The impact of diurnal variability in sea surface temperature on the atlantic air-sea CO 2 flux

The impact of diurnal variability in sea surface temperature on the atlantic air-sea CO2 flux H. Kettle, C. J. Merchant, C. D. Jeffery, M. J. Filipiak, and C. L. Gentemann School of GeoSciences, The University of Edinburgh, UK National Oceanography Centre, Southampton, UK Remote Sensing Systems, Santa Rosa, CA, USA Received: 18 July 2008 – Accepted: 26 July 2008 – Published: 19 August 2008 Correspondence to: H. Kettle (h.kettle@ed.ac.uk) Published by Copernicus Publications on behalf of the European Geosciences Union.


Introduction
During the day, the upper 2 m of the ocean typically absorbs about 50% of the solar radiation reaching its surface.At night this layer then cools, losing heat to the atmosphere through radiative latent and sensible heat fluxes.This diurnal heating and cooling can lead to significant variations in the sea surface temperature (SST) (e.g., Stuart-Menteth et al., 2003;Gentemann et al., 2003).Here we investigate the impact of diurnal variability in SST on CO 2 fluxes by using SST data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) geostationary satellite.Typical regional and seasonal variations in diurnal warming over the SEVIRI disk region are shown in individual days localised warming can be as much as 6 K within a shallow 'warm layer' at the sea surface (Merchant et al., 2008;Stramma et al., 1986).The sea-air flux of CO 2 , F , is controlled by the transfer of CO 2 across the aqueous boundary layer, such that, (McGillis and Wanninkhof, 2006) where k is the gas transfer velocity, [CO 2w ] is the CO 2 concentration at the base of the mass boundary layer and [CO 2s ] is the CO 2 concentration at the surface skin.Because dissolved CO 2 in the ocean is strongly buffered by dissolved inorganic carbon species, the transfer of CO 2 across the interface does not significantly affect the total dissolved CO 2 concentration (i.e., we assume [CO 2w ] is not affected by the flux).The concentration of CO 2 may be expressed as a combination of the solubility of CO 2 in sea water and its partial pressure, so that Eq. 1 becomes: where α is the solubility of CO 2 in sea water, pCO 2 is the partial pressure of CO 2 and the subscripts w, s and a denote the bottom of the mass boundary layer, the skin and the air respectively.Note that pCO 2 is assumed to be equivalent to the fugacity of CO 2 (<0.5% error over the relevant temperature range; McGillis and Wanninkhof, 2006).Since each of these factors (k, pCO 2 and α) vary with SST (Fig. 2), F will also vary diurnally.Thus, this study uses high resolution satellite measurements of the ocean skin to estimate CO 2 flux.Previous studies have suggested the "thermal skin effect" (cooling/warming of the upper few millimetres of the ocean) affects flux (e.g.Robertson and Watson, 1992;Van Scoy et al., 1995), as does the warming of the upper few metres of the ocean by solar radiation (McNeil and Merlivat, 1996).Work by Olsen et al. (2004) and Mc-Neil and Merlivat (1996) on this topic differs from the study herein, in that they use a wind-based parameterisation for the gas transfer velocity and averaged values of Introduction

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Interactive Discussion diurnal warming.These two simplifications may underestimate the importance of diurnal warming.This is because averaging eliminates covariations between variables and wind-based transfer velocities predict no gas flux when there is no wind, which are the conditions under which large ∆ SSTs may occur.Moreover, a field experiment has shown that it is possible for CO 2 fluxes to have only a weak dependence on wind speed but a strong dependence on the diurnal heating cycle (e.g., GasEx-2001 in the Equatorial Pacific;McGillis et al., 2004).Therefore, in this study we use a more complex physically-based parameterisation that includes buoyancy driven, as well as wind driven, gas transfer, by Fairall et al. (2000) with modifications by Jeffery et al. (2007); along with a slightly different formulation for the flux (Eq.2) as recommended by McGillis and Wanninkhof (2006).
Since we are investigating gas flux through the air-sea interface, we define the SST to be the temperature of the ocean skin (T s ).We can express this in terms of a foundation (or bulk) temperature (Donlon et al., 2007) below the diurnally warmed layer (T f ), the temperature difference associated with diurnal heating (∆T d w ) and the temperature drop across the skin, (∆T s ) such that, (3) The impact of temperature on CO 2 flux is investigated by computing fluxes over the SEVIRI disk region for the following three scenarios: Scenario 1.The specified temperature is equivalent to the foundation temperature.This is the most commonly used temperature and ignores both the skin effect and diurnal variations.
We use this to calculate CO 2 flux over the complete SEVIRI disk with no account taken of diurnal warming -the flux generated (using Eq. 2) is denoted F f .
Scenario 2. This examines the effect of diurnal variability on CO 2 flux by using a temperature with hourly estimates of the diurnal variability included (Eq.3).

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Interactive Discussion For a given time slot this will only cover a small fraction of the SEVIRI disk because we only use locations where warming occurs and where there are data available (i.e.pixels not obscured by cloud).∆T d w − ∆T s is estimated from satellite measurements (see Sect. 3.1) and T f is as in Scenario 1.The flux computed under these conditions is denoted F d v .
Scenario 3.This investigates the impact of using a monthly average diurnal variability, instead of hourly estimates, on CO 2 flux.The temperature used is the foundation temperature plus the monthly mean warming, (denoted by ∆T d w −∆T s ): This gives flux estimates accounting for the warming but not the time variability, denoted F w The difference between the CO 2 flux fields resulting from scenarios 1 and 2 (F d v -F f ) examines the effect of the increase in SST caused by diurnal warming.The difference between scenarios 2 and 3 (F d v -F w ) examines the effect of the covariability of SST with the other factors affecting flux.

Data
Satellite observations of SST, surface solar irradiance (SSI) and downward longwave irradiance (DLI) are provided by EUMETSAT's Ocean and Sea-Ice Satellite Application Facility (OSISAF), and consist of hourly fields over a field of view that encompasses the east Atlantic Ocean and the Mediterranean Sea (Fig. 1).SSTs are derived from the SEVIRI radiances (OSISAF, Atlantic Sea Surface Temperature Product Manual, Version 1.6, October 2006, http://www.osi-saf.org/biblio/docs/ss1pmatlsst 1 6.pdf).The resolution of the data is 0.05 • and geographical coverage is 60 • S to 60 • N, 60 screening and poorer SST precision.The difference between the SEVIRI SSTs and matched drifting buoys (between July 2004 and July 2005) has mean standard deviation of 0.01±0.49K which includes both drifter errors and spatially correlated retrieval errors.SSTs are only measurable when the sky is clear, so each data point is assigned a confidence level ranging from 1 ("bad") to 5 ("excellent"), depending on the possible cloud contamination (LeBorgne et al., 2006).We bin the data onto a 0.2 • grid to increase the apparent completeness (in space and time) of the SST data and to decreases the SST error in a cell due to retrieval noise.This spatial averaging may dampen the amplitudes of very localised diurnal warming but was necessary due to computing constraints.This SST dataset is used to compute the diurnal warming, ∆T d w − ∆T s (see Methods section).
In addition to the SST dataset described above, a foundation SST data set, provided by Meteo-France, is also used.This is an analysis of night-time sub-skin SSTs optimally interpolated to 00:00 UTC daily.It is this dataset that is used to provide the values of T f in the three scenarios (Eq.4-6).
The wind speeds used in this analysis are the NASA Atlas First-LooK (FLK) version 1.1 derived surface winds level 3.0 product which uses available passive microwave satellite wind speeds produced by Remote Sensing Systems and described at http://sivo.gsfc.nasa.gov/oceanwinds/.All satellite measurements are processed in a consistent manner using a physically-based retrieval algorithm to determine the wind speed (Wentz, 1997).These wind speeds are used to derive a global 10-m wind speed every 6 h on a 25 km grid using variational analysis method (VAM).These data were linearly interpolated in time and space onto the hourly SEVIRI 0.05 • grid.Finally the wind speed data coincident with the grid points of the 0.2 • grid used in this study are extracted.
Other meteorolgical data, pressure (P ), dew point temperature (T dew ) and air temperature (T air ), are taken from the ECMWF operational dataset (N80 Gaussian gridded analysis on surface levels; in ERA-40 format) at 6-hourly intervals) and we linearly interpolate these in time and space.pCO 2w and salinity (S) are taken from Taka-Introduction

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Interactive Discussion hashi's climatology (Takahashi et al., 2002) -hereafter refered to as Taka02 -which is monthly and on 4 • lon×5 • lat grid.Where the Taka02 data are not fully resolved we interpolate longitudinally.We use a monthly climatological dataset for the mixed layer depth (MLD) obtained from Scripps Institution of Oceanography (available from http://ingrid.ldgo.columbia.edu/SOURCES/.IGOSS/.sio)with 5 • lon×2 • lat resolution.

Deriving the diurnal variations in SST
The SEVIRI satellite measures T s but the processed dataset is corrected for the cool skin by adding 0.2 K.We reverse this correction to retain the original T s measurement.
To calculate the diurnal warming, at each hour where there is a SST measurement with confidence level 5, we compute the difference between it and the "satellite foundation temperature" (T sf ) which we define to be the satellite measured temperature just before the time of local dawn (t d ).Note this is not the same as the foundation temperature previously mentioned (T f ) which is from a different dataset.T sf throughout the rest of the day is approximated using a linear interpolation between consecutive pre-dawn temperatures, such that The diurnal temperature difference at time t is then given by: The sea-air flux of CO 2 (Eq.2) contains 3 factors which depend on temperature in different ways (see Fig. 2).The following subsections describe the details of how each of these factors is computed and its reliance on SST.Introduction

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Solubility, α
The solubility, α of CO 2 in sea water is a physical property that determines how much CO 2 will dissolve.CO 2 is poorly soluble in water and its solubility is highly temperature dependent.Solubility (in mol m −3 atm) can be calculated according to Weiss (1974) by where T k is the water temperature (Kelvin), As the temperature increases the solubility decreases, e.g., dropping to 40% of its value for a temperature increase from 5 • C to 40 • C (Fig. 2).To compute the CO 2 flux (Eq.2) for the different scenarios we evaluate α using the foundation temperature (α w ) and using the skin temperature (α s ).

Gas transfer velocity, k
The gas transfer velocity describes the rate at which a gas moves between the sea and air.The magnitude of the transfer rate is controlled by the thickness of the boundary layer which is a function of near surface turbulence and diffusion.Thus, the transfer rate is determined by the state of the sea surface: by factors such as wave age, fetch, wind speed, the prevalence of bubbles, boundary layer stability and naturally occurring surfactants (e.g.Woolf, 1997;Monahan and Spillane, 1984;Liss and Merlivat, 1986;Asher and Wanninkhof, 1998).It is highly unlikely, therefore, that only one physical variable can completely determine the spatial scales and environmental conditions necessary to predict k.Despite this, many empirical relationships for k in practical use are solely functions of wind speed as this is an influential and easily obtainable parameter.Three commonly used wind-based parameterisations are the piecewise linear relation (Liss  and Merlivat, 1986), the quadratic relation (Wanninkhof, 1992;Nightingale et al., 2000), and the cubic relation (Wanninkhof and McGillis, 1999).Using this type of parameterisation to examine the influence of diurnal warming on gas flux will likely result in an under-estimation of the effect because at low wind speeds (when diurnal warming is at its most significant) these parameterisations predict virtually no gas flux.To overcome this limitation we use the NOAA Coupled Ocean Atmosphere Response Experiment (COARE) gas transfer parameterisation (Fairall et al., 2000) which is physically (rather than empirically) based.We also include a modification to this parameterisation by Jeffery et al. (2007) to include the effects of nighttime convective overturn of the water column.A brief description of this method is given below.Fairall et al. (2000) express the transfer velocity as: where α n is non-dimensionalised solubility ( αR gas T ; where R gas is the universal gas constant), r is the 'resistance' and u * is the friction velocity (subscripts a and w refer to the air and water sides respectively).The resistances are given by: where S c is Schmidt number, z wr is the measurement depth, δ is the thickness of the cool skin, C d a is the airside drag coefficient and κ is the von Karman constant (0.41).
The h factors are concerned with the transport through the cool skin layer and are given by h a =13.3 and h w = 13.3λ6A (Saunders, 1967;Soloviev and Schl üssel, 1994) where λ is computed according to Fairall et al. (1996a) and A is a tunable constant (≈1).If there is no cool skin present λ is set to 6.

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Printer-friendly Version Interactive Discussion Fairall et al. (2000) define the water-side friction velocity u * w by However, in order to include the increased gas transfer caused by convective overturn, Jeffery et al. (2007) modified the expression for u * w to include waterside "gustiness".Thus u * w is newly defined as where C d w is the waterside drag coefficient and S w is an average value of "wind speed", which following Stull (1994) and Godfrey and Beljaars (1991) for the airside, is expressed as where u ref is analagous to a wind speed at some reference depth (z ref ), which we can define as The convective buoyancy/velocity scale, w g is defined as where β is the (tunable) "gustiness parameter", Z m is the depth of the convective layer (we use monthly climatological MLD) and B f is the buoyancy flux given as the sum of the buoyancy caused by heating and that caused by freshening through evaporation, such that where g is the acceleration due to gravity, a 1 is the thermal expansion coefficient (2.1×10 −5 (T +3.2) 0.79 K −1 ), b e is the saline expansion coefficient (0.026), C p is the thermal heat capacity of water and ρ w is the density of seawater (both functions of temperature), L v is the latent heat of vaporization ((2.501-0.00237T )×10 6 J kg −1 ), Q net is the net heat flux (positive into the water) and Q l at is the latent heat of evaporation (positive out of water).When the buoyancy flux is positive w g is set to zero as the fluxes serve to stabilize the exchange by adding buoyancy to the surface.Bubble mediated gas transfer (k b ) is accounted for by modifiying the gas transfer Eq. ( 10) as follows: where k b is defined by Woolf's (1997) parameterisation: where and V 0 =6.8×10 −3 m s −1 , e=14, n=1.2 and B is a tunable constant.
To solve Eqs. 10 to 21 we first compute the heat fluxes (Q lat and Q net ), the cool skin parameters (δ and λ) and the drag coefficients (C d ) using code from the air-sea matlab toolbox from Woods Hole Science Center (http://woodshole.er.usgs.gov/operations/sea-mat/index.html;Fairall et al., 1996aFairall et al., , 2000)).This requires relative humidity (function of T air and T dew ), pressure, air temperature (all from ECMWF), wind speed (satellite data), net short wave radiation (SEVIRI SSI) and net long wave radiation (SEVIRI DLI minus the long wave radiation emitted from the ocean).
The (dimensionless) Schmidt number (used in Eqs.11 and 12) is the kinematic viscosity of the fluid divided by the molecular diffusion coefficient of the gas.For CO 2 Introduction

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Full in seawater S cw can be estimated from a relationship with temperature (Wanninkhof, 1992) such that where T is in • C. The Schmidt number for CO 2 in air, S ca is kept constant at 0.8 (Fairall et al., 2000) and is much smaller than its waterside equivalent (∼600) so that the transfer resistance for CO 2 is much greater in water than in air.
The gas transfer parameterisation thus contains three empirical parameters which allow tuning to specific data sets: A (related to the thermal sublayer), B (related to bubble mediated transfer) and β (the "gustiness" parameter which is related to convective buoyancy effects).Published values of A and B derived from CO 2 air-sea flux field experiments are: A=0.625, B=2.0 (GasEx 98 -a warm core eddy; Hare et al., 2004), and A=1.3, B=0.82 (GasEx 2001 -in the eastern Pacific south of the upwelling region; derived from results by McGillis et al., 2004).Soloviev and Sch üssel (1994) use A=1.85 and B=1 based on radon experiments.Thus, there is a significant amount of uncertainty in these two parameters.The gustiness parameter, β, has published values of 1.25 (Fairall et al., 1996b), 1.0 (Miller et al., 1991) and 0.7 (Schumann, 1988) -but note that these are for air.Here we are not tuning the parameterisation to a particular data set so we take the generic values of β=1, A=1, and B=1, since these are roughly the mean of the previously published values and are a neutral choice with no scaling up or down.The effect of SST on the gas transfer velocity is shown in Fig. 2 for a steady wind speed of 2 m s −1 and in Fig. 3 for a range of wind speeds.
The temperature used to evaluate k (Eq.10-22) is changed according to the three scenarios outlined in the Introduction.change in dry air pressure have been shown to be important for flux calculations by Kettle and Merchant (2005).Thus we compute pCO 2a from: where salinity, S, is taken from Taka02.
The change in pCO 2w with temperature is given by Takahashi et al. (1993) as However, it should be noted that 0.0423 • C −1 is an approximation and can range between 0.037 to 0.053 • C −1 depending upon the carbonate dissociation constants used (McGillis and Wanninkhof, 2006).We compute pCO 2w (t) based on changes from a reference field, such that: where t is time, pCO Tak 2s and T Tak are pCO 2s and temperature from Taka02 (monthly).
Note that pCO 2w is always computed using the foundation temperature but pCO 2a will vary between the three scenarios.Introduction

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Full Since the effect of diurnal covariability is small, in the rest of this section we focus on the differences between using F d v (because it is based on the most detailed data available) and F f (because it is the foundation SST which is most commonly used for estimating CO 2 flux).Figure 6 shows the spatial distribution of the mean monthly differences in flux caused by diurnal variability, (i.e., F d v − F f ), averaged over 2005-2006 for each month.Here we see that including diurnal variability in SST either causes an increase in the outgassing of CO 2 from the ocean or no change in flux, everywhere over this region.The maximum increase in monthly-averaged outgassing is ∼0.2 mol CO 2 m −2 a −1 .The seasonal maximum is around 5 mol CO 2 m −2 a −1 (Fig. 4), however, large regions have zero net flux so the impact of diurnal variability in SST on flux, is regionally very significant.As expected, the impact changes spatially with time of year, with large increases in the Mediterranean in the northern summer (∼0.1 mol CO 2 m −2 a −1 ) and around South America from June to January.The spatial distribution of the available data points is shown in Fig. 7.The figure shows there are some areas where data is very sparse -this is discussed further in the following section.

Discussion
The results show that including the increase in SST due to diurnal warming acts to increase the outgassing/reduce the ingassing flux of CO 2 from the ocean over the SEVIRI disk region (all other factors being equal).The main factor in the flux Eq. ( 2) through which ∆SST affects flux can be ascertained by differentiating with respect to temperature: Numbering the terms on the right hand side of Eq. 27 from 1-6; we can ignore terms 1 and 3 since these are evaluated at the foundation temperature and will not be affected by diurnal warming.Term 4 can be assumed negligible since pCO 2a does not vary much with temperature (Fig. 2) so that Eq. 27 becomes: Since the partial pressure of CO 2 in the ocean and atmosphere is approximately in balance the first term on the right hand side of Eq. 28 is close to zero, implying that the diurnal change in flux is dominated by the change in solubility caused by variations in the ocean skin temperature.Solubility decreases with temperature so this term is negative, indicating that the flux in the outgassing direction will be increased by diurnal warming.In other words, the change in flux due to diurnal warming can be estimated very approximately by: ∆F diurnal warming ≈ −pCO 2a k∆α s .
(29) However, CO 2 flux is not just affected by temperature but also by biological activity.Photosynthesis by phytoplankton removes dissolved inorganic carbon (DIC) from the surface waters, lowering pCO 2w when there is sufficent light and nutrient available.Since light availability also varies diurnally the biological affect, which acts to increase CO 2 ingassing, may eliminate the diurnal increases in outgassing caused by diurnal warming.We estimate the approximate magnitude of the biological effect as follows: Morel and Antoine (2002) show the average net primary production (NPP) over June 2001 or December 2000 to have a global maximum of 2 gC m −2 d −1 (which incidentally is higher than estimates given by Behrenfeld et al. (2005), and Behrenfeld and Falkowski, 1997).We convert this to the NPP over daylight hours by doubling it (assuming no photosynthesis over night, and assuming this is half of the day), and we assume photosynthesis occurs over a depth of 100 m, so that the uptake of carbon caused by an increase in SST of ∼0.5-0.7 K. Therefore if surface nutrient is available it is possible that biological activity could eliminate the temperature-induced increase in outgassing for ∆SST≤∼0.7 K.However, since diurnal warming generally occurs when the ocean is strongly stratified, these are the times when there is less surface nutrient available and biological activity is probably much lower than we have estimated.
The analysis presented herein required the Taka02 pCO 2w climatology to be interpolated over the shelf sea regions.This is not ideal but, in the absence of a shelf sea pCO 2w climatology, was the only approach.Figure 8 shows examples of the interpolation results for January and July.The method appears sensible through the Meditteranean Sea and around the coasts but very high values are estimated in the Red Sea (NE Africa) in July due to the high values in the Arabian sea in the Taka02 climatology.Whether or not the fluxes predicted over these regions are reasonable is unknown, however, since our concern is the difference in flux caused by SST variability, this is not critical.Similarly the Taka02 climatology is referenced to the year 1995, therefore the fluxes shown in this study are computed using driving data from 2005-2006 but can not be thought to be the actual fluxes for this period as the pCO 2 fields have undoubtably changed since 1995.
Finally, there is the issue of missing data.Since satellites measurements of SST are not possible through cloud, there are many missing data points.In fact in some Introduction

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Full Screen / Esc Printer-friendly Version Interactive Discussion regions there may not be a single satellite measurement for a month (see Fig. 7).Therefore, although the extreme diurnal warming events will occur under clear-sky conditions there may be more moderate warming events that are missed due to cloud.This is due to the decrease in solubility associated with ∆SST rather than covariations between diurnally varying variables (which causes a decrease in outgassing of ∼1 Tg C a −1 ).Therefore, it is important that the additional outgassing of CO 2 due to ∆SST is accounted for, but it may be estimated using monthly-averaged values of ∆SST along with a foundation SST rather than high resolution SST data.Introduction

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Full Fig. 1.Due to averaging, diurnal changes in SST (∆SST) shown in Fig. 1 are only up to 1.5 K but on 3.2.3Thepartial pressures of CO 2 in the air and sea, pCO 2We calculate pCO 2a and pCO 2w in Eq. 2 based on changes to the Takak02 climatology caused by short term changes in air pressure and SST.Variations in pCO 2a with

Figure 4
Figure 4 shows seasonal averages of the CO 2 flux field for 2005-2006 computed using the analysed foundation SST (T f ).There is strong outgassing of around 2 mol CO 2 m −2 a −1 from the ocean around the equator, changing to ocean uptake of CO 2 beyond the Tropics towards the North and South poles.Regions around 30-40 • N and 30-40 • S change seasonally between being sources and sinks of CO 2 .The plots compare well in terms of magnitude and spatial distribution with the mean annual flux for 1995 shown by Taka02 and serve as a check that the more complex physicallybased flux parameterisation, with far more variables, is generally equivalent (at these scales) to other methods.Using the foundation SST (F f , scenario 1) gives a mean net mass flux of 9.6 Tg C a −1 out of the ocean over the SEVIRI disk region for 2005-2006.When satellite-measured diurnal variations are included (F d v , scenario 2) this is increased to 30.4 Tg C a −1 , and when diurnal warming is represented by a monthly-averaged value (F w , scenario 3) the mass flux is 31.2Tg C a −1 .Figure 5 shows how using the three different SST datasets affects the total mass flux over the SEVIRI disk for each month during 2005 and 2006.Using satellite-measured diurnal variations increases outgassing by 21.7 Tg C (2005) and 20.0 Tg C (2006) (Fig. 5b).When time covariations are eliminated by using the monthly averaged ∆SST the outgassing is increased (or ingassing is reduced) by a further 0.92 Tg C (2005) and 0.69 Tg C (2006).Diurnal covariations reduce the outgassing flux because ∆SST and wind speed (the dominant factor affecting flux) are negatively correlated leading to less flux when ∆SST (and α s ) is high (due to the low wind speed).However, the difference between F d v and F w is small compared with the difference with F f implying that the covariation effects of diurnal variability are much less important than the mean effect of diurnal warming.In Fig. 5c the number of ∆SST data points derived from satellite measured SSTs each month is plotted.The number of valid measurements range from a minimum in January 2006 (0.67 million) to a maximum in June 2005 (2.01 million).
from the water in one day is 40 mg C m −3 .This is equivalent to a decrease in DIC of 3.3 µmol C l −1 .Using equations representing the sea-water acid-base system with dissociation constants of carbonic acid, hydrogen carbonate, boric acid and water fromDOE (1994)  we can compute the change in pCO 2w for a given change in DIC.If DIC decreases by 3.3 µmol C l −1 , and assuming a standard DIC concentration of 2058 µmol l −1 , alkalinity of 2396 µmol l −1 (e.g.Palmer and Totterdell, 2001) and SST=25 • C, the decrease in pCO 2w is 5.7 µatm.This would result in a change in flux (∆F bio ) of 5.7×10 −6 k α w , which at 25 • C is ∼1.65×10 −4 k.Equating this with Eq. 29 (and assuming pCO 2a ≈350 µatm), indicates that a change in solubility of 0.47 mol atm −1 m −3 is required to offset the biological influence.The change in solubility with temperature ranges from -0.92 to -0.6 for SSTs of 20-30 • C. Thus the increase in ingassing flux due to biological activity is equivalent to the increase in outgassing flux SST have a significant impact on CO 2 flux over the SEVIRI disk region (central Atlantic ocean and Meditteranean).Including diurnal variability in SST increases the mass net flux out of the ocean from 9.6 Tg C a −1 to 30.4 Tg C a −1 .At a local scale, average monthly fluxes may be increased by up to ∼0.2 mol CO 2 m −2 a −1 .

Fig. 6 .
Fig. 6.Average difference in the net CO 2 flux (mol CO 2 m −2 yr −1 ) averaged over each month (numbered) over 2005-2006 caused by diurnal variations in SST (F d v −F f ).Positive values indicate an increase in outgassing of CO 2 from the ocean.White regions indicate missing data.

Fig. 7 .
Fig. 7. Number of data points available to compute fluxes.Colorscale is saturated at 1000 to show data distribution by in data rich areas numbers go up to around 2600.