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Florent Gasparin, Mathieu Hamon, Elisabeth Rémy, and Pierre-Yves Le Traon

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.

Open access
Mathieu Hamon, Eric Greiner, Pierre-Yves Le Traon, and Elisabeth Remy

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.

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Nicolas Kolodziejczyk, Mathieu Hamon, Jacqueline Boutin, Jean-Luc Vergely, Gilles Reverdin, Alexandre Supply, and Nicolas Reul

Abstract

Ten years of L-band radiometric measurements have proven the capability of satellite sea surface salinity (SSS) to resolve large-scale-to-mesoscale SSS features in tropical to subtropical ocean. In mid-to-high latitudes, L-band measurements still suffer from large-scale and time-varying errors. Here, a simple method is proposed to mitigate the large-scale and time-varying errors. First, an optimal interpolation using a large correlation scale (~500 km) is used to map independently Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) level-3 (L3) data. The mapping is compared with the equivalent mapping of in situ observations to estimate the large-scale and seasonal biases. A second mapping is performed on adjusted SSS at the scale of SMOS/SMAP spatial resolution (~45 km). This procedure merges both products and increases the signal-to-noise ratio of the absolute SSS estimates, reducing the root-mean-square difference of in situ satellite products by about 26%–32% from mid- to high latitudes, respectively, in comparison with the existing SMOS and SMAP L3 products. However, in the Arctic Ocean, some issues on satellite retrieved SSS related to, for example, radio frequency interferences, land–sea contamination, and ice–sea contamination remain challenging to reduce given the low sensitivity of L-band radiometric measurements to SSS in cold water. Using the International Thermodynamic Equation Of Seawater—2010 (TEOS-10), the resulting level-4 SSS satellite product is combined with satellite-microwave SST products to estimate sea surface density, spiciness, and haline contraction and thermal expansion coefficients. For the first time, we illustrate how useful these satellite-derived parameters are to fully characterize the surface ocean water masses at large mesoscale.

Open access