Assessment of sea surface temperature observational networks in the Baltic Sea and North Sea

The satellite and in situ Sea Surface Temperature (SST) observational networks in the Baltic Sea and North Sea are evaluated based on the quality of the gridded SST products generated from the networks. A multi-indicator approach is applied in the assessment. It includes evaluation of data quality, effective data coverage, field reconstruction error and model nowcast error. The results show that the best available full-coverage SST product is generated by assimilating the SST observations to obtain a yearly mean model bias of 0.07 °C and RMSE of 0.64 °C. The effective data coverage rate is 31% by using AVHRR (Advanced Very High Resolution Radiometer) data from NOAA (National Ocean and Atmosphere Administration) satellites 12, 14 and 16. The data redundancy increases rapidly with the number of infrared sensors. Using either NOAA satellite 12 or all 3 satellites makes a small difference with regard to derived effective coverage and the ocean model nowcast error. The influence of using the in situ SST observations in the SST field reconstruction is negligibly small. Instead, the major role of in situ SST observations is in calibrating the satellite observations. To study the relative importance of data quality and data coverage, an assessment is done for two satellite products: one product is based entirely on NOAA 12 data and has larger coverage but lower quality. The other product is a subset of the SAF products (derived from NOAA 14 and 16) and has lower coverage but higher quality. Based on current monitoring, modelling and assimilation technology, the results suggest that the data quality is an important factor in further improving the quality of the gridded SST products. Recommendations are made for possible further improvements of the existing SST observational networks.     

Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea

Sea surface temperature fields of the North Sea and Baltic Sea have been constructed for the year 2001 using a multiplatform Optimal Interpolation scheme. The analyzed fields are constructed every 12 h on a 10 km spatial grid. The product is based upon observations from the three NOAA satellites 12, 14 and 16 together with a large amount of in situ observations. Space dependent covariance functions are estimated from the satellite observations and account for spatial and temporal lags. Several independent methods have been used to assess the error on the sea surface temperature product. Compared against independent in situ observations, the mean RMS difference for the year 2001 is 0.78 °C. The spatial distribution of the errors reveals that the Baltic Sea in general show higher errors than the North Sea. The error statistics throughout the year show a temporal variation of the errors with maximum during summer and winter. Tests with a varying number of satellite observations show that the accuracy of the satellite observations is the most important parameter in terms of reducing the errors on the interpolated sea surface temperature product.     

1/32° real-time global ocean prediction and value-added over 1/16° resolution

A 1/32° global ocean nowcast/forecast system has been developed by the Naval Research Laboratory at the Stennis Space Center. It started running at the Naval Oceanographic Office in near real-time on 1 Nov. 2003 and has been running daily in real-time since 1 Mar. 2005. It became an operational system on 6 March 2006, replacing the existing 1/16° system which ceased operation on 12 March 2006. Both systems use the NRL Layered Ocean Model (NLOM) with assimilation of sea surface height from satellite altimeters and sea surface temperature from multi-channel satellite infrared radiometers. Real-time and archived results are available online at http://www.ocean.nrlssc.navy.mil/global_nlom. The 1/32° system has improvements over the earlier system that can be grouped into two categories: (1) better resolution and representation of dynamical processes and (2) design modifications. The design modifications are the result of accrued knowledge since the development of the earlier 1/16° system. The improved horizontal resolution of the 1/32° system has significant dynamical benefits which increase the ability of the model to accurately nowcast and skillfully forecast. At the finer resolution, current pathways and their transports become more accurate, the sea surface height (SSH) variability increases and becomes more realistic and even the global ocean circulation experiences some changes (including inter-basin exchange). These improvements make the 1/32° system a better dynamical interpolator of assimilated satellite altimeter track data, using a one-day model forecast as the first guess. The result is quantitatively more accurate nowcasts, as is illustrated by several model-data comparisons. Based on comparisons with ocean color imagery in the northwestern Arabian Sea and the Gulf of Oman, the 1/32° system has even demonstrated the ability to map small eddies, 25–75 km in diameter, with 70% reliability and a median eddy center location error of 22.5 km, a surprising and unanticipated result from assimilation of altimeter track data. For all of the eddies (50% small eddies), the reliability was 80% and the median eddy center location error was 29 km. The 1/32° system also exhibits improved forecast skill in relation to the 1/16° system. This is due to (a) a more accurate initial condition for the forecast and (b) better resolution and representation of critical dynamical processes (such as upper ocean – topographic coupling via mesoscale flow instabilities) which allow the model to more accurately evolve these features in time while running in forecast mode (forecast atmospheric forcing for the first 5 days, then gradually reverting toward climatology for the remainder of the 30-day forecast period). At 1/32° resolution, forecast SSH generally compares better with unassimilated observations and the anomaly correlation of the forecast SSH exceeds that from persistence by a larger amount than found in the 1/16° system.     

The Mercator global ocean operational analysis system: Assessment and validation of an 11-year reanalysis

This paper presents Prototype Système 2 Global (PSY2G), the first Mercator global Ocean General Circulation Model (OGCM) to assimilate along-track sea level anomaly (SLA) satellite data. Based on a coarse resolution ocean model, this system was developed mainly for climatic purposes and will provide, for example, initial oceanic states for coupled ocean-atmosphere seasonal predictions. It has been operational since 3 September 2003 and produces an analysis and a two-week forecast for the global ocean every week. The PSY2G system uses an incremental assimilation scheme based on the Cooper and Haines [Cooper, M., Haines, K., 1996. Data assimilation with water property conservation. J. Geophys. Res., 101, 1059-1077.] lifting–lowering of isopycnals. The SLA increment is obtained using an optimal interpolation method then the correction is partitioned into baroclinic and barotropic contributions. The baroclinic ocean state correction consists of temperature, salinity and geostrophic velocity increments and the barotropic correction is a barotropic velocity increment. A reanalysis (1993–2003) was carried out that enabled the PSY2G system to perform its first operational cycle. All available SLA data sets (TOPEX/Poséïdon, ERS2, Geosat-Follow-On, Jason1 and Envisat) were assimilated for the 1993–2003 period. The major objective of this study is to assess the reanalysis from both an assimilation and a thermodynamic point of view in order to evaluate its realism, especially in the tropical band which is a key region for climatic studies. Although the system is also able to deliver forecasts, we have mainly focused on analysis. These results are useful because they give an a priori estimation of the qualities and capabilities of the operational ocean analysis system that has been implemented. In particular, the reanalysis identifies some regional biases in sea level variability such as near the Antarctic Circumpolar Current, in the eastern Equatorial Pacific and in the Norwegian Sea (generally less than 1 cm) with a small seasonal cycle. This is attributed to changes in mean circulation and vertical stratification caused by the assimilation methodology. But the model's low resolution, inaccurate physical parameterisations (especially for ocean–ice interactions) and surface atmospheric forcing also contribute to the occurrence of the SLA biases. A detailed analysis of the thermohaline structure of the ocean reveals that the isopycnal lifting–lowering tends to diffuse vertically the main thermocline. The impact on temperature is that the surface layer (0–200 m) becomes cooler whereas in deeper waters (from 500 to 1500 m), the ocean becomes slightly warmer. This is particularly true in the tropics, between 30°N and 30°S. However it can be demonstrated that the assimilation improves the variability in both surface currents and sub-surface temperature in the Equatorial Pacific Ocean.     

Wave data assimilation with the Kalman filter

A new data assimilation method for ocean waves is presented, based on an efficient low-rank approximation to the Kalman filter. Both the extended Kalman filter and a truncated second-order filter are implemented. In order to explicitly estimate past wind corrections based on current wave measurements, the filter is extended to a fixed-lag Kalman smoother for the wind fields. The filter is tested in a number of synthetic experiments with simple geometries. Propagation experiments with errors in the boundary condition showed that the KF was able to accurately propagate forecast errors, resulting in spatially varying error correlations, which would be impossible to model with time-independent assimilation methods like OI. An explicit comparison with an OI assimilation scheme showed that the KF also is superior in estimating the sea state at some distance from the observations. In experiments with errors in the driving wind, the modeled error estimates were also in agreement with the actual forecast errors. The bias in the state estimate, which is introduced through the nonlinear dependence of the waves on the driving wind field, was largely removed by the second-order filter, even without actually assimilating data. Assimilation of wave observations resulted in an improved wave analysis and in correction of past wind fields. The accuracy of this wind correction depends strongly on the actual place and time of wave generation, which is correctly modeled by the error estimate supplied by the Kalman filter. In summary, the KF approach is shown to be a reliable assimilation scheme in these simple experiments, and has the advantage over other assimilation methods that it supplies explicit dynamical error estimates.     

Data assimilation into a Princeton Ocean Model of the Mediterranean Sea using advanced Kalman filters

This study investigates the effectiveness of the Singular Evolutive Extended Kalman filter (SEEK) and its variants (SEIK and SFEK filters) for data assimilation into a Princeton Ocean Model (POM) of the Mediterranean Sea. The SEEK filters are sub-optimal Kalman filters based on the approximation of the filter's error covariance matrices by singular low-rank matrices, reducing in this way extensive computational burden. At the initialization, the filters error covariance matrix is parameterized by a set of multivariate empirical orthogonal functions (EOFs) which describe the dominant modes of the system's variability. The Mediterranean model is implemented on a 1/4° × 1/4° horizontal grid with 25 sigma levels and is forced with 6-hour ECMWF re-analysis atmospheric data. Several twin experiments, in which pseudo-observations of altimetric data and/or data profiles were assimilated, were first performed to evaluate the filters performances and to study their sensitivities to different parameters and setups. The results of these experiments were very encouraging and helped in setting up an effective configuration for the assimilation of real data in near-real time situation. In the hindcast experiments, Topex/Poseidon and ERS weekly sea level anomaly data were first assimilated during 1993 and the filters solution was evaluated against independent Reynolds sea surface temperature (SST) analysis. The assimilation system was able to significantly enhance the consistency between the model and the assimilated data, although the improvement with respect to independent SST data was significantly less pronounced. The model SST was only improved after including SST data in the assimilation system.     

Multivariate assimilation in MERCATOR project: New statistical parameters from forecast error estimation

The new operational prototype of Mercator (french Global Ocean Data Assimilation Experiment contribution) is composed of a North Atlantic primitive equation ocean model OPA (Ocean Parallel Algorithm between 20°S and 70°N, [Madec, G., P. Delecluse, M. Imbard and C. Lévy (1998). OPA8.1 ocean general circulation model reference manuel. Notes du pôle de modélisation IPSL. n°11: 91p]) and of a multivariate and multidata assimilation scheme [De Mey, P. and M. Benkiran (2002). “A multivariate reduced-order optimal interpolation method and its application in Mediterranean basin-scale circulation.” Ocean Forecasting : Conceptual basis and application, Pinardi, N., Springer Verlag.] This system has already given some significant improvements from previous Mercator configurations (M. Benkiran, personal communication). However some biases on ocean state still remain in the tropics where the reduced-order optimal interpolation scheme is suspected to be ill-parameted in the model forecast error. Indeed the guess error covariance matrix is decomposed into an error variance value and a spatio-temporal correlation function which are assumed to have some “good” properties (spatial homogeneity of the correlation function, constant ratio between signal and error variance). This study shows how we can use ensemble methods to validate these assumptions. We can see that the correlation function can reach negative values locally, mostly in regions of high variability contradictory with the homogeneous hypothesis. The reduced space used in the operational configuration is based on the signal seasonal Empirical Orthogonal Functions (EOFs). An empirical relationship between signal and error variance has been set and the correlation function is the same on every dimension of the reduced space. By projection of the estimated guess error variance onto the reduced space, we find a repartition of this quantity quite different to what was set in the system. The error statistics is found to be inhomogeneous compared to hypothesis made in the assimilation scheme. These two new parameters tested separately in the assimilation scheme gives significant improvements of the forecast and analysis results. This is particularly obvious in the tropics. But relationship between signal and error statistics (as assumed in the optimal interpolation) is found to be complex.     

Recovery of North-East Atlantic temperature fields from profiling floats: Determination of the optimal float number from sampling and instrumental error analysis

Argo is an international project that is deploying an array of temperature and salinity profiling floats over the global ocean. Here we use the error formulation derived from Optimal Statistical Interpolation to estimate statistical errors associated with the recovery of the temperature field in the North-East Atlantic ocean. Results indicate that with the present distribution of floats (119 in the considered domain), scales of wavelength larger than 500 km can be recovered with a relative uncertainty (rms error relative to the standard deviation of the field) of about 7% at 50 m, 8% at 200 m and 10% at 1000 m. This corresponds to mean absolute errors of 0.111 °C at 50 m, 0.104 °C at 200 m and 0.073 °C at 1000 m.The splitting of total errors into instrumental and sampling contributions reveals that, in the present scenario, errors are more due to the small number of floats than to instrumental errors, especially at upper levels. For scales larger than 500 km this will hold true until 200–250 floats are deployed (less than 200 for deep levels). In such a simulated scenario, the number of observations and the technology become approximately equally limiting factors for the accuracy of the temperature field mapping, with total relative errors of less than 2% at upper levels and about 3% at 1000 m.     

Assimilation of sea surface temperature in the POL Coastal Ocean Modelling System

A one-dimensional scheme is used to assimilate satellite Sea Surface Temperature data into the Proudman Oceanographic Laboratory Coastal Ocean Modelling System, set up in the Irish Sea with a fine resolution ( 1.8 km). The capabilities of the assimilation scheme are investigated using two different sets of satellite data, of lower and similar resolution to that of the model respectively. Comparison of results with independent data show that assimilation improves the modelled Sea Surface Temperature, but does not address model representation of the temperature vertical structure. It is concluded that for the Irish Sea and at the scales resolved by the model, the assimilation problem cannot be approached in a one-dimensional framework. It is also pointed out that forecast error needs to account explicitly for errors in the representation of the vertical structure of the thermal field.Three-dimensional methods that are suited for coastal systems are then suggested.     

Comparison between a reanalyzed product by 3-dimensional variational assimilation technique and observations in the Ulleung Basin of the East/Japan Sea

Reanalyzed products from a MOM3-based East Sea Regional Ocean Model with a 3-dimentional variational data assimilation module (DA-ESROM), have been compared with the observed hydrographic and current datasets in the Ulleung Basin (UB) of the East/Japan Sea (EJS). Satellite-borne sea surface temperature and sea surface height data, and in-situ temperature profiles have been assimilated into the DA-ESROM. The performance of the DA-ESROM appears to be efficient enough to be used in an operational ocean forecast system.Comparing with the results from Mitchell et al. [Mitchell, D. A., Watts, D. R., Wimbush, M., Teague, W.J., Tracey, K. L., Book, J. W., Chang, K.-I., Suk, M.-S., Yoon, J.-H., 2005a. Upper circulation patterns in the Ulleung Basin. Deep-Sea Res. II, 52, 1617-1638.], the DA-ESROM fairly well simulates the high variability of the Ulleung Warm Eddy and Dok Cold Eddy as well as the branching of the Tsushima Warm Current in the UB. The overall root-mean-square error between 100 m temperature field reproduced by the DA-ESROM and the observed 100-dbar temperature field is 2.1 °C, and the spatially averaged grid-to-grid correlation between the two temperature fields is high with a mean value of 0.79 for the inter-comparison period.The DA-ESROM reproduces the development of strong southward North Korean Cold Current (NKCC) in summer consistent with the observational results, which is thought to be an improvement of the previous numerical models in the EJS. The reanalyzed products show that the NKCC is about 35 km wide, and flows southward along the Korean coast from spring to summer with maximum monthly mean volume transport of about 0.8 Sv in August–September.     

Multigrid state vector for data assimilation in a two-way nested model of the Ligurian Sea

A system of two nested models composed by a coarse resolution model of the Mediterranean Sea, an intermediate resolution model of the Provençal Basin and a high resolution model of the Ligurian Sea is coupled with a Kalman-filter based assimilation method. The state vector for the data assimilation is composed by the temperature, salinity and elevation of the three models. The forecast error is estimated by an ensemble run of 200 members by perturbing initial condition and atmospheric forcings. The 50 dominant empirical orthogonal functions (EOF) are taken as the error covariance of the model forecast. This error covariance is assumed to be constant in time. Sea surface temperature (SST) and sea surface height (SSH) are assimilated in this system.     

Assimilation of altimetric data in the mid-latitude oceans using the Singular Evolutive Extended Kalman filter with an eddy-resolving, primitive equation model

A new data assimilation scheme has been elaborated for ocean circulation models based on the concept of an evolutive, reduced-order Kalman filter. The dimension of the assimilation problem is reduced by expressing the initial error covariance matrix as a truncated series of orthogonal perturbations. This error sub-space evolves during the assimilation so as to capture the growing modes of the estimation error. The algorithm has been formulated in quite a general fashion to make it tractable with a large variety of ocean models and measurement types. In the present paper, we have examined three possible strategies to compute the evolution of the error subspace in the so-called Singular Evolutive Extended Kalman (SEEK) filter: the steady filter considers a time-independent error sub-space, the apprentice filter progressively enriches the error sub-space with the information learned from the innovation vector after each analysis step, and the dynamical filter updates the error sub-space according to the model dynamics. The SEEK filter has been implemented to assimilate synthetic observations of the surface topography in a non-linear, primitive equation model that uses density as vertical coordinate. A simplified box configuration has been adopted to simulate a Gulf Stream-like current and its associated eddies and gyres with a resolution of 20 km in the horizontal, and 4 levels in the vertical. The concept of twin experiments is used to demonstrate that the conventional SEEK filter must be complemented by a learning mechanism in order to model the misrepresented tail of the error covariance matrix. An approach based on the vertical physics of the isopycnal model, is shown particularly robust to control the velocity field in deep layers with surface observations only. The cost of the method makes it a suitable candidate for large-size assimilation problems and operational applications.     

Improving assimilation of SeaWiFS data by the application of bias correction with a local SEIK filter

Ocean-biogeochemical models show typically significant errors in the representation of chlorophyll concentrations. The model state can be improved by the assimilation of satellite chlorophyll data with algorithms based on the Kalman filter. However, these algorithms do usually not account for the possibility that the model prediction contains systematic errors in the form of model bias. Accounting explicitly for model biases can improve the assimilation performance. To study the effect of bias estimation on the estimation of surface chlorophyll concentrations, chlorophyll data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) are assimilated on a daily basis into the NASA Ocean Biogeochemical Model (NOBM). The assimilation is performed by the ensemble-based SEIK filter combined with an online bias correction scheme. The SEIK filter is simplified here by the use of a static error covariance matrix. The performance of the filter algorithm is assessed by comparison with independent in situ data over the 7-year period 1998–2004. The bias correction results in significant improvements of the surface chlorophyll concentrations compared to the assimilation without bias estimation. With bias estimation, the daily surface chlorophyll estimates from the assimilation show about 3.3% lower error than SeaWiFS data. In contrast, the error in the global surface chlorophyll estimate without bias estimation is 10.9% larger than the error of SeaWiFS data.     

Air–sea CO2 fluxes in the Caribbean Sea from 2002–2004

Air–sea fluxes in the Caribbean Sea are presented based on measurements of partial pressure of CO₂ in surface seawater, pCO_2sw, from an automated system onboard the cruise ship Explorer of the Seas for 2002 through 2004. The pCO_2sw values are used to develop algorithms of pCO_2sw based on sea surface temperature (SST) and position. The algorithms are applied to assimilated SST data and remotely sensed winds on a 1° by 1° grid to estimate the fluxes on weekly timescales in the region. The positive relationship between pCO_2sw and SST is lower than the isochemical trend suggesting counteracting effects from biological processes. The relationship varies systematically with location with a stronger dependence further south. Furthermore, the southern area shows significantly lower pCO_2sw in the fall compared to the spring at the same SST, which is attributed to differences in salinity. The annual algorithms for the entire region show a slight trend between 2002 and 2004 suggesting an increase of pCO_2sw over time. This is in accord with the increasing pCO_2sw due the invasion of anthropogenic CO₂. The annual fluxes of CO₂ yield a net invasion of CO₂ to the ocean that ranges from − 0.04 to − 1.2 mol m^− 2 year^− 1 over the 3 years. There is a seasonal reversal in the direction of the flux with CO₂ entering into the ocean during the winter and an evasion during the summer. Year-to-year differences in flux are primarily caused by temperature anomalies in the late winter and spring period resulting in changes in invasion during these seasons. An analysis of pCO_2sw before and after hurricane Frances (September 4–6, 2004), and wind records during the storm suggest a large local enhancement of the flux but minimal influence on annual fluxes in the region.     

Estimation of the surface and mid-depth currents from Argo floats in the Pacific and error analysis

With the large deployment, the Array for Real-time Geostrophic Oceanography program has great potential for measuring the ocean currents both on the surface and at mid-depth. However the positioning error of fixes in a trajectory varies from 150 m to 1000 m, and thus created difficulty for accurate estimations of the surface and mid-depth currents. Also the reliability of the estimated surface and mid-depth currents requires accurate error estimations.In this study a new sequential method of Argo float surface trajectory tracking and extrapolating is proposed based on Kalman Filter (KF), under the presumption that a surface trajectory of Argo float is dominated by a constant current plus inertial oscillation. This trajectory tracking and extrapolating method is able to reduce the positioning uncertainties of Argo surface trajectories and provides error estimations. When this method was applied to extrapolate the positions when float resurfacing and descending, the estimation error of the mid-depth currents can be reduced. Utilizing this method in the Pacific, surface and mid-depth currents were estimated from surface trajectories of Argo floats from 2001 to 2004, along with their detailed error estimations. The average error for surface currents is about 4.4 cm s^− 1 which is equivalent to the accuracy order (5 cm s^− 1) of the Surface Velocity Program drifters. The estimation error of the mid-depth currents at 1000 db is reduced to about 0.21 cm s^− 1 without considering the effect of vertical shear.This study shows that the surface trajectory from Argo float provides a new means to measure surface circulations in the global ocean at real time, and that the estimated mid-depth current could be one of the important sources to improve the understanding for ocean dynamic.     

Inter-comparing five forecast operational systems in the North Atlantic and Mediterranean basins: The MERSEA-strand1 methodology

The methodology to achieve a real time inter-comparison of five state-of-the-art operational forecast systems for the North Atlantic and Mediterranean basins is presented. All systems provide analysis and near real-time prediction of the three-dimensional ocean through Opendap servers. A standard set of diagnostics called metrics, is described. Definition and examples of metrics are given. An inter-comparison of the five systems is conducted over a 1 year period using those metrics. It is shown that the methodology developed allows a successful inter-comparison. It has been adopted by the GODAE community. It is also shown that the systems are consistent with the current knowledge of the ocean circulation and climatologies. Systems are deficient in the representation of specific water masses characteristics as Mode waters. Data assimilation of vertical profiles of temperature and salinity solve such deficiencies. Metrics also allow a monitoring of the system's North Atlantic overturning stream function and will allow detecting any changes in the coming year system's thermohaline circulation.     

Forecast verification of a 3D model of the Mediterranean Sea. The use of discrete wavelet transforms and EOFs in the skill assessment of spatial forecasts

The quality assessment of a nested model system of the Mediterranean Sea is realised. The model has two zooms in the Provençal Basin and in the Ligurian Sea, realised with a two-way nesting approach. The experiment lasts for nine weeks, and at each week sea surface temperature (SST) and sea level anomaly are assimilated. The quality assessment of the surface temperature is done in a spatio-temporal approach, to take into account the high complexity of the SST distribution. We focus on the multi-scale nature of oceanic processes using two powerful tools for spatio-temporal analysis, wavelets and Empirical Orthogonal Functions (EOFs). We apply two-dimensional wavelets to decompose the high-resolution model and observed SST into different spatial scales. The Ligurian Sea model results are compared to observations at each of those spatial scales, with special attention on how the assimilation affects the model behaviour. We also use EOFs to assess the similarities between the Mediterranean Sea model and the observed SST. The results show that the assimilation mainly affects the model large-scale features, whereas the small scales show little or no improvement and sometimes, even a decrease in their skill. The multiresolution analysis reveals the connection between large- and small-scale errors, and how the choice of the maximum correlation length of the assimilation scheme affects the distribution of the model error among the different spatial scales.     

Measurement of ocean temperatures using instruments carried by Antarctic fur seals

The study aimed to test the utility of instruments deployed on marine mammals for measuring physical oceanographic variation and, using this method, to examine temperature variation in the coastal waters around South Georgia. There was a significant correlation between temperature measurements made using a towed undulating oceanographic recorder (UOR) and concurrent measurements from time-depth recorders (TDRs) fitted to lactating Antarctic fur seals foraging from the coast of South Georgia. Congruence was found at horizontal spatial scales from 0.01°×0.01° to 0.5°×0.5° (degrees of latitude and longitude), and at a vertical scale of 10 m. However, there was no significant correlation between temperature measured by TDRs in the top 5 m and sea surface temperature (SST) measured by satellite remote sensing. TDR data provided information about temperature variation vertically through the water column, and through time. The UOR data were used to recalibrate the TDR data in order to correct for the slow response time of the TDR thermistor relative to the speed of seal movements through the water column. Seasonal temperature variation was apparent, and temperatures also varied between regions, and with bathymetry. These results were consistent with the current interpretation of the coastal oceanography around South Georgia. In particular, the relationship between on- and off-shelf waters showed larger amounts of warmer surface water in a region in which more run-off was to be expected. The study also showed that Antarctic fur seals concentrate their activity in regions of colder, and presumably oceanic, water. Such instrumented animals could provide near real time data for assimilation into ocean models.     

Effect of wave boundary layer on sea-to-air dimethylsulfide transfer velocity during typhoon passage

A full-spectral third-generation ocean wind–wave model (Wavewatch-III) implemented in the South China Sea is used to investigate the effects of the wave boundary layer on the drag coefficient and the sea-to-air transfer velocity of dimethylsulfide (DMS) during passage of Typhoon Wukong (September 5–11, 2000) with a maximum sustained wind speed of 38 m s^− 1. The model is driven by the reanalyzed surface winds (1° × 1°, four times daily) from the National Centers for Environmental Prediction. It is found that the wave boundary layer evidently enhances (16.5%) the drag coefficient (in turn increases the momentum flux across the air–sea interface), and reduces (13.1%) the sea-to-air DMS transfer velocity (in turn decreases the sea-to-air DMS flux). This indicates the possibility of important roles of wave boundary layer in atmospheric DMS contents and global climate system.     

Parameterization of air–sea gas fluxes at extreme wind speeds

Air–sea flux measurements of O₂ and N₂ obtained during Hurricane Frances in September 2004 [D'Asaro, E. A. and McNeil, C. L., 2006. Measurements of air–sea gas exchange at extreme wind speeds. Journal Marine Systems, this edition.] using air-deployed neutrally buoyant floats reveal the first evidence of a new regime of air–sea gas transfer occurring at wind speeds in excess of 35 m s^− 1. In this regime, plumes of bubbles 1 mm and smaller in size are transported down from near the surface of the ocean to greater depths by vertical turbulent currents with speeds up to 20−30 cm s^− 1. These bubble plumes mostly dissolve before reaching a depth of approximately 20 m as a result of hydrostatic compression. Injection of air into the ocean by this mechanism results in the invasion of gases in proportion to their tropospheric molar gas ratios, and further supersaturation of less soluble gases. A new formulation for air–sea fluxes of weakly soluble gases as a function of wind speed is proposed to extend existing formulations [Woolf, D.K, 1997. Bubbles and their role in gas exchange. In: Liss, P.S., and Duce, R.A., (Eds.), The Sea Surface and Global Change. Cambridge University Press, Cambridge, UK, pp. 173–205.] to span the entire natural range of wind speeds over the open ocean, which includes hurricanes. The new formulation has separate contributions to air–sea gas flux from: 1) non-supersaturating near-surface equilibration processes, which include direct transfer associated with the air–sea interface and ventilation associated with surface wave breaking; 2) partial dissolution of bubbles smaller than 1 mm that mix into the ocean via turbulence; and 3) complete dissolution of bubbles of up to 1 mm in size via subduction of bubble plumes. The model can be simplified by combining “surface equilibration” terms that allow exchange of gases into and out of the ocean, and “gas injection” terms that only allow gas to enter the ocean. The model was tested against the Hurricane Frances data set. Although all the model parameters cannot be determined uniquely, some features are clear. The fluxes due to the surface equilibration terms, estimated both from data and from model inversions, increase rapidly at high wind speed but are still far below those predicted using the cubic parameterization of Wanninkhof and McGillis [Wannikhof, R. and McGillis, W.R., 1999. A cubic relationship between air–sea CO₂ exchange and wind speed. Geophysical Research Letters, 26:1889–1892.] at high wind speed. The fluxes due to gas injection terms increase with wind speed even more rapidly, causing bubble injection to dominate at the highest wind speeds.