A NEW HYDROGRAPHIC GRIDDED PRODUCT FOR THE...

Gabriel JORDÀ, Loïc HOUPERT, Damià GOMIS, Anthony BOSSE, Pierre TESTOR, Josep LLASSES, Charles TROUPIN

[email protected]

INSTITUT MEDITERRANI D’ESTUDIS AVANÇATS

A NEW HYDROGRAPHIC GRIDDED PRODUCT FOR THE MEDITERRANEAN. IMPACT OF XBT CORRECTION ON

HEAT CONTENT ESTIMATES.

The 4th XBT Workshop: XBT Science and the Way Forward 11-13 November 2014, 2014

Beijing, China

mailto:[email protected]�

The Mediterranean Sea

Marginal Sea

Highly populated coasts

Large Biodiversity (8% of known marine species with

0.8% of surface)

Hot spot for Climate Change (Giorgi 2006)

Historical hydrographic data are sparse in time and space, which is inconvenient for climate studies

Gridded products are required for scientific studies and model validation BUT … A lot of extrapolation is required … Are they accurate enough?

Motivation

Optimal interpolation is usually chosen to generate the maps and to provide an estimate of the formal error, .. but it depends on a-priori assumptions : A more accurate estimate of the associate uncertainties is required

Motivation

Optimal Interpolation

The goal is to produce a new and updated hydrographic gridded product for the Mediterranean

along with a realistic measure of its errors.

Motivation

Data base as collected in Houpert et al (2014) Includes XBT, MBT and CTD data collected by the Medar-MEDATLAS project (till 2000), WDO and National programmes not included in the international databases (from Italy , France and Spain). It also includes ARGO profilers (from Coriolis Data Center) and GLIDER data (EGO project) Quality control procedure applied to all raw data as well as specific corrections for XBT and MBTs (see later). About 140.000 temperature profiles and 75.000 salinity profiles are kept

Methodology -> The in – situ data


· Relative importance of each type of measurements evolves on time · Before 1965 only MBTs (less accurate) · Few CTDs during last decade (dissemination problem?) · Large contribution of Gliders during the last decade but profiles can be redundant · Few data in the deeper layers

Termosalinometer data is also available but not considered here (it provides only surface data only) Bottles have also not be included


· Accuracy 0.001 psu (CTD), 0.002 psu (Argo), 0.01 psu (Glider, moorings) · Similar to temperature BUT without XBT and MBT · Calibration of salinity data is very important. An offset O(0.01) psu is possible between historical data · Thermal lag problem specially in GLIDERs and ARGO floats

Termosalinometer data is also available but not considered here (it provides only surface data only) Bottles have also not be included

-Mapping algorithm based on a variational approach (close to Optimal Interpolation). The DIVA tool (Troupin et al., 2012) has been implemented -Correlation length scale and noise-to-signal ratio tuned using data from models -Analysis performed in a grid of 0.2º of spatial resolution and 33 levels from surface to bottom

Methodology -> Mapping procedure

PRODUCT Time span DX Zmax Time Resolution

XBT/MBT Correction

IMEDEA 1950-2013 0.2º Bottom Monthly Cowley 2013

MEDATLAS2 1950-2001 0.2º Bottom Yearly Not

EN3 1950-2011 1º Bottom Monthly Not


ISHII 6.7 1945-2006 1º 700m Monthly Ishii 2006


Methodology -> Comparison to other existing products


XBT/MBT Correction







IMEDEA PRODUCT EN3 PRODUCT

SST January 1950



XBT/MBT Correction







7.1 7.15 7.2 7.25 7.3 7.35

x 105

13.4

13.5

13.6

13.7

13.8

13.9

14

14.1MEDITERRANEAN MEAN TEMPERATURE FROM EN3

Volume correctedVolume UNcorrectedSurface Area in EN3=3.8 ·1012

m2 while in IMEDEA is 2.6 ·1012 m2, much closer to the actual values. Also, the averaged depth in EN3 is ~1900m while in reality is 1350 m.



XBT/MBT Correction








The error of the product is estimated using an ensemble of surrogate data From a numerical model (NEMOMED8, Beuvier et al. 2010) we extract virtual observations at the same locations and time than the actual observations were. Then, we generate the maps using the same procedure than with real data Finally we compare them with the original model fields “the reality”.

-5 0 5 10 15 20 25 30 3530

35

40

45

NEMO ANOMALY Field for 01-Dec-1999

-2

-1

0

1

2

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35

40

45

OI ANOMALY Map for 16-Dec-1999 12:00:00

-2

-1

0

1

2

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35

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OI-NEMO for 16-Dec-1999 12:00:00

-1

0

1

Methodology -> Error estimate

We repeat the procedure N times randomly chosing the year, so for each data distribution we have an estimate of the associated uncertainty

Methodology -> Error estimate

Uncertainties linked to data gaps but also to unresolved scales

and background (first guess) unaccuracies

Estimated Error for surface salinity maps associated to different data coverage

Validation

Mediterranean Averaged SST

Deseasoned and 12-months smoothed time series

PRODUCT Correlation RMSE (ºC)

IMEDEA 0.84 0.13

EN3 0.69 0.21

EN4 0.73 0.19

ISHII v6.7 0.72 0.28

ISHII 6.12 0.81 0.17

Uncertainty of the new product

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46test23 Correlation

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1

1

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T10m - Correlation

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46test23 RMSE

0

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1 1ºC

0ºC

T10m - RMSE

(From the experiments with model data)

1960 1965 1970 1975 1980 1985 1990 1995 2000

500

1000Number of observations per month TEST23

1960 1965 1970 1975 1980 1985 1990 1995 20000

0.5

1

ºC

RMSE of anomalies TEST23

Number of observations per month

Averaged RMSE per month

Quality evolves in time (seasonally)

Better results in winter (less variability, easier to capture even with less observations)


Different from Formal Error!!!

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46test11 RMSE

0

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0ºC

1ºC

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0

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46test15 RMSE

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0 psu

1psu

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RMSE Correlation

T100m

S 10m

When the characteristic spatial scales of the dominating signal are short the uncertainties increase

(From the experiments with model data)


Averaging for different layers and subbasins may increase the reliability of the product

The uncertainty range must be compared to the range of the variability


The uncertainty range must be compared to the range of the variability

Averaging for different layers and subbasins may increase the reliability of the product


Sources of uncertainty -Lack of data -Background (in particular biases related to decadal variability) -Errors in the raw profiles (those which are beyond instrumental uncertainty; not considered here )

Impact of the XBT-MBT correction

We produce three versions of the product using : -No correction for the XBT/MBT -The Hanawa Correction -Cowley et al., 2013 The MBT data was used until ~1965 The XBT data from then to 1985. After that it is still used although in a relatively small fraction


3D AVERAGE


3D AVERAGE 0-150 m

150 – 600m 600m - bottom

- New hydrographic historical database for the Mediterranean has been generated for the period 1950-2013

- Realistic estimate of uncertainties provided for the maps and averaged quantities

- Non-negligible impact of XBT corrections during the 70’s . However, uncertainty related to sparseness of data is larger …

- Freely available in few weeks upon request

- However, uncertainties are large, specially for salinity and intermediate/Deep layers. Please, use the uncertainty estimates!

Conclusions

A NEW HYDROGRAPHIC GRIDDED PRODUCT FOR THE...

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