WP11 highlights: introduction and overview

EU FP6 Integrated Project CARBOOCEAN ”Marine carbon sources and sinks assessment”

5th 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 2009.

…Observed oceanic pCO2 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 pCO2 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

CO2 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 evaluation5

Simulate the evolution of the atmospheric CO2 concentration at the global scale is a necessary condition To evaluate a model Be confident in future projections

300ppmin 2100

Friedlingstein et al., 2006

Future period

SRES A2 scenario

60ppmin 2005

Historical period

In 2005Obs: 379 ppm (Foster et al. 2007)

C4MIP: 380 ± 14 ppm

No de

fore

stat

ion

Atmospheric CO2

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)

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How can we exploit the spatio-temporal variation of the atmospheric CO2 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 CO2 emissions for the 3 models

(fossil fuel and land use)– Study period: 1979-2003– Same transport model (LMDZ4) forced by observed winds

Cadule et al. 2009b, GBC (in revision)

7

Evaluation of atmospheric CO2 8

Mauna Loa (MLO)

Latitude : 19°32’N

Longitude : 155°35’WConstraint 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 tothe 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. CO2 (phase, amplitude,…) at selected stations

• Interannual Variability (IAV):

Relationship bewteen ENSO and CO2 growth rate

Evaluation of atmospheric CO2

Results: Seasonal cycle (SC)

10

Cadule et al. 2009b, GBC (in revision)

SC

markMLO 0.47 0.37 0.67A

tm. C

O2 (

pp

m)

Atm

. CO

2 (

pp

m)

year

Phase and amplitude

Change of amplitude of the peak-to-peak

H I L

Atm

. CO

2 (

pp

m)

Total 0.27 0.42 0.52

Evaluation of atmospheric CO2

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)

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HarvardNorth American Temperate

IPSL-CM2-C IPSL-CM4-LOOPHadCM3LC

Inter-annual variability of atmospheric CO2 growth rate (solid) and SST anomalies (dash)

Evaluation of atmospheric CO2

IAV: Sensitivity of the CO2 variability to climate variability

12

Cadule et al. 2009b, GBC (under review)

Climatevariability(ENSO)

Climate anomalies(Tropics)

Anomalies of the CO2 fluxes

(Tropics)

Anomalies in measured

atmospheric CO2

September 17th, 2009

Mauna Loa

Evaluation of atmospheric CO2

Results: Sensitivity of the CO2 variability to the climate variability

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• At global scale the three models do not reproduce well the sensitivity of the CO2 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 CO2 growth rate to the SST anomalies is based on slope and intercept

H I L

Total 0.60 0.01 0.13

Evaluation of atmospheric CO2

Global metrics results

14

Cadule et al. 2009b, GBC (in revision)

Conclusions

• The CO2 metrics constitute a stronger constraint than the evaluation based on atmospheric CO2 concentration at global scale

• These metrics help identify processes needing better representation & aid model improvement

• Atm. CO2 (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.

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Cadule et al. 2009b, GBC (in revision)