WP6: Model-based flux assessment

CarboOcean final meeting, Os, Norway, 5-9 October 2009

Funding: EU (GOSAC, NOCES), NASA, DOE, Swiss NSF, CSIRO

PIs R. Schlitzer, J. Schneider (AWI)A. Oschlies, T. Friedrich (LIMK)C. Moulin, J. Orr (LSCE)I. Totterdell (UK Met. Office)

Objectives:

Improve interpolation of surface-ocean pCO2 (seasonal maps of N. Atlantic)

Initiate data assimilation activities to better quantify air-sea CO2 flux

Improve prognostic ocean models to assess key gaps

State of the art in 2005

• First signs of trouble in North Atlantic:

Lefevre et al. (2004) A decrease in the sink for atmospheric CO2 in the North Atlantic, GRL.

Lefevre et al. (2005) A comparison of multiple linear regression and neural network techniques for mapping in situ pCO2 data, Tellus B.

• Rare efforts to assimilate data to improve CO2 fluxes (Schlitzer, AWI: annual mean assessment)

• Coarse-resolution global ocean BGC models

• Higher-resolution regional ocean BGC models (short simulations)

Objectives

• Improve interpolation of surface-ocean pCO2 (seasonal maps of North Atlantic): IfM-GEOMAR, LSCE

• Initiate data assimilation activities to better quantify air-sea CO2 flux: AWI, LSCE, Met. Office

• Improve prognostic ocean models to assess some glaring limitations: LSCE

Pre-Industrial Sea-to-Air COPre-Industrial Sea-to-Air CO22 Fluxes FluxesReiner Schlitzer, Alfred Wegener Institute for Polar and Marine ResearchReiner Schlitzer, Alfred Wegener Institute for Polar and Marine Research

• Use CFC and 14C calibrated global model• Assimilate carbon data from ocean interior

Data with significant Cant not used!

• Outgasing in tropics (especially Pacific)

• Net outgasing around Antarctica

• Uptake in subtropical and subpolar areas (especially Southern Ocean and North Pacific; North Atlantic sink relatively small)

• near-zero interhemispheric near-zero interhemispheric oceanic C transport oceanic C transport (+0.14 PgC/yr)(+0.14 PgC/yr)

• Comparison with other work:

10 – 50% difference

larger sink in south Pacific and Indian

Mikaloff Fletcher, 2007Mikaloff Fletcher, 2007

Assimilate time-dependent C data to find monthly gas-exchange rates that explain the data (AWI, Schlitzer & Schneider)

Optimization test case:• Good general agreement at BATSGood general agreement at BATS

• Overall RMS = 20 Overall RMS = 20 mol/kgmol/kg

Takahashi, 1999Takahashi, 1999 ModelModel

Zonally integrated sea-to-air CO2 flux

COCO22 Fluxes: Fluxes:

•Initialize with Takahashi (1999) fluxesInitialize with Takahashi (1999) fluxes

•Modify to achieve agreement with dataModify to achieve agreement with data

•N. Atlantic POC export much too low in N. Atlantic POC export much too low in North Atlantic) lead to unrealistically North Atlantic) lead to unrealistically large summer outgasing. C draw-down is large summer outgasing. C draw-down is realized by outgasing, when much of it in realized by outgasing, when much of it in reality is caused by POC export. reality is caused by POC export.

!! Need to better constrain POC export !!!! Need to better constrain POC export !!

Assimilate ocean pCO2 data to improve basin-scale, air-sea CO2 flux estimates (UK Met. Office)

New assimilation scheme developed

CARINA data used for assimilation Assimilation scheme

See Poster – Totterdell et al.

First tests encouraging

RMS reduced near densely spaced obs.

Error reduction (July)pCO2: assim. – control

Participants: C. Moulin, A.-S. Krémeur, A. El Moussaoui, C. Ethé, L. Bopp, E. Dombrowski, E. Greiner, O. Aumont, P. Brasseur

Annual-meanchlorophyll

Effect of physical data assimilation on simulated Chl a (LSCE)

¼° with assimilation

¼° no assimilation2° no assimilation

SeaWIFS

Improvements:• Southern Ocean• Subtropical Gyres

*using French MERCATOR modeling framework

Effect of physical data assimilation on simulated Chl a

Improvements:• Reduced Chl a• Gulf Stream• E-W distribution

Ongoing analysis:• pCO2

• air-sea CO2 flux• carbon-related tracers

Problems:• Gyre - Chl a too low• Labrodor Sea• Coastal regions …

SeaWIFS

Annual-meanchlorophyll

¼° with assimilation

¼° no assimilation2° no assimilation

10

Implemented BGC model (1-D PISCES) with assimilation software (YAO):[LSCE: A. Kane, C. Moulin]

- Identified key parameters through sensitivity tests, definition of error matrices, validation using twin experiments,…

- Optimized 6 parameters at 1 station.

- Optimized 45 parameters at 5 stations and validation through a comparison between a 3D simulation and satellite data.

Optimized 45 parameters (surface Chl)

- Variables Chl, NO3, POC, Si used at 5 JGOFS stations- Improvements, butr 5 stations remain inadequate- Assimilation of satellite-derived Chl a in progress

State of the art in 2005

• First signs of trouble in North Atlantic:

Lefevre et al. (2004) A decrease in the sink for atmospheric CO2 in the North Atlantic, GRL.

Lefevre et al. (2005) A comparison of multiple linear regression and neural network techniques for mapping in situ pCO2 data, Tellus B.

• Rare efforts to assimilate data to improve CO2 fluxes (Schlitzer, AWI: annual mean assessment)

• Coarse-resolution global ocean BGC models

• Higher-resolution regional ocean BGC models (short simulations)

WP6 Publications:Friedrich, T., and A. Oschlies (2009), Neural-network based estimates of North Atlantic surface

pCO2 from satellite data - a methodological study, J. Geophys. Res., 114, C03020, doi:10.1029/2007JC004646.

Friedrich, T., and A. Oschlies (2009), A neural-net based method for estimating North Atlantic surface pCO2 from ARGO float data, J. Geophys. Res., in press.

Kane, A., C. Moulin, S. Thiria, L. Bopp, M. Berranda, A. Tagliabue, M. Crépon, F. Badran, O. Aumont, Improving the parameters of a global ocean biogeochemical model by using variational assimilation of in situ data a five JGOFS stations, in prep., 2009.

Lachkar, Z., J. C. Orr, J.-C. Dutay, P. Delecluse. On the role of mesoscale eddies in the ventilation of Antarctic Intermediate Water, Deep-Sea Research I , 56, 909-925, 2009.

Lachkar, Z., J. C. Orr, and J.-C. Dutay. Seasonal and mesoscale variability of oceanic transport of anthropogenic CO2, Biogeosciences Discuss., 6, 4233-4277, 2009.

Lachkar, Z., J. C. Orr, J.-C. Dutay, and P. Delecluse. Effects of mesoscale eddies on global distributions of CFC-11, CO2, and Δ14C, Ocean Science, 3, 461-482, 2007.

Raynaud, S., J. C. Orr, O. Aumont, K. Rodgers, and P. Yiou. Interannual-to-decadal variability of North Atlantic air-sea CO2 fluxes, Ocean Science, 2, 43—60, 2006.

Schlitzer, R., 2007: Assimilation of radiocarbon and chlorofluorocarbon data to constrain deep and bottom water transports in the world ocean. Journal of Physical Oceanography, 37, 259–276.

Telszewski, M., Chazottes, A., Schuster, U., Watson, A. J., Moulin, C., Bakker, D. C. E., González-Dávila, M., Johannessen, T., Körtzinger, A., Lüger, H., Olsen, A., Omar, A., Padin, X. A., Ríos, A. F., Steinhoff, T., Santana-Casiano, M., Wallace, D. W. R., and Wanninkhof, R.: Estimating the monthly pCO2 distribution in the North Atlantic using a self-organizing neural network, Biogeosciences, 6, 1405-1421, 2009.

Assimilate ocean pCO2 data to improve basin-scale, air-sea CO2 flux estimates (Met. Office)

New assimilation scheme developed

CARINA data used for assimilation Assimilation scheme

See Poster – Totterdell et al.

First tests encouraging

RMS reduced near densely spaced obs.

Error reduction (July)pCO2: assim. – control

Monthly Sea-to-Air COMonthly Sea-to-Air CO22 Fluxes FluxesR. Schlitzer & J. Schneider, Alfred Wegener Institute for Polar and Marine ResearchR. Schlitzer & J. Schneider, Alfred Wegener Institute for Polar and Marine Research

Objective: Assimilate time-dependent C data and find monthly CO2 gas exchange rates that explain the data.

Optimization works:•Good agreement at BATS StationGood agreement at BATS Station

•Overall rms=20 Overall rms=20 mol/kgmol/kg

COCO22 Fluxes: Fluxes:

•Initialized with Takahashi, 99 fluxesInitialized with Takahashi, 99 fluxes

•Modified by the model to achieve Modified by the model to achieve agreement with dataagreement with data

•Shortcomings in the model POC export Shortcomings in the model POC export (much too low in North Atlantic) lead to (much too low in North Atlantic) lead to unrealistically large summer outgasing. C unrealistically large summer outgasing. C draw-down is realized by outgasing, draw-down is realized by outgasing, when much of it in reality is caused by when much of it in reality is caused by POC export. POC export.

!! Need to better constrain POC export !!!! Need to better constrain POC export !!

Takahashi, 1999Takahashi, 1999 ModelModel

Zonally integrated CO2 gas exchange fluxes

1870-20011870-20012002 - 2007Run

44 years44 years3 yearsSpin up

ERA40ERA40MERCATOR-Vert

Forcings

onlineonlineofflineCoupling

2 °1/2 °1° forced by 1/4 ° degraded physics

Resolutions

ORCA2 (P2)ORCA05 (P05)

Green-MERCATOR (MV025)

Configurations

IDRIS (Orsay)Earth Simulator (Japan)

MERCATOR (Toulouse)

Running center

Summary of the different configurations

Plans for Plans for OceanCarbon2010

•Extend and apply the global adjoint model for quantification of monthly CO2 air-sea fluxes, POC exports, and 3D carbon transports.

•Additionally assimilate monthly nutrient (WOA05) and satellite chlorophyll (SeaWiFS, MODIS) data to better constrain model POC exports.

•Additionally assimilate the new CARINA and SOCAT datasets.

Simulated interannual variability (from LSCE) 4th year progress on sensitivity to 3 factors : 1. Effect of model resolution ?

– Mercator-vert collaboration • ORCA/PISCES model simulations (2º, ½ º, & ¼º) completed• Mercator ocean reanalysis fields: 2001 to 2005 completed

– DRAKKAR collaboration• Global: Forced NEMO (OPA9) global ocean-model dynamic simulations (2º,

½ º, & ¼º) over last 50 years completed (started - coupling with BGC model)• Southern Ocean, high-res (1/12º) regional simulations with NEMO/PISCES

in preparation (started - thesis study of Carolina Dufour) Does explictly resolving eddies change estimated ongoing slowdown in

Southern-Ocean CO2 uptake?

2. Effect of forcing fields ? NEMO/PISCES simulations over last ~50 years Coding and tests underway (started, Jennifer Simeon)

• at 3 resolutions (2º, ½ º, & ¼º) & different sets of forcing fields • with different sets of forcing fields (NCEP, ERA40, CORE, DFS3, DFS4)

• Effect of physical data assimilation (SST, altimetry, …)?– Ongoing study within the Mercator-vert project (Moulin et al., 2008)

Conclusions:• Arctic surface [CO3

2-]: 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