a hydrographic and bio-chemical climatology of the mediterranean and the black sea: some statistical...

A HYDROGRAPHIC AND BIO-CHEMICAL CLIMATOLOGY OF THE

MEDITERRANEAN AND THE BLACK SEA: SOME STATISTICAL PITFALLS

(modb.oce.ulg.ac.be/medar)

Michel Rixen1, Jean-Marie Beckers2 and Catherine Maillard3

The Color of Ocean DataBrussels, Belgium, November 2002

1. SOC, Southampton, UK (myr@soc.soton.ac.uk)

2.GHER, University of Liège, Belgium, (JM.Beckers@ulg.ac.be)

3. SISMER, Ifremer, Centre de Brest, BP70, 29280 Plouzane, France

Task I, II, III, V

At 15:20

Recent advances in oceanographic data management of the Mediterranean and Black Seas: The MEDAR/MEDATLAS 2002 data base

By C. Maillard and E. Balopoulos (France, Greece)

• Objective analysis– Optimal interpolation (OI) (+ sub-optimal schemes)

– Successive corrections (SC) (+ sub-optimal schemes)

– Variational inverse model (VIM) (stat. Equiv. to OI)

– ….

Task IV: climatology

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The Variational Inverse Model

• Dimensional analysis+Bessel K1 correlation function

L ,S/N , ,2

2

i10

Finite element mesh

VIM:no bias

OI:Information crosses boundaries

Computational cost

Field

Errorfield

OI

VIM

OI

VIM

Climatology: some details

• 25 standard vertical levels• (Obsolete: automatic QC:

– data rejected if outside 3*std locally)• Sandwell bathymetry at 2’

– Used for contours and FEM• Reference field=climatic field

– (semi-normed analysis)• T,S,Alkalinity,DOX,NH4,NO2,NO3,PO4, SiO4,H2S,pH,Chl• Climatologic, seasonal, monthly, inter-annual and decadal temporal

windows when relevant• 20km x 20 km, 8 km x8 km or 5 km x 5 km resolution• Analyzed and error fields

A good example: enough data

Another good example: enough data

Even more good examples…

Alboran, 200m, many data Levantine basin, 200m, few data

VIM and OI: statistical hypotheses

- gaussian frequency distributions- statistics are homogeneous and isotrope- uncorrelated noise

Nitrite Salinity

Phosphate Silicate

Ph Temperature

Vertical distribution of temperature

Yearly distribution of salinity

Monthly distribution (salinity)

Ionian

1980 Months 21986 Months 3 4 91988 Months 71990 Months 10 111992 Months 51994 Months 1

Levantine

1984 Months 101986 Months 8 9 10 11 1988 Months 3 8 91990 Months 7 10 111994 Months 1 2

Temp

Possible bias ?

Temp

2D analysis appropriate?

PO4 at 30m: coastal (<18km) and/or shallow sounding (<50m)

With coastal data

Without coastal data

Difference

Phosphate (mmole/m3)

Ionian (36-37 ºN , 19-20ºE)

Some potential problems…

• Statistical hypothesis• Correlation length=100-300km, so at least 200-

2000 data homogeneously distributed needed!• Few data at deeper levels:

– extrapolate from upper levels?

• Coastal data bias beyond the physical diffusion/ advection through correlation length: in several areas the only existing data

• Last but not least: obvious errors in the raw data (e.g. instrument calibration)

Selection of robust fields

• Annual , seasonal and monthly climatology – Temperature, Salinity

• Annual and seasonal– Oxygen, Silicate, Phosphate – Hydrogen sulphide (H2S) in the Black Sea

• Annual only– Nitrate, Nitrite, PH, Ammonium, Alkalinity,

Chlorophyll

• So far: (almost) the best we can do…

Future

• More data!

• Other parameters (e.g. ADCP, DIC, POM, DOC,…)

• Multivariate analysis and QC (bio-chemical data!)

• 3D Variational analysis?

• More high level final products

• Your feedback

MEDAR Climatology: “modb.oce.ulg.ac.be/medar”

Free access to - 2000 fields- 20000 figures- 600 animations