Sea Surface Temperature Fields Research Articles

Influence of mesoscale sea‐surface temperature structures on the Mediterranean cyclone Ianos in convection‐permitting simulations: Contributions of surface warming and cold wakes

AbstractThis study examined the influence of mesoscale sea‐surface temperature (SST) structures and warming on the tropical‐like cyclone Ianos. The controlling mechanisms were investigated using a set of sensitivity experiments with the non‐hydrostatic weather research and forecasting model. A simulation forced by a high‐resolution SST field from the mesoscale eddy‐resolving HYCOM reanalysis (CTRLH) performed better than a simulation forced by a coarse‐resolution SST field from Optimum Interpolation Sea Surface Temperature (OISST) in terms of precipitation, wind intensity, and landfall location. The suppression of mesoscale SST variability affected the cyclone's intensity, its path, and associated processes. This effect depends on the nature of the anomalies (cold/warm). The suppression of mesoscale SST forcing intensified wind and precipitation in the presence of cold anomalies, leading to a northwestward shift of the storm's path during its mature phase. The magnitude of these changes varied with the scale of smoothing (mesoscale SST). Compared to the CTRLH simulation, a simulation in which a 60‐km smoothing filter was applied to the SST field resulted in a cyclone centered slightly to the west (or delayed by 3–6 hr), which was further shifted to the west (or delayed) when the smoothing filter was increased to 150 km. The intensification of wind and precipitation may be associated with the reduction in the storm's cold wake, which would lead to enhanced turbulent fluxes and increased atmospheric water vapor. These results highlight the role of small‐scale air–sea interaction processes that could have a strong impact on Mediterranean cyclones' path and intensity.

Hierarchical Bayesian modeling of Atlantic storms based on the sea surface temperature field

Hierarchical Bayesian modeling of Atlantic storms based on the sea surface temperature field

Presenting a Long-Term, Reprocessed Dataset of Global Sea Surface Temperature Produced Using the OSTIA System

Over the past few decades, the oceans have stored the majority of the excess heat in the climate system resulting from anthropogenic emissions. An accurate, long-term sea surface temperature (SST) dataset is essential for monitoring and researching the changes to the global oceans. A variety of SST datasets have been produced by various institutes over the years, and here, we present a new SST data record produced originally within the Copernicus Marine Environment Monitoring Service (which is therefore named CMEMS v2.0) and assess: (1) its accuracy compared to independent observations; (2) how it compares with the previous version (named CMEMS v1.2); and (3) its performance during two major volcanic eruptions. By comparing both versions of the CMEMS datasets using independent in situ observations, we show that both datasets are within the target accuracy of 0.1 K, but that CMEMS v2.0 is closer to the ground truth. The uncertainty fields generated by the two analyses were also compared, and CMEMS v2.0 was found to provide a more accurate estimate of its own uncertainties. Frequency and vector analysis of the SST fields determined that CMEMS v2.0 feature resolution and horizontal gradients were also superior, indicating that it resolved oceanic features with greater clarity. The behavior of the two analyses during two volcanic eruption events (Mt. Pinatubo and El Chichón) was examined. A comparison with the HadSST4 gridded in situ dataset suggested a cool bias in the CMEMS v2.0 dataset versus the v1.2 dataset following the Pinatubo eruption, although a comparison with sparser buoy-only observations yielded less clear results. No clear impact of the El Chichón eruption (which was a smaller event than Mt. Pinatubo) on CMEMS v2.0 was found. Overall, with the exception of a few specific and extreme events early in the time series, CMEMS v2.0 possesses high accuracy, resolution, and stability and is recommended to users.

Near-Term Mediterranean Summer Temperature Climate Projections: A Comparison of Constraining Methods

Abstract There are several methods to constrain multimodel projections of future climate. This study assesses the quality of four constraining methods in representing the near-term summer temperature projections of the Mediterranean region. Three are based on phasing in ocean surface temperature variations based on observations or decadal predictions, and the method is based on the measuring performance and independence of the individual simulations. The comparison has been carried out with a new framework inspired by the forecast quality assessment of decadal predictions. The framework led to quality estimates of the constrained projection approaches obtained by producing 20-yr temperature estimates every year from 1970 to 2000 and computing quality metrics against observational references. The evaluation results show some differences between constraining approaches. The improvement or deterioration against quality measures of the full, unconstrained, phase 6 of the Coupled Model Intercomparison Project (CMIP6) ensemble shows strong spatial heterogeneity. From the analysis of the selection approaches it is found that the constraints based on sea surface temperature (SST) fields are affected not only by the variability but also by the warming trend. The weighting method generally shows small quality differences with respect to the full CMIP6 ensemble. Despite caveats of the different methods, there is potential to improve the near-term climate projections as some significant quality enhancements were found in some approaches according to the evaluation metrics used. This study suggests a good understanding of the constraining methods, and their forecast quality is required before using them to take informed decisions. Our study opens the door to optimizing these methods for the Mediterranean climate and highlights the need for evaluating the constraints through retrospective assessments against observational references.

Deep learning for the super resolution of Mediterranean sea surface temperature fields

Abstract. Sea surface temperature (SST) is one of the essential variables of the Earth's climate system. Being at the air–sea interface, SST modulates heat fluxes in and out of the ocean, provides insight into several upper and interior ocean dynamical processes, and is a fundamental indicator of climate variability potentially impacting the health of marine ecosystems. Its accurate estimation and regular monitoring from space is therefore crucial. However, even if satellite infrared/microwave measurements provide much better coverage than what is achievable from in situ platforms, they cannot sense the sea surface under cloudy and rainy conditions. Large gaps are present even in merged multi-sensor satellite products, and different statistical strategies, mostly based on optimal interpolation (OI) algorithms, have thus been proposed to obtain gap-free (L4) images. These techniques, however, filter out the signals below the space–time decorrelation scales considered, significantly smoothing most of the small mesoscale and submesoscale features. Here, deep learning models, originally designed for single-image super resolution (SR), are applied to enhance the effective resolution of SST products and the accuracy of SST gradients. SR schemes include a set of computer vision techniques leveraging convolutional neural networks to retrieve high-resolution data from low-resolution images. A dilated convolutional multi-scale learning network, which includes an adaptive residual strategy and implements a channel attention mechanism, is used to reconstruct features in SST data at 1/100° spatial resolution starting from 1/16° data over the Mediterranean Sea. The application of this technique shows an improvement in the high-resolution reconstruction, capturing small-scale features and providing a root-mean-squared-difference improvement of 0.02 °C with respect to the L3 ground-truth data.

The South Atlantic Dipole via multichannel singular spectrum analysis

This study analyzes coupled atmosphere–ocean variability in the South Atlantic Ocean. To do so, we characterize the spatio-temporal variability of annual mean sea-surface temperature (SST) and sea-level pressure (SLP) using Multichannel Singular Spectrum Analysis (M-SSA). We applied M-SSA to ERA5 reanalysis data (1959–2022) of South Atlantic SST and SLP, both individually and jointly, and identified a nonlinear trend, as well as two climate oscillations. The leading oscillation, with a period of 13 years, consists of a basin-wide southwest–northeast dipole and is observed both in the individual variables and in the coupled analysis. This mode is reminiscent of the already known South Atlantic Dipole, and it is probably related to the Pacific Decadal Oscillation and to El Niño–Southern Oscillation in the Pacific Ocean. The second oscillation has a 5-year period and also displays a dipolar structure. The main difference between the spatial structure of the decadal, 13-year, and the interannual, 5-year mode is that, in the first one, the SST cold tongue region in the southeast Atlantic’s Cape Basin is included in the pole closer to the equator. Together, these two oscillatory modes, along with the trend, capture almost 40% of the total interannual variability of the SST and SLP fields, and of their co-variability. These results provide further insights into the spatio-temporal evolution of SST and SLP variability in the South Atlantic, in particular as it relates to the South Atlantic Dipole and its predictability.

Mitigating Masked Pixels in a Climate-Critical Ocean Dataset

Clouds and other data artefacts frequently limit the retrieval of key variables from remotely sensed Earth observations. We train a natural language processing (NLP)-inspired algorithm with high-fidelity ocean simulations to accurately reconstruct masked or missing data in sea surface temperature (SST) fields—one of 54 essential climate variables identified by the Global Climate Observing System. We demonstrate that the resulting model, referred to as Enki, repeatedly outperforms previously adopted inpainting techniques by up to an order of magnitude in reconstruction error, while displaying exceptional performance even in circumstances where the majority of pixels are masked. Furthermore, experiments on real infrared sensor data with masked percentages of at least 40% show reconstruction errors of less than the known uncertainty of this sensor (root mean square error (RMSE) ≲0.1 K). We attribute Enki’s success to the attentive nature of NLP combined with realistic SST model outputs—an approach that could be extended to other remotely sensed variables. This study demonstrates that systems built upon Enki—or other advanced systems like it—may therefore yield the optimal solution to mitigating masked pixels in in climate-critical ocean datasets sampling a rapidly changing Earth.

Intercomparison of ocean temperature and circulation near the Galápagos islands in high-resolution models and observations

The mean structure and variability of the Equatorial Undercurrent (EUC) have important implications for upwelling, sea surface temperature (SST), and productivity in the ecologically vital Galápagos Cold Pool. Historically, global coupled climate model simulations have exhibited considerable biases in their simulation of the EUC due to the requirement of relatively high spatial resolution to represent its dynamics. Particularly in the eastern equatorial Pacific, models must also adequately resolve important topographic features to accurately simulate the regional circulation. Here, we examine the extent to which a high-resolution configuration of the NCAR Community Earth System Model (CESM) and a suite of models from the High-Resolution Model Intercomparison project (HighResMIP) adequately represent the regional ocean circulation and other important climatological features of the eastern equatorial Pacific such as the EUC and the associated temperature patterns defining the cold tongue/Galápagos Cold Pool complex. Comparisons with satellite SST and in situ velocity observations, and a high-resolution ocean reanalysis product, illustrate that the high-resolution configuration of the CESM captures many key aspects of the SST field and EUC uniquely well, including its seasonal-to-interannual variability in the eastern equatorial Pacific. Specific strengths and biases of this model with direct comparison to the HighResMIP ensemble are discussed in detail, along with the potential for application of these models to interdisciplinary research topics such as projecting climate change impacts on marine ecosystems.

A Self‐Supervised Framework for Refined Reconstruction of Geophysical Fields via Domain Adaptation

AbstractReconstructing fine‐grained, detailed spatial structures from time‐evolving coarse‐scale geophysical fields has been a long‐standing challenge. Current deep learning approaches addressing this issue generally require massive fine‐scale fields as supervision, which is often unavailable due to limitations in existing observational systems and the scarcity of widespread high‐precision sensors. Here, we present AdaptDeep, a self‐supervised framework for refined reconstruction of geophysical fields via domain adaptation from the coarse‐scale source domain to the fine‐scale target domain. This method incorporates two pretext tasks, cropped field reconstruction and temporal augmentation‐assisted contrastive learning, to leverage spatial and temporal correlations in the target domain. A global propagation structure is proposed in the feature extraction network to leverage bidirectional information for enhanced long‐range dependencies and robustness against estimation errors. In experiments, AdaptDeep correctly identifies local, fine structures and significantly recovers 81.2% detailed information in sea surface temperature fields.

Sea surface temperature prediction by stacked generalization ensemble of deep learning

Accurate sea surface temperature (SST) prediction is of great significance for fishery farming, marine ecological protection, and planning of maritime activities. In this paper, the stacked generalization ensemble is demonstrated for SST prediction to improve every single deep-learning model. Long-term high-resolution satellite-derived SST is used with the sub-regions of the Taiwan Strait and East China Sea taken as the study area. We select the Multilayer Perceptron, Long short-term memory (LSTM), Convolutional Neural Networks (CNN), CNN-LSTM as individual learners, and Convolutional LSTM as the meta-learner. The individual learners are trained and validated on the retained data subset I, while the meta-learner is trained and validated by constructing the samples with the predictions of validated individual learners on the retained data subset II. The two types of models are evaluated on the same test dataset with root-mean-square error and coefficient of efficiency as the scoring criteria. We find that the meta-model outperforms any individual model and other baselines for the one-day-/three-day-ahead forecasts in the Taiwan Strait and one-day-/three-day-/five-day-ahead predictions in the East China Sea. Furthermore, when the lead time is 1 day and 3 days, the meta-model has a better spatial distribution of prediction metrics across all grid points in the Taiwan Strait sub-area. For the East China Sea sub-region, the meta-model advantage is extended to the lead time of 5 days. Probably due to the higher quality of offshore satellite data, the prediction ability enhancement of the stacked ensemble applied in the East China Sea is better than that in the Taiwan Strait. The better-performing meta-model prediction suggests that the stacked generalization ensemble is encouraging and promising for improving the short-term prediction of the daily SST field.

On Predicting Offshore Hub Height Wind Speed and Wind Power Density in the Northeast US Coast Using High-Resolution WRF Model Configurations during Anticyclones Coinciding with Wind Drought

We investigated the predictive capability of various configurations of the Weather Research and Forecasting (WRF) model version 4.4, to predict hub height offshore wind speed and wind power density in the Northeast US wind farm lease areas. The selected atmospheric conditions were high-pressure systems (anticyclones) coinciding with wind speed below the cut-in wind turbine threshold. There are many factors affecting the potential of offshore wind power generation, one of them being low winds, namely wind droughts, that have been present in future climate change scenarios. The efficiency of high-resolution hub height wind prediction for such events has not been extensively investigated, even though the anticipation of such events will be important in our increased reliance on wind and solar power resources in the near future. We used offshore wind observations from the Woods Hole Oceanographic Institution’s (WHOI) Air–Sea Interaction Tower (ASIT) located south of Martha’s Vineyard to assess the impact of the initial and boundary conditions, number of model vertical levels, and inclusion of high-resolution sea surface temperature (SST) fields. Our focus has been on the influence of the initial and boundary conditions (ICBCs), SST, and model vertical layers. Our findings showed that the ICBCs exhibited the strongest influence on hub height wind predictions above all other factors. The NAM/WRF and HRRR/WRF were able to capture the decreased wind speed, and there was no single configuration that systematically produced better results. However, when using the predicted wind speed to estimate the wind power density, the HRRR/WRF had statistically improved results, with lower errors than the NAM/WRF. Our work underscored that for predicting offshore wind resources, it is important to evaluate not only the WRF predictive wind speed, but also the connection of wind speed to wind power.

Experimental and numerical investigation of shelf flow crossing over a strait

Motivated by the phenomenon of Scotian Shelf Crossover events, the problem of a shelf flow that is interrupted by a strait is considered. Laboratory experiments in a rotating tank with barotropic and baroclinic flow over flat and sloping shelves confirm that the flow is steered by the bathymetric contours and mainly circumnavigates the gulf. In order to jump across the strait, as suggested by earlier theories, the flow must have unrealistically high Rossby numbers. However, the near bottom friction relaxes the bathymetric constraint and causes the formation of a peculiar jet crossing the strait diagonally. For the dissipation values such that a half of the transport goes around the gulf and half crosses the strait diagonally, the diagonal crossover jet becomes most evident. Numerical solutions for realistic values of the frictional parameter reproduce the results of the laboratory experiments and consideration of the actual Gulf of Maine bathymetry reproduces patterns similar to those observed by drift trajectories and in the satellite derived sea surface temperature fields.

Climatology and variability of sea surface temperature in the Azores region and its relation to climate indices

Daily sea surface temperature (SST) data from the Advanced Very High Resolution Radiometer from 1982 to 2020 were used to study the spatiotemporal variability and climatology of SST in the Azores. In addition, the relationships between the SST anomaly (SSTA) and three climate index time series for the period 1982–2020 were investigated: the Oceanic Niño Index (ONI), the Southern Oscillation Index (SOI) and the North Atlantic Oscillation (NAO) index. The spatiotemporal variability and climatology of the SST were studied by analyzing annual, seasonal, and monthly mean SST time series and climate fields. Relationships between the SSTA and climate index time series were investigated using Pearson's correlation analysis and wavelet coherence analysis. The annual mean SST time series shows a positive linear trend of about 0.027°C per year over the period 1982–2020. The spatial variability of the linear trend shows that the positive trend is consistent throughout the study area, ranging from 0.024 to 0.034°C per year. The spatial variability is not uniform, and high values of the trend are observed in the upper northwestern part and southwestern half of the basin. Seasonal mean SST fields show a positive North-South latitudinal gradient, with higher temperatures in the south, especially in the southwestern basin. In addition, the seasonal mean SST fields show possible coastal upwelling signatures along the coast of the Azores islands in different seasons. The results of Pearson's correlation analysis between basin-averaged, de-trended monthly mean SSTA and climate indices show that SSTA shows a significant positive correlation with SOI and a negative correlation with ONI from 1982 to 2020. NAO shows positive correlation with SSTA for the December-January-February season. Wavelet coherence analysis confirms significant coherences between the Azores SSTA and the climate-ocean indices at certain time scales and periods.

On the Mechanisms Driving Latent Heat Flux Variations in the Northwest Tropical Atlantic

AbstractThe Northwest Tropical Atlantic (NWTA) is a region of complex surface ocean circulation. The most prominent feature is the North Brazil Current (NBC) and its retroflection at 8°N, which leads to the formation of numerous mesoscale eddies known as NBC rings. The NWTA also receives the outflow of the Amazon River, generating freshwater plumes that can extend up to 100,000 km2. We show that these two processes influence the spatial variability of the region's surface latent heat flux (LHF). On the one hand, the presence of surface freshwater modifies the vertical stratification of the ocean, the mixed layer heat budget, and thus the air‐sea heat exchanges. On the other hand, NBC rings create a highly heterogeneous mesoscale sea surface temperature (SST) field that directly influences the near‐surface atmospheric circulation. These effects are illustrated by observations from the ElUcidating the RolE of Cloud‐Circulation Coupling in ClimAte ‐ Ocean Atmosphere (EUREC4A‐OA) and Atlantic Tradewind Ocean‐Atmosphere Interaction Campaign (ATOMIC) experiments, satellite and reanalysis data. We decompose the LHF budget into several terms controlled by different atmospheric and oceanic processes to identify the mechanisms leading to LHF changes. We find LHF variations of up to 160 W m2, of which 100 W m2 are associated with wind speed changes and 40 W m2 with SST variations. Surface currents or heat release associated with stratification changes remain as second‐order contributions with LHF variations of less than 10 W m2 each. This study highlights the importance of considering these three components to properly characterize LHF variability at different spatial scales, although it is limited by the scarcity of collocated observations.

Spatial structure of the temporary variability of the Arctic seas surface temperature

Interannual oscillations in the surface temperature of the Arctic Ocean and the North Atlantic with the southern boundary (instead “border”) at latitude 55° 25′ N between 1949 and 2007 are investigated based on the MPIOM (Max Planck Institute Ocean Model) solution. It is a free surface ocean model based on primitive equations in the Boussinesq and incompressibility approximations. High-resolution spectra were estimated via fast Fourier transform with a maximum resolution (Welch’s method). Factor analysis method, which makes it possible to identify areas with highly correlated oscillations and reduce the study of the characteristics in question to their analysis in local points, is used to minimize the significant amount of the initial information about monthly average sea surface temperature fields. Аnalysis of the main factors made it possible to identify 10 areas with quasi-synchronous variability of temperature anomalies by including the points correlated with relevant factors with correlation exceeding 0.6. Spectral structure compliance classification revealed that the areas of the Chukchi Sea, the Hudson Bay, the Irminger Sea, and the Labrador Sea have oscillation peak similarities for the periods of 5–6 years and 8–9 years. Central and western areas of the Norwegian Sea, the area affected by the North Atlantic Current, the eastern part of the Norwegian Sea, and some areas of the Kara Sea have similar spectral structure defined by the peaks at the 11-year and 6-year periods. The Baffin Bay with two main peaks at the 16-year and 5–6-year periods, and the central and the western parts of the Barents Sea, where oscillations are similar to the ones in the Chukchi Sea at short periods, and to the ones in the south-eastern part of the Barents Sea and in the eastern part of the Norwegian Sea at 7–8-year periods, stand out significantly. In some cases, spectrum peaks in different areas appear shifted and attenuated, so presumably the frequency characteristics of the temperature signal change as it moves across the water area.

Long-Term Spatiotemporal Characteristics of Ulva prolifera Green Tide and Effects of Environmental Drivers on Its Monitoring by Satellites: A Case Study in the Yellow Sea, China, from 2008 to 2023

Ulva prolifera (U. prolifera) green tide outbreaks have occurred in the Yellow Sea of China for many years, causing serious losses to marine ecology and the marine economy. The monitoring and tracking of U. prolifera green tide is a crucial aspect of marine ecological disaster prevention and control management. This paper aims to investigate the spatiotemporal distribution changes in U. prolifera green tide in the Yellow Sea throughout its life cycle. A survey of the Yellow Sea from 2008 to 2023 was conducted using multi-source remote sensing images. Long-term monitoring and analysis of U. prolifera revealed the evolution process of the green tide, including the early development, outbreak, decline, and extinction stages, considering time, space, and frequency of occurrence. Additionally, this study examined peak coverage change patterns over the past 16 years and analyzed the influence of environmental factors such as sea surface temperature and sea surface wind field on the development of U. prolifera. The research results serve as a valuable reference for the monitoring, early warning, and scientific prevention and control of U. prolifera green tide in the Yellow Sea, as well as other similar marine disaster areas.

Data-driven dimensionality reduction and causal inference for spatiotemporal climate fields.

We propose a data-driven framework to describe spatiotemporal climate variability in terms of a few entities and their causal linkages. Given a high-dimensional climate field, the methodology first reduces its dimensionality into a set of regionally constrained patterns. Causal relations among such patterns are then inferred in the interventional sense through the fluctuation-response formalism. To distinguish between true and spurious responses, we propose an analytical null model for the fluctuation-dissipation relation, therefore allowing us for uncertainty estimation at a given confidence level. We showcase the methodology on the sea surface temperature field from a state-of-the-art climate model. The usefulness of the proposed framework for spatiotemporal climate data is demonstrated in several ways. First, we focus on the correct identification of known causal relations across tropical basins. Second, we show how the methodology allows us to visualize the cumulative response of the whole system to climate variability in a few selected regions. Finally, each pattern is ranked in terms of its causal strength, quantifying its relative ability to influence the system's dynamics. We argue that the methodology allows us to explore and characterize causal relations in spatiotemporal fields in a rigorous and interpretable way.

A Simple Statistical Postprocessing Scheme for Enhancing the Skill of Seasonal SST Predictions in the Tropics

Abstract Seasonal prediction systems are subject to systematic errors, including those introduced during the initialization procedure, that may degrade the forecast skill. Here we use a novel statistical postprocessing correction scheme that is based on canonical correlation analysis (CCA) to relate errors in ocean temperature arising during initialization with errors in the predicted sea surface temperature fields at 1–12-month lead time. In addition, the scheme uses CCA of simultaneous SST fields from the prediction and corresponding observations to correct pattern errors. Finally, simple scaling is used to mitigate systematic location and phasing errors as a function of lead time and calendar month. Applying this scheme to an ensemble of seven seasonal prediction models suggests that moderate improvement of prediction skill is achievable in the tropical Atlantic and, to a lesser extent, in the tropical Pacific and Indian Ocean. The scheme possesses several adjustable parameters, including the number of CCA modes retained, and the regions of the left and right CCA patterns. These parameters are selected using a simple tuning procedure based on the average of four skill metrics. The results of the present study indicate that errors in ocean temperature fields due to imperfect initialization and SST variability errors can have a sizable negative impact on SST prediction skill. Further development of prediction systems may be able to remedy these impacts to some extent. Significance Statement The prediction of year-to-year climate variability patterns, such as El Niño, offers potential benefits to society by aiding mitigation and adaptation efforts. Current prediction systems, however, may still have substantial room for improvement due to systematic model errors and due to imperfect initialization of the oceanic state at the start of predictions. Here we develop a statistical correction scheme to improve prediction skill after forecasts have been completed. The scheme shows some moderate success in improving the skill for predicting El Niño and similar climate patterns in seven prediction systems. Our results not only indicate a potential for improving prediction skill after the fact but also point to the importance of improving the way prediction systems are initialized.

Biophysical coupling of seasonal chlorophyll-a bloom variations and phytoplankton assemblages across the Peninsula Front in the Bransfield Strait

Abstract. This study investigates the spatio-temporal variations in the chlorophyll-a (chl-a) blooms in the Bransfield Strait (BS) at a climatological scale (1998–2018). We propose that suitable monitoring of these blooms can be achieved through remotely sensed observations only if the BS is divided following the Peninsula Front (PF), which ultimately influences the phytoplankton assemblage. Our analysis is based on characterizing climatological fields of sea surface temperature (SST), air temperature, sea ice coverage, chl-a concentrations and wind stress, guided by synoptic novel and historical in situ observations which reveal two niches for phytoplankton assemblage: the Transitional Bellingshausen Water (TBW) and Transitional Weddell Water (TWW) pools. The TBW pool features stratified, less saline, warmer waters with shallow mixed layers, while the TWW pool features well-mixed, saltier, and colder waters. We identify that the 0.6 °C isotherm corresponds to the summertime climatological PF location, effectively dividing the BS into two different scenarios. Furthermore, the 0.5 mg m−3 chl-a isoline aligns well with the 0.6 °C isotherm, serving as a threshold for chl-a blooms of the highest concentrations around the South Shetland Islands. For the first time, these thresholds enable the monthly climatological descriptions of the two blooms developing in the BS on both sides of the PF. We think this approach underscores the potential of combining SST and chl-a data to monitor the year-to-year interplay of the chl-a blooms occurring in the TBW and TWW pools contoured by the PF.

CLOINet: ocean state reconstructions through remote-sensing, in-situ sparse observations and deep learning

Combining remote-sensing data with in-situ observations to achieve a comprehensive 3D reconstruction of the ocean state presents significant challenges for traditional interpolation techniques. To address this, we developed the CLuster Optimal Interpolation Neural Network (CLOINet), which combines the robust mathematical framework of the Optimal Interpolation (OI) scheme with a self-supervised clustering approach. CLOINet efficiently segments remote sensing images into clusters to reveal non-local correlations, thereby enhancing fine-scale oceanic reconstructions. We trained our network using outputs from an Ocean General Circulation Model (OGCM), which also facilitated various testing scenarios. Our Observing System Simulation Experiments aimed to reconstruct deep salinity fields using Sea Surface Temperature (SST) or Sea Surface Height (SSH), alongside sparse in-situ salinity observations. The results showcased a significant reduction in reconstruction error up to 40% and the ability to resolve scales 50% smaller compared to baseline OI techniques. Remarkably, even though CLOINet was trained exclusively on simulated data, it accurately reconstructed an unseen SST field using only glider temperature observations and satellite chlorophyll concentration data. This demonstrates how deep learning networks like CLOINet can potentially lead the integration of modeling and observational efforts in developing an ocean digital twin.