1. ARGO and other observing system elements – Issues and Challenges Uwe Send IfM Kiel With contributions from P.Testor J.Karstensen M.Lankhorst J.Fischer A.Koertzinger D.Roemmich P.-Y. Le Traon

2. ARGO is the single most important and most successful element of the in-situ Ocean Observing System that is being developed. Examples: boundary currents choke points (passages, straits, overflows) deep ocean (>2000m) processes that require higher temporal sampling dedicated observations in small regions/sub-basins biogeochemical observations more Yet, there are various other elements of the system that are required for addressing the scientific and operational needs (see OCEANOBS’99).

3. ARGO and the other required systems are very complementary Examples Altimetry XBT network moorings tomography biogeochemical observations

4. altimetry ARGO and Satellite Altimetry Sea surface height (SSH) consists of - the steric (dynamic height H dyn ) contribution of T and S - a barotropic flow component (reference level pressure P ref ) Symbolically SSH = P ref + H dyn = SSH’ + SSH Altimetry has good spatial and temporal coverage but cannot determine - steric and non-steric components - mean SSH field (relative to geoid) - T and S contributions (spiciness) - interior structure (vertical distribution) of H dyn ARGO data can help resolve these issues

5. altimetry Float profiles Symbolically SSH = P ref + H dyn = SSH’ + SSH scatter is a measure for non-steric contributions (plus errors) altimetric SSH‘ vs in-situ H‘ dyn Compare SSH‘ and float H‘ dyn : large barotropic contributions at high latitudes Correlation vs latitude (from P.-Y. Le Traon)

6. altimetry Float profiles deep trajectories residual Symbolically SSH = P ref + H dyn = SSH’ + SSH Deep mean flow (p ref ) from float trajectories : (from R.Davis)

7. ARGO and the XBT network Large-scale heat budgets: - broadscale heat anomalies (storage) from floats - heat transport estimates from the high resolution XBT lines

8. Tasman Box HRX lines (D.Roemmich) Storage (floats) Advection/convergence (XBT) Surface flux (ECMWF) Heat budget Surface Flux reference buoy

9. ARGO and moorings (1-D timeseries) How can ARGO and the observing system benefit from moorings: 1) temporal behaviour / statistics (of T and S) quantification of timescales in ocean regions (“provinces”) quantification of natural variance/”noise” for QC and assimilation of profiles description/insight into how changes observed with floats are induced separation of spatial and temporal changes in float data S/N increase or resolving of fast processes (see Schiller talk) removal of aliasing (e.g. check if trends in float data are real) 2) detailed mixed-layer evolution follow changes in m.l. depth, flow, T/S diurnal cycle (e.g. to correct float SST for satellite SST reference) 3) additional variables CO2, chlorophyll, nutrients, carbon flux, etc upper-layer currents (Ekman flows, Richardson numbers,…) 4) calibrations and validations pre/post calibration allows check of floats that pass near a mooring good way to observe changes in deep (reference) water masses use of mooring data to validate assimilated products (flow, T, S)

10. a) spatial distribution and spatial statistics (T, S, deep flow) correlation scales/ “area of influence” (also for mooring assimilations) mean gradients around moorings advection estimates proxy for other properties ? b) trajectories, deep currents, reference level representativeness of mooring currents (mean flow and variability) reference level for large-scale dynamic height differences advection/spreading of downward-mixed properties (T,S,PV,CO2,...) c) space-time variability analyze floats approaching a mooring to calculate space-time covariances How can timeseries observations benefit from ARGO:

11. Example: Deep Labrador Current: Fischer and Schott 2002, JPO  helps to use moorings for long-term observations (also for interpretation of historical mooring data) Occasional/few floats provide spatial structure

12. Example: Water mass formation in Labrador Sea Floats at ARGO density are too sparse to allow observation of water mass formation interannually This information is best obtained from a convection mooring

13. The subsequent spreading of newly formed water is best observed by floats. Example: Tracking Labrador Sea Water properties observed at the mooring in 2003 ARGO data

14. Example: Useage of floats to define spatial field around multi- disciplinary mooring in the Irminger Sea Nearby floats during a 10-month period Systematic temperature offset between mooring and floats

15. ARGO and tomography (2-D timeseries) Challenge: how to sample in a volume-sense (changes in heat content or water mass volume) on a sub-basin scale (Labrador Sea, Mediterranean) ? Floats are good for obtaining long-term mean and spatial statistics. At any time, coverage is insufficient to follow any temporal changes. Use tomography lines to get time-varying basin integrals, based on mean field and statistics from floats.

16. Surface water layer 0-150m LIW layer 150-600m Essential to correctly estimate mean field – otherwise covariance functions will be artificially elevated due to constant component Resulting covariance functions

17. Also allows array design studies combination of tomography and floats The covariance functions from floats define linear estimators for 3-D heat content from the tomography lines H =  a i D i plus uncertainties of the estimate  timeseries of 3-D heat content with error bars ˆ 0-150m layer150-600m layer Time-means can be compared: float data over 5 years tomography array over (other) 9 months

18. 0-150m layer150-600m layer Mean square error as floats are added to the tomography array:

19. ARGO and biogeochemical observing systems

20. First O 2 optode profiles from floats (Labrador Sea). Two PROVOR floats with Optodes will be deployed next year. (A.Koertzinger, J.Schimanski)

21. The challenge Global timeseries pilot project As we build and expand the global ocean observing system components, to recognize the synergies between them and to develop insight and tools to exploit these.