This study presents a novel cyclostrophic balance correction method for estimating submesoscale ocean surface currents in the Northern Arabian Sea using surface water and ocean topography (SWOT) mission altimetry. High-resolution Ocean Color Monitor (OCM-3) data from the EOS-06 satellite reveal fine-scale eddies and filaments with high chlorophyll-a concentrations (>0.4 mg m−3), spatially coherent with geostrophic current patterns from SWOT. At these scales, the geostrophic assumption is invalid; therefore, we introduce a curvature-based cyclostrophic correction that accounts for enhanced centripetal accelerations. Validation against high-resolution model simulations shows that our approach is in better agreement with model outputs than uncorrected and previously published corrected fields, particularly in regions with strong vorticity and strain. When applied to SWOT data, the corrected velocities demonstrate spatial correspondence with chlorophyll patterns and suppress spurious gradients. Probability density functions of normalized vorticity and strain also match theoretical expectations, emphasizing the potential of SWOT for advancing submesoscale ocean dynamics.
The advanced 2-D sea surface height (SSH) observations from the surface water and ocean topography (SWOT) satellite have achieved a global average geoid resolution better than 13 km, as estimated from spectral analysis of adjacent single-cycle observations. However, in regions with complex oceanic dynamics (the ocean off southwestern Argentina and east of Japan), SWOT observations show significant temporal variability across cycles. This variability greatly hampers the satellite’s ability to detect and precisely measure steady-state marine geoid signals. Even with multicycle data stacking, resolution remains poor in some regions, sometimes underperforming conventional satellite altimetry. To solve this issue, this study employs a multiresolution coherent spatio-temporal scale separation (mrCOSTS) method to analyze sea surface height anomaly (SSHA) data from 26 SWOT cycles, aiming to isolate stable signal components and reconstruct the geoid. The approach notably improves geoid resolution in regions with intricate ocean features, with the maximum enhancement reaching up to 76%. This resolution reflects the spatial scales of features reproducibly resolved by two independent SWOT geoid estimates, rather than a direct improvement in absolute marine geoid accuracy. The method effectively suppresses ocean dynamic signals while preserving the static oceanic component, and it also contributes to improving the recovery of the marine gravity field in regions characterized by complex ocean dynamics. Furthermore, in regions with relatively high resolution, the method achieves additional subtle improvements, which are of considerable significance for obtaining a stable static field.
Borey is a high-resolution regional modeling and operational forecasting system for the Barents and Kara Seas. It combines WRF for the atmosphere, NEMO-SI3 for the ocean and sea ice, and WW3 for waves on approximately 3--6\,km grids, and generates daily forecasts to 72 hours. We describe the model chain and production workflow and present an accompanying hourly hindcast of surface conditions from August 2015 to August 2023. The archive provides aligned atmosphere, ocean, sea ice, and wave fields for regional marine studies and a baseline for evaluating the operational system. Comparisons with observations and observation-based products show that Borey captures much of the variability in near-surface atmospheric conditions and ocean temperature. Skill in the evaluated WRF, NEMO, and SI3 forecasts changes only modestly across the three-day window. The main limitations are persistent rather than rapidly growing errors: sea surface temperature is generally too cold, sea ice concentration and occurrence are overestimated during seasonal retreat, and significant wave height is underestimated. Borey should therefore complement observation-constrained products. The planned public release will provide hourly surface fields, native grids, provenance information, and validation outputs for regional analysis, model development, and carefully evaluated data-driven forecasting and data-assimilation research.
Utilizing two-dimensional sea level anomalies from the Surface Water and Ocean Topography (SWOT) wide-swath altimeter, we conduct a global census of fine-scale eddies (with equivalent radii < 30 km) that are beyond the resolution capacity of traditional altimeters. We find that these eddies are concentrated in regions with energetic mesoscale currents such as western boundary current extensions, the Antarctic Circumpolar Current and prominent interior frontal zones. While these eddies are smaller than classical mesoscale eddies (O(100) km), they exhibit dynamics distinct from the mesoscale, including a pronounced winter peak in occurrence frequency and a striking dominance of cyclonic over anticyclonic polarities. A global composite reveals a net enhancing effect of these fine-scale eddies on surface chlorophyll-a (approximately +7% within eddy cores relative to surrounding waters). Our findings shed light on the dynamical characteristics and surface chlorophyll-a signatures of fine-scale ocean eddies, necessitating their adequate representation in next-generation ocean models.
High-resolution projections of near-surface winds are crucial for ocean circulation and sea level studies in Southeast Asia, a region characterized by complex coastlines and monsoon variability. This study evaluates the added value of dynamical downscaling using the Weather Research and Forecasting (WRF) model at 9 km resolution, driven by two CMIP6 global climate models (EC-Earth3 and MPI-ESM1-2-HR), to simulate 10 m wind climatology over the Southeast Asian seas. Comparisons were made against ERA5 reanalysis and the parent CMIP6 GCMs, focusing on seasonal mean patterns, interannual variability, and the annual cycle. The WRF simulations demonstrate substantial improvement in capturing the spatial structures of monsoonal winds and regional circulation features. Future wind projections under SSP2-4.5 and SSP5-8.5 scenarios reveal seasonally and spatially heterogeneous trends. The downscaled models project strengthening of winter monsoon winds over the Southeast Asian seas and a weakening of summer monsoon flows, with implications for upper ocean dynamics and regional sea level patterns. The leading modes of variability from EOF analysis indicate basin-wide wind anomalies modulated by periodic signals at ~1 year and ~2–7 years, likely driven by ENSO and the Asian monsoon. The power spectra of principal components reveal that internal variability persists across scenarios, though with increased signal-to-noise ratios (SNRs) in the WRF projections toward the end of the 21st century.
Despite significant advances in observational systems such as the global Argo array of autonomous profiling floats, the spatiotemporal coverage of subsurface ocean observations remains limited compared to the dense data provided by satellite platforms. This study develops a data‐driven framework to reconstruct synthetic profiles of upper ocean temperature and salinity by training a self‐attention‐based neural network with satellite‐derived sea surface height (SSH) and sea surface temperature anomalies, using 17 years of collocated Argo float measurements. Daily synthetic profiles for the upper 650 m of the Philippine Sea were generated for the entirety of 2010 and assimilated into a regional ocean model via 4D‐Var data assimilation. Results show overall improved effectiveness of state estimation when synthetic profiles are utilized. Diagnostic variables like temperature, salinity, SSH, horizontal velocity all show improvement. Synthetic profiles of subsurface temperature had an overall positive impact on SSH analysis and forecast, especially in regions east of the Luzon Strait and the southern domain influenced by the North Equatorial Current. In the vertical range of 150–600 m, the impact of synthetic profiles on various observations was promising, leading to substantial reductions in the analysis and forecast error.