a hydrographic and bio-chemical climatology of the mediterranean and the black sea: some statistical...
TRANSCRIPT
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 ([email protected])
2.GHER, University of Liège, Belgium, ([email protected])
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