| CARS home page is now http://www.cmar.csiro.au/cars. That page gives access to the latest version (CARS2009). |
The version described on this page is CARS2006. |
The earliest and now superceded version is CARS2000. It is described and still accessible via the CARS2000 page |
On this page: Using CARS2006 | Bottom water | Software | Access | About CARS | References & Metadata | Sample plots
CARS is a digital climatology, or atlas of seasonal ocean water properties.
CARS2009 covers all the world's oceans, and uses a significantly enlarged dataset, but only provides temperature and salinity (oxygen and nutrients should be added in early 2011.
CARS2006 spans the southern 2/3 of the world's oceans, from 70S to 26N, except in the Atlantic where is reaches only to 10N. The six water properties mapped in are temperature, salinity, oxygen, nitrate, silicate, phosphate. It comprises historic mean fields and average seasonal cycles, derived from all available historical subsurface ocean property measurements (primarily research vessel instrument casts and autonomous profiling buoys.)
A temperature-only version based on the CSIRO Quality Controlled Ocean Temperature Archive (QuOTA) is also available in a monthly-values netCDF file spanning the Indian and South-West Pacific Oceans.
CARS does not provide information for any given year. CARS is created by averaging/interpolating all available oceanographic cast data, most of which was collected in the last 50 years. Especially when trying to provide an estimate at every location and every depth in the world's oceans, there is not enough data to resolve any one year, so we ignore the year of collection of each observation and retain only the day-of-year - and then fit a mean and mean-seasonal-cycle at each point.
In the Western Equatorial Pacific and the Gulf of Carpentaria we apply corrections for interannual signals, but in general any such signals will effect the maps, both by these signals being aliased into spatial structure or seasonal cycles, and by biasing towards the interannual anomaly of data-rich periods.
CARS2006 is stored and available online in netCDF files. The T and S fields are also available via ftp in a collection of GIS-suitable ASCII files. The nutrient fields can be made available in this format, by arrangement.
| Standard CARS2006 | Mapped using Argo T & S Data | Bottom water |
|---|---|---|
| temperature_cars2006.nc | temperature_cars2006a.nc | t_cars2006a_bot.nc |
| salinity_cars2006.nc | salinity_cars2006a.nc | s_cars2006a_bot.nc |
| oxygen_cars2006.nc | o2_cars2006_bot.nc | |
| nitrate_cars2006.nc | no3_cars2006_bot.nc | |
| phosphate_cars2006.nc | po4_cars2006_bot.nc | |
| silicate_cars2006.nc | si_cars2006_bot.nc |
It is mapped on version 3 CSL (CSIRO standard depth levels), on a .5X.5 degree grid covering the region 0E - 360E, 70S - 26N (10N only in the Atlantic). Seasonal cycles are esitmated in the upper ocean. The following variables are likely to be most useful:
| Name | Description |
|---|---|
| depth | depths of the 79 mapping levels (in metres) |
| depth_ann | depths of the levels for which annual cycles are estimated |
| depth_semiann | depths of the levels for which semiannual cycles are estimated |
| mean | estimate of mean value |
| an_cos | cosine of annual cycle |
| an_sin | sine of annual cycle |
| sa_cos | cosine of semiannual cycle |
| sa_sin | sine of semiannual cycle |
| std_dev | estimate of standard deviation of measurements |
| map_error | estimate of mapping Standard Error of the Mean |
| lat,lon | grid point locations |
Note that unrealistic values can arise from fitting sinusoidal seasonal cycles. The most obvious example occurs when nutrients are seasonally depleted so that the "true" seasonal curve would have a peak or two and intervening zero flatspots. A best fit sinusoid will undershoot, creating negative values. By resetting these to zero we get a more realistic representation.
Seawater properties at the ocean floor are required for some analyses. CARS is derived from oceanographic cast data, and such casts typically do not approach closer than 5 to 10m from the ocean floor (to prevent damage to the instruments.) However we can estimate the seafloor properties because in many places there is nearby deeper water which has been sampled. In making CARS, at the "coastline" for each depth level we extrapolate landwards by one gridpoint. This means we often have a CARS value just below, as well as above, the bottom. The seafloor maps are computed by vertically interpolating those near-bottom CARS values to the ocean depth at each gridpoint. Where we cannot interpolate because there is no below-bottom value, we use the value immediately above the bottom. However there are still a portion of grid points for which there is no value immediately above the bottom, due to absence of nearby deep observations (as particularly occurs in hollows in the seafloor.) The bottom water fields necessarily have gaps in these locations.
As well as mean values, seasonal values, seasonal range, and standard deviation values may also be provided for the seafloor, and these are all derived from CARS in the same way.
Many popular software products have interfaces or library routines to interrogate and extract data from netCDF files.
A small package of unsupported Matlab access routines can be downloaded along with the CARS data. They will require local installation of the Matlab-netcdf "toolbox".
| Name | Use |
|---|---|
| getchunk | extracts a 3D chunk |
| getmap | extracts a single depth layer or horizontal slice |
| get_clim_casts | extract vertical profiles at the lats/longs and optionally time of year (and so can be used to create sections, for example.) |
| get_clim | alternative to get_clim_casts |
| atday | evaluate the mean and temporal harmonics at a particular day-of-year. |
| atdaypos | as for atday, but also interpolates to desired locations |
| dep_csl, csl_dep | convert between depth (m) and CSIRO standard depth levels (CSL) |
Ridgway K.R., J.R. Dunn, and J.L. Wilkin, Ocean interpolation by four-dimensional least squares -Application to the waters around Australia, J. Atmos. Ocean. Tech., Vol 19, No 9, 1357-1375, 2002
Dunn J.R., and K.R. Ridgway, Mapping ocean properties in regions of complex topography, Deep Sea Research I : Oceanographic Research, 49 (3) (2002) pp. 591-604
CARS2006 metadata record: MarLIN record: 8540, Anzlic identifier: ANZCW0306008540
This webpage is itself the authoritative reference for CARS2006.
If you use CARS in your work, please acknowledge CARS and CSIRO Marine Laboratories as its source.
CARS2006 is available as netCDF files via ftp or as ASCII GIS-friendly format or via netCDF access via OpenDAP (formerly known as DODS.)
Before retrieving data please read the conditions below and acknowledge that you accept them. Acceptance of the conditions will activate the download addresses page.
The User acknowledges that the Product was developed by CSIRO for its own research purposes. The CSIRO will not therefore be liable for interpretation of or inconsistencies, discrepancies, errors or omissions in any or all of the Product as supplied.
Any use of or reliance by the User on the Product or any part thereof is at the User's own risk and CSIRO shall not be liable for any loss or damage howsoever arising as a result of such use.
The User agrees that whenever the Product or imagery/data derived from the Product are published by the User, the CSIRO Marine Laboratories shall be acknowledged as the source of the Product.
The User agrees to indemnify and hold harmless CSIRO in respect of any loss or damage (including any rights arising from negligence or infringement of third party intellectual property rights) suffered by CSIRO as a result of User's use of or reliance on the Data.
If you accept these conditions please enter the information below for our records, and press Accept.
Data was screened for duplicates and bad positions, outliers to global t-s relations, and outliers of residuals to intermediate mappings.
The mapping algorithm is adapted from the weighted least-squares quadratic smoother, known as a "loess" smoother. Quadratics were fitted in horizontal and vertical coordinates, with bathymetry-influenced weighting. Annual and semiannual harmonics were simultaneously fitted, with them being damped with depth until first semiannual and then annual fit is extinguished.
For every mapped point, a (zonally stretched) radius was calculated that provided 400 data points at that depth. Other points were used from one standard depth above and below, if their combined XY-radius, Z-distance, and bathymetry-weight-distance fell within the 400-point horizontal radius. That is, in ocean of uniform depth, the data source region roughly forms a 3 dimensional ellipse. An important characteristic of this type of mapping is that length scales are automatically adapted to data density, providing maximum resolution in areas of high sample density.
A value is provided everywhere the ocean is deep enough, and one gridpoint landwards of each depth "shoreline" (this allows interpolation between gridpoints to locations near the shorelines).
These gif images are also freely available by anonymous ftp from ftp.marine.csiro.au in directory /pub/dunn/CARS2006/gifs/.
| T,S 0m | T,S 100m | T,S 2000m |
| Property | Surface | 100m |
| Temperature (degreeC) | T 0m - stats T0 | T 100m - stats T100 |
| Salinity - Practical Salinity Units (PSU) | S 0m - stats S0 | S 100m - stats S100 |
| Oxygen - millilitre per litre (ml/l) | O2 0m - stats o2 0 | O2 100m - stats o2 100 |
| Silicate - micromole per litre (uM) | Si 0m - stats si 0 | Si 100m - stats si 100 |
| Phosphate - micromole per litre (uM) | PO4 0m | PO4 100m |
| Nitrate - micromole per litre (uM) | NO3 0m | NO3 100m |
Jeff Dunn CSIRO CMAR - last updated 5/8/2010
