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Mesoscale eddies are phenomena that widely exist in the ocean and have a significant impact on the ocean's temperature and salt structure, as well as on acoustic propagation effects. Currently, utilizing the limited data on mesoscale eddy environments for refined acoustic field reconstruction in offshore conditions at mid-to-far-ocean distances is an urgent problem that needs to be addressed. In this paper, we propose a mesoscale eddy reconstruction method (EddyGAN) based on the generative adversarial network (GAN) model which is inspired by the concept of global localization. We adopt a hybrid algorithm for eddy identification using JCOPE2M high-resolution reanalysis data and Archiving, Validation, and Interpretation of Satellite Oceanographic (AVISO) satellite altimeter data to extract mesoscale eddy sound speed profile (SSP) sample data, and then apply the EddyGAN model to train this dataset and perform mesoscale eddy acoustic field reconstruction. We also propose an evaluation method for mesoscale eddy acoustic field reconstruction that uses RMSE, SSIM, and convergence zone (CZ) accuracy based on World Ocean Atlas (WOA) climate state data completion as indicators. The reconstruction result of this model achieves an RMSE of 1.7 m/s, an SSIM of 0.77, and an average CZ accuracy of over 70%. This method better characterizes the mesoscale eddy sound field than the native GAN and other reconstruction methods, improves the accuracy of mesoscale eddy acoustic field reconstruction, and provides superior performance, offering significant reference value for mesoscale eddy reconstruction technology and subsequent ocean acoustic research. [ABSTRACT FROM AUTHOR]

Changes in the phenology of physical and ecological variables associated with climate change are likely to have significant effect on many aspects of the Baltic ecosystems. We apply a set of phenological indicators to multiple environmental variables measured by satellite sensors for 17-35 years to detect possible changes in the seasonality in the Baltic Sea environment. We detect significant temporal changes such as earlier start of the summer season and prolongation of the productive season in multiple variables ranging from basic physical drivers to ecological status indicators. While increasing trends in the absolute values of variables like sea-surface temperature (SST), diffuse attenuation of light (Ked490) and satellite-detected chlorophyll concentration (CHL) are detectable, the corresponding changes in their seasonal cycles are more dramatic. For example, the cumulative sum of 30 000Wm-2 of surface incoming shortwave irradiance (SIS) was reached 23 days earlier in 2014 compared to the beginning of the time series in 1983. The period of the year with SST of at least 17 °C has almost doubled (from 29 days in 1982 to 56 days in 2014), the period with Ked490 over 0.4m-1 has increased from about 60 days in 1998 to 240 days in 2013, i.e. quadrupled. The period with satellite-detected CHL of at least 3mgm-3 has doubled from approximately 110 days in 1998 to 220 days in 2013. While the timing of both the phytoplankton spring and summer blooms have advanced, the annual CHL maximum that in the 1980s corresponded to the spring diatom bloom in May has now switched to the summer cyanobacteria bloom in July. [ABSTRACT FROM AUTHOR]

Medium-voltage DC (MVDC) technology has gained increasing attention in recent years. Power electronics devices dominate these grids. Accurate simulation of such a grid, with detailed models of switching semiconductors, can quickly became very time-consuming, according to the number of connected devices to be simulated. A simulation approach based on interactions on a continuous time model can be very interesting, especially for developing a system-level control model of such a modern MVDC distribution grid. The aim of this paper is to present all the steps required for obtaining a continuous time modelling of a +/−10 kV MVDC grid case study, including a solid-state transformer (SST)- and modular multilevel converter (MMC)-based active front end (AFE). An additional aim of this paper is to supply educational content about the use of the continuous time simulation approach, thanks to a detailed description of the various devices modelled into the presented MVDC grid. The results of a certain number of simulation scenarios are eventually presented. [ABSTRACT FROM AUTHOR]

This review will serve as an entry point for international researchers who would like to tap into the vast scientific potential of Chinese-language literature on oceanic fronts. We focused on observational physical oceanography studies of marine fronts. A thorough bibliographic search netted 95 papers published in 1982–2023, with a sharp increase in the total number of papers from 2006–2010 to 2011–2015, when this number almost tripled. This trend continued unabated through the early 2020s. The sharp increase in Chinese-language publications preceded by several years a rapid increase in English-language publications in the same field. Regionally, the overwhelming majority of papers is focused on the China Seas, particularly the East China Seas and northern South China Sea. Elsewhere, a number of papers were dedicated to the Southern Ocean and North Atlantic. Thematically, papers on remote sensing of ocean fronts dominate the literature, with special attention to the development of front-detection algorithms that can be applied to satellite data on sea surface temperature, salinity, chlorophyll, and sea level anomaly. Numerous papers on marine fronts in the China Seas present important results that have to be considered by international researchers. Overall, this review emphasizes the significant contributions made by Chinese oceanographers, particularly to studies of the frontal oceanography of the China Seas. [ABSTRACT FROM AUTHOR]

Light limitation can affect the distribution of biota and nutrients in the ocean. Light absorption by colored detrital material (CDM) was included in a fully coupled Earth System Model using a new parameterization for shortwave attenuation. Two model runs were conducted, with and without light attenuation by CDM. In a global average sense, greater light limitation associated with CDM increased surface chlorophyll, biomass and nutrients together. These changes can be attributed to the movement of biological productivity higher up the water column, which increased surface chlorophyll and biomass while simultaneously decreasing total biomass. Meanwhile, the reduction in biomass resulted in greater nutrient availability throughout the water column. Similar results were found on a regional scale in an analysis of the oceans by biome. In coastal regions, surface chlorophyll increased by 35% while total integrated phytoplankton biomass diminished by 18 %. The largest relative increases in modeled surface chlorophyll and biomass in the open ocean were found in the equatorial biomes, while largest decreases in depth-integrated biomass and chlorophyll were found in the subpolar and polar biomes. This mismatch of surface and subsurface trends and their regional dependence was analyzed by comparing the competing factors of diminished light availability and increased nutrient availability on phytoplankton growth in the upper 200 m. Overall, increases in surface biomass were expected to accompany greater nutrient uptake and therefore diminish surface nutrients, but changes in light limitation decoupled trends between these two variables. Understanding changes in biological productivity requires both surface and depth-resolved information. Surface trends may be minimal or of the opposite sign to depth-integrated amounts, depending on the vertical structure of phytoplankton abundance. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) prediction plays an important role in scientific research, environmental protection, and other marine-related fields. However, most of the current prediction methods are not effective enough to utilize the spatial correlation of SSTs, which limits the improvement of SST prediction accuracy. Therefore, this paper first explores spatial correlation mining methods, including regular boundary division, convolutional sliding translation, and clustering neural networks. Then, spatial correlation mining through a graph convolutional neural network (GCN) is proposed, which solves the problem of the dependency on regular Euclidian space and the lack of spatial correlation around the boundary of groups for the above three methods. Based on that, this paper combines the spatial advantages of the GCN and the temporal advantages of the long short-term memory network (LSTM) and proposes a spatiotemporal fusion model (GCN-LSTM) for SST prediction. The proposed model can capture SST features in both the spatial and temporal dimensions more effectively and complete the SST prediction by spatiotemporal fusion. The experiments prove that the proposed model greatly improves the prediction accuracy and is an effective model for SST prediction. [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]

In the process of ocean exploration, highly accurate and sensitive measurements of seawater temperature and pressure significantly impact the study of seawater's physical, chemical, and biological processes. In this paper, three different package structures, V-shape, square-shape, and semicircle-shape, are designed and fabricated, and an optical microfiber coupler combined Sagnac loop (OMCSL) is encapsulated in these structures with polydimethylsiloxane (PDMS). Then, the temperature and pressure response characteristics of the OMCSL, under different package structures, are analyzed by simulation and experiment. The experimental results show that structural change hardly affects temperature sensitivity, and square-shape has the highest pressure sensitivity. In addition, with an input error of 1% F.S., temperature and pressure errors were calculated, which shows that a semicircle-shape structure can increase the angle between lines in the sensitivity matrix method (SMM), and reduce the effect of the input error, thus optimizing the ill-conditioned matrix. Finally, this paper shows that using the machine learning method (MLM) effectively improves demodulation accuracy. In conclusion, this paper proposes to optimize the ill-conditioned matrix problem in SMM demodulation by improving sensitivity with structural optimization, which essentially explains the cause of the large errors for multiparameter cross-sensitivity. In addition, this paper proposes to use the MLM to solve the problem of large errors in the SMM, which provides a new method to solve the problem of the ill-conditioned matrix in SMM demodulation. These have practical implications for engineering an all-optical sensor that can be used for detection in the ocean environment. [ABSTRACT FROM AUTHOR]

Heavy rain events are frequently recorded in the Western Mediterranean causing economic losses and even human casualties. The Western Mediterranean is a deep and almost closed sea surrounded by high mountain ranges and with little exchange of water with the Atlantic ocean. A main factor in the development of torrential rains are ocean-atmosphere exchanges of heat and moisture that can potentially destabilize air masses travelling over the sea. The study of air mass trajectories previous to the rain event permits the identification of sea areas that could probably contribute to the development or intensification of rainfall. From a previous Mediterranean sea surface temperature climatology, its spatio-temporal distribution patterns have been studied showing two main distribution modes in winter and summer and transitional regimes in spring and autumn. Hence, three heavy precipitation events, for such winter and summer sea temperature regimes and for fall transition, affecting the Valencia region have been selected to study the effect of sea surface temperature in torrential rains. Simula tions with perturbed sea surface temperature in different areas along the air mass path were run to compare results with unperturbed simulation. The variation of sea surface temperature in certain areas caused significant changes in model accumulated values and its spatial distribution. Therefore, the existence of recharge areas where air-sea interaction favors the development of torrential rainfall in Valencia region has been shown. This methodology could be extended to the whole Mediterranean basin to look for such potential recharge areas. The identification of sea areas that contribute to the development or intensification of heavy rain events in the Mediterranean countries could be a useful prognosis and/or monitoring tool. [ABSTRACT FROM AUTHOR]

In this study, a method is presented to retrieve the surface reflectance using reflectance measured at the top of the atmosphere for the two views provided by the Along-Track Scanning Radiometer (AATSR). In the first step, the aerosol optical depth (AOD) is obtained using the AATSR dual view algorithm (ADV) by eliminating the effect of the surface on the measured radiances. Hence the AOD is independent of surface properties and can thus be used in the second step to provide the aerosol part of the atmospheric correction which is needed for the surface reflectance retrieval. The method is applied to provide monthly maps of both AOD and surface reflectance at two wavelengths (555 and 659 nm) for the whole year of 2007. The results are validated vs. surface reflectance provided by the AERONET-based Surface Reflectance Validation Network (ASRVN). Correlation coefficients are 0.8 and 0.9 for 555 and 659 nm, respectively. The standard deviation is 0.001 for both wavelengths and the absolute error is less than 0.02. Pixel-by-pixel comparison with MODIS (MODerate resolution Imaging Spectrometer) monthly averaged surface reflectances show a good correlation (0.91 and 0.89 for 555 and 659 nm, respectively) with some (up to 0.05) overestimation by ADV over bright surfaces. The difference between the ADV and MODIS retrieved surface reflectance is smaller than ±0.025 for 68.3% of the collocated pixels at 555nm and 79.9% of the collocated pixels at 659 nm. An application of the results over Australia illustrates the variation of the surface reflectances for different land cover types. The validation and comparison results suggest that the algorithm can be successfully used for the both AATSR and ATSR-2 (which has characteristics similar to AATSR) missions, which together cover 17 years period of measurements (1995-2012), as well as a prototype for The Sea and Land Surface Temperature Radiometer (SLSTR) to be launched in 2015 onboard the Sentinel-3 satellite. [ABSTRACT FROM AUTHOR]

Numerical simulations provide a considerable aid in studying past climates. Out of the various approaches taken in designing numerical climate experiments, transient simulations have been found to be the most optimal when it comes to comparison with proxy data. However, multi-millennial or longer simulations using fully coupled general circulation models are computationally very expensive such that acceleration techniques are frequently applied. In this study, we compare the results from transient simulations of the present and the last interglacial with and without acceleration of the orbital forcing, using the comprehensive coupled climate model CCSM3 (Community Climate System Model 3). Our study shows that in most parts of the world, the simulation of long-term variations in interglacial surface climate is not significantly affected by the use of the acceleration technique (with an acceleration factor 10) and, hence, large-scale model-data comparison of surface variables is not hampered. However, in high-latitude regions where the surface climate has a direct connection to the deep ocean, e.g. in the Southern Ocean or the Nordic Seas, acceleration-induced biases in sea-surface temperature evolution may occur with potential influence on the dynamics of the overlying atmosphere. [ABSTRACT FROM AUTHOR]

Sea Surface Temperature is the key variable when tackling seasonal to decadal climate forecast. Dynamical models are unable to properly reproduce tropical climate variability, introducing biases that prevent a skillful predictability. Statistical methodologies emerge as an alternative to improve the predictability and reduce these biases. Recent studies have put forward the non-stationary behavior of the teleconnections between tropical oceans, showing how the same tropical mode has different impacts depending on the considered sequence of decades. To improve the predictability, the Sea Surface Temperature based Statistical Seasonal foreCAST model (S4CAST) introduces the novelty of considering the non-stationary links between the predictor and predictand fields. This paper describes the development of S4CAST model whose operation is focused on the study of the predictability of any variable related to sea surface temperature. An application focused on West African rainfall predictability has been implemented as a benchmark example. [ABSTRACT FROM AUTHOR]

We present a numerical model of the ocean that couples a three-stream radiative transfer component with a marine biogeochemical-ecosystem in a dynamic threedimensional physical framework. The radiative transfer component resolves spectral irradiance as it is absorbed and scattered within the water column. We explicitly include the effect of several optically important water constituents (the phytoplankton community, detrital particles, and coloured dissolved organic matter, CDOM). The model is evaluated against in situ observed and satellite derived products. In particular we compare to concurrently measured biogeochemical, ecosystem and optical data along a north-south transect of the Atlantic Ocean. The simulation captures the patterns and magnitudes of these data, and estimates surface upwelling irradiance analogous to that observed by ocean colour satellite instruments. We conduct a series of sensitivity experiments to demonstrate, globally, the relative importance of each of the water constituents, and the crucial feedbacks between the light field and the relative fitness of phytoplankton types, and the biogeochemistry of the ocean. CDOM has proportionally more importance at short wavelengths and in more productive waters, phytoplankton absorption is especially important at the deep chlorophyll a (Chl a) maximum, and absorption by water molecules is relatively most important in the highly oligotrophic gyres. Sensitivity experiments in which absorption by any of the optical constituents was increased led to a decrease in the size of the oligotrophic regions of the subtropical gyres: lateral nutrient supplies were enhanced as a result of decreasing high latitude productivity. Scattering does not as strongly affect the ecosystem and biogeochemistry fields within the water column but is important for setting the surface upwelling irradiance, and hence sea surface reflectance. Having a model capable of capturing bio-optical feedbacks will be important for improving our understanding of the role of light and optical constituents on ocean biogeochemistry, especially in a changing environment. The potential benefits of capturing surface upwelling irradiance will be important for making closer connections to satellite derived products in the future. [ABSTRACT FROM AUTHOR]

Mg / Ca ratios in foraminiferal tests are routinely used as paleo temperature proxy, but on long timescales, also hold the potential to reconstruct past seawater Mg / Ca. Impact of both temperature and seawater Mg/Ca on Mg incorporation in foraminifera have been quantified by a number of studies. The underlying mechanism responsible for Mg incorporation in foraminiferal calcite and its sensitivity to environmental conditions, however, is not fully identified. A recently published biomineralization model (Nehrke et al., 2013) proposes a combination of transmembrane transport and seawater leakage or vacuolization to link calcite Mg / Ca to seawater Mg /Ca and explains inter-species variability in Mg / Ca ratios. To test the assumptions of this model, we conducted a culture study in which seawater Mg / Ca was manipulated by varying [Ca2+] and keeping [Mg2+] constant. Foraminiferal growth rates, test thickness and calcite Mg / Ca of newly formed chambers were analyzed. Results showed optimum growth rates and test thickness at Mg / Ca closest to that of ambient seawater. Calcite Mg / Ca is positively correlated to seawater Mg / Ca, indicating that not absolute seawater [Ca2+] and [Mg2+], but the telative ratio controls Mg / Ca in tests. These results demonstrate that the calcification process cannot be based only on seawater vacuolization, supporting the mixing model proposed by Nehrke et al. (2013). Here we, however, suggest a transmembrane transport fractionation that is not as strong as suggested by Nehrke et al. (2013). [ABSTRACT FROM AUTHOR]

Haiyang-1D (HY-1D) is the second operational satellite in China's Haiyang-1 series of satellites, carrying the Chinese Ocean Color and Temperature Scanner (COCTS) to provide ocean color and temperature observations. The radiometric calibration is a prerequisite to guarantee the quality of the satellite observations and the derived products, and the radiometric calibration of the thermal emissive bands of HY-1D/COCTS can effectively improve the accuracy of sea surface temperature (SST) derived from the thermal infrared data. In this paper, a study on the regional cross-calibration of the COCTS thermal emissive bands is conducted for high-accuracy SST observations in the South China Sea. The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the NOAA-20 satellite launched by the National Oceanic and Atmospheric Administration (NOAA) is selected as the calibration reference sensor, and a double-difference cross-calibration method is used for HY-1D/COCTS thermal infrared brightness temperature (BT) evaluation. The results show that the bias of the 11 µm and 12 µm thermal emissive bands of COCTS and VIIRS in the South China Sea are 0.101 K and 0.892 K, respectively, and the differences in BTs between the two sensors show temperature dependence. The cross-calibration coefficients are obtained and used to correct the BT of the COCTS thermal emissive bands. The bias of the BT of the 11 µm and 12 µm bands of COCTS are about 0.01 K after cross-calibration. To further validate the results, COCTS post-calibration data were examined using the NOAA-20 Cross-track Infrared Sounder (CrIS) data as a third-party source. The BT is calculated with the spectral response functions of the COCTS thermal emissive bands using the convolution calculation of the CrIS hyperspectral region observations. The comparison shows a small bias between the post-calibration COCTS thermal emissive band observations and CrIS, which is consistent with the comparison between VIIRS and CrIS. The accuracy of the post-calibration COCTS thermal emissive band BT data in the South China Sea has been significantly improved. [ABSTRACT FROM AUTHOR]

The mobile ocean weather observation system, named Marine Weather Observer (MWO), developed by the Institute of Atmospheric Physics (IAP), consists of a fully solar-powered, unoccupied vehicle and meteorological and hydrological instruments. One of the MWOs completed a long-term continuous observation, actively approaching the center of Typhoon Sinlaku from 24 July to 2 August 2020, over the South China Sea. The in situ and high-temporal-resolution (1 min) observations obtained from MWO were analyzed and evaluated through comparison with the observations made by two types of buoys during the evolution of Typhoon Sinlaku. First, the air pressure and wind speed measured by MWO are in good agreement with those measured by the buoys before the typhoon, reflecting the equivalent measurement capabilities of the two methods under normal sea conditions. The sea surface temperature (SST) between MWO and the mooring buoys is highly consistent throughout the observation period, indicating the high stability and accuracy of SST measurements from MWO during the typhoon evolution. The air temperature and relative humidity measured by MWO have significant diurnal variations, generally lower than those measured by the buoys, which may be related to the mounting height and sensitivity of sensors. When actively approaching the typhoon center, the air pressure from MWO can reflect some drastic and subtle changes, such as a sudden drop to 980 hPa, which is difficult to obtain by other observation methods. As a mobile meteorological and oceanographic observation station, MWO has shown its unique advantages over traditional observation methods, and the results preliminarily demonstrate the reliable observation capability of MWO in this paper. [ABSTRACT FROM AUTHOR]

In the marine ecosystem, phytoplankton plays a vital role as the primary supplier of oxygen, contributing to 50% of the total oxygen production. Not only does it serve as a significant source of food for other species, but it also sustains life in the ocean. However, the rising ocean temperatures caused by global warming have severely hindered the ability of phytoplankton to generate oxygen. Furthermore, fishes are crucial consumers of oxygen within the marine ecosystem. This research paper presents a model that intricately connects the dynamics of oxygen, phytoplankton, zooplankton, and fish using the Caputo fractional derivative. The model aims to examine the impact of global warming on the collective dynamics by considering the relationship between the rate of oxygen generation, temperature, and time. The paper establishes the existence and uniqueness of solutions and also analyzes the stability of equilibrium points. Numerical simulations are conducted to demonstrate the impact of fractional derivatives and global warming on oxygen depletion and species extinction. [ABSTRACT FROM AUTHOR]

We implement a new stomatal conductance model, based on the optimality approach, within the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model. Coupled land-atmosphere simulations are then performed using CABLE within the Australian Community Climate and Earth Systems Simulator (ACCESS) with prescribed sea surface temperatures. As in most land surface models, the default stomatal conductance scheme only accounts for differences in model parameters in relation to the photosynthetic pathway, but not in relation to plant functional types. The new scheme allows model parameters to vary by plant functional type, based on a global synthesis of observations of stomatal conductance under different climate regimes over a wide range of species. We show that the new scheme reduces the latent heat flux from the land surface over the boreal forests during the Northern Hemisphere summer by 0.5 to 1.0 mm day-1. This leads to warmer daily maximum and minimum temperatures by up to 1.0 °C and warmer extreme maximum temperatures by up to 1.5 °C. These changes generally improve the climate model's climatology and improve existing biases by 10-20 %. The change in the surface energy balance also affects net primary productivity and the terrestrial carbon balance. We conclude that the improvements in the global climate model which result from the new stomatal scheme, constrained by a global synthesis of experimental data, provide a valuable advance in the long-term development of the ACCESS modelling system. [ABSTRACT FROM AUTHOR]

The North Atlantic Oscillation (NAO) and North Atlantic tripole sea surface temperature (SST_tri) are important modes in the atmosphere and ocean over the North Atlantic, respectively. The link between the two is well‐known. However, this link weakened during 1980–2001, which is particularly pronounced in late winter and was not detected in early winter. This phenomenon has not been well revealed. The role of NAO in the above correlation changes was discussed. In late winter, a significant eastward shift (up to 20° longitude) of NAO south center during 1980–2001 was observed in both observation and CMIP6, accompanied by the eastward expansion of NAO north center. Spatial shift of the NAO forced the region of strong air‐sea interactions to shift and resulting in the collapse of NAO‐related SST_tri. These findings deepen our understanding of the NAO on the subseasonal scale. Plain Language Summary: A significant correlation between the North Atlantic Oscillation (NAO) and the North Atlantic tripole sea surface temperature (SST_tri) is widely recognized. However, this study found that the correlation between the two was significantly weakened in late winter during 1980–2001 while no such change was detected in early winter. Therefore, this paper will focus on the late winter and discuss the role of NAO spatial structure in the above correlation changes. When the NAO south center is located over the North Atlantic, both observations and the climate models indicate that the corresponding North Atlantic SST shows a significant tripole pattern. However, during 1980–2001, the NAO south center shift significantly eastward from the North Atlantic toward Western Europe (up to 20° longitude). At the same time, the NAO north center expanded eastward significantly. The eastward shift of the NAO results in the significant eastward shift of the turbulent heat flux and wind stress anomalies. Shift in the region of strong air‐sea interaction led to the collapse of NAO‐related SST_tri. These findings deepen our understanding of the NAO on the subseasonal scale and also provide implications for subseasonal‐seasonal predictions of Eurasian climate extremes. Key Points: The link between NAO and North Atlantic tripole SST weakened obviously during 1980–2001 late winter, which was not detected in early winterThe eastward shift of ∼20° longitude in NAO south center forced the strong air‐sea interactions region to shift in observations and CMIP6NAO's spatial shift in late winter caused the changes in the link between NAO and North Atlantic tripole SST [ABSTRACT FROM AUTHOR]

This paper aims to analyze the North Brazil Current (NBC) rings during the initial 5 months of 2020 using surface currents derived from Automatic Identification System (AIS) data in comparison with altimetry-based Archiving, Validation and Interpretation of Satellite Oceanographic Data (AVISO) current fields. The region of NBC rings is characterized by relatively high marine traffic, facilitating an accurate current estimation. Our investigation primarily focused on a brief period coinciding with intensive in situ measurements (EUREC4A-OA experiment). The Angular Momentum Eddy Detection and tracking Algorithm (AMEDA) detection algorithm was then employed to detect and track eddies in both fields. Subsequently, a particular NBC ring present in the region in January and February 2020 was examined. The comparison demonstrated that AIS data exhibited the precision and resolution necessary to effectively identify the NBC rings and smaller surrounding eddies, aligning well with other datasets such as in situ measurements, sea surface temperature (SST), and sea surface salinity (SSS) data. Moreover, we established that AIS data yielded accurate regional velocity fields, as evidenced by an analysis of energy spectra. Furthermore, our analysis revealed that AIS data captured aspects of eddy–eddy interactions which were not adequately depicted in AVISO fields. [ABSTRACT FROM AUTHOR]

Data on the reproductive biology of elasmobranchs are essential for understanding their life history. Published studies on batoid ray reproductive biology are comparatively scarce, leading to limited understanding and data gaps. The Oceania fantail ray, Taeniura lessoni, is a good example. This Data Deficient nearshore stingray is restricted to Melanesia, with lacking biological and ecological data, including reproduction. To expand upon the limited life-history data for this species, this short paper provides observational data on the reproductive condition in female T. lessoni, at Drawaqa Island, Fiji. Field work involved direct observations and ocean temperature measurements. Over 40 days spanning three months, 105 surveys were conducted across five sites, resulting in 71 sightings of the species. Based on spot patterns and body markings, four female individuals were identified. Between January and March 2024, these females exhibited convex dorsa indicating advanced gestation, transitioning to concave dorsa suggesting parturition. The presence of neonates from early March onwards coincided with the estimated parturition period inferred from the rays' condition. Furthermore, a female previously pregnant was photographed with a dermal abrasion around her pectoral fin, possibly indicating pre-copulatory biting, suggesting a continuous reproductive cycle. The average monthly water temperature at the surveyed sites remained relatively stable throughout the study. Collectively, our findings suggest that Drawaqa Island provides suitable habitat niches for reproductive activities in female T. lessoni. Repeated and long-term data is certainly needed to confirm either a continuous reproductive cycle or seasonal peaks. While preliminary, our observational data represents the first documentation on female reproductive condition in a stingray in Fiji. [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]

In this paper, we explore different prognostic methods to account for skin sea surface temperature diurnal variations in a coupled ocean–atmosphere regional model of the Mediterranean Sea. Our aim is to characterise the sensitivity of the considered methods with respect to the underlying assumption of how the solar radiation shapes the warm layer of the ocean. All existing prognostic methods truncate solar transmission coefficient at a warm-layer reference depth that is constant in space and time; instead, we implement a new scheme where this latter is estimated from a chlorophyll dataset as the e -folding depth of solar transmission, which thus allows it to vary in space and time depending on seawater's transparency conditions. Comparison against satellite data shows that our new scheme, compared to the one already implemented within the ocean model, improves the spatially averaged diurnal signal, especially during winter, and the seasonally averaged one in spring and autumn, while showing a monthly basin-wide averaged bias smaller than 0.1 K year-round. In April, when most of the drifters' measurements are available, the new scheme mitigates the bias during nighttime, keeping it positive but smaller than 0.12 K during the rest of the monthly averaged day. The new scheme implemented within the ocean model improves the old one by about 0.1 K, particularly during June. All the methods considered here showed differences with respect to objectively analysed profiles confined between 0.5 K during winter and 1 K in summer for both the eastern and the western Mediterranean regions, especially over the uppermost 60 m. The new scheme reduces the RMSE on the top 15 m in the central Mediterranean for summertime months compared to the scheme already implemented within the ocean model. Overall, the surface net total heat flux shows that the use of a skin sea surface temperature (SST) parameterisation brings the budget about 1.5 Wm-2 closer to zero on an annual basis, despite all simulations showing an annual net heat loss from the ocean to the atmosphere. Our "chlorophyll-interactive" method proved to be an effective enhancement of existing methods, its strength relying on an improved physical consistency with the solar extinction implemented in the ocean component. [ABSTRACT FROM AUTHOR]

Wave-induced radiation stress (RS), as a primary driver of ocean currents influenced by waves, plays an important role in the response of upper ocean temperatures under typhoons. Previous studies have mainly focused on wave-generated currents and coastal currents in nearshore areas. This paper incorporates the geostrophic effect into the wave-induced radiation stress of wave-current interaction, and the effect of waves on the changes in upper ocean temperature (including sea surface temperature (SST) and mixed layer temperature) under typhoon Nanmadol (2022) is studied. The FVCOM-SWAVE model is used to conduct a preliminary numerical study in the western Pacific Ocean. The RS with the geostrophic effect increased the horizontal and vertical components, leading to an enhancement in turbulent mixing and a decrease in SST by up to 1.0 °C to 1.4 °C, which is closer to the SST obtained by OISST remote sensing fusion observation data. In the strong divergence domain, the direction of the vortex flow exhibits a more pronounced turn to the right, accompanied by an increase in water velocity. The vertical temperature profile of the ocean shows that the water below is perturbed by the RS component of the geostrophic effect, and the depth of the mixed layer increases by about 2 m, which is closer to the depth of the mixed layer observed by the Argo floats, indirectly enhancing the vertical mass transport of the ocean. In general, this shows that RS, which takes into account geostrophic effects, enhances the effect of waves on the water below, indirectly leading to lower temperatures in the upper ocean, and the simulated results align more closely with the observed data, offering valuable insights for enhancing marine numerical forecasting accuracy. [ABSTRACT FROM AUTHOR]

The number and coverage of weather observations over the oceans were considerably reduced during World War II (WW2) due to disruptions to normal trade routes. The observations that do exist for this period are often unavailable to science as they are still only available as paper records or scanned images. We have rescued the detailed hourly weather observations contained in more than 28,000 logbook images of the US Navy Pacific Fleet stationed at Hawai'i during 1941–1945 to produce a dataset of more than 630,000 records. Each record contains the date and time, positional information and several meteorological measurements, totalling more than 3 million individual observations. The data rescue process consisted of a citizen science project asking volunteers to transcribe the observations from the available images, followed by additional quality control processes. This dataset not only contains hourly weather observations of air temperature, sea surface temperature, atmospheric pressure, wind speed and wind direction, mainly in the Pacific Ocean but also includes some observations from the Atlantic and Indian Oceans. The new observations are found to be of good quality by inter‐comparing independent measurements taken on ships travelling in convoy and by comparing with the 20th‐Century Reanalysis. This dataset provides invaluable instrumental weather observations at times and places during WW2, which fill gaps in existing reconstructions. [ABSTRACT FROM AUTHOR]

We present a new mechanistic model, Stochastic Lagrangian Aggregate Model of Sinking particles (SLAMS) for the biological pump in the ocean, which tracks the evolution of individual particles as they aggregate, disaggregate, sink, and are altered by chemical and biological processes. SLAMS considers the impacts of ballasting by mineral phases, binding of aggregates by transparent exopolymer particles (TEP), zooplankton grazing, and the fractal geometry (porosity) of the aggregates. Parameterizations for age-dependent organic carbon (orgC) degradation kinetics, and disaggregation driven by zooplankton grazing and TEP degradation, are motivated by observed particle fluxes and size spectra throughout the water column. The model is able to explain observed variations in orgC export efficiency and rain ratio from the euphotic zone and to the sea floor as driven by sea surface temperature and the primary production rate and sea-sonality of primary production. The model provides a new mechanistic framework with which to predict future changes on the flux attenuation of orgC in response to climate change forcing. [ABSTRACT FROM AUTHOR]

A well-defined surface wind divergence (SWD) belt with distinct cloud properties forms over the equatorial Atlantic during the boreal summer months. This belt separates the deep convective clouds of the intertropical convergence zone (ITCZ) from the shallow marine stratocumulus cloud decks forming over the cold-water subtropical region of the southern Hadley cell. Using the QuikSCAT-SeaWinds and Aqua-MODIS instruments, we examined the large-scale spatiotemporal variability of the SWD belt during a 6 year period (2003-2008) and the related links to cloud properties over the Atlantic Ocean. The Atlantic SWD belt was found to be most pronounced from May to August, between the equator and 2° N latitude. A positive correlation and a strong link were observed between formation of the SWD belt and a sharp sea-surface temperature gradient on the northern border of the cold tongue, supporting Wallace's vertical-mixing mechanism. The dominant cloud type over this region was shallow cumulus. Cloud properties were shown to be strongly linked to the formation and strength of the SWD zone. Our findings help understand the link between ocean-atmosphere dynamics and cloud properties over this region, and suggest that the SWD zone be considered a unique cloud belt of the southern branch of the Atlantic Hadley cell. [ABSTRACT FROM AUTHOR]

Complex oceanic circulation and air--sea interaction make the eastern tropical Pacific Ocean (ETPO) a highly variable source of CO2 to the atmosphere. Although the scientific community have amassed 70 000 surface partial-pressure of carbon dioxide (pCO2) datapoints within the ETPO region over the past 25 years, the spatial and temporal resolution of this dataset is insufficient to fully quantify the seasonal to inter-annual variability of the region, a region where pCO2 has been observed to fluctuate by >300 µatm. Upwelling and rainfall events dominate the surface physical and chemical characteristics of the ETPO, with both yielding unique signatures in sea surface temperature and salinity. Thus, we explore the potential of using a statistical description of pCO2 within sea-surface salinity-temperature space. These SSS/SST relationships are based on in-situ SOCAT data collected within the ETPO. This statistical description is then applied to high resolution (0.25°) SMOS sea surface salinity and OSTIA sea surface temperature in order to compute regional pCO2. As a result, we are able to resolve pCO2 at sufficiently high resolution to elucidate the influence various physical processes have on the pCO2 of the surface ETPO. Normalised (to 2014) oceanic pCO2 between July 2010 and June 2014 within the entire ETPO was 41 µatm supersaturated with respect to 2014 atmospheric partial pressures. Values of pCO2 within the ETPO were found to be broadly split between southeast and a northwest regions. The north west, central and South Equatorial Current regions were supersaturated, with wintertime wind jet driven upwelling found to be the first order control on pCO2 values. This contrasts with the southeastern/Gulf of Panama region, where heavy rainfall combined with rapid stratification of the upper water-column act to dilute dissolved inorganic carbon, and yield pCO2 values under-saturated with respect to atmospheric partial pressures of CO2. [ABSTRACT FROM AUTHOR]

Nitrogen fixation in temperate oceans is a potentially important, but poorly understood process that may influence the marine nitrogen budget. This study determined seasonal variations in nitrogen fixation and nifH gene diversity within the euphotic zone in the temperate coastal region of the northwestern North Pacific. Nitrogen fixation as high as 13.6 nmolN L-1 d-1 was measured from early summer to fall when the surface temperature exceeded 14.2 °C and the surface nitrate concentration was low (≤ 0.30 µM), although we also detected nitrogen fixation in subsurface layers (42-62 m) where nitrate concentrations were high (> 1 µM). During periods with high nitrogen fixation, the nifH sequences of UCYN-A were recovered, suggesting that these groups played a key role in nitrogen fixation. The nifH genes were also recovered in spring and winter when nitrogen fixation was undetectable. These genes consisted of many sequences affiliated with Cluster III diazotrophs (putative anaerobic bacteria), which hitherto have rarely been reported to be abundant in surface diazotroph communities in marine environments. [ABSTRACT FROM AUTHOR]

N2 fixation rates were measured daily in large (~ 50 m³) mesocosms deployed in the tropical South West Pacific coastal ocean (New Caledonia) to investigate the spatial and temporal dynamics of diazotrophy and the fate of diazotroph-derived nitrogen (DDN) in a low nutrient, low chlorophyll ecosystem. The mesocosms were intentionally fertilized with ~ 0.8 μM dissolved inorganic phosphorus (DIP) to stimulate diazotrophy. Bulk N2 fixation rates were replicable between the three mesocosms, averaged 18.5 ± 1.1 nmol N L-1 d-1 over the 23 days, and increased by a factor of two during the second half of the experiment (days 15 to 23) to reach 27.3 ± 1.0 nmol N L-1 d-1. These rates are higher than the upper range reported for the global ocean, indicating that the waters surrounding New Caledonia are particularly favourable for N2 fixation. During the 23 days of the experiment, N2 fixation rates were positively correlated with seawater temperature, primary production, bacterial production, standing stocks of particulate organic carbon, nitrogen and phosphorus, and alkaline phosphatase activity, and negatively correlated with DIP concentrations, DIP turnover time, nitrate, and dissolved organic nitrogen and phosphorus concentrations. The fate of DDN was investigated during the bloom of the unicellular diazotroph, UCYN-C, that occurred during the second half of the experiment. Quantification of diazotrophs in the sediment traps indicates that ~ 10% of UCYN-C from the water column were exported daily to the traps, representing as much as 22.4 ± 5.5% of the total POC exported at the height of the UCYN-C bloom. This export was mainly due to the aggregation of small (5.7 ± 0.8 μm) UCYN-C cells into large (100-500 μm) aggregates. During the same time period, a DDN transfer experiment based on high-resolution nanometer scale secondary ion mass spectrometry (nanoSIMS) coupled with 15N2 isotopic labelling revealed that 16 ± 6% of the DDN was released to the dissolved pool and 21 ± 4% was transferred to non-diazotrophic plankton, mainly picoplankton (18 ± 4%) followed by diatoms (3 ± 2%) within 24 h of incubation. This is consistent with the observed dramatic increase in picoplankton and diatom abundances, primary production, bacterial production and standing stocks of particulate organic carbon, nitrogen and phosphorus during the second half of the experiment in the mesocosms. These results offer insights into the fate of DDN during a bloom of UCYN-C in low nutrient, low chlorophyll ecosystems. [ABSTRACT FROM AUTHOR]

Insights into oceanographic environmental conditions such as paleoproductivity, sea-surface temperatures, deep-water temperatures, salinity, ice volumes, circulation patterns, and nutrient cycling have all been obtained from geochemical analyses of biomineralized carbonate of marine organisms. However, we cannot fully understand geochemical proxy incorporation and the fidelity of such in species until we better understand fundamental aspects of their ecology such as where and when these (micro)organisms calcify. Here, we present an innovative method using osmotic pumps and the fluorescent marker calcein to help identify where and when calcareous meiofauna calcify in situ. Method development initially involved juvenile quahogs (Mercenaria mercenaria); subsequent method refinement involved a neritic benthic foraminiferal community. Future applications of this method will allow determinations of in situ growth rate in calcareous organisms and provide insights about microhabitats where paleoceanographically relevant benthic foraminifera actually calcify. [ABSTRACT FROM AUTHOR]

Over the last decade, event prediction has drawn attention from both academic and industry communities, resulting in a substantial volume of scientific papers published in a wide range of journals by scholars from different countries and disciplines. However, thus far, a comprehensive and systematic survey of recent literature has been lacking to quantitatively capture the research progress as well as emerging trends in the event prediction field. Aiming at addressing this gap, we employed CiteSpace software to analyze and visualize data retrieved from the Web of Science (WoS) database, including authors, documents, research institutions, and keywords, based on which the author co-citation network, document co-citation network, collaborative institution network, and keyword co-occurrence network were constructed. Through analyzing the aforementioned networks, we identified areas of active research, influential literature, collaborations at the national level, interdisciplinary patterns, and emerging trends by identifying the central nodes and the nodes with strong citation bursts. It reveals that sensor data has been widely used for predicting weather events and meteorological events (e.g., monitoring sea surface temperature and weather sensor data for predicting El Nino). The real-time and multivariable monitoring features of sensor data enable it to be a reliable source for predicting multiple types of events. Our work offers not only a comprehensive survey of the existing studies but also insights into the development trends within the event prediction field. These findings will assist researchers in conducting further research in this area and draw a large readership among academia and industrial communities who are engaged in event prediction research. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) constitutes a pivotal physical parameter in the investigation of atmospheric, oceanic, and air–sea exchange processes. The retrieval of SST through satellite passive microwave (PMW) technology effectively mitigates the interference posed by cloud cover, addressing a longstanding challenge. Nevertheless, conventional functional representations often fall short in capturing the intricate interplay of factors influencing SST. Leveraging neural networks (NNs), known for their adeptness in tackling nonlinear and intricate problems, holds great promise in SST retrieval. Nonetheless, NNs exhibit a high sensitivity to initial weights and thresholds, rendering them susceptible to local optimization issues. In this study, we present a novel machine learning (ML) approach for SST retrieval using PMW measurements, drawing from the Sparrow Search Algorithm (SSA) and Back-Propagation neural network (BPNN) methodologies. The core premise involves the optimization of the BP neural network's initial weights and thresholds through an enhanced SSA algorithm employing various optimization strategies. This optimization aims to provide superior parameters for the training of the BP neural network. Employing AMSR2 brightness temperature data, sea surface wind speed data, and buoy SST measurements, we construct the ISSA-BP model for sea surface temperature retrieval. The validation of the ISSA-BP model against the test data is conducted and compared against the multiple linear regression (MLR) model, an unoptimized BP model, and an unimproved SSA-BP model. The results manifest an impressive R-squared (R2) value of 0.9918 and a root-mean-square error (RMSE) of 0.8268 °C for the ISSA-BP model, attesting to its superior accuracy. Furthermore, the ISSA-BP model was applied to retrieve global sea surface temperatures on 15 July 2022, yielding an R2 of 0.9926 and an RMSE of 0.7673 °C for the OISST product on the same day, underscoring its excellent concordance. The results indicate that SST can be efficiently and accurately retrieved using the model proposed in this paper, based on satellite PMW measurements. This finding underscores the potential of employing machine learning algorithms for SST retrieval and offers a valuable reference for future studies focusing on the retrieval of other sea surface parameters. [ABSTRACT FROM AUTHOR]

The accurate temperature background field plays a vital role in the numerical prediction of sea surface temperature (SST). At present, the SST background field is mainly derived from multi-source data fusion, including satellite SST data and in situ data from marine stations, buoys, and voluntary observing ships. The characteristics of satellite SST data are wide coverage but low accuracy, whereas the in situ data have high accuracy but sparse distribution. For obtaining a more accurate temperature background field and realizing the fusion of measured data with satellite data as much as possible, we propose a sparse data-extended fusion method to predict SST in this paper. By using this method, the actual observed sites and buoys data in the East China Sea area are fused with Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Version 5.0 SST data. Furthermore, the temperature field in the study area were predicted by using Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) deep learning methods, respectively. Finally, we obtained the results by traditional prediction methods to verify them. The experimental results show that the method we proposed in this paper can obtain more accurate prediction results, and effectively compensate for the uncertainty caused by the parameterization of ocean dynamic process, the discrete method, and the error of initial conditions. [ABSTRACT FROM AUTHOR]

The accurate prediction of sea surface temperature (SST) is the basis for our understanding of local and global climate characteristics. At present, the existing sea temperature prediction methods fail to take full advantage of the potential spatial dependence between variables. Among them, graph neural networks (GNNs) modeled on the relationships between variables can better deal with space–time dependency issues. However, most of the current graph neural networks are applied to data that already have a good graph structure, while in SST data, the dependency relationship between spatial points needs to be excavated rather than existing as prior knowledge. In order to predict SST more accurately and break through the bottleneck of existing SST prediction methods, we urgently need to develop an adaptive SST prediction method that is independent of predefined graph structures and can take full advantage of the real temporal and spatial correlations hidden indata sets. Therefore, this paper presents a graph neural network model designed specifically for space–time sequence prediction that can automatically learn the relationships between variables and model them. The model automatically extracts the dependencies between sea temperature multi-variates by embedding the nodes of the adaptive graph learning module, so that the fine-grained spatial correlations hidden in the sequence data can be accurately captured. Figure learning modules, graph convolution modules, and time convolution modules are integrated into a unified end-to-end framework for learning. Experiments were carried out on the Bohai Sea surface temperature data set and the South China Sea surface temperature data set, and the results show that the model presented in this paper is significantly better than other sea temperature model predictions in two remote-sensing sea temperature data sets and the surface temperature of the South China Sea is easier to predict than the surface temperature of the Bohai Sea. [ABSTRACT FROM AUTHOR]

Regime shifts have been reported in many marine ecosystems, and are often expressed as an abrupt change occurring in multiple physical and biological components of the system. In the Gulf of Alaska, a regime shift in the late 1970s was observed, indicated by an abrupt increase in sea surface temperature and major shifts in the catch of many fish species. This late 1970s regime shift in the Gulf of Alaska was followed by another shift in the late 1980s, not as pervasive as the 1977 shift, but which nevertheless did not return to the prior state. A thorough understanding of the extent and mechanisms leading to such regime shifts is challenged by data paucity in time and space. We investigate the ability of a suite of ocean biogeochemistry models of varying complexity to simulate regime shifts in the Gulf of Alaska by examining the presence of abrupt changes in time series of physical variables (sea surface temperature and mixed layer depth), nutrients and biological variables (chlorophyll, primary productivity and plankton biomass) using change-point analysis. Our study demonstrates that ocean biogeochemical models are capable of simulating the late 1970s shift, indicating an abrupt increase in sea surface temperature forcing followed by an abrupt decrease in nutrients and biological productivity. This predicted shift is consistent among all the models, although some of them exhibit an abrupt transition (i.e. a significant shift from one year to the next), whereas others simulate a smoother transition. Some models further suggest that the late 1980s shift was constrained by changes in mixed layer depth. Our study demonstrates that ocean biogeochemical can successfully simulate regime shifts in the Gulf of Alaska region, thereby providing better understanding of how changes in physical conditions are propagated from lower to upper trophic levels through bottom-up controls. [ABSTRACT FROM AUTHOR]

This study was designed to determine the effects of flooding on plankton community respiration (CR) in the East China Sea (ECS). In July 2010, a devastating flood occurred in the Changjiang River; the mean monthly discharge was 60 527m³ s-1. To compare, the variables were also examined in the low riverine flow of July 2009 (33 955 m³ s-1). During the flooding, the Changjiang diluted water (CDW) zone, the sea surface salinity (SSS) was ≤ 31 psu, covering almost two thirds of the ECS, which was approximately six times that in the non-flooding period. The mean nitrate concentration was higher in 2010 (6.2 µM) than in 2009 (2.0 µM). However, in the 2010 flood, the mean values of Chl a and the bacterial biomass were only slightly higher or even lower than in 2009. Surprisingly, however, the CR was still higher in the flood period than in the non-flood period, with mean values of 105.6 and 73.2mg C m-3 d-1, respectively. The higher CR in 2010 could be attributed to vigorous plankton activities, especially phytoplankton, at stations in the CDW zone, which were not mostly covered by low SSS in 2009. There was a huge amount of fCO2 drawdown in the 2010 flood. These results suggested that the devastating flood in 2010 had a significant effect on the carbon balance in the ECS. This effect might become more pronounced as extreme rainfall events and flooding magnitudes increase dramatically throughout the world. [ABSTRACT FROM AUTHOR]

Subpolar regions are key areas to study natural climate variability due to their high sensitivity to rapid environmental changes, particularly through sea surface temperature (SST) variations. Here, we have tested three independent organic temperature proxies (U37K', TEX86 and LDI) on their potential applicability for SST reconstruction in the subpolar region around Iceland. U37K', TEX86 and TEX86L temperature estimates from suspended particulate matter result in a substantial discrepancy with instrumental data, while long chain alkyl diols were below detection limit in most of the stations. In the northern Iceland basin sedimenting particles revealed a seasonality in lipid fluxes similar to that in the bulk flux and the net primary production, i.e. highest flux of alkenones and GDGTs were measured during late spring-summer, and late summer for long chain alkyl diols. The flux-weighted average temperature estimates had a significant negative (ca. 2.3 °C for U37K') and positive (up to 5 °C for TEX86) offset with satellite-derived SSTs and the temperature derived from the underlying surface sediment. In contrast, sedimentary U37K' estimates temperatures around Iceland correlate well with summer mean temperatures, while TEX86 derived temperatures correspond well with either annual or winter mean 0-200m temperatures, suggesting a subsurface temperature signal. Anomalous LDI-SST values in surface sediments, and low mass flux of 1,13- and 1,15-diols compared to 1,14-diols, suggest that Proboscia diatom is the major source of long chain alkyl diols in this area rather than eustigmatophyte algae, and therefore the LDI cannot be applied in this region. [ABSTRACT FROM AUTHOR]

This paper evaluates Indo-Pacific warm pool (IPWP) sea surface temperature (SST) warming biases of Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6. The IPWP warming trend in the CMIP5 multi-model ensemble (MME) is closer to observation than in CMIP6 MME, but the IPWP expanding trend is the opposite. There is no qualitative improvement in the simulation of IPWP warming from CMIP5 to CMIP6. In addition, four metrics were used to investigate the performance of Indo-Pacific region warming trends in all models. CMIP6 models perform better than CMIP5 with smaller root mean square error and bias in MME and higher skill scores in MME and top models, which is tightly linked to their better performance in simulating associated physical processes in CMIP6 models. IPWP warming biases are mainly attributed to the combined effects of positive atmospheric process biases and negative ocean dynamics term biases. The positive atmospheric process biases are primarily related to the shortwave radiation and latent heat flux from atmospheric forcing, the latter of which can be attributed to the biases in surface wind fields. Compared with CMIP5 models, the IPWP warming simulated by CMIP6 models is weaker, related to the less robust atmospheric processes and the shallower thermocline anomalies simulated by CMIP6. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs), responsible for extreme weather conditions, are mid-latitude systems that can cause significant damage to coastal areas. While forecasting ARs beyond two weeks remains a challenge, past research suggests potential benefits may come from properly accounting for the changes in sea surface temperature (SST) through air–sea interactions. In this paper, we investigate the impact of ARs on SST over the North Pacific by analyzing 25 years of ocean reanalysis data using an SST budget equation. We show that in the region of strong ocean modification, ocean dynamics can offset over 100% of the anomalous SST warming that would otherwise arise from atmospheric forcing. Among all ocean processes, ageostrophic advection and vertical mixing (diffusion and entrainment) are the most important factors in modifying the SST tendency response. The SST tendency response to ARs varies spatially. For example, in coastal California, the driver of enhanced SST warming is the reduction in ageostrophic advection due to anomalous southerly winds. Moreover, there is a large region where the SST shows a warming response to ARs due to the overall reduction in the total clouds and subsequent increase in total incoming shortwave radiation. Strong air-sea interactions during atmospheric rivers often lead to modest upper ocean heat changes. The authors show that interior ocean dynamics are compensating for these air-sea exchanges. These findings can help improve subseasonal forecasts. [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]

The analysis of tephra layers in maar lake sediments of the Eifel shows 14 well-visible tephra during the last glacial cycle from the Holocene to the Eemian (0–130,000 yr b2k). These tephra were analyzed for their petrographic composition, which allows us to connect several tephra to eruption sites. All tephra were dated by application of the ELSA-20 chronology, developed using the late Pleistocene infilled maar lake of Auel and the Holocene lake Holzmaar (0–60,000 yr b2k). We extend the ELSA-20 chronology with this paper for the millennia of 60,000–130,000 yr b2k (ELSA-23 chronology), which is based on the infilled maar lake records from Dehner, Hoher List, and Jungferweiher. The evaluation of the tephra from the entire last glacial cycle shows that all 14 tephra were close to interstadial warming of the North Atlantic sea surface temperatures. In particular, phreatomagmatic maar eruptions were systematically associated with Heinrich events or C-events. These events represent times of warming of the Southern Hemisphere, global sea level rise, and CO2 increase, which predate the abrupt interstadial warming events of the Northern Hemisphere. This synchroneity indicates a physical relationship between endogenic and exogenic processes. Changes in the lithospheric stress field in response to changes in continental ice loads have already been suggested as a potential candidate to explain the exogenic forcing of endogenic processes. The chronology of volcanic activity in the Eifel demonstrates that intraplate mantle plumes are also affected by the exogenic forcing of endogenic processes. [ABSTRACT FROM AUTHOR]

Accurate prediction of ocean surface currents is important for marine safety, ship routing, tracking of pollutants and in coupled forecasting. Presently, velocity observations are not routinely assimilated in global ocean forecasting systems, largely due to the sparsity of the observation network. Several satellite missions are now being proposed with the capability to measure Total Surface Current Velocities (TSCV). If successful, these would substantially increase the coverage of ocean current observations and could improve accuracy of ocean current forecasts through data assimilation. In this paper, Observing System Simulation Experiments (OSSEs) are used to assess the impact of assimilating TSCV in the Met Office's global ocean forecasting system. Synthetic observations are generated from a high-resolution model run for all standard observation types (sea surface temperature, profiles of temperature and salinity, sea level anomaly and sea ice concentration) as well as TSCV observations from a Sea surface KInematics Multiscale monitoring (SKIM) like satellite. The assimilation of SKIM like TSCV observations is tested over an 11 month period. Preliminary experiments assimilating idealised single TSCV observations demonstrate that ageostrophic velocity corrections are not well retained in the model. We propose a method for improving ageostrophic currents through TSCV assimilation by initialising Near Inertial Oscillations with a rotated incremental analysis update (IAU) scheme. The OSSEs show that TSCV assimilation has the potential to significantly improve the prediction of velocities, particularly in the Western Boundary Currents, Antarctic Circumpolar Current and in the near surface equatorial currents. For global surface velocity the analysis root-mean-squareerrors (RMSEs) are reduced by 23% and there is a 4-day gain in forecast RMSE. There are some degradations to the subsurface in the tropics, generally in regions with complex vertical salinity structures. However, outside of the tropics, improvements are seen to velocities throughout the water column. Globally there are also improvements to temperature and sea surface height when TSCV are assimilated. The TSCV assimilation largely corrects the geostrophic ocean currents, but results using the rotated IAU method show that the energy at inertial frequencies can be improved with this method. Overall, the experiments demonstrate significant potential benefit of assimilating TSCV observations in a global ocean forecasting system. [ABSTRACT FROM AUTHOR]

Recent studies have highlighted the increasingly dominant role of external forcing in driving Atlantic and Pacific Ocean variability during the second half of the 20th century. This paper provides insights into the underlying mechanisms driving interactions between modes of variability over the two basins. We define a set of possible drivers of these interactions and apply causal discovery to reanalysis data, two ensembles of pacemaker simulations where sea surface temperatures in either the tropical Pacific or the North Atlantic are nudged to observations, and a pre-industrial control run. We also utilize large-ensemble means of historical simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to quantify the effect of external forcing and improve the understanding of its impact. A causal analysis of the historical time series between 1950 and 2014 identifies a regime switch in the interactions between major modes of Atlantic and Pacific climate variability in both reanalysis and pacemaker simulations. A sliding window causal analysis reveals a decaying El Niño–Southern Oscillation (ENSO) effect on the Atlantic as the North Atlantic fluctuates towards an anomalously warm state. The causal networks also demonstrate that external forcing contributed to strengthening the Atlantic's negative-sign effect on ENSO since the mid-1980s, where warming tropical Atlantic sea surface temperatures induce a La Niña-like cooling in the equatorial Pacific during the following season through an intensification of the Pacific Walker circulation. The strengthening of this effect is not detected when the historical external forcing signal is removed in the Pacific pacemaker ensemble. The analysis of the pre-industrial control run supports the notion that the Atlantic and Pacific modes of natural climate variability exert contrasting impacts on each other even in the absence of anthropogenic forcing. The interactions are shown to be modulated by the (multi)decadal states of temperature anomalies of both basins with stronger connections when these states are "out of phase". We show that causal discovery can detect previously documented connections and provides important potential for a deeper understanding of the mechanisms driving changes in regional and global climate variability. [ABSTRACT FROM AUTHOR]

A multi-season convection-permitting regional climate simulation of the Maritime Continent (MC) using the Met Office Unified Model (MetUM) with 2.2 km grid spacing is presented and evaluated. The simulations pioneer the use of atmosphere–ocean coupling with the multi-column K profile parametrisation (KPP) mixed-layer ocean model in atmospheric convection-permitting climate simulations. Comparisons are made against a convection-parametrised simulation in which it is nested and which in turn derives boundary conditions from the ERA5 reanalysis. This paper describes the configuration, performance of the mean state and variability in the two simulations compared against observational datasets. The models have both minor sea surface temperature (SST) and wet precipitation biases. The diurnal cycle, representation of equatorial waves, and relationship between SST and precipitation are all improved in the convection-permitting model compared to the convection-parametrised model. The Madden–Julian oscillation (MJO) is present in both models with a faster-than-observed propagation speed. However, it is unclear whether fidelity of the MJO simulation is inherent to the model or whether it predominantly arises from the forcing at the boundaries. [ABSTRACT FROM AUTHOR]

In this paper, based on an Fe-based amorphous alloy, four kinds of RGO/Cu/Fe-based amorphous composite coatings with mass ratios of 5%, 10%, 15%, and 20% of RGO/Cu were prepared on the surface of 45# steel by using high-velocity oxy-fuel (HVOF) spraying. The coatings were immersed in simulated seawater at room temperature and at 90 °C for different lengths of time, and their corrosion resistance was tested using electrochemical impedance spectroscopy (EIS), scanning electron microscopy (SEM), and X-ray diffraction (XRD), and the surface morphology and phase distribution of the samples were observed. The results showed that with the increase in the introduction ratio of RGO/Cu, when the addition ratio reached 15%, the composite coating had the best corrosion resistance. After soaking in simulated seawater at 90 °C for 18 days, the surface of the coating showed slight peeling and crack propagation, but no obvious pitting phenomenon occurred. The corrosion mechanism of the RGO/Cu/Fe coating in high-temperature seawater is mainly that high temperature causes the cracking of the coating, which opens up a transport channel for corrosion media. However, due to the addition of RGO, the corrosion has a certain self-limitation effect, which is mainly due to the toughening effect of RGO on the coating and its effect on extending the corrosion channel. [ABSTRACT FROM AUTHOR]

Sea ice, as an important component of the Earth's ecosystem, has a profound impact on global climate and human activities due to its thickness. Therefore, the inversion of sea ice thickness has important research significance. Due to environmental and equipment-related limitations, the number of samples available for remote sensing inversion is currently insufficient. At high spatial resolutions, remote sensing data contain limited information and noise interference, which seriously affect the accuracy of sea ice thickness inversion. In response to the above issues, we conducted experiments using ice draft data from the Beaufort Sea and designed an improved GBDT method that integrates feature-enhancement and active-learning strategies (IFEAL-GBDT). In this method, the incident angle and time series are used to perform spatiotemporal correction of the data, reducing both temporal and spatial impacts. Meanwhile, based on the original polarization information, effective multi-attribute features are generated to expand the information content and improve the separability of sea ice with different thicknesses. Taking into account the growth cycle and age of sea ice, attributes were added for month and seawater temperature. In addition, we studied an active learning strategy based on the maximum standard deviation to select more informative and representative samples and improve the model's generalization ability. The improved GBDT model was used for training and prediction, offering advantages in dealing with nonlinear, high-dimensional data, and data noise problems, further expanding the effectiveness of feature-enhancement and active-learning strategies. Compared with other methods, the method proposed in this paper achieves the best inversion accuracy, with an average absolute error of 8 cm and a root mean square error of 13.7 cm for IFEAL-GBDT and a correlation coefficient of 0.912. This research proves the effectiveness of our method, which is suitable for the high-precision inversion of sea ice thickness determined using Sentinel-1 data. [ABSTRACT FROM AUTHOR]