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Abstract
A nonlinear finite-difference inverse model is used for estimating the North Atlantic general circulation between 20° and 50°N. The inverse model with grid spacing 2° latitude and 2.5° longitude is based on hydrography and is in geostrophic and hydrostatic balances. The constraints of the inverse model are surface and subsurface float mean velocities; Ekman pumping derived from wind data; conservations of mass, heat, and salt; and the planetary vorticity equation at the reference level. The mass, heat, and salt conservations are applied in a vertically integrated form. The model does not have explicit mixing or air–sea flux terms. Vertical velocities result from the nondivergence of the 3D velocity field.
After inversion, float velocities, hydrographic data, and dynamical constraints are generally compatible within error bounds. A few float velocities are, however, rejected by the model mainly due to inadequate time or space sampling of the 2° latitude by 5° longitude boxes for which mean float velocities are computed.
The resulting circulation shows a maximum Gulf Stream transport close to 130 × 106 m3 s−1 at 64°W. Residuals of the vertically integrated heat and salt conservation constraints may be interpreted as air–sea fluxes and are of the right order of magnitude as compared to in situ measurements.
The float database used is already important particularly at the surface. However, its addition to the inversion does not change substantially the estimation by the model of integrated quantities, such as Gulf Stream transports, as compared to an inversion using hydrography and dynamical constraints alone. But floats significantly affect the estimation of the deep circulation increasing, for instance, the estimated velocity amplitude for the deep western boundary current flowing westward south of the Grand Banks.
Abstract
A nonlinear finite-difference inverse model is used for estimating the North Atlantic general circulation between 20° and 50°N. The inverse model with grid spacing 2° latitude and 2.5° longitude is based on hydrography and is in geostrophic and hydrostatic balances. The constraints of the inverse model are surface and subsurface float mean velocities; Ekman pumping derived from wind data; conservations of mass, heat, and salt; and the planetary vorticity equation at the reference level. The mass, heat, and salt conservations are applied in a vertically integrated form. The model does not have explicit mixing or air–sea flux terms. Vertical velocities result from the nondivergence of the 3D velocity field.
After inversion, float velocities, hydrographic data, and dynamical constraints are generally compatible within error bounds. A few float velocities are, however, rejected by the model mainly due to inadequate time or space sampling of the 2° latitude by 5° longitude boxes for which mean float velocities are computed.
The resulting circulation shows a maximum Gulf Stream transport close to 130 × 106 m3 s−1 at 64°W. Residuals of the vertically integrated heat and salt conservation constraints may be interpreted as air–sea fluxes and are of the right order of magnitude as compared to in situ measurements.
The float database used is already important particularly at the surface. However, its addition to the inversion does not change substantially the estimation by the model of integrated quantities, such as Gulf Stream transports, as compared to an inversion using hydrography and dynamical constraints alone. But floats significantly affect the estimation of the deep circulation increasing, for instance, the estimated velocity amplitude for the deep western boundary current flowing westward south of the Grand Banks.
Abstract
An experiment design problem–-that of drifter cast strategy–-is discussed. Different optimization techniques used as part of preparations for the Semaphore-93 air-sea experiment, during which drifters were deployed, are examined. The oceanographic experiment objective was to sample a 500-km-square zone cantered at 33°N, 22°W in the Azores current area, using an average of 25 surface drifters for at least one month. We investigate different “orders of merit” for determining the performance of a particular cast strategy, as well as the method of genetic algorithms for optimizing the strategy. Our technique uses dynamic reference knowledge of the area where the simulation takes place. Two reference sets were used: a steady-state field calculated with data collected from the Kiel University April 1982 hydrographic experiment, and data output from a regional quasigeostrophic model assimilating two years of Geosat altimetric data. The strategies obtained via the genetic algorithm method were compared with regular array drifter deployments, which is the intuitive and commonly used approach. It was also found that a cheap objective function gives strategies comparable to one which was more accurate but computationally expensive. Optimization by the genetic algorithm method is shown to be efficient and robust.
Abstract
An experiment design problem–-that of drifter cast strategy–-is discussed. Different optimization techniques used as part of preparations for the Semaphore-93 air-sea experiment, during which drifters were deployed, are examined. The oceanographic experiment objective was to sample a 500-km-square zone cantered at 33°N, 22°W in the Azores current area, using an average of 25 surface drifters for at least one month. We investigate different “orders of merit” for determining the performance of a particular cast strategy, as well as the method of genetic algorithms for optimizing the strategy. Our technique uses dynamic reference knowledge of the area where the simulation takes place. Two reference sets were used: a steady-state field calculated with data collected from the Kiel University April 1982 hydrographic experiment, and data output from a regional quasigeostrophic model assimilating two years of Geosat altimetric data. The strategies obtained via the genetic algorithm method were compared with regular array drifter deployments, which is the intuitive and commonly used approach. It was also found that a cheap objective function gives strategies comparable to one which was more accurate but computationally expensive. Optimization by the genetic algorithm method is shown to be efficient and robust.
Impact of Multiple Altimeter Data and Mean Dynamic Topography in a Global Analysis and Forecasting SystemAbstract
Satellite altimetry is one of the main sources of information used to constrain global ocean analysis and forecasting systems. In addition to in situ vertical temperature and salinity profiles and sea surface temperature (SST) data, sea level anomalies (SLA) from multiple altimeters are assimilated through the knowledge of a surface reference, the mean dynamic topography (MDT). The quality of analyses and forecasts mainly depends on the availability of SLA observations and on the accuracy of the MDT. A series of observing system evaluations (OSEs) were conducted to assess the relative importance of the number of assimilated altimeters and the accuracy of the MDT in a Mercator Ocean global 1/4° ocean data assimilation system. Dedicated tools were used to quantify impacts on analyzed and forecast sea surface height and temperature/salinity in deeper layers. The study shows that a constellation of four altimeters associated with a precise MDT is required to adequately describe and predict upper-ocean circulation in a global 1/4° ocean data assimilation system. Compared to a one-altimeter configuration, a four-altimeter configuration reduces the mean forecast error by about 30%, but the reduction can reach more than 80% in western boundary current (WBC) regions. The use of the most recent MDT updates improves the accuracy of analyses and forecasts to the same extent as assimilating a fourth altimeter.
Abstract
Satellite altimetry is one of the main sources of information used to constrain global ocean analysis and forecasting systems. In addition to in situ vertical temperature and salinity profiles and sea surface temperature (SST) data, sea level anomalies (SLA) from multiple altimeters are assimilated through the knowledge of a surface reference, the mean dynamic topography (MDT). The quality of analyses and forecasts mainly depends on the availability of SLA observations and on the accuracy of the MDT. A series of observing system evaluations (OSEs) were conducted to assess the relative importance of the number of assimilated altimeters and the accuracy of the MDT in a Mercator Ocean global 1/4° ocean data assimilation system. Dedicated tools were used to quantify impacts on analyzed and forecast sea surface height and temperature/salinity in deeper layers. The study shows that a constellation of four altimeters associated with a precise MDT is required to adequately describe and predict upper-ocean circulation in a global 1/4° ocean data assimilation system. Compared to a one-altimeter configuration, a four-altimeter configuration reduces the mean forecast error by about 30%, but the reduction can reach more than 80% in western boundary current (WBC) regions. The use of the most recent MDT updates improves the accuracy of analyses and forecasts to the same extent as assimilating a fourth altimeter.
Abstract
A method to estimate mass and heat transports across hydrographic sections using hydrography together with altimetry data in a geostrophic inverse box model is presented. Absolute surface velocities computed from Archiving, Validation, and Interpretation of Satellite Oceanographic data (AVISO) altimetry products made up of a combination of sea surface height measurements and geoid estimate are first compared to ship acoustic Doppler current profiler (S-ADCP) measurements of the Observatoire de la Variabilité Interannuelle et Décennale (OVIDE) project along hydrographic sections repeated every 2 yr in summer from Portugal to Greenland. The RMS difference between S-ADCP and altimetry velocities averaged on distances of about 100 km accounts for 3.3 cm s−1. Considering that the uncertainty of S-ADCP velocities is found at 1.5 cm s−1, altimetry errors are estimated at 3 cm s−1. Transports across OVIDE sections previously obtained using S-ADCP data to constrain the geostrophic inverse box model are used as a reference. The new method is found useful to estimate absolute transports across the sections, as well as part of their variability. Despite associated uncertainties that are about 50% larger than when S-ADCP is used, the results for the North Atlantic Current and heat transports, with uncertainties of 10%–15%, reproduce the already observed variability. The largest uncertainties are found in the estimates of the East Greenland Irminger Current (EGIC) transport (30%), induced by larger uncertainties associated with altimetry data at the western boundary.
Abstract
A method to estimate mass and heat transports across hydrographic sections using hydrography together with altimetry data in a geostrophic inverse box model is presented. Absolute surface velocities computed from Archiving, Validation, and Interpretation of Satellite Oceanographic data (AVISO) altimetry products made up of a combination of sea surface height measurements and geoid estimate are first compared to ship acoustic Doppler current profiler (S-ADCP) measurements of the Observatoire de la Variabilité Interannuelle et Décennale (OVIDE) project along hydrographic sections repeated every 2 yr in summer from Portugal to Greenland. The RMS difference between S-ADCP and altimetry velocities averaged on distances of about 100 km accounts for 3.3 cm s−1. Considering that the uncertainty of S-ADCP velocities is found at 1.5 cm s−1, altimetry errors are estimated at 3 cm s−1. Transports across OVIDE sections previously obtained using S-ADCP data to constrain the geostrophic inverse box model are used as a reference. The new method is found useful to estimate absolute transports across the sections, as well as part of their variability. Despite associated uncertainties that are about 50% larger than when S-ADCP is used, the results for the North Atlantic Current and heat transports, with uncertainties of 10%–15%, reproduce the already observed variability. The largest uncertainties are found in the estimates of the East Greenland Irminger Current (EGIC) transport (30%), induced by larger uncertainties associated with altimetry data at the western boundary.
Abstract
Global ocean sampling with autonomous floats going to 4000–6000 m, known as the deep Argo array, constitutes one of the next challenges for tracking climate change. The question here is how such a global deep array will impact ocean reanalyses. Based on the different behavior of four ocean reanalyses, we first identified that large uncertainty exists in current reanalyses in representing local heat and freshwater fluxes in the deep ocean (1 W m−2 and 10 cm yr−1 regionally). Additionally, temperature and salinity comparison with deep Argo observations demonstrates that reanalysis errors in the deep ocean are of the same size as, or even stronger than, the deep ocean signal. An experimental approach, using the 1/4° GLORYS2V4 (Global Ocean Reanalysis and Simulation) system, is then presented to anticipate how the evolution of the global ocean observing system (GOOS), with the advent of deep Argo, would contribute to ocean reanalyses. Based on observing system simulation experiments (OSSE), which consist in extracting observing system datasets from a realistic simulation to be subsequently assimilated in an experimental system, this study suggests that a global deep Argo array of 1200 floats will significantly constrain the deep ocean by reducing temperature and salinity errors by around 50%. Our results also show that such a deep global array will help ocean reanalyses to reduce error in temperature changes below 2000 m, equivalent to global ocean heat fluxes from 0.15 to 0.07 W m−2, and from 0.26 to 0.19 W m−2 for the entire water column. This work exploits the capabilities of operational systems to provide comprehensive information for the evolution of the GOOS.
Abstract
Global ocean sampling with autonomous floats going to 4000–6000 m, known as the deep Argo array, constitutes one of the next challenges for tracking climate change. The question here is how such a global deep array will impact ocean reanalyses. Based on the different behavior of four ocean reanalyses, we first identified that large uncertainty exists in current reanalyses in representing local heat and freshwater fluxes in the deep ocean (1 W m−2 and 10 cm yr−1 regionally). Additionally, temperature and salinity comparison with deep Argo observations demonstrates that reanalysis errors in the deep ocean are of the same size as, or even stronger than, the deep ocean signal. An experimental approach, using the 1/4° GLORYS2V4 (Global Ocean Reanalysis and Simulation) system, is then presented to anticipate how the evolution of the global ocean observing system (GOOS), with the advent of deep Argo, would contribute to ocean reanalyses. Based on observing system simulation experiments (OSSE), which consist in extracting observing system datasets from a realistic simulation to be subsequently assimilated in an experimental system, this study suggests that a global deep Argo array of 1200 floats will significantly constrain the deep ocean by reducing temperature and salinity errors by around 50%. Our results also show that such a deep global array will help ocean reanalyses to reduce error in temperature changes below 2000 m, equivalent to global ocean heat fluxes from 0.15 to 0.07 W m−2, and from 0.26 to 0.19 W m−2 for the entire water column. This work exploits the capabilities of operational systems to provide comprehensive information for the evolution of the GOOS.
Abstract
High-resolution numerical experiments of ocean mesoscale eddy turbulence show that the wind-driven mixed layer (ML) dynamics affects mesoscale motions in the surface layers at scales lower than O(60 km). At these scales, surface horizontal currents are still coherent to, but weaker than, those derived from sea surface height using geostrophy. Vertical motions, on the other hand, are stronger than those diagnosed using the adiabatic quasigeotrophic (QG) framework. An analytical model, based on a scaling analysis and on simple dynamical arguments, provides a physical understanding and leads to a parameterization of these features in terms of vertical mixing. These results are valid when the wind-driven velocity scale is much smaller than that associated with eddies and the Ekman number (related to the ratio between the Ekman and ML depth) is not small. This suggests that, in these specific situations, three-dimensional ML motions (including the vertical velocity) can be diagnosed from high-resolution satellite observations combined with a climatological knowledge of ML conditions and interior stratification.
Abstract
High-resolution numerical experiments of ocean mesoscale eddy turbulence show that the wind-driven mixed layer (ML) dynamics affects mesoscale motions in the surface layers at scales lower than O(60 km). At these scales, surface horizontal currents are still coherent to, but weaker than, those derived from sea surface height using geostrophy. Vertical motions, on the other hand, are stronger than those diagnosed using the adiabatic quasigeotrophic (QG) framework. An analytical model, based on a scaling analysis and on simple dynamical arguments, provides a physical understanding and leads to a parameterization of these features in terms of vertical mixing. These results are valid when the wind-driven velocity scale is much smaller than that associated with eddies and the Ekman number (related to the ratio between the Ekman and ML depth) is not small. This suggests that, in these specific situations, three-dimensional ML motions (including the vertical velocity) can be diagnosed from high-resolution satellite observations combined with a climatological knowledge of ML conditions and interior stratification.
“Deep-Arvor”: A New Profiling Float to Extend the Argo Observations Down to 4000-m DepthAbstract
The international Argo program, consisting of a global array of more than 3000 free-drifting profiling floats, has now been monitoring the upper 2000 m of the ocean for several years. One of its main proposed evolutions is to be able to reach the deeper ocean in order to better observe and understand the key role of the deep ocean in the climate system. For this purpose, Ifremer has designed the new “Deep-Arvor” profiling float: it extends the current operational depth down to 4000 m, and measures temperature and salinity for up to 150 cycles with CTD pumping continuously and 200 cycles in spot sampling mode. High-resolution profiles (up to 2000 points) can be transmitted and data are delivered in near–real time according to Argo requirements. Deep-Arvor can be deployed everywhere at sea without any preballasting operation and its light weight (~26 kg) makes its launching easy. Its design was done to target a cost-effective solution. Predefined spots have been allocated to add an optional oxygen sensor and a connector for an extra sensor. Extensive laboratory tests were successful. The results of the first at-sea experiments showed that the expected performances of the operational prototypes had been reached (i.e., to perform up to 150 cycles). Meanwhile, the industrialization phase was completed in order to manufacture the Deep-Arvor float for the pilot experiment in 2015. This paper details all the steps of the development work and presents the results from the at-sea experiments.
Abstract
The international Argo program, consisting of a global array of more than 3000 free-drifting profiling floats, has now been monitoring the upper 2000 m of the ocean for several years. One of its main proposed evolutions is to be able to reach the deeper ocean in order to better observe and understand the key role of the deep ocean in the climate system. For this purpose, Ifremer has designed the new “Deep-Arvor” profiling float: it extends the current operational depth down to 4000 m, and measures temperature and salinity for up to 150 cycles with CTD pumping continuously and 200 cycles in spot sampling mode. High-resolution profiles (up to 2000 points) can be transmitted and data are delivered in near–real time according to Argo requirements. Deep-Arvor can be deployed everywhere at sea without any preballasting operation and its light weight (~26 kg) makes its launching easy. Its design was done to target a cost-effective solution. Predefined spots have been allocated to add an optional oxygen sensor and a connector for an extra sensor. Extensive laboratory tests were successful. The results of the first at-sea experiments showed that the expected performances of the operational prototypes had been reached (i.e., to perform up to 150 cycles). Meanwhile, the industrialization phase was completed in order to manufacture the Deep-Arvor float for the pilot experiment in 2015. This paper details all the steps of the development work and presents the results from the at-sea experiments.
