocean Dipole Mode. Our findings provide a fresh insight into the prediction of the East Asian winter precipitation. [ABSTRACT FROM AUTHOR]

The heat storage capacity of Southern Ocean (SO) buffers future atmospheric warming but differs vastly across climate models. Reducing its projection uncertainty is vital for understanding and evaluating future global sustainability. Using Coupled Model Intercomparison Project Phase 6, we show that the present-day SO high-latitude easterly wind anomalies induced by El Niño is an effective constraint for the projected increase in SO heat content. Models simulating weaker El Niño-induced easterlies generate more equatorward atmospheric teleconnection in the present day. Under global warming, these models have greater capacity in the poleward shift of atmospheric circulation, thus generate stronger future increase in El Niño-induced high-latitude easterlies, slowing the SO heat storage by weakening the northward Ekman transport that underpins the dynamical process for SO heat storage. However, most models overestimate the present-day El Niño-induced easterlies, implying that alleviating this bias would reduce future SO heat storage, thus exacerbating atmospheric warming. [ABSTRACT FROM AUTHOR]

This study examines the asymmetry in the Indian summer monsoon rainfall (ISMR) response over India and its four homogeneous regions to two distinct types of temporal evolution in La Niña. We have shown this uneven response by analysing the large‐scale dynamics over tropical Indo‐Pacific region for the period 1951–2022. We have identified two types of La Niña events during monsoon season (June–September) based on whether they evolved from El Niño or La Niña from preceding boreal winter season (December–February). India receives significantly more (less) rainfall during La Niña years, when it was preceded by El Niño (La Niña) in the preceding winter. We further observed the spatial diversity of rainfall over India with a northeast–southwest dipole pattern. When La Niña years were preceded by El Niño, positive surface pressure anomaly over west‐north Pacific, low‐level westerlies and moisture transport favoured the rainfall over south peninsula and west‐central India. Whereas moisture divergence associated with anomalous lower‐tropospheric anticyclone over west‐north Pacific suppressed the rainfall over Indo‐Gangetic plains. However, when La Niña years were preceded by La Niña in winter, the absence of westerlies and weak moisture transport subdued the rainfall over south peninsula and west‐central India. At the same time, moisture convergence and a greater number of monsoon depressions favoured rainfall over north‐west India. This study also looked at how well eight Copernicus Climate Change Service (C3S) models predicted ISMR and SST for two types of La Niña with April initial conditions during the period 1993–2016. Models were able to capture the spatial pattern of SST anomalies over Indo‐Pacific Ocean, but all models could not capture the spatial pattern of ISMR. However, in terms of intensity, six out of eight models could predict more (less) ISMR when it was preceded by El Niño (La Niña), coinciding with the observed anomaly. [ABSTRACT FROM AUTHOR]

Knowledge of spring warm pool in the Bay of Bengal (BoBWP) is key for further understanding the climate variability in this region and beyond, but little is known about the BoBWP climatological state and the related mechanisms. In this study, we investigate the spatial structure and evolution of the BoBWP using daily Optimum Interpolation SST data from 1982-2022 in combination with multi-source data. Our analysis shows that the BoBWP is located in the central bay (6°-13°N) with a thickness around 20m~ 40 m. Composite analysis indicates that the BoBWP emerges in early April, peaks in early May and dissipates in early June. During the developing period, the net heat flux dominates the formation of spring warm pool through significant air-sea coupling processes, and induces the warming rate of 0.27°C/10d in the mixed layer, which is far larger than the contribution of oceanic dynamical processes (0.01°C/10d). During the decaying period, the net heat flux also plays a dominant role, with a cooling rate of -0.21°C/10d, meanwhile ocean dynamical processes contribute to the cooling of the warm pool with a rate of -0.01°C/10d. Additionally, the SST and the area of the BoBWP are significantly correlated with ENSO (r=0.66 and 0.73, p=0.05). During El Niño decaying year, the BoBWP primarily expands in a southward direction, with a 75% increase in area. Conversely, during La Niña decaying year, the BoBWP almost disappears, with a 52% decrease. [ABSTRACT FROM AUTHOR]