Satellite-derived Sea Surface Height Research Articles

Enhanced regional ocean ensemble data assimilation through atmospheric coupling in the SKRIPS model

We investigate the impact of ocean data assimilation using the Ensemble Adjustment Kalman Filter (EAKF) from the Data Assimilation Research Testbed (DART) on the oceanic and atmospheric states of the Red Sea. Our study extends the ocean data assimilation experiment performed by Sanikommu et al. (2020) by utilizing the SKRIPS model coupling the MITgcm ocean model and the Weather Research and Forecasting (WRF) atmosphere model. Using a 50-member ensemble, we assimilate satellite-derived sea surface temperature and height and in situ temperature and salinity profiles every three days for one year, starting January 01 2011. Atmospheric data are not assimilated in the experiments. To improve the ensemble realism, perturbations are added to the WRF model using several physics options and the stochastic kinetic energy backscatter (SKEB) scheme. Compared with the control experiments using uncoupled MITgcm with ECMWF ensemble forcing, the EAKF ensemble mean oceanic states from the coupled model are better or insignificantly worse (root-mean-square errors are 23% to −1.3% smaller), especially when the atmospheric model uncertainties are accounted for with stochastic perturbations. We hypothesize that the ensemble spreads of the air–sea fluxes are better represented in the downscaled WRF ensembles when uncertainties are well accounted for, leading to improved representation of the ensemble oceanic states from the new experiments with the coupled model. This indicates the ocean model assimilation will be improved with coupled models and may relax the need for operational centers to provide atmospheric ensembles to drive ocean forecasts. Although the feedback from ocean to atmosphere is included in this two-way regional coupled configuration, we find no significant effect of ocean data assimilation on the ensemble mean latent heat flux and 10-m wind speed over the Red Sea. This suggests that the improved skill using the coupled model is not from the two-way coupling, but from downscaling the ensemble atmospheric forcings (one-way coupled) to drive the ocean model.

Comparison of 4-dimensional variational and ensemble optimal interpolation data assimilation systems using a Regional Ocean Modeling System (v3.4) configuration of the eddy-dominated East Australian Current system

Abstract. Ocean models must be regularly updated through the assimilation of observations (data assimilation) in order to correctly represent the timing and locations of eddies. Since initial conditions play an important role in the quality of short-term ocean forecasts, an effective data assimilation scheme to produce accurate state estimates is key to improving prediction. Western boundary current regions, such as the East Australia Current system, are highly variable regions, making them particularly challenging to model and predict. This study assesses the performance of two ocean data assimilation systems in the East Australian Current system over a 2-year period. We compare the time-dependent 4-dimensional variational (4D-Var) data assimilation system with the more computationally efficient, time-independent ensemble optimal interpolation (EnOI) system, across a common modelling and observational framework. Both systems assimilate the same observations: satellite-derived sea surface height, sea surface temperature, vertical profiles of temperature and salinity (from Argo floats), and temperature profiles from expendable bathythermographs. We analyse both systems' performance against independent data that are withheld, allowing a thorough analysis of system performance. The 4D-Var system is 25 times more expensive but outperforms the EnOI system against both assimilated and independent observations at the surface and subsurface. For forecast horizons of 5 d, root-mean-squared forecast errors are 20 %–60 % higher for the EnOI system compared to the 4D-Var system. The 4D-Var system, which assimilates observations over 5 d windows, provides a smoother transition from the end of the forecast to the subsequent analysis field. The EnOI system displays elevated low-frequency (>1 d) surface-intensified variability in temperature and elevated kinetic energy at length scales less than 100 km at the beginning of the forecast windows. The 4D-Var system displays elevated energy in the near-inertial range throughout the water column, with the wavenumber kinetic energy spectra remaining unchanged upon assimilation. Overall, this comparison shows quantitatively that the 4D-Var system results in improved predictability as the analysis provides a smoother and more dynamically balanced fit between the observations and the model's time-evolving flow. This advocates the use of advanced, time-dependent data assimilation methods, particularly for highly variable oceanic regions, and motivates future work into further improving data assimilation schemes.

Impact of surface and subsurface-intensified eddies on sea surface temperature and chlorophyll a in the northern Indian Ocean utilizing deep learning

Abstract. Mesoscale eddies, including surface-intensified eddies (SEs) and subsurface-intensified eddies (SSEs), significantly influence phytoplankton distribution in the ocean. Nevertheless, due to the sparse in situ data, understanding of the characteristics of SSEs and their influence on chlorophyll a (Chl a) concentration is still unclear. Consequently, the study utilized a deep learning model to extract SEs and SSEs in the northern Indian Ocean (NIO) from 2000 to 2015, using satellite-derived sea surface height (SSH) and sea surface temperature (SST) data. The analysis revealed that SSEs accounted for 39 % of the total eddies in the NIO, and their SST signatures exhibited opposite behaviour compared to SEs. Furthermore, by integrating ocean colour remote-sensing data, the study investigated the contrasting impacts of SEs and SSEs on Chl a concentration in two basins of the NIO, the Arabian Sea (AS) and the Bay of Bengal (BoB), known for their disparate biological productivity. In the AS, SEs induced Chl a anomalies that were 2 to 3 times higher than those caused by SSEs. Notably, there were no significant differences in Chl a anomalies induced by the same type of eddies between summer and winter. In contrast, the BoB exhibited distinct seasonal variations, where SEs induced slightly higher Chl a anomalies than SSEs during the summer, while substantial differences were observed during the winter. Specifically, subsurface-intensified anticyclonic eddies (SSAEs) led to positive Chl a anomalies, contrasting the negative anomalies induced by surface-intensified anticyclonic eddies (SAEs) with comparable magnitudes. Moreover, while both subsurface-intensified cyclonic eddies (SSCEs) and surface-intensified cyclonic eddies (SCEs) resulted in positive Chl a anomalies during winter in the BoB, the magnitude of SSCEs was only one-third of that induced by SCEs. Besides, subsurface Chl a induced by SSAEs (SSCEs) is ∼0.1 mg m−3 greater (less) than that caused by SAEs (SCEs) in the upper 30 (50) m using Biogeochemical Argo profiles. The distinct Chl a between SEs and SSEs can be attributed to their contrasting subsurface structures revealed by Argo profiles. Compared to SAEs (SCEs), SSAEs (SSCEs) enhance (decrease) production via the convex (concave) of the isopycnals that occur around the mixed layer. The study provides a valuable approach to investigating subsurface eddies and contributes to a comprehensive understanding of their influence on chlorophyll concentration.

Northward shift of the Kuroshio Extension during 1993–2021

The Kuroshio Extension (KE) flows eastward at the northern boundary of the North Pacific subtropical gyre. By transporting large amounts of seawater with heat, the KE contributes significantly to the formation of sea surface temperature (SST) fields. Recently, poleward shifts of major ocean gyres in the world ocean, including the North Pacific subtropical gyre, have been highlighted based on basin-scale changes in SST and sea surface height (SSH) distributions. However, a detailed investigation of the long-term meridional KE movement has not been presented. Investigation of KE path changes helps provide insights into long-term changes in the physical fields in the western North Pacific. In this study, we identified the KE path from satellite-derived SSH and surface current velocity data using a front identification method and showed that the KE migrated northward by approximately 210 km during 1993–2021. We further explored the cause of the northward KE shift based on atmospheric reanalysis data and numerical experiments using a high-resolution ocean general circulation model. It was revealed that the northward KE shift is mostly caused by the trend of wind stress curl in the North Pacific during 1993–2021.

Amplitude modulations of seasonal variability in the Karimata Strait throughflow

The Karimata Strait (KS) throughflow between the South China Sea (SCS) and Java Sea plays an essential role in heat and freshwater budget in the SCS and dual roles in strengthening/reducing the primary Indonesian throughflow (ITF) in the Makassar Strait. A sustained long-term monitoring of the ITF is logistically challenging and expensive; therefore, proxies are needed. Here, we use a combination of in situ measurement of the KS throughflow and satellite-derived sea surface height (SSH) and sea surface wind (SSW) to determine the interannual and decadal modulations in seasonal amplitude of the KS throughflow associated with El Niño-Southern Oscillation (ENSO), Indian Ocean dipole (IOD), Pacific Decadal Oscillation (PDO). Linear regression, correlation, harmonic and power spectrum analyses are used. The results manifest that there are significant interannual to decadal modulations in the seasonal amplitude of the KS throughflow. The modulations of the seasonal amplitude in the volume and heat transports range 1.36-1.92 Sv (1 Sv = 106 m3 s-1) and 126.41-173.36 TW (1 TW = 1012 W), respectively, with a significant cycle of ~9 years. From 1994 to 2020, the seasonal amplitude of volume transport through the KS shows an increasing trend of 37.75 ± 15.69 mSv decade-1 (1 mSv = 103 m3 s-1). The seasonal amplitude of the heat transport also increases, at a rate of 4.78 ± 1.52 TW decade-1. The KS volume transport is positively correlated with PDO and ENSO indices (r2 = 0.69 and r2 = 0.58), with a lag of 12 and 10 months, respectively. The results of composite analysis suggest that the interannual variability of the KS transport is related to the interannual anomalies of the SSH gradient and the local SSW fields in boreal winter.

Impact of Yucatan Channel Subsurface Velocity Observations on the Gulf of Mexico State Estimates

Abstract The Gulf of Mexico (GoM) surface circulation variability is dominated by the Loop Current (LC) and the episodically released anticyclonic Loop Current eddies (LCEs). The Yucatan Current feeds the LC through the Yucatan Channel (YC), and its flow structure at the YC is hypothesized to affect the LC evolution critically. This study examines the impact of assimilating YC subsurface velocity observations from a tall mooring array across the YC on the GoM circulation. State estimates and forecasts of the LC circulation were produced using a regional implementation of the Massachusetts Institute of Technology general circulation model (MITgcm) and its adjoint-based four-dimensional variational (4DVAR) assimilation system. The estimates were constrained by combinations of the YC observations and satellite-derived sea surface height (SSH) and sea surface temperature (SST). The results show that assimilation of both moored and satellite data improves the model hindcasts and forecasts for all LC phases. Additionally, one realization of the state estimate that assimilates only moored data matches the LCE detachment timing with that of AVISO SSH. Observations from the moorings close to the Yucatan Peninsula significantly impact the LCE detachment. A finite-time Lyapunov exponent analysis reveals the differences among the assimilation experiments, such as eddylike structures intruding into the GoM through the YC, and its relation to the typical LC sudden growth. Finally, an adjoint sensitivity analysis is used to verify the dynamic link between the LC extension and the intrusion of eddylike structures into the GoM.

Multimodal 4DVarNets for the Reconstruction of Sea Surface Dynamics From SST-SSH Synergies

The space-time reconstruction of sea surface dynamics from satellite observations is a challenging inverse problem due to the associated irregular sampling. Satellite altimetry provides a direct observation of the sea surface height (SSH), which relates to the divergence-free component of sea surface currents. The associated sampling pattern prevents operational schemes from retrieving fine-scale dynamics, typically below 10 days. By contrast, other satellite sensors provide higher-resolution observations of sea surface tracers such as sea surface temperature (SST). Multimodal inversion schemes then arise as appealing approaches. Though theoretical evidence supports the existence of an explicit relationship between sea surface temperature and sea surface dynamics under specific dynamical regimes, the generalization to the variety of upper ocean dynamical regimes is complex. Here, we investigate this issue from a physics-informed learning perspective. We introduce a trainable multimodal inversion scheme for the reconstruction of sea surface dynamics from multi-source satellite-derived observations, namely satellite-derived SSH and SST data. The proposed multimodal 4DVarNet schemes combine a variational formulation involving trainable observation and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> terms with a trainable gradient-based solver. An observing system simulation experiment for a Gulf Stream region supports the relevance of our approach compared with state-of-the-art schemes. We report a relative improvement greater than 60% compared with the operational altimetry product in terms of root mean square error and resolved space-time scales. We discuss further the potential and the limitations of the proposed approach for the reconstruction and forecasting of geophysical dynamics from irregularly-sampled satellite observations.

Oceanic Eddy Detection and Analysis from Satellite-Derived SSH and SST Fields in the Kuroshio Extension

Vigorous mesoscale eddies are broadly distributed in the Kuroshio Extension and can generally be identified from sea surface height (SSH) and sea surface temperature (SST) fields. Nevertheless, the changes in SSH and SST caused by mesoscale eddies and their seasonal correlation in the Kuroshio Extension are not clear, as well as the difference between identified eddy results from the two data. Combining in situ Argo float profiles data, the correlation between SSH anomaly (SSHA) and SST anomaly (SSTA) signals in mesoscale eddies are analyzed. The result shows that SSTA–SSHA signals inside eddies are generally more correlated in winter than in summer. Argo subsurface temperature anomalies θ′ and SSHA signals inside eddies show a high correlation, with a regression coefficient θ′/SSHA of about 7 °C·m−1, while correlations of Argo θ′–SSTA inside eddies are low. Generally, the lifetime and propagation distance of SSTA-based eddies are shorter and smaller than those of SSHA-based eddies, which may be related to the rapid changes in SSTA field and the interference of small-scale oceanic signal in the SST field. Comparing with SSHA-based eddies, which exist primarily around the region of the Kuroshio mainstream (33°–36°N), SSTA-based eddies are concentrated in the Oyashio Extension (39°–42°N), where SST gradient is large, and changes in SST fields caused by mesoscale eddies are more obvious and more likely to be captured by satellites there. In addition, the geographical distributions of SSHA- and SSTA-based eddy amplitudes are consistent with the absolute dynamic topography and SST gradient.

Estimating the Ocean Interior from Satellite Observations in the Kerguelen Area (Southern Ocean): A Combined Investigation Using High-Resolution CTD Data from Animal-Borne Instruments

Abstract Observational surface data are utilized to reconstruct the subsurface density and geostrophic velocity fields via the “interior + surface quasigeostrophic” (isQG) method in a subdomain of the Antarctic Circumpolar Current (ACC). The input variables include the satellite-derived sea surface height (SSH), satellite-derived sea surface temperature (SST), satellite-derived or Argo-based sea surface salinity (SSS), and a monthly estimate of the stratification. The density reconstruction is assessed against a newly released high-resolution in situ dataset that is collected by a southern elephant seal. The results show that the observed mesoscale structures are reasonably reconstructed. In the Argo-SSS-based experiment, pattern correlations between the reconstructed and observed density mostly exceed 0.8 in the upper 300 m. Uncertainties in the SSS products notably influence the isQG performance, and the Argo-SSS-based experiment yields better density reconstruction than the satellite-SSS-based one. Through the two-dimensional (2D) omega equation, we further employ the isQG reconstructions to diagnose the upper-ocean vertical velocities (denoted wisQG2D), which are then compared against the seal-data-based 2D diagnosis of wseal. Notable discrepancies are found between wisQG2D and wseal, primarily because the density reconstruction does not capture the seal-observed smaller-scale signals. Within several subtransects, the Argo-SSS-based wisQG2D reasonably reproduce the spatial structures of wseal, but present smaller magnitude. We also apply the isQG reconstructions to the 3D omega equation, and the 3D diagnosis of wisQG3D is very different from wisQG2D, indicating the limitations of the 2D diagnostic equation. With reduced uncertainties in satellite-derived products in the future, we expect the isQG framework to achieve better subsurface estimations.

Directional decomposition of internal tides propagating from multiple generation sites

Internal tides generated from multiple sites create complicated interference patterns, often making it difficult to identify the origin, direction of propagation, and dissipation of individual waves. To identify these constituent waves, we apply a directional Fourier filter (DFF) method to analyse multimodal, broad-wavenumber, internal wave fields found over varying topography. We apply the method to data derived from both a numerical model and satellite remote sensing observations. Using a series of two-dimensional (i.e. x–z) simulations, we first demonstrate the capability of the DFF method to separate incoming and reflected internal tides emanating from a supercritical slope. The results show the DFF method requires model sampling rates of Δt∕T<0.15 and horizontal cell sizes of Δx∕λ<0.2, where T is the wave period and λ is the wavelength. Remote internal tides can significantly affect the generation and propagation of local internal tides in regional ocean simulations. Using a gridded satellite altimetry product at three globally diverse regions, we demonstrate how the DFF method can be used to specify the internal tide boundary conditions at the open boundaries of regional ocean models. Lastly, we apply the DFF method to a three-dimensional hydrostatic numerical simulation of the southern Australian North West Shelf to estimate the energy decay characteristics of internal tides. The energy decay length scale, for offshore-propagating waves from the continental slope, in the model was estimated to be two mode-1 wavelengths (i.e. 240 km), in close agreement with an estimate derived from a satellite-derived sea surface height anomaly.

Sea Surface Salinity as a Proxy for Arctic Ocean Freshwater Changes

AbstractThe changing Arctic freshwater content and distribution have a significant implications for ocean circulation, climate, and water and biogeochemical cycles. The paucity of in situ salinity measurements in the Arctic Ocean has limited our ability to study Arctic‐Ocean freshwater variability. Although satellite‐derived sea surface height (SSH) and ocean bottom pressure (OBP) have been used together to infer depth‐integrated freshwater content changes, these measurements are limited in sampling and resolution. Motivated by the recent development of sea surface salinity (SSS) remote sensing, we explore the use of SSS as a proxy for Arctic freshwater changes. As a first step, here we conduct a proof‐of‐concept study by analyzing the output of an ocean‐ice state estimation product. We find that SSS variations are coherent with those of SSH‐minus‐OBP across the Arctic basin (with R ~ −0.8) except for those in the center of the Beaufort Gyre and the region affected by the subpolar North Atlantic inflow. On Arctic shelves, the linear regression coefficient between SSS and SSH‐minus‐OBP is −0.3 pss/cm or larger in magnitude. The results suggest that SSS is a good proxy for Arctic freshwater changes, and satellite SSH‐minus‐OBP can be used to evaluate satellite SSS quality.

Estimation and prediction of the upper ocean circulation in the Bay of Bengal

The upper ocean stratification and circulation in the Bay of Bengal (BoB) plays a key role in the northward propagating monsoon intraseasonal oscillation during the months of June–August. This region is highly influenced by strong, seasonal atmospheric forcing and the oceanic circulation is characterized by dominant mesoscale variability and strong horizontal gradients in salinity and temperature during the monsoon period. Given the role of the ocean in the monsoon circulation, it is important to investigate accurate ocean state estimates and forecasts of the BoB ocean circulation in preparation for coupled ocean-atmosphere modeling and predictions. Hence, we use a mesoscale-permitting regional implementation of Massachusetts Institute of Technology general circulation model (MITgcm) and its adjoint-based four-dimensional variational (4DVAR) system to assimilate satellite-derived Sea Surface Height (SSH) and Sea Surface Temperature (SST) data in the BoB for a period of one month (June 1 – 30, 2017). It is shown that the MITgcm-BoB 4DVAR assimilation system is able to significantly improve the model consistency with the assimilated observations in the BoB region, reducing the model-data misfit by 50% and provided a dynamically-consistent BoB ocean circulation for the one-month hindcast period. We performed forecasting experiments using the state estimate to initialize two forecasts for a period of 30-days (July 1 – 30, 2017) from the end of the hindcast period. These forecasts used either atmosphere reanalysis and ocean analysis forcings or monthly climatology of atmosphere reanalysis and ocean analysis forcings. They therefore do not represent a “true” regional ocean forecast, forced using actual atmosphere and ocean forecasts, but bound the performance between climatological and nearly perfect forecasts. The model forecast is a cross-validation against future observations and showed that the initial conditions from the state estimate improves the prediction of the three-dimensional circulation in the BoB. The model hindcast and forecasts were also cross-validated against independent Argo temperature and salinity observations in the BoB. Additional state estimation and forecast experiments for other periods showed similar model performance with improved hindcasts and forecasts for the BoB region.

Propagating Modes of Variability and Their Impact on the Western Boundary Current in the South Atlantic

AbstractStudies have suggested that the South Atlantic Ocean plays an important role in modulating climate at global and regional scales and thus could serve as a potential predictor of extreme rainfall and temperature events globally. To understand how propagating modes of variability influence the circulation of the subtropical gyre and the southward flowing Brazil Current (BC) at interannual frequencies, a Complex Empirical Orthogonal Function (CEOF) analysis was performed on the satellite‐derived sea surface height (SSH). The first three CEOF modes explain about 23%, 16%, and 11% of the total interannual variability and show clear westward propagation with phase speeds comparable to that of theoretical baroclinic mode 1 Rossby waves. Results suggest that there is a change in the way energy is distributed among the modes before and after 2005. Before 2005, the SSH variability in the western boundary in the South Atlantic and the volume transport of the BC are more closely linked to the first and the second modes, while the third mode dominates after 2005. This change in energy distribution around 2005 is associated with the recent El Niño‐Southern Oscillation (ENSO) regime shift in the Pacific Ocean via atmospheric teleconnections. We found that the first CEOF mode is strongly correlated with eastern Pacific (i.e., canonical) ENSO events and the Pacific Decadal Oscillation, whereas the third CEOF is correlated to central Pacific (i.e., Modoki) ENSO. These results are useful to understand the overall dynamics of the South Atlantic and to potentially improve predictability of Meridional Overturning Circulation and monsoon pattern changes around the world.

Reanalysis of the PacIOOS Hawaiian Island Ocean Forecast System, an implementation of the Regional Ocean Modeling System v3.6

Abstract. A 10-year reanalysis of the PacIOOS Hawaiian Island Ocean Forecast System was produced using an incremental strong-constraint 4-D variational data assimilation with the Regional Ocean Modeling System (ROMS v3.6). Observations were assimilated from a range of sources: satellite-derived sea surface temperature (SST), salinity (SSS), and height anomalies (SSHAs); depth profiles of temperature and salinity from Argo floats, autonomous Seagliders, and shipboard conductivity–temperature–depth (CTD); and surface velocity measurements from high-frequency radar (HFR). The performance of the state estimate is examined against a forecast showing an improved representation of the observations, especially the realization of HFR surface currents. EOFs of the increments made during the assimilation to the initial conditions and atmospheric forcing components are computed, revealing the variables that are influential in producing the state-estimate solution and the spatial structure the increments form.

Influence of ENSO Events on the Agulhas Leakage Region

This study explores the subsurface oceanic response to the El Nino Southern Oscillation (ENSO) in the southern tropical Indian Ocean and Agulhas leakage region. The subsurface temperature and salinity responses of the southern tropical Indian Ocean and Agulhas leakage are studied using Argo float data. In addition, AVISO satellite-derived sea surface height (SSH) data is used to examine the propagation of an ENSO signal across the Indian Ocean to the Agulhas leakage region and its impact on eddy propagation within the leakage region. During the peak of an ENSO event, the Indian Ocean basin is anomalously warm up to 500 m depth in response to El Nino, but anomalously cool in response to La Nina. Sea surface salinity is anomalously fresh for both events in the eastern and western Indian Ocean basins, but the subsurface salinity signal during El Nino is anomalously saline to about 150 m in both basins. The subsurface signals last approximately 1 year (2 years) after the peak of ENSO in the eastern (western) Indian Ocean basin. The subsurface signal in the Agulhas leakage region is anomalously warm 2 years after El Nino and anomalously cool 2 years after La Nina. Westward propagation of SSH anomalies across the Indian Ocean basin is evident at 12°S and 25°S latitudes and it takes 2 years for the initial signal of Rossby waves to reach Agulhas leakage region after the peak of an ENSO event.

Adjusting Neural Network to a Particular Problem: Neural Network-Based Empirical Biological Model for Chlorophyll Concentration in the Upper Ocean

The versatility of the neural network (NN) technique allows it to be successfully applied in many fields of science and to a great variety of problems. For each problem or class of problems, a generic NN technique (e.g., multilayer perceptron (MLP)) usually requires some adjustments, which often are crucial for the development of a successful application. In this paper, we introduce a NN application that demonstrates the importance of such adjustments; moreover, in this case, the adjustments applied to a generic NN technique may be successfully used in many other NN applications. We introduce a NN technique, linking chlorophyll “a” (chl-a) variability—primarily driven by biological processes—with the physical processes of the upper ocean using a NN-based empirical biological model for chl-a. In this study, satellite-derived surface parameter fields, sea-surface temperature (SST) and sea-surface height (SSH), as well as gridded salinity and temperature profiles from 0 to 75m depth are employed as signatures of upper-ocean dynamics. Chlorophyll-a fields from NOAA’s operational Visible Imaging Infrared Radiometer Suite (VIIRS) are used, as well as Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) chl-a concentrations. Different methods of optimizing the NN technique are investigated. Results are assessed using the root-mean-square error (RMSE) metric and cross-correlations between observed ocean color (OC) fields and NN output. To reduce the impact of noise in the data and to obtain a stable computation of the NN Jacobian, an ensemble of NN with different weights is constructed. This study demonstrates that the NN technique provides an accurate, computationally cheap method to generate long (up to 10 years) time series of consistent chl-a concentration that are in good agreement with chl-a data observed by different satellite sensors during the relevant period. The presented NN demonstrates a very good ability to generalize in terms of both space and time. Consequently, the NN-based empirical biological model for chl-a can be used in oceanic models, coupled climate prediction systems, and data assimilation systems to dynamically consider biological processes in the upper ocean.

Relations between salinity in the northwestern Bering Sea, the Bering Strait throughflow and sea surface height in the Arctic Ocean

The correlation between transport via the Bering Strait throughflow (BTF) and sea surface salinity (SSS) in the Bering Sea has been examined mainly using an atmosphere–ocean–ice coupled climate model that has an eddy-permitting ocean component. The SSS anomaly in the northwestern Bering Sea is high from winter to spring when the BTF transport anomaly is large in the cold season. Similar features can be seen in an observation dataset and two kinds of ocean data assimilation product. BTF transport is strongly correlated with sea surface height (SSH) in the northeastern Bering Sea, the southwestern Chukchi Sea and the East Siberian Sea. The SSH along the Russian coast in the Arctic Ocean is uncorrelated with the SSH in the Bering Sea, the meaning being that the Arctic SSH affects the BTF and the SSS independently of the SSH in the Bering Sea. The low SSH along the Siberian coast is correlated with easterly wind anomalies over the Laptev Sea and north of the New Siberian Islands. The relationship between the low Siberian-coast SSH and the high SSS in the Bering Sea, however, is not confirmed in 10 years of satellite-derived SSH. Mixed-layer salt budget analysis has revealed that the high SSS anomalies are mainly caused by the increases of horizontal advective salt convergence north of 62.5°N, and by the decreases of sea-ice melting south of 62.5°N, through the strengthening of the near-surface northeastward currents. In the warm season, these two factors fade and the salinization disappears.

On the dynamics of the Sri Lanka Dome in the Bay of Bengal

AbstractEast of Sri Lanka, in the northern Indian Ocean, a cold dome, known as the Sri Lanka Dome (SLD), develops during southwest monsoon season (June–September). The SLD first forms around May, matures in July, and decays around September, in association with the strong cyclonic wind stress curl. In this study, the structure and dynamics of SLD in response to the climatic events such as the Indian Ocean Dipole (IOD) are examined. Our results reveal that these climatic events modulated the subsurface temperature variability in the thermocline at ∼100 m depth, whose signature was also evident in the satellite‐derived sea surface height (SSH) anomaly in the SLD region. We find that the mechanisms for the formation of SLD are consistent with previous research, and there is year‐to‐year variability in the SLD dynamics. This study also reveals that the atmospheric parameters including vertical wind shear and midtropospheric relative humidity are high over the SLD region and adjacent southern Bay of Bengal and show an upward (increasing) trend over the decades. This has impacted the atmospheric parameters over the northern Bay of Bengal over the decades and as a consequence the total number of monsoon depressions (June–September) decreased over the decades from 1980 to 2015, as reported by the India Meteorological Department, New Delhi. Thus, the new insight emerged from this study is the variability in the cyclogenesis and the occurrence of total number of monsoon depressions over northern Bay of Bengal over the decades is much related to the SLD dynamics.

Reconstruction of ocean's interior from observed sea surface information

Observational surface data are used to reconstruct the ocean's interior through the “interior + surface quasigeostrophic” (isQG) method. The input data include the satellite-derived sea surface height, satellite-derived sea surface temperature, satellite-derived or Argo-based sea surface salinity, and an estimated stratification of the region. The results show that the isQG retrieval of subsurface density anomalies is quite promising compared to Argo profile data. At ∼1000 m depth, the directions of retrieved velocity anomalies are comparable to those derived from Argo float trajectories. The reconstruction using surface density input field approximated only by SST (with constant SSS) performs less satisfactorily than that taking into account the contribution of SSS perturbations, suggesting that the observed SSS information is important for the application of the isQG method. Better reconstruction is obtained in the warm season than in the cold season, which is probably due to the stronger stratification in the warm season that confines the influence of the biases in the surface input data (especially SSS) in a shallow layer. The comparison between the performance of isQG with Argo-based SSS input and that with satellite-derived SSS input suggests that the biases in the SSS products could be a major factor that influences the isQG performance. With reduced biases in satellite-derived SSS in the future, the measurement-based isQG method is expected to achieve better reconstruction of ocean interior and thus is promising in practical application.

Spatio-Temporal Decomposition of Satellite-Derived SST–SSH Fields: Links Between Surface Data and Ocean Interior Dynamics in the Agulhas Region

In this paper, we develop a novel observation-driven methodology to explore spatio-temporal dependencies between satellite-derived sea surface height (SSH) and sea surface temperature (SST) fields. Level-set-based and registration-based criteria are defined to evaluate and detect spatial links between SSH and SST anomaly fields. The method is applied to one-year SST and SSH time series in the highly dynamical Aghulas return current region. The analysis evidences a seasonal variation of the overall correlation between SST and SSH fields. As further revealed, the coldest SST anomalies are reported to efficiently trace the lowest SSH anomalies for all seasons, while the warmest SST anomalies solely match the largest SSH anomalies during winter. The second criterion relies on the registration of SSH and SST anomaly fields. The registration energy is shown to corresponds to the seasonal influence of the mixed-layer depth revealed here by atmospheric forcing. These results show us that the SST-derived SSH reconstruction using the surface quasigeostrophic approximation should take into account stratification effects, especially during summer. As discussed, the proposed methodology can enforce the reconstruction of sea surface current from a joint analysis of satellite altimetry data and high-resolution satellite-derived SST data.