1. Session on Simulating variability of air-sea CO2 fluxes CarboOcean final meeting, Os, Norway, 5-9 October 2009 Funding: EU (GOSAC, NOCES), NASA, DOE, Swiss NSF, CSIRO R. Matear (CSIRO, Hobart, Australia) : Impact of Historical Climate Change on the Southern Ocean Carbon Cycle J. Orr (LSCE, Gif-sur-Yvette, France) Effects of forcing and resolution on simulated variability of air-sea CO 2 fluxes

2. CarboOcean final meeting, Os, Norway, 5-9 October 2009 Funding: EU (GOSAC, NOCES), NASA, DOE, Swiss NSF, CSIRO J. Orr (LSCE) Contributors: LSCE - J. Simeon, M. Gehlen, L. Bopp LEGI (Grenoble) – C. Dufour, B. Barnier, J. LeSommer, J.-M. Molines

3. Outline Hints from North Atlantic (Raynaud et al., 2006) Hints from transient tracer simulaitons (Lachkar et al., 2007) Forcing Resolution

4. BATS: Sea-air CO 2 flux anomalies ( 12-mo running mean ) Why general underprediction? “Data” errors? Low horizontal resolution (near west. boundary) Weak Forcing (atm. reanalysis) o from above o affects lateral lags Raynaud et al., 2006 (Ocean Science, 2, 43-60)

5. NCEP underestimates real wind speed variability Interannual var. in wind speed:  NCEP < (1/3)  ERA40 NCEP wind speeds lower than WOCE ship track winds NCEP atm. transport variability only half that observed (Waliser et al., 1999) North Atlantic Smith et al. (2001, J. Climate) Raynaud et al. (2006, Ocean Science)

6. HOT: Sea-air CO 2 flux anomalies ( 12-mo running mean ) Raynaud et al., 2006 (Ocean Science, 2, 43-60)

7. LSCE testing importance of resolving eddies (global model): Non-eddying 2° Eddying ½° Data* de Boyer Montégut (2004, JGR) Mixed layer depth non-eddying  non-eddying + GM  eddying + GM  eddying CFC-11 burden (integrated vertically & zonally) *Lachkar et al (2007, Ocean Science) Zonal Integral of CFC-11 (Mmol degree -1 ) CFC-11 inventory Improvements:

8. Southern Ocean carbon sink – different stories Le Quéré et al. (2007): slower than expected [coarse-resolution model, NCEP forcing] Matear and McNeil (2008): not slower [another coarse-res. model, NCEP forcing] Sarmiento et al. (2009): slower [4 coarse-res. models, NCEP forcing] Bopp (2009): [coarse-res. model] –slower with NCEP forcing; –not slower with ERA40

9. Changes in observed T across ACC reveal fingerprint of anthropogenic climate change Boening et al. (2008, Nature Geoscience) 52 447 Argo Profiles Mean for neutral densities 26.9 to 27.7

10. Observed T trend on density surfaces Bin by dynamic height (0.09 levels) Average Remap onto mean bin latitudes Boening et al. (2008, Nature Geoscience)

11. Observed trends on depth surfaces TemperatureSalinity Boening et al. (2008, Nature Geoscience)

12. In forcing ocean GCM’s, there is much room for artistry … and error Atmospheric surface variables Bulk formulas L. Brodeau, B. Barnier, T. Penduff, J.-M. Molines (2009) An ERA40- based atmospheric forcing for simulations and reanalyses of the global ocean circulation between 1958 to present, submitted. Large uncertainties

13. Building adequate forcing requires huge effort Strategy to blend –corrected ERA40 surface atmospheric state fields (wind, air temperature, humidity) with –satellite products (ISCCP for radiation, CMAP for precipitation) processed by Large & Yeager (2004) for CORE data set. Procedure: –Replace CORE’s NCEP with ERA40 (surface T, humidity, wind) Extend ERA40 until 2004 with ECWMF operational product Correct major ERA40 flaws (biases, inter-annual discontinuities) –Adjust CORE shortwave radiation and precipitation products –Quantify changes in forcing with a series of 1958-2004 interannual 2° (ORCA2) simulations  assess impact of every forcing variable on the model solution. Example from high-res. ocean modeling consortium (DRAKKAR DFS3, DFS4):

14. DFS4.1 forcing in 2° model (NEMO/ORCA2) T trendS trend Density (σ) Depth (m) LSCE simulations (J. Simeon et al.) with LEGI forcing (DRAKKAR DFS4.1)

15. NCEP-2 forcing in 2° model (NEMO/ORCA2) T trendS trend Density (σ) Depth (m) LSCE simulations (J. Simeon et al.) with NCEP-2 forcing

16. Different forcing results in different air-sea fluxes of natural CO 2 during pre-satellite era NEMO/ORCA2 model Southern Ocean (south of 45°S) Ocean efflux

17. Preliminary comparison of resolution (2° vs. 0.5°) + many other differences:

18. Conclusions Different forcing fields – strengthen ties to evolving developments of ocean circulation modeling community Different resolutions – ibid Different models – need more concerted evaluation, comparison & strategy Different BGC components – minimum complexity to properly simulate interannual variability & trends?

20. Conclusions: Arctic surface [CO 3 2- ]: high in summer, low in winter (as elsewhere: Bering Sea, Norwegian Sea, Southern Ocean) High summertime [CO 3 2- ] from Biologically driven increase (from DIC drawdown) overwhelms Physically driven decrease (freshening, i.e., dilution) Opposite trend in models with excessive fresh-water input Chukchi Sea surface water: –observed seasonal amplitude (≥12 μmol kg -1 ) (equivalent to past 30+ years of transient change) –That annual cycle + Beringia 2005 summer data, yields Wintertime Ωa < 1 already by 1990 (pCO 2 atm = 354 ppmv), i.e., 30 years sooner than summertime observations