Climatological variations of total alkalinity and total inorganic carbon Climatological variations of

36 Pages·2017·0.75 MB·English

Climatological variations of total alkalinity and total
inorganic carbon Climatological variations of total
alkalinity and total inorganic carbon in the
Mediterranean Sea surface waters
Elissar Gemayel, Abed El Rahman Hassoun, Mohamed Benallal, Catherine
Goyet, P Rivaro, M Abboud-Abi Saab, E Krasakopoulou, Franck Touratier, P
Ziveri
To cite this version:
Elissar Gemayel, Abed El Rahman Hassoun, Mohamed Benallal, Catherine Goyet, P Rivaro, et al..
Climatological variations of total alkalinity and total inorganic carbon Climatological variations of
total alkalinity and total inorganic carbon in the Mediterranean Sea surface waters. Earth System
Dynamics, 2015, 6, pp.1499-1533. ￿10.5194/esdd-6-1499-2015￿. ￿hal-01229908￿
HAL Id: hal-01229908
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EarthSyst.Dynam.Discuss.,6,1499–1533,2015 D
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variations of total
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alkalinity and total
Climatological variations of total alkalinity D
inorganic carbon
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and total inorganic carbon in the s E.Gemayeletal.
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Mediterranean Sea surface waters P
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1,2,3 3 1,2 1,2 4
E. Gemayel , A. E. R. Hassoun , M. A. Benallal , C. Goyet , P. Rivaro ,
| Abstract Introduction
3 5 1,2 6,7
M. Abboud-Abi Saab , E. Krasakopoulou , F. Touratier , and P. Ziveri
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1UniversitédePerpignanViaDomitia,IMAGES_ESPACE-DEV,52avenuePaulAlduy, isc
66860PerpignanCEDEX9,France us Tables Figures
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2ESPACE-DEV,UGUAURUMIRD,Maisondelatélédétection,500rueJean-François io
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Breton,34093MontpellierCEDEX5,France P
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3NationalCouncilforScientificResearch,NationalCenterforMarinesSciences,P.O.Box534, p
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4UniversityofGenova,DepartmentofChemistryandIndustrialChemistry, | Back Close
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5UniversityoftheAegean,DepartmentofMarineSciences,UniversityHill, is FullScreen/Esc
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6UniversitatAutònomadeBarcelona(UAB),InstituteofEnvironmentalScienceand sio Printer-friendlyVersion
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Received:28June2015–Accepted:4August2015–Published:24August2015 D
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Correspondenceto:E.Gemayel([email protected]) u
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Abstract D
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Acompilationofseveralcruisesdatafrom1998to2013wasusedtoderivepolynomial s
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fits that estimate total alkalinity (A ) and total inorganic carbon (C ) from measure- io 6,1499–1533,2015
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ments of salinity and temperature in the Mediterranean Sea surface waters. The opti- P
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5 mal equations were chosen based on the 10-fold cross validation results and revealed pe Climatological
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that a second and third order polynomials fit the AT and CT data respectively. The AT variations of total
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surface fit showed an improved root mean square error (RMSE) of ±10.6µmolkg . alkalinity and total
Furthermore we present the first annual mean C parameterization for the Mediter- D inorganic carbon
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ranean Sea surface waters with a RMSE of ±14.3µmolkg−1. Excluding the marginal cu
s E.Gemayeletal.
seas of the Adriatic and the Aegean, these equations can be used to estimate A s
10 T io
and C in case of the lack of measurements. The seven years averages (2005–2012) n
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mappedusingthequarterdegreeclimatologiesoftheWorldOceanAtlas2013showed a
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thatinsurfacewatersAT andCT havesimilarpatternswithanincreasingeastwardgra- er TitlePage
dient. The surface variability is influenced by the inflow of cold Atlantic waters through | Abstract Introduction
the Strait of Gibraltar and by the oligotrophic and thermohaline gradient that charac-
15 D Conclusions References
terize the Mediterranean Sea. The summer-winter seasonality was also mapped and is
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showed different patterns for AT and CT. During the winter, the AT and CT concentra- us Tables Figures
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tions were higher in the western than in the eastern basin, primarily due to the deep- io
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ening of the mixed layer and upwelling of dense waters. The opposite was observed (cid:74) (cid:73)
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20 in the summer where the eastern basin was marked by higher AT and CT concentra- pe (cid:74) (cid:73)
tions than in winter. The strong evaporation that takes place in this season along with r
the ultra-oligotrophy of the eastern basin determines the increase of both A and C | Back Close
T T
concentrations.
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1 Introduction D
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The role of the ocean in mitigating climate change is well known as it absorbs about s
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2PgCyr−1 ofanthropogenicCO (Wanninkhofetal.,2013).Worldwidemeasurements io 6,1499–1533,2015
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of surface seawater CO properties are being conducted as they are important for P
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5 advancing our understanding of the carbon cycle and the underlying processes con- pe Climatological
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trolling it. For instance, the buffer capacity of the CO2 system varies with temperature, variations of total
the distribution of total inorganic carbon and total alkalinity (Omta et al., 2011). | alkalinity and total
Our understanding of the open-ocean CO2 dynamics has drastically improved over Dis inorganic carbon
theyears(Rödenbecketal.,2013;Sabineetal.,2004;Takahashietal.,2009;Watson c
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10 and Orr, 2003). However our understanding of marginal seas such as the Mediter- ssio E.Gemayeletal.
ranean remains poor due to the limited measurements combined with the enhanced n
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complexity of the land-ocean interactions. In the Mediterranean Sea, available mea- a
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surements of the carbonate system are still scarce and only available in specific re- e TitlePage
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gions such as the Alboran Sea (Copin-Montégut, 1993), the Gibraltar Strait (Santana-
| Abstract Introduction
Casianoetal.,2002),theDyfamedtime-seriesintheLigurianSea(BégovicandCopin-
15
Montégut, 2002; Copin-Montégut and Bégovic, 2002; Touratier and Goyet, 2009) and Dis Conclusions References
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the Otranto Strait (Krasakopoulou et al., 2011). Large geographical repartition of CO2 us Tables Figures
data are often confined to cruises with a short sampling period (Álvarez et al., 2014; s
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Goyet et al., 2015; Rivaro et al., 2010; Schneider et al., 2007; Touratier et al., 2012). n (cid:74) (cid:73)
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NumericalmodelshaveprovidedsomeinsightsofthecarbondynamicsintheMediter- a
20 p
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ranean Sea (Cossarini et al., 2015; D’Ortenzio et al., 2008; Louanchi et al., 2009), but r
itremainsimportanttoconstrainthesystemfrominsitumeasurementstovalidatetheir
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output.
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The scarcity of the CO2 system measurements in the Mediterranean Sea make it isc FullScreen/Esc
difficult to constrain the CO uptake in this landlocked area and also limits our under- u
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standingofthemagnitudeandmechanismsdrivingthenaturalvariabilityontheocean sio Printer-friendlyVersion
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carbonsystem(TouratierandGoyet,2009).Empiricalmodelinghasbeensuccessfully P InteractiveDiscussion
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used to study the marine carbon biogeochemical processes such as the estimation of p
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biologically produced O in the mixed layer (Keeling et al., 1993), estimation of global D
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inventories of anthropogenic CO (Sabine et al., 2004) and estimation of the CaCO c ESDD
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cycle(Koeveetal.,2014).EmpiricalalgorithmswerealsousedtorelatelimitedA and ss
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C measurements to more widely available physical parameters such as salinity and n
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5 temperature (Bakker et al., 1999; Ishii et al., 2004; Lee et al., 2006). The AT and CT ap
fieldscanthenbeusedtocalculatepCO fieldsandthuspredicttheCO fluxesacross e Climatological
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the air–sea interface (McNeil et al., 2007). variations of total
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Previous empirical approaches to constrain A in the Mediterranean Sea have only alkalinity and total
T
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coveredselectedcruises(Schneideretal.,2007;TouratierandGoyet,2009)orlocalar- inorganic carbon
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10 eassuchastheDyfamedtime-seriesstationortheStraitofGibraltar(Copin-Montégut, u
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1993; Santana-Casiano et al., 2002). As for C , empirical models have only been ap- s
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plied to data below the mixed layer depth (MLD) following the equation of Goyet and n
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Davis (1997) at the Dyfamed time series station (Touratier and Goyet, 2009) or using a
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the composite dataset from Meteor 51/2 and Dyfamed (Touratier and Goyet, 2011). r
15 Also Lovato and Vichi (2015) proposed an optimal multiple linear model for CT using | Abstract Introduction
theMeteor84/3fullwatercolumdata.Tothebestofourknowledgethereconstruction
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of CT in surface waters has not been yet performed in the Mediterranean Sea. This is isc
probablyduetothelackofmeasurementsavailableforpreviousstudiestocapturethe us Tables Figures
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more complex interplay of biological, physical and solubility processes that drive the io
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C variability in surface waters. (cid:74) (cid:73)
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InthisstudywehavecompiledCO systemmeasurementsfrom14cruisesbetween p
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1995and2013,thatallowedustoconstrainanimprovedandnewempiricalalgorithms r
for A and C in the Mediterranean Sea surface waters. We also evaluated the spatial | Back Close
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andseasonalvariabilityofthecarbonsystemintheMediterraneanSeasurfacewaters,
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bymappingthe2005–2012annualandseasonalaveragesofsurfaceA andC using is FullScreen/Esc
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the quarter degree climatologies of salinity and temperature from the World Ocean s
Atlas 2013 (WOA13). sio Printer-friendlyVersion
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2 Methods D
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2.1 Surface A and C data in the Mediterranean Sea s
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Between1998and2013,therehavebeenmultipleresearchcruisessamplingthesea- P
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water properties throughout the Mediterranean Sea. This includes parameters of the p
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carbonatesystemmorespecificallyA ,pHandC andphysico-chemicalpropertiesof r
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in situ salinity, and temperature. However, the number of the nutrients concentrations |
alkalinity and total
wasverylimited.Inthisstudywehavecompiledsurfacewatersamplesbetween0and D
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10m depth, totaling 490 and 426 measurements for AT and CT respectively (Table 1). iscu
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2.2 Polynomial model for fitting A and C data io
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10 Two polynomial equations for fitting AT or CT from salinity (S) alone or combined with pe TitlePage
sea surface temperature (T) in the surface waters (0–10m) of the Mediterranean Sea r
were chosen from the results of the 10-fold cross validation method (Breiman, 1996; | Abstract Introduction
Stone, 1974). This type of analysis was previously performed by Lee et al. (2006) for
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global relationships of A with salinity and temperature. This model validation tech- is
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15 niqueisperformedbyretainingasinglesubsampleusedfortestingandtrainingtheal- uss Tables Figures
gorithmonthe9remainingsubsamples.Thecrossvalidationprocessisthenrepeated io
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10times.Thebestfitischosenbycomputingtheresidualsfromeachregressionmodel, P (cid:74) (cid:73)
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and computing independently the performance of the selected optimal polynomial on p
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the remaining subsets. The analysis was applied for polynomials of order 1 to 3, and
theoptimalequationwaschosenbasedonthelowestRootMeanSquareError(RMSE) | Back Close
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and the highest coefficient of determination (r2). D
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To ensure the same spatial and temporal distribution of A and C polynomial fits c
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we only selected stations where A and C were simultaneously measured (Table 1; s
Fig.1).TovalidatethegeneraluseoTftheprToposedparameterizationswetestedtheal- sion Printer-friendlyVersion
gorithmswithmeasurementswhicharenotincludedinthefits(Testingdataset).Hence P InteractiveDiscussion
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for the A , 375 and 115 data points are used for the training and testing datasets re- e
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spectively. For the C the training dataset is formed from 381 data points and the D
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validation dataset is the same as the testing subset of the 10th fold (45 data points). c ESDD
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2.3 Climatological and seasonal mapping of A and C io 6,1499–1533,2015
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The climatological and seasonal averages of salinity (Zweng et al., 2013) and tem- p
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perature (Locarnini et al., 2013) in 1/4×1/4◦ grid cells were downloaded from the r
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World Ocean Atlas 2013 (WOA13). The seven years averages (2005–2012) and the |
alkalinity and total
summer-winter seasonality of A and C fields were mapped at 5m depth by applying D
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the respective derived algorithms in their appropriate ranges of S and T. isc
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3 Results and discussion
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10 3.1 Fitting AT in the Mediterranean Sea surface waters r
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In the surface ocean the A variability is controlled by freshwater addition or the effect
T
of evaporation, and salinity contributes to more than 80% of the AT variability (Millero Dis Conclusions References
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et al., 1998). In the Mediterranean Sea, several studies have shown that the relation- us Tables Figures
shipbetweenA andS islinear(Copin-Montégut,1993;Copin-MontégutandBégovic, s
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15 2002; Hassoun et al., 2015b; Rivaro et al., 2010; Schneider et al., 2007). In other nP (cid:74) (cid:73)
studies, the sea surface temperature (T) has been included as an additional proxy for a
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changes in surface water A related to convective mixing (Lee et al., 2006; Touratier r
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and Goyet, 2011).
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Theresultsofthe10-foldcrossvalidationanalysisrevealedthattheoptimalmodelfor
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20 AT is a second order polynomial in which AT is fitted to both S and T (Table 2, Eq. 1). isc FullScreen/Esc
Aa lloinweearrrr2el(a0ti.o9n1s)hthipanbeEtwq.ee(1n).AITn aansdeSmi-yeienlcdlsosaedhibgahseirnRsMucShEa(s14th.5eµMmeodliktegr−r1a)neaannd ussio Printer-friendlyVersion
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Sea, the insulation and high evaporation as well as the input of rivers and little pre- P InteractiveDiscussion
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cipitation leads to a negative freshwater balance (Rohling et al., 2009). The resulting p
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anti-estuarinethermohalinecirculationcouldexplainthecontributionoftemperatureto D
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The residuals of training dataset used to generate the second order polynomial fit s
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for A are presented in Fig. 2a. Most of the A residuals (340 over 375) were within n
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a range of ±15µmolkg−1 (1σ). However 35 residuals were high up to ±30µmolkg−1 Pa
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(1σ). Applying the A algorithm to the testing dataset (Fig. 2b), yields a mean resid- e Climatological
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ual of 0.91±10.30µmolkg−1 (1σ), and only 6 data points have residuals higher than variations of total
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±15µmolkg−1 (1σ). alkalinity and total
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The comparison of the RMSE as reported by other studies with that of Eq. (1) does is inorganic carbon
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not indicate if the parameterization developed here has advanced or not on previous u
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attempts in the Mediterranean Sea. In that order, we independently applied each of io
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the previous equations on the same training dataset used to develop Eq. (1) and then
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computed the RMSE and r2 for every one (Table 3). The results show that Eq. (1) has ap
a lower RMSE and a higher r2 than all of the parameterizations presented in Table 3. er TitlePage
15 For instance, the global relationship of Lee et al. (2006) applied to the dataset of this | Abstract Introduction
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study yields a RMSE as high as ±40.50µmolkg . The RMSE of other studies devel-
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oped strictly in the Mediterranean Sea varied from ±13.81 to ±26.11µmolkg−1 using is
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the equations of Touratier and Goyet (2011) and Schneider et al. (2007) respectively. us Tables Figures
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By applying directly the previous parameterizations to our training dataset, the cal- io
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culated RMSE are significantly higher than the ones reported in their respective stud- P (cid:74) (cid:73)
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gions is ±8µmolkg and that of Schneider et al. (2007) for the Meteor 51/2 cruise
is ±4.2µmolkg−1. This shows that previous models were constrained by their spatial | Back Close
coverage,timespananduseddatasets.Infactthepreviousequationswerecalculated D
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25 in local areas such as the Alboran Sea (Copin-Montégut, 1993), the Strait of Gibral- cu
tar (Santana-Casiano et al., 2002) or the Dyfamed Site (Copin-Montégut and Bégovic, s
2002; Touratier and Goyet, 2009). On a large scale, equations were applied using lim- sion Printer-friendlyVersion
ited datasets such as the Meteor 51/2 cruise in October–November 2001 (Schneider P InteractiveDiscussion
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etal.,2007),theTransmedcruiseinMay–June2007(Rivaroetal.,2010)ortheMeteor D
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51/2 and the Dyfamed time series station (Touratier and Goyet, 2011). c ESDD
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The proposed algorithm including surface data from multiple cruises, and on a large s
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time span, presents a more global relationship to estimate A from S and T than the n
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5 previously presented equations (Table 3). In Eq. (1), T and S contribute to 96% of the a
A variability and the RMSE of ±10.6µmolkg−1 presents a significant improvement of pe Climatological
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the spatial and temporal estimations of A in the Mediterranean Sea surface waters. variations of total
T |
alkalinity and total
3.2 Fitting CT in the Mediterranean Sea surface waters Dis inorganic carbon
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The surface C concentrations are influenced by lateral and vertical mixing, photosyn- s E.Gemayeletal.
T s
thesis, oxidation of organic matter and changes in temperature and salinity (Poisson io
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et al., 1993; Takahashi et al., 1993). All these processes are directly or indirectly cor- P
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related with sea-surface temperature (Lee et al., 2000). Hence, the parameterization e TitlePage
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of C in surface waters includes both physical (S and T) and/or biological parameters
(BakTkeretal.,1999;Batesetal.,2006;Koffietal.,2010;Leeetal.,2000;Sasseetal., | Abstract Introduction
2013). D Conclusions References
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Theresultsofthe10-foldcrossvalidationanalysisshowedthatafirstorderpolynome c
fits C to S and T with an RMSE of 16.25µmolkg−1 and r2=0.87. These values are uss Tables Figures
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comparabletotheRMSEandr2 foundbypreviousempiricalapproachesappliedinthe n
(cid:74) (cid:73)
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Eastern Atlantic (Bakker et al., 1999; Koffi et al., 2010). However we found that a third a
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orderpolynomeimprovedtheRMSEandr2 oftheequationcomparedtothefirstorder e (cid:74) (cid:73)
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fit (Table 4, Eq. 2). Hence we will retain the large dataset used to develop Eq. (2),
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where temperature and salinity contribute to 90% of the C variability encountered in
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the Mediterranean Sea surface waters. The remaining 10% could be attributed to the is FullScreen/Esc
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biological and air–sea exchange contributions to the C variability. u
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uncertaintythanthatofAT regardingbothRMSEandr2.Infact,theinterpolationofCT nP InteractiveDiscussion
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inthemixedlayeraddsahighuncertaintyduetotheseasonalvariability.Alsoinsurface p
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