Ocean reanalyses are reconstructions of the recent state of the ocean, using all available observational datasets together with models. They are necessary tools for understanding ocean and climate dynamics and studying the evolution of recent ocean and climate change. They are also used to derive climatologies and anomalies for applied studies, initialisation of forecasts, and training for deep learning applications.
This session aims at deepening our understanding of the way reanalyses are used by the scientific community, by providing a forum for ocean reanalysis producers and users. The session will focus, among others, on the following applications:
- Reanalyses intercomparison studies to understand the strengths and limitations of these data
- Impact of long-term observations on reanalyses quality as well as potential uncertainties stemming from the lack of such observations.
- Representation, analysis and dynamical interpretation of specific events such as extremes.
- Synergies with deep learning applications for ocean reanalyses
The outcome of the session will provide useful insights to ocean reanalysis producers for further developments to meet the community’s needs for their applications.
Data Assimilation:
This session also focuses on recent developments and research on ocean data assimilation. Data assimilation is essential for ocean forecasting, reanalysis, and climate studies. By optimally combining numerical simulations with various observations, data assimilation provides a dynamically consistent and comprehensive estimate of the present and past ocean state. This session invites abstract submissions on developments of data assimilation for the physical ocean together with coupled components such as sea ice, marine ecosystems, land-sea interface and atmosphere for ensuring consistency with other parts of the Earth system. The session also focuses on impact of the assimilation and deployment of novel space-borne and in-situ observations such as autonomous platforms, wide-swath satellite tracks, and deep in-situ observations and biogeochemistry profilers.
Beyond state estimation, this session also welcomes contributions in parameter estimation, uncertainty quantification, and hybrid machine learning and data assimilation methods focused on the ocean.
Ensemble-variational ocean data assimilation systems combine static, climatological background error covariances with flow-dependent, ensemble-based estimates to balance robustness and adaptivity. In current operational practice, however, the relative weight between these two components is typically prescribed through a fixed scalar parameter, limiting the ability of hybrid schemes to fast and locally respond to changes in the flow regime, observation density, and model error characteristics.
Recent advances in machine learning for ocean data assimilation have highlighted both the potential and the limitations of data-driven approaches, emphasizing the need for hybrid strategies that remain physically grounded while adapting to evolving dynamical conditions.
Within this context, and as part of HYDRA (HYbrid Data-driven Reconstruction and Adaptation), a research project aimed at enhancing hybrid ensemble-variational data assimilation schemes with machine learning components, the present work will focus specifically on the HYDRA-α module, which targets the adaptive estimation of the hybrid weight α in variational and hybrid 3DVar frameworks. Rather than treating α as a fixed tuning parameter, HYDRA-α explores its spatio-temporal variability and its impact on assimilation skill, consistency, and error statistics, by learning optimal α values conditioned on location, season, and dynamical regime.
Preliminary work has focused on developing the complete validation infrastructure, from data preparation and statistical analysis through to optimal α mapping. Current efforts are directed toward feature engineering and dataset creation for the ML component, with planned development of architectures capable of learning the complex, nonlinear relationships between ocean dynamics and optimal assimilation parameters.
This work represents a concrete step toward realizing hybrid systems that combine embedded physical knowledge with systematic validation across different oceanic regimes to unlock the full potential of machine learning-enhanced ocean data assimilation. By enabling location-specific, seasonally-aware, and dynamically-adaptive localization, our work aims to improve the efficiency and accuracy of ocean data assimilation systems, particularly in regions where static parameters are known to be suboptimal.
During this talk, our methodology will be presented, and some preliminary results obtained within the CNR ISMAR CIGAR reanalysis framework, composed of the NEMO ocean model and a hybrid 3DVar data assimilation system, over the 1995-2005 pre-Argo period will be discussed.
How to cite: Grande, D., Storto, A., and Buizza, R.: An adaptive hybrid weighting for ensemble-variational ocean data assimilation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18434, https://doi.org/10.5194/egusphere-egu26-18434, 2026.
Nudging still is a cost-effective data assimilation technique in coupled climate models, but conventional schemes apply fixed spatial strengths and are less effective in representing heterogeneous ocean processes. An adaptive nudging framework based on a spatially varying gain matrix is proposed to dynamically balance model and observational errors. The method not only preserves the merits of the latitude-dependent nudging approach but also provides a more physically consistent determination of the spatial distribution of nudging coefficients. Implemented in the SPEEDY-NEMO coupled model, the framework is systematically evaluated against the traditional latitude-dependent scheme. Results show that the adaptive approach substantially improves subsurface temperature assimilation, particularly in the Niño3.4 region, the tropical Indian Ocean, North Pacific, North Atlantic, and the northeastern Pacific. In the tropics, the improvement is mainly achieved above and within the thermocline (roughly 100--200 m), where strong vertical stratification and sharp gradients make fixed nudging strengths inadequate:the RMSE decreases by 20% and the correlation with observations increases by 30% compared with the traditional latitude-dependent scheme. By dynamically adjusting the assimilation strength, the adaptive scheme better constrains the thermocline variability and surface-subsurface interactions. In mid- to high-latitude regions, the improvement extends to greater depths, consistent with a deeper thermocline, where oceanic processes dominated by the mixed layer dynamics and convection exhibit large regional biases that require spatially adaptive correction. In addition, compared with the latitude-dependent nudging scheme, the adaptive approach achieves simultaneous corrections of both the systematic bias term and the variance term of temperature deviations, thereby enhancing not only the mean state but also the model’s ability to capture variability. Generally, the root-mean-square errors decrease by 20-30% and the correlation with observations increases around 30-50% by the adaptive scheme. Beyond temperature, improvements are also evident in salinity, currents, and sea surface height anomalies, indicating the broader benefits of the adaptive scheme. These results indicate that spatially adaptive nudging provides a more effective and practical alternative to fixed schemes, offering a solid basis for improving ocean state estimation in coupled models.
How to cite: Wang, Y., Zheng, F., Yan, C., and Abid, M. A.: An Adaptive Nudging Scheme with Spatially Varying Gain for Improving the Ability of Ocean Temperature Assimilation in SPEEDY-NEMO, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3263, https://doi.org/10.5194/egusphere-egu26-3263, 2026.
Assimilation of sea ice observations has been limited by the difficulty to define adequate forecast errors, key challenges include the non-Gaussianity of sea ice variables and a chronic underdispersion of forecast ensembles. In this presentation, we show two advancements in the TOPAZ coupled ocean and sea ice model system, where a 100-member ensemble is utilized to provide flow-dependent estimates of uncertainties. Firstly, the representation of atmospheric uncertainties is improved by an updated perturbation scheme. A comparison between the operational TOPAZ perturbation scheme with the ECMWF ensemble forecast revealed the deficiencies in sea ice drift spread at large scales. The new scheme takes a multiscale approach to compensate for this deficiency. Secondly, a radiative transfer model was implemented as a new observation operator in TOPAZ, which enabled the direct assimilation of brightness temperature measurements from the Advanced Microwave Scanning Radiometer 2 (AMSR2) satellite mission. Compared to the conventional assimilation of sea ice concentration products retrieved from AMSR2, the direct assimilation approach better accounts for the uncertainties from the sea ice and the atmospheric forcing, avoiding the biases due to assumptions made in the traditional retrieval process. We will also discuss the future direction to integrate these advancements in the TOPAZ system, in preparation for the upcoming Copernicus Imaging Microwave Radiometer (CIMR) mission. This work is funded by the European Horizon project ACCIBERG.
How to cite: Ying, Y., Durán Moro, M., Lavergne, T., Xie, J., and Bertino, L.: Advancements in satellite data assimilation in TOPAZ coupled ocean and sea ice model system, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7563, https://doi.org/10.5194/egusphere-egu26-7563, 2026.
Earth System Models (ESMs) are sophisticated tools for simulating the global ocean and its ecosystems through tightly coupled physical and biogeochemical (BGC) processes. A key factor in improving ESM predictive skill is data assimilation, which incorporates observations to produce reanalyses used as initial conditions. Despite its crucial role, ocean data assimilation is challenged by limited BGC observations and physically unrealistic diapycnal mixing, which hinder progress in coupled physical-biogeochemical prediction. Here we develop an ocean reanalysis production system using the Parallel Data Assimilation Framework (PDAF) within GFDL-ESM4 to initialize coupled physical-biogeochemical prediction. The system produces a 27-year reanalysis (1991–2017) by assimilating only physical observations (e.g., temperature and salinity) and does not exhibit spurious diapycnal mixing. Comparisons with observations and existing reconstructions indicate that our reanalysis similarly represents the physical mean state and climate variability. Notably, the reanalyzed BGC variables show consistency with observations—carbon flux exhibiting similar spatial patterns, and chlorophyll anomalies showing significant correlations (~0.67) across several ocean regions—even without assimilating BGC data. These findings highlight the potential of our developed system to initialize coupled physical-biogeochemical predictions.
How to cite: Jeon, W., Park, J.-Y., Jung, H.-C., and Song, H.-J.: Development of Ocean Reanalysis for Coupled Physical-Biogeochemical Prediction Using PDAF with GFDL-ESM4, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6344, https://doi.org/10.5194/egusphere-egu26-6344, 2026.
We present a data assimilative multi-tracer circulation optimization framework to refine the Ocean Circulation Inverse Model (OCIM) by jointly assimilating radiocarbon, CFCs (chlorofluorocarbons), and SF6 (sulfur hexafluoride). The framework uses a newly compiled global radiocarbon data set that combines dissolved inorganic carbon measurements with coral and mollusk shell records, together with CFC and SF6 observations from GLODAPv2. These tracers provide complementary constraints on ocean ventilation from centennial to decadal timescales. In a steady-state OCIM formulation, mixing and advection parameters are optimized by minimizing a global tracer misfit cost function using gradient information and Bayesian inverse techniques. Joint optimization combines tracers with different temporal sensitivities and improves constraints on large-scale transport pathways and diapycnal mixing compared to single-tracer approaches. The optimized steady-state circulation provides an observationally constrained baseline for studies of ocean heat uptake, carbon storage, and marine biogeochemistry, and offers a flexible framework for multi-tracer data assimilation in climate-relevant ocean modeling.
How to cite: Mamnun, N., DeVries, T., Tournebise, N., Primeau, F., and Graven, H.: Optimizing Global Ocean Circulation with Transient Ocean Tracers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10932, https://doi.org/10.5194/egusphere-egu26-10932, 2026.
In physical oceanography, observations are mainly acquired by space-borne
sensors that cannot measure the interior ocean state. As a result, most avail-
able data are limited to the sea surface, with gaps arising from satellite tra-
jectories and sensor coverage. To fill in these gaps, data inversion is usually
performed using reduced-order models. These models are based on assumptions
which simplify the flow dynamics. One such framework is the 1.5-layer quasi-
geostrophic model which resolves only the upper layer flow by considering that
the underlying layer remains at rest. However, it is actually a truncation of
the quasi-geostrophic (QG) framework which describes the dynamics of a three-
dimensional flow. Although motivated by the lack of sublayer data, reducing
the dynamics solely to its surface component remains a strong assumption.
We wish to mitigate the systematic error introduced by this truncation to
improve surface flow reconstruction. We also aim at keeping a sparse expression
for the correction as it allows the problem to remain well-constrained by the
observations. Using the QG equations, we introduce a truncation-correcting
term bridging the gap between the 1.5-layer model and its multilayer counter-
part. This correction is prescribed through the transport of the sublayer stream
function by the upper layer flow. Since the dynamics of the correction is re-
fined by the surface flow, it can be parameterized with a reasonable number of
parameters.
Using simulations of different complexity, we evaluate the performance of the
proposed method. As it identifies the correction term to a physical quantity, we
make use of the potential vorticity conservation in the sublayer to constrain our
parameterization. Correction is finally estimated using a variational method.
Results show a significant improvement over a state-of-the-art error modeling
strategy. The additional constraint helps reconstructing a much smoother field
for the surface flow. Our method also allows the correction term to compensate
for an incorrect deformation radius. This approach then mitigates the pas-
sive sublayer assumption while improving the reconstruction capabilities of the
model.
How to cite: Rigaut, G., Lahaye, N., and Mémin, E.: Enhancing simplified models for the inversion of surface mesoscale dynamics from satellite data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12211, https://doi.org/10.5194/egusphere-egu26-12211, 2026.
Over the past decade, CMCC has focused on developing state-of-the-art global ocean reanalyses that can be suited for diverse purposes, from climate studies to initialization of forecasting systems and downscaling applications. The robustness of the C-GLORS series on key oceanic variables has been demonstrated through its participation in numerous reanalysis inter-comparison projects and is continuously monitored as part of the GREP ensemble product (GLOBAL_MULTIYEAR_PHY_ENS_001_031) within the CMEMS catalogue. The latest consolidated version, C-GLORSv8, spans the altimetry era from 1993 to the present. The same spatial resolution as version 7 is preserved, alongside substantial upgrades. New advances consider the use of hourly ERA5 atmospheric reanalysis, the inclusion of a sea-level spatial unbias scheme, a new multi-category sea-ice model and a bivariate SIC/SIT assimilation system.
The new product shows a reduced temperature bias within the upper 1500m, leading to a more accurate depiction of global ocean heat content. The new configuration has a lower error in sea-level anomaly, and a more realistic representation of the salinity field in the Southern Ocean, especially during the pre-argo era. Furthermore, the new system shows higher surface eddy activity and a more realistic surface eddy kinetic energy field, leading to an improved representation of mesoscale dynamics. In addition, the system demonstrates enhanced ocean convection and a more accurate representation of sea ice extent at both poles, including a reduced low bias in Southern Hemisphere sea ice volume.
How to cite: Selivanova, J., Cipollone, A., Gonzalez, N., and Iovino, D.: Assessing the new version of the CMCC Global Ocean Reanalysis System (C-GLORS), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12888, https://doi.org/10.5194/egusphere-egu26-12888, 2026.
The ongoing decline in Arctic sea-ice extent and thickness underscores the scientific importance of monitoring the marginal ice zone (MIZ), a transitional region between the open ocean and compact pack ice. As the Arctic continues to warm and summer sea-ice extent retreats, the MIZ has been widening and shifting poleward, providing clear evidence of an ongoing transition toward a new Arctic state. The MIZ plays a central role in atmosphere–ocean exchanges, with important implications for weather systems, marine ecosystems, and human activities. It is highly dynamic and characterized by strong spatial gradients and pronounced variability in sea-ice concentration, thickness, and ice composition.
Here, using global ocean reanalyses together with satellite products, we adopt an integrated ocean–ice perspective on the MIZ to identify robust indicators of the evolving Arctic climate state. We characterise Arctic MIZ properties in terms of area fraction and thickness, and their relationship with surface ocean conditions, and analyse the seasonal cycle and interannual variability of MIZ characteristics at hemispheric and regional scales from 1993 onward. Overall, our findings underline the importance of improving the understanding and representation of MIZ dynamics, ice-type distinctions, and coupled ocean–ice interactions to better constrain models and enhance projections of the future Arctic sea-ice cover.
How to cite: Iovino, D., Cocetta, F., and Cipollone, A.: Arctic Marginal Ice Zone Variability: Area, Thickness, and Ocean Surface Links, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14812, https://doi.org/10.5194/egusphere-egu26-14812, 2026.
The model-dependency has been a challenging issue for traditional data assimilation (DA)-based targeted observational method. This study developed a new strategy to address this challenge using multiple-model prediction ensemble. It was found that while the ensemble size reaches a sufficiently large number the optimal observational sites detected tend to stable and model-independent. This new finding answers the long-standing challenge question on the model dependence in targeted observational analysis, offering an efficient and objective way to identify optimal observational sites.
With this strategy, we designed an optimal observational array in the tropical Pacific for the El Niño-Southern Oscillation (ENSO) prediction using the multiple historical simulation datasets from Coupled Model Intercomparison Project Phase 6 (CMIP6) and reanalysis datasets. Sensitive experiments show that while number of datasets reaches 12, a robust optimal observational array is obtained. The first 10 optimal observational sites, mostly located in the equatorial central eastern Pacific, can reduce initial uncertainties by 67%. This was further confirmed by the observation system simulation experiments (OSSE), which is implemented by the EAKF (Ensemble Adjustment Kalman Filter) assimilation system developed in the Community Earth System Model (CESM). This newly developed model-independent strategy makes it feasible to design a robust oceanic observational network for ENSO prediction even using the current targeted observational algorithm, well serving the goal of international Tropical Pacific Observation System (TPOS) 2020 project.
How to cite: Rao, W., Tang, Y., and Wu, Y.: A model-independent strategy for the targeted observation analysis and its application in ENSO prediction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5635, https://doi.org/10.5194/egusphere-egu26-5635, 2026.
Offshore wind farms can influence the physical marine environment in several ways. During construction, they may increase suspended sediment concentrations and sedimentation. During operation, they can alter wave dynamics and hydrodynamics through turbine foundations, wake-induced wind reduction, and potential discharges of warmer, saline water (brine) from on-site hydrogen production. Assessing these effects is essential for responsible planning, but remains challenging due to limited operational experience, scarce large-scale and long-term observations, and the complex interplay of atmospheric, hydrodynamic, and sedimentary processes.
To overcome these challenges, this contribution demonstrates how freely available Copernicus Marine Service (CMEMS) ocean reanalyses data [1], combined with Atmospheric Re-Analysis 5 (ERA5) data developed by the European Centre for Medium Range Weather Forecasts (ECMWF) [2], can support efficient modelling offshore wind farm impacts in the Baltic Sea. We present a systematic workflow that employs these datasets to characterize baseline hydrodynamic conditions and setup locally refined, project-specific numerical models (DHI MIKE).
The reanalysis data is applied to identify seasonal patterns, stratification regimes, and interannual variability, guide decisions on appropriate model complexity (e.g., 2D versus 3D, barotropic versus baroclinic), provide initialization and boundary forcing of the project-specific numerical hydrodynamic models, and enable the validation of numerical models through comparison of three-dimensional current, temperature, and salinity fields where spatially dense measurements are missing.
Our calibrated models reveal how wind farm layouts influence wind, wave, and current patterns, as well as salinity and temperature. For a planned offshore wind farm in the Bay of Bothnia, key findings include:
- Wind: annual mean wind speed reduction of ~0.1 m/s within a 10–15 km radius,
- Waves: significant wave height reduction of ~5% within the farm and ~1.5% up to 20 km beyond,
- Currents: mean surface current changes of −0.015 m/s inside the farm and +0.006 m/s outside,
- Salinity: variations <0.05 PSU (<1% of natural variability),
- Temperature: annual changes within ±0.25°C, with summer surface warming (+0.25–0.50°C) and subsurface cooling (−0.25 to −0.50°C) under stratified conditions.
This workflow illustrates how publicly available ocean reanalyses can support robust, cost-effective impact assessments, enabling more reliable planning for offshore wind farm development.
Datasets used:
[1] E.U. Copernicus Marine Service Information (CMEMS). Marine Data Store (MDS). Multiple products accessed:
Baltic Sea Physics Analysis and Forecast (BALTICSEA_ANALYSISFORECAST_PHY_003_006, https://doi.org/10.48670/moi-00010),
Baltic Sea Physics Reanalysis (BALTICSEA_MULTIYEAR_PHY_003_011, https://doi.org/10.48670/moi-00013),
Baltic Sea Wave Analysis and Forecast (BALTICSEA_ANALYSISFORECAST_WAV_003_010, https://doi.org/10.48670/moi-00011),
Baltic Sea Wave Hindcast (BALTICSEA_MULTIYEAR_WAV_003_015, https://doi.org/10.48670/moi-00014)
Baltic Sea Biogeochemistry Reanalysis (BALTICSEA_MULTIYEAR_BGC_003_012, https://doi.org/10.48670/moi-00012),
Baltic Sea Biogeochemistry Analysis and Forecast (BALTICSEA_ANALYSISFORECAST_BGC_003_007, https://doi.org/10.48670/moi-00009).
[2] Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J.,Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J.-N. (2023). ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://doi.org/10.24381/cds.adbb2d47
How to cite: Irniger, A. and Bergøe, T. E.: High-Resolution Modelling of Offshore Wind Farm Impacts in the Baltic Sea with Ocean Reanalyses, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5721, https://doi.org/10.5194/egusphere-egu26-5721, 2026.
We address the problem of designing an optimal drifter‐release strategy in the Levantine Mediterranean in order to obtain accurate estimates of surface currents. Building on the assimilation framework of Issa et al. (2016), we generate synthetic drifter trajectories from an ocean re-analysis velocity field (Mediterranean Sea Physics Reanalysis. MEDSEA_MULTIYEAR_PHY_006_004) and assimilate them to quantify the improvement, or “gain,” in the reconstructed flow. For each hypothetical launch point we compute a time and space averaged gain, thereby producing a map that links initial drifter locations to the expected percentage correction of the background field. These gain maps are then used to train a machine-learning model based on a U-NET architecture, which learns to predict, from a given background velocity field alone, a spatial map of the anticipated correction associated with any drifter launching point. The resulting tool provides a fast surrogate for expensive observing-system simulation experiments and directly suggests optimal release locations tailored to the instantaneous flow. We compare our strategy with deployments based on random placement and on seeding along the unstable manifolds of the background flow.
How to cite: Issa, L., Hammoud, A., and Brajard, J.: Learning Optimal Drifter Release Locations for Surface Current Estimation in the Levantine Mediterranean, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11568, https://doi.org/10.5194/egusphere-egu26-11568, 2026.
High-resolution satellite observations increasingly enable characterization of mesoscale and submesoscale ocean variability, but their ability to inform reconstruction of subsurface circulation remains uncertain, particularly in regions where intense multi-scale interactions challenge geostrophic constraints. The Surface Quasi-Geostrophic (SQG) framework offers potential in reconstructing three-dimensional upper-ocean dynamics from surface fields, yet its performance across methods and dynamical regimes has not been systematically quantified. Using two parallel physically consistent mesoscale-resolving (1/20°) and submesoscale-permitting (1/60°) model simulations, we investigate four established SQG-based reconstruction methods for their applicability to reconstruct three-dimensional subsurface velocity and density anomalies from surface information in the core Agulhas region. The extended “interior + surface quasigeostrophic” numerical solution-based method (L19), which refines the representation of higher baroclinic modes following the “effective” SQG framework, emerges as the most skillful. L19 effectively reconstructs mesoscale structures (>100 km) and maintains strong spectral agreement with model simulations down to ~50 km near the surface. Its skill varies with seasonal mixed‐layer depth and regional eddy activity, improving under shallow, stable mixed layers and in energetic areas along the Agulhas Retroflection, ring pathways, and the Agulhas Return Current. While density reconstructions remain robust across dynamical regimes, velocity reconstructions deteriorate when submesoscale (<50 km) surface variability dominates, reflecting unresolved ageostrophic motions and rapid vertical decorrelation at submesoscales. These results delineate the effective operating range of SQG-based methods and provide a benchmark for applying submesoscale-resolving satellite observations (e.g., Surface Water and Ocean Topography) to investigate upper-ocean circulation within the Agulhas Current system and other dynamically active regions.
How to cite: Chen, Z., Jing, Z., Wang, X., Schwarzkopf, F. U., Schubert, R., and Biastoch, A.: SQG-based Reconstruction of Mesoscale-to-Submesoscale Dynamics: Applicability of Different Methods in the Core Agulhas System, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3747, https://doi.org/10.5194/egusphere-egu26-3747, 2026.
Recent decades have been marked by pronounced changes in Antarctic sea ice, including record-breaking highs and lows and significant regional contrasts. This variability is puzzling and not captured by climate models; furthermore, while long-term records of sea ice extent are available since 1979, estimates of sea ice thickness are more difficult to obtain. Such estimates are, however, essential for understanding underlying processes, regional dynamics, for evaluating model performance, and for supporting climate studies in polar regions. Data assimilation (DA) offers a robust framework to combine satellite observations with numerical models to generate estimates of sea ice evolution over multi-decadal periods.
Here, we present a reconstruction of the sea ice state over the period 1979–2025 based on the assimilation of satellite-derived sea ice concentration (SIC) into the NEMO–SI3 sea ice–ocean model using an Ensemble Kalman Filter (EnKF). The ensemble consists of 25 members generated through perturbations of ERA5 atmospheric forcing. Monthly SIC observations from the OSI SAF dataset are assimilated throughout the satellite era, yielding a dynamically consistent reconstruction of Antarctic sea ice variability. The reconstructed product is primarily analysed for the Antarctic, with a detailed regional assessment across the main sea ice sectors.
This reconstruction provides a physically consistent description of Antarctic sea ice evolution over the last four decades and offers a basis for regional process studies, climate variability analyses. In parallel, this work represents a first step toward improving polar ocean and sea ice initial conditions for coupled prediction systems, including the Earth System Model EC-Earth. Future developments will involve the assimilation of additional satellite observations, such as sea ice freeboard, with the aim of extending this reconstruction toward coupled ocean–sea ice reanalyses and prediction applications.
How to cite: Delhasse, A. and Massonnet, F.: Reconstruction of Antarctic Sea Ice State since 1979 using data assimilation of sea ice concentration in NEMO-SI3, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12301, https://doi.org/10.5194/egusphere-egu26-12301, 2026.
The Gulf Stream and its associated eddies play a vital role in the transport of energy, momentum, and biogeochemical tracers across the northwest Atlantic [1] as well as regulating the European Climate [2]. In modeling investigations of the Gulf Stream, submesoscale-permitting resolutions (up to 1/50°, i.e., kilometer-scale), are reported to increase realism in current separation, path, and vertical penetration [3] when compared to mesoscale-resolving resolutions. However, running long-term basin-scale simulations at sub-mesoscale-permitting resolutions remains impractical with current computational resources. Here, data assimilation is a valuable tool to help bridge this resolution gap by using observations to constrain ocean models. In large-scale western boundary current regions like the Gulf Stream, the most widely available data are satellite observations, which provide information only at the sea surface. While assimilating these surface-only datasets can directly correct surface features (e.g., front location and intensity), how to best use this data to constrain subsurface conditions remains an area of active research for both physics [4] and biogeochemistry [5].
This study evaluates the extent to which the assimilation of sea-surface observations alone can accurately reconstruct subsurface frontal structures and vertical profiles. Specifically, we investigate the degree of fidelity with which surface-only data can constrain the seasonal subsurface temperature and density gradients, originally resolved in a submesoscale-permitting simulation (1/50°), when assimilated into a coarser, mesoscale-resolving configuration (1/16°) and 59 vertical levels. Data assimilation is performed using the DART system [6] in an ensemble Kalman filter framework. The DART system is coupled with MITgcm [7], which is configured with hydrostatic primitive equations at a horizontal resolution of 1/16°. Starting in January 2017, the model is integrated with hourly ERA5 atmospheric forcing, with initial and lateral boundary conditions derived from GLORYS12V1 reanalysis. Pseudo-observations of sea level anomaly (SLA), sea surface temperature (SST), and sea surface salinity (SSS) are sampled from a reference dataset. We evaluate three assimilation scenarios (SLA-only, SLA+SST, and SLA+SST+SSS), including vertical profiles of velocity and potential temperature, depth of the mixed layer, and surface biases in SSH/SLA and SST. Results are compared with a submesoscale-permitting simulation (1/50°) and the GLORYS12V1 reanalysis. The results from this study will provide a benchmark for more advanced data-assimilation techniques, including techniques utilizing machine learning algorithms.
References
[1] D. Kang et al. (2016), Journal of Physical Oceanography, 16(4), 1189–1207.
[2] J. B. Palter (2015), Annual review of marine science, 7(1), 113–137.
[3] E. P. Chassignet and X. Xu (2017), Journal of Physical Oceanography, 47, 1999–2021.
[4] Z. Chen et al. (2022), Frontiers in Marine Science, 9.
[5] B. Wang et al. (2021), Ocean Science, 17(4), 1141–1156.
[6] J. L. Anderson et al. (2009), Bulletin of the American Meteorological Society, 90(9), 1283–1296, 2009.
[7] J. Marshall et al. (1997), Journal of Geophysical Research: Oceans, 102(C3), 5733–5752.
How to cite: Irie, R., Stewart, H., Kohyama, K., and Hisada, M.: Evaluating surface-only data assimilation for subsurface state estimation in the Gulf Stream, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16742, https://doi.org/10.5194/egusphere-egu26-16742, 2026.
Ocean reanalyses are reconstructed past ocean states by combining ocean numerical models and observations through data assimilation techniques. Thanks to their temporal and spatial consistency, continuity, and high accuracy, ocean reanalyses are an important tool for a wide range of applications
The MER-EP project we present, endorsed by UN Ocean decade action, is an international effort build on previous ocean reanalysis intercomparison exercises such as the Ocean Reanalyses Intercomparison Project (ORA-IP), and on the joint efforts of the ocean prediction community (Copernicus Marine Service, Oceanpredict/ForeSea/OP-DCC), the ocean and climate modelling research community (CLIVAR/GSOP), and on the Ocean Physics and Climate panel of the Global Ocean Observing System (GOOS/OOPC) research program.
Previous intercomparison exercises of ocean reanalyses have targeted specific variables to assess the consistency and discrepancies among various ocean reanalysis products. MER-EP will complement this approach including more systematic to evaluate different ocean reanalyses to determine their quality and fitness-for-purpose for specific applications.
Therefore, the main objective of MER-EP is to improve our knowledge of the ocean by understanding and ultimately improving the reliability and usability of global and regional ocean reanalyses, including physics, waves, biogeochemistry, and sea ice. This work is based on representative and high-priority use cases identified after extensive discussions with academic and private sectors ocean reanalyses users. In the proposed presentation, the MER-EP general organization, the development plans and first results will be given.
How to cite: Yang, C., Bourdallé-Badie, R., and Drévillon, M.: The Marine Environment Reanalyses Evaluation Project MER-EP: towards an improved knowledge of the global ocean environment of the past decades, to support ocean applications and ocean prediction. , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9403, https://doi.org/10.5194/egusphere-egu26-9403, 2026.
The Copernicus Marine Service is the marine component of the Earth Observation Copernicus program of the European Union. It provides free, regular and systematic authoritative information on the state of the Blue (physical), White (sea ice) and Green (biogeochemical) ocean, at global and regional scales. In this context, Mercator Ocean International develops and proposes global (1/12° and 1/4°) blue and white ocean reanalysis (model/data combinations) covering the 3 last decades, among others reanalysis products. With more than 1.2 Peta octet downloaded and 5000 users in 2023, these reanalyzes are ones of the most more downloaded product of Copernicus Marine catalogue.
This presentation, firstly, provides a description and an overall assessment of each global blue reanalyzes produced by Mercator Ocean International: the 1/12° (GLORYS12V1) targeting a robust representation of meso-scale activity and the 1/4° resolution which is a member of small multi-model ensemble approach (GREP) distributed by the Copernicus Marine Service.
Then, we describe the ongoing developments and first assessment of the future version of these reanalyzes. The main improvements concern evolution of the ocean (NEMO) and Assimilation (SAM) codes, the control of the mass and steric repartition, the use of ERA5 atmospheric reanalyze, the switch from 50 to 75 vertical levels for 1/12° reanalysis, the use of interannual river discharges. The first statistical comparisons to observed data (temperature, salinity, sea level) show good results and these new reanalysis release show an improvement of general trends (Ocean Heat Content, steric, thermosteric, halosteric, mass).
How to cite: Bourdallé-Badie, R., lellouche, J.-M., Greiner, E., Hamon, M., Ruggiero, G., Chanut, J., Garric, G., Drévillon, M., and Melet, A.: The Mercator Ocean global “blue” ocean reanalysis: past, present future, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14474, https://doi.org/10.5194/egusphere-egu26-14474, 2026.
Ocean mesoscale eddies (10–250 km) play a key role in transporting heat, momentum, and nutrients. Accurately evaluating their representation in ocean reanalysis becomes important given that ocean reanalysis is widely used in ocean and climate research due to their temporal and spatial consistent coverage. This study aims to assess two global ocean reanalysis products—GLORYS12V1 (1/12° resolution) and GLORYS2V4 (1/4° resolution) available from the Copernicus Marine Service—against satellite altimetry observations. We have used both AVISO SSALTO/DUACS (1/4°) and the higher-resolution SWOT MIOST Science product (1/8°), as reference datasets that allow us to understand better the advantage of high-resolution altimetry data and have a fair evaluation for eddy-resolving ocean reanalysis (1/12°). The evaluation approach is based on a feature-based eddy-verification method to compare eddy properties for example amplitude, radius, centroid, shape using a cost-function metric, Probability of Detection (POD) and False Alarm Ratio (FAR) are then used to quantify the reanalysis skill. GLORYS12V1 demonstrates better agreement with observations than GLORYS2V4, especially for eddies with amplitudes >10 cm. For both DUACS and SWOT MIOST Science (1/8°), the POD increases by more than 30% when moving from GLORYS2V4 to GLORYS12V1, while the FAR also rises by about 20%, mainly due to the detection of more small and weak eddies. The hit-cost metrics also improve: with SWOT, the total hit cost decreases by more than 9%, and the amplitude error is reduced by about 6%. Small increases in radius and distance between centroids are still observed, but these are smaller in the SWOT comparison, showing better compatibility with the high-resolution reanalysis. Overall, POD values remain above 50% and reach around 60% with SWOT, while FAR stays below 30%. These results underline the added value of high model resolution for the representation mesoscale eddies in ocean reanalysis and highlight the value of wide-swath altimetry for model verification.
How to cite: Mauriello, P., Smith, G. C., Yang, C., and Storto, A.: Quantitative Evaluation of Mesoscale Eddies in the North Atlantic Using Satellite Altimetry and Ocean Reanalyses, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8249, https://doi.org/10.5194/egusphere-egu26-8249, 2026.
The summer upwelling along the southeast coast of Vietnam (SUEV) is a prominent seasonal feature of the South China Sea (SCS), characterized by cold sea surface temperatures (SSTs). The SUEV is induced by the southwest monsoon during boreal summer (June–August, JJA) and is probably modulated by the El Niño–Southern Oscillation (ENSO). Generally, when an El Niño event occurs in the previous winter followed by a La Niña event in the following winter, easterly anomalies in the summer weaken the southwest monsoon, leading to a significant reduction in the SUEV. This type of summer has occurred in the past 30 years in 1998, 2010, and 2016, and is hereafter referred to as the Niño-Niña SUEV event. However, the Niño-Niña SUEV in August 2016 exhibited an anomalous intensification caused by positive westerly anomalies induced by the Madden–Julian Oscillation (MJO). This raises a question of how oceanic processes contributed to this unexpected intensification. To address this issue, this study uses reanalysis and satellite datasets, including ERA5 winds and SSTs, Copernicus Marine Environment Monitoring Service (CMEMS) “GLOBCURRENT” surface current fields (Ekman+geostrophic), and altimeter data (sea level anomaly). The results show that the late-summer intensification of the 2016 Niño-Niña SUEV was strongly modulated by a mesoscale anticyclonic eddy propagating from northeast to southwest along the eastern flank of the SUEV. The persistent northeastward flow on the western flank of this eddy enhanced the 2016 Niño-Niña SUEV by +19.8 cm/s. In contrast, the Niño-Niña SUEV weakened by −43.4 cm/s and −48.4 cm/s in 1998 and 2010, respectively. These findings highlight the role of oceanic internal variability in modulating the 2016 SUEV intensity. This study provides new insights into ocean climate variability, with implications for the United Nations 2030 Sustainable Development Goal 13 (Climate Action).
How to cite: Tseng, Y.-H. and Ho, C.-R.: Modulation of the Vietnam coastal upwelling in the 2016 Summer by an abnormal anticyclonic eddy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4685, https://doi.org/10.5194/egusphere-egu26-4685, 2026.
Originating from the North Equatorial Current, the Kuroshio plays a crucial role in transporting heat and transmitting climate signals from the tropics to the mid-latitudes. As the Kuroshio flows northward through the Luzon Strait, its pathway, velocity, and vertical structure are strongly modulated by the complex bathymetry and the strait's gap. To examine how the vertical velocity structure of the Kuroshio changes during its passage through the Luzon Strait, this study utilizes the Global Ocean Physics Reanalysis dataset (GLORYS12V1) provided by the Copernicus Marine Environment Monitoring Service. This dataset has a horizontal resolution of 1/12° and comprises 50 vertical layers, ranging in depth from 0.5 m to 5728 m. The data used in this study spans from 1993 to 2024. Empirical orthogonal function analysis was employed to identify the dominant modes of variability in the vertical velocity structure. The result reveals that the zero-crossing depth of the first baroclinic mode of the Kuroshio’s vertical velocity in the Luzon Strait lies approximately between 180 and 320 m, with stronger velocities associated with shallower zero-crossing depths. Furthermore, wavelet analysis of the corresponding principal component indicates that the influence of interannual variability originating from the tropical Pacific weakens near 20°–21°N in the Luzon Strait and is progressively replaced by decadal variability signals as the Kuroshio continues northward. These findings offer new insights into the vertical structure and climate variability of the Kuroshio, contributing to a deeper understanding of how climate signals are conveyed from the tropics into the ocean interior and toward higher latitudes. This is closely related to the United Nations 2030 Sustainable Development Goal (SDG) 13.
How to cite: Ho, C.-R. and Tseng, Y.-H.: First Baroclinic Mode Variability of Kuroshio in the Luzon Strait, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4689, https://doi.org/10.5194/egusphere-egu26-4689, 2026.
Posters virtual: Tue, 5 May, 14:00–18:00 | vPoster spot 1a
EGU26-22059 | ECS | Posters virtual | VPS20
Indirect assimilation of remote sensing reflectance: case study in the Liguria SeaTue, 05 May, 14:54–14:57 (CEST) vPoster spot 1a
The dataset with the most spatial coverage for data assimilation of biogeochemical models in operational systems is the satellite-derived data. Nevertheless, variables derived from Remote Sensing Reflectance (RSR), like the sea surface chlorophyll concentration, for regions like coastal areas, can reach big errors if compared with in situ measurements. For this reason, a suggestion with the aim of improving the assimilated results comes from the direct assimilation of Remote Sensing Reflectance, removing the error derived from inferring the biogeochemical variable before assimilating. In this work, we focus on a case study, using the Biogeochemical Flux Model (BFM) merged with a hydrological model, we study the effects of the direct and indirect assimilation of RSR in a region located in the Ligurian Basin of the northwestern Mediterranean Sea. For both assimilation experiments, the algorithm used was an Error Subspace Kalman Filter. To assess the results, we compared them with climatologies computed with in situ measurements, highlighting the advantages and disadvantages of both approaches.
How to cite: Soto Lopez, C. E., Lazzari, P., Anselmi, F., and Teruzzi, A.: Indirect assimilation of remote sensing reflectance: case study in the Liguria Sea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22059, https://doi.org/10.5194/egusphere-egu26-22059, 2026.
