Satellite Sea Surface Temperature Data Research Articles

Spatiotemporal Evolution of Marine Heatwaves Globally

Abstract The spatiotemporal evolution of marine heatwaves (MHWs) is explored using a tracking algorithm called Ocetrac that provides objective characterization of MHW spatiotemporal evolution. Candidate MHW grid points are defined in detrended gridded sea temperature data using a seasonally varying temperature threshold. Identified MHW points are collected into spatially distinct objects using edge detection with weak sensitivity to edge detection and size percentile threshold criteria at each timestep. Ocetrac then uses 3D connectivity to determine if these objects are part of the same event, but Ocetrac only defines the full MHW event after all timesteps have been processed, limiting its use in predictability studies. Here Ocetrac is applied to monthly satellite sea surface temperature data from September 1981 through January 2021. The resulting MHWs are characterized by their intensity, duration, and total area covered. The global analysis shows that MHWs in the Gulf of Maine and Mediterranean Sea are spatially isolated, while major MHWs in the Pacific and Indian Oceans are connected in space and time. The largest and most long lasting MHW using this method lasts for 60 months from November 2013 to October 2018, encompassing previously identified MHW events including those in the Northeast Pacific (2014-2015), the Tasman Sea (2015-2016, 2017-2018), and the Great Barrier Reef (2016).

Understanding bottom and surface marine heatwaves along the continental shelf of China

Marine heatwaves (MHWs) have become longer and more frequent over the past century under anthropogenic climate change, with devastating impacts on marine ecosystems. Surface MHWs (SMHWs) and their drivers have been extensively studied using satellite sea surface temperature data, yet the mechanism and characteristics of subsurface MHWs, especially bottom MHWs (BMHWs) along continental shelves, remain unclear. Based on a high-resolution ocean reanalysis dataset, we compare SMHWs and BMHWs along the continental shelf of China and find that BMHWs are typically longer (0–16 d) and more intense (0 °C–50 °C days) than SMHWs. The categorizing of both the BMHW and SMHW shows that moderate and strong events commonly occur in most areas with relatively large spatial coverage, whereas severe and extreme events occur with relatively small spatial coverage. There is a clear negative relationship between the BMHW intensity and ocean depth along the continental shelf, while the BMHW annual days and ocean depth are positively correlated in the Bohai and East China Seas. Generally, BMHWs and SMHWs occur more frequently in shallow coastal regions where the mixed layer depth is more likely to extend to the seafloor, resulting in high BMHW and SMHW synchrony. In addition to spatial coherence, there is a good temporal correspondence between BMHWs and SMHWs across the continental shelf of China from 1993 to 2020.

Assessing the accuracy of MUR high resolution satellite sea surface temperature data

Marine bivalve mollusks are valuable proxies for El Niño-Southern Oscillation (ENSO) events in paleoclimate research, reflecting changes in sea surface temperature (SST) and other ambient marine conditions in their shell chemistry. However, the use of several Peruvian species of bivalves as paleoENSO proxies needs to be established by comparing their shells’ δ18O-derived SST records with instrumental SST measurements. Along the Peruvian coast, the limited spatial coverage of in-situ coastal monitoring stations hinders such comparisons. This study aims to assess the accuracy of the Multi-scale Ultra-high Resolution (MUR) satellite-derived SST dataset as an alternative source of SST information for paleoENSO studies in Peru, which could advance research on Peruvian bivalve species. This study compares MUR satellite data with in-situ measured SST from ten coastal monitoring stations operated by the Instituto del Mar del Perú (IMARPE). The correlation between the datasets is evaluated, and systematic and non-systematic variations are examined. Environmental factors that may impact the accuracy of satellite SST measurements, such as clouds and aerosols, are also investigated. Results show that MUR data generally correlate with overall trends in SST variability captured by in-situ measurements. However, there are regional variations in the accuracy of MUR data, with some areas showing consistent temperature offsets. Differences in measurement methodologies and environmental variables are explored as potential causes of variations between the datasets. Overall, the study demonstrates the potential of using high-resolution satellite SST data for assessing δ18O SST records from bivalves in locations without local SST records. It provides insights into the accuracy and limitations of using MUR data as an alternative source of SST information for paleoENSO studies in coastal Peru.

Spatial and Temporal Variability of Ocean Thermal Energy Resource of the Pacific Islands

A lack of natural resources drives the oil dependency in Pacific Island Countries and Territories (PICTs), hampering energy security and imposing high electricity tariffs in the region. Nevertheless, the Western Equatorial Pacific is known for its large Sea Surface Temperature (SST) and deep-sea water (DSW) temperature difference favorable for harvesting thermal energy. In this study, we selected 18 PICTs in the western Equatorial Pacific to estimate Annual Energy Production (AEP) for a 1 MW class Ocean Thermal Energy Conversion (OTEC) plant. We combined the DSW temperature from the mean in situ Argo profiles and 1 km resolution satellite SST data to estimate the thermal energy resource resolving the fine features of the island coastline. Furthermore, the twenty-year-long SST dataset was used to analyze the SST variability. The analysis showed that Equatorial islands and Southern islands have the highest inter-annual variability due to El Nino Southern Oscillation (ENSO). The power density varied from 0.26 to 0.32 W/m2 among the islands, with the lowest values found for the southernmost islands near the South Equatorial Countercurrent. Islands within the South Equatorial Current, Equatorial Undercurrent, and North Equatorial Countercurrent showed the highest values for both power density and gross power. Considering a 1 MW class OTEC plant, Annual Energy Production (AEP) in 2022 varied from 7 GWh to 8 GWh, with relatively low variability among islands near the Equator and in low latitudes. Considering the three variables, AEP, SST variability, and distance from the shore, Nauru is a potential candidate for OTEC, with a net power of 1.14 MW within 1 km from the shore.

Performance of high-resolution MUR satellite sea surface temperature data as a proxy for near-surface in situ temperatures on neotropical reefs

As ocean temperatures increase, so does the need for accurate, regular, and widespread temperature data. This is especially important for researchers studying corals, which can be pushed over their bleaching thresholds by small, sustained changes in water temperature. Satellite data products offer longitudinal estimates of sea surface temperature (SST) taken at regular intervals without the ongoing costs and geospatial limits associated with underwater temperature loggers. However, previous work indicates satellite data are sometimes characterized by significant temperature biases, particularly in stratified coastal waters. Here, we compared the foundation sea surface temperature (fSST) estimated from a highly processed, gridded satellite data set (GHRSST MUR L4 Global SST) to in situ temperature (IST) data collected from coral reef habitats in the top 20 m of the water column at 19 sites in Panama. Comparisons between Pacific and Caribbean coasts and between upwelling and non-upwelling seasons provide a robust test of the utility of this product to estimate subsurface temperatures. The mean differences between fSST and IST show that MUR fSST accurately reflects temperatures at shallow (<9 m) depths on the Caribbean coast (mean bias ≤0.55°C ± standard error (SE) 0.01°C) and during the non-upwelling season on the Pacific coast (mean bias ≤0.26°C ± SE 0.03°C). MUR fSST poorly reflects IST during the Pacific upwelling season, especially at sites 12 m and deeper. In such cases, 66% of satellite estimates deviated from ISTs by >1°C. Our findings underscore how highly processed SST data products should be ground-truthed before being used as a substitute for IST, especially in upwelling areas.

Characterizing the California Current System through Sea Surface Temperature and Salinity

Characterizing temperature and salinity (T-S) conditions is a standard framework in oceanography to identify and describe deep water masses and their dynamics. At the surface, this practice is hindered by multiple air–sea–land processes impacting T-S properties at shorter time scales than can easily be monitored. Now, however, the unsurpassed spatial and temporal coverage and resolution achieved with satellite sea surface temperature (SST) and salinity (SSS) allow us to use these variables to investigate the variability of surface processes at climate-relevant scales. In this work, we use SSS and SST data, aggregated into domains using a cluster algorithm over a T-S diagram, to describe the surface characteristics of the California Current System (CCS), validating them with in situ data from uncrewed Saildrone vessels. Despite biases and uncertainties in SSS and SST values in highly dynamic coastal areas, this T-S framework has proven useful in describing CCS regional surface properties and their variability in the past and in real time, at novel scales. This analysis also shows the capacity of remote sensing data for investigating variability in land–air–sea interactions not previously possible due to limited in situ data.

Impacts of Marine Heatwave Events on Three Distinct Upwelling Systems and Their Implications for Marine Ecosystems in the Northwestern South China Sea

Under global warming, the frequency and intensity of marine heatwaves are increasing. However, the inhibition of atmospheric-forcing marine heatwaves (AMHW) on upwelling and their impacts on marine ecosystems remain poorly understood. To address this issue, the satellite sea surface temperature and reanalysis data during 1998–2021 were analyzed in three distinct upwelling systems, in the northwestern South China Sea. The results showed that the coastal tide-induced upwelling in the west (W) of Hainan Island is primarily suppressed by enhanced stratification during the AMHW events, since the coastal tide-induced upwelling is insensitive to wind weakening. Contrarily, the wind-driven upwelling in the east (E) and northeast (NE) of Hainan Island are jointly regulated by wind and stratification during the AMHW. Specifically, the AMHW events have a stronger inhibitory effect on the upwelling and phytoplankton growth in the NE than that in the E. The causes could be the following: (1) the background upwelling in the NE region is stronger than in the E; thus, the NE region has a higher susceptibility to the wind weakening; (2) the wind-driven upwelling begins to be suppressed by AMHW when the high-pressure system is aligned with the coastline of the upwelling. In the NE region, the location of the high-pressure center during the occurrence of AMHW is positioned in closer proximity to the upwelling area. Moreover, the inhibitory effect of wind weakening and stratification enhancing on upwelling changes with the development of the AMHW. Before and during the mature phase of AMHW, stratification and wind jointly inhibit upwelling and phytoplankton growth, while a shift to stratification-dominated (>85%) occurs during the decline phase. This study suggests that MHW has a great impact on the upwelling ecosystem, especially the wind-driven upwelling, which should be given high attention under global warming (with increasing MHW events in the future).

Satellite and High-Spatio-Temporal Resolution Data Collected by Southern Elephant Seals Allow an Unprecedented 3D View of the Argentine Continental Shelf

High spatial and temporal resolution hydrographic data collected by Southern Elephant Seals (Mirounga leonina, SESs) and satellite remote sensing data allow a detailed oceanographic description of the Argentine Continental Shelf (ACS). In-situ data were obtained from the CTD (Conductivity, Temperature, and Depth), accelerometer, and hydrophone sensors attached to five SESs that crossed the ACS between the 17th and 31st of October 2019. The analysis of the temperature (T) and salinity (S) along the trajectories allowed us to identify two different regions: north and south of 42°S. Satellite Sea Surface Temperature (SST) data suggests that north of 42°S, warm waters are coming from the San Matias Gulf (SMG). The high spatio-temporal resolution of the in-situ data shows regions with intense gradients along the T and S sections that were associated with a seasonal front that develops north of Península Valdés in winter due to the entrance of cold and fresh water to the SMG. The speed of the SESs is correlated with tidal currents in the coastal portion of the northern region, which is in good agreement with the macrotidal regime observed. A large number of Prey Catch Attempts (PCA), a measure obtained from the accelerometer sensor, indicates that SESs also feed in this region, contradicting suggestions from previous works. The analysis of wind intensity estimated from acoustic sensors allowed us to rule out the local wind as the cause of fast thermocline breakups observed along the SESs trajectories. Finally, we show that the maximum depth reached by the elephant seals can be used to detect errors in the bathymetry charts.

Evaluation of Leftward Biased Cold Wakes Induced by Tropical Cyclones in the North Hemisphere

AbstractThe cold wake features are one of the most significant characteristics left behind by tropical cyclones (TCs) over the warm ocean. In the past several decades, the cold wakes have been widely conceived to be rightward biased (in the Northern Hemisphere). This basic cognition is examined in this study based on satellite sea surface temperature (SST) data with the high‐resolution of ∼0.09° between 2002 and 2021. Statistical results reveal that roughly one third (∼30.58%) of TC cold wakes are actually leftward biased in real world. On average, the maximum cooling of leftward cold wakes is approximately −0.7°C, appearing at ∼60 km to the left of the storm track. The proportion of leftward cases decreases steadily with increasing TC intensity, while shows little relation with the translation speed. The leftward biased phenomena are found to be independent from meso‐scale oceanic features and mainly attributed to inhomogeneous large‐scale background oceanic environment. The generally deeper (shallower) pre‐storm mixed layer and weaker (stronger) stratification found in the right (left) of track can effectively suppress rightward (enhance leftward) surface cooling and synchronously deform the normal spatial patterns of cold wakes. These effects cooperate with leftward‐biased surface enthalpy flux resulted from asymmetric pre‐storm SST, and eventually contributes to the left‐shifted feature. A series of three‐dimensional simulations using the three‐dimensional Price‐Weller‐Pinkel (3DPWP) model consolidate these physical explanations. Further observations suggest that distributions of cold wakes could significantly affect the asymmetric precipitation of TCs, confirming the importance of leftward cases to the TCs structure.

Tropical Cyclone-Induced Sea Surface Temperature Responses in the Northern Indian Ocean

Tropical cyclones (TCs) exert a significant influence on the upper ocean, leading to sea surface temperature (SST) changes on a global scale. However, TC-induced SST responses exhibit considerable variability in the northern Indian Ocean (NIO), and the general understanding of these responses remains limited. This paper investigates the SST changes caused by 96 TCs over an 18-year period in the NIO. Through a composite analysis utilizing satellite SST data, a comprehensive study is conducted to examine the relationship between TC characteristics, including wind speed and translation speed, and the associated SST changes. The overall findings reveal that within a radius of 300 km from the TC center, SST decreases were observed at 1702 (86%) locations, with an average SST response to TC of −0.46 °C and a maximum decrease of −2.07 °C. The most significant reduction in SST typically occurred two days after the passage of TCs, followed by a gradual recovery period exceeding 15 days for the SSTs to return to their initial values. Consistent with findings in other ocean basins, stronger and slower-moving TCs induced more substantial cooling effects. Conversely, at 279 (14%) locations, particularly associated with TCs of weaker intensities, SST increases were observed following the TC passage. Notably, 140 of these locations were situated at low latitudes, specifically between 8° N and 15° N. This study provides a quantitative analysis of the comprehensive SST response to TCs in the NIO.

Advancing ocean subsurface thermal structure estimation in the Pacific Ocean: A multi-model ensemble machine learning approach

Estimation of the ocean subsurface thermal structure (OSTS) is important for understanding thermodynamic processes and climate variability. In the present study, a novel multi-model ensemble machine learning (Ensemble-ML) model is developed to retrieve subsurface thermal structure in the Pacific Ocean by integrating sea surface data with Argo observations. The Ensemble-ML model integrates four individual machine learning models to enhance estimation accuracy and reliability. Our results exhibit good agreement between the satellite sea surface temperature (SST) and sea surface salinity (SSS) data and Argo observations, providing validation for the utilization of these datasets in the Ensemble-ML model. The Ensemble-ML model exhibits better performance compared to individual machine learning models, with an average root mean square error (RMSE) of 0.3273 °C and an average coefficient of determination (R²) of 0.9905. Notably, incorporating geographical information as input variables enhance model performance, emphasizing the importance of considering spatial context in OSTS estimation. The Ensemble-ML model accurately captures the spatial distribution of OSTS across depths and seasons in the Pacific Ocean, effectively reproducing critical temperature features while maintaining strong agreement with Argo observations. Nevertheless, its performance shows relative weakness within the thermocline layer and the equatorial Pacific region (spanning from 10°S to 10°N latitude), which are characterized by complex circulation systems. Despite these challenges, the Ensemble-ML model effectively reproduces the spatial distribution of OSTS of the Pacific Ocean. This indicates the potential of machine learning models, particularly ensemble models, for enhancing OSTS estimation in the Pacific Ocean and other regions, offering valuable insights for future research and applications in physical oceanography.

Extreme marine heatwaves and cold-spells events in the Southern North Sea: classifications, patterns, and trends

In this study, we examined the long-term spatiotemporal trend of marine heatwaves (MHW) and marine cold spells (MCS) characteristics in the southern North Sea over the last four decades (1982-2021). We then estimated the difference between their annual mean values and the possible relationship with the large-scale climate modes of natural sea surface temperature (SST) and atmospheric variability using satellite SST data. The SST warming rate was 0.33 ± 0.06°C/decade and was associated with an increase in MHW frequency (0.85 ± 0.39 events/decade) and a decrease in MCS frequency (-0.92 ± 0.40 events/decade) over the entire period. We found a distinct difference between the annual mean values of MHW and MCS characteristics, with a rapid increase in total MHW days (14.36 ± 8.16 days/decade), whereas MCS showed an opposite trend (-16.54 ± 9.06 days/decade). The highest MHW frequency was observed in the last two decades, especially in 2014 (8 events), 2020 (5 events), and 2007 (4 events), which were also the warmest years during the study period. Only two years (2010 and 2013) in the last two decades had higher MCS frequency, which was attributed to the strong negative phase of the North Atlantic Oscillation (NAO). Our results also show that on the annual scale, both the East Atlantic Pattern (EAP) and the Atlantic Multidecadal Oscillation (AMO) play a more important role in the formation of the MHW in the southern North Sea than the other teleconnections (e.g., the NAO). However, the NAO made the largest contribution only in the winter. Strong significant (p < 0.05) positive/negative correlations were found between oceanic and atmospheric temperatures and the frequency of MHW/MCS. This suggests that with global warming, we can expect an increase/decrease in MHW/MCS occurrences in the future.

Analysis of Seasonal and Long-Term Variations in the Surface and Vertical Structures of the Lofoten Vortex

The Lofoten Vortex (LV) is a quasi-permanent anticyclonic eddy with the characteristic of periodic regeneration in the Lofoten Basin (LB), which is one of the major areas of deep vertical mixing in the Nordic Sea. Our analysis of the LV contributes to our understanding of the variations in convective mixing in the LB. Based on drifter data and satellite altimeter data, the climatological results show that the LV has the sea surface characteristics of relative stability in terms of its spatial position and significant seasonal variations in its physical characteristics. Combined with the temperature and salinity data of Argo profiles, the vertical structures of the LV are presented here in terms of their spatial distribution and monthly variations. The wavelet analysis of the satellite sea surface temperature (SST) data shows that the period of SST anomaly (SSTA) in the LV sea area is 8–16 years. In the stage marked by a decreasing (increasing) trend of SSTA, the vertical mixing is strengthened (weakened). Current vertical mixing is clearly revealed by the Argo profiles, and the SSTA shows a significant impact of cooling. However, against a background of warming and freshening, this vertical mixing will be greatly weakened in the next increasing trending stage of the SSTA.

Co-Occurrence of Atmospheric and Oceanic Heatwaves in the Eastern Mediterranean over the Last Four Decades

Heatwaves are now considered one of the main stressors of global warming. As a result of anthropogenic warming, atmospheric and oceanic heatwaves have increased in frequency, intensity and duration in recent decades. These extreme events have recently become a major concern in climate research due to their economic and environmental impacts on ecosystems. In this study, we investigated the co-occurrence and relationship between atmospheric and marine heatwaves (AHW/MHW) in the Eastern Mediterranean (EMED) over the last four decades (1982–2021). Furthermore, the spatio-temporal variability and trends of sea surface temperature (SST), near-surface air temperature (SAT), AHW and MHW characteristics (frequency and duration) were examined. For these objectives, we used daily gridded high-resolution satellite SST data (0.05° × 0.05°) and the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF-ERA5) atmospheric reanalysis SAT and wind components (0.25° × 0.25°). The results showed an average warming trend of about 0.38 ± 0.08 °C/decade and 0.43 ± 0.05 °C/decade for SAT and SST, respectively. A high statistically significant (p < 0.05) correlation (R = 0.90) was found between AHW and MHW frequency. Our results showed that more than half of the MHWs in the EMED co-occurred with AHWs throughout the study period. The most intense summer MHW in 2021, which co-occurred with AHW, was associated with higher positive anomalies of SAT and SST, and a decrease in the wind speed anomaly.

Methods of Assimilation of Sea Surface Temperature Satellite Data and Their Influence on the Reconstruction of Hydrophysical Fields of the Black, Azov, and Marmara Seas Using the Institute of Numerical Mathematics Ocean Model (INMOM)

Methods of Assimilation of Sea Surface Temperature Satellite Data and Their Influence on the Reconstruction of Hydrophysical Fields of the Black, Azov, and Marmara Seas Using the Institute of Numerical Mathematics Ocean Model (INMOM)

Heat Flux Estimates From a Synthesis of Satellite Observations and a Hydrodynamic Model (With Application to Long Island Sound)

AbstractEstimating surface heat fluxes via direct covariance measurements or bulk formulae is observation‐intensive and costly. We present a methodology whereby we estimate net surface heat fluxes as the difference between the depth‐integrated heat tendencies and the depth‐integrated horizontal heat exchanges in a hydrodynamic model. We calibrate the model to achieve a good representation of mixing and advection and then assimilate satellite sea surface temperature (SST) observations into the model at an 8‐day scale. The SST data assimilation forces a good representation of observed temperatures and heat tendencies both at the surface and throughout the water column. We estimate the horizontal heat exchange directly from the model output and then infer the surface fluxes required to close the budget. When we apply this methodology to a model with prescribed surface heat fluxes and without data assimilation, we can recover the prescribed fluxes with an RMS error of ±10 W m−2 and an r2 of 0.998. When we compare our results to those estimated using Coupled Ocean‐Atmosphere Response Experiment bulk formulae with observations in western Long Island Sound, we find similarly good agreement.

Marine heat waves: Characterizing a major climate impact in the Mediterranean

Marine heat waves (MHW), considered as persistent and spatially extensive sea surface temperature (SST) anomalies, have emerged as one of the global change-induced high impact events on the oceans. The study of MHWs received significant progress in recent years, although many unknowns remain. One of the most notable weaknesses is related to the absence of a universally established definition that would allow better intercomparison of results. It is our aim to contribute to this debate by considering the spatial extent to define a MHW. By applying this hypothesis to a relatively small, but complex, basin such as the Mediterranean, MHWs have been characterized and long-term trends assessed from SST satellite data analysis. Our results show that the inclusion of a minimum area threshold, 5 % of the area basin, greatly reduces the population of MHW events by not considering local SST anomalies that do not constitute a MHW event. A trend to more frequent, intense, and longer MHWs is found in the 1982–2021 period in the Mediterranean. In the spatial characterization and long-term trend analysis, regional differences were apparent. Results evidenced variations in MHWs characteristics and trends across the different sub-basins evidencing the fact that, even in a relatively small basin such as the Mediterranean, significant regional differences make it necessary to include a spatial perspective in the studies, beyond purely local analysis at each observation point in a large basin or even in the global ocean. Regarding the characterization of MHWs and trend analysis in the Mediterranean basin, a growing trend has been found in terms of frequency, duration, and intensity that accelerated since 2000 and especially in the last decade, pointing not just to a steady intensification and higher frequency of MHWs but to the emergence of a new set of more intense, long-lasting and spatially extensive MHWs in the recent years.

Climate Change Assessment of the Spatial Potential Aggregation Zones of Plectropomus pessuliferus marisrubri and Plectropomus areolatus along the Saudi Coast, Using RS and GIS

Climate change is becoming one of the main threats to fishery resources, with the attendant possibilities of decreasing income and food security. Sea surface temperature (SST) is considered a major environmental indicator of climate change, one that impacts the marine ecosystem and habitat. Studying the impacts of SST changes necessitates regular effective monitoring; remote sensing techniques provide researchers with the ability to track changes on various spatial and temporal scales. This study provides an integrated approach, using the advantages of remote sensing data and GIS tools, to assess the SST changes in the spatial potential aggregation zones of Plectropomus pessuliferus marisrubri and Plectropomus areolatus along the Red Sea’s Saudi coast. This study used SST satellite data for 2011 and 2021 to detect changes and develop suitability and risk assessment maps. The SST showed an increase of 0.46 °C from 2011 to 2021, particularly during the summer months. As a result, the suitability of spatial potential aggregation from 2011 to 2021 has dropped in the summer months. The risk assessment analysis revealed a decrease in the suitable potential aggregation zones in the summer months, as it reached about −35.7% in August, while it increased in the winter months, reaching +2.52% in January.

Cataloguing environmental influences on the spatiotemporal variability of Adriatic anchovy early life stages in the eastern Adriatic Sea using an artificial neural network

The anchovy (Engraulis encrasicolus, Linnaeus, 1758), one of the most important small pelagic fish species in the Adriatic, is currently described as a species that can be considered overfished. From 2013 to 2020, samples of anchovy eggs and larvae were collected through scientific surveys during the summer months. The collected ichthyoplankton data were combined with environmental data (measured satellite sea surface temperature and chlorophyll data, numerically simulated salinity, maps of primary production) to identify anchovy spawning habitats and environmental conditions affecting the anchovy early life stages. For this large dataset, a nonlinear method called Growing Neural Gas Network analysis was used to explain the multiple dependencies between anchovy and the explanatory environmental variables and represent them in 9 patterns called Best Matching Unit (BMU). Obtained values of anchovy early life stages abundances (eggs/m2; larvae/m2) showed a clear negative trend, which was easily observed both in the time series and in the annual spatial distributions. Among all measured environmental parameters that were previously mentioned, salinity showed a significant increase, which can be attributed to the cyclonic phase of the bimodal oscillatory system of the Adriatic and Ionian Seas. The calculated BMUs showed several interesting results that shed new light on previous findings: (a) there is a split between the richer northern and poorer southern parts of the Adriatic in terms of anchovy eggs and larvae abundances, (b) the Kvarner Bay, the west coast of Istria and the area around Dugi otok are consistently rich spawning grounds, (c) decreased abundance in the southern areas is a result of the influence of salinity, (d) an increase in chlorophyll can lead to an increase in egg count, (e) the positive effects of upwelling can be negated by an increase in salinity, (f) increased primary production is followed by increased egg count. Upwelling, as one of the factors that can influence larval and egg abundance by bringing nutrients up from the seafloor, showed increased spatial and temporal variability during the investigated period, which depended on the wind regime. Our analysis showed that neural network analysis can successfully describe the effects and interplay of environmental factors on the abundance of anchovy early life stages.

Influence of the El Niño-Southern Oscillation on SST Fronts Along the West Coasts of North and South America.

Along the west coasts of North, Central, and South America, sea surface temperature (SST) fronts are important for circulation dynamics and promoting biological activity. Prevailing equatorward winds during summer results in offshore Ekman transport and upwelling along the coast, where fronts often form between cold, upwelled water and warmer offshore waters. The interannual variability in winds, coastal upwelling, sea level anomalies, and SST in these regions have been linked to the El Niño-Southern Oscillation (ENSO), however SST fronts have received less attention. Here, we investigate the interannual variability of SST fronts off North, Central, and South America using satellite SST data spanning 1982-2018. Anomalies of fronts within 0-300km offshore indicate interannual variability that coincides with ENSO events in most regions. Frontal activity generally decreases during El Niño events and increases during La Niña events. The decrease in fronts off Peru and Chile during El Niño coincides with the seasonal peak in frontal activity, while off the United States the decrease occurs when frontal activity is at a seasonal minimum. We also utilized satellite measurements of wind stress and sea level anomaly to investigate how ENSO oceanic and atmospheric forcing mechanisms affect frontal activity. Decreases in frontal activity during El Niño events are largely due to oceanic forcing (i.e., coastal Kelvin waves) off Central and South America and to both oceanic forcing and atmospheric teleconnections off the United States. This study furthers our understanding of the influence of ENSO on coastal upwelling regions in the eastern Pacific Ocean.