# Data-based estimates of ocean carbon uptake biased high from neglect of submonthly atmospheric pressure variability

Jeanne Dombret, Hugo Bellenger, Xavier Perrot, Laetitia Parc, Lester

Kwiatkowski, Frédéric Chevallier, Laurent Bopp, Marion Gehlen, Roland

Séférian, Sarah Berthet, et al.

► To cite this version:

Jeanne Dombret, Hugo Bellenger, Xavier Perrot, Laetitia Parc, Lester Kwiatkowski, et al. Data-based estimates of ocean carbon uptake biased high from neglect of submonthly atmospheric pressure variability. 2025. hal-05266848

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# Data-based estimates of ocean carbon uptake biased high from neglect of submonthly atmospheric pressure variability

Jeanne Dombret¹, Hugo Bellenger¹, xavier Perrot², Laetitia Parc³, Lester Kwiatkowski⁴,
Frederic Chevallier⁵, Laurent Bopp⁶, Marion Gehlen⁷, Roland Séférian⁸, Sarah Berthet⁹,
and James C Orr¹⁰

¹Laboratoire de Meteorologie Dynamique

²Ecole Normale Supérieure

³Lab Météorologie Dynamique

⁴Laboratoire de Météorologie Dynamique (LMD)

⁵Laboratoire des Sciences du Climat et de l'Environnement

⁶LMD/IPSL

⁷LSCE

⁸CNRM (Météo-France/CNRS)

⁹Centre National de Recherches Météorologiques

¹⁰Laboratoire des Sciences du Climat et de l'environnement

July 21, 2025

## Abstract

Current estimates of the global ocean carbon sink based on measurements of CO2 fugacity are inconsistent with those obtained from global ocean biogeochemistry models. Here we investigate how this gap might be partially closed by more fully accounting for submonthly variability in observation-based estimates. While these estimates compute the air-sea CO2 flux based on hourly wind speed, other variables have only monthly resolution. Thus they neglect some high-frequency variability from short-term events such as storms. To evaluate this error, we compare fluxes at different temporal resolutions between 2009 and 2018. We find that accounting for hourly variations in atmospheric pressure and daily variations in sea surface temperature decreases the global carbon uptake estimate by 5% (0.12 Pg C yr-1) which is driven by the submonthly covariance between wind speed and atmospheric pressure, particularly in the southern extratropics. This represents 25% of the average gap between observation-based and model estimates.

## Hosted file

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## Hosted file

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1 Data-based estimates of ocean carbon uptake biased high from neglect of

2 submonthly atmospheric pressure variability

3 Authors: Jeanne Dombret¹, Hugo Bellenger¹, Xavier Perrot¹, Laëtitia Parc¹, Lester

4 Kwiatkowski², Frédéric Chevallier³, Laurent Bopp¹, Marion Gehlen³, Roland

5 Séférian⁴, Sarah Berthet⁴ and James C. Orr³

6 Affiliations :

7 ¹Laboratoire de Météorologie Dynamique, LMD-IPSL, École Normale Supérieure,

8 Université PSL, École polytechnique, Institut Polytechnique de Paris, Sorbonne

9 Université, CNRS, Paris France.

10 ²Laboratoire d'Océanographie et du Climat: Expérimentations et Approches

11 Numériques, LOCEAN-IPSL, Sorbonne Université-CNRS-IRD-MNHN, Paris, France

12 ³Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL, CEA-CNRS-

13 UVSQ, Université Paris Saclay, Gif-sur-Yvette, France

14 ⁴Centre National de Recherches Météorologiques (CNRM), Université de Toulouse,

15 Météo-France, CNRS, Toulouse, France.

16

17 Manuscript submitted to Geophysical Research Letters

18 **Key points** (3, 140 characters per point) :

19 • Neglect of submonthly variability of atmospheric pressure in data-based ocean carbon sink estimates causes a positive bias of 0.12 Pg C yr⁻¹

20 • This bias represents on average 25% of the gap between model and data product estimates of the sink between 2008 and 2019
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* This bias is driven by the covariance between wind speed and atmospheric pressure, 85% of which stems from the southern midlatitudes

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## Abstract

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Current estimates of the global ocean carbon sink based on measurements of CO₂ fugacity are inconsistent with those obtained from global ocean biogeochemistry models. Here we investigate how this gap might be partially closed by more fully accounting for submonthly variability in observation-based estimates. While these estimates compute the air-sea CO₂ flux based on hourly wind speed, other variables have only monthly resolution. Thus they neglect some high-frequency variability from short-term events such as storms. To evaluate this error, we compare fluxes at different temporal resolutions between 2009 and 2018. We find that accounting for hourly variations in atmospheric pressure and daily variations in sea surface temperature decreases the global carbon uptake estimate by 5% (0.12 Pg C yr⁻¹) which is driven by the submonthly covariance between wind speed and atmospheric pressure, particularly in the southern extratropics. This represents 25% of the average gap between observation-based and model estimates.

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## Plain language summary (200 words)

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The ocean absorbs about a quarter of CO₂ released by human activities, but exact estimates vary depending on the approach used. One approach relies on different types of atmospheric and oceanic observations, typically monthly averages except for wind speed. So that approach partly neglects effects from shorter-term events, such as storms. Here, we account for the effect of this previously neglected shorter-term variability on ocean carbon uptake, finding it closes 25% of the gap between these
46 observation-based estimates of ocean carbon uptake and model estimates. The main
47 reason is that storms lower atmospheric pressure, reducing the effective atmospheric
48 CO2 concentration. This effect is observed in temperate regions and is particularly
49 important south of 30°S where storms are more frequent.

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# 1. Introduction

52 The ocean plays a major role in the global carbon cycle, absorbing one-fourth of
53 anthropogenic CO₂ emissions (Friedlingstein et al., 2025). In the Global Carbon Budget
54 (GCB) of Friedlingstein et al. (2025), the ocean absorbed an average of 2.9 Pg C yr⁻¹
55 during 2014-2023. But this number comes with considerable uncertainty, having a
56 spread of more than 1 Pg C yr⁻¹ between the lowest and highest estimates. The GCB is
57 updated annually to reflect the evolution in the data and methods used to obtain the
58 latest estimates of global carbon emissions and carbon storage in different reservoirs
59 (sinks). In particular, the GCB relies on estimates of the anthropogenic carbon stored in
60 the global ocean based on two different approaches. The first approach relies on Global
61 Ocean Biogeochemistry Models (GOBMs), which resolve global ocean circulation along
62 with the main biogeochemical processes of the carbon cycle. GOBMs simulate the
63 transport of dissolved inorganic carbon throughout the ocean, affecting oceanic surface
64 CO₂ concentrations and thus the air-sea CO₂ flux. These GOBMs are forced by
65 atmospheric reanalyses with a typical timestep of a few hours. The second approach
66 uses observation-based products for ocean fugacity of CO₂ (fCO₂), a variable nearly
67 identical to the ocean partial pressure of CO₂ (pCO₂). These data products rely on
68 sparse in situ measurements of fCO₂ archived in the Surface Ocean CO₂ Atlas (SOCAT,
69 Bakker et al., 2016). Various groups have produced data products by mapping the $fCO_2$ from SOCAT's sparse gridded data typically to a 1°×1° grid with monthly resolution, 70 filling missing grid cells based on a series of global predictors (sea surface temperature, 71 mixed layer-depth, etc.) obtained from satellite products, meteorological and ocean 72 reanalyses (Landschützer et al., 2016; Rödenbeck et al., 2022; Chau et al., 2022; 73 Watson et al., 2020; Zeng et al., 2022; Iida et al., 2021; Gregor et al., 2024; Gloege et al., 74 2022; Gregor et al., 2019). 75

76 The uncertainty of the global ocean carbon sink is estimated to be ±0.4 Pg C yr⁻¹ from 77 the ensemble standard deviation of 8 data-based products and 10 GOBMs while 78 considering known systematic biases (Friedlingstein et al. 2025). In particular, there is a 79 systematic discrepancy between the two approaches, where the sink from data-based 80 products is 0.5 Pg C yr⁻¹ more on average than that from GOBMs. One potential source 81 of bias between the two types of estimates is the 0.65 ± 0.15 Pg C yr⁻¹ adjustment of the 82 data-based estimates for the net preindustrial outgassing from the imbalance between 83 riverine input and sediment losses (Regnier et al., 2022), as discussed by Planchat et al. 84 (2025). Another important source of uncertainty is the sparsity of SOCAT measurements 85 particularly in key regions such as the Southern Ocean (Hauck et al. 2020). Finally, 86 biases may exist between the two approaches because of their vastly different temporal 87 resolutions. While GOBMs compute air-sea $CO_2$ fluxes with a time step on the order of 88 an hour, all but one data-based product used in GCB 2024 rely on monthly averaged 89 variables. Although data-based products account for the effect of wind-speed variance 90 by using monthly means of the square of observed 1- to 6-hourly wind speed, they 91 neglect nonlinearities from submonthly variability of other physical drivers, including 92 atmospheric pressure, temperature, and salinity. 93
To address this limitation, Gregor et al. (2024) developed a higher-frequency product with 8-day averages, but that had minimal effect on their sink estimate. Yet many meteorological events, such as tropical cyclones and extratropical storms, cause variations of atmospheric and oceanic variables on time scales shorter than a week. Previous case studies have shown that tropical cyclones can cause substantial regional uptake (Gregor et al., 2024) or outgassing (Bates et al., 1998; Huang & Imberger, 2010), which depends largely on the initial partial pressure difference at the air-sea interface. Globally, the contribution of tropical cyclones to the ocean carbon sink has been shown to be negligible, because opposing effects nearly balance, and many tropical cyclones occur in regions with weak air-sea differences in fCO₂ (Lévy et al. 2012). However, recent studies using floats or gliders indicate that storms in the Southern Ocean can cause outgassing anomalies over 1-3 days, driven by concomitant variations in wind speed, atmospheric pressure, and DIC concentrations (Carranza et al., 2024; Nicholson et al., 2022). Southern Ocean storms may reduce the global carbon sink by up to 0.51 Pg C yr⁻¹ (Carranza et al., 2024). How much this high-frequency variability affects data-based estimates of air-sea fluxes, particularly how nonlinearities in the calculation are affected by using only monthly-averaged physical variables, remains uncertain and warrants further investigation.

Although submonthly variability of biogeochemical variables (fCO₂, DIC...) is hard to constrain due to the scarcity of observations, physical variables such as atmospheric pressure, sea surface temperature and salinity are measured more frequently, allowing higher temporal resolutions. We ask here how much this submonthly variability might affect observation-based estimates of the global ocean carbon sink, particularly in mid-latitudes where storms induce outgassing through
simultaneous decreases in atmospheric pressure and increases in wind speed. Our aim is to quantify the importance of this effect, identifying the mechanisms involved and the regions where they are the most important.

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## 2. Methods

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### 2.1 CO₂ flux calculations

Two data-based estimates are computed between 2009 and 2018. One, consistent with the GCB, accounts only for submonthly variability of wind speed; the second is computed from higher temporal resolution data for atmospheric pressure, sea-surface temperature and salinity. We calculate the air-sea CO₂ flux F using the formulation:

$$F = k_{660}(u) A(T, S) (fCO_{2 \text{ atm}} - fCO_{2 \text{ sea}}) \quad (1)$$

where $k_{660}$ is the gas transfer (piston) velocity at 20°C, while $fCO_{2 \text{ atm}}$ and $fCO_{2 \text{ sea}}$ are the fugacities of CO₂ in the atmosphere and the ocean, and $A = K_0(T, S) \left(\frac{Sc(T)}{660}\right)^{-\frac{1}{2}}$. The $K_0$ is the solubility of CO₂ in seawater computed from the sea surface temperature (T) and salinity (S) following Weiss (1974), and the final ratio with the Schmidt number (Sc), a function of T (Wanninkhof, 2014), is used to compute piston velocities at other temperatures and for other gases. Positive fluxes indicate an air-to-sea CO₂ flux. The $k_{660}$ is calculated using a quadratic function of the 10-m wind speed $k_{660} = au^2$ (Wanninkhof, 1992) where $a$ is a scaling factor depending on the wind dataset used for the calculation (Naegler, 2009) and $u$ is the wind speed at 10 m. The CO₂ fugacity in the
139 atmosphere, fCO₂atm, is the partial pressure of CO₂ in moist air, corrected for the non-ideal behaviour of the gas:
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$$141 \qquad fCO_2 \text{ atm} = \phi_{CO_2} (P_{\text{atm}}, T) X_{CO_2} (P_{\text{atm}} - P_{\text{vap}}(T, S)) \#(2)$$

142 where φCO₂ is the fugacity coefficient from Weiss (1974), XCO₂ is the CO₂ mole fraction in 143 dry air, Patm is the surface atmospheric pressure, and Pvap is the partial pressure of water 144 vapor from Weiss & Price (1980), an empirical function of T and S assuming that the air 145 is fully saturated (100% humidity) at the sea interface. Here, the oceanic fugacity 146 fCO₂sea is not from an ocean fCO₂ product but rather calculated from gridded products 147 of T, S, dissolved inorganic carbon (DIC), alkalinity (Alk), and total inorganic phosphorus 148 and silicon concentrations ([P] and [Si]) using mocsy (Orr & Epitalon, 2015).

149 Consistent with the temporal resolution of GCB 2024 data products, the 150 reference flux Fref is calculated using the monthly mean of the squared wind speed u 151 computed from hourly data and monthly means of the other variables:

$$152 \qquad F_{\text{ref}} = a \overline{u^2} A(\bar{T}, \bar{S}) \Delta f \text{CO}_2(\overline{P_{\text{atm}}}, \bar{T}, \bar{S}, \overline{X_{\text{CO}_2}}, \overline{\text{DIC}}, \overline{\text{Alk}}, [\overline{P}]^c, [\overline{S1}]^c) \#(3)$$

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154 The overbar denotes a monthly mean, while the appended c indicates a climatological
155 monthly mean. The high-frequency flux Fh is calculated using each variable at its
156 highest available temporal resolution (hourly u and Patm, daily T, 8-daily S, and monthly
157 for all others). We compare Fref to Fh, the monthly mean of Fh:

$$158 \qquad \overline{F_h} = a \overline{u^2} A(T, S) \Delta f \text{CO}_2(P_{\text{atm}}, T, S, \overline{X_{\text{CO}_2}}, \overline{\text{DIC}}, \overline{\text{Alk}}, [\overline{P}]^c, [\overline{S1}]^c) \#(4)$$

159 To help assess the causes of differences, we also calculate two additional
160 intermediate air-sea CO₂ fluxes: Fp (using hourly Patm and monthly T and S) and FP,τ,
(using hourly Patm, daily T, and monthly S).
Finally, the corresponding global ocean sinks of anthropogenic carbon $S_h$ and $S_{ref}$ are obtained by spatially integrating the air-sea CO$_2$ flux and correcting for the preindustrial outgassing by subtracting the global $S_{river} = 0.65 \pm 0.15$ Pg C yr$^{-1}$ (Regnier et al., 2022). Here we consider that differences between $F_h$ and $F_{ref}$ and between $S_h$ and $S_{ref}$ are statistically significant only if they meet the 99% level of the Student's t-test.

We use hourly fields of wind speed $u$ and surface pressure $P_{atm}$, and daily fields of sea surface temperature $T$ and sea ice are from the fifth generation ECMWF reanalysis (ERA5, Hersbach et al., 2020) at 0.25°×0.25° spatial resolution. The sea-surface salinity $S$ is from the Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density dataset (Droghei et al., 2018) at 0.25°×0.25° spatial resolution and 8-day temporal resolution. The atmospheric CO$_2$ dry air mole fraction $X_{CO2}$ is from the NOAA Greenhouse Gas Marine Boundary Layer Reference (Lan et al., 2023), initially provided as 4.5° latitudinal bands at a temporal resolution of ~8 days. Because of the limited observational network used to produce global maps of $X_{CO2}$, we used its monthly means, leaving for future studies the impact of its submonthly variability on the global sink. Fields of DIC and Alk are from OceanSODA-ETHZ (Gregor & Gruber, 2021), which has 1°×1° spatial resolution and monthly temporal resolution. Fields of inorganic $[P]$ and $[Si]$ are from a monthly climatology (Broullón et al., 2019) derived from the 2013 World Ocean Atlas dataset (Boyer et al., 2013), also on a 1°×1° grid. All datasets are regridded bilinearly to a common 0.25° resolution before computing the fluxes. In the transfer velocity formulation, the scaling factor is taken to be $a=0.271$ corresponding to ERA5 wind speed following Fay et al. (2021). Fluxes and sinks are calculated only for
184 latitudes between 60°S and 60°N to avoid sea ice areas. Calculations consider only the time steps when regions are completely ice-free.

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187 **2.2 Reynolds decomposition**

188 To investigate the mechanisms responsible for differences between $F_{\text{ref}}$ and $\overline{F_h}$, a Reynolds decomposition is used with respect to $u$ and $\Delta = fCO_2_{\text{atm}} - fCO_2_{\text{sea}}$ for each month from 2009 to 2018, assuming that solubility and Schmidt number variations cancel out to give $A = \underline{A}$ (Etcheto et Merlivat 1988), an assumption good to within 5% locally (Wanninkhof, 2014):

$$ \overline{F_h} = \underbrace{aA\bar{u}^2\bar{\Delta}}_{\text{mean bulk}} + \underbrace{aA(\overline{u'})^2\bar{\Delta}}_{\text{variance}} + \underbrace{2aA\bar{u}\overline{u'\Delta'}}_{\text{covariance}} + \underbrace{aA(\overline{u'})^2\overline{\Delta'}}_{\text{3rd order moment}} \quad \#(5) $$

193 The combination of the first term (mean bulk) and 2nd term (variance) is referred to as the bulk term, thus by definition it includes the enhancement from wind speed variance. The bulk term is very close to the reference flux $F_{\text{ref}}$. This agreement along with the assumption that $A = \bar{A}$, is illustrated by the mean difference between $F_{\text{ref}}$ and the sum of the first 2 terms ($aA\bar{u}^2\bar{\Delta}$), which ranges from 0 to -0.2 g C m⁻² yr⁻¹ (Figure S1), an order of magnitude smaller than the mean differences between $\overline{F_h}$ and $F_{\text{ref}}$ (Figure 1c). 194 195 196 197 198 199 Other data-based reconstructions do not take into account the final two terms of equation (5), the correction terms. The first of those ($u$ and $\Delta$ covariance term) accounts for how the flux is affected by covariation between wind speed anomalies and $\Delta$ anomalies (2nd-order moment). The second of the correction terms (3rd-order mixed moment) accounts for nonlinear interactions between wind speed variance and $\Delta$ anomalies, thus capturing effects from skewness or asymmetry in fluctuations. 200 201 202 203 204
## 3. Results

### 3.1 Global ocean carbon sink sensitivity to submonthly variability

Our estimate $S_{ref}$ is comparable to the average from the GCB data products (Figure 1a). Both account for submonthly variability of $u$. Our sink estimate $S_h$ which also accounts for submonthly variability of P, T, and S, is 0.12 P g C yr$^{-1}$ lower than $S_{ref}$. Therefore, the standard approach which neglects all submonthly variability besides that of $u$ results in an overestimation of the carbon sink by 0.12 P g C yr$^{-1}$. On average between 2009 and 2018, this difference corresponds to 5% of the carbon sink $S_{ref}$ but 25% of the discrepancy between models and data products from GCB (~0.5 Pg C yr$^{-1}$). Our computed difference does not change over 2009 to 2018.

The spatial distribution of $F_{ref}$ (Fig. 1b) is consistent with the fluxes from the SeaFlux product (Fay et al. 2021, Figure S2). The 2009-2018 mean difference $\overline{F_h} - F_{ref}$ shows that the discrepancy between the two fluxes stems largely from latitudes between 30° and 60° in both hemispheres (Figure 1c). In these regions relative to $F_{ref}$, $\overline{F_h}$ has either more outgassing (North Atlantic, Southern Ocean) or less uptake (North Pacific, by 1.5 g C m$^{-2}$ yr$^{-1}$ on average). A weaker opposing effect is seen in tropics, where sinks are increased by 0.5 g C m$^{-2}$ yr$^{-1}$ on average, and by up to 1 g C m$^{-2}$ yr$^{-1}$ in the Arabian and South China Sea.

The differences in the mid-latitudes are mainly due to atmospheric pressure variations, whereas differences in the tropics are driven by sea surface temperature variations, based on maps of $\overline{F_P} - F_{ref}$ and $\overline{F_{P,T}} - \overline{F_P}$ (Figure 2). Atmospheric pressure variations reduce the carbon sink estimate by 0.136 Pg C yr$^{-1}$ on average, an effect that
228 is slightly weakened by sea surface temperature variations which increase the sink by
229 0.016 Pg C yr⁻¹. On synoptic timescales, low pressure perturbations are associated with
230 stronger winds, so periods of strong CO₂ exchange have weaker atmospheric pressure
231 and thus weaker atmospheric CO₂ fugacity. Thus wind speed and atmospheric pressure
232 covariance leads to an anomalous outgassing correction term in the midlatitudes.
233 Further separation of $\overline{\mathrm{F}}_{\mathrm{P},\mathrm{T}} - \overline{\mathrm{F}}_{\mathrm{P}}$ into atmospheric and oceanic components (Figure S3b)
234 reveal that temperature variations mainly affect ocean $f\mathrm{CO}_2\mathrm{sea}$, but have negligible
235 effect on vapor pressure. The anomalous ingassing in the Arabian Sea appears to result
236 from an increase in submonthly variability of wind speed driving stronger upwelling and
237 hence surface cooling, thus reducing $f\mathrm{CO}_2\mathrm{sea}$. The remaining effect of salinity ($\overline{\mathrm{F}}_h$ –
238 $\overline{\mathrm{F}}_{\mathrm{P},\mathrm{T}}$) is negligible.

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240 **3.2 Relative importance of the correction terms**

241 The Reynolds decomposition (5) reveals the relative importance of the different
242 terms in equation (5). The wind speed variance term, which is already accounted for in
243 the GCB data-based products, is the dominant source of submonthly nonlinearity (Fig
244 3a). It results in an increase in the estimated global ocean carbon sink by 0.36 Pg C yr⁻¹.
245 This increase is situated mainly in the mid-latitudes, peaking at 40°S and 40°N (Fig. 3,
246 S4).

247 Our first correction term (covariance) accounts for 90% of the global correction,
248 whereas the third-order moment is weaker. Together they result in a global outgassing
249 anomaly whose magnitude is one-third of the wind variance term and opposite in sign.
250 About 85% of this contribution is situated between 60°-30°S and amounts to a reduction
of 0.1 Pg C yr⁻¹ (Fig. 3a). The other main region where the correction terms reduce the sink is between 30°-60°N, but that reduction of 0.03 Pg C yr⁻¹ is largely offset by the 0.026 Pg C yr⁻¹ increase between 30°S-30°N. Although the covariance term is generally weaker than the wind speed variance term, it has a similar magnitude in the tropics and dominates south of 50°S (Figure 3b).

## 4. Discussion

This study reveals that accounting for hourly variability in atmospheric pressure, while including the small compensation from daily variability in sea surface temperature, reduces data-based estimates of the global ocean carbon sink by 0.12 Pg C yr⁻¹, closing 25% of the gap between those products and the GOBMs. While the 8-day product from Gregor et al. (2024) offers higher frequency atmospheric and oceanic CO₂ fugacities, the resulting ocean sink estimates are essentially unchanged from the monthly equivalent product. Thus even higher frequency is needed to better capture the effect of atmospheric variability. Whether or not 6-hourly or daily atmospheric pressure data would suffice remains a question for future research.

The Reynolds decomposition of the high-resolution CO₂ flux confirms that the submonthly variation in wind speed, already accounted for in the ocean data products, is the most important nonlinearity. It generally increases sink estimates outside the tropics with peaks at 40°S and 40°N. Conversely, our correction due to the submonthly covariance of wind speed and atmospheric pressure leads to outgassing anomalies in the midlatitudes. The reason is that midlatitude synoptic disturbances have strong winds that favor air-sea exchange but also reduce atmospheric pressure, thus lowering atmospheric CO₂ fugacity, which favors outgassing. This correction is particularly large
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South of 50°S, it becomes the main source of nonlinearity.

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While the nonlinearities underlined here explain 25% of the gap between models and data-based products, we found no temporal evolution in that gap, unlike Friedlingstein et al. (2025). Furthermore, we leave for future work the question of whether submonthly variations in atmospheric $X_{CO_2}$, notably potential covariations with $P_{atm}$, may also alter data-based estimates of ocean carbon uptake. The gap between data-based products and GOBMs also depends on model resolution and their representation of coastal regions (e.g. Resplandy et al. 2024) as well as on processes not yet commonly included in the flux parameterization, such as the ocean cool skin (+0.3 Pg C yr$^{-1}$, Dong et al. 2022), rain (+0.14-0.19 Pg C yr$^{-1}$, Parc et al. 2024) or wave breaking-induced bubbles (+0.07 Pg C yr$^{-1}$, Rustogi et al. 2024). Bellenger et al. (2023) showed that the cool skin temperature effect is three times smaller in a model framework because gas exchange is not as rate limiting as carbon transfer to the ocean interior. Including the cool skin effect in both data-based products and GOBMs could thus increase the gap by about +0.2 Pg C yr$^{-1}$. The addition of new processes in the calculation of carbon fluxes may further increase the gap between data-based products and GOBMs estimates.

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Our results at the global scale are consistent with previous observations in the Southern Ocean from ARGO floats (Carranza et al., 2024) and gliders (Nicholson et al., 2022). But these studies also show that submonthly variability of biogeochemical variables needs more attention, both for data-based and model estimates. Their observations suggest that entrainment of cold, DIC-rich subsurface water into the surface mixed layer is the leading cause of storm-driven CO2 mean outgassing in the Southern Ocean, potentially
297 leading to a total decrease of 0.51 Pg C yr-1 of the sink in this region (Carranza et al., 2024). Likewise, GOBMs underestimate storm-driven vertical transport of DIC (Carranza et al. 2024). While our study should help to narrow the data vs. model gap, its continued 298 299 growth and the large disparities among data-based products and among models 300 underscore the need for further advances to constrain ocean carbon uptake, advances 301 that are also key to reducing, by difference, much larger uncertainties associated with 302 the terrestrial biosphere. 303

304 Acknowledgments:

305 This study has been conducted using EU Copernicus Marine Service Information; 306 https://doi.org/10.48670/moi-00051. To process the flux data, this study benefited from 307 the IPSL mesocenter ESPRI facility which is supported by CNRS, UPMC, Labex L-IPSL, 308 CNES and Ecole Polytechnique. JCO and MG were funded by EU grant 101083922 309 (OceanICU). RS acknowledges funding from the EU's Horizon Europe research and 310 innovation programme under ESM2025 (grant agreement No 101003536) and OptimESM 311 (grant agreement No 101081193) as well as the ANR - France 2030 as part of the PEPR 312 TRACCS programme under grant number ANR-22-EXTR-0009.

313 Data availability

314 Estimates of the CO2 fluxes for the different diagnostics done in this study are 315 publicly available at the Zenodo repository at 10.5281/zenodo.15848192. ERA5 316 reanalysis data from the ECMWF (Hersbach, H. et al., 2020) was obtained through 317 the Copernicus Climate Change Service (C3S, 2023) 318 (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels). 319 The results contain modified Copernicus Climate Change Service information 2020.
Neither the European Commission nor ECMWF is responsible for any use that may
be made of the Copernicus information or data it contains. The CO2 dry air mole
fraction data were from the NOAA Greenhouse Gas Marine Boundary Layer
Reference (https://gml.noaa.gov/ccgg/mbl/data.php). The sea surface salinity values
have been downloaded from EU Copernicus Marine Service Information
(https://doi.org/10.48670/moi-00051). Global DIC and Alk bulk values was obtained
from the OceanSODA-ETHZ climatology dataset
(https://www.ncei.noaa.gov/access/metadata/landing-
page/bin/iso?id=gov.noaa.nodc:0220059). The Broullón et al. (2020) Silicon and
Phosphorus concentrations monthly climatologies are available at
https://digital.csic.es/handle/10261/184460.

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<figure><img src="image_2.png" /></figure>

477

**Figure 1.** a) Time series of the global annual mean CO₂ sink (Pg C yr⁻¹) computed from Sₜref (thick green line), S<sub>h</sub> (thick red line), and those from the GCB 2024 data products (thin orange lines) and their mean (thick dashed orange line) as well as the GCB 2024 models (thin blue lines) and their mean (thick dashed blue line). Also shown are maps for the 2009-2018 mean of b) the air-sea CO₂ reference flux Fₜref and c) the difference between the monthly mean of the high frequency flux F̄<sub>h</sub> and Fₜref, where positive values indicate less outgassing or more uptake with F̄<sub>h</sub> than with Fₜref.
<figure><img src="image_3.png" /><figcaption>**Figure 2.** Maps averaged over 2009-2018 for effects on observation-based air-sea CO2 fluxes from **a)** submonthly variability of atmospheric pressure (F̄P - Fref) and **b)** submonthly variability of temperature (F̄P,T - F̄P).</figcaption></figure>

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<figure><img src="image_4.png" /><figcaption>**Figure 3.** a) Contribution of the final 3 terms in equation 5 to ocean carbon uptake in different latitudinal bands. b) Zonal integral of the final three terms in equation 5 (blue, pink and red lines) and their sum (dashed black line).</figcaption></figure>

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