Sea Surface Height Research Articles

Studying Conditions of Intense Harmful Algal Blooms Based on Long-Term Satellite Data

Harmful algal blooms (HABs) adversely impact aquatic organisms, human health, and the marine economy. The need to understand the origins and mechanisms of HAB occurrence and development determines the relevance of the study of these phenomena, including using remote sensing methods and assets. Here we present the results of a comprehensive study of conditions and precursors of some intense HABs detected in the water areas near the island of Chiloe (Chile, 2016), near the Kamchatka Peninsula (Russia, 2020), near the island of Hokkaido (Japan, 2021), among others. The study involves statistical analysis of long-term satellite and model data arrays on significant parameters of the marine environment and near-surface atmosphere, as well as empirical modeling of HAB risks. Information products on the following environmental parameters were used: sea surface temperature (SST, NOAA OISST, since 1981), the level of photosynthetically active radiation (PAR) and chlorophyll-a concentration (MODIS Ocean Color SMI, since 2000), sea surface salinity and height (HYCOM, since 1993), and near-surface wind speed and direction (NCEP CFSv2, since 1979). Quantitative assessments of the dynamics of informative criteria were applied. The key criterion is the ratio (Δσ) of the absolute deviation of the studied parameter from the expected norm to the RMS deviation of its values. Intense HABs were often preceded by excessive SST (up to Δσ ~1.99) and PAR (up to Δσ ~2.25) values, as well as low near-surface wind speed (up to Δσ ~−1.83). These environmental parameters considerably contribute to HAB generation and intensification. An approach and empirical function were proposed that allow us to assess the risk of HAB phenomena and reveal their precursors. Using the proposed approach and empirical function, the precursors of ten HABs were identified, nine of which were confirmed by in situ data. The results can be used as a tool for forecasting and studying the conditions for the occurrence of HABs, representing one of the promising directions for monitoring these dangerous phenomena.

Steric heights of submesoscale processes and internal gravity waves in the subtropical northwestern Pacific and northern South China Sea as revealed by moored observations

The Surface Water and Ocean Topography (SWOT) altimeter mission provides a good opportunity to globally detect submesoscale processes (submesoscales) from high-resolution sea surface height (SSH) measurements. However, in addition to the balanced mesoscale eddies and submesoscales, the measured SSH also contains signals of unbalanced internal gravity waves (IGWs) including internal tides (ITs) and supertidal IGWs (IGWs with frequencies of 3–8 cycles per day). Therefore, a quantitative understanding of the contributions of the above multiscale dynamic processes to the SSH is a prerequisite for exploring submesoscales from the SWOT-derived SSH data. Here, this issue was investigated through analyzing one-year moored observation-derived near-surface steric heights (SHs) in the subtropical northwestern Pacific (NWP) and northern South China Sea (SCS) which have different dynamic backgrounds. In the sense of annual mean, the root-mean-squared (RMS) SHs of submesoscales (defined as processes with periods of 2–15 days here) and IGWs in the SCS are 1.30 and 5.54 cm, respectively, while they are only 0.70 and 3.19 cm in the NWP. Seasonally, the RMS SH of submesoscales is larger in winter than summer in both regions; with respect to the IGWs, their RMS SH is larger in summer than winter in the SCS but it is opposite in the NWP. Generally, the SHs of diurnal and semidiurnal ITs are dominated by the stationary component in both regions but the proportion of the nonstationary component is much larger in the NWP than the SCS. The sum of the SHs by nonstationary diurnal and semidiurnal ITs exceeds that of submesoscales except in winter in the SCS. Furthermore, we also found that the SHs of supertidal IGWs are always larger than those of submesoscales in both regions and both seasons. By decomposing the IGWs with frequencies of 1–8 cpd (i.e., cycles per day) into different baroclinic modes, we found that for all of the IGWs bands, the SHs are overwhelmingly attributed to the first mode. The results in this study will provide useful information for the application of SWOT data in regions with active IGWs.

Winter storms drive offshore transport and modulate phytoplankton blooms in Northern Taiwan, China

The East China Shelf Sea (ECSS) is subject to high-frequency storms during winter and spring, with these storm processes serving as a significant driving factor for initiating the outward diffusion of materials from the inner shelf. Inner shelf waters tend to be rich in nutrients, thus their horizontal diffusion is crucial to the marine primary productivity of the outer shelf. However, our understanding of the processes that govern the offshore water mass transport under winter storm oscillations and the mechanisms driving their impact on phytoplankton is currently limited. In this study, we used satellite and reanalysis data from 2003 to 2020 to investigate the modulation mechanism of winter offshore currents (WOC) on phytoplankton bloom (PB) stimulated by storms in Northern Taiwan (NTW). The results revealed that at synoptic scales, winter storms result in higher sea surface height (SSH) and potential energy on the inner side of the front. However, during periods of weakened storms (for example, during north wind relaxation or a shift to southerly winds), a strong WOC is produced in the top 30–50 m of the ocean due to continuous SSH adjustments caused by changed pressure gradients and induced by topography. PB occurs in the offshore waters of the front, driven by a continuous nutrient supply and an increase in photosynthetically available radiation (PAR). On an interannual scale, offshore transport is controlled by the storm's duration and the extent of its weakening. As the climate warms and winter winds in NTW have consistently weakened over the past 20 years, lower turbulent kinetic energy (TKE) and a shallower mixed layer depth (MLD) will promote phytoplankton growth. The results suggest that water level fluctuations caused by high-frequency changes in winter storms are strongly linked to the offshore sediment transport from near-shore waters, impacting the material transport and the health of the ecosystem on the continental shelf.

Multivariate Sea Surface Prediction in the Bohai Sea Using a Data-Driven Model

Data-driven predictions of marine environmental variables are typically focused on single variables. However, in real marine environments, there are correlations among different oceanic variables. Additionally, sea–air interactions play a significant role in influencing the evolution of the marine environment. Both internal dynamics and external drivers contribute to these changes. In this study, a data-driven model is proposed using sea surface height anomaly (SSHA), sea surface temperature (SST), and sea surface wind (SSW) in the Bohai Sea. This model combines multivariate empirical orthogonal functions (MEOFs) with long and short-term memory (LSTM). MEOF analysis is used on the multivariate dataset of SSHA and SST, considering the correlation among sea surface variables. SSW is introduced as a predictor to enhance the predictability of the multivariate sea surface model. In the case of the Bohai Sea, the comparative tests of the model without wind field effect, the fully coupled model, and the proposed prediction model were carried out. MEOF analysis is employed in comparative experiments for oceanic variables, atmospheric variables, and combined atmospheric and oceanic variables. The results demonstrate that using wind field as a predictor can improve the forecast accuracy of SSHA and SST in the Bohai Sea. The root mean square errors (RMSE) for SSHA and SST in a 7-day forecast are 0.016 m and 0.3200 °C, respectively.

Clarifying the Distinction between Steric and Baroclinic Sea Surface Height

Abstract One of the most fundamental uses of ocean models is for the prediction of sea level. Vertical integration of the hydrostatic equation leads to the partitioning of sea level in terms of atmospheric pressure, steric height, and bottom pressure. In an effort to validate the baroclinic wave dynamics of numerical ocean models, some researchers have compared the steric height from models with the sea level anomaly derived from satellite altimetry. The use of steric height in these comparisons captures the qualitative aspects of the baroclinic waves, but it neglects a nonnegligible contribution from bottom pressure. A more accurate evaluation of baroclinic wave dynamics using sea level would involve projecting the pressure field onto orthogonal barotropic and baroclinic components to isolate the baroclinic sea level anomaly. This note illustrates the quantitative difference between steric height and baroclinic sea level, which amounts to approximately a 20% bias of steric height over baroclinic sea level, depending on location. Significance Statement The bottom pressure variability associated with superinertial internal waves is not negligible, but it has been neglected in a recent series of ocean model validation studies. Baroclinic surface pressure, and its associated sea level anomaly, should be used instead of steric height for model–data comparisons of baroclinic sea level variability.

Analysis of the potential of full-waveform stacking techniques applied to coastal airborne LiDAR bathymetry data of the German Wadden Sea National Park

As a result of natural processes and human activities, water bodies and in particular the seabed are in a constant state of change. Collecting data on the topography of the seabed for monitoring tasks, coastal protection or to ensure safe navigation is a major challenge. Airborne LiDAR bathymetry is an efficient area-wide method for acquiring seabed topography. However, this measurement method is limited in water depth penetration due to the attenuation of the measurement signal in the water column and water turbidity. Therefore, it is only suitable for bottom detection in shallow water areas. However, recent developments in full-waveform processing techniques allow an increase in the usable portion of the signal waveform, resulting in an improved representation of the seabed. In this contribution, two novel full-waveform processing techniques are evaluated for the first time on a dataset from the German Wadden Sea National Park. In addition, an enhanced water surface correction method is introduced, which accounts for the local sea surface topography with the goal of improving the accuracy potential of the full-waveform stacking processing. The study demonstrates an increase in the analyzable water depth on the order of 26 %. This results in an improved coverage of the seabed in terms of point density and area covered (+ 14.6 %). A comprehensive analysis of the results shows that the additional seabed points represent the seabed well.

Downscaled seasonal forecasts for the California Current System: Skill assessment and prospects for living marine resource applications

Ocean forecasting is now widely recognized as an important approach to improve the resilience of marine ecosystems, coastal communities, and economies to climate variability and change. In particular, regionally tailored forecasts may serve as the foundation for a wide range of applications to facilitate proactive decision making. Here, we describe and assess ~30 years of retrospective seasonal (1–12 month) forecasts for the California Current System, produced by forcing a regional ocean model with output from a global forecast system. Considerable forecast skill is evident for surface and bottom temperatures, sea surface height, and upper ocean stratification. In contrast, mixed layer depth, surface wind stress, and surface currents exhibit little predictability. Ocean conditions tend to be more predictable in the first half of the year, owing to greater persistence for forecasts initialized in winter and dynamical forecast skill consistent with winter/spring influence of the El Niño–Southern Oscillation (ENSO) for forecasts initialized in summer. Forecast skill above persistence appears to come through the ocean more than through the atmosphere. We also test the sensitivity of forecast performance to downscaling method; bias correcting global model output before running the regional model greatly reduces bias in the downscaled forecasts, but only marginally improves prediction of interannual variability. We then tailor the physical forecast evaluation to a suite of potential ecological applications, including species distribution and recruitment, bycatch and ship-strike risk, and indicators of ecosystem change. This evaluation serves as a template for identifying promising ecological forecasts based on the physical parameters that underlie them. Finally, we discuss suggestions for developing operational forecast products, including methodological considerations for downscaling as well as the respective roles of regional and global forecasts.

CMIP6 model evaluation for sea surface height responses to ENSO

CMIP6 model evaluation for sea surface height responses to ENSO

Regional mapping of energetic short mesoscale ocean dynamics from altimetry: performances from real observations

Abstract. For over 25 years, satellite altimetry has provided invaluable information about the ocean dynamics at many scales. In particular, gridded sea surface height (SSH) maps allow us to estimate the mesoscale geostrophic circulation in the ocean. However, conventional interpolation techniques rely on static optimal interpolation schemes, hence limiting the estimation of non-linear dynamics at scales not well sampled by altimetry (i.e., below 150–200 km at mid-latitudes). To overcome this limitation in the resolution of small-scale SSH structures (and thus small-scale geostrophic currents), a back-and-forth nudging algorithm combined with a quasi-geostrophic model, a technique called BFN-QG, has been successfully applied on simulated SSH data in observing system simulation experiments (OSSEs). The result is a significant reduction in interpolation error and an improvement in the space–time resolutions of the experimental gridded product compared to those of operational products. In this study, we propose that the BFN-QG be applied to real altimetric SSH data in a highly turbulent region spanning a part of the Agulhas Current. The performances are evaluated within observing system experiments (OSEs) that use independent data (such as independent SSH, sea surface temperature and drifter data) as ground truth. By comparing the mapping performances to the ones obtained with operational products, we show that the BFN-QG improves the mapping of short, energetic mesoscale structures and associated geostrophic currents both in space and time. In particular, the BFN-QG improves (i) the spatial effective resolution of the SSH maps by a factor of 20 %, (ii) the zonal and (especially) the meridional geostrophic currents, and (iii) the prediction of Lagrangian transport for lead times up to 10 d. Unlike the results obtained in the OSSEs, the OSEs reveal more contrasting performances in low-variability regions, which are discussed in the paper.

Marine distribution and habitat use by Snow Petrels Pagodroma nivea in East Antarctica throughout the non-breeding period

The relationships between flying Antarctic seabirds and their at-sea environments remain poorly understood, particularly outside of the breeding season. Improving our knowledge of how these marine predators use their habitat is a critical step towards conservation of these species. We tracked 27 adult Snow Petrels from two large breeding colonies in East Antarctica during the Austral winter (non-breeding period) – when they are primarily at sea away from their nesting sites. During this time, Snow Petrel habitat use was most associated with bathymetry (> 5000 m), low sea-surface height, relatively close distance to the southern boundary of the Antarctic Circumpolar Current, relatively close distance to the ice-edge, relatively high sea-ice concentration, and low sea-surface temperature. Individuals displayed various movement patterns: 20 birds occupied overlapping winter areas that ranged broadly (up to 2000 km) from their breeding sites. The remaining birds ventured far beyond their breeding sites – reaching a maximal distance from the colony of 5,268 km. One individual circumnavigated Antarctica. Daily activity patterns were related to day length, with peak activity occurring near dawn and dusk. Nocturnal activity increased from March until August/September. Key results reveal and depict Snow Petrel habitat maps for the non-breeding period in the East Antarctic region.

An ocean front dataset for the Mediterranean sea and southwest Indian ocean

Fronts are ubiquitous discrete features of the global ocean often associated with enhanced vertical velocities, in turn boosting primary production. Fronts thus form dynamical and ephemeral ecosystems where numerous species meet across all trophic levels. Fronts are also targeted by fisheries. Capturing ocean fronts and studying their long-term variability in relation with climate change is thus key for marine resource management and spatial planning. The Mediterranean Sea and the Southwest Indian Ocean are natural laboratories to study front-marine life interactions due to their energetic flow at sub-to-mesoscales, high biodiversity (including endemic and endangered species) and numerous conservation initiatives. Based on remotely-sensed Sea Surface Temperature and Height, we compute thermal fronts (2003–2020) and attracting Lagrangian coherent structures (1994–2020), in both regions over several decades. We advocate for the combined use of both thermal fronts and attracting Lagrangian coherent structures to study front-marine life interactions. The resulting front dataset differs from other alternatives by its high spatio-temporal resolution, long time coverage, and relevant thresholds defined for ecological provinces.

Characterizing Ionospheric Effects on GNSS Reflectometry at Grazing Angles from Space

Coherent observations in GNSS reflectometry are prominent in regions with smooth reflecting surfaces and at grazing elevation angles. However, within these lower elevation ranges, GNSS signals traverse a more extensive atmospheric path, and increased ionospheric effects (e.g., delay biases) are expected. These biases can be mitigated by employing dual-frequency receivers or models tailored for single-frequency receivers. In preparation for the single-frequency GNSS-R ESA “PRETTY” mission, this study aims to characterize ionospheric effects under variable parameter conditions: elevation angles in the grazing range (5° to 30°), latitude-dependent regions (north, tropic, south) and diurnal changes (day and nighttime). The investigation employs simulations using orbit data from Spire Global Inc.’s Lemur-2 CubeSat constellation at the solar minimum (F10.7 index at 75) on March, 2021. Changes towards higher solar activity are accounted for with an additional scenario (F10.7 index at 180) on March, 2023. The electron density associated with each reflection event is determined using the Neustrelitz Electron Density Model (NEDM2020) and the NeQuick 2 model. The results from periods of low solar activity reveal fluctuations of up to approximately 300 TECUs in slant total electron content, 19 m in relative ionospheric delay for the GPS L1 frequency, 2 Hz in Doppler shifts, and variations in the peak electron density height ranging from 215 to 330 km. Sea surface height uncertainty associated with ionospheric model-based corrections in group delay altimetric inversion can reach a standard deviation at the meter level.

Estimation of the barrier layer thickness in the Indian Ocean based on hybrid neural network model

Accurately and effectively estimating of the barrier layer thickness (BLT) is essential for the research of ocean thermodynamics, ocean dynamics, and air-sea interaction. Artificial intelligence model provides an effective means for accurately estimating BLT from sea surface and gridded Argo data. The present study focuses on the application of a hybrid particle swarm optimization-based artificial neural networks model (PSO-ANN) for estimating the BLT in the Indian Ocean. The input variables of the hybrid model include sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH) and precipitation (P), and the output variable is the BLT value. Multivariate satellite and gridded Argo data collected from the Indian Ocean between January 2012 and December 2019 (i.e., a database consists 239,568 training datasets and 34,224 testing datasets) are provided to prepare the training and testing datasets for the model. The parameters of ANN model, such as network parameter, network weights, and dropout rates are optimized using the PSO algorithm to achieve the best estimation model. R-squared (R2) and root mean square error (RMSE) are used to evaluate the performance of the model. Two groups of comparative experiments (Case 1 and Case 2) on the performance of the PSO-ANN model demonstrate that the model in Case 2 can better capture the complex features of the BLT in the ocean region. The performance of the PSO-ANN model in Case 2 is further compared with the data-driven estimation models such as the traditional ANN model and the known multilinear regression model (MRM), as well as the CESM2-WACCM dynamic model from CMIP6. The comparison results show that the dynamic model has the worst performance among the four models. Moreover, the annual average RMSE value for the PSO-ANN model is 1.83 m, which is 12% and 84% lower than that of the traditional ANN and MRM, respectively. The R2 value for the model of 0.85 is improved by 4% and 40% compared to that of two models. Furthermore, three regions with significant seasonal fluctuations of the BLT in the Indian Ocean are selected to further evaluate the estimation accuracy of the hybrid model in 2019; the Southeast Arabian Sea (SEAS), the Bay of Bengal (BoB), and the Eastern Equatorial Indian Ocean (EEIO). As a result, the hybrid model is capable of reflecting seasonal variation trends in these regions, but there is room for improvement in the estimation accuracy. In addition, the correlation analysis between BLT and sea surface parameters indicates that there exist significant correlations between the BLT and SSS, P. The results of this study show that the proposed hybrid model can be used to estimate and analyze BLT in certain regions with complex ocean dynamics processes. Moreover, the model can be extended to estimate other key ocean parameters and provide effective technical support for studying the internal ocean parameters.

Machine Learning Provides a Clearer Window into Ocean Motion

A new method could translate satellite information about sea surface heights into insights on current, heat flow, and—ultimately—climate change.

Are Near-Coastal Sea Levels Accelerating Faster Than Global during the Satellite Altimetry Era?

Impact and risk assessments in coastal areas are informed by current and future sea level rise and acceleration, which demands a better understanding of drivers for regional sea level acceleration. In our study, we analyze the near-coastal sea level acceleration compared with global values during satellite altimetry (1993–2020) and discuss the potential drivers of regional sea level acceleration. We estimate regional sea level acceleration using high-resolution satellite altimetry sea surface height anomalies. Our study reveals a wide range of regional acceleration estimates, varying from −1.2 to 1.2 mm·yr−2, which can be up to 20 times larger or smaller than the global mean sea level acceleration of 0.07 mm·yr−2. Notably, sea level acceleration near the global coastline is calculated at 0.10 ± 0.03 mm·yr−2, exceeding the global mean sea level acceleration by 40%. Regional patterns of sea level acceleration are in good agreement with acceleration patterns calculated from the steric sea level. However, the magnitude of acceleration is only partially explained by the changes in steric sea level, with increasing contributions from the non-steric component.

Intra-Seasonal Variability of Sea Level on the Southwestern Bering Sea Shelf and Its Impact on the East Kamchatka and East Sakhalin Currents

The East Kamchatka and East Sakhalin Currents (EKC and ESC) are the western boundary currents of the subarctic North Pacific and Okhotsk Sea. Variability in the EKC and ESC velocities could exert a substantial effect on ecosystems and fish stocks in the southwestern Bering Sea and Okhotsk Sea. Using satellite-derived data (sea surface heights, geostrophic current velocities, and sea surface temperatures, 2002–2020), we demonstrate that changes in zonal wind generate sea level variations on the shelf in the southwestern Bering Sea over a period of 18–29 days and with an amplitude of 5–20 cm. The ebb/flood events on the shelf lead to changes in the velocity, direction, and position of the EKC. The sea level anomalies propagate along the western Kamchatka, northern Kuril Islands and the northern and western Okhotsk Sea and result in the variability of geostrophic current velocities in the ESC zone. The strengthening (weakening) of ESC leads to an increase (a decrease) in SST in the southern part of the Okhotsk Sea by 1–3 °C. In the northwestern Okhotsk Sea, in addition to wind-induced variability, there are temporary changes in the geostrophic currents with a period of 14 days caused by fortnightly tides.

Implementation of the Pelagic Hotspot Index in detecting the habitat suitability area for bigeye tuna (Thunnus obesus) in the eastern Indian Ocean

Abstract. Istnaeni ZD, Gaol JL, Zainuddin M, Fitrianah D. 2023. Implementation of the Pelagic Hotspot Index in detecting the habitat suitability area for bigeye tuna (Thunnus obesus) in the eastern Indian Ocean. Biodiversitas 24: 5044-5056. Bigeye tuna (Thunnus obesus Lowe, 1839) is a species with a high economic value that can migrate horizontally and vertically over a large area. Although sea temperature has been the main focus of previous findings, other variables can serve as a reasonable proxy. Here we used sea surface chlorophyll-a, sea surface height, subsurface sea salinity, and subsurface temperature to predict the suitable habitat area for bigeye tuna in the Eastern Indian Ocean Off Java. A Generalized Additive Model was performed to analyze the best-fit model evaluated from the p-value and Cumulative Deviance Explained. The suitability index of the selected model was calculated using Pelagic Hotspot Index constructed from multi-spectrum satellite data. The results showed that the high catches were located on the high suitable index value and supported by chlorophyll-a as the most significant factor, followed by sea surface height, temperature, and salinity. This condition was stimulated by the high feeding opportunity, which may relate to the EIO's oceanic front, eddies, and specific current patterns. This study helps identify ecological hotspots, track the migration, and monitor the seasonal closure for bigeye tuna, particularly in EIO.

Hurricane Wave Loads on Spar-Type Floating Wind Turbines: A Comparison of Simulation Schemes

Floating wind turbines are sensitive to hurricane events. Since the turbine rotors are parked and the blades are feathered during hurricanes, the aerodynamic loads due to boundary-layer winds are relatively small compared to the hydrodynamic loads due to sea surface elevations. Hence, accurate modeling of the hurricane wave loads is crucial to ensure the safety of floating wind turbines. During a hurricane, large wave heights with severe flow separation make it inaccurate to use either linear panel method-based models (without nonlinear consideration associated with fluid viscosity) or Morison equation-based models (without unsteady consideration associated with fluid memory). Efforts have been made to advance simulation schemes of hurricane wave loads on spar-type floating wind turbines. This study systematically compares and assesses the efficacy of six hydrodynamic models available in the literature along with a newly proposed model. The ability of these seven hydrodynamic models to capture nonlinear and/or unsteady effects is investigated. As a demonstration example, the wave loads on a spar-type wind turbine are calculated using these seven models to highlight the underlying role of each simulation scheme in accurately acquiring the dynamic responses of this type of offshore floating structure in severe hurricane seas. It is found that the nonlinear viscous term in the Morison equation and hybrid model serves as an important nonlinear damping mechanism. The reduction of the low-frequency wave load and added mass in the modified hybrid model collectively leads to larger displacements compared to those based on the hybrid model. While the displacements based on the stretching method and Rainey’s equation are similarly larger than those based on the Morison equation, their nonlinear wave loads are much smaller than those in FNV theory.

Long-term climate change impacts on regional sterodynamic sea level statistics analyzed from the MPI-ESM large ensemble simulation

Statistics of regional sterodynamic sea level variability are analyzed in terms of probability density functions of a 100-member ensemble of monthly mean sea surface height (SSH) timeseries simulated with the low-resolution Max Planck Institute Grand Ensemble. To analyze the impact of climate change on sea level statistics, fields of SSH variability, skewness and excess kurtosis representing the historical period 1986–2005 are compared with similar fields from projections for the period 2081–2100 under moderate (RCP4.5) and strong (RCP8.5) climate forcing conditions. Larger deviations of the models SSH statistics from Gaussian are limited to the western and eastern tropical Pacific. Under future climate warming conditions, SSH variability of the western tropical Pacific appear more Gaussian in agreement with weaker zonal easterly wind stress pulses, suggesting a reduced El Niño Southern Oscillation activity in the western warm pool region. SSH variability changes show a complex amplitude pattern with some regions becoming less variable, e.g., off the eastern coast of the north American continent, while other regions become more variable, notably the Southern Ocean. A west (decrease)-east (increase) contrast in variability changes across the subtropical Atlantic under RCP8.5 forcing is related to changes in the gyre circulation and a declining Atlantic Meridional Overturning Circulation in response to external forcing changes. In addition to global mean sea-level rise of 16 cm for RCP4.5 and 24 cm for RCP8.5, we diagnose regional changes in the tails of the probability density functions, suggesting a potential increased in variability-related extreme sea level events under global warmer conditions.

Ocean 2D eddy energy fluxes from small mesoscale processes with SWOT

Abstract. We investigate ocean dynamics at different scales in the Agulhas Current system, a region of important interocean exchange of heat and energy. While ocean observations and some of the most advanced climate models capture the larger mesoscale dynamics (> 100 km), the smaller-scale fronts and eddies are underrepresented. The recently launched NASA–CNES Surface Water and Ocean Topography (SWOT) wide-swath altimeter mission observes the smaller ocean geostrophic scales down to 15 km in wavelength globally. Here we will analyse different eddy diagnostics in the Agulhas Current region and quantify the contributions from the larger mesoscales observable today and the smaller scales to be observed with SWOT. Surface geostrophic diagnostics of eddy kinetic energy, strain, and energy cascades are estimated from modelled sea surface height (SSH) fields of the Massachusetts Institute of Technology general circulation model (MITgcm) latitude–longitude polar cap (LLC4320) simulation subsampled at 1/10∘. In this region, the smaller scales (<150 km) have a strong signature on the horizontal geostrophic strain rate and for all eddy diagnostics in the Western Boundary Current and along the meandering Agulhas Extension. We investigate the horizontal cascade of energy using a coarse-graining technique, and we observe that the wavelength range where the inverse cascade occurs is biased towards larger mesoscale wavelengths with today’s altimetric sampling. We also calculate the projected sampling of the eddy diagnostics under the SWOT swaths built with the NASA–CNES simulator to include the satellite position and realistic noise. For the swaths, a neural network noise mitigation method is implemented to reduce the residual SWOT random error before calculating eddy diagnostics. In terms of SSH, observable wavelengths of 15 to 20 km are retrieved after neural network noise mitigation, as opposed to wavelengths larger than 40 km before the noise reduction.