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

Save this metadata as an ISO19139 Marine Community Profile XML fileSave this metadata as a Version 1.4 ISO19139 Marine Community Profile XML fileSave this metadata as an OAI Dublin Core XML fileSave this metadata as an ANDS rifcs XML filePDF

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  • NESP MB Project C1 - Improving our understanding of pressures on the marine environment
    • Twenty years of high-resolution sea surface temperature imagery around Australia: inter-annual and annual variability



Identification info
Data identification (MCP)
Citation
Citation
Title  Twenty years of high-resolution sea surface temperature imagery around Australia: inter-annual and annual variability
Date
Date
Date  2014-02-01
Date type  creation: date identifies when the resource was brought into existence
Date
Date
Date  2014-05-06
Date type  publication: date identifies when the resource was issued
Cited responsible party
Individual name  Piers Dunstan
Organisation name  CSIRO Marine and Amospheric Research
Position name  Research Scientist
Role  pointOfContact: party who can be contacted for acquiring knowledge about or acquisition of the resource
Contact Information
Contact
Address
Address
Delivery point  GPO BOX 1538
City  Hobart
Administrative area  Tasmania
Postal code  7001
Country  Australia
Electronic mail address  piers.dunstan@csiro.au
Abstract  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.
Credit  Data was sourced from the National Environmental Science Program (NESP) Marine Biodiversity Hub
Status  completed: production of the data has been completed
Point of contact
Individual name  Piers Dunstan
Organisation name  CSIRO Marine and Amospheric Research
Position name  Research Scientist
Role  pointOfContact: party who can be contacted for acquiring knowledge about or acquisition of the resource
Contact Information
Contact
Address
Address
Delivery point  GPO BOX 1538
City  Hobart
Administrative area  Tasmania
Postal code  7001
Country  Australia
Electronic mail address  piers.dunstan@csiro.au
Resource maintenance
Maintenance information
Maintenance and update frequency  notPlanned: there are no plans to update the data
Descriptive keywords 
Oceans | Ocean Temperature | Sea Surface Temperature (theme).
Resource constraints
Security constraints
Classification  unclassified: available for general disclosure
Resource constraints
Commons License
Current License
Jurisdiction: au Attribution-Share Alike 2.5 Australia Attribution-Share Alike 2.5 Australia
Attribution Constraints  Data was sourced from the NESP Marine Biodiversity Hub - The Marine Biodiversity Hub is supported through funding from the Australian Government's National Environmental Science Program (NESP), administered by the Department of the Environment (DOE).
Language  English
Character set  utf8: 8-bit variable size UCS Transfer Format, based on ISO/IEC 10646
Topic category
Topic category code  oceans
Extent
Extent
Geographic element
Geographic bounding box
North bound
West bound
East bound
South bound
Polygon((108 -47,162 -47,162 -9,108 -9,108 -47))
Extent
Extent
Temporal element
Temporal Extent (MCP)
Extent
Begin date  1993-01-01
End date  2013-12-31
Distribution and On-line Resource(s)
OnLine resource
OnLine resource  DATA ACCESS - SST Long Term Trend [WMS]
OnLine resource
OnLine resource  DATA ACCESS - SST Long Term Trend SE [WMS]
OnLine resource
OnLine resource  DATA ACCESS - SST Long Term Trend RMSE [WMS]
OnLine resource
OnLine resource  DATA ACCESS - SST Annual RMSE [WMS]
OnLine resource
OnLine resource  DATA ACCESS - SST e-fold time [WMS]
OnLine resource
OnLine resource  Point of truth URL of this metadata record
OnLine resource
OnLine resource  NESP Marine Biodiversity Hub Project C1 website
OnLine resource
OnLine resource  NESP Project C1 [ANDS RDA record]
Data quality info
Data quality
Scope
Scope
Hierarchy level  series: information applies to the series
Lineage
Lineage
Statement 

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)

Metadata constraints
Security constraints
Classification  unclassified: available for general disclosure
Metadata Info
File identifier  b8f48127-495e-42e6-8d53-db3c56ee3a7f
Metadata language  English
Character set  utf8: 8-bit variable size UCS Transfer Format, based on ISO/IEC 10646
Parent identifier  NESP MB Project C1 - Improving our understanding of pressures on the marine environment
Hierarchy level  series: information applies to the series
Contact
Individual name  Piers Dunstan
Organisation name  CSIRO Marine and Amospheric Research
Position name  Research Scientist
Role  pointOfContact: party who can be contacted for acquiring knowledge about or acquisition of the resource
Contact Information
Contact
Address
Address
Delivery point  GPO BOX 1538
City  Hobart
Administrative area  Tasmania
Postal code  7001
Country  Australia
Electronic mail address  piers.dunstan@csiro.au
Date stamp  2014-05-09
Metadata standard name  Australian Marine Community Profile of ISO 19115:2005/19139
Metadata standard version  1.5-experimental
Revision Date  2017-01-24T15:55:55