Near real time forecasting of biogeochemistry in global GCMs Rosa Barciela, NCOF, Met Office

1. Near real time forecasting of biogeochemistry in global GCMsRosa Barciela, NCOF, Met Office rosa.barciela@metoffice.gov.uk

2. The Talk • What are the aims? • What tools are we using? • What have we developed so far? • Some preliminary results • Assimilation of satellite-derived chlorophyll • What will we be doing next? • Are there any issues to be addressed? • Conclusions

3. What are the aims? • Pre-operational coupled physical-biogeochemical model by 2008 • Different users have different needs: • - NERC-CASIX: • estimates of air-sea fluxes of CO2 • decadal re-analysis (1997-2006) with/without ocean colour DA • - Royal Navy • water clarity forecasts in the open ocean (5 to 7 days ahead) • improvement of light attenuation estimates: SST, MLD, sea-ice • minimise risks to maritime environment when deploying active sonar systems

4. The Talk • What are the aims? • What tools are we using? • What have we developed so far? • Some preliminary results • Assimilation of satellite-derived chlorophyll • What will we be doing next? • Are there any issues to be addressed? • Conclusions

5. HadOCC • Hadley Centre Ocean Carbon Cycle Model What tools are we using? • Coupling together two models … • FOAM • Forecasting Ocean Assimilation Model

6. Forecasting the open ocean: the FOAM system Input boundary data NWP 6 hourly fluxes Obs QC Forecast to T+144 Analysis Output boundary data Real-time data Automatic verification Product delivery FOAM = Forecasting Ocean Assimilation Model • Operational real-time deep-ocean forecasting system • Daily analyses and forecasts out to 6 days • Low resolution global to high resolution nested configurations • Relocatable system deployable in a few weeks • Hindcast capability (back to 1997) • Assimilates T and S profiles, SST, SSH, sea-ice concentration

7. Hadley Centre Ocean Carbon Cycle Model (HadOCC) Model description: - ‘NPZD’ ecosystem model - Coupled to carbon & alkalinity - Variable C:Chl ratio - Transported around the ocean by physical processes - Normally used for climate studies

8. The Talk • What are the aims? • What tools are we using? • What have we developed so far? • Some preliminary results • Assimilation of satellite-derived chlorophyll • What will we be doing next? • Are there any issues to be addressed? • Conclusions

9. What have we developed so far? • HadOCC embedded into FOAM at different resolutions (1º, 1/3º & 1/9º) • Initial tests have been run with 1˚ global, 1/3˚ N Atlantic and Arctic and 1/9˚ N Atlantic FOAM configurations. • Nested system running successfully 1/3º North Atlantic • Data assimilation scheme for derived chlorophyll (ocean colour)

10. The Talk • What are the aims? • What tools are we using? • What have we developed so far? • Some preliminary results • Assimilation of satellite-derived chlorophyll • What will we be doing next? • Are there any issues to be addressed? • Conclusions

11. The impact of a phytoplankton bloom on air-sea CO2 flux FOAM-HadOCC at 1º resolution, April 29th – May 19th 2000

12. Validation of FOAM-HadOCC results Validation of surface chlorophyll against SeaWiFS data Daily mean North Atlantic fields for 20th April 2003 1/3º North Atlantic & Arctic 1º Global 1/9º North Atlantic SeaWiFS 5-day composite

13. Temperature Salinity Chlorophyll Validation of FOAM-HadOCC results Validation of subsurface structure vs AMT cruise data 20.0W, 41.5N, 11th June 2003 32.6W, 24.3N, 6th June 2003 Temperature Salinity Chlorophyll AMT obs 1/3º 1/9º 1º

14. The Talk • What are the aims? • What tools are we using? • What have we developed so far? • Some preliminary results • Assimilation of satellite-derived chlorophyll • What will we be doing next? • Are there any issues to be addressed? • Conclusions

15. Chlorophyll data assimilation scheme • Two stage analysis scheme: • Model chlvs. satellite obs: increments (ACS) • Balancing increments to biogeochemical variables • Phytoplankton increments derived using model biomass:chlorophyll ratio • Increments to other pools depend on the likely contributions to phytoplankton error from errors in growth and loss • Increments constrained to conserve total nitrogen & carbon at each grid point (if sufficient nitrogen is available)

16. Assimilation of derived chlorophyll Results from 3-D twin experiments Phytoplankton background error before the first analysis. Phytoplankton analysis error after the first analysis, with data everywhere. Phytoplankton errors (mmolN/m3)

17. Ocean colour DA: tests in 3-D win experiments • “True” run • - start from a spun-up model state, • - model run for 1 year (Jan 2003 – Jan 2004) • - forced by NWP 6 hourly surface fluxes • - with physical (T, S, SST) data assimilation • Observations of Chl are taken from this “true” model state once a day. • Assimilation and control runs • - HadOCC initialised using the fields from March 2003 • - physical fields taken from true run from April 2003 • Assimilation run assimilates chl observations from the “true” run • Control run does not

18. 3-D Twin experiments: daily mean RMS errors in the North Atlantic Phytoplankton (mmol N/m3) Zooplankton (mmol N/m3) Total DIC (mmol C/m3) Control - truth Detritus (mmol N/m3) Nutrients (mmol N/m3) Assimilation - truth • air-sea exchange of CO2 significantly improved after assimilating ocean colour data

19. Real world experiments • Global average RMS (solid lines) and mean (dashed lines) errors compared to the satellite chlorophyll data. Green: no DA Black: only physical DA Red: physical and biological DA

20. Real world experiments – on 1st July 2003 Log(chl) observations Log(chl) from model with no biological assimilation Log(chl) from model with biological assimilation

21. The Talk • What are the aims? • What tools are we using? • What have we developed so far? • Some preliminary results • Assimilation of satellite-derived chlorophyll • What will we be doing next? • Are there any issues to be addressed? • Conclusions

22. What will be doing next? • The key next steps are: • further quantitatively validation to initial FOAM-HadOCC integrations • parameter tuning (required to improve performance) • further refinement of ocean colour assimilation scheme • explicit biological feedback to physical model: downward radiation • run a 10-year re-analysis of FOAM-HadOCC with ocean colour and physical assimilation

23. The Talk • What are the aims? • What tools are we using? • What have we developed so far? • Some preliminary results • Assimilation of satellite-derived chlorophyll • What will we be doing next? • Are there any issues to be addressed? • Conclusions

24. Issues … • Data assimilation: • Impact of physical assimilation on biogeochemistry: vertical mixing? • Quality of chl product: target accuracy in open ocean ~ 35% !!! • Chlorophyll versus IOPs/absorption? • Validation: • Good temporal and spatial coverage for chlorophyll only (global – remotely sensed since 1997) • Other verifiable variables are: pCO2 (North Atlantic?-VOS), nutrient (climatology, cruise data, time-series from monitoring stations) • Lack of verification for remaining fields: biomass (P,Z), detritus.

25. The Talk • What are the aims? • What tools are we using? • What have we developed so far? • Some preliminary results • Assimilation of satellite-derived chlorophyll • What will we be doing next? • Are there any issues to be addressed? • Conclusions

26. Conclusions Model development • the FOAM-HadOCC system has been run for 1 year at three resolutions • the system appears to be effective at simulating the onset of the spring bloom (good qualitative agreement with SeaWiFS and AMT data) but chl levels subsequently appear to be over-estimated. • higher resolution provides improved representation of advectiveprocesses in particular. However, benefits masked by large scale errors Data assimilation • an ocean colour data assimilation scheme has been designed and implemented within FOAM-HadOCC. • joint collaboration between University of Plymouth, NOC-Southampton and Met Office • real-world experiments show that the scheme is able to improve the chlorophyll: other biological fields are difficult to verify but some work is underway in this area

27. Rosa Barcielarosa.barciela@metoffice.gov.uk