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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]

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]

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]

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 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]

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]

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]

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]

This paper demonstrates the effectiveness and superiority of Empirical Mode Decomposition (EMD) in projecting non-stationary hydrological data. The study focuses on daily Sea Surface Temperature (SST) sequences in the Niño 3.4 region and uses EMD to forecast the probability of El Niño events. Applying the Mann–Kendall test at the 5% significance level reveals a significant increasing trend in SST changes in this region, particularly noticeable after 1980. This trend is associated with the occurrence of El Niño and La Niña events, which have a recurrence interval of approximately 8.4 years and persist for over a year. The modified Oceanic Niño Index (ONI) proposed in this study demonstrates high forecast accuracy, with 97.56% accuracy for El Niño and 89.80% for La Niña events. Additionally, the EMD of SST data results in 13 Intrinsic Mode Functions (IMFs) and a residual component. The oscillation period increases with each IMF level, with IMF7 exhibiting the largest amplitude, fluctuating between ±1 °C. The residual component shows a significant upward trend, with an average annual increase of 0.0107 °C. These findings reveal that the EMD-based data generation method overcomes the limitations of traditional hydrological models in managing non-stationary sequences, representing a notable advancement in data-driven hydrological time series modeling. Practically, the EMD-based 5-year moving process can generate daily sea temperature sequences for the coming year in this region, offering valuable insights for assessing El Niño probabilities and facilitating annual updates. [ABSTRACT FROM AUTHOR]

The atmosphere is a complex nonlinear system, with the information of its temperature, water vapor, pressure, and cloud being crucial aspects of remote-sensing data analysis. There exist intricate interactions among these internal components, such as convection, radiation, and humidity exchange. Atmospheric phenomena span multiple spatial and temporal scales, from small-scale thunderstorms to large-scale events like El Niño. The dynamic interactions across different scales, along with external disturbances to the atmospheric system, such as variations in solar radiation and Earth surface conditions, contribute to the chaotic nature of the atmosphere, making long-term predictions challenging. Grasping the intrinsic chaotic dynamics is essential for advancing atmospheric analysis, which holds profound implications for enhancing meteorological forecasts, mitigating disaster risks, and safeguarding ecological systems. To validate the chaotic nature of the atmosphere, this paper reviewed the definitions and main features of chaotic systems, elucidated the method of phase space reconstruction centered on Takens' theorem, and categorized the qualitative and quantitative methods for determining the chaotic nature of time series data. Among quantitative methods, the Wolf method is used to calculate the Largest Lyapunov Exponents, while the G–P method is used to calculate the correlation dimensions. A new method named Improved Saturated Correlation Dimension method was proposed to address the subjectivity and noise sensitivity inherent in the traditional G–P method. Subsequently, the Largest Lyapunov Exponents and saturated correlation dimensions were utilized to conduct a quantitative analysis of FY-4A and Himawari-8 remote-sensing infrared observation data, and ERA5 reanalysis data. For both short-term remote-sensing data and long-term reanalysis data, the results showed that more than 99.91% of the regional points have corresponding sequences with positive Largest Lyapunov exponents and all the regional points have correlation dimensions that tended to saturate at values greater than 1 with increasing embedding dimensions, thereby proving that the atmospheric system exhibits chaotic properties on both short and long temporal scales, with extreme sensitivity to initial conditions. This conclusion provided a theoretical foundation for the short-term prediction of atmospheric infrared radiation field variables and the detection of weak, time-sensitive signals in complex atmospheric environments. [ABSTRACT FROM AUTHOR]

Estimating the ocean's subsurface thermohaline information from satellite measurements is essential for understanding ocean dynamics and the El Niño phenomenon. This paper proposes an improved double-output residual neural network (DO-ResNet) model to concurrently estimate the subsurface temperature (ST) and subsurface salinity (SS) in the tropical Western Pacific using multi-source remote sensing data, including sea surface temperature (SST), sea surface salinity (SSS), sea surface height anomaly (SSHA), sea surface wind (SSW), and geographical information (including longitude and latitude). In the model experiment, Argo data were used to train and validate the model, and the root mean square error (RMSE), normalized root mean square error (NRMSE), and coefficient of determination (R2) were employed to evaluate the model's performance. The results showed that the sea surface parameters selected in this study have a positive effect on the estimation process, and the average RMSE and R2 values for estimating ST (SS) by the proposed model are 0.34 °C (0.05 psu) and 0.91 (0.95), respectively. Under the data conditions considered in this study, DO-ResNet demonstrates superior performance relative to the extreme gradient boosting model, random forest model, and artificial neural network model. Additionally, this study evaluates the model's accuracy by comparing its estimations of ST and SS across different depths with Argo data, demonstrating the model's ability to effectively capture the most spatial features, and by comparing NRMSE across different depths and seasons, the model demonstrates strong adaptability to seasonal variations. In conclusion, this research introduces a novel artificial intelligence technique for estimating ST and SS in the tropical Western Pacific Ocean. [ABSTRACT FROM AUTHOR]

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]

Understanding past and present hydrosystem feedbacks to global ocean–atmospheric interactions represents one of the main challenges to preventing droughts, extreme events, and related human catastrophes in the face of global warming, especially in arid and semiarid environments. In eastern Africa, the El Niño–Southern Oscillation (ENSO) was identified as one of the primary drivers of precipitation variability affecting water availability. However, the northern East African Rift System (EARS) still suffers from the underrepresentation of predictive and ENSO teleconnection models because of the scarcity of local to regional historical or palaeo-data. In this paper, we provide a 50-year seasonal flood and drought chronicle of the Awash River catchment from the study of laminated sediment from Gemeri and Afambo lakes (central Afar region, Ethiopia) with the aim of reconstructing the magnitude of regional hydroclimatic events. Pluricentimetric micro-laminated lithogenic facies alternating with plurimillimetric carbonate-enriched facies are investigated in both lakes. We couple dating methods including radiocarbon, short-lived radionuclides, palaeomagnetic field variations, and varve counting on both lake deposits to build a high-resolution age model and to discuss the regional hydrosedimentary dynamics of the Awash River over the last ∼ 700 years with a focus on the last 50 years. Using a multiproxy approach, we observe that following a multicentennial enhanced hydrological period, the two lakes have experienced a gradual decrease in river load inflow since 1979 CE, attaining extreme drought and high evaporative conditions between 1991 and 1997 CE. In 2014, the construction of a dam and increased agricultural water management in the lower Awash River plain impacted the erodibility of local soils and the hydrosedimentary balance of the lake basins, as evidenced by a disproportionate sediment accumulation rate. Comparison of our quantitative reconstruction with (i) lake water surface evolution, (ii) the interannual Awash River flow rates, and (iii) the El Niño 3.4 model highlights the intermittent connections between ENSO sea surface temperature anomalies, regional droughts, and hydrological conditions in the northern EARS. [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]

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 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]

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 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]

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]

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]

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]

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]

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]

Monitoring the spatio-temporal variations of surface chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) greatly benefited from the availability of continuous and global ocean color satellite measurements from 1997 onward. These two decades of satellite observations are however still too short to provide a comprehensive description of Chl variations at decadal to multi-decadal timescales. This paper investigates the ability of a machine learning approach (a non-linear statistical approach based on Support Vector Regression, hereafter SVR) to reconstruct global spatio-temporal Chl variations from selected surface oceanic and atmospheric physical parameters. With a limited training period (13 years), we first demonstrate that Chl variability from a 32- years global physical-biogeochemical simulation can generally be skillfully reproduced with a SVR using the model surface variables as input parameters. We then apply the SVR to reconstruct satellite Chl observations using the physical predictors from the above numerical model and show that the Chl reconstructed by this SVR more accurately reproduces some aspects of observed Chl variability and trends compared to the model simulation. This SVR is able to reproduce the main modes of interannual Chl variations depicted by satellite observations in most regions, including El Niño signature in the tropical Pacific and Indian Oceans. In stark contrast with the trends simulated by the biogeochemical model, it also accurately captures spatial patterns of Chl trends estimated by satellite data, with a Chl increase in most extratropical regions and a Chl decrease in the center of the subtropical gyres, although the amplitude of these trends are underestimated by half. Results from our SVR reconstruction over the entire period (1979--2010) also suggest that the Interdecadal Pacific Oscillation drives a significant part of decadal Chl variations in both the tropical Pacific and Indian Oceans. Overall, this study demonstrates that non-linear statistical reconstructions can be complementary tools to in situ and satellite observations as well as conventional physical-biogeochemical numerical simulations to reconstruct and investigate Chl decadal variability. [ABSTRACT FROM AUTHOR]

Mathematical algorithms relate satellite data of ocean color with the surface Chlorophyll-a concentration (Chl-a), a proxy of phytoplankton biomass. These mathematical tools work best when they are adapted to the unique bio-optical properties of a particular oceanic province. Ocean color algorithms should also consider that there are significant differences between datasets derived from different sensors. Common solutions are to provide different parameters for each sensor or use merged satellite data. In this paper, we use satellite data from the Copernicus merged product suite and in situ data from the southernmost part of the California Current System to test two widely used global algorithms, OCx and CI, and a regional algorithm, CalCOFI2. The OCx algorithm yielded the most favorable results. Consequently, we regionalized it and conducted further testing, leading to significant improvements, especially in eutrophic and oligotrophic waters. The database was then separated according to (a) dynamic boundaries in the area, (b) bio-optical properties, and (c) climatic conditions (El Niño/La Niña). Regional algorithms were obtained and tested for each partition. The Chl-a retrievals for each model were tested and compared. The best fit for the data was for the regional algorithms that considered the climatic conditions (El Niño/La Niña). These results will allow for the construction of consistent regionally adapted time series and, therefore, will demonstrate the importance of El Niño/La Niña events on the bio-optical properties of the area. [ABSTRACT FROM AUTHOR]

As the most significant interannual signal in the tropical Pacific, the influence of ENSO on the interannual variability in TC genesis location in the western North Pacific (WNP) has received much attention in previous studies. This paper mainly emphasizes the underlying SST factors independent of the ENSO signal and explores how they modulate interannual tropical cyclone genesis (TCG) latitude variability. Our study finds that the meridional sea temperature gradient (SSTG) between the Kuroshio Extension and the WNP still has a significant effect on the interannual variability in the TCG latitude after removing the effect of ENSO (r = 0.6). The interannual forecasts of the TCG latitude were effectively improved from 0.67 to 0.81 when the ENSO-independent SSTG and ENSO were regressed together in a multi-linear regression. We then propose an ENSO-independent physical mechanism affecting the TCG latitude. The equatorward (poleward) SSTG excited the positive (negative) Pacific–Japan telecorrelation pattern over the WNP, forming Rossby wave trains and propagating northward. A significant cyclonic vortex (anticyclonic vortex) with strong convective development (suppression) developed near 20° N, leading more TCs to the northern (southern) part of the WNP. These findings provide a new perspective for the prediction of the interannual variability in the TCG latitude. [ABSTRACT FROM AUTHOR]

Systematic, routine, and comprehensive evaluation of Earth system models (ESMs) facilitates benchmarking improvement across model generations and identifying the strengths and weaknesses of different model configurations. By gauging the consistency between models and observations, this endeavor is becoming increasingly necessary to objectively synthesize the thousands of simulations contributed to the Coupled Model Intercomparison Project (CMIP) to date. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) Metrics Package (PMP) is an open-source Python software package that provides quick-look objective comparisons of ESMs with one another and with observations. The comparisons include metrics of large- to global-scale climatologies, tropical inter-annual and intra-seasonal variability modes such as the El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO), extratropical modes of variability, regional monsoons, cloud radiative feedbacks, and high-frequency characteristics of simulated precipitation, including its extremes. The PMP comparison results are produced using all model simulations contributed to CMIP6 and earlier CMIP phases. An important objective of the PMP is to document the performance of ESMs participating in the recent phases of CMIP, together with providing version-controlled information for all datasets, software packages, and analysis codes being used in the evaluation process. Among other purposes, this also enables modeling groups to assess performance changes during the ESM development cycle in the context of the error distribution of the multi-model ensemble. Quantitative model evaluation provided by the PMP can assist modelers in their development priorities. In this paper, we provide an overview of the PMP, including its latest capabilities, and discuss its future direction. [ABSTRACT FROM AUTHOR]

The Tarim Basin is a large inland arid basin in the arid region of northwest China and has been experiencing significant "warming and wetting" since 1987. As a result, the purpose of this paper is to determine whether the climate transition phenomenon occurred in the Tarim Basin as well as the role of atmospheric circulation in this process. We use meteorological data and atmospheric circulation indexes to study the seasonal trends of climate change in this region from 1987 to 2020 to understand how they are affected by atmospheric circulation. The findings show that, from 1987 to 2020, the Tarim Basin experienced significant warming and wetting; with the exception of the winter scale, all other seasonal scales exhibited a clear warming and wetting trend. From the perspective of spatial distribution, most of the areas showed a significant warming trend, and the warming amplitude around the basin is greater than that in the central area of the basin. However, there are significant regional differences in precipitation change rates. Meanwhile, wavelet analysis shows that there is a significant oscillation period of 17–20 years between climate change and the atmospheric circulation index during 1987–2020. The correlation analysis shows that the Pacific decadal oscillation (PDO) and El Niño-Southern Oscillation (ENSO) are the main influencing factors of climate change in the Tarim Basin at different seasonal scales, while the teleconnection of the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) is low and the PDO dominates the summer and autumn temperature changes in the Tarim Basin. The research results of this paper show that, despite the warming and wetting trends since 1987 in the Tarim Basin, the climate type did not change. From 1987 to 2020, the main teleconnection factors of climate change in the Tarim Basin were PDO and ENSO. [ABSTRACT FROM AUTHOR]

This article, titled "Editorial: Impact of solar activities on weather and climate," explores the relationship between solar activities and weather and climate patterns on Earth. It discusses various ways in which solar activities can affect atmospheric conditions, such as temperature, precipitation, tropical cyclones, and ocean circulation. The article includes several research papers that investigate the link between solar activity and surface climate variability, TC genesis frequency, the North Atlantic Oscillation, El Niño-Southern Oscillation, and the Atlantic meridional overturning circulation. It also examines the influence of solar forcing on the South Asian jet, high-altitude clouds, and energy transmission in the climate system. The article acknowledges the challenges and questions in studying the impact of the Sun on Earth's atmosphere and climate. [Extracted from the article]

The possible impact of El Niño-Southern Oscillation (ENSO) events on the main components of CO2 and H2O fluxes in a pristine mountainous tropical rainforest growing in Central Sulawesi in Indonesia is described. The fluxes were continuously measured using the eddy covariance method for the period from January 2004 to June 2008. During this period, two episodes of El Niño and one episode of La Niña were observed. All these ENSO episodes had moderate intensity and were of Central Pacific type. The temporal variability analysis of the main meteorological parameters and components of CO2 and H2O exchange showed a very high sensitivity of Evapotranspiration (ET) and Gross Primary Production (GPP) of the tropical rain forest to meteorological variations caused by both El Niño and La Niña episodes. Incoming solar radiation is the main governing factor that is responsible for ET and GPP variability. Ecosystem Respiration (RE) dynamics depend mainly on the air temperature changes and are almost insensitive to ENSO. Changes of precipitation due to moderate ENSO events did not cause any notable effect on ET and GPP, mainly because of sufficient soil moisture conditions even in periods of anomalous reduction of precipitation in the region. [ABSTRACT FROM AUTHOR]

In the California Current System (CCS), changes in the phenology (i.e., seasonal timing) of coastal upwelling alter the functioning of this productive marine ecosystem. Recently developed coastal upwelling indices that account for upwelling strength and nutrient flux to the surface provide a more complete understanding of bottom‐up forcing in the region. Using these indices, we describe CCS upwelling phenological variability in vertical transport and nutrient flux. Physical and biogeochemical spring transitions generally co‐occur in winter or spring, followed by increased upwelling and nutrient flux. In the latter half of the year, upwelling continues but nutrient flux wanes as declining source nutrient concentrations limit the biological efficacy of coastal upwelling. Earlier spring transitions and higher season‐integrated upwelling intensity occur during strong La Niña events at all latitudes, driven largely by stronger alongshore wind stress. Understanding phenological changes in coastal upwelling is critical, as they could have significant ecosystem consequences. Plain Language Summary: In the California Current System (CCS), coastal upwelling carries nutrient‐rich waters to the surface, supporting primary production and driving the coastal ecosystem. This upwelling varies on a seasonal and interannual basis, as reflected in recently developed indices that account for the amount of water upwelled to the surface as well as the amount of nutrients carried in that water. Generally upwelling and nutrient transport are high in the first half of the year. Upwelling persists into the second half of the year, but nutrient transport decreases as the deep‐water sources of these nutrients are depleted. Upwelling in the CCS is also affected by the El Niño Southern Oscillation. During La Niña conditions, strong trade winds enhance upwelling and nutrient transport on the California coast. This paper presents regional, seasonal and interannual patterns of upwelling and nutrient delivery in the CCS, which are important drivers of change to this coastal ecosystem. Key Points: We define new upwelling phenology indices for the California Current System that include nutrient transportWe identify spatial, seasonal, and interannual patterns of upwelling and nutrient deliveryWe relate the physical mechanisms of coastal upwelling with its biological efficacy [ABSTRACT FROM AUTHOR]

As an update on the current NOAA/NCEP operational ocean reanalysis systems, a new system named GLobal Ocean Reanalysis (GLORe) is recently built up based on the JEDI‐SOCA 3DVar scheme. In this study, the quality of GLORe is assessed in initializing ENSO predictions using the NOAA Unified Forecast System (UFS). In details, initialized by GLORe, 9‐month ensemble hindcasts are conducted from each May/November during 1982–2021. The ENSO prediction skill is compared to the current NOAA operational system CFSv2, suggesting that UFS initialized with GLORe has an improved skill in ENSO predictions. By conducting another set of hindcasts with UFS and the same initializations as CFSv2, it is found that the skill improvement is largely attributed to the ocean initialization with GLORe, but with some contributions from model improvements as well. The effect of ocean initializations is further confirmed by the superiority of GLORe over CFSR as validated against an objective analysis. Plain Language Summary: The operational climate monitoring and outlooks heavily rely on ocean data assimilation systems. At NOAA/NCEP, there are two such systems running in real time—GODAS and CFSR. As two highly related systems, GODAS and CFSR share some common weaknesses, and are lagging to meet the latest operational requirements. In this study, a new ocean reanalysis system (GLORe) is built based on the latest scientific advances. As a first evaluation about GLORe, this work focuses on its performance in initializing dynamical ENSO predictions, a critical component of climate outlooks. In particular, we complete a set of 9‐month ensemble hindcasts initialized with GLORe, by using the NOAA Unified Forecast System as the forecast model. The hindcasts are compared to the present NOAA operational system CFSv2 that is initialized with CFSR, and significant skill enhancements in ENSO predictions are seen in our hindcasts. Further experiments and diagnostics suggest that the skill improvement is mostly attributed to the GLORe initialization, but with some contributions from model improvements as well. As the configurations for our hindcasts are highly relevant to a future operational Seasonal Forecast System (SFS) at NOAA, the hindcasts reported in this paper will serve as an important benchmark for its developments. Key Points: A newly developed ocean reanalysis (GLORe) is introduced and evaluated for its quality in initializing ENSO predictionsUFS initialized with GLORe presents a better performance in ENSO predictions than the current operational system at NOAAThe hindcasts will be a benchmark for the Seasonal Forecast System (SFS) development at NOAA [ABSTRACT FROM AUTHOR]

This paper uses a 30 m record of valley alluviation in the Lockyer Creek, a major tributary of the mid-Brisbane River in Southeast Queensland, to document the timing and nature of Quaternary fluvial response. A combination of radiocarbon and optically stimulated luminescence dating reveals a sequence of major cut and fill episodes. The earliest aggradation phase is represented by a basal gravel unit, dating to ~220 ka (marine isotope sub-stage 7d), and although little evidence supports higher fluvial discharges during MIS 5, a MIS 3 fluvial episode characterised by incision and aggradation dates to ~60 ka. A penultimate phase of incision to a depth of 30 m prior to ~14 ka saw the lower Lockyer occupy its current position within the valley floor. The Lockyer Creek shows evidence of only minor fluvial activity during MIS 2, suggesting a drier LGM climate. The appearance of alternating fine- and coarse-grained units at about 2 ka is notable and may represent higher-energy flood conditions associated with a strengthening of El Niño Southern Oscillation activity as observed in the flood of 2011. The aggradation rate for this Holocene floodplain unit is ~11 times higher than the long-term rate. [ABSTRACT FROM AUTHOR]

Tropical sea surface temperature (SST) variability, mainly driven by the El Niño–Southern Oscillation (ENSO), influences the atmospheric circulation and hence the transport of heat and chemical species in both the troposphere and stratosphere. This paper uses Met Office, ERA5 and MERRA2 reanalysis data to examine the impact of SST variability on the dynamics of the polar stratosphere and ozone layer over the period from 1980 to 2020. Particular attention is paid to studying the differences in the influence of different types of ENSO (East Pacific (EP) and Central Pacific (CP)) for the El Niño and La Niña phases. It is shown that during the CP El Niño, the zonal wind weakens more strongly and changes direction more often than during the EP El Niño, and the CP El Niño leads to a more rapid decay of the polar vortex (PV), an increase in stratospheric air temperature and an increase in the concentration and total column ozone than during EP El Niño. For the CP La Niña, the PV is more stable, which often leads to a significant decrease in Arctic ozone. During EP La Niña, powerful sudden stratospheric warming events are often observed, which lead to the destruction of PV and an increase in column ozone. [ABSTRACT FROM AUTHOR]

The first paper focuses on the impact of the El Niño - Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) on South American rainfall during austral spring [[9]]. Since these sub-periods include years under ENSO extremes, the authors could study the ENSO impact on the mean climatic states in different AMO/PDO phases. The authors used composite and linear regression techniques to analyze the rainfall anomalies of different ENSO/IOD phases and (without the influence of the second principal variable) residual signals for the period from 1951 to 2016. [Extracted from the article]

Marine heatwaves (MHWs), defined as prolonged period of extremely warm sea surface temperature (SST), have been receiving a lot of attention in the past decade as their frequency and intensity increase in a warming climate. This paper investigates the extent to which the seasonal occurrence and duration of MHWs can be predicted with the European Centre for Medium-Range Weather Forecast (ECMWF) operational seasonal forecast system. The prediction of the occurrence of MHW events, the number of MHW days per season, and their intensity and spatial extent are derived from seasonal SST forecasts and evaluated against an observation-based SST analysis using both deterministic and probabilistic metrics over the 1982–2021 period. Forecast scores show useful skill in predicting the occurrence of MHWs globally for the two seasons following the starting date. The skill is the highest in the El Niño region, the Caribbean, the wider tropics, the north-eastern extra-tropical Pacific, and southwest of the extra-tropical basins. The skill is not as good for other midlatitude eastern basins nor for the Mediterranean, with the forecast system being able to represent the low-frequency modulation of MHWs but showing poor skill in predicting the interannual variability of the MHW characteristics. Linear trend analysis shows an increase in MHW occurrence at a global scale, which the forecasts capture well. [ABSTRACT FROM AUTHOR]

Aims: We investigate the associations between major Australian climate drivers and extreme weekly fire danger throughout the year. Methods: We use a composite-based approach, relating the probability of top-decile observed potential fire intensity to the positive and negative modes of the El Niño Southern Oscillation, Indian Ocean Dipole, Madden–Julian Oscillation, Southern Annular Mode, split-flow blocking and Subtropical Ridge Tasman Highs, both concurrently and at a variety of lag times. Key results: The chance of extreme fire danger increases over broad regions of the continent in response to El Niño and positive Indian Ocean Dipole events, the negative mode of the Southern Annular Mode, split-flow Blocking Index and Subtropical Ridge Tasman High, and Madden–Julian Oscillation phases 5, 6, 2 and 8 in Austral summer, autumn, winter and spring respectively. These relationships exist not only concurrently, but also when a climate event occurs up to 6 months ahead of the season of interest. Conclusions: These findings highlight the importance of considering the influence of diverse climate drivers, at a range of temporal lag periods, in understanding and predicting extreme fire danger. Implications: The results of this study may aid in the development of effective fire management strategies and decision-making processes to mitigate the impacts of fire events in Australia. This paper explores the relationships between the major forces influencing Australian weather and climate, and the chance of severe fire seasons. The findings could be valuable in decision making and preparation for upcoming fire seasons to avoid more seasons with devastating outcomes such as the 2019–2020 Black Summer. This article belongs to the Collection Fire and Climate. [ABSTRACT FROM AUTHOR]

The surface currents in coastal areas are closely related to the ecological environment and human activities, and are influenced by both local and remote factors of different timescales, resulting in complex genesis and multi-timescale characteristics. In this research, 9-year-long, hourly high-frequency radar (HFR) surface current observations are utilized together with satellite remote sensing reanalysis products and mooring data, and based on the Empirical Orthogonal Function (EOF) and correlation analysis, we revealed the multi-timescale characteristics of the surface currents in Fremantle Sea (32°S), Southwestern Australia, and explored the corresponding driving factors as well as the impact of El Niño-Southern Oscillation (ENSO) on the nearshore currents. Results show that the currents on the slope are dominated by the southward Leeuwin Current (LC), and the currents within the shelf are dominated by winds, which are subject to obvious diurnal and seasonal variations. The strong bathymetry variation there, from a wide shelf in the north to a narrow shelf in this study region, also plays an important role, resulting in the frequent occurrence of nearshore eddies. In addition, the near-zonal winds south of 30°S in winter contribute to the interannual variability of the Leeuwin Current at Fremantle, especially in 2011, when the onshore shelf circulation is particularly strong because of the climatic factors, together with the wind-driven offshore circulation, which results in significant and long-lasting eddies. The southward Leeuwin Current along Southwestern Australia shows a strong response to interannual climatic variability. During La Niña years, the equatorial thermal anomalies generate the westward anomalies in winds and equatorial currents, which in turn strengthen the Leeuwin Current and trigger the cross-shelf current as well as downwelling within the shelf at Fremantle, whereas during El Niño years, the climate anomalies and the response of coastal currents are opposite. This paper provides insights into the multi-timescale nature of coastal surface currents and the relative importance of different driving mechanisms. It also demonstrates the potential of HFR to reveal the response of nearshore currents to climate anomalies when combined with other multivariate data. Meanwhile, the methodology adopted in this research is applicable to other coastal regions with long-term available HFR observations. [ABSTRACT FROM AUTHOR]

Previous studies suggested that tropical sea surface salinity (SSS) can influence tropical Pacific sea surface temperature (SST) through mixing and entrainment and thus it may be a signal for El Niño‐Southern Oscillation (ENSO) prediction. This paper explores the influence of SSS on ENSO spring predictability barrier (SPB) using an empirical dynamic model ‐ Linear Inverse Model (LIM). By coupling and decoupling SSS in the LIM, we find that tropical Pacific SSS plays a significant role in weakening both Central‐Pacific and Eastern‐Pacific ENSO SPB. The evolution of optimal initial structure also shows the importance of SSS dynamics in ENSO. We found an SSS mode that plays the dominant role in SSS impacting ENSO prediction. By the analysis of lead‐lag correlation, we find that this mode can induce easterlies during the spring, which finally leads to a La Niña‐like SST pattern in the winter through zonal advective and thermocline feedbacks. Plain Language Summary: The spring predictability barrier (SPB) is a phenomenon of forecast skill reduction of the El Niño‐Southern Oscillation (ENSO) when it comes to the boreal spring, regardless of the initial month. Sea surface salinity (SSS) can influence sea surface temperature by altering sea surface density and thus it may be a signal for ENSO prediction. Using a linear dynamical model, we find that SSS plays an important role in improving the forecast skill of ENSO and weakening SPB. We further find that SSS can induce the easterlies during the spring, which finally leads to a La Niña‐like SST pattern. Our study suggests that SSS can be used to predict ENSO about 1 year later. Key Points: A linear dynamical model suggests that taking sea surface salinity (SSS) into consideration can strongly weaken Central‐Pacific and Eastern‐Pacific El Niño‐Southern Oscillation (ENSO) spring predictability barrier (SPB)A new SSS mode is found to be important for weakening ENSO SPBThe SSS mode can predict ENSO events about 1 year earlier by inducing sea surface temperature and wind anomalies in the early spring [ABSTRACT FROM AUTHOR]

Accurate climate data at fine spatial resolution are essential for scientific research and the development and planning of crucial social systems, such as energy and agriculture. Among them, sea surface temperature plays a critical role as the associated El Niño–Southern Oscillation (ENSO) is considered a significant signal of the global interannual climate system. In this paper, we propose an implicit neural representation-based interpolation method with temporal information ( T _ I N R I ) to reconstruct climate data of high spatial resolution, with sea surface temperature as the research object. Traditional deep learning models for generating high-resolution climate data are only applicable to fixed-resolution enhancement scales. In contrast, the proposed T _ I N R I method is not limited to the enhancement scale provided during the training process and its results indicate that it can enhance low-resolution input by arbitrary scale. Additionally, we discuss the impact of temporal information on the generation of high-resolution climate data, specifically, the influence of the month from which the low-resolution sea surface temperature data are obtained. Our experimental results indicate that T _ I N R I is advantageous over traditional interpolation methods under different enhancement scales, and the temporal information can improve T _ I N R I performance for a different calendar month. We also examined the potential capability of T _ I N R I in recovering missing grid value. These results demonstrate that the proposed T _ I N R I is a promising method for generating high-resolution climate data and has significant implications for climate research and related applications. [ABSTRACT FROM AUTHOR]