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This contribution provides an introduction to a sequence of five papers (CCE I- CCE V) that describe the impact of the Warm Anomaly of 2014–15 and El Niño 2015–16 on the pelagic food web of the southern California Current Ecosystem. These contributions analyze the influence of these two warm water perturbations on satellite-based measures of ocean fronts, export efficiency out of the euphotic zone, copepod egg production, mesozooplankton community structure, and a synthesis of primary production, mesozooplankton grazing, and gravitational fluxes of organic carbon.

This contribution provides an introduction to a sequence of five papers (CCE I- CCE V) that describe the impact of the Warm Anomaly of 2014–15 and El Niño 2015–16 on the pelagic food web of the southern California Current Ecosystem. These contributions analyze the influence of these two warm water perturbations on satellite-based measures of ocean fronts, export efficiency out of the euphotic zone, copepod egg production, mesozooplankton community structure, and a synthesis of primary production, mesozooplankton grazing, and gravitational fluxes of organic carbon.

The Madden–Julian oscillation (MJO) identified by Madden and Julian in the early 1970s has been well recognized as the most prominent intraseasonal signal in the tropics. Its discovery and its relationship with other weather phenomena such as tropical cyclones (TCs) are among the most significant advancements in modern meteorology with broad and far-reaching impacts. The original study by Madden and Julian used radiosonde data on Canton Island, and their spectral analysis revealed the signal of a 40–50-day oscillation. It has come to our attention that an earlier study by Xie et al. published in a Chinese journal documented an oscillatory signal of a 45-day period using radiosonde data from several stations between 70° and 125°E in the tropics. The 40–50-day signal found by Xie et al. is strikingly evident without any filtering. Xie et al. identified that occurrences of TCs are correlated with the 40–50-day variation of low-level westerlies at these stations. The original figures in Xie et al.'s article were hand drawn. Their results are verified using data from a longer period of 1958–70. The 40–50-day oscillation in the monsoon westerlies and its relationship with the occurrence of TCs are confirmed and further expanded upon. This study serves the purpose of bringing recognition to the community of the identification of a 40–50-day signal published in Chinese in 1963 and the discovery of the correlation between MJO phases and TC genesis three decades earlier than studies on this subject published outside China. [ABSTRACT FROM AUTHOR]

Abstract This contribution provides an introduction to a sequence of five papers (CCE I- CCE V) that describe the impact of the Warm Anomaly of 2014–15 and El Niño 2015–16 on the pelagic food web of the southern California Current Ecosystem. These contributions analyze the influence of these two warm water perturbations on satellite-based measures of ocean fronts, export efficiency out of the euphotic zone, copepod egg production, mesozooplankton community structure, and a synthesis of primary production, mesozooplankton grazing, and gravitational fluxes of organic carbon. [ABSTRACT FROM AUTHOR]

To discover the spatial–temporal patterns of sea surface temperature (SST) in the South China Sea (SCS), this paper proposes a spatial–temporal co-clustering algorithm optimized by information divergence. This method allows for the clustering of SST data simultaneously across temporal and spatial dimensions and is adaptable to large volumes of data and anomalous data situations. First, the SST data are initially clustered using the co-clustering algorithm. Second, we use information divergence as the loss function to refine the clustering results iteratively. During the iterative optimization of spatial clustering results, we treat the temporal dimension as a constraint; similarly, during the iterative optimization of temporal clustering, we treat the spatial dimension as a constraint. This is to ensure better robustness of the algorithm. Finally, this paper conducts experiments in the SCS to verify our algorithm. According to the analysis of the experimental results, we have drawn the following conclusions. First, the use of the spatial–temporal co-clustering algorithm reveals that the SST in the SCS exhibits strong seasonal patterns in the temporal clustering results. The spatial distribution of SST varies significantly in different seasons. There is a slight difference in SST between the northern and southern regions of the SCS in winter, but the largest difference is in summer. Second, during ocean anomalies, our proposed algorithm can identify the corresponding abnormal patterns. When ENSO occurs, the seasonal distribution pattern of SST in the SCS is destroyed and replaced by an abnormal temporal pattern. The results indicate that during ENSO events, the SST in specific months in the SCS exhibits a correlation with the SST observed 4–5 months afterward. [ABSTRACT FROM AUTHOR]

A combination of careful harmonic analysis and high-resolution numerical modelling of tides in altimeter data from TOPEX/Poseidon and ERS-2 satellites allows detailed calculation of seasonal and interannual variability of sea level in coastal areas of the north-east Pacific Ocean. These calculations show large changes in sea level in marginal seas during the 1997-8 El Niño and La Niña events, as well as providing information on the origin and life cycle of large anticyclonic eddies, observed to be generated off the west coast of Canada. [ABSTRACT FROM AUTHOR]

The first two years of SeaWiFS (Sea viewing Wide Field of view Sensor) data (1997-1999) are used to document the variability of large-scale surface chlorophyll patterns within the coastal region along the full latitudinal extent of each of the four major global eastern boundary currents; the California, Humboldt, Benguela and Canary Currents. Seasonal chlorophyll patterns are compared to coincident seasonal cycles of Ekman transport calculated from satellite scatterometer data. In all four regions, maximum chlorophyll concentrations are generally temporally and latitudinally coincident with the seasonal maximum in upwelling (offshore Ekman transport) over most of their latitudinal range, but exceptions are documented. Interannual differences are evident in each region, most notably in the two Pacific regions where the 1997-1998 chlorophyll seasonality was affected by El Niño conditions. Significant differences between previously published chlorophyll seasonality deduced from the relatively sparse coverage of the Coastal Zone Color Scanner (CZCS) and the more complete coverage of SeaWiFS in both Southern Hemisphere regions are evident. [ABSTRACT FROM AUTHOR]

The El Niño phenomenon – and its associated phenomena El Niño Southern Oscillation (ENSO) and La Niña – have become probably the most well-known forms of natural climatic variability. El Niño forecasts underpin regional Climate Outlook Forums in many parts of the world. The declaration of El Niño conditions can unlock development aid money and El Niño events commonly receive widespread media coverage. Yet 'El Niño' has not always meant what it does today. The name was originally applied to an annually-occurring ocean current that affected northern Peru and Ecuador, so called because it arrived at Christmas (the Christ Child). The transition in meaning to a complex global phenomenon was related as much to commercial and geopolitical priorities as to the oceanic and atmospheric observations that underpin theories of El Niño dynamics. In this paper, I argue that scientific conceptualisations of El Niño are an example of path dependency. Badging ocean-atmosphere variability as 'El Niño' is unnecessary either for the advancement of science or effective disaster risk reduction; in fact, current definitions are confusing and can create problems in preparing for El Niño-related hazards, as occurred with the 2017 'coastal' El Niño in Peru. This paper outlines the historical processes that led to the current conceptualisations of El Niño and suggests an alternative way of understanding ocean-atmosphere dynamics in the Pacific and beyond. It then considers the implications of this path-dependency on El Niño's ontological politics; that is, who gets to define El Niño, and to what end. [ABSTRACT FROM AUTHOR]

The El Niño–Southern Oscillation (ENSO) is a significant climate phenomenon that appears periodically in the tropical Pacific. The intermediate coupled ocean–atmosphere Zebiak–Cane (ZC) model is the first and classical one designed to numerically forecast the ENSO events. Traditionally, the conditional nonlinear optimal perturbation (CNOP) approach has been used to capture optimal precursors in practice. In this paper, based on state-of-the-art statistical machine learning techniques Generally, the statistical machine learning techniques refer to the marriage of traditional optimization methods and statistical methods, or, say, stochastic optimization methods, where the iterative behavior is governed by the distribution instead of the point due to the attention of noise. Here, the sampling algorithm used in this paper is to numerically implement the stochastic gradient descent method, which takes the sample average to obtain the inaccurate gradient. , we investigate the sampling algorithm proposed in to obtain optimal precursors via the CNOP approach in the ZC model. For the ZC model, or more generally, the numerical models with a large number O(104-105) of degrees of freedom, the numerical performance, regardless of the statically spatial patterns and the dynamical nonlinear time evolution behaviors as well as the corresponding quantities and indices, shows the high efficiency of the sampling method compared to the traditional adjoint method. The sampling algorithm does not only reduce the gradient (first-order information) to the objective function value (zeroth-order information) but also avoids the use of the adjoint model, which is hard to develop in the coupled ocean–atmosphere models and the parameterization models. In addition, based on the key characteristic that the samples are independently and identically distributed, we can implement the sampling algorithm by parallel computation to shorten the computation time. Meanwhile, we also show in the numerical experiments that the important features of optimal precursors can still be captured even when the number of samples is reduced sharply. [ABSTRACT FROM AUTHOR]

The interannual variability (IAV) in atmospheric CO2 growth rate (CGR) is closely connected with the El Niño-Southern Oscillation. However, sensitivities of CGR to temperature and precipitation remain largely uncertain. This paper analyzed the relationship between Mauna Loa CGR and tropical land climatic elements. We find that Mauna Loa CGR lags precipitation by 4 months with a correlation coefficient of -0.63, leads temperature by 1 month (0.77), and correlates with soil moisture (-0.65) with zero lag. Additionally, precipitation and temperature are highly correlated (-0.66), with precipitation leading by 4-5 months. Regression analysis shows that sensitivities of Mauna Loa CGR to temperature and precipitation are 2.92 ± 0.20 Pg C yr-1 K-1 and -0.46 ± 0.07 Pg C yr-1 100 mm-1, respectively. Unlike some recent suggestions, these empirical relationships favor neither temperature nor precipitation as the dominant factor of CGR IAV. We further analyzed seven terrestrial carbon cycle models, from the TRENDY project, to study the processes underlying CGR IAV. All models capture well the IAV of tropical land-atmosphere carbon flux (CFTA). Sensitivities of the ensemble mean CFTA to temperature and precipitation are 3.18 ± 0.11 Pg C yr-1 K-1 and -0.67 ± 0.04 Pg C yr-1 100 mm-1, close to Mauna Loa CGR. Importantly, the models consistently show the variability in net primary productivity (NPP) dominates CGR, rather than soil respiration. Because NPP is largely driven by precipitation, this suggests a key role of precipitation in CGR IAV despite the higher CGR correlation with temperature. Understanding the relative contribution of CO2 sensitivity to precipitation and temperature has important implications for future carbon-climate feedback using such "emergent constraint". [ABSTRACT FROM AUTHOR]

Marine heatwave (MHW) events have significant consequences for marine ecosystems and human society. This paper investigates a MHW's spatial–temporal characteristics in the Changjiang River Estuary and its surrounding coastal regions (CRESs), as well as analyzes the drivers, using satellite and reanalysis data spanning from 1982–2021. The findings show that, during the last 40 years, all four of the MHW indicators have increased. The summer MHW is more severe than other seasons, showing a rising pattern from southeast to northwest. The rise of MHWs can be attributed to the increase in sea surface heat flux, weak wind speed, and powerful El Niño events. Additionally, two special MHW events were detected during the entire study period: Event A lasted for 191 days from 9 October 2006 to 17 April 2007; Event B had an average intensity of 4.93 °C from 5 July 1994 to 1 August 1994. For locations so close to each other, the characteristics of MHWs can also vary, and the mechanisms behind them are highly complex. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) are among the most destructive natural hazards and yet, quantifying their financial impacts remains a significant methodological challenge. It is therefore of high societal value to synthetically simulate TC tracks and winds to assess potential impacts along with their probability distributions for example, land use planning and financial risk management. A common approach to generate TC tracks is to apply storm detection methodologies to climate model output, but such an approach is sensitive to the method and parameterization used and tends to underestimate intense TCs. We present a global TC model (the UQAM‐TCW model thereafter) that melds statistical modeling, to capture historical risk features, with a climate model large ensemble, to generate large samples of physically coherent TC seasons. Integrating statistical and physical methods, the model is probabilistic and consistent with the physics of how TCs develop. The model includes frequency and location of cyclogenesis, full trajectories with maximum sustained winds and the entire wind structure along each track for the six typical cyclogenesis basins from IBTrACS. Being an important driver of TCs globally, we also integrate ENSO effects in key components of the model. The global TC model thus belongs to a recent strand of literature that combines probabilistic and physical approaches to TC track generation. As an application of the model, we show global hazard maps for direct and indirect hits expressed in terms of return periods. The global TC model can be of interest to climate and environmental scientists, economists and financial risk managers. Plain Language Summary: Tropical cyclones (TCs) are among the most destructive natural hazards and yet, quantifying their financial impacts remains a difficult task. Being able to randomly simulate TCs and their features (such as wind speed) with mathematical models is therefore critical to build scenarios (and their corresponding probability) for land use planning and financial risk management. A common approach is to simulate TCs by tracking them directly in climate model outputs but this often underestimates the frequency of intense TCs while being computationally costly overall to generate a large number of events. For these reasons, many authors have looked into alternative approaches that replicate key physical features of TCs but rather using statistical models that are much less computationally demanding. This paper therefore presents a global TC model that leverages the strengths of both statistical and climate models to simulate a large number of TCs whose features are consistent with the physics and observations. As an important global phenomenon that affects TCs globally, we also integrate in our model the effects of El Niño. The paper focuses on the methodology and validation of each model component and concludes with global hazard maps for direct and indirect hits. Key Points: We present a global tropical cyclone (TC) wind model built upon a climate model large ensemble that can be used for risk analysisWe integrate ENSO into our model since it is a strong driver of storm annual frequency, cyclogenesis, trajectories, and intensityWe present global hazard maps consistent with statistical features of TC components and coherent with a global climate model [ABSTRACT FROM AUTHOR]

As arid lands expand across the globe, scholars increasingly turn to the archaeological record for examples of sustainable farming in extreme environments. The arid north coast of Peru was the setting of early and intensive irrigation-based farming; it is also periodically impacted by sudden, heavy rainfall related to the El Niño Southern Oscillation (ENSO) phenomenon. While the sociopolitical effects, technologies, and engineering expertise of these irrigation systems have been thoroughly examined and theorized, little is known about how farmers managed periods of water stress. The aim of this study is to test whether arid zone farming was supported by hybrid, intermittent flood and perennial water source systems in the prehispanic past. An arroyo in the Chicama Valley was selected for preliminary data collection, and these data are presented here: (1) drone photography of the arroyo capturing the aftermath of a recent (2023) rain event; and (2) potassium (K) soil test kit results from samples collected near suspected prehispanic check dam features in the same area. The paper combines these data with comparative examples from the literature to suggest that the prehispanic features functioned as water-harvesting infrastructure. The paper concludes that sustainable, arid zone farming can be supported by hybrid, intermittent flood and perennial water source systems. [ABSTRACT FROM AUTHOR]

The frequency of El Niño occurrences in southern Africa surpasses the norm, resulting in erratic weather patterns that significantly impact food security, particularly in Zimbabwe. The effects of these weather patterns posit that El Niño occurrences have contributed to the diminished maize yields. The objective is to give guidelines to policymakers, researchers, and agricultural stakeholders for taking proactive actions to address the immediate and lasting impacts of El Niño and enhance the resilience of the agricultural industry. This brief paper provides prospective strategies for farmers to anticipate and counteract the El Niño-influenced dry season projected for 2023/24 and beyond. The coefficient of determination R2 between yield and ENSO was low; 11 of the 13 El Niño seasons had a negative detrended yield anomaly, indicating the strong association between El Nino's effects and the reduced maize yields in Zimbabwe. The R2 between the Oceanic Nino Index (ONI) and rainfall (43%) and between rainfall and yield (39%) indirectly affects the association between ONI and yield. To safeguard farmers' livelihoods and improve their preparedness for droughts in future agricultural seasons, this paper proposes a set of strategic, tactical, and operational decision-making guidelines that the agriculture industry should follow. The importance of equipping farmers with weather and climate information and guidance on drought and heat stress was underscored, encompassing strategies such as planting resilient crop varieties, choosing resilient livestock, and implementing adequate fire safety measures. [ABSTRACT FROM AUTHOR]

The Journal of the Meteorological Society of Japan (JMSJ) is a well-established journal in the field of meteorology and related sciences. The editorial board is committed to continuously improving the journal for authors, readers, and reviewers. In 2024, the JMSJ will publish its 102nd volume and will discontinue the note service for submissions. The journal has recently implemented minor reforms in the technical editing process. The JMSJ Award for 2023 was presented to authors who conducted novel research on important topics. The most accessed papers in 2023 included topics such as geostationary meteorological satellites and reanalysis data. The journal also organized three special editions on various topics. JMSJ authors are encouraged to use J- STAGE Data for archiving datasets related to their papers. The journal expresses gratitude to the meteorological research community for their support and looks forward to continued success in 2024. [Extracted from the article]

The current paper examines the strength of variability between the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) in influencing October to December (OND) rainfall over the bimodal rainfall regime of Tanzania. The empirical orthogonal function (EOF), correlation analysis, and composite analysis were used during the data analysis. The results show more rainfall distribution over the western part of the Lake Victoria basin and to the peripheral of the northern coast of the country, thus suggesting that, during the OND rainfall season, the onset starts in the western part of the Lake Victoria basin, then spreads to the rest of the areas under investigation as the season progresses. Furthermore, on the spatial scale, the findings revealed that there is a strong correlation between IOD and ENSO indices and OND rains in the northeastern highlands. Furthermore, a robust temporal correlation is revealed between the mean OND rains over the bimodal rainfall areas and IOD (r = 0.70) compared to ENSO (r = 0.62). The anomalous warming over the western Indian Ocean (positive IOD) has a faster response to OND rains over the bimodal rainfall regime of Tanzania compared to the remote influence induced by anomalous warming from the central equatorial Pacific Ocean (warm phase of ENSO). Meanwhile, dry years are associated with negative IOD and the cold phase of ENSO conditions. The findings offer valuable insights on strategies for mitigating the effects associated with extreme weather events and improving resilience in Tanzania. [ABSTRACT FROM AUTHOR]

The El Ni"no Southern Oscillation (ENSO) and the Atlantic Multidecadal Oscillation (AMO) are known to influence coastal water levels along the east coast of the United States. By identifying empirical orthogonal functions (EOFs) which coherently contribute from the Multivariate ENSO Index (MEI) to the AMO index (AMOI), we characterize both the expression of ENSO in the unsmoothed AMOI, and coherent relationships between these indices and interannual sea level anomalies at six stations in the Gulf of Mexico and Western North Atlantic. Within the ENSO band (2-7 yr periods) the total contribution of MEI to unsmoothed AMOI variability is 79 %. Cross correlation suggests that the MEI leads expression of the ENSO signature in the AMOI by six months, consistent with the mechanism of an atmospheric bridge. Within the ENSO band, essentially all of the coupling between the unsmoothed AMOI and sea level anomalies is the result of ENSO expression in the AMOI. At longer periods we find decadal components of sea level anomalies linked to the AMOI at three southern stations (Key West, Pensacola, Charleston), but not at the northern stations (Baltimore, Boston, Portland), with values of coherence ranging from 20 to 50 %. The coherence of MEI to coastal sea level anomalies has a different structure and is generally weaker than that of the ENSO expressed AMOI influence, suggesting distinct physical mechanisms are influencing sea level anomalies due to a direct ENSO teleconnection when compared to teleconnections based on ENSO expression in the AMOI. It is expected that applying this analysis to extremes of sea level anomalies will reveal additional influences. [ABSTRACT FROM AUTHOR]

A simultaneous analysis of 13 years of remotely sensed data of land cover, fires, precipitation, and aerosols from the MODIS, TRMM, and MISR satellites and the AERONET network over Southeast Asia is performed, leading to a set of robust relationships be- tween land-use change and fire being found on inter-annual and intra-annual scales over Southeast Asia, reflecting the heavy amounts of anthropogenic influence over land use change and fires in this region of the world. First, we find that fires occur annually, but with a considerable amount of variance in their onset, duration, and intensity from year to year, and from two separate regions within Southeast Asia from each other. This variability is already partially understood from previous works, including the impacts of both inter-annually and intra-annually occurring influences such as the Monsoon and El-Nino events, but yet there are other as of yet unknown influences that also are found to strongly influence the results. Second, we show that a simple regression-model of the land-cover, fire, and precipitation data can be used to recreate a robust representation of the timing and magnitude of measured AOD from multiple measurements sources of this region using either 8-day (better for onset and duration) or monthly based (better for magnitude) measurements, but not daily measurements. We find that the reconstructed AOD matches the timing and intensity from AERONET measurements to within 70 to 90% and the timing and intensity of MISR measurements from to within 50 to 95%. This is a unique finding in this part of the world, since could-covered regions are large, yet the robustness of the model is still capable of holding over many of these regions, where otherwise no fires are observed and hence no emissions source contribution to AOD would otherwise be thought to occur. Third, we determine that while Southeast Asia is a source region of such intense smoke emissions, that it is also impacted by transport of smoke from other regions as well. There are regions in northern Southeast Asia which have two annual AOD peaks, one during the local fire season, and the second smaller peak corresponding to a combination of some local smoke sources as well as transport of aerosols from fires in southern Southeast Asia, and possibly even from anthropogenic sources in South Asia. Conversely, we show that southern Southeast Asia is affected exclusively by its own local fire sources during its own local fire season. Overall, this study highlights the importance of taking into account a simultaneous use of land-use, fire, and precipitation for understanding the impacts of fires on the atmospheric loading and distribution of aerosols in Southeast Asia over both space and time. [ABSTRACT FROM AUTHOR]

The article presents a study which investigated climate variability's effects on savannah fires in West Africa. It highlights the use of multivariate El Niño southern oscillation (ENSO) index (MEI) in the analysis of ENSO's effect on burned areas. It mentions that temporal and spatial variability showed a large burned area activity from November to February 2000-2007.

Tropical and extra-tropical storms generate extreme waves, impacting both nearby and remote regions through swell propagation. Despite their devastating effects in tropical areas, the contribution of tropical cyclones (TCs) to global wave-induced coastal risk remains unknown. Here, we enable a quantitative assessment of TC's role in extreme waves approaching global coastlines, by designing twin oceanic wave simulations with and without realistic TC wind forcing. We find that TCs substantially contribute to extreme breaking heights in tropical regions (35-50% on average), reaching 100% in high-density TC areas like the North Pacific. TCs also impact remote TC-free regions, such as the equatorial Pacific experiencing in average 30% of its extreme wave events due to TCs. Interannual variability amplifies TC-induced wave hazards, notably during El Niño in the Central Pacific, and La Niña in the South China Sea, Caribbean Arc, and South Indian Ocean coastlines. This research offers critical insights for global risk management and preparedness. This paper shows that tropical cyclones contribute between 40% and 100% of the extreme wave events approaching coastlines in basins prone to tropical cyclone activity, while they can also impact remote areas via swell propagation across the ocean. [ABSTRACT FROM AUTHOR]

The co-occurrence of multiple hazards can either exacerbate or mitigate risks. The interrelationships between multiple hazards greatly depend on the spatiotemporal scale and can be difficult to detect from large to local scales. In this paper, we identified coastal regions worldwide where the leading tropical (El Niño-Southern Oscillation, ENSO) and polar (Arctic Oscillation, AO; Southern Annular Mode, SAM) modes of climate variability simultaneously modify the seasonal conditions of multiple hazards, including the near-surface wind speed and swell and wind-sea wave powers. We classified the results at the national and municipal levels, with a focus on multiple hazards simultaneously occurring in space and time. The results revealed that the ENSO modulates multiple hazards, affecting approximately 40% of coastal countries, while the polar annular modes affect approximately 30% of coastal countries. The ENSO induced a greater diversity of multiple hazards, with Asian countries (e.g., Indonesia experienced increases of + 2% in wind and + 7% in swell) and countries in the Americas (e.g., Peru exhibited increases of + 1.5% in wind and + 6% in wind-sea) the most notably affected. The SAM imposed a greater influence on swells in the eastern countries of ocean basins (+ 2.5% in Chile) than in other countries, while the influence of the AO was greater in Norway and the UK (+ 12% for wind-sea and 8% for swell). Low-lying islands exhibited notable variations in pairwise hazards between phases and seasons. Our results could facilitate the interpretation of multihazard interactions and pave the way for a wide range of potential implementations of different coastal industries. [ABSTRACT FROM AUTHOR]

The deep ocean, a vast thermal reservoir, absorbs excess heat under greenhouse warming, which ultimately regulates the Earth's surface climate. Even if CO2 emissions are successfully reduced, the stored heat will gradually be released, resulting in a particular pattern of ocean warming. Here, we show that deep ocean warming will lead to El Niño-like ocean warming and resultant increased precipitation in the tropical eastern Pacific with southward shift of the intertropical convergence zone. Consequently, the El Niño-Southern Oscillation shifts eastward, intensifying Eastern Pacific El Niño events. In particular, the deep ocean warming could increase convective extreme El Niño events by 40 to 80% relative to the current climate. Our findings suggest that anthropogenic greenhouse warming will have a prolonged impact on El Niño variability through delayed deep ocean warming, even if CO2 stabilization is achieved. This paper shows that anthropogenic greenhouse warming will have a prolonged impact on El Niño variability through delayed deep ocean warming, even if CO2 stabilization is achieved. [ABSTRACT FROM AUTHOR]

Much research has raised concerns about how a warming planet will interact with natural cyclical climatic variations, and the implications for the resilience and vulnerability of coastal communities. As the anticipated effects of climate change will continue to intensify, it is necessary to understand the response and adaptive capacity of individuals and communities. Coastal communities in Ecuador have evolved in an environment of such cyclical climatic variations referred to as El Niño-Southern Oscillation (ENSO) and La Niña. These climatic events are frequently characterized by extreme variations in precipitation, violent storms, and coastal flooding during El Niño and lowered sea water temperatures and drought during La Niña. This paper draws on survey data and long-term ethnographic research in Ecuadorian coastal communities to explore how fishers understand the impacts of ENSO and implications for their livelihood decisions and resilience to climate variability. The results suggest that fishers along the coast of Ecuador understand and respond differentially to the impacts of ENSO depending on social, cultural, environmental, and geographical factors. These differential levels of response suggest that livelihood diversification may uphold social resilience, which has implications for how coastal communities may adapt to the increasingly harsh weather conditions predicted by many climate models. Our findings further suggest that the impacts of El Niño are more salient than the impacts of La Niña; these findings have significant implications for fisheries management and science communication. [ABSTRACT FROM AUTHOR]

The intricate interplay of oceanic-atmospheric variations has intensified erosive processes on sandy beaches in recent decades, with climate change expected to exacerbate these impacts in the future. Projections for the southern Atlantic and Pacific regions of continental Latin America predict increased extreme events and heightened impacts on sandy beaches, highlighting disparities in studies addressing coastal erosion and its causes. To address these risks, a systematic review is proposed to analyze historical trends and projections, aiming to inform local-level studies and management strategies for at-risk coastal communities. Reviewing 130 research papers, insights reveal the influence of climatic events like El Niño and La Niña on coastal dynamics, as well as the effects of storm intensification and extreme events such as high-intensity waves and storm surges on Latin American coasts, resulting in ecosystem, economic, and infrastructure losses. Projections indicate a rise in the population inhabiting Low Elevation Coastal Zones (LECZ) by the century's end, emphasizing the urgent need for effective management and planning. Community engagement in erosion monitoring and adaptation programs is crucial for addressing these challenges and developing robust, sustainable, long-term adaptation strategies. This study aims to enhance the understanding of coastal erosion in Latin American communities addressing future coastal risks. [ABSTRACT FROM AUTHOR]

Sandy coastlines are very dynamic spaces affected by a variety of natural and human factors. In Central Chile, changes in oceanographic and wave conditions, modes of inter-annual climate variability such as El Niño Southern Oscillation (ENSO), and extreme events such as earthquakes and tsunamis condition the beach morphology. At the same time, direct human actions alter the arrival of sediments to the coast and their alongshore distribution. Despite the relevance of the beaches for this coastal region and the interesting relationship their morphology has with the aforementioned factors, there is a lack of robust morphological datasets to provide a deep characterization and understanding of the dynamism of the Chilean coast. Based on the information provided by satellite-derived shorelines (SDSs) defined by using the SHOREX algorithm, this paper characterizes the morphological changes of Playa Grande in Cartagena Bay (Central Chile) during the period 1985–2019. The shoreline position data are analyzed in the context of changing beach transforming elements, allowing for a better understanding of the changes according to multiple drivers. While some of these factors, such as earthquakes or coastal storms, have a punctual character, changes in wave patterns vary at different time scales, from seasonal to multi-annual, linked to climate phases such as ENSO. Its effects are translated into shoreline erosion and accretion conditioned by the morphology and orientation of the coast while influenced by the availability of sediment in the coastal system. According to that, a conceptual model of the dynamism and redistribution of sediment in the Bay of Cartagena is proposed. The work proves the high utility that the systematic analysis of multi-decadal SDS datasets obtained from the images acquired in the optical by the Landsat and Sentinel-2 offer for beach monitoring and understanding the coastal dynamism. [ABSTRACT FROM AUTHOR]

Autoregressive Markov switching (ARMS) time series models are used to represent real-world signals whose dynamics may change over time. They have found application in many areas of the natural and social sciences, as well as in engineering. In general, inference in this kind of systems involves two problems: (a) detecting the number of distinct dynamical models that the signal may adopt and (b) estimating any unknown parameters in these models. In this paper, we introduce a new class of nonlinear ARMS time series models with delays that includes, among others, many systems resulting from the discretisation of stochastic delay differential equations (DDEs). Remarkably, this class includes cases in which the discretisation time grid is not necessarily aligned with the delays of the DDE, resulting in discrete-time ARMS models with real (non-integer) delays. The incorporation of real, possibly long, delays is a key departure compared to typical ARMS models in the literature. We describe methods for the maximum likelihood detection of the number of dynamical modes and the estimation of unknown parameters (including the possibly non-integer delays) and illustrate their application with a nonlinear ARMS model of El Niño–southern oscillation (ENSO) phenomenon. [ABSTRACT FROM AUTHOR]

The amount of radiative energy received at the Earth's surface depends on two factors: Earth–Sun distance and sunlight angle. Because of the former, high-eccentricity cycles can induce the appearance of seasons in the tropical ocean. In this paper, we use the Earth system model IPSL-CM5A2 to investigate the response of the low-latitude ocean to variations in Earth's orbit eccentricity. Sea surface temperature (SST) and primary production (PP) were simulated under six precession configurations at high eccentricity and two configurations at low eccentricity, representing extreme configurations observed over the past 1 million years. Results show that high eccentricity leads to increased seasonality in low-latitude mean SST, with an annual thermal amplitude of approximately 2.2 °C (vs. 0.5 °C at low eccentricity). Low-latitude mean PP, which already exhibits inherent seasonality under low-eccentricity conditions, sees its seasonality largely increased under high eccentricity. As a consequence, we show that on long timescales the intensity of SST seasonality exhibits only the eccentricity frequency, whereas that of PP additionally follows precession dynamics. Furthermore, the seasonal variations in both SST and PP at high eccentricities are influenced by the annual placement of the perihelion with its direct impact of radiative energy received in tropical regions. This leads to a gradual and consistent transition of seasons within the calendar. We introduce the concept of "eccentriseasons", referring to distinct annual thermal differences observed in tropical oceans under high-eccentricity conditions, which shift gradually throughout the calendar year. These findings have implications for understanding low-latitude climate phenomena such as the El Niño–Southern Oscillation (ENSO) and monsoons in the past. [ABSTRACT FROM AUTHOR]

The yellowfin tuna represents a significant fishery resource in the Pacific Ocean. Its resource endowment status and spatial variation mechanisms are intricately influenced by marine environments, particularly under varying climate events. Consequently, investigating the spatial variation patterns of dominant environmental factors under diverse climate conditions, and understanding the response of yellowfin tuna catch volume based on the spatial heterogeneity among these environmental factors, presents a formidable challenge. This paper utilizes comprehensive 5°×5° yellowfin tuna longline fishing data and environmental data, including seawater temperature and salinity, published by the Western and Central Pacific Fisheries Commission (WCPFC) and the Inter-American Tropical Tuna Commission (IATTC) for the period 2000–2021 in the Pacific Ocean. In conjunction with the Niño index, a multiscale geographically weighted regression model based on geographically weighted principal component analysis (GWPCA-MGWR) and spatial association between zones (SABZ) is employed for this study. The results indicate the following: (1) The spatial distribution of dominant environmental factors affecting the catch of Pacific yellowfin tuna is primarily divided into two types: seawater temperature dominates in the western Pacific Ocean, while salinity dominates in the eastern Pacific Ocean. When El Niño occurs, the area with seawater temperature as the dominant environmental factor in the western Pacific Ocean further extends eastward, and the water layers where the dominant environmental factors are located develop to deeper depths; when La Niña occurs, there is a clear westward expansion in the area with seawater salinity as the dominant factor in the eastern Pacific Ocean. This change in the spatial distribution pattern of dominant factors is closely related to the movement of the position of the warm pool and cold tongue under ENSO events. (2) The areas with a higher catch of Pacific yellowfin tuna are spatially associated with the dominant environmental factor of mid-deep seawater temperature (105–155 m temperature) to a greater extent than other factors, the highest correlation exceeds 70%, and remain relatively stable under different ENSO events. The formation of this spatial association pattern is related to the vertical movement of yellowfin tuna as affected by subsurface seawater temperature. (3) The GWPCA-MGWR model can fully capture the differences in environmental variability among subregions in the Pacific Ocean under different climatic backgrounds, intuitively reflect the changing areas and influencing boundaries from a macro perspective, and has a relatively accurate prediction on the trend of yellowfin tuna catch in the Pacific Ocean. [ABSTRACT FROM AUTHOR]

There have been several incidences of flood recently, which are believed to be aggravated by increased climatic variables as a result of perceived changes in climatic conditions (due to climate change) in the Cross River Basin. The basin is the most extensively developed and used river basin in the management of the water resources of the Cross River and Akwa Ibom States in Nigeria. In this paper, 30 years (from 1992 to 2021) of hydro-meteorological data (annual average rainfall, maximum and minimum temperatures, hu midity, duration of sunlight (sunshine hours), evaporation, wind speed, soil temperature, cloud cover, solar radiation, and atmospheric pressure) from four stations in the Cross River Basin were obtained from the Nigerian Meteorological Agency (NIMET), Abuja and subjected to trend detection analysis using the Mann–Kendall test to determine the trend in climatic parameters. The results indicate that there is a significant upward trend in annual rainfall in Ogoja but a downward trend in Calabar. The evaporation trend is significantly downward in Eket, whereas in Calabar, there is an upward trend in solar radiation. Generally, there is a significant rise in annual maximum temperature across the basin. Serial correlation and segmented regression analyses were performed to measure the impact of fluctuations in monthly and long-term Tahiti and Darwin's Sea level pressures on the climatic variables at the Cross River Basin catchment. These analyses were necessary to determine the extent of the influence of the El Nino Southern Oscillation climatic cycle. The analyses show no significant association between the El Niño Southern Oscillation (ENSO) and rainfall or between the ENSO and runoff in the catchment. This implies that the impact of the ENSO on rainfall and runoff in the Cross River Basin catchment is not considerable. The intercepts derived from the segmented regression in Eket and Ogoja show significant positive trends in both areas for rainfall and runoff. The trends in intercepts suggest that there are external factors influencing rainfall and runoff other than ENSO events, thus strengthening the assertion of climate change. Results from this study will facilitate the understanding of the variability in climatic parameters by stakeholders in the basin, researchers, policymakers, and water resource managers. [ABSTRACT FROM AUTHOR]