Abstract
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.
Highlights
Water temperature is an important predictor of coastal diversity (Tittensor et al, 2010) and distribution (Block et al, 2011; Last et al, 2011; Wernberg et al, 2011) across a wide range of taxa
The mean temperatures showed clear seasonal patterns across each of the different latitudes included in this study (Figure 2)
This study demonstrates that using well-chosen area averaged satellite sea surface temperatures (SSTs) data can increase the likelihood of obtaining an SST measurement for coastal areas
Summary
Water temperature is an important predictor of coastal diversity (Tittensor et al, 2010) and distribution (Block et al, 2011; Last et al, 2011; Wernberg et al, 2011) across a wide range of taxa. Previous studies have demonstrated biases in SST data when comparing it to in situ measurements within coastal areas (Smale and Wernberg, 2009; Lathlean et al, 2011; Stobart et al, 2016), with some studies recording up to 6◦C differences (Smit et al, 2013) These biases are due to contamination of the satellite processing due to the presence of coastal features such as the shoreline, estuaries and embayments as well as complex coastal dynamics such as tides and upwelling (Smit et al, 2013) that vary over short spatial scales. Despite this well documented bias of satellite SST data in coastal areas, no studies have assessed if using spatial area averaging (based on known decorrelation length and/or time scales) can reduce the bias or increase the availability of satellite derived temperature data
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