Physical and biogeochemical spatial scales of variability in the  East Australian Current separation from shelf glider measurements

Schaeffer, Amandine; Roughan, Moninya; Jones, Emlyn; White, Dana

doi:10.5194/bg-13-1967-2016

Cited by 32 publications

(30 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The daily temperature mean and range (maximum minus minimum temperature) were calculated for each logger and averaged across all the locations within 0.20 • latitudes (∼20 km alongshore to match the distances used in the area averaging, see below, and within temperature decorrelation lengths and within 2 m depth intervals (8-9.9, 10-11.9, 12-13.9, 14-15.9, 16-17.9, and 18-20.0 m) for each month. Data were not differentiated by across-shelf distances as all sites were less than the known decorrelation length scales, even at depth (all sites were ≤ 11.09 km offshore; decorrelation length scales: 19 km on the surface and 14 km at 50 m depth- Schaeffer et al, 2016).…”

Section: Satellite Derived Sstmentioning

confidence: 99%

“…Schaeffer et al (2016) determined that the mean Frontiers in Marine Science | www.frontiersin.orgtemperature de-correlation length scales at a latitude of ∼29.5-33.0 • S, upstream of the EAC separation point, were 19 and 29 km in the across-and along-shelf directions in the surface mixed layer. The across-shelf (along-shelf) distances decreased (increased) with increasing water depth.…”

Section: Using De-correlation Length Scales From In Situ Data To Infomentioning

confidence: 99%

“…The combined area averaging and use of a rolling mean (i.e., the area-and time-averaged method) provides researchers working in coastal areas with simple method by which SST data can be extracted for stationary, point locations. Future studies using satellite data as a proxy for in situ temperatures should consider the dominant oceanographic conditions of their study site, preferably using known de-correlation length and time scales (such as those quantified by Schaeffer et al, 2016 andRoughan et al, 2015, repsectively), to determine the appropriate number of satellite pixels for obtaining their SST measurements and the number of days over which to average. However, users should be aware that SST measurements from polar-orbiting satellites (even with no area averaging) may be significantly different from in situ measurements and may not capture the full seasonal variability in short-term changes in temperature (such as the daily temperature ranges observed from the in situ data in this study).…”

Section: Area Averaging and Gap Filling Satellite Datamentioning

confidence: 99%

See 2 more Smart Citations

Assessing the Use of Area- and Time-Averaging Based on Known De-correlation Scales to Provide Satellite Derived Sea Surface Temperatures in Coastal Areas

et al. 2018

Self Cite

View full text Add to dashboard Cite

Satellite derived sea surface temperatures (SSTs) are often used as a proxy for in situ water temperatures, as they are readily available over large spatial and temporal scales. However, contamination of satellite images can prohibit their use in coastal areas. We compared in situ temperatures to SST foundation (∼10 m depth) at 31 sites inshore of the East Australian Current (EAC), the dynamic western boundary current of the south Pacific gyre, using an area averaging approach to overcome coastal contamination. Varying across-and along-shelf distances were used to area average SST measurements and de-correlation time scales were used to gap fill data. As the EAC is typically anisotropic (dominant along-shore flow) the choice of across-shelf distances influenced the correlation with in situ temperatures more than along-shelf distances. However, the "optimal" distances for both measurements were within known de-correlation length scales. Incorporating both SST area and time averaging (based on de-correlation time scales) produced data for an average of 96% of days that in situ loggers were deployed, compared to 27% (52%) without (with) area averaging. Temperature differences between the in situ data and SSTs varied depending on time of year, with higher differences in the austral summer when daily in situ temperatures can range by up to 4.20 • C. The differences between the in situ and SST measurements were, however, significant with or without area averaging (t-test: p-values < 0.05). Nevertheless, when using the area averaging approaches SSTs were only an average of ∼1.05 • C different from in situ temperatures and less than in situ temperature fluctuations. Linear mixed models revealed that latitude, distance to the coast and nearest estuary did not influence the difference between the in situ and satellite data as much as the water depth. This study shows that using de-correlation length and time scales to inform how to process satellite data can overcome contamination and missing data thereby greatly increasing the coverage and utility of SST data, particularly in coastal areas.

show abstract

Section: Satellite Derived Sstmentioning

confidence: 99%

Section: Using De-correlation Length Scales From In Situ Data To Infomentioning

confidence: 99%

Section: Area Averaging and Gap Filling Satellite Datamentioning

confidence: 99%

See 1 more Smart Citation

Assessing the Use of Area- and Time-Averaging Based on Known De-correlation Scales to Provide Satellite Derived Sea Surface Temperatures in Coastal Areas

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although relevant sensor systems are improving rapidly (e.g., Moore et al 2009) and increasingly sophisticated efforts are being made (e.g., Banas et al 2009), we still have a long way to go before the requisite quality, consistent calibrations, and absolute accuracy can be regularly obtained on appropriate space and time scales. The problem is made more difficult when sampling scales are considered: there is evidence (e.g., Brink et al 2007;Schaeffer et al 2016) that correlation length scales for chlorophyll (a dynamically passive tracer) are shorter than those for temperature over the shelf. Further, these scales vary in the vertical (Fig.…”

Section: Parameters Of Interestmentioning

confidence: 99%

Some considerations about coastal ocean observing systems

Brink¹,

Kirincich²

2017

j mar res

View full text Add to dashboard Cite

Coastal ocean observing capabilities are evolving rapidly, both in terms of sensors and in terms of the volume of information available. We discuss the aspects of the coastal ocean that make it a unique environment, both in terms of physical processes and measurement techniques. Although many global-level systems are relevant to the coastal ocean, we concentrate on treating systems that are unique to the continental shelf environment. Further, we briefly discuss examples of measurement systems that would be useful for developing and driving ocean prediction systems.

show abstract

“…These error terms are discussed in detail in Sect. 2.5.2 (additionally see Schaeffer et al, 2016, for a further discussion of these error sources relating to BGC variables and methods to estimate them from glider data). The background error covariance matrix, P, is given by…”

Section: Data Assimilation Algorithmmentioning

confidence: 99%

Use of remote-sensing reflectance to constrain a data assimilating marine biogeochemical model of the Great Barrier Reef

et al. 2016

Self Cite

View full text Add to dashboard Cite

Abstract. Skillful marine biogeochemical (BGC) models are required to understand a range of coastal and global phenomena such as changes in nitrogen and carbon cycles. The refinement of BGC models through the assimilation of variables calculated from observed in-water inherent optical properties (IOPs), such as phytoplankton absorption, is problematic. Empirically derived relationships between IOPs and variables such as chlorophyll-a concentration (Chl a), total suspended solids (TSS) and coloured dissolved organic matter (CDOM) have been shown to have errors that can exceed 100 % of the observed quantity. These errors are greatest in shallow coastal regions, such as the Great Barrier Reef (GBR), due to the additional signal from bottom reflectance. Rather than assimilate quantities calculated using IOP algorithms, this study demonstrates the advantages of assimilating quantities calculated directly from the less error-prone satellite remote-sensing reflectance (RSR). To assimilate the observed RSR, we use an in-water optical model to produce an equivalent simulated RSR and calculate the mismatch between the observed and simulated quantities to constrain the BGC model with a deterministic ensemble Kalman filter (DEnKF). The traditional assumption that simulated surface Chl a is equivalent to the remotely sensed OC3M estimate of Chl a resulted in a forecast error of approximately 75 %. We show this error can be halved by instead using simulated RSR to constrain the model via the assimilation system. When the analysis and forecast fields from the RSR-based assimilation system are compared with the non-assimilating model, a comparison against independent in situ observations of Chl a, TSS and dissolved inorganic nutrients (NO 3 , NH 4 and DIP) showed that errors are reduced by up to 90 %. In all cases, the assimilation system improves the simulation compared to the non-assimilating model. Our approach allows for the incorporation of vast quantities of remote-sensing observations that have in the past been discarded due to shallow water and/or artefacts introduced by terrestrially derived TSS and CDOM or the lack of a calibrated regional IOP algorithm.

show abstract

Physical and biogeochemical spatial scales of variability in the East Australian Current separation from shelf glider measurements

Cited by 32 publications

References 32 publications

Assessing the Use of Area- and Time-Averaging Based on Known De-correlation Scales to Provide Satellite Derived Sea Surface Temperatures in Coastal Areas

Assessing the Use of Area- and Time-Averaging Based on Known De-correlation Scales to Provide Satellite Derived Sea Surface Temperatures in Coastal Areas

Some considerations about coastal ocean observing systems

Use of remote-sensing reflectance to constrain a data assimilating marine biogeochemical model of the Great Barrier Reef

Contact Info

Product

Resources

About