1. EUTROPHICATION – NE ATLANTIC
EMODnet Chemistry 3 – Final meeting January 2019
Madrid, Spain
EUTROPHICATION – NE ATLANTIC
Products generation
The Aggregated & Qced dataset
The global DIVA maps
The High resolution maps near river mouth
Delphine LEROY
Julie GATTI
IFREMER

2. Outline Results on the Aggregated&Qced dataset Data distribution
Data access / Conclusions
Results on Maps generation
Global DIVA maps
High resolution DIVAnd maps near LOIRE river mouth
Conclusions
Madrid, January 2019

3. Eutrophication – Ocean acidification
Aggregated & Qced dataset
NE Atlantic region : [20°N to 48°N] and [42°W to 0°] from 12 CDI partners
→ stations after aggregation and QC
- Only 6% of restricted data!
- Mainly along the coast from monitoring studies
Madrid, January 2019

4. Eutrophication – Ocean acidification
Aggregated & Qced dataset
Global spatio-temporal distribution by data types
Vertical Profiles (VP)
Time Series (TS)
1980s
1970s
Madrid, January 2019

5. Eutrophication – Ocean acidification
Aggregated & Qced dataset
Distribution by parameters :
– 16 parameters for Vertical Profiles (VP)
– 7 parameters for Time Series (TS)
5 parameters with the best spatiotemporal coverage for both VP/TS:
→ Chla, O2, Phosphate, Silicate and DIN (NO3+NO2+NH4)
Madrid, January 2019

6. Eutrophication – Ocean acidification
Aggregated & Qced dataset
Status after QC
Check format errors
Check duplicates
Check broad ranges, ≤0 values, LOD,…
Mean of :
90% Good data (QF=1,2,5,6,8)
10% Bad data (QF=3,4)
Distribution of Good/bad data by 5 parameters
Madrid, January 2019

7. Eutrophication – Ocean acidification
Aggregated & Qced dataset
Few results of parameter distribution (VP)
Oxygen
Phosphate
Madrid, January 2019

8. Data Access / Conclusions
Eutrophication and Ocean Acidification Aggregated Dataset
Visualisation on Oceanbrowser (section Plots)
Full Web description on Sextant (filter=aggregated datasets) and DOI
Detailed documentation
Aggregation and Quality checks are improved by the upgrades of ODV and OCTOPUS.
In Chemistry, the Quality control becomes trickier with the new high technologies of continuous water body measurements (e.g. Ferrybox) and the mixed of calibrated/uncalibrated data.
Madrid, January 2019

9. Global DIVA maps Running DIVA. Our strategy to :
Final product : 6 years climatologies for each season
Dissolved oxygen, Phosphate, Silicate, Chlorophyll-a and DIN
34 depth levels from surface to 3000 m deep
Running DIVA. Our strategy to :
Compute correlation lengths
Use both vertical profiles and timeseries
Results overview
: Comparison with WOA
Comparison with timeseries data
Madrid, January 2019

10. Global DIVA maps Correlation lengths (L)
Correlation lengths depend on the amount of data you have. Correlation lengths’ estimates are less relevant when only few data are available
The amount of available data can vary a lot between one 6-years climatology to the next.
We’d rather to ensure a certain consistency between consecutive 6-years maps
Nutrients concentrations change with season
We choose to compute 4 L profiles : one for each season but over the whole period ( ) and then apply the corresponding profile to all 6-year climatologies
Madrid, January 2019

11. Global DIVA maps Vertical profiles versus Timeseries Timeseries
- measurements available in the open waters
- give information in depth
- one date only
 important measures but RARE compared to timeseries
Timeseries
- mostly close to coasts
a lot of measurements at the same point !
 for a fixed time range, much more measures than vertical profiles !
time
We choose to use weights on datas : 1 (i.e. 100%) for vertical profiles but only 0.1 (10%) for timeseries
Madrid, January 2019

12. Global DIVA maps Comparison with World Ocean Atlas
Example : Spring season – Fields of dissolved O2 with increasing depth
Good agreement between DIVA simulations and WOA !
Madrid, January 2019

13. Global DIVA maps Comparison with timeserie data
For a given location, observa-tions have been averaged over 6 years and by season
Even with different correlation lengths for each season, DIVA reproduces quite well the time variations. Maximum and minimum values are found for the right season.
Madrid, January 2019

14. High resolution DIVAnd maps near river mouths
Final product : 6 years climatologies for each season
Phosphate, Silicate and DIN
Depth levels : from surface to the river basin depth
River selection
Domain definition
Data availability
Few results
Madrid, January 2019

15. High resolution DIVAnd maps near river mouths
Loire and Garonne account for 75% of freshwater inputs to the Bay of Biscay (Lazure et al. 2009)
Loire
Catchment area : km2
Mean flow : 880 m3/s
Flow changes : between 1 and 50 l/s/km2
Mean [NO3] : 212 µmol/L
Mean [PO2] : 1.4 µmol/L
Garonne
Catchment area : km2
Mean flow : 495m3/s
Flow changes : between 1 and 135 l/s/km2
Mean [NO3] : 137 µmol/L
Mean [PO4] : 1.6 µmol/L
Loire > Garonne
Menesguen et al., Ocean and Coastal Management,
I. Khojasteh Pour Fard, Thèse de l’Université de Bordeaux.
Plan d’action pour le milieu marin. Evaluation initiale des eaux marines. Sous-région marine golfe de Gascogne
Madrid, January 2019

16. High resolution DIVAnd maps near river mouths : Loire river
Domain definition
Domain area : from 4°W to 1°W & from 46.25°N to 48°N
Grid resolution : 0.01°
Time resolution: seasonal maps for the whole period ( ) and for 6 years periods
Data weights :
timeseries and profiles have different epsilon2 values : 1 for profiles and 10 for timeseries (as espilon2= 1/SNR)
Depths levels : 0, 2, 4, 6, 8, 10, 15, 20, 25, 30, 40, 50, 75, 100, 125m
(phosphate)
Loire
~ 200 km
100 m
50 m
~ 230 km
Madrid, January 2019

17. High resolution DIVAnd maps near river mouths : Loire river
Data availability
Number of cases with no observation available over the domain (i.e. no map are produced) for a 6-year hypothesis
season
Phosphate
Silicate
DIN
Winter 7
13% 20
38% 33
62%
Spring 4 8% 28
53%
Summer 3 6% 15
28% 26
49%
Autumn 9
17% 36
68%
all 23
11% 62
29%
123
58%
 Unfortunately, a lot of maps are empty because of the low amount of data available over a reduced domain and for limited-time period
Madrid, January 2019

18. High resolution DIVAnd maps near river mouths : Loire river
Example of results for phosphate ( )
The most challenging point was the autumn season because in this case we have no profiles at all, only timeseries.
Madrid, January 2019

19. Conclusions DIVA & DIVAnd map products Global DIVA maps
All maps for the Atlantic Ocean are available on oceanbrowser
Comparisons with WOA maps and timeseries tend to show that the product’s quality is good.
High resolution DIVAnd maps near river mouth
We managed to use DIVAnd to produce maps for the Loire river
The production of this kind of maps is complicated by the fact that the data coverage is strongly reduced compared to the global case  we should really think of a larger time resolution to improve the results’quality
One challenging point was the autumn maps as we only have timeseries for this season (no profiles).
Madrid, January 2019

20. Thank you!