CarboOcean final meeting, Os, Norway, 5-9 October 2009 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 CO2 fluxes Session on Simulating variability of air-sea CO2 fluxes Funding: EU (GOSAC, NOCES), NASA, DOE, Swiss NSF, CSIRO
CarboOcean final meeting, Os, Norway, 5-9 October 2009 J. Orr (LSCE) Contributors: LSCE - J. Simeon, M. Gehlen, L. Bopp LEGI (Grenoble) – C. Dufour, B. Barnier, J. LeSommer, J.-M. Molines Effects of forcing and resolution on simulated variability of air-sea CO2 fluxes Funding: EU (GOSAC, NOCES), NASA, DOE, Swiss NSF, CSIRO
Outline • Hints from North Atlantic (Raynaud et al., 2006) • Hints from transient tracer simulaitons (Lachkar et al., 2007) • Forcing • Resolution
BATS: Sea-air CO2 flux anomalies (12-mo running mean) Raynaud et al., 2006 (Ocean Science, 2, 43-60) Why general underprediction? • “Data” errors? • Low horizontal resolution (near west. boundary) • Weak Forcing (atm. reanalysis) • from above • affects lateral lags
NCEP underestimates real wind speed variability North Atlantic • 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) Raynaud et al. (2006, Ocean Science) Smith et al. (2001, J. Climate)
HOT: Sea-air CO2 flux anomalies (12-mo running mean) Raynaud et al., 2006 (Ocean Science, 2, 43-60)
LSCE testing importance of resolving eddies (global model): CFC-11 burden (integrated vertically & zonally) non-eddying non-eddying + GM eddying eddying + GM Zonal Integral of CFC-11 (Mmol degree-1) *Lachkar et al (2007, Ocean Science) Improvements: Eddying ½° Non-eddying 2° • Mixed layer depth • CFC-11 inventory Data* de Boyer Montégut (2004, JGR)
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
52 447 Argo Profiles Mean for neutral densities 26.9 to 27.7 Changes in observed T across ACC reveal fingerprint of anthropogenic climate change Boening et al. (2008, Nature Geoscience)
Bin by dynamic height (0.09 levels) Average Remap onto mean bin latitudes Observed T trend on density surfaces Boening et al. (2008, Nature Geoscience)
Observed trends on depth surfaces Temperature Salinity Boening et al. (2008, Nature Geoscience)
In forcing ocean GCM’s, there is much room for artistry … and error • Atmospheric surface variables • Bulk formulas Large uncertainties 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.
Building adequate forcing requires huge effort Example from high-res. ocean modeling consortium (DRAKKAR DFS3, DFS4): • 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.
DFS4.1 forcing in 2° model (NEMO/ORCA2) T trend S trend Density (σ) Depth (m) LSCE simulations (J. Simeon et al.) with LEGI forcing (DRAKKAR DFS4.1)
NCEP-2 forcing in 2° model (NEMO/ORCA2) T trend S trend Density (σ) Depth (m) LSCE simulations (J. Simeon et al.) with NCEP-2 forcing
Different forcing results in different air-sea fluxes of natural CO2 during pre-satellite era Southern Ocean (south of 45°S) Ocean efflux NEMO/ORCA2 model
Preliminary comparison of resolution (2° vs. 0.5°) + many other differences:
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?
Conclusions: • Arctic surface [CO32-]: high in summer, low in winter (as elsewhere: Bering Sea, Norwegian Sea, Southern Ocean) • High summertime [CO32-] 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 (pCO2 atm = 354 ppmv), i.e., 30 years sooner than summertime observations