Twenty years of high-resolution sea surface temperature imagery around Australia: inter-annual and annual variability

Researchers: CSIRO O&A, Information & Data Centre (Point of contact ) ,  Piers Dunstan (Point of contact ) ,  Piers, Dunstan (Processor)

Brief description The physical climate defines a significant portion of the habitats in which biological communities and species reside. It is important to quantify these environmental conditions, and how they have changed, as this will inform future efforts to study many natural systems. We present the results of a statistical summary of the variability in sea surface temperature (SST) time-series data for the waters surrounding Australia, from 1993 to 2013. We partition variation in the SST series into annual trends, inter-annual trends, and a number of components of random variation. We utilise satellite data and validate the statistical summary from these data to summaries of data from long-term monitoringstations and from the global drifter program. The spatially dense results show clear trends that associate with oceanographic features. Noteworthy oceanographic features include: average warming was greatest off southern West Australia and off eastern Tasmania where the warming was around 0.6 C per decade for a twenty year study period, and; insubstantial warming in areas dominated by the East Australian Current but this area did exhibit high levels of inter-annual variability (long-term trend increases and decreases but does not increase on average). The results of the analyses can be directly incorporated into (biogeographic) models that explain variation in biological data where both biological and environmental data are on a fine scale.

Lineage The primary goal of the analysis is to produce a map of summaries of the observed SST change in the Australasian region. The SST data set is spatial and temporal and can be thought of a large set of time series, one for each spatial grid cell. Each time series spans a period of approximately 20 years. The SST spatial resolution is high, so there is no need to do spatial interpolation; analyses on individual spatial locations is sufficient. There are almost 2 million non-empty grid cells with more than 750 observation days throughout the entire period which are analysed separately. Each of these 2 million grid cells has a model fitted to it, which is a substantial computational challenge. Any grid cell with less than 750 observation days is not analysed as the amount of information\text it{may} not be sufficient to support the model. This is a conservative approach but it excludes only a tiny proportion of grid cells. Summaries of the individual analyses can be represented spatially to give an idea about spatial variation but neighbouring locations are not incorporated into each grid cell's analysis. The basic principle is that the temperature time-series, for any spatial location, can be decomposed into: Inter-annual variability This includes the long-term trend and any variability with multi-year time-scale. This is modelled as a smooth function of time f (t), say. Here t reflects the day since the startof the observation and 0 = t = 7091 days, where 7091 is the number of observation days includedin the study. Annual cycle This is a periodic function with the same timing and amplitude every year. It is assumed to be a smooth function of day-within-year but not necessarily trigonometric or a function of trigonometric functions. Denote this function as g(d) say, where 0 = d = 365 days (or 366 days ina leap year). Residual All random (and some non-random) deviations from the model's expectation. It includes: a) patterns that occur on a time scale that is shorter than the 1-day data (diurnal effects - a cell is not measured at the same time each day), and 2) non-smooth trends and other model misfit issues.The latter can occur when one of the modelling assumptions fails. An example is when the annual cycle changes abruptly between years, as can happen in an El Nino year, for example. The components of variation in the time-series data can be formally included into a statistical model, viz y(t, d) = f (t) + g(d) + et where y(t, d) is the SST observation on the tth day after the time-series starts (0 = t = 7091 days) that is observed on the dth day of the year (0 = d = 365). The functional form of the longer-term trend, f (t), and the seasonal cycle, g(d), could take many forms. Here, a penalised cubic regression spline is used for f (t) and a penalised cyclic regression spline is used for g(d)

Notes Credit
Data was sourced from the National Environmental Science Program (NESP) Marine Biodiversity Hub

Data time period: 1993-01-01 to 2013-12-31

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162,-9 162,-47 108,-47 108,-9 162,-9


text: northlimit=-9; southlimit=-47; westlimit=108; eastLimit=162

Other Information
DATA ACCESS - SST Long Term Trend [WMS]

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DATA ACCESS - SST Long Term Trend SE [WMS]

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DATA ACCESS - SST e-fold time [WMS]

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NESP Marine Biodiversity Hub Project C1 website

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NESP Project C1 [ANDS RDA record]

purl :

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