1. WP 11 Optimisation of ocean observing capability
Ute Schuster
University of Exeter, UK
UNEXE (UK), NERC (UK), HCMR (Greece), UiB (Norway), CNRS (France), ULPGC (Spain)
WP leaders: Please prepare
General Assembly 2017
Vilanova i la Geltrú, Spain; 27 – 29 June 2017

2. Objectives The overall objective is research
WP 11 – Optimisation of ocean observing capability
The overall objective is research
on the specification for an optimum observational network of FixO3 platforms
integrated and complemented by other platforms,
to optimise the development of multinational, multiplatform observing networks such as ICOS and EMSO.

3. WP Highlights
WP 11 – Optimisation of ocean observing capability
Top: Graphical representation of the 17 biomes, as defined by Fay and McKinley (2014).
Bottom: Comparison of trends in surface pH in the fully sampled and sub-sampled according to the observational coverage in SOCATv2 from the NorESM1-ME historical and future RCP8.5 model run. The figure is modified from Fig. 5 in Lauvset et al. (2015).
NorESM1-ME – has fully interactive biogeochemistry.
Only in the SP-STPS biome—which is in the southern Pacific Ocean that is known to have poor data density—are the two trends significantly different. A need for careful consideration of representativeness when comparing model-derived future changes and trends based on data. In order to reduce the large uncertainties in the pH trend estimates improved sampling strategies, especially in the South Pacific, are necessary.

4. WP Highlights
WP 11 – Optimisation of ocean observing capability
The figure shows the observations of DIC in the North Atlantic in
The figure shows the error (visualized as (estimated error/basin-wide average error)*100 incurred by gap-filling the observations
These figures show how the error associated with a statistical gap-filling method is linked to the data density.

5. WP Highlights
WP 11 – Optimisation of ocean observing capability
The map shows the phenomena of interest and in which regions they are highly relevant.
The work done in WP11 also made a significant contribution to the AtlantOS report D1.3: “Analysis of the capacities and gaps of the present Atlantic Ocean Observing System.» This map is from that report and shows the phenomena of interest in different biomes where they are particularly relevant.

6. WP Highlights WP 11 – Optimisation of ocean observing capability
The DIVA gap-filling of oxygen from GLODAPv2 leads to significant errors in the oxygen minimum zones in the eastern equatorial Atlantic. In this area ship-based observations are not sufficient to capture the deoxygenation phenomena.

7. WP Highlights WP 11 – Optimisation of ocean observing capability
Ocean acidification is an important phenomena throughout the Atlantic Ocean. The DIVA gap-filling shows that ship-based ocean acidification observations are too currently too few and the errors are too large to fully capture the phenomena. Especially in the equatorial and southern Atlantic. Incorporating fixed stations (especially long-term time series like BATS) and Argo floats into such maps is being planned.

8. WP Highlights Truth is Medusa 2.0
WP 11 – Optimisation of ocean observing capability
An observational system sampling experiment (OSSE)
e.g. sea surface temperature [oC]
Truth is
Medusa 2.0
Exists 3-dimensional on a 1 o lat / lon grid

9. Starting with an idealised sea surface temperature field
WP Highlights
WP 11 – Optimisation of ocean observing capability
Starting with an idealised sea surface temperature field

10. Adding more and more sampling locations
WP Highlights
WP 11 – Optimisation of ocean observing capability
Adding more and more sampling locations

11. WP Highlights Repeating for different parameters
WP 11 – Optimisation of ocean observing capability
Repeating for different parameters
e.g. SST [oC] and surface pCO2 [µatm]

12. Another observational system sampling experiment (OSSE)
WP Highlights
WP 11 – Optimisation of ocean observing capability
Another observational system sampling experiment (OSSE)
e.g. surface pCO2 [µatm]

13. Running OSSEs with different idealised sampling regimes
WP Highlights
WP 11 – Optimisation of ocean observing capability
Running OSSEs with different idealised sampling regimes

14. Recreating maps using different ocean regions
WP Highlights
WP 11 – Optimisation of ocean observing capability
Recreating maps using different ocean regions
Fay and McKinley, 2014, ESSD
Gruber et al., 2009, GBC
Schuster et al., 2013, BG
Peylin et al., 2013, BG
Longhurst, 1995, PiO

15. Calculating sea-air flux of CO2
WP Highlights
WP 11 – Optimisation of ocean observing capability
Calculating sea-air flux of CO2

16. WP Highlights Using Monte Carlo runs of OSSEs Towards the uncertainty
WP 11 – Optimisation of ocean observing capability
Using Monte Carlo runs of OSSEs
Towards the uncertainty

17. Collaboration
WP 11 – Optimisation of ocean observing capability
The work done in FixO3 WP11 was used in AtlantOS to produce their “Analysis of the capacities and gaps of the present Atlantic Ocean Observing System.»
It is highlighted that «the low % of biological measurements relative to biogeochemical parameters measured hinders correct interpretation of observed changes in the phenomena of interest»

18. Lessons Learned
WP 11 – Optimisation of ocean observing capability
All error maps produced to evaluate the current network were derived using the ship-based measurements in GLODAPv2 or SOCATv4.
It is now clear thata multi-platform observational network is required.
For each parameter, the optimum observational network is slightly different.
Uncertainty determinations are crucial

19. Remaining Activity
WP 11 – Optimisation of ocean observing capability
Determine the uncertainty to obtain results of “high confidence level”
Write/complete the final document

20. FixO3 Legacy
WP 11 – Optimisation of ocean observing capability
Cross-platform collaboration is highly beneficial for quality of science and research
not only cross-platform, but also cross-parameter
The only way to reduce uncertainty and increase confidence levels

21. FixO3 Legacy More is more: Data synthesis from the PAP SO
WP 11 – Optimisation of ocean observing capability
More is more:
Data synthesis from the PAP SO
combined with other observing systems produces
a bigger and clearer picture.
The Pteropod question:
Why did Diacria trispinosa occur in significant numbers in 2006, but not much before or after?
What triggers
the change
in distribution?
Physics
Physics
Biogeochemistry
NAO
PAP-SO
Physics
ARGO
Satellite
Physics
Biology
Biology
SAHFOS

22. Finding answers at PAP FixO3 Legacy
WP 11 – Optimisation of ocean observing capability
Finding answers at PAP
Diacra trispinosa northern distribution affected by deeper water temperature change and food supply.
Answers were found in biology – chlorophyll – Satellites
And in physics – temperature- ARGO
The answers were more complicated than the question
The answers drew on many different data sources