Ocean surface current multiscale observation mission (OSCOM): Simultaneous measurement of ocean surface current, vector wind, and temperature
Yan DuXiaolong DongXingwei JiangYuhong ZhangDi ZhuQiwei SunZhenzhan WangXinhua NiuWen ChenCheng ZhuZhiyou JingShilin TangYineng LiChen JuXiaoqing ChuChi XuTianyu WangYinghui HeBing HanYing ZhangMinyang WangWei WuYifan XiaKun ChenYu‐Kun QianPing ShiHaigang ZhanShiqiu Peng
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Ocean observations
Ocean surface topography
Sea-surface height
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Boundary current
This work compares the performance of three ocean model frameworks that currently produce outputs of the ocean properties specific to the US Caribbean ocean; the Global Ocean Forecast System (GOFS), US Navy Coastal Ocean Model for the American Seas (AMSEAS) and the Daily Global Physical Bulletin (PSY4). Separate comparisons are done for the ocean properties in the open ocean and nearshore regions. For the open ocean, the model outputs are compared with the AVISO satellite altimetry data for the sea-surface height anomaly (SSHA), the OSCAR data for surface current velocities and the G1SST satellite data for sea-surface temperature (SST). For the nearshore analysis, the model outputs are compared with in-situ buoy measurements and HOBO logger data in the nearshore regions. Our analysis shows that the PSY4 produces the most realistic outputs of SSHA and surface current velocities in the open ocean, whereas all the models produce a strong correlation in terms of the seasonal variability of the surface temperature when compared to the G1SST data. The AMSEAS model, despite being a fine resolution regional model, underperforms in terms of the surface current velocity outputs in the open ocean due to the influence of the simulated submesoscale turbulence on the mesoscale variability. In the nearshore regions, none of the models produce agreeable outputs on the SSHA and current velocities. These findings provide useful insight on the applicability of the model outputs for various operations that require oceanographic data specific to the US Caribbean ocean.
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Observational surveys have shown significant oceanic bottom water warming, but they are too spatially and temporally sporadic to quantify the deep ocean contribution to the present-day sea level rise (SLR). In this study, altimetry sea surface height (SSH), Gravity Recovery and Climate Experiment (GRACE) ocean mass, and in situ upper ocean (0-700 m) steric height have been assessed for their seasonal variability and trend maps. It is shown that neither the global mean nor the regional trends of altimetry SLR can be explained by the upper ocean steric height plus the GRACE ocean mass. A non-Boussinesq ocean general circulation model (OGCM), allowing the sea level to rise as a direct response to the heat added into the ocean, is then used to diagnose the deep ocean steric height. Constrained by sea surface temperature data and the top of atmosphere (TOA) radiation measurements, the model reproduces the observed upper ocean heat content well. Combining the modeled deep ocean steric height with observational upper ocean data gives the full depth steric height. Adding a GRACE-estimated mass trend, the data-model combination explains not only the altimetry global mean SLR but also its regional trends fairly well. The deep ocean warming is mostly prevalent in the Atlantic and Indian oceans, and along the Antarctic Circumpolar Current, suggesting a strong relation to the oceanic circulation and dynamics. Its comparison with available bottom water measurements shows reasonably good agreement, indicating that deep ocean warming below 700 m might have contributed 1.1 mm/yr to the global mean SLR or one-third of the altimeter-observed rate of 3.11 +/- 0.6 mm/yr over 1993-2008.
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Abstract The Global Ocean Biogeochemistry (GO-BGC) Array is a project funded by the US National Science Foundation to build a global network of chemical and biological sensors on Argo profiling floats. The network will monitor biogeochemical cycles and ocean health. The floats will collect from a depth of 2,000 meters to the surface, augmenting the existing <ext-link ext-link-type="uri" xlink:href="https://argo.ucsd.edu/">Argo array</ext-link> that monitors ocean temperature and salinity. Data will be made freely available within a day of being collected via the Argo data system. These data will allow scientists to pursue fundamental questions concerning ocean ecosystems, monitor ocean health and productivity, and observe the elemental cycles of carbon, oxygen, and nitrogen through all seasons of the year. Such essential data are needed to improve computer models of ocean fisheries and climate, to monitor and forecast the effects of ocean warming and ocean acidification on sea life, and to address key questions identified in “Sea Change: 2015‐2025 Decadal Survey of Ocean Sciences” such as: What is the ocean's role in regulating the carbon cycle? What are the natural and anthropogenic drivers of open ocean deoxygenation? What are the consequences of ocean acidification? How do physical changes in mixing and circulation affect nutrient availability and ocean productivity?
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Biogeochemical Cycle
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General ocean circulation models are not perfect. Forced with observed atmospheric fluxes they gradually drift away from measured distributions of temperature and salinity. We suggest data assimilation of absolute dynamical ocean topography (DOT) observed from space geodetic missions as an option to reduce these differences. Sea surface information of DOT is transferred into the deep ocean by defining the analysed ocean state as a weighted average of an ensemble of fully consistent model solutions using an error-subspace ensemble Kalman filter technique. Success of the technique is demonstrated by assimilation into a global configuration of the ocean circulation model FESOM over 1 year. The dynamic ocean topography data are obtained from a combination of multi-satellite altimetry and geoid measurements. The assimilation result is assessed using independent temperature and salinity analysis derived from profiling buoys of the AGRO float data set. The largest impact of the assimilation occurs at the first few analysis steps where both the model ocean topography and the steric height (i.e. temperature and salinity) are improved. The continued data assimilation over 1 year further improves the model state gradually. Deep ocean fields quickly adjust in a sustained manner: A model forecast initialized from the model state estimated by the data assimilation after only 1 month shows that improvements induced by the data assimilation remain in the model state for a long time. Even after 11 months, the modelled ocean topography and temperature fields show smaller errors than the model forecast without any data assimilation.
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Interannual variability of sea surface height (SSH) in western Pacific Ocean and eastern Indian Ocean occurs as a result of the ENSO phenomena. This variability affects SSH difference between both of those oceans. In normal condition, SSH in western Pacific Ocean is higher than in eastern Indian Ocean which causes a current that passes through Indonesia which known as the Indonesian Throughflow (ITF). This study used SSH and geostrophic currents data from 1993 to 2015 which obtained from AVISO satellite altimetry data to determine the variability of SSH difference in ENSO condition which represented by the sea surface temperature (SST) anomaly of Niño 3.4 data from NOAA and its relation to the geostrophic currents in Lombok Strait which is one of the ITF crossing path. The result of the correlation calculation of ENSO condition to SSH difference between western Pacific Ocean and eastern Indian Ocean shows the negative value. It means that SSH difference at both oceans have opposite condition that when El Nino happened SSH in western Pacific Ocean is lower than the eastern Indian Ocean and when La Niña happened SSH in the western Pacific Ocean is higher than the eastern Indian Ocean. The SSH difference does not affect the direction of geostrophic currents but affects its speed.
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Sea surface height anomalies (SSHA) derived from the Topex/Poseidon (T/P) satellite are used for computing heat storage anomalies (HSA) and heat storage rates (HSR) over the north Indian Ocean [20 o S – 25 o N and 35 o E – 115 o E] for a period of 10 years (1993-2002). In normal years during September to November positive HSA and HSR were observed in the region 10 o S - Equator, 90 o E 110 o E. But during the years 1994 and 1997 negative HSA and HSR were observed in this region, this interannual variability has recently been addressed as Indian Ocean Dipole (IOD). The heat content anomaly clearly showed the existence of the dipole like structure in the equatorial Indian Ocean (IO) in 1994 and 1997. The T/P measurement showed large SSHA in the western equatorial Indian Ocean during 1994-1995 and 1997 -1998 IOD events that represent the oceanic response to the surface wind forcing. These anomalies in turn played an important role in forming the sea surface temperature anomalies (SSTA). The 1997 Dipole mode structure was observed to be stronger than 1994 and that can be clearly seen in calculated HSA, HSR, T/P SSH anomalies, thermocline depth (D20) anomaly derived from Simple Ocean Data Assimilation (SODA) and in HADISST anomaly. The Rossby wave propagation is found to have a good correlation with the heat content anomaly derived from Topex/Poseidon sea surface height anomalies. During the dipole years 199495 and 1997-98 the anomalous westward propagation of SSHA and HSA were clearly observed especially in the region south of 7 o S and strengthened in 80 - 90 o E belt. Wind stress curl anomalies play an important role in strengthening this propagation in 80-90 o E and hence warming the west Indian Ocean in the early months of 1998. It was seen that positive and negative dipole years are inversely correlated in the southeastern equatorial Indian Ocean (10 o S - Equator, 90 o E -110 o E). To understand the interannual variability of upper ocean SSHA, Complex Empirical Orthogonal Function (CEOF) has been applied to T/P SSHA and HSA. IOD has been shown to be the leading mode of the interannual variability of the upper ocean SSHA and HSA. The westward propagation of the phase is in agreement with the sea saw thermocline variability observed in the equatorial Indian Ocean.
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Anomaly (physics)
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[1] Observational surveys have shown significant oceanic bottom water warming, but they are too spatially and temporally sporadic to quantify the deep ocean contribution to the present-day sea level rise (SLR). In this study, altimetry sea surface height (SSH), Gravity Recovery and Climate Experiment (GRACE) ocean mass, and in situ upper ocean (0–700 m) steric height have been assessed for their seasonal variability and trend maps. It is shown that neither the global mean nor the regional trends of altimetry SLR can be explained by the upper ocean steric height plus the GRACE ocean mass. A non-Boussinesq ocean general circulation model (OGCM), allowing the sea level to rise as a direct response to the heat added into the ocean, is then used to diagnose the deep ocean steric height. Constrained by sea surface temperature data and the top of atmosphere (TOA) radiation measurements, the model reproduces the observed upper ocean heat content well. Combining the modeled deep ocean steric height with observational upper ocean data gives the full depth steric height. Adding a GRACE-estimated mass trend, the data-model combination explains not only the altimetry global mean SLR but also its regional trends fairly well. The deep ocean warming is mostly prevalent in the Atlantic and Indian oceans, and along the Antarctic Circumpolar Current, suggesting a strong relation to the oceanic circulation and dynamics. Its comparison with available bottom water measurements shows reasonably good agreement, indicating that deep ocean warming below 700 m might have contributed 1.1 mm/yr to the global mean SLR or one-third of the altimeter-observed rate of 3.11 ± 0.6 mm/yr over 1993–2008.
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Argo
Ocean observations
Marine ecosystem
Abrupt climate change
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