Optics of the offshore Columbia River plume from glider observations and satellite imagery

Saldías, Gonzalo S.; Shearman, R. Kipp; Barth, John A.; Tufillaro, Nicholas B.

doi:10.1002/2015jc011431

Cited by 28 publications

(15 citation statements)

References 63 publications

(88 reference statements)

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“…The model without assimilation reproduces the plume dynamics qualitatively correctly. For instance, the extent and geometry of the plume is similar to that revealed in satellite ocean color data (Saldías et al, ). Events of surface water freshening over the Oregon shelf following upwelling wind relaxation and reversal are observed at the NH10/NDBC46094 mooring (124.308°W, 44.634°N, see NDBC ).…”

Section: The Dynamical Regime and The Model Setupsupporting

confidence: 72%

“…Glider temperature and salinity observations along the Newport line (44.65°N, see Figure ) are available on a grid with a 1‐km resolution in the horizontal direction, along the glider track, and 4 m in the vertical direction. The profiles are provided together with the variance of the raw observations for each point (Erofeev, ; Mazzini et al, ; Saldías et al, ). For each vertical profile the observations are boxed and averaged within each model grid cell.…”

Section: The Dynamical Regime and The Model Setupmentioning

confidence: 99%

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Why Gliders Appreciate Good Company: Glider Assimilation in the Oregon‐Washington Coastal Ocean 4DVAR System With and Without Surface Observations

et al. 2019

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Gliders are low-power autonomous underwater vehicles used to obtain oceanic measurements in vertical sections. Assimilation of glider temperature and salinity into coastal ocean circulation models holds the potential to improve the ocean subsurface structure estimate. In this study, the impact of assimilation of glider observations is studied using a four-dimensional variational (4DVAR) data assimilation and forecast system set offshore of Oregon and Washington on the U.S. West Coast. Four test cases are compared: (1) no assimilation, (2) assimilation of glider temperature and salinity data alone, (3) assimilation of the glider data in combination with the surface observations including satellite sea surface temperature, sea surface height, and high-frequency radar surface velocities, and (4) assimilation of the surface data alone. It is found that the assimilation of glider observations alone creates unphysical eddies in the vicinity of the glider transect. As a consequence, the forecast errors in the surface velocity and temperature increase compared to the case without data assimilation. Assimilation of surface and subsurface observations in combination prevents these features from forming and reduces the errors in the forecasts for the subsurface fields compared to the other three experiments. These improvements persisted in 21-day forecasts run after the last data assimilation cycle.Plain Language Summary Ocean forecast systems, like our system for the ocean offshore Oregon-Washington, USA, use numerical models to predict future temperature, currents, sea surface height, and salt concentration. Eventually, these predictions are corrected using observations in a process called data assimilation (DA) in order to better approximate the true state of the ocean. Assimilation of subsurface observations made by autonomous underwater vehicles called gliders can potentially improve the predictions of the ocean state below the surface. In this study, we have assimilated glider observations together with, and in absence of, surface observations. We found that assimilation of glider observations alone creates predictions that are less accurate than those obtained from a model without DA as the DA creates unphysical features. Assimilating the glider observations in tandem with surface observations prevents these features from forming and realizes local improvements to the subsurface ocean predictions that last for 21 days after the last DA correction. These results show that one has to be careful with using glider observations in DA and should always assimilate glider observations in combination with observations that cover large swaths of the ocean surface.

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Section: The Dynamical Regime and The Model Setupsupporting

confidence: 72%

Section: The Dynamical Regime and The Model Setupmentioning

confidence: 99%

Why Gliders Appreciate Good Company: Glider Assimilation in the Oregon‐Washington Coastal Ocean 4DVAR System With and Without Surface Observations

et al. 2019

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“…So the fact that some reflectances are significant while others are not, within the studied period (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017), is probably a result of different dispersal in mixing conditions experimented by the plume. In any case, it is difficult to produce ESS estimates using a single ocean color band because optical properties related to SSS tend to show a nonlinear spatial variability (Saldías et al, 2016). Despite these complexities, the reconstructed monthly climatology using the ESS (Figure 4) captures all the main surface mesoscale features, particularly those related to the main path of the low-salinity water.…”

Section: Empirical Surface Salinitymentioning

confidence: 99%

“…The necessity to account for the large diversity of constituents found in plume waters (apart from DOM) that covary with salinity limits the interpretation of SSS fields based on adg443 alone. One can also argue that this approach may fail to detect the complex and rapid changes in SSS observed in the river plume (Geiger et al, 2013;Saldías et al, 2016), when the assumed conservative mixing between high CDOM river water and ocean water may not hold. Salisbury et al (2011), for example, reported large variability of the relation between salinity and absorption coefficient of colored detrital matter at 443 nm (a cdm 443) in the first 500 km of the Amazon river plume.…”

Section: Empirical Surface Salinitymentioning

confidence: 99%

The Salinity Structure of the Amazon River Plume Drives Spatiotemporal Variation of Oceanic Primary Productivity

et al. 2019

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The Amazon river is a major source of terrestrially derived organic carbon to the tropical Atlantic Ocean. Field, satellite and a vertically generalized production model data were used to estimate empirical surface salinity and fit an inverse logit function to investigate the limiting effect of salinity on the productivity in the Amazon river plume. Satellite data included Moderate Resolution Imaging Spectroradiometer, Soil Moisture and Ocean Salinity, and Aquarius missions. Previous empirical surface salinity models have relied on a very narrow range of salinity values and satellite data to estimate the spatial extent of the river plume. The empirical surface salinity model presented here extended the range of salinity values and captures all the main surface mesoscale features, particularly those related to the main path of the low‐salinity water. We also show that it is possible to gain new insights on the spatiotemporal variability of the Amazon river plume by improving the empirical surface salinity and expanding its sampling period with the aid of remote sensing data. The variability of primary productivity is dominated by the subannual (6 month) and annual (12 month) frequency bands. Low‐salinity river water influences surface primary productivity continuously during the year through mechanisms associated with the western tropical Atlantic circulation and vertical mixing.

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“…Wind forcing also drives fluctuations in surface currents, water properties, and sea level, with typical scales of variability on the order of three to 10 days (Hickey and Banas, 2003). Additionally, upwelling-favorable winds drive crossshelf transport that actively pushes the freshwater plume of the Columbia River southward and offshore of the Oregon shelf in the spring and summer months (Barnes et al, 1972;Hickey et al, 2005;Saldías et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Dynamics of Physical and Biological Properties along the Endurance Array of the California Current Ecosystem

Freitas¹,

Saldías²,

Goñi³

et al. 2018

Oceanog

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The coastal margin of the Pacific Northwest of the United States is a highly dynamic and productive region. Here, we use satellite, high-frequency mooring, and glider estimates of biologically relevant physical and optical variables to characterize seasonal patterns and latitudinal and cross-shore gradients in particle concentrations between the Washington and Oregon shelves. Consistent with prior research, we find that the Columbia River exerts a strong seasonal influence on the Washington shelf, but smaller coastal rivers and resuspension processes also appear important in determining particle distributions nearshore during winter across the full study region. We find fluorescencebased measurements of chlorophyll to be similar in magnitude across the two shelves over the time period examined, although the much weaker wind stresses off Washington indicate that processes other than upwelling are important determinants of chlorophyll changes in those areas, as previously suggested. These in situ observations contrast with the overall differences observed from satellite data, which consistently show higher chlorophyll concentrations off the Washington coast. This research suggests that latitudinal differences in chromophoric dissolved organic matter may be a partial explanation for perceived trends in satellite-derived chlorophyll. The observations presented are nascent; maturation of temporal and spatial coverage of OOI data sets will be necessary to more conclusively link physical forcing and biogeochemical responses.

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Optics of the offshore Columbia River plume from glider observations and satellite imagery

Cited by 28 publications

References 63 publications

Why Gliders Appreciate Good Company: Glider Assimilation in the Oregon‐Washington Coastal Ocean 4DVAR System With and Without Surface Observations

Why Gliders Appreciate Good Company: Glider Assimilation in the Oregon‐Washington Coastal Ocean 4DVAR System With and Without Surface Observations

The Salinity Structure of the Amazon River Plume Drives Spatiotemporal Variation of Oceanic Primary Productivity

Temporal and Spatial Dynamics of Physical and Biological Properties along the Endurance Array of the California Current Ecosystem

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