WP 11 Optimisation of ocean observing capability Ute Schuster (u.schuster@exeter.ac.uk) 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

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.

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 1981-2011 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.

WP Highlights WP 11 – Optimisation of ocean observing capability The figure shows the observations of DIC in the North Atlantic in 1972-2013. 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.

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.

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.

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.

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

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

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

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]

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]

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

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

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

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

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»

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

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

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

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

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