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Air-sea CO 2 flux climatology for the Baltic Sea Gaëlle Parard 1, A.A.Charantonis 2 S. Raj Parampil 1 and A. Rutgerson 1 1 Department of Earth Sciences,

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Presentation on theme: "Air-sea CO 2 flux climatology for the Baltic Sea Gaëlle Parard 1, A.A.Charantonis 2 S. Raj Parampil 1 and A. Rutgerson 1 1 Department of Earth Sciences,"— Presentation transcript:

1 Air-sea CO 2 flux climatology for the Baltic Sea Gaëlle Parard 1, A.A.Charantonis 2 S. Raj Parampil 1 and A. Rutgerson 1 1 Department of Earth Sciences, Uppsala University, Sweden 2 LOCEAN, IPSL/UPMC,/CNRS, Paris, France ESA SOLAS EGU meeting 28 th- 31 st October

2 Context 2  Exchange of CO 2 between coastal environments and atmosphere ≈16 % of the open ocean sink but continental shelf < 7 % of the oceanic surface  Problem for the quantification the oceanic sink and large uncertainty related to sparse of in-situ data coverage in time and space  Baltic Sea is comparably well monitoring (program for several decades)  Nevertheless air-sea CO 2 flux estimation in Baltic Sea remains particularly challenging  Neural network have the potential to generate additional information  Estimate pCO 2 variability in the global Baltic Sea with satellite data  Estimate the CO 2 flux in Baltic Sea

3 In situ Data: monthly average 3  1445 measurement between at monthly scale : -Measurement of pCO 2 (SAMI sensor) at Östergarnsholm station since June SMHI mooring in Baltic Sea ( ) (pCO 2 compute with carbonate relation (TA and pH ) ) -Ship measurements VOS ( )(Schneider et al., 2006)  Problem : concentrate in the middle of the Baltic Sea  Combined methods : Self Organizing Map and Multiple Linear regression (SOMLO from Sasse et al., 2013) GB

4 Satellite DATA 4  SST: -Federal Maritime and Hydrographic Agency (BSH) processing the data from AVHRR-NOAA( ) -GRHSST (Group for High Resolution Sea Surface Temperature) product for Baltic Sea ( ). -NOAA/NASA Advanced Very High Resolution Radiometer (AVHRR) data stream conducted by the University of Miami’s Rosenstiel School of Marine and Atmospheric Science (RSMAS) ( )  Chlorophyll : -SeaWiFS (Sept Dec. 2002) 4 km monthly -MODIS-Aqua (Jul Jun. 2011) 4 km monthly  CDOM : -MODIS data 4km monthly average.  Primary Production: -EMIS: depth-integrated model (Lee et al., 2005). The primary production calculation is based on Platt and Sathyendranath (1993). monthly average (Oct to Sept. 2008). -Vertically Generalized Production Model ( VGPM) ( ). The VGPM is a "chlorophyll-based" model that estimate net primary production from chlorophyll using a temperature-dependent description of chlorophyll-specific photosynthetic efficiency.  MLD -One3D hydrodynamic model currently used at the JRC/IES is the public domain GETM model (General Estuarine Transport Model monthly averages ( ), -Carbon-based production model at monthly scale (Behrenfeld et al. 2005) (2008 and 2011)  WIND to July 1999, July 1999 to December 1999: QuikSCAT 2000 to 2009: Flux-merged

5 Distribution of each parameters 5  Strongly dependant of SST and chlorophyll. MLD : Higher value during winter

6 Validation of the method  Monthly data : 10 % of the data set  RMS = 36.7 μatm  45 % > 20 μatm  Good correlation R=0.93  Close from some other studies with more vectors ( ) 6

7 pCO 2 Map MARCH 2010 JULY 2010 SEPTEMBER Reconstruction from 1998 to 2011 : Seasonal cycle well reproduce (winter : 437 μatm and summer :274 μatm similar to Wesslander, 2011)

8 A simple flag 8 MARCH 2010JULY 2010SEPTEMBER 2010 Simple Flag : Value neuron – Value estimated 1. <20 μatm 2.20 μatm 30 μatm

9 Comparison with model 9 Model output : 13 basins in Baltic Sea ( Horizontal average, vertical resolution) Higher correlation for BS & CB : 0.7 Higher variation : GF Lower variation : BB BS GF GB CP

10 Climatology of pCO 2 10

11 Air-sea CO 2 Fluxes : Interannual variability 11  FCO 2 =k(U)*ΔpCO 2 pCO 2atm =x trend +x anthropogeni + x tnatural (Norman et al., 2013) U : Satellite product : (R= 0.8 & RMSD = 1.3)  Baltic Sea : source or sink : several studies from model not agree : -3.6

12 Summary Promising results : Study based on 1445 vectors : R=0.93 and RMS=36 μatm –Other study with lower and close result but more vectors (e.g.:Friedrich and Oschlies 2009, Hales et al.,2012) Central basin well reproduce compare to Gulf of Bothnia : due to the lake of data Climatology of pCO 2 computed from in-situ data Air-sea CO 2 flux: order of magnitude seems in agreement with other studies –Wind product lower resolution  difficult to compute 12

13 Perspectives Improvement of the results due to acquisition of new data –Sami sensor, SMHI, VOS transect New acquisition : –Data from ferry boat from Gothenburg –Utö Atmospheric and Marine research station Improve the estimation of pCO 2 in Gulf of Bothnia Succeeds to have data in January and December 13

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15 Method : Self Organising Maps 15

16 Air-Sea flux of CO 2 FCO 2 =k(U)*(pCO 2water -pCO 2atm ) pCO 2atm =x trend +x anthropogeni +x tnatural (Norman et al., 2013) U : Wind (Satellite product) : –Comparison in-situ data & satellite product : R= 0.8 RMSD = 1.3 –Lower resolution than pCO 2 –Problem with 1998 and 1999 : Lower resolution and low correlation :  Flux really high not realistic 16

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