WP11 highlights: introduction and overview EU FP6 Integrated Project CARBOOCEAN ”Marine carbon sources and sinks assessment” 5 th Annual & Final Meeting – Solstrand Hotel Norway 5-9 October 2009

WP11 Model performance assessment and initial fields for scenarios Objectives: To determine, how well biogeochemical ocean general circulation models (BOGCMs) are able to reproduce carbon cycle observations from the real world with respect to temporal and spatial distributions. To refine criteria for model performance with respect to observations and other model. To establish a quality check for the initial conditions for future scenarios with BOGCMs (BOGCM = biogeochemical ocean general circulation model).

Still to come, see SOLAS OSC, Barcelona, Nov …Observed oceanic pCO 2 trends are a valuable metric of climate change, because they integrate the changes in dissolved inorganic carbon (DIC), alkalinity, salinity and temperature; we separate the observed pCO 2 trends into components driven by each of these fields to assess how well they are captured by the models…

Benchmarking coupled climate-carbon models against long-term atmospheric CO 2 measurements Patricia Cadule, Pierre Friedlingstein, Laurent Bopp, Stephen Sitch, Chris Jones, Philippe Ciais, Shilong Piao, Philippe Peylin CARBOOCEAN Annual Meeting – Solstrand, Norway 5-9 October 2009 WP11 Highlights: (IPSL & Hadley Center)

“Traditional” model evaluation 5 Simulate the evolution of the atmospheric CO 2 concentration at the global scale is a necessary condition To evaluate a model Be confident in future projections 300 ppm in 2100 Friedlingstein et al., 2006 Future period SRES A2 scenario 60 ppm in 2005 Historical period In 2005 Obs: 379 ppm (Foster et al. 2007) C 4 MIP: 380 ± 14 ppm No deforestation

Atmospheric CO 2 Available data Spatial variations Temporal variations –Seasonal cycle (SC) Vast network of measurement stations across the globe –Inter-annual variability (IAV) –Long term trend (TR) 6 How can we exploit the spatio-temporal variation of the atmospheric CO 2 to evaluate the numerical models?

Methodology Objective: evaluate the simulated carbon exchange against observation data from atmospheric monitoring stations 3 coupled carbon-climate models –H: HadCM3LC (Cox et al., 2000) –I: IPSL-CM2-C (Dufresne et al., 2002) –L: IPSL-CM4-LOOP (Cadule et al., 2009a) Protocol –Same anthropogenic CO 2 emissions for the 3 models (fossil fuel and land use) –Study period: –Same transport model (LMDZ4) forced by observed winds Cadule et al. 2009b, GBC (in revision) 7

Evaluation of atmospheric CO 2 8 Mauna Loa (MLO) Latitude : 19°32’N Longitude : 155°35’W Constraint on sinks Constraint mainly on the terrestrial ecosystems of the mid and high latitudes Constraint on the terrestrial ecosystems of the Tropics Signal decomposition according to the method of Thoning et al. (1989): Fourier transform and low-pass filters year Cadule et al. 2009b, GBC (in revision)

Examples Seasonal Cycle (SC): Atm. CO 2 (phase, amplitude,…) at selected stations Interannual Variability (IAV): Relationship bewteen ENSO and CO 2 growth rate

Evaluation of atmospheric CO 2 Results: Seasonal cycle (SC) 10 Cadule et al. 2009b, GBC (in revision) SC mark MLO Atm. CO 2 (ppm) year Phase and amplitude Change of amplitude of the peak-to-peak HIL Atm. CO 2 (ppm) Total

Evaluation of atmospheric CO 2 Analysis: Seasonal cycle (SC) At Harvard and at regional scale, HadCM3LC simulates –A carbon sink too soon –A carbon source during summer At Harvard, IPSL-CM2-C simulates a too weak sink Cadule et al. 2009b, GBC (in revision) 11 HarvardNorth American Temperate

IPSL-CM2-C IPSL-CM4-LOOP HadCM3LC Inter-annual variability of atmospheric CO 2 growth rate (solid) and SST anomalies (dash) Evaluation of atmospheric CO 2 IAV: Sensitivity of the CO 2 variability to climate variability 12 Cadule et al. 2009b, GBC (under review) Climate variability (ENSO) Climate anomalies (Tropics) Anomalies of the CO 2 fluxes (Tropics) Anomalies in measured atmospheric CO 2 September 17 th, 2009

Mauna Loa Evaluation of atmospheric CO 2 Results: Sensitivity of the CO 2 variability to the climate variability 13 At global scale the three models do not reproduce well the sensitivity of the CO 2 growth rate to the climate variability Cadule et al. 2009b, GBC (in revision) Analysis performed at 12 stations Evaluation of the sensitivity of the atmospheric CO 2 growth rate to the SST anomalies is based on slope and intercept HIL Total

Evaluation of atmospheric CO 2 Global metrics results 14 Cadule et al. 2009b, GBC (in revision)

Conclusions The CO 2 metrics constitute a stronger constraint than the evaluation based on atmospheric CO 2 concentration at global scale These metrics help identify processes needing better representation & aid model improvement Atm. CO 2 (SC, IAV) mainly used to evaluate the land carbon cycle models. For the ocean carbon cycle, other tracers (APO) may do a better job. Use also other CAARBOOCEAN models (MPI, NCAR, BCCR): in progress More research is required to turn this analysis into a constraint on future climate-carbon cycle feedbacks. 15 Cadule et al. 2009b, GBC (in revision)