Sea Surface Height Observations Research Articles

Arctic and Southern Ocean polar sea level maps and along-tracks from multi-mission satellite altimetry from 2011 to 2021

Polar sea surface height observation by radar altimeters requires missions with high-latitude orbit and specific processing to observe the sea-ice-covered region within fractures in the ice. Here, we combine sea surface height estimates from different radar satellites over the ice-free and ice-covered polar oceans to create cross-calibrated along-tracks and gridded products over the Arctic Ocean (2011–2021) and the Southern Ocean (2013–2021). The sea surface height from our regional polar products is in great agreement with tide gauges and bottom pressure recorders at monthly timescales in seasonally to year-round ice-covered regions. Thanks to the use of several missions and the mapping strategy, our multi-mission products have a greater resolution than mono-mission products. Part of the sea level variability of the Arctic Ocean product is related to the Arctic Oscillation atmospheric circulation. At long term, the Arctic altimetry sea level is coherent with in-situ steric height evolution in the Beaufort gyre, and negative sea level trends over the 10-year period are observed in the East Siberian slope region, which may be related to the local freshwater decrease observed by other studies. Our regional polar sea level products are limited by current understanding of the sea-ice lead measurements, and homogenization of these polar products with global sea level products needs to be tackled.

Deep Learning Improves Global Satellite Observations of Ocean Eddy Dynamics

AbstractOcean eddies affect large‐scale circulation and induce a kinetic energy cascade through their non‐linear interactions. However, since global observations of eddy dynamics come from satellite altimetry maps that smooth eddies and distort their geometry, the strength of this cascade is underestimated. Here, we use deep learning to improve observational estimates of global surface geostrophic currents and explore the implications for the cascade. By synthesizing multi‐modal satellite observations of sea surface height (SSH) and temperature, we achieve up to a 30% improvement in spatial resolution over the community‐standard SSH product. This reveals numerous strongly interacting eddies that were previously obscured by smoothing. In many regions, these newly resolved eddies lead to nearly an order‐of‐magnitude increase in the upscale kinetic energy cascade that peaks in spring and is strong enough to drive the seasonality of large mesoscale eddies. Our study suggests that deep learning can be a powerful paradigm for satellite oceanography.

Surface Quasi Geostrophic Reconstruction of Vertical Velocities and Vertical Heat Fluxes in the Southern Ocean: Perspectives for SWOT

AbstractMesoscale currents account for 80% of the ocean's kinetic energy, whereas submesoscale currents capture 50% of the vertical velocity variance. SWOT's first sea surface height (SSH) observations have a spatial resolution an order of magnitude greater than traditional nadir‐looking altimeters and capture mesoscale and submesoscale features. This enables the derivation of submesoscale vertical velocities, crucial for the vertical transport of heat, carbon and nutrients between the ocean interior and the surface. This work focuses on a mesoscale energetic region south of Tasmania using a coupled ocean‐atmosphere simulation at km‐scale resolution and preliminary SWOT SSH observations. Vertical velocities (w), temperature anomalies and vertical heat fluxes (VHF) from the surface down to 1,000 m are reconstructed using effective surface Quasi‐Geostrophic (sQG) theory. An independent method for reconstructing temperature anomalies, mimicking an operational gridded product, is also developed. Results show that sQG reconstructs 90% of the modeled w and VHF rms at scales down to 30 km just below the mixed layer and 50%–70% of the rms for scales larger than 70 km at greater depth, with a spatial correlation of 0.6. The reconstruction is spectrally coherent for scales larger than 30–40 km at the surface, slightly degrading ( 0.55) at depth. Two temperature anomaly data sets yield similar results, indicating the dominance of w on VHF. The RMS of sQG and VHF derived from SWOT are twice as large as those derived from conventional altimetry, highlighting the potential of SWOT for reconstructing energetic meso and submesoscale dynamics in the ocean interior.

Learning Sea Surface Height Interpolation From Multi‐Variate Simulated Satellite Observations

AbstractSatellite‐based remote sensing missions have revolutionized our understanding of the Ocean state and dynamics. Among them, space‐borne altimetry provides valuable Sea Surface Height (SSH) measurements, used to estimate surface geostrophic currents. Due to the sensor technology employed, important gaps occur in SSH observations. Complete SSH maps are produced using linear Optimal Interpolations (OI) such as the widely used Data Unification and Altimeter Combination System (duacs). On the other hand, Sea Surface Temperature (SST) products have much higher data coverage and SST is physically linked to geostrophic currents through advection. We propose a new multi‐variate Observing System Simulation Experiment (OSSE) emulating 20 years of SSH and SST satellite observations. We train an Attention‐Based Encoder‐Decoder deep learning network (abed) on this data, comparing two settings: one with access to ground truth during training and one without. On our OSSE, we compare abed reconstructions when trained using either supervised or unsupervised loss functions, with or without SST information. We evaluate the SSH interpolations in terms of eddy detection. We also introduce a new way to transfer the learning from simulation to observations: supervised pre‐training on our OSSE followed by unsupervised fine‐tuning on satellite data. Based on real SSH observations from the Ocean Data Challenge 2021, we find that this learning strategy, combined with the use of SST, decreases the root mean squared error by 24% compared to OI.

Tidal modeling based on satellite altimetry observations of TOPEX/Poseidon, Jason1, Jason2, and Jason3 with high prediction capability: A case study of the Baltic Sea

This research aims to optimize the utilization of long-term sea level data from the TOPEX/Poseidon, Jason1, Jason2, and Jason3 altimetry missions for tidal modeling. We generate a time series of along-track observations and apply a developed method to produce tidal models with specific tidal constituents for each location. Our tidal modeling methodology follows an iterative process: partitioning sea surface height (SSH) observations into analysis/training and prediction/validation parts and ultimately identifying the set of tidal constituents that provide the best predictions at each time series location. The study focuses on developing 1256 time series along the altimetry tracks over the Baltic Sea, each with its own set of tidal constituents. Verification of the developed tidal models against the SSH observations within the prediction/validation part reveals mean absolute error (MAE) values ranging from 0.0334 m to 0.1349 m, with an average MAE of 0.089 m. The same validation process is conducted on the FES2014 and EOT20 global tidal models, demonstrating that our tidal model, referred to as BT23 (short for Baltic Tide 2023), outperforms both models with an average MAE improvement of 0.0417 m and 0.0346 m, respectively. In addition to providing details on the development of the time series and the tidal modeling procedure, we offer the 1256 along-track time series and their associated tidal models as supplementary materials. We encourage the satellite altimetry community to utilize these resources for further research and applications.

An ensemble-based data assimilation system for forecasting variability of the Northwestern Pacific ocean

An adjoint-free four-dimensional variational (a4dVar) data assimilation (DA) is implemented in an operational ocean forecast system based on an eddy-resolving ocean general circulation model for the Northwestern Pacific. Validation of the system against independent observations demonstrates that fitting the model to time-dependent satellite altimetry during a 10-day DA window leads to substantial skill improvements in the succeeding 60-day hindcast. The a4dVar corrects representation of the Kuroshio path variation south of Japan by adjusting the dynamical balance between amplitude/wavelength of the meander and flow advection. A larger ensemble spread tends to reduce the skill in representing the observed sea surface height anomaly, suggesting that it is possible to use the ensemble information for quantifying the forecast error. The ensemble information is also utilized for modification of the background error covariance (BEC), which improves the accuracy of temperature and salinity distributions. The modified BEC yields the skill decline of the Kuroshio path variation during the 60-day hindcast period, and the ensemble sensitivity analysis shows that changes in the dynamical balance caused by the ensemble BEC result in such skill deterioration.

A Deep Learning Approach to Extract Balanced Motions From Sea Surface Height Snapshot

AbstractExtracting balanced geostrophic motions (BM) from sea surface height (SSH) observations obtained by wide‐swath altimetry holds great significance in enhancing our understanding of oceanic dynamic processes at submesoscale wavelength. However, SSH observations derived from wide‐swath altimetry are characterized by high spatial resolution while relatively low temporal resolution, thereby posing challenges to extract the BM from a single SSH snapshot. To address this issue, this paper proposes a deep learning model called the BM‐UBM Network, which takes an instantaneous SSH snapshot as input and outputs the projection corresponding to the BM. Training experiments are conducted both in the Gulf Stream and South China Sea, and three metrics are considered to diagnose model's outputs. The favorable results highlight the potential capability of the BM‐UBM Network to process SSH measurements obtained by wide‐swath altimetry.

Enhancing the Assimilation of SWOT Simulated Observations Using a Multi-Scale 4DVAR Method in Regional Ocean Modeling System

This paper presents an innovative approach to enhance the assimilation of high-resolution simulated observations, specifically targeting Surface Water Ocean Topography (SWOT) Ka-band Radar Interferometer Sea Surface Height (SSH) products, within the Regional Ocean Modeling System (ROMS). Responding to the demand for improved assimilation techniques, we developed a multi-scale Four-Dimensional Variational Data Assimilation (4DVAR) system, building upon validated fine-scale correction capabilities from prior studies. The multi-scale strategy was extended to the ROMS-4DVAR system, providing a comprehensive solution for assimilating high-resolution observations. Leveraging the Observing System Simulation Experiment (OSSE) framework, we conducted a twin experiment comprising a nature run and a free run case. Subsequently, synthetic SWOT SSH measurements were decomposed, considering the model configuration resolution. These components, derived from dense SSH observations, were integrated into a two-step 4DVAR assimilation scheme. The first cycle targets large-scale features for model field correction, and the updated analysis serves as the background for the second assimilation step, addressing fine-scale observation components. Comparisons with the primitive ROMS-4DVAR using a single-scale scheme highlight the superiority of the multi-scale strategy in reducing gaps between the model and the SSH observations. The Root Mean Squared Error (RMSE) is halved, and the Mean Absolute Percentage Error (MAPE) decreases from 2.237% to 0.93%. The two-step assimilation procedure ensures comprehensive multi-scale updates in the SSH field simulation, enhancing fine-scale features in the analysis fields. The quantification of three-dimensional-model dynamic fields further validates the efficiency and superiority of the multi-scale 4DVAR approach, offering a robust methodology for assimilating high-resolution observations within the ROMS.

A direct insertion technique to assimilate sea surface height into a storm surge model

Natural hazards like hurricanes, cyclones, and typhoons frequently cause major economic and livelihood loss in coastal areas around the world. However, accurately predicting sea surface height and flood inundation extent at the operational level remains a challenge. This work tests the hypothesis that assimilating satellite-like sea surface observations within a storm surge model can improve the estimation of storm surge dynamics for operational forecasting. A synthetic experiment is proposed based on Hurricane Irene, which moved along the United States East Coast in 2011. With a focus on the Chesapeake Bay area, a framework is developed to simulate coastal sea surface height using a storm surge model and merging synthetic satellite observations. To represent the “truth” of the system, a hindcast simulation was run for the Hurricane Irene event. Different satellite revisit times are considered to assess the improvement over the numerical model open-loop (i.e., no data assimilation) simulation. Results indicate that the assimilation of synthetic sea surface height observations has the potential to reduce errors and improve the accuracy in the hydrodynamic model simulations by up to 25% (in terms of correlation coefficient), especially when the satellite has short revisit times (i.e., daily, half-daily and flood tides). Such improvements are consistent regardless of the bias sign in the satellite observations and the location, which is fundamental in an operational setting where errors in satellite observations are often unknown.

A Circulation Model for Baynes Sound, British Columbia, Canada

ABSTRACT An application of the three-dimensional Finite Volume Community Ocean Model was developed for Baynes Sound, British Columbia, Canada and a simulation was carried for the period of May 2016 to April 2017. The objective was to provide highly resolved spatial estimates of the regional hydrodynamics that could be used in a coupled biogeochemical model to assess the cultured shellfish carrying capacity of the region. The issue of how representative the particular simulation period was in the context of typical seasonal features was addressed by comparing temperatures, salinities and river discharges with long-term statistics. Circulation model results were generally in good agreement with salinity, temperature, velocity, and sea surface height observations, providing confidence in subsequent seasonal estimates of volume fluxes through the two entrances and water-renewal. Approximately sixty percent of the variability in near-surface temperature observations at five locations was shown to be linearly dependent on a combination of the along-sound wind, air temperature, and the daily sea surface range, which was taken as a proxy for tidal mixing.

Reconstruction of Surface Kinematics From Sea Surface Height Using Neural Networks

AbstractThe Surface Water and Ocean Topography (SWOT) satellite is expected to observe sea surface height (SSH) down to scales approaching ∼15 km, revealing submesoscale patterns that have never before been observed on global scales. Features at these soon‐to‐be‐observed scales, however, are expected to be significantly influenced by internal gravity waves, fronts, and other ageostrophic processes, presenting a serious challenge for estimating surface velocities from SWOT observations. Here we show that a data‐driven approach can be used to estimate the surface flow, particularly the kinematic signatures of smaller scale flows, from SSH observations, and that it performs significantly better than using the geostrophic relationship. We use a Convolutional Neural Network (CNN) trained on submesoscale‐permitting high‐resolution simulations to test the possibility of reconstructing surface vorticity, strain, and divergence from snapshots of SSH. By evaluating success using pointwise accuracy and vorticity‐strain‐divergence joint distributions, we show that the CNN works well when inertial gravity wave amplitudes are relatively weak. When the wave amplitudes are strong, reconstructions of vorticity and strain are less accurate; however, we find that the CNN naturally filters the wave‐divergence, making divergence a surprisingly reliable field to reconstruct. We also show that when applied to realistic simulations, a CNN model pretrained with simpler simulation data performs well, indicating a possible path forward for estimating real flow statistics with limited observations.

Efficient Optimization of a Regional Water Elevation Model With an Automatically Generated Adjoint

AbstractCalibration of unknown model parameters is a common task in many ocean model applications. We present an adjoint‐based optimization of an unstructured mesh shallow water model for the Baltic Sea. Spatially varying bottom friction parameter is tuned to minimize the misfit with respect to tide gauge sea surface height (SSH) observations. A key benefit of adjoint‐based optimization is that computational cost does not depend on the number of unknown variables. Adjoint models are, however, typically very laborious to implement. In this work, we leverage a domain specific language framework in which the discrete adjoint model can be obtained automatically. The adjoint model is both exactly compatible with the discrete forward model and computationally efficient. A gradient‐based quasi‐Newton method is used to minimize the misfit. Optimizing spatially‐variable parameters is typically an under‐determined problem and can lead to over‐fitting. We employ Hessian‐based regularization to penalize the spatial curvature of the friction field to overcome this problem. The SSH dynamics in the Baltic Sea are simulated for a 3‐month period. Optimization of the bottom friction parameter results in significant improvement of the model performance. The results are especially encouraging in the complex Danish Straits region, highlighting the benefit of unstructured meshes. Domain specific language frameworks enable automated model analysis and provide easy access to adjoint modeling. Our application shows that this capability can be enabled with few efforts, and the optimization procedure is robust and computationally efficient.

Evaluation of GNSS reflectometry method for sea level estimation in Indonesia

Over the last decade, GNSS-R (Global Navigation Satellite System reflectometry) has emerged as a technique for observing sea level height using data from GNSS observations. GNSS-R estimates sea level height from the phase center antenna using the reflection of the extracted GNSS signal at sea level. With a large number of tidal stations equipped with GNSS antennas, GNSS-R has the potential to be implemented in Indonesia. GNSS-R observations can also cover sea surface areas tens to hundreds of kilometres away from where the antenna is located over the coast. Furthermore, the installation of a GNSS antenna at a safe height on land allows for the observation of sea levels under extreme conditions such as during storms and cyclones. To employ GNSS-R effectively, several factors must be considered, including signal-to-noise ratio (SNR) data analysis, data processing, filtering control variables, and increased frequency extraction. This study will focus on trying to use GNSS-R method by identifying the best control variables for each study area and evaluating the results. This study used three months of GPS and GLONASS satellite SNR data from two stations, Barus and Morotai. The separated multipath data was then analyzed using the Lomb-Scargle Periodogram (LSP) based on its frequency. The results of GNSS-R sea surface height observations were validated using tide gauge data at both stations. Based on the test results, the RMSE values were 8.7 cm and 8.4 cm at Barus and Morotai stations, respectively. GNSS-R results strongly correlate with tide gauge data, with correlation coefficients of 95% and 98% at Barus and Morotai stations, respectively. Based on these findings, the GNSS-R method can be used to complement tidal sensor data by applying proper quality control.

Comparison of Freshwater Content and Variability in the Arctic Ocean Using Observations and Model Simulations

Freshwater content (FWC), generally characterized in the Arctic Ocean by salinities lower than 34.8 psu, has shifted in both quantity and distribution in recent decades in the Arctic Ocean. This has been largely driven by changes in the volume and salinity of freshwater sources and the direction and magnitude of major currents. In this study, we analyze the variability in FWC and other physical oceanographic variables from 1993 to 2021 in the Arctic Ocean and Beaufort Gyre (BG) using in situ and remote sensing observations and five ocean models and reanalysis products. Generally, ocean models and reanalysis products underestimate FWC in the BG when compared with observations. Modeled FWC and sea surface height (SSH) in the BG are well correlated during the time period and are similar to correlations of the observational data of these variables. ORAS5 compares best to EN4 salinity over the entire study period, although GLORYS12 agrees well pre-2007 and SODA post-2007. Outside the BG, consistency between modeled SSH, FWC, and limited observations varies between models. These comparisons help identify discrepancies in ocean model and reanalysis products while highlighting areas where future improvements are necessary to further our understanding of Arctic FWC. As observations are scarce in the Arctic, these products and their accuracy are important to studying this dynamic and vulnerable ocean.

Calibration of the Instrumental Errors on Marine Gravity Recovery from SWOT Altimeter

The wide-swath altimetry mission, Surface Water and Ocean Topography (SWOT), makes it possible to further break through the accuracy and resolution of marine gravity field recovery. However, as its main payload, the Ka-band radar interferometer (KaRIN) is easy to be affected by the spacecraft attitude, mechanical deformations and dual antenna signal reception status, which generates instrumental errors including roll errors, phase errors, baseline dilation errors, timing errors, and KaRIN noise. Together with ocean temporal variations, the instrumental errors have large effects on the SWOT Sea Surface Height (SSH) observations and hence the marine gravity field recovery. Here, taking the sea area around Japan as an example, we investigated the calibrations of instrumental errors and ocean temporal variations on the recovery of the marine gravity field. The SSH observations of SWOT are first simulated by Mean Sea Surface (MSS), Absolute Dynamic Topography (ADT), Mean Dynamic Topography (MDT) and instrumental errors. Next, the influence of instrumental errors on determining vertical deflection within single-cycle data was analyzed. Then, three calibration methods (KaRIN/KaRIN cross-calibration, Representative KaRIN/KaRIN cross-calibration, and KaRIN/Nadir cross-calibration) are used to reduce the instrumental errors; the experimental results show KaRIN/KaRIN cross-calibration is the optimal one. Last, for the multi-cycle observations containing ocean temporal variations, crossover calibration is done in single cycle and then followed by collinear adjustment in multi-cycles. This approach is verified by considering 18 simulated cycles that cover almost 1 year. Our result indicates an improvement in the accuracy of marine gravity anomaly by about 45% compared to that of one cycle. The calibration strategy of instrumental errors and ocean temporal variations can be used for high-precision marine gravity field recovery with abundant SWOT observations in the near future.

Southwestern Atlantic ocean fronts detected from the fusion of multi-source remote sensing data by a deep learning model

In the Southwestern Atlantic, the Falkland Current intrudes onto the South American shelf, resulting in the meeting of two water masses which are completely different in temperature and dynamic characteristics, thus generating the Southwestern Atlantic Front (SAF). Therefore, the SAF has prominent characteristics of thermal and dynamics. The current ocean front detection is mainly by performing gradient operations on sea surface temperature (SST) data, where regions with large temperature gradients are considered as ocean fronts. The thermal gradient method largely ignores the dynamical features, leading to inaccurate manifestation of SAF. This study develops a deep learning model, SAFNet, to detect the SAF through the synergy of 10-year (2010-2019) satellite-derived SST and sea surface height (SSH) observations to achieve high accuracy detection of SAF with fused thermal and dynamic characteristics. The comparative experimental results show that the detection accuracy of SAFNet reaches 99.45%, which is significantly better than other models. By comparing the frontal probability (FP) obtained by SST, SSH and SST-SSH fusion data respectively, it is proved that the necessity of fusion multi-source remote sensing data for SAF detection. The detection results of fusion data can reflect the spatial distribution of SAF more comprehensively and accurately. According to the meridional variation of FP, the main reason for the seasonal variation of the SAF is the change in its thermal characteristics, and the SAF has stable dynamic characteristics.

Causal Forcing Analysis on the Low-Frequency Variations of Eddy Kinetic Energy in the Kuroshio Extension Region

Abstract The eddy kinetic energy (EKE) in the Kuroshio Extension (KE) region may be affected by two factors: EKE in the Kuroshio large meander (KLM) region and the North Pacific Gyre Oscillation (NPGO). Previous studies reported that the Kuroshio path variations south of Japan may affect the low-frequency variability of the KE jet and related EKE, but the linear correlation between these phenomena derived from long time series is low and not significant, implying that the linkage between EKE in the KLM and KE regions is still unclear. Besides, whether NPGO has a causal effect on the KE EKE remains under debate. In this study, we investigate the causal forcing of the KLM EKE and NPGO on the KE EKE using the convergent cross mapping (CCM) approach based on satellite sea surface height observations. The analysis shows that the KLM EKE affects the EKE only in the KE upstream area (west of 146°E), with no significant causal effect on the EKE in the downstream area; the NPGO plays, instead, a remarkable role on the EKE in both areas. The effect of the KLM EKE on the KE EKE is found to depend on the Kuroshio latitudinal position over the Izu Ridge. Changes in the KLM EKE affect the downstream advection of eddies and induce changes in the Kuroshio position over the ridge, which cause different EKE levels in the KE upstream region. The NPGO affects the KE EKE through the westward propagation of sea surface height anomalies remotely forced by wind stress anomalies associated with the North Pacific Oscillation.

Characteristics of submesoscale eddy structures within mesoscale eddies in the Gulf of Mexico from 1/48° ECCO estimates

Submesoscale oceanic structures (<10-20 km) such as eddies and fronts are often difficult to describe given the influence of the mesoscale. In order to characterize the surface signatures of submesoscale structures, we utilize a custom spatial filtering function to separate the meso- and large-scale sea surface height (SSH) signal from the small scale SSH signal of 1/48° high resolution estimates provided by NASA’s Estimating the Circulation and Climate of the Oceans (ECCO) project. In this study, we use ECCO estimates from a 14-month global simulation between September 2011 and November 2012 with a 2 km horizontal grid spacing in the Gulf of Mexico. We then use an eddy detection and tracking algorithm to identify persistent circular features on both scales, giving rise to an atlas of submesoscale eddy-like variabilities (SEVs). We briefly investigate the geographic and temporal variability of SEVs as a whole before collocating SEVs inside mesoscale eddies, allowing us to evaluate the characteristics of internal SEVs and the impact of SEVs on mesoscale eddies. We find that SEVs, both anticyclonic and cyclonic, are ubiquitous inside mesoscale eddies with lifetimes longer than a week, accounting for an average of 10-20% of the spatial area and eddy kinetic energy of mesoscale eddies. We also show that internal SEVs are persistently associated with temperature and salinity anomalies in both eddy centers and edges of up to 0.1 °C and 0.05 psu, with anticyclonic internal SEVs being warmer and fresher while cyclonic internal SEVs are colder and saltier. Finally, we examine the life cycle of an anticyclonic Loop Current eddy, demonstrating that the number and intensity of internal SEVs within increases as the eddy approaches separation from the Loop Current until a maximum is obtained just after separation. In light of forthcoming submesoscale SSH observations from NASA’s Surface Water and Ocean Topography (SWOT) mission, our results showcase the variability of submesoscale eddy structures and their possible implications for biogeochemical cycling, the inverse energy cascade, and Loop Current prediction techniques.

Mesoscale Variability Linked to Interannual Displacement of Gulf Stream

AbstractThe impacts of interannual oscillations of the Gulf Stream (GS) on oceanic mesoscale variability are investigated using satellite observations of sea surface height (SSH) and sea surface temperature (SST) from 1993 to 2018. We show that variations in GS position, strength, and meandering status are the dominant spatiotemporal modes in regional SSH variability as they explain over 50% of the total variance. In particular, meridional shift of the GS associated with the large‐scale wind variation over the North Atlantic contributes to approximately 30% of SSH variability. We further find that this path displacement mode can drive approximately 15% of regional mesoscale variability in eddy kinetic energy and divergent eddy heat flux. This observational‐based evidence of ocean mesoscale response to GS shift infers a potentially important forcing mechanism that could drive eddy‐scale ocean variability and has far‐reaching implications for regional ocean and ecosystem dynamics in response to climate variation.

Surface and Interior Dynamics of Arctic Seas Using Surface Quasi-Geostrophic Approach

This study assesses the capability of Surface Quasi-Geostrophy (SQG) to reconstruct the three-dimensional (3D) dynamics in four critical areas of the Arctic Ocean: the Nordic, Barents, East Siberian, and Beaufort Seas. We first reconstruct the upper ocean dynamics from TOPAZ4 reanalysis of sea surface height (SSH), surface buoyancy (SSB), and surface velocities (SSV) and validate the results with the geostrophic and total TOPAZ4 velocities. The reconstruction of upper ocean dynamics using SSH fields is in high agreement with the geostrophic velocities, with correlation coefficients greater than 0.8 for the upper 400 m. SSH reconstructions outperform surface buoyancy reconstructions, even in places near freshwater inputs from river discharges, melting sea ice, and glaciers. Surface buoyancy fails due to the uncorrelation of SSB and subsurface potential vorticity (PV). Reconstruction from surface currents correlates to the total TOPAZ4 velocities with correlation coefficients greater than 0.6 up to 200 m. In the second part, we apply the SQG approach validated with the reanalysis outputs to satellite-derived sea level anomalies and validate the results against in-situ measurements. Due to lower water column stratification, the SQG approach’s performance is better in fall and winter than in spring and summer. Our results demonstrate that using surface information from SSH or surface velocities, combined with information on the stratification of the water column, it is possible to effectively reconstruct the upper ocean dynamics in the Arctic and Subarctic Seas up to 400 m. Future remote sensing missions in the Arctic Ocean, such as SWOT, Seastar, WaCM, CIMR, and CRISTAL, will produce enhanced SSH and surface velocity observations, allowing SQG schemes to characterize upper ocean 3D mesoscale dynamics up to 400 m with higher resolutions and lower uncertainties.