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CORA description

Szekely, T., Gourrion, J., Brion, E., Von Schuckmann, K. Reverdin, G., Grouazel, A., and Pouliquel, S., 2015: CORA4.1: A delayed-time validated temperature and salinity profiles and timeseries product. Proceeding from 7e EuroGOOS conference, To be published.

Cabanes, C., A. Grouazel, K. von Schuckmann, M. Hamon, V. Turpin, C. Coatanoan, F. Paris, S. Guinehut, C. Boone, N. Ferry, C. de Boyer Montégut, T. Carval, G. Reverdin, S. Pouliquen, and P. Y. Le Traon, 2013: The CORA dataset: validation and diagnostics of in-situ ocean temperature and salinity measurements. Ocean Science, 9, 1-18, http://www.ocean-sci.net/9/1/2013/os-9-1-2013.html, doi:10.5194/os-9-1-2013

CORA : COriolis ocean database for ReAnalysis

by T.szekely(7), J. Gourrion (7), C. Cabanes (1),(7)*, A. Grouazel (1), K. von Schukmann (2), M.Hamon (3), V. Turpin (4)

C.Coatanoan (4), F. Paris (4), S. Guinehut (5), C.Boone (5), N.Ferry (6), C. de Boyer Montégut (3)

T. Carval (4), G. Reverdin (2), S. Pouliquen (3) and P.-Y. Le Traon (3)

* Corresponding author : Cécile CABANES, Cecile.Cabanes@ifremer.fr

(1) : Division Technique de l’INSU, UPS855, CNRS Plouzané France

(2) : LOCEAN - CNRS, Paris France

(3) : LOS IFREMER, Plouzané France

(4) : SISMER IFREMER, Plouzané France

(5) : CLS - Space Oceanography Division, Ramonville St Agne, France

(6) : MERCATOR OCEAN, Ramonville St Agne, France

(7) : now at IUEM, UMS3113, CNRS-UBO-IRD, Plouzané France

 

If you use the CORA data, please cite : 

Szekely, T., Gourrion, J., Brion, E., Von Schuckmann, K. Reverdin, G., Grouazel, A., and Pouliquel, S., 2015: CORA4.1: A delayed-time validated temperature and salinity profiles and timeseries product. Proceeding from 7e EuroGOOS conference, To be published.

Cabanes, C., A. Grouazel, K. von Schuckmann, M. Hamon, V. Turpin, C. Coatanoan, F. Paris, S. Guinehut, C. Boone, N. Ferry, C. de Boyer Montégut, T. Carval, G. Reverdin, S. Pouliquen, and P. Y. Le Traon, 2013: The CORA dataset: validation and diagnostics of in-situ ocean temperature and salinity measurements. Ocean Science, 9, 1-18, http://www.ocean-sci.net/9/1/2013/os-9-1-2013.html, doi:10.5194/os-9-1-2013

The French programme CORIOLIS, as part of the French operational oceanographic system, produces the COriolis dataset for Re-Analysis (CORA) on a yearly basis. The latest release CORA4.1 covers the period 1950 to 2013 and contains in-situ temperature and salinity data from different instrument types extracted from the Coriolis database. Several tests have been developed to ensure a homogenous quality control of the dataset and to meet the requirements of the physical ocean reanalysis activities (assimilation and validation). The CORA dataset is a product of the CMEMS catalogue (available at http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b ), and aims to be a reference as a dataset produced by in situ TAC of this European project. 

 

Motivations

An ideal set of in situ oceanographic data should cover the entire global ocean and be continuous in time, subject to regular quality control and calibration procedures, and encompass several spatial and temporal scales. This goal is not an easy one to achieve in reality, especially with in situ oceanographic profiles because they have basically many origins as there are various scientific campaigns to collect them.

Efforts to produce such ideal global dataset have been done for many years, especially since Levitus (1982). The most comprehensive and widely used today is the World Ocean Database produced by the National Oceanographic Data Centre (NODC) Ocean Climate Laboratory (OCL), whose latest version WOD13 contains historical and modern temperature, salinity and plankton data from the 19th century to 2013. More recently, the MetOffice Hadley Centre produced and maintained a quality-controlled subsurface ocean and salinity dataset (Ingleby and Huddleston, 2007, Good et al. 2013), whose last version EN4 spans the period from 1900 until the present. 

Launched at the beginning of the 2000’s, the French operational oceanographic programme CORIOLIS aimed at providing ocean in situ measurements to the French operational ocean analysis and forecasting system (Mercator Ocean) and to contribute to a continuous, automatic and permanent observation networks (Coatanoan and Petit de la Villeon, 2005). The CORIOLIS Data Centre (DAC) has been set up to gather, qualify and distribute data from the global ocean both in real and delayed time.

Since 2010, The DAC and the R&D Coriolis team worked together to produce a quality-controlled dataset on a regular basis. The new CORA4.1 global dataset is now available with the objective to update it each year with all the data acquired during the last full year available and to update the entire CORA dataset (full times-pan) every two years. This demand is a necessity for reanalysis projects (such as GLORYS, see Ferry et al., 2010) including validation, initialization and assimilation of models (Lellouche et al., 2012) and those of general oceanographic research studies including ones on climate change (von Schukmann and Le Traon, 2011 ; Souza et al., 2011 ; Guinehut et al., 2012).

 

Data and Methodology

Every day, the Coriolis data centre collects temperature  and salinity measurements from french projects (SO ARGO, SO PIRATA, SO SSS,...), as well as european parteners (SeaDataNet, EuroGOOS and COPERNICUS) and international projects (ARGO, GOSUD, OceanSITES, GTSPP,etc...). While the CORIOLIS database (http://www.coriolis.eu.org/cdc/data_selection.htm) is updated every day as new data arrive, the CORA database corresponds to an extraction of all in situ T/S profiles and timeseries from the CORIOLIS database at a given time. In this context, measurements covering the periode from 1990 to 2013 have been extracted from the Coriolis database on june 2014 and several delayed time validated datasets have been agreggated to the CORA database along year 2014.

  • XBT and CTD measurements extracted from the EN4 dataset (http://www.metofice.gov.uk/hadobs/en4/) and covering the period 1950-1989.
  • Temperature and salinity measured by elephant sea carying sensors (www.biology.st-andrews.ac.uk/seaos/, Roquet et al, 2013, 2014).
  • Delayed time worldwide surface drifters measurements (G. Reverdin, personal communication).
  • Temperature and salinity measurements by thermosalinographs embedded in trans-oceanic cargos. (Delcroix et al, 2005, www.legos.obs-mip.fr/observations/sss/).

 

Data received by the Coriolis data centre from different sources are put through a set of real or near-real time quality control procedures (Coatanoan and Petit de la Villeon, 2005) to ensure a consistent dataset. Each measurement for each profile is associated with a control quality flag, ranging from 0 to  with flag 1 for good data and flag 4 for bad data. Beside those basic tests, several other quality checks have been developed to produce CORA3, in order to reach the quality level required by the physical ocean reanalysis activities. At the DAC, more systematic tests are performed in real or near-real time. A statistical test based on objective analysis method (Gaillard et al., 2009), a visual quality check run on data managed by the Coriolis centre or an altimetric test operated on ARGO data (Guinehut et al.2009) led to improve the quality of the CORA dataset. 

The data go then through delayed-time validation procedures. Only the data considered as good or probably good (flag 1 or 2) after real and near-real time tests or those which have never been checked (flag 0) are further verified in delayed mode. Comparisons with climatology (the anomaly method), and tests designed for Argo floats are applied before a model background check based on global ocean reanalysis GLORYS2V1 (Ferry et al., 2010). Each time a profile fails a test, it is checked visually and control quality flags of each measurement at each level are examined and changed if necessary. The visual quality check is a very important step in the quality control procedure of the CORA dataset since it allows the rejection of observations that have passed the other tests, or allows the requalification of rejected observations as good measurements.  

 

Figure 1: Decimal logarithm of the number of temperature (top) and salinity (bot.) profiles in CORA4.1 per year at different depths and as a function of time.

 

The CORA4.1 dataset not only contains the raw parameters such as temperature, salinity, pressure or depth, as received from the instrument, it can also include adjusted parameters, i.e. temperature, salinity, pressure or depth corrected from a drift or offset. The data types affected by these adjustments are Argo floats and XBTs. For Argo data, it is the responsibility of each DAC to provide data corrections both in real time and delayed mode. The Coriolis data centre, as a GDAC, gathers these corrections and stores raw and adjusted parameters in the Coriolis database. No supplementary correction has been made or applied to the Argo data in the CORA4.1 dataset.

On the contrary, it is chosen to apply a correction on XBT measurements in the CORA 4.1 dataset, and to write the corrected temperature in an adjusted field apart from the original one, so one can still use the original TEMP quantity and apply his own correction. This XBT correction method is based on the work by Hamon et al. 2012. This method first applies a correction on the mean temperature offset in the upper layer, between XBT measurements and CTD measurements. Then, it applies a correction based on the computation of the quadratic regression of the mean depth error between XBTs and a local mean temperature measured by CTDs. The XBT measurements are separated into 2 categories: the shallow XBTs, with a maximal depth lower than 500 m, and the deep XBTs with a maximal depth lower than 500 m. These two groups are then divided into 2 sub-categories, the "Warm" XBT's, for which mean temperature between 0 and 200 m  is higher than 8°C , and the "Cold" XBTs for which this temperature is lower than 12°C, the two categories overlapping for lowering the gap between the two corrections. 

 

Figure 2: Yearly mean anomaly between XBT measurements and reference CTDs before (top) and after (bot.) correction.

 

Figure 2 gives the amplitude of the anomaly between XBT profiles and reference CTDs before and after the correction. It shows that the XBT correction is efficient between 150 and 800m depth and is very efficient before 1980 and after 2005. The noise observed at the top and the bottom of the water column is influenced by the higher temperature variability at the surface and the lower number of matchpoints below 800m.

Uses of the CORA dataset

Oceanic parameters form in situ T/S profiles can be useful for analysing the physical state of the global ocean and have a large range of vital applications in the multidisciplinary fields of climate research studies. Several GSSL (Global Steric Sea Level) estimations based on Argo and/or other in situ observations have been derived over the past couple of years (Willis et al., 2008 ; Cazenave and Llovel, 2010; von Schukmann and Le Traon, 2011). But these global statistic analyses are not consistent mainly because of differences in estimation periods, instrumental biases, quality control and processing issues, the role of the salinity and the influence of the reference depth for GSSL calculations. 

Results obtained with the previous CORA3 dataset on GSSL estimations have been compared with GSSL estimated from von Schukmann and Le Traon (2011) (figure 3) based on Argo data only. Using the CORA3 dataset, the 6-year GSSL trend is 0.64+/- 0.12 mm.yr-1 and lies within the error bars of the von Schukmann and Le Traon (2011) estimates. This quite good agreement may be due to the fact that the method of GSSL computation is not very sensitive to any bad data that possibly remains in the dataset or that some residual positive and negative biases in in situ data compensate each other. More sensitivity studies are therefore needed to estimate GSSL from the CORA3 dataset.

CORA can be used also to construct climatologies of heat content, depth of the thermocline or climate indices which are very useful for validating ocean model outputs and improve their quality or assess their results. For example, de Boyer Montégut et al., 2007 validate their OGCM mixed layer depth outputs against in situ observations in the northern Indian Ocean. 

Finally, an important application of the CORA database is its use in ocean reanalysis. In France, global ocean reanalysis activity is a joint collaboration between Mercator-Océan, Coriolis Data Centre and several oceanographic and atmospheric research laboratories in the framewrok of GLORYS project (Ferry et al., 2010). 

 

 

fig3_GSSL

Figure 3: Estimation of GSSL for the year 2005–2010 with a 1500 m reference depth. The calculation is based on a simple box aver- aging method described in von Scuckmann and Le Traon (2011) (VST2011). Results obtained with CORA3 (red and green curves) are compared to those obtained by VST2011. The 6-yr trends obtained are 0.64 ± 0.12 mm yr−1 with CORA3 (0.58 ± 0.10 mm yr−1 for CORA3 with only Argo data) and 0.69±0.14mmyr−1 for VST2011. Error bars (red areas) are shown for CORA3 (all data) and are calculated as described in VST2011.This total error includes the uncertainties on the averaged parameter in every 5◦ × 10◦ × 3- month box and the choice of the reference climatology, but it does not take into account possible unknown systematic measurement errors (Cabanes et al., 2013)

 

Interesting Links

CMEMS Service Desk : servicedesk.cmems@mercator-ocean.eu 

GTS system : http://www.wmo.int

Coriolis project : http://www.coriolis.eu.org/

GOSUD project : http://www.gosud.org

Voluntary Observing Ships project - VOS : http://www.vos.noaa.gov/vos_scheme.shtml

Gliders data : http://www.ego-network.org

MEDS : http://www.meds-sdmm.dfo-mpo.gc.ca/isdm-gdsi/index-eng.html

 

References

 

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  • Cabanes, C., A. Grouazel, K. von Schuckmann, M. Hamon, V. Turpin, C. Coatanoan, F. Paris, S. Guinehut, C. Boone, N. Ferry, C. de Boyer Montégut, T. Carval, G. Reverdin, S. Pouliquen, and P. Y. Le Traon, 2013: The CORA dataset: validation and diagnostics of in-situ ocean temperature and salinity measurements. Ocean Science, 9, 1-18, http://www.ocean-sci.net/9/1/2013/
  • Cazenave, A. and Llovel, W., 2010 : Contemporary Sea Level Rise, Annual Review of Marine Science, 2, 145–173, doi:10.1146/annurev- marine-120308-081105.
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  • Delcroix, T. and co-authors (2005). Time and space scales for sea surface salinity in the tropical ocean. Deep-sea Research, 52, 787-813.
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