Spatiotemporal variability of air–sea CO2 fluxes in the Barents Sea, as determined from empirical relationships and modeled hydrography

Journal of Marine Systems 98-99 (2012) 40–50

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Spatiotemporal variability of air–sea CO2 fluxes in the Barents Sea, as determined from empirical relationships and modeled hydrography M. Årthun a, b,⁎, R.G.J. Bellerby a, b, c, A.M. Omar b, c, C. Schrum a, b a b c

Geophysical Institute, University of Bergen, Bergen, Norway Bjerknes Centre for Climate Research, Bergen, Norway Uni Bjerknes Centre, Uni Research AS, Bergen, Norway

a r t i c l e

i n f o

Article history: Received 7 June 2011 Received in revised form 14 March 2012 Accepted 19 March 2012 Available online 29 March 2012 Keywords: Barents Sea Numerical model CO2 fluxes Carbon dioxide Spatial variability Interannual variability

a b s t r a c t Shelf seas play a major role in the global carbon cycle, but estimates of regional oceanic CO2 uptake are limited in time and space due to scarcity of observed carbon parameters. Here, air–sea CO2 fluxes in the Barents Sea and the dominant drivers of variability during the period 2000–2007 are investigated using a carbon system model based on hydrography coupled to a hydrodynamic model. The strong thermohaline control on the surface ocean CO2 system allows for estimates of alkalinity and partial pressure of CO2 (pCO2) based on simulated temperature and salinity. Biological drawdown of CO2 is calculated from changes in total inorganic carbon based on prescribed values of carbon to nitrate uptake ratio and a prescribed seasonal cycle of nitrate. Compared to available measurements the use of temperature and salinity data to reconstruct spatial and temporal variability of carbon system variables in the Barents Sea is shown to be reasonable. This allows, for the first time, an estimate of the spatiotemporal variability of air–sea CO2 exchange for the whole Barents Sea. Our analysis indicates that the Barents Sea is a sink for atmospheric CO2 throughout the year during the study period. The mean annual air– sea flux is 40± 5 g C m− 2, corresponding to an ocean uptake of 0.061 ± 0.007 Gt C yr− 1. Higher fluxes are found in the Atlantic southern Barents Sea (45 ± 5 g C m− 2), whereas less gas exchange takes place in the seasonally ice covered northern Barents Sea (33 ± 4 g C m− 2). Due to the combined effect of large concentration gradients across the air–sea interface (ΔpCO2) and high wind speeds, the largest CO2 uptake occurs in September and October. Interannually, the fluxes vary by ±12% of the mean oceanic uptake, mostly driven by variations in wind speed. © 2012 Elsevier B.V. All rights reserved.

1. Introduction The ocean plays an important role in the biogeochemical cycling of carbon. Due to intense ocean–atmosphere heat exchange in winter and large biological production in summer shelf seas have been reported to contribute substantially to the global uptake of atmospheric CO2 (Borges et al., 2006; Chen and Borges, 2009; Thomas et al., 2004; Tsunogai et al., 1999). The Barents Sea (Fig. 1) is the largest shelf sea adjacent to the Arctic Ocean, and it is also one of the most biologically productive (Bates and Mathis, 2009; Wassmann et al., 2006). Observations show that the surface waters across the Barents Sea are undersaturated with respect to atmospheric CO2 (Kelley, 1970; Nakaoka et al., 2006; Omar et al., 2003, 2007), indicating a strong potential for significant uptake of CO2 from the atmosphere.

⁎ Corresponding author at: British Antarctic Survey, Natural Environment Research Council, Cambridge, UK. Tel.: +44 1223221205. E-mail addresses: [email protected] (M. Årthun), [email protected] (R.G.J. Bellerby), [email protected] (A.M. Omar), corinna.schrum@gfi.uib.no (C. Schrum). 0924-7963/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.jmarsys.2012.03.005

The ocean circulation in the southern Barents Sea is dominated by warm, saline Atlantic Water (AW) which enters the Barents Sea as the North Cape Current (NCaC; Ingvaldsen et al., 2004; Loeng et al., 1997) between Norway and Bear Island (Fig. 1), often called the Barents Sea Opening (BSO). Upon entering the Barents Sea the NCaC splits into two main branches; one following the Norwegian/Russian coast and the other flowing north into the Hopen Trench. Part of the northern branch recirculates within the Hopen Trench (Skagseth, 2008), while the rest either continues north or flows north of the Central Bank and into the Central Basin (Aksenov et al., 2010; Ozhigin et al., 2000). In the eastern Barents Sea the flow is generally northeastward along the coast of Novaya Zemlya. The fresher Norwegian/ Murmansk Coastal Current also enters the Barents Sea in the southwest, after which it flows eastward and either enters the Kara Sea south of Novaya Zemlya or continues northward along the Central Basin (Loeng et al., 1997; Ozhigin et al., 2000). The low salinity of this water also reflects freshening due to river discharge in the southern part of the Barents Sea. The circulation in the north-western Barents Sea is dominated by cold and fresh Arctic Water (ArW) transported southward by the East Spitsbergen Current and Persey Current (Pfirman et al., 1994). The separation between the Atlantic influenced

M. Årthun et al. / Journal of Marine Systems 98-99 (2012) 40–50

41

Fig. 1. Schematic of main water masses and distribution in the Barents Sea. Colored arrows indicate currents, while black lines are crude representations of frontal areas (see e.g., Loeng, 1991). Warm, saline Atlantic Water (AW) enters the Barents Sea between Norway and Bear Island. Flowing east/north-east it gets colder and fresher and exits the Barents Sea mainly as modified Atlantic Water (mAW) between Novaya Zemlya and Frans Josef Land. The north-western Barents Sea is dominated by cold, fresh Arctic Water (ArW) flowing south-westward. The separation between the Atlantic influenced southern Barents Sea and the Arctic waters in the north is denoted the Polar Front. In the eastern Barents Sea the Polar Front is less distinct indicated by the dashed black line. A salinity front also separates the AW from the fresher Coastal Water (CW) flowing along the coast of Norway and Russia.

southern Barents Sea and the Arctic waters in the north (Fig. 1) is denoted as the Polar Front. Substantial oceanic heat loss takes place in the Barents Sea (Årthun and Schrum, 2010; Häkkinen and Cavalieri, 1989; Serreze et al., 2007), which leads to a gradual cooling of the AW during its passage from south to north. The combination of high biological production and the transformation of the inflowing AW into colder, denser waters masses (Årthun et al., 2011; Schauer et al., 2002) results in a strong CO2 uptake, followed by the absorbed carbon being transported into the interior ocean, thus being isolated from interaction with the atmosphere. The dense waters mainly exit the Barents Sea between Frans Josef Land and Novaya Zemlya (Gammelsrød et al., 2009; Schauer et al., 2002), and are a source of intermediate and deep waters of the Arctic Ocean (e.g., Rudels et al., 1994). Using volume transport estimates from Maslowski et al. (2004) to construct a carbon budget for the Barents Sea, Kivimäe et al. (2010) estimated the export of total dissolved inorganic carbon (CT) from the Barents Sea to the Arctic Ocean to be 2.5 Gt C yr − 1, ~ 70% being a sub-surface transport. The carbon contained in Barents Sea waters is thus sequestered for decades to centuries when the waters exit into the neighboring deep basins of the Nordic Seas and Arctic Ocean (Anderson et al., 1998). The Barents Sea is, however, sparsely sampled; observations often being limited to the Atlantic region during summer months. To enable calculation of surface ocean partial pressure of CO2 (pCOw 2 ), empirical relationships are often applied which relate CO2 system variables to hydrographic and nutrient properties (e.g., Bellerby et al., 2005; Millero et al., 1998; Nakaoka et al., 2006; Olsen et al., 2003; Omar et al., 2007), taking advantage of the strong thermohaline control on the surface ocean CO2 system. This includes a strong total alkalinity (AT) dependence on salinity, and a thermal control on CO2 solubility. Knowledge of two of the four carbon system variables (CT, AT, pH, and pCOw 2 ) then allows for the calculation of the remaining CO2 system variables through thermodynamic equations (Zeebe and Wolf-

Gladrow, 2001). This is thus a useful approach for the purpose of understanding spatial and temporal variability of contemporary air– sea fluxes in a region with few measurements. In the Barents Sea, Nakaoka et al. (2006) derived seasonally varying relationships between NCEP/NCAR reanalysis (Kalnay et al., 1996) sea surface temperature and pCOw 2 , while Omar et al. (2007) identified an empirical relationship to compute pCOw 2 using observed temperature, salinity and phosphate. However, both applications were mainly limited to the Atlantic inflow region (Fig. 1). The present study uses the thermohaline output from a regional coupled ice–ocean model together with a carbon system module with algorithms for the CO2 system based on hydrography. An estimate of air–sea CO2 exchange and its variability is therefore presented, for the first time, for the Barents Sea as a whole. Our approach is not applicable to integrated climate studies as there is no feedback to atmospheric CO2, but it provides a useful tool for assessing the variability of air–sea CO2 fluxes in the Barents Sea on a seasonal and regional basis at a higher resolution than that obtainable from direct observational platforms. Regional models can also provide details and resolve local effects not captured by climate models (e.g., Melsom et al., 2009) due to the increased resolution. The paper is organized as follows: first, the model system is presented in Section 2 and evaluated in Section 3. Spatial and temporal variability of air–sea CO2 fluxes are thereafter investigated and discussed with respect to dominant drivers in Section 4. 2. Data and methods 2.1. Ocean model A model run for the period 2000 to 2007 was performed using the regional coupled ice–ocean model Hamburg Self Ocean Model (HAMSOM; Schrum and Backhaus, 1999). HAMSOM is a threedimensional, baroclinic, coupled ice–ocean model. The model uses non-linear primitive equations of motion, which are discretized on

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M. Årthun et al. / Journal of Marine Systems 98-99 (2012) 40–50

an Arakawa C-grid with a semi-implicit numerical scheme and a time step of 20 min. HAMSOM applies a monotonic TVD advection scheme (Sweby, 1984) and an analytical k–
approach for turbulence closure (Schrum, 1997). The ocean model is coupled to a modified Hiblertype (Hibler, 1979) dynamic–thermodynamic sea ice model. The ice dynamics are based upon a viscous-plastic rheology described in Leppäranta and Zhang (1992). Model boundaries are defined by Norway and Russia in the south (including the White Sea), Svalbard and Frans Josef Land in the north, Novaya Zemlya in the east, and the continental slope toward the Norwegian Sea in the west (Fig. 1). The model configuration has a horizontal resolution of 7 km × 7 km and 16 vertical z-levels. The model topography is extracted from the International Bathymetric Chart of the Arctic Ocean (IBCAO; Jakobsson, 2002). At the lateral open boundaries the sea surface elevation is prescribed based on data from the global model MICOM (Bleck, 1998). Additionally, sea surface elevation caused by the semidiurnal lunar tide (M2) is prescribed based on a large-scale tidal model (Zahel et al., 2000). Temperature and salinity boundary conditions are from the Barents and Kara Seas Oceanographic Database (BarKode; Golubev and Zuyev, 1999). Output from a previous model run (Årthun et al., 2011) was used as initial conditions. Freshwater from land is added through river runoff from four Russian rivers; Pechora, Mesen, Dvina, Onega (Lammers and Shiklomanov, 2000). Atmospheric forcing is based on NCEP/NCAR reanalysis data (Kalnay et al., 1996), except for turbulent heat fluxes which are calculated by the model itself using bulk formulae (Schrum and Backhaus, 1999). The model has previously been successfully applied to the Barents Sea (Årthun and Schrum, 2010; Årthun et al., 2011; Harms, 1997) and other shelf seas (Pohlmann, 1996; Schrum and Backhaus, 1999), and found to reproduce well the mean distribution and interannual variability of hydrography and sea ice extent. 2.2. Carbon system algorithm The inorganic CO2 system can be determined by knowing two of the four major carbon system parameters (CT, AT, pH, and pCOw 2 ). Following Olsen et al. (2003) pCOw 2 is calculated as a function of the sea surface temperature (T):
 w 2 3 fCO2 ¼ 391:13−8:71⋅T−0:36⋅T þ 0:011⋅T ⋅ expð0:0423⋅ðT−5ÞÞ

between 1967 and 2000/2001. The fact that Olsen et al. (2003) calibrated (identified) Eq. (1) using 1995 pCOw 2 climatology implies that the equation produces pCOw 2 estimates containing a time invariant anthropogenic CO2 concentration which is at the level of 1995. In other words, data obtained from Eq. (1) are expected to include all kinds of temporal and spatial variability except for the anthropogenic trend. Therefore, the model pCOw 2 estimates do not need to be normalized to 1995. The observed data, on the other hand, contain different concentrations of anthropogenic carbon due to the increase of atmospheric CO2 and need to be adjusted to 1995 in order to be compared to the modeled pCOw 2. Several salinity–alkalinity relationships exist for the North Atlantic (e.g., Bellerby et al., 2005; Millero et al., 1998; Nondal et al., 2009). Shelf seas represent more heterogeneous environments where salinities are influenced by, e.g., river runoff and sea ice melt, and we have adapted the linear regression formula from Kivimäe (2007): AT ¼ 57:6⋅S þ 288;

ð3Þ

as this is based solely on data from the Barents Sea. Using the thermohaline output from HAMSOM combined with the equations for alkalinity and pCOw 2 the carbon system variables can then be calculated at each model grid point using an inorganic speciation model (csys3.m; Zeebe and Wolf-Gladrow, 2001). Carbonate system parameters are calculated using the first and second dissociation constants of carbonic acid, K1 and K2, according to Mehrbach et al. (1973), refit by Lueker et al. (2000), while the solubility coefficient, K0, is taken from Weiss (1974). The dissociation constants for borate, Kb, and water, Kw, are based on Dickson (1990) and Millero (1995), respectively. Following Millero (1995), a correction on the constants is applied due to the effect of pressure. A number of sets of equilibrium constants that govern the carbonate equations have been published, and there is no universally accepted set of coefficients. However, the constants by Mehrbach et al. (1973), and recommended by Lueker et al. (2000), have the advantage of being derived from natural as opposed to artificial seawater, and are therefore preferred here. The effect of biology on the carbon system is considered using a mean (temporal and spatial) seasonal cycle of nitrate (NO3, Fig. 2) based on data from the SINMOD numerical ocean model (Slagstad et al., 2011) between 2002 and 2006. The change in CT due to biology is then:

ð1Þ w

w

pCO2 ≈fCO2 ;

ΔCT ¼ C=N⋅ΔNO3 ;

ð4Þ

ð2Þ 12

10

Nitrate [µmol/kg]

where the isochemical relationship from Takahashi et al. (1993) is used to convert back to in situ temperatures, as Olsen et al. (2003) w used pCOw 2 normalized to 5 °C. The assumption that fugacity (fCO2 ) equals partial pressure of CO2 is correct within a few μatm Zeebe and Wolf-Gladrow, 2001. Empirical relationships for the Barents Sea have also been identified elsewhere (e.g., Kelley, 1970; Nakaoka et al., 2006; Omar et al., 2003, 2007). The latter two require nutrients or dissolved oxygen for which we had no data with the necessary coverage. More importantly, all the above relationships include spatial and/or temporal limitations which would be violated if we used them for this study. One can say the same about the use of Eq. (1) from Olsen et al. (2003) which is valid for the northern north Atlantic during winter (October–March). We chose the latter because it reproduces the observations satisfactorily (to be shown in Section 3), and because of its applicability for a much wider region. When constructing their climatology Olsen et al. (2003) adjusted the pCOw 2 data to the year 1995 by assuming an oceanic mixed layer −1 increase in pCOw over their study period. This was 2 of 1.4 μatm yr based on Omar et al. (2003) who showed that the surface ocean pCOw 2 in the Barents Sea had tracked the atmospheric increase

8

6

4

2

W E S N Mean

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Time [month] Fig. 2. Seasonal cycle of surface nitrate from west (W), east (E), south (S), and north (N) Barents Sea. The applied (mean) values are indicated by the thick black line. Data are from the physical–chemical–biological model SINMOD (Slagstad et al., 2011).

M. Årthun et al. / Journal of Marine Systems 98-99 (2012) 40–50

where the carbon to nitrate (C/N) uptake ratio is set to 6.625 following Redfield et al. (1963). A proportional change in AT is also calculated for a change in nitrate. Nutrient levels are maximal in the winter and decrease rapidly during the spring bloom. In the model, the seasonal change is highest at the surface and decreases linearly to 100 m. Below this no seasonal change in nitrate is prescribed. This is consistent with Reigstad et al. (2002) who observed reduced nutrient conw centrations down to 100 m in the central Barents Sea. The pCO2 calculated from Eq. (1) does not take into account the influence of biw ological production, and pCO2 is therefore recalculated based on the nitrate corrected CT and AT. Air–sea CO2 fluxes (Fco2) are calculated using the concentration gradient between the atmosphere and the ocean, the gas solubility of CO2 (K0; Weiss, 1974) and the gas transfer velocity (k) suggested by Sweeney et al. (2007):  a w Fco2 ¼ K 0 ⋅k⋅ pCO2 −pCO2 ⋅ð1−Ic Þ −1=2

k ¼ 0:27⋅ðSc=660Þ

2

⋅U :

ð5Þ ð6Þ

Fluxes are scaled with the open water area (1 − Ic) in each grid cell assuming that ice prevents gas exchange. Sea ice concentrations (Ic) are taken from the numerical model. Wind speeds, U, are 6 hourly reanalysis data from NCEP and Sc is the Schmidt number, which is related to temperature (Wanninkhof, 1992). Monthly atmospheric pCO2 (pCOa2 ) was calculated using the atmospheric mole fraction of CO2, xCO2, from Ny-Ålesund, Svalbard (78.90°N, 11.88°E; World Meteorological Organisation (WMO) World Data Centre for Greenhouse Gases (WDCGG)):
 a pCO2 ¼ xCO2 ⋅ p−VPH2 O ;

ð7Þ

where p is the sea-level pressure and VPH2O is the water vapor pressure calculated according to Cooper et al. (1998). 3. Evaluation of calculated carbon system parameters The applied empirical relationships need to be evaluated in order to determine their predictive skills in the Barents Sea. For instance, only data from the extreme south-western Barents Sea are included in the Olsen et al. (2003) climatology, and Eq. (1) is thus calibrated for different conditions than it is being applied to. Here, an evaluation of the calculated carbon system parameters is given using independent observations described below. 3.1. Observations Three datasets are used in this study to evaluate the modelderived values. The CARINA (CARbon IN the Atlantic) database (http://cdiac.ornl.gov/oceans/CARINA) contains water column data of carbon and hydrographic parameters. Details on data distribution and quality control are found in Olsen et al. (2009) and references therein. The second dataset is high frequency pCOw 2 measurements acquired from a mooring in BSO (72.5°N, 19.6°E, 17 m depth) using an autonomous sensor (SAMI-pCO2) between June 2006 and July 2007. This dataset is described in detail in Omar et al. (in prep.) and only a brief description is given here. The SAMI pCOw 2 measurement is based on equilibration of a pH indicator solution contained in a gas permeable membrane with ambient pCOw 2 and subsequent spectrophotometric pH determination in the equilibrated solution (DeGrandpre et al., 1995). We added a constant offset of 38 μatm to the data as recommended by Omar et al. (in prep.) based on comparison with underway pCOw 2 data acquired onboard R/V G.O. Sars around the mooring. The accuracy of the offset-corrected SAMI pCO2 data is assumed to be

43

equal to the typical accuracy of the sensor in the field; ±10.4 μatm (Körtzinger et al., 2008). pCOw 2 measurements from the south-western Barents Sea have also been carried out on R/V G.O. Sars using an underway pCOw 2 system (e.g., Pierrot et al., 2009). The system is calibrated with three referenced standard gases at regular intervals. Here, we use data from January–March and October–December 2005–2007. 3.2. Evaluation Based on the data coverage in the Barents Sea the following comparison between observed (CARINA) and modeled carbon system parameters is restricted to the period June–September 2000–2003. Three areas are also specified (Fig. 3a) which represent areas with different water mass characteristics; the AW inflow region south of Bear Island (BSO), the area between Bear Island and Svalbard (BISV), and an area in the north-west (NWBS) dominated by cold and fresh ArW (e.g., Pfirman et al., 1994). A comparison between modeled and observed CT and AT in these areas at different depth intervals is presented in Fig. 3b–d, while the bias, root mean square error (RMSE) and the normalized mean absolute error, χ;

χ¼

N 1X jM n −Dn j : N n¼1 σD

ð8Þ

are given in Table 1. Here, Dn is the observation at each individual location, Mn is the model output at its matching model grid cell (colocated to observations within ±0.05°N and ±0.15°E), and σD is the standard deviation of the observed data calculated over each area. The average modeled AT values are within ±6 μmol kg − 1 of observed values at all depth intervals in BSO and BISV (Fig. 3b, c). This lends credibility to the applied salinity–AT relationship in the southern Barents Sea. The largest discrepancy between observed and modeled AT is found in the surface layer in the north-western Barents Sea (Fig. 3d; Table 1). The weighted bias (χ) shows that this error is about 1.5 standard deviations. In NWBS the freshwater content is substantially influenced by ice melt (Ellingsen et al., 2009), which might influence the applicability of the alkalinity–salinity relationship (Kivimäe, 2007). Modeled CT is in good agreement with observations, except between 50 and 100 m in BSO where it is outside the observed standard deviation. The highest RMSE is found in NWBS, although the error is within one standard deviation (χ b 1; Table 1). pCOw 2 was also calculated based on observed (CARINA) CT and AT (Zeebe and Wolf-Gladrow, 2001). Model-derived pCOw 2 is lower than these calculated values (negative bias). This is also the case if compared to direct wintertime pCOw 2 measurements from the southwestern Barents Sea (> 17°E and b76°N; Fig. 4a) in 2005–2007, and to mooring data from 2006 to 2007 in BSO (Fig. 4b). The RMSE between model-derived values and ship-based observations is 22.9 μatm. The difference between modeled and observed surface pCOw 2 in BSO is largest during spring (May–June; Fig. 4b). For the rest of the year the mean offset is 9 μatm, which is similar to the values presented in Table 1. This might be due to interannual variations in the start of the spring bloom. Although annual variability in primary production is small, Wassmann et al. (2010) found large monthly variability in the beginning of the productive period. The difference between simulated and observed temperature in May 2007 is 0.03 °C, and should therefore not have a significant influence on the offset. The applied pCOw 2 relationship (Eq. (1)) is assumed to be valid for wintertime (October–March) when low biological productivity conditions prevail. However, nutrient concentrations (Fig. 2) indicate that the winter season is limited to January–March. The difference in observed pCOw 2 between October–December and January–March at the same temperatures also supports biological activity during

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M. Årthun et al. / Journal of Marine Systems 98-99 (2012) 40–50

80 o N

a)

78 o N

NWBS

76 o N 74 o N

BISV

72 o N

BSO

70 o N

oE

70

20 o E µmol kg

30 oE

−1

40oE

o 50 E

o 60 E

1900 1920 1940 1960 1980 2000 2020 2040 2060 2080 2100 2120

AT[µmol kg−1]

AT[µmol kg−1]

2260 2280 2300 2320 2340

b)

2240 2260 2280 2300 2320

c)

BSO

AT[µmol kg−1] 2100

0−8

0−8

0−8

8−50

8−50

8−50

50−100

50−100

50−100

100−200

100−200

100−200

200−400

200−400

200−400

2200

d)

BISV

2300

2400

NWBS

Observed Modeled

2050

2100

2150

2000 2050 2100 2150 2200

CT[µmol kg−1]

CT[µmol kg−1]

1900

2000

2100

2200

CT[µmol kg−1]

Fig. 3. a) Surface (0–10 m) CT (μmol kg− 1) from CARINA from June to September 2000–2003. Abbreviations indicate geographical locations. BSO: Barents Sea Opening. BISV: Bear Island–Svalbard. NWBS: North-west Barents Sea. b–d) Observed (crosses) and simulated average (squares) CT (black) and AT from defined boxes and binned into depth intervals. Observations have been adjusted to 1995 assuming an increase of 0.7 μmol kg− 1 yr− 1. Lines indicate observed standard deviations. Note the different scales.

early winter. This would lead to an underestimation in modeled pCOw 2 , consistent with the discrepancy between model and observations (Fig. 4a,b). During periods of the year with biological activity, modeled pCOw 2 will also be influenced by errors in the nitratecorrected CT and AT, and low values can be due to excessive biological drawdown related to regional differences in nitrate not captured by the applied mean profile. From Fig. 2 it is seen that the bloom starts in April in the southern Barents Sea and then progresses north as the ice melts. Nutrient depletion is also lower in the northern domain (N). The mean profile is most representative of conditions in the Atlantic domain (W; includes BSO) and the eastern domain (E), which includes the area around the Central Basin (Fig. 1). The southern Barents Sea (S) is influenced by the fresher Norwegian Coastal Current and has lower (diluted) nutrient concentrations. The offset is, Table 1 Bias (modeled minus observed or calculated), root mean square error (RMSE) and normalized mean absolute error, χ; Eq. (8), at different depth intervals based on observed (CARINA) and calculated parameters from June to September 2000–2003. Model data were co-located within ±0.05°N and ±0.15°E. Model-derived pCOw 2 is here compared with calculated pCOw 2 (from CARINA CT and AT). Depth [m]

BSO BISV NWBS

0–100 100–200 0–100 100–200 0–100 100–200

CT

AT

Bias

RMSE

χ

Bias

RMSE

χ

Bias

RMSE

χ

− 8.8 1.6 3.1 2.5 7.2 10.9

13.8 7.1 14.9 12.9 31.7 19.2

1.2 0.9 0.8 0.6 0.9 0.7

− 7.4 − 1.3 − 3.0 − 0.5 2.0 21.2

14.9 7.3 12.4 7.2 25.0 22.4

0.8 0.6 0.6 0.8 1.5 1.3

− 11.4 − 4.9 − 17.1 − 25.7 − 2.9 − 22.2

21.7 19.2 27.1 40.1 22.0 31.1

0.9 0.8 1.1 1.2 1.0 0.8

however, nearly constant and carbon consumption will therefore be similar to that based on the mean profile. Hence, we do not consider this to be a major source of the discrepancy seen in Fig. 4. Based on the presented evaluation we consider the approach and empirical relationships to be applicable to all areas of the Barents Sea, although we recognize that a majority of the observations and, hence, the model evaluation, are from the Atlantic sector. 3.3. Estimate of uncertainty The uncertainty in our flux calculations (σF) was estimated by error propagation (Omar et al., 2007): 2 31=2 2  2  2 ∂F co2 ∂F co2 ∂F co2 ∂F ∂F ⋅σ K0 þ ⋅σ ΔpCO2 þ 2 co2 co2 C ðK 0 ; kÞþ 7 ⋅σ k þ 6 6 7 ∂k ∂ΔpCO2 ∂K 0 ∂k σ F ¼ 6 ∂K 0 7 ; 4 ∂F co2 ∂F co2 5 ∂F ∂F co2 C ðK 0 ; ΔpCO2 Þ þ 2 co2 C ðk; ΔpCO2 Þ 2 ∂K 0 ∂ΔpCO2 ∂k ∂ΔpCO2

ð9Þ where σx is the uncertainty associated with x and C (x,y) is the covariance between variables x and y. The mean RMSE of modeled pCOw 2 in the upper 100 m is 23.6 μatm (Table 1), and is thus considered as the uncertainty in ΔpCO2. This is also close to the RMSE value obtained when comparing to ship-based observations. The uncertainty in k is based on an estimated 9% uncertainty in wind speed (Smith et al., 2001), whereas a 2% uncertainty in K0 stems from 0.5% uncertainty in the calculation of K0 (McGillis and Wanninkhof, 2006) and a 0.08 °C (1.5%) difference between observed and modeled upper ocean temperature (Årthun et al., 2011). Covariances were calculated

M. Årthun et al. / Journal of Marine Systems 98-99 (2012) 40–50

a) 450 Observations (Oct−Dec) Observations (Jan−Mar) Model (Oct−Dec) Model (Jan−Mar)

350

w

pCO 2 [µatm]

400

300

250

200 −2

0

2

4

6

8

10

12

Temperature [°C]

b)

350 Observations Model

pCO

We have chosen to use a modified version of Wanninkhof (1992) formulation for gas transfer velocity. Wanninkhof (1992) used a scaling factor (Γ) of 0.39. However, recent improvements in wind speed and 14C data have resulted in smaller values for Γ (Wanninkhof, 2007). Using NCEP reanalysis wind speeds between 1954 and 2000, Sweeney et al. (2007) obtained Γ = 0.27. Our calculations are also based on NCEP winds, and we therefore applied this k-parameterization. Secondly, Bender et al. (2011) compared k-values constrained by global 222Rn measurements with those obtained from different k–U parameterizations. The best agreement was found for the Atlantic Ocean and with the parameterizations of Nightingale et al. (2000) and Sweeney et al. (2007). Gas transfer velocities obtained with these parameterizations and NCEP winds differed from those calculated from the 222Rn data by 23% in the North Atlantic. The use of different k-parameterizations also exert an influence on the interannual variability of CO2 fluxes. Olsen et al. (2005) evaluated this effect and found that it corresponds to the effect of interannual variability of ΔpCO2 on the order of 5–10 μatm in the Northern Hemisphere, i.e. less than the assumed accuracy of pCOw 2 measurements (Section 3.1). Furthermore, different transfer velocities result in the same pattern of interannual variability, both geographically and seasonally (Olsen et al., 2005).

330

4. Air–sea CO2 fluxes

320

4.1. Mean state and regional variability

310

w 2

[µatm]

340

300 290 280 270 260

Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May

Time [month] Fig. 4. a) Observed and modeled surface ocean pCOw 2 (μatm) from the south-western Barents Sea (> 17°E and b 76°N) in January–March and October–December 2005–2007. All available observations (n = 37596) are plotted, whereas model data are monthly averages co-located within ±0.05°N and ±0.15°E. The data have been normalized to 5 °C. The map displays the cruise tracks with color code corresponding to time period. b) Observed and modeled seasonal cycle in pCOw 2 from BSO (72.5°N, 19.6°E). Observations have been adjusted to 1995 following Olsen et al. (2003).

following Omar et al. (2007) using time series whose variability only depends on sea surface temperature. The flux uncertainty was calculated from monthly averages from which the random error associated with the mean annual flux was calculated as the quadric sum of the uncertainties in the monthly mean fluxes. This results in an uncertainty in the modeled air–sea CO2 fluxes of 12%. CO2 flux calculations are also sensitive to the choice of gas transfer velocity parameterization (k; Wanninkhof, 2007). This is a function of the turbulence in the atmosphere–ocean boundary layer, but is most often parameterized as a function of wind speed as this has the dominant effect (Wanninkhof et al., 2009). However, relationships of gas exchange with wind include both linear (Liss and Merlivat, 1986), quadratic (Wanninkhof, 1992) and cubic dependencies (Wanninkhof and McGillis, 1999), and estimates of CO2 fluxes will scale proportionally if applied to the same winds. Different gas exchange–wind speed relationships have been derived using different wind speed products, and it is therefore important to use consistent winds when deciding on the appropriate k-relationships to calculate air–sea CO2 fluxes.

45

The Barents Sea is an annual sink for atmospheric CO2 (Fig. 5), the mean air–sea CO2 flux (Fco2) being 40 g C m − 2 with an uncertainty of ± 5 g C m − 2. Integrated over the model domain this yields 0.061 ± 0.007 Gt C yr − 1. Since most measurements are from the south-western Barents Sea the model domain was divided into a South (Atlantic) sector and a North sector based on the winter mean 0 °C isotherm (Fig. 5) to enable better comparison with published results. Considering the southern and northern area the annual uptake is 0.036 ± 0.004 Gt C yr− 1 (45 ± 5 g C m− 2) and 0.025 ± 0.003 Gt C yr− 1 (33 ± 4 g C m − 2), respectively. Water mass specific computations allow for a further description of regional differences. Highest uptake occurs in AW (see Table 2 for water mass definitions) with 0.022 ±0.003 Gt C yr− 1 corresponding to 47 ±6 g C m − 2. Fresher coastal waters (CW) contribute with 0.021 ± 0.003 Gt C yr − 1 (46 ± 6 g C m− 2). Lower uptake is found in the colder water masses which are influenced by the seasonal ice cover. The CO2 uptake in ArW is 0.012 ±0.001 Gt C yr− 1 (25 ± 3 g C m− 2), whereas in modified Atlantic water (mAW) the uptake is 0.006 ±0.001 Gt C yr− 1 (41 ±5 g C m− 2).

Fig. 5. Average annual air–sea CO2 flux (g C m− 2) between 2000 and 2007. Positive flux equals oceanic carbon uptake. Note that the CO2 fluxes have been scaled with the open water fraction. The solid line is the winter mean 0 °C surface isotherm indicative of the position of the Polar Front, and is used to separate “North” and “South” sub-regions.

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M. Årthun et al. / Journal of Marine Systems 98-99 (2012) 40–50

Table 2 Estimates of air–sea CO2 fluxes in the Barents Sea. North/South is based on the subdivision shown in Fig. 5. It should be noted that water mass characteristics vary from different regions, and that the applied temperature and salinity boundaries only serve as an indicator of the main water masses found within the Barents Sea. The AW definition is adapted from Aksenov et al. (2010), whereas the others are modified from Loeng (1991). CO2-flux [g C m− 2] Fransson et al. (2001) Kaltin et al. (2002) Nakaoka et al. (2006) Kivimäe (2007) Omar et al. (2007) This study Total North South AW (T > 1 °C, S > 34.8) mAW (T b 1 °C, S > 34.8) CW (T > 0 °C, S b 34.8) ArW (T b 0 °C, S b 34.8)

44 ± 10 29 ± 11 46 ± 18 15 ± 2 51 ± 8 40 ± 5 33 ± 4 45 ± 5 47 ± 6 41 ± 5 46 ± 6 25 ± 3

Period 1996 1999 1992–2001 2003 1990–1999 2000–2007

Several investigators have previously estimated Fco2 in the southern (Atlantic) Barents Sea using different approaches. Nakaoka et al. (2006) and Omar et al. (2007) estimated an annual ocean CO2 uptake of 46 ± 18 g C m − 2 and 51 ± 8 g C m − 2, respectively (Table 2). A similar uptake (44 ± 10 g C m − 2) was calculated by Fransson et al. (2001) using mass balance approaches on data from the Atlantic inflow (BSO) and from the St. Anna Trough downstream of the main outflow from the Barents Sea (Schauer et al., 2002). Using a similar approach on data from a transect in the central Barents Sea Kaltin et al. (2002) estimated an ocean CO2 uptake of 29 ± 11 g C m − 2 between May and July 1999. Lower uptake (8–17 g C m − 2) was found at stations north of 76°N. This is similar to Kivimäe (2007) who estimated an uptake in the waters of the north-western Barents Sea of 15 ± 2 g C m − 2 during spring 2003. Our result for the southern Barents Sea is thus in close agreement with the annual estimates from Fransson et al. (2001), Nakaoka et al. (2006) and Omar et al. (2007) (Table 2), while the lower estimates by Kaltin et al. (2002) and Kivimäe (2007) from the northern Barents Sea are comparable to the flux estimate for ArW and the north–south gradient seen in Fig. 5. Similar results using different approaches give further confidence in our results. The discrepancy in flux estimates can, as we will show in Section 4.2, be partly due to seasonality and interannual variability in the flux. Also, the fact that we have used atmospheric pCO2 data from different years produces a trend in ΔpCO2 (superimposed on natural variability in pCOw 2 due to, e.g., changes in sea surface temperatures and ventilation) and our flux values could be biased high. Calculations based on detrended atmospheric pCO2 reduce the average air–sea flux by about 4 g C m − 2. The combined effect of ΔpCO2, wind speed, hydrography, and sea ice cover determines variations in air–sea CO2 fluxes. The highest fluxes in the Barents Sea are found in the seasonally ice free southeastern region and the area east of Bear Island (Fig. 5). The former is associated with the highest average wind speeds (~8 m s − 1). Furthermore, the local maximum in the eastern Barents Sea is found in the area around the Central Basin (Fig. 1) where a cyclonic circulation (Ozhigin et al., 2000) brings cold, low pCOw 2 water from the north (not shown). The outflow south of Bear Island (Fig. 1) consists of cold ArW (Loeng et al., 1997) and has previously been observed to exhibit strong CO2 undersaturation (Kelley, 1970). Strong tidal currents are also found in this area, causing an increased generation of leads (Kowalik and Proshutinsky, 1995) which will act to increase air–sea exchange. Sea ice is assumed to act as a barrier to gas exchange resulting in lower fluxes in the northern Barents Sea. To identify areas of significant variability in CO2 fluxes the interannual standard deviation is provided in Fig. 6a. The largest

interannual variability, up to 50% of the respective mean values, is found in the northern Barents Sea. This is due to fluctuations of the ice edge (Fig. 6b) combined with larger interannual variations in wind speed in the north-eastern Barents Sea (not shown; a quantification of the influence of this variability on CO2 uptake is given in Section 4.2). Also, the air–sea fluxes are low in this region and small changes thus lead to large variability. Less Fco2 variability is seen in the southern Barents Sea (5–15%) and in the AW influenced northeast (15–20%), despite greater variability in ΔpCO2 (Fig. 6c). Both sea ice and ΔpCO2 anomalies in the central Barents Sea can be related to the position of the Polar Front, separating the warm, ice free southern Barents Sea from the colder, ice covered northern Barents Sea.

a)

b)

c)

Fig. 6. Interannual standard deviation of a) Fco2 (%; percent of mean value), b) Ic (%; concentration), and c) ΔpCO2 (%; percent of mean value).

M. Årthun et al. / Journal of Marine Systems 98-99 (2012) 40–50

This thus suggests that the interannual dynamics of inflowing AW exert major control on regional CO2 uptake.

47

12% (standard deviation/mean) if integrated over the whole Barents Sea, and by 9% and 17% in the southern and northern region, respectively. The variability is larger for individual winter months; ±29% in January and ±31% in April (Fig. 7a).

4.2. Seasonal and interannual variability

a) 60 Air−sea CO2 flux [g C m−2 yr−1]

The mean seasonal cycle of Fco2 and its spatial variability are depicted in Fig. 7a,b. Wind speeds are higher during winter, whereas ΔpCO2 variability is related to the seasonal cycle in sea surface temperature which reduce (increase) the pCOw 2 during winter (summer), and the spring phytoplankton bloom and corresponding nutrient consumption (Fig. 2) which counteracts the temperature effect. From April to July the increase in Fco2 is due mainly to increased ΔpCO2, despite reduced wind speeds. Increased wind speeds from August yields a maximum flux (uptake) in September, after which Fco2 returns to winter values. The September maximum also coincides with minimum sea ice extent, which further increases the air–sea exchange. This is most evident in the northern Barents Sea (Fig. 7b). The seasonal variability is less distinct in the southern Barents Sea and more influenced by wind speed variations alone (not shown). Fig. 8a shows the interannual variability of CO2 flux for the whole Barents Sea, and for defined sub-regions, between 2000 and 2007. Similar variability is seen for both areas with higher values toward the end of the period. The carbon uptake varied interannually by

40

30

20

10

0

2000 2001 2002 2003 2004 2005 2006 2007

Time [year]

CO2 flux anomaly [g C m−2 yr−1]

b) a)

50

Total South North

8

North

6 4 2 0 −2

Total Wind Ice w pCO

−4 −6

pCO −8

2 a 2

2000 2001 2002 2003 2004 2005 2006 2007

Time [year]

b)

CO2 flux anomaly [g C m−2 yr−1]

c)

4

South

3 2 1 0 −1 −2 −3 −4

2000 2001 2002 2003 2004 2005 2006 2007

Time [year]

Fig. 7. a) Mean seasonal cycle (2000–2007) of air–sea CO2 flux (g C m− 2 yr− 1). Error bars show 1 standard deviation. In b) the fluxes are calculated for 0.5°latitude bins between 35 and 45°E.

Fig. 8. a) Interannual variability of air–sea CO2 flux. Positive values indicate oceanic carbon uptake. b) and c) Respective contributions of wind speed, sea ice extent, and seawater and atmospheric pCO2 to annual anomalies (detrended) in the northern and southern Barents Sea. Anomalies were calculated based on Eq. (5) allowing only one parameter to vary, whereas the others were kept constant at their mean value (cf. text).

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To assess the main drivers of interannual variability, Fco2 values were re-computed (Eq. (5)) allowing only one parameter to vary at a time while the others were kept constant at their mean value. This simple method of decomposition allows us to determine the relative influence of each property on the interannual variations in ocean CO2 uptake, and has previously been used by, e.g., Olsen et al. (2003) and Nakaoka et al. (2006) in the North Atlantic and Greenland Sea, respectively. The results are shown for the northern and southern Barents Sea in Fig. 8b, c. The increased flux from 2002 to 2006 mainly corresponds to higher wind speeds in both areas; a 0.6 m s− 1 wind speed increase in the south being associated with 6 g C m− 2. Sea ice variability is dominated by larger ice extent in 2003–2004 related to increased ice import from the Arctic Ocean (Kwok, 2009), followed by a steady decline (1.4 · 10 5 km 2 sea ice reduction) which contribute to the increased air–sea flux (7 g C m − 2) in the north. Conversely, low ocean temperatures (~0.5 °C below average) and thus pCOw 2 constitutes a positive flux contribution between 2002 and 2004. In the south, the contribution of sea ice to the uptake variability is small, and wind and pCOw 2 are the dominant factors. Interannual variability of atmospheric pCO2 is the smallest contributor to air–sea CO2 flux variability in both areas. Wind speed as a dominant driver to interannual variability in Fco2 concurs with Olsen et al. (2003, North Atlantic), Nakaoka et al. (2006, Greenland Sea and western Barents Sea), and Omar et al. (2007, Atlantic Barents Sea). The importance of atmospheric and seawater pCO2 differs in the cited literature. Olsen et al. (2003) found atmospheric pCO2 to be more important, whereas Omar et al. (2007) identified seawater pCO2 as the main driver of variability. Our results (Fig. 8) indicate that seawater pCO2 is the dominant factor. In the northern Barents Sea the calculated pCO2 shows little interannual variability compared to wind speed and sea ice, and is thus in agreement with Nakaoka et al. (2006) in that ΔpCO2 is a minor factor in determining Fco2 variability on interannual time scales in this region. Seawater pCO2 constitutes a larger contribution to variations in Fco2 in the southern Barents Sea. Both Olsen et al. (2003) and Omar et al. (2007) pointed out the fact that the usage of a constant temperature– pCOw 2 relationship, might lead to underestimation of interannual variability in seawater pCOw 2 . In that case our computations would underestimate the role of ΔpCO2 variability. Larger variability in observed pCOw 2 is clearly seen in Fig. 4a. It should, however, be noted that model values are monthly averages, which will smooth some of the variability seen in the observations. The role of sea ice coverage was highlighted by Nakaoka et al. (2006). Our results also indicate that this is a major source of variability, both seasonally and interannually, in the northern Barents Sea. There is, however, still a controversy over the importance of sea ice formation controlling the air–sea CO2 flux. Historically (and in this study) sea ice has been regarded as a barrier to gas exchange, but recently ice formation has been hypothesized to enhance CO2 uptake by efficient exchange through the air–sea interface (Anderson et al., 2004) and by CT rejection from growing ice into the water column (Rysgaard et al., 2009). Seasonal ice formation in the Barents Sea might therefore have a significant impact on CT concentrations during winter, and especially in areas with high ice production. The above results suggest an annual change in ocean CO2 uptake of 10 g C m − 2 per 1 m s − 1 change in wind speed, whereas 1 · 105 km2 sea ice loss is associated with an additional 5 g C m− 2. Flux variations due to wind speed and sea ice anomalies tend to have the same sign in the northern Barents Sea (stronger winds and less ice), whereas they are inversely related to pCOw 2 . There is a close connection between the local atmospheric forcing and AW inflow to the western Barents Sea (Ådlandsvik and Loeng, 1991; Ingvaldsen et al., 2004). Variations in atmospheric forcing thus influence not only wind speeds, but also pCOw 2 values and ice extent through advection of ocean heat anomalies from the North Atlantic. It is therefore to be expected that the largely atmospherically driven variability in hydrography (Ingvaldsen et al., 2003)

and sea ice extent (Sorteberg and Kvingedal, 2006) observed in the Barents Sea during recent decades have been manifested in the uptake of atmospheric CO2. 5. Summary and conclusions Due to high primary production and the substantial cooling of the Atlantic Water (AW) throughflow, the Barents Sea is a sink region for atmospheric CO2. To elucidate on the temporal and spatial variability of air–sea CO2 exchange and assess the dominant drivers of variability, empirical relationships based on hydrography were applied to the Barents Sea using the thermohaline output from a regional coupled ice–ocean model. The use of temperature and salinity data combined with redfieldian biological production to reconstruct spatial and temporal variability of carbon system variables in the Barents Sea is shown to be reasonable. Modeled total alkalinity (AT), dissolved inorganic carbon (CT), and partial pressure of CO2 (pCOw 2 ) compare well to available measurements (Table 1, Figs. 3 and 4), and the annual CO2 uptake in the Atlantic domain is in agreement with previous estimates (Table 2). The Barents Sea is an annual sink for atmospheric CO2 in all areas. The mean air–sea flux is 40 ± 5 g C m − 2, corresponding to an uptake of 0.061 ± 0.007 Gt C yr − 1. Higher fluxes are found in waters of Atlantic origin in the south-east, while less gas exchange takes place in the seasonally ice covered northern Barents Sea. Maximum CO2 uptake occurs in September and October (56 ± 7 g C m − 2). This is due to the combined effect of a large pCO2 gradient across the air–sea interface (ΔpCO2) and high wind speeds. The September maximum also coincides with minimum sea ice extent, which further increases the air–sea exchange. Interannually, fluxes vary by ±12% mainly driven by variations in wind speed and ice cover/ pCOw 2 in the northern/ southern Barents Sea. Due to its limited area the annual CO2 uptake in the Barents Sea is not large in a global perspective (Takahashi et al., 2009). However, the presented flux estimates identify the southern Barents Sea as a highly efficient sink of atmospheric CO2, and the uptake per area is among the highest in the world's oceans. The present model application is suitable for developing seasonal and interannual CO2 flux information based on locally derived products, thus providing regional detail not available from in-situ measurements and reducing uncertainties associated with coarse climate models. This assertion is corroborated from the independent model-observation comparison. The uncertainty in the flux estimate is about the same order of magnitude as the interannual variability, highlighting the need for further observational assessment in the region. However, our results provide a first order estimate of the main drivers of variability in the Barents Sea which can serve as a base for comparison to evaluate developments in CO2 uptake. The approach can be easily transferred to other model platforms covering other ocean regions providing there is a sound understanding of the relation between regional hydrography and two of the four major carbonate system variables (pH, CT, AT and pCOw 2 ). Acknowledgments This work was funded by the IPY project Bipolar Atlantic Thermohaline Circulation (BIAC). R. Bellerby has also been partially funded by the project Marine Ecosystem Response to a Changing Climate (MERCLIM No 184860) financed by the program NORKLIMA through the Norwegian Research Council, and by Theme 6 of the EC seventh framework program through the Marine Ecosystem Evolution in a Changing Environment (MEECE No 212085) Collaborative Project. The authors want to thank Anna Silyakova for providing nitrate model data, and Siv K. Lauvset and the chemical oceanography group at BCCR for providing quality controlled observation from G.O. Sars. This is publication no. A395 from the Bjerknes Centre for Climate Research.

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