Since the Industrial Revolution, less than half of the carbon emitted into the atmosphere by human activities remains in the atmosphere (Friedlingstein et al., 2020; Costa et al., 2023), highlighting the pivotal role of terrestrial and ocean sinks in regulating atmospheric CO2 concentrations. From 2013 to 2022, the ocean absorbed and stored CO2 approximately 26 % of total anthropogenic CO2 emissions (Friedlingstein et al., 2023). The ocean stores a vast amount of CO2, with inorganic carbon reservoir approximately 50 times greater than those in the atmosphere (Sabine et al., 2004). Therefore, studying oceanic CO2 sources and sinks is crucial for developing mitigation strategies and mitigate climate change. The most widely and extensively applied, and long-established method for ocean carbon sink investigation involves measuring the partial pressure difference of CO2 (ΔpCO2) across the air–sea interface (Song et al., 2023). Continuous observations used to calculate the air–sea CO2 flux, providing the most direct characterization of the ocean carbon cycle system (Wanninkhof et al., 2019; Song et al., 2023).

Owing to the past several decades of continuous observations, a large amount of sea surface pCO2 data has been accumulated, yet it remains insufficient relative to the vast ocean area. The Surface Ocean CO2 Atlas (SOCAT v2024) revealed that the ocean area covered by monthly CO2 measurements has decreased by nearly half since 2017, reflecting the decline in global open-ocean CO2 observation capacity (Bakker et al., 2024). Although recently studies have increasing employed artificial intelligence and big data technologies to investigate the dynamics of ocean carbon sinks (Landschützer et al., 2013; Xu et al., 2019; Yu et al., 2023), the fundamental limitation of in-situ field observation remains unresolved. Due to the limited spatial and temporal coverage of ΔpCO2 measurements, as well as uncertainties in wind forcing and transport velocity parameterization, the uncertainty in global and regional fluxes estimated from ΔpCO2 measurements can reach up to ± 50 % (Wanninkhof et al., 2013; Rhein et al., 2013). In addition to ΔpCO2 data, air–sea CO2 fluxes can also be estimated using a top-down inversion method that integrates atmospheric CO2 concentrations with atmospheric transport models (Jacobson et al., 2007; Wanninkhof et al., 2019). Spatial and temporal variations in atmospheric CO2 concentrations reflect the pattern of sources and sinks across large spatial scales. Consequently, top-down atmospheric inversion methods are suitable for assessing global and regional CO2 fluxes and are currently widely adopted to estimate CO2 emissions from fossil fuels and carbon sinks in terrestrial ecosystems (Piao et al., 2022; Han et al., 2024). However, due to the sparse sampling of CO2 concentration over the open ocean, significant uncertainties persist in those flux estimations, limiting its applicability (Rödenbeck et al., 2006; Wanninkhof et al., 2019). In summary, the scarcity of marine atmospheric CO2 concentration observations is the primary obstacle to accurately quantify the oceanic carbon sink.

For marine atmosphere, buoy observations excel at meeting the requirements of expanding field observation coverage and significantly increasing data volume compared to research vessel observations constrained by voyage frequency, range, and cost, or satellite observations limited by operational cycles and atmospheric conditions (e.g., clouds, aerosols). Currently, both the ARGO Global Ocean Observing System and the Southern Ocean Carbon and Climate Observation and Modeling (SOCCOM) project have proposed utilizing buoys for seawater CO2 observations (Sarmiento et al., 2023), but none of them have conducted atmospheric CO2 concentrations over the ocean. Urban CO2 monitoring efforts provide valuable experience for selecting CO2 observation instruments suitable for deployment on buoys. In recently years, high-density monitoring networks based on low-cost CO2 sensors have been established in numerous cities worldwide (Karion et al., 2020), as supplements to the land-based observations from the sites within the World Meteorological Organization's (WMO) Global Atmosphere Watch Programme (GAW). For instance, Shusterman et al. (2016) established a CO2 observation network (BEACO2N) which consists of 34 sensors in and around Oakland, California. After applying environmental parameter and drift corrections, the network achieved an accuracy of ± 1.2–2.0 ppm for 1 min average dry-air concentrations between, effectively capturing CO2 variations across multiple temporal scales in urban areas and abnormal short-term CO2 emission events. Delaria et al. (2021) further corrected the temperature-dependent zero bias of the BEACO2N sensor, reducing the error to 1.6 ppm or less. Han et al. (2024) established a 134-station SenseAir K30 sensor observation network and developed a CO2 calibration system. Data accuracy was enhanced through averaging raw observations, environmental corrections, and calibration with standard gases. After applying long-term drift correction, the sensors (SENSE – IAP) maintained a root mean square error (RMSE) of 2.4 ± 0.2 ppm after 30 months of operation (Cai et al., 2025). Compared to high-precision instruments, low-cost sensors exhibited relatively lower accuracy (several ppm versus ∼ 0.1 ppm) but at drastically reduced cost (under USD 15 000 versus over USD 150 000). This cost-performance balance enables the construction of dense observation networks to reveal significant spatial variations of CO2 induced by emission sources, vegetation carbon sinks, and meteorological conditions (Shusterman et al., 2016; Bakker et al., 2024; Han et al., 2024; Cai et al., 2025).

Considering the advantages of low-cost sensors in land-based CO2 monitoring and inversion, and the relative maturity of buoy-based observation technologies, we designed a sea–air interface buoy platform equipped with SenseAir K30 sensors (SenseAir AB, Delsbo, Sweden) in the coastal waters off Maoming, Guangdong, China, and evaluated its performance in monitoring marine atmospheric CO2 concentration. Satisfying measurement accuracy was obtained after instrumental calibration, data processing, and correction strategies, validating the feasibility of marine CO2 monitoring with low-cost sensors. Here we presented the preliminary results of the buoy-based low-cost sensor system for atmospheric CO2 monitoring at the sea–air interface. Section 2 introduces the instruments and data correction methods; Sect. 3 presents the results of the land-based experiments for calibration and correction; Sect. 4 demonstrates a short-term marine observation case study; and Sect. 5 presents and discusses the results of post-deployment laboratory validation of the sensors.

Temperature variations affect the sensor's light source intensity, detector response, and absorption cross-section, leading to systematic drifts in output (Yasuda et al., 2012). Pressure influences gas density and infrared absorption line broadening, making corrections based on the equation of state or sensor sensitivity particularly important in regions with strong pressure fluctuations (Chen et al., 2010; Curcoll et al., 2022). Water vapor exerts the most complex effects on NDIR sensors: it not only dilutes CO2 mole fractions in moist air relative to dry air but also causes spectral line broadening within the CO2 absorption band, introducing biases (Chen et al., 2010; Dubey et al., 2024). Accordingly, the multivariate linear calibration of CMs focuses on 3 key environmental factors – T, P, and RH. Figure 5 shows the variations of ΔCO2 between the three CMs and the collocated Picarro measurements as a function of environmental parameters (T, P, and RH) during the land-based field observations, where the orange and green colors correspond to the data before and after environmental correction, respectively. Figure 6 presents the linear relationships between the CMs and the Picarro, along with the histograms of ΔCO2.

Prior to correction, the ΔCO2 of all three CMs exhibited linear relationships with environmental parameters (Fig. 5): negative correlation with T, and positive correlation with P and RH. Among these, CM2 demonstrated the most pronounced correlations. The correlation coefficients (r) between ΔCO2 and T, P, and RH were -0.79, 0.71, and 0.43, respectively, while the r values for CM1 and CM3 were -0.49, 0.52, 0.22, and -0.39, 0.44, 0.16, respectively, all significant at the 0.01 level (p < 0.01). After correction, the systematic drift of ΔCO2 across the three CMs due to environmental parameters was successfully eliminated. The corresponding r values were all 0, indicating no significant correlations, with ΔCO2 fluctuating around the zero line. Beyond environmental factors, the multiple linear regression also incorporated the true CO2 concentration in the atmosphere, represented by Picarro co-located measurements in field observations. The values of the first three CMs before correction all exhibit zero bias relative to the true values (the fit between CO2 observations and true values includes intercepts) (Fig. 6a–c), with respective r values of 0.98, 0.93, and 0.98, all significant at the 0.01 level (p < 0.01). After correction, the correlations all improved to 0.99 (p < 0.01), and data points largely converged on either side of the 1:1 line. The bias between CMs and Picarro shifted from -3.7, +43.6, and -16 to +0.1, +1.6, and +0.6 ppm, respectively, shifting from significantly biased to essentially unbiased (Fig. 6d–f). The results above suggest that all three CMs are influenced by environmental variables, but to markedly different extents. Whether for CM2, which inherently exhibits substantial systematic errors, or CM1, which shows minimal data offset prior to correction, our environmental correction method significantly enhances observational accuracy, improves data quality, and demonstrates good universality.

Environmental correction effectively reduced the offset values between CMs and Picarro (Fig. 7). Before correction, CMs captured CO2 concentration trends like Picarro but exhibited significant deviations. For the best performing CM1, this deviation was particularly noticeable at low CO2 concentrations, while CM2 and CM3 showed overall high and low biases, respectively. The ΔCO2 of CMs exhibited a certain “downward” drift trend over the one-week observation period, CM1 and CM3 moved from zero toward negative values, while CM2 shifted from a relatively high positive value around 50 ppm toward zero. The calibrated results showed high consistency with Picarro, with the RMSE decreasing from 11.04, 30.51, and 18.70 to 4.03, 3.96, and 3.88 ppm, respectively. The correction effectively eliminated the linear drift trend of ΔCO2 over time, stabilizing it to fluctuate around the zero line. During the land-based field observations in Shanghai in early spring, the atmospheric temperature and humidity exhibited pronounced diurnal variations, fluctuating between 5–30 °C and 10 %–60 %, respectively. The average RMSE of corrected CMs was 3.64 ppm, which is sufficient to capture terrestrial CO2 variations (400–600 ppm), even during periods of significant CO2 fluctuations with pronounced peaks and troughs, such as cases on 22–23 March and 25–26 March, where the environmental correction method performed well.

Based on the instrument deployment and marine environment described in Sect. 2, and the environmental correction method validated through land-based field observations in Sect. 3, the CMs-equipped air–sea interface buoy observation platform commenced operation in May 2025 in the northern South China Sea off the coast of Dianbai District, Guangdong Province. Following signal debugging and regular equipment maintenance, observational data were obtained over 3 months from 28 May to 28 August. The hourly moving-averaged time series of CO2 concentration, T, and RH from the 3 CMs are shown in Fig. 8. During environmental calibration, measurements are first corrected using the environmental coefficients at, ap, aRH, and ac obtained from land-based calibration; baseline correction is then applied by updating a0 with reference CO2 concentrations from the marine atmospheric boundary layer. Over the open ocean, strong horizontal atmospheric mixing results in small zonal variations in marine boundary layer CO2 concentrations, indicating high zonal uniformity at similar latitudes (Bakwin et al., 2004; Palter et al., 2023). Given the strong real-time nature of this study's observations and the limited availability of co-located and near-surface observation resources, CO2 observation data from the Mauna Loa atmospheric background station in Hawaii, USA (MLO, 19.54° N, 155.58° W) – located at a latitude similar to the observation site – served as the reference value. The CO2 datasets were obtained from the NOAA Global Monitoring Laboratory (GML) (https://gml.noaa.gov/ccgg/trends/data.html, last access: 23 April 2026; Lan and Keeling, 2025). The relatively stable period (17–24 June) of CMs concentrations during the observation was regarded as the atmospheric background state. Both values were substituted into a multiple linear regression calculation to obtain a0. After environmental correction, the RMSE of the CMs significantly decreased from 9.27, 52.39, and 11.24 to 1.57, 1.86, and 1.52 ppm, respectively (Figs. 8a and S4–S6 in the Supplement). The lower RMSE during the marine test can be partly explained by the substantially lower ambient CO2 variability over the ocean, as reflected by the smaller standard deviation of the MLO reference data (1.81 ppm) compared to that of the Picarro in-situ measurements at the land site (29.29 ppm).

During the 3 month marine observations, the atmosphere at the observation site exhibited high temperatures and humidity, with temperatures ranging from 25 to 37.5 °C and humidity levels between 50 % and 100 % (Fig. 8b). Both showed pronounced diurnal and weekly variations. The mean CMs values before and after correction were 459.48 and 436.94 ppm, with medians of 456.53 and 433.75 ppm, respectively (Fig. 8c). The means consistently exceeded the medians, and the ranges surpassed 100 ppm in both cases, which indicates many signal peaks in CO2 during the observation, and the observation site is susceptible to terrestrial anthropogenic CO2 emissions. After correction, the overall concentration was approximately 22 ppm lower than the original values, consistent with the CO2 range at the atmospheric background station MLO (mean 429.32 ppm). The correction eliminated systematic overestimation, bringing the results closer to background concentrations.

The SD of the raw data is 9.79 ppm, showing a slight difference from the corrected value of 9.62 ppm. The first quartile (Q1) changed from 453.08 to 430.10 ppm, and the third quartile (Q3) changed from 463.29 to 441.32 ppm, with an inter-quartile range (IQR) of 10.21 ppm, slightly below the corrected value of 11.22 ppm. These indicate that the correction process reduced the overall CO2 concentrations but did not significantly decrease data variability. In fact, the distribution of the middle 50 % of data points became wider. Whether considering the extreme fluctuation range (extreme value difference) influenced by anthropogenic land effects, the typical fluctuation range IQR after removing most extreme signals, or the average fluctuation amplitude SD of overall concentrations, the accuracy of CMs corrected data is sufficiently to capture the corresponding signals.

During the 3 month observation period, CO2 at the monitoring site exhibited short-term fluctuation peaks as well as periods of stable concentrations, both of which can be analyzed and interpreted from the perspective of atmospheric transport. Using the NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, 36 h backward trajectories were calculated for the observation point to identify the primary transport pathways influencing the air mass sources at the location (Cohen et al., 2015). The trajectory origin is set at the observation point, with a time resolution of 6 h. Meteorological driving data were from the Global Data Assimilation System (GDAS1, 1° × 1°) reanalysis data provided by NOAA (Rolph et al., 2017). After obtaining a series of backward trajectories, the HYSPLIT clustering module was employed to classify the trajectories. This approach mitigates the impact of uncertainty inherent in individual trajectories and extracts key transport pathway characteristics, providing a basis for subsequent analysis of the relationship between air mass transport and observational results (Cohen et al., 2015). The entire period of marine observations was divided into four segments for clustering. The periods from 28 May to 16 June (Fig. 9a) and 25 June to 1 August (Fig. 9c) constituted CO2 fluctuation phases (the background is light red in Fig. 8a), with many concentration peaks occurring during these periods. The periods from 17 to 24 June (Fig. 9b) and 2 to 13 August (Fig. 9d) constituted CO2 stable phases (the background is light blue in Fig. 8a). During these phases, CO2 concentrations fluctuated minimally and approached the background levels of the marine boundary layer. It is particularly noteworthy that during the two-week period from 14 to 28 August (the background is white in Fig. 8a), CO2 exhibited alternating patterns of relatively dense peaks and sustained background concentration levels, with each state lasting no more than five days. The limited number of trajectories obtained from segmented analysis makes it inconvenient for cluster analysis.

The trajectory clustering results (Fig. 9) indicate that the atmospheric transport pathways corresponding to concentration fluctuation periods are relatively complex, significantly influenced by air masses transported from land. Trajectories from 28 May to 16 June were classified into 17 categories, with 40 % originating from land and 60 % from the ocean. The most typical inland air mass, represented by Trajectory 4, was transported from northern Guangdong all the way to western Guangdong. Trajectory 12, although originating from the sea, reached the observation point via the western coast of Guangdong within the first 6 h of the observation period. From 25 June to 1 August, trajectories ending over land accounted for 54 %, while those ending over the ocean accounted for 46 %. Trajectory 2, corresponding to short-range inland transport, accounted for 13 %. The clustering results indicate that local urban emissions from land areas significantly contributed to CO2 concentrations at the observation point, effectively explaining the observed large fluctuations and multiple peaks during the corresponding period. Correspondingly, air masses at observation point during concentration stable periods were predominantly transported from clean marine atmospheres. The trajectories from 17 to 24 June were clustered into 11 groups, all originating from the South China Sea. Consequently, the observed CO2 concentrations during this period remained near background levels, which can be considered the CO2 concentration level in clean air without anthropogenic pollution. This also demonstrates that using CO2 observations from this period combined with MLO atmospheric background concentrations for baseline correction of CMs is reasonable. Trajectories from 2 to 13 August were categorized into 4 types, with 92 % originating from marine sources and 8 % from land sources. Consequently, although CO2 concentrations at observation point during this period were slightly elevated above background values, they remained generally stable.

By comparing the corrected CMs data during the concentration stable periods (including 19 complete days) with the diurnal variations in CO2 concentration during the summer of 2024 (June to August) at the MLO station (Fig. 10), we can further understand how CMs captures the diurnal variation of oceanic boundary layer CO2. Hourly CO2 mole fractions at MLO were obtained from NOAA GML (https://gml.noaa.gov/data/dataset.php?item=mlo-co2-observatory-hourly, last access: 23 April 2026; Thoning et al., 2025). Both CMs and MLO exhibit daily variations in background CO2 concentrations characterized by lower daytime values and higher nighttime values. This pattern likely stems from the primary influence of air–sea CO2 fluxes and atmospheric convective transport on oceanic CO2 concentrations (Lv et al., 2015). During the day, solar radiation heats the Earth's surface, which enhances photosynthesis in marine ecosystems and promotes the uptake of atmospheric CO2. As atmospheric temperatures rise, enhanced turbulent activity thickens the atmospheric boundary layer, diluting CO2 through vertical atmospheric mixing and reducing its concentration. At night, photosynthesis ceases in regional marine ecosystems, leaving only respiration. The sea surface cools, and weakened turbulent mixing restricts vertical air exchange.CO2 struggles to diffuse into the upper layers, accumulating in the near-surface layer and increasing in concentration. The daily amplitude of CO2 at MLO station during summer is approximately 2 ppm, while CMs is 3 ppm. This indicates that the environmentally calibrated CMs is sufficiently sensitive to capture the daily variation signal of CO2 in clean background air unaffected by terrestrial anthropogenic emissions, demonstrating its potential for observing atmospheric CO2 concentrations in open ocean environments. The daily amplitude of CO2 at MLO Station during summer is approximately 2 ppm, while CMs is 3 ppm. This indicates that the environmentally calibrated CMs is sufficiently sensitive to capture the daily variation signal of CO2 in clean background air unaffected by terrestrial anthropogenic emissions, demonstrating its potential for observing atmospheric CO2 concentrations in open ocean environments.

Observations of the CMs show that the daily maximum and minimum values of CO2 exhibit a 3 h lag compared to the MLO station, which may be attributed to differences in the surrounding environments of the two sites. The MLO station is situated on high altitude land, exhibiting a typical mountainous diurnal variation in CO2 concentration. Mountainous terrain induces strong upslope and downslope airflows, leading to earlier atmospheric mixing. During the day, upslope flow and mixing intensify (NOAA GML, 2024a), resulting in the lowest concentrations around 4 p.m. when vertical mixing is strongest. At night, the mountain air becomes isolated from the free atmosphere, with downslope flows carrying high CO2 air (NOAA GML, 2024a), reaching peak concentrations around 6 a.m. The observation site in this study is over an ocean surface, which possesses high thermal capacity, exhibits small diurnal temperature variations, and experiences delayed turbulence enhancement and boundary layer development (Nemoto et al., 2009). Consequently, the nocturnal accumulation of CO2 persists until 8 a.m., several hours after sunrise. Daytime sea breezes and mixing intensify later, with the lowest CO2 values occurring around 7 p.m. The average CO2 concentrations at MLO and CMs were 424.85 and 431.38 ppm, respectively, differing by approximately 6.5 ppm. This discrepancy may be due to the fact that the comparison uses 2024 data. According to the NOAA, the annual growth rates of CO2 concentrations for MLO 2023 and 2024 were 3.36 and 3.33 ppmyr-1 (NOAA GML, 2024b). Furthermore, even after filtering out short term terrestrial sources, the East Asian monsoon transport can still cause regional increases in atmospheric CO2. This monsoon-driven transport represents mesoscale or large-scale processes, not local pollution peaks, and thus systematic differences between CO2 concentrations and MLO can still be observed during stable periods (Fang et al., 2014; Lin et al., 2018).

To evaluate long-term sensor stability, post-deployment laboratory calibrations were performed against the Picarro reference in March 2026, approximately 9 months after the initial June 2025 calibration. The results are summarized in Table 1.

After marine deployment, noticeable sensor temporal drift is evident in the Corrected data (mean bias: -9.28 ppm, mean RMSE: 9.67 ppm). Performance varied among sensors; CM3 showed reduced accuracy under the original correction compared to its raw data, likely due to individual sensor variability over time. Crucially, sensor sensitivities to temperature, humidity, and pressure remained consistent throughout the deployment. Re-correction by updating the baseline parameter a0? effectively compensates for this temporal drift, reducing the mean bias to 0.17 ppm and mean RMSE to 2.70 ppm. These results confirm that the environmental correction approach remains reliable for short-term deployments, while periodic re-calibration is essential for maintaining measurement accuracy in long-term marine observations.

We successfully established a sea–air interface buoy platform along the coast of Maoming, Guangdong Province, employing low-cost sensors to observe marine CO2 concentrations in the nearshore region of the northern South China Sea. Environmental correction methods effectively eliminated the impact of environmental factors such as temperature, pressure, and humidity fluctuations on CO2 measurements while correcting for zero bias. With land-based Picarro G2301 co-located observations for comparison, CO2 accuracy improved from 8.03 to 3.64 ppm (corresponds to the 1 h moving average of minute-by-minute data). Over ocean, baseline correction using MLO atmospheric background station data improved accuracy from 24.26 to 1.59 ppm, meeting the precision requirements for capturing marine CO2 concentration signals (e.g., 420–480 ppm in this research). Systematic errors were eliminated, ensuring observed overall concentration levels align with MLO background stations, thereby meeting CO2 concentration levels in the marine boundary layer. The temporal variations in CO2 observed by the CMs, including both fluctuating and steady-state phases, can be explained by the long-range atmospheric transport simulated by HYSPLIT. Moreover, the CMs successfully captured the diurnal variations of background CO2 in the marine atmospheric boundary layer, with an amplitude of approximately 3 ppm.

In summary, this study proposes an environmental correction method for calibrating low-cost sensors, demonstrating its reliability and scientific validity. It has successfully facilitated the application of low-cost sensors aboard buoys for observing marine atmospheric CO2 concentrations, providing valuable experience for field deployment of marine atmospheric CO2 monitoring. To our knowledge, this marks the first application of this method, holding significant importance for acquiring CO2 concentration data in under-observed marine regions. This approach significantly reduces the cost of observing CO2 in the ocean, opening new possibilities for achieving the goal of substantially increasing observation data. At the same time, high-precision instruments demand stringent environmental conditions. When deployed on buoy platforms in the harsh field observation environment of the ocean, with its powerful winds and waves, maintenance becomes extremely difficult. Low-cost sensors, however, overcome these technical challenges to a large extent.

This study represents the first trial in deploying low-cost sensors aboard buoys to monitor marine CO2 concentrations. The buoy platform equipped with CMs has withstood several typhoon events, demonstrating excellent watertightness, mechanical robustness, and stability under wave conditions. During deployment, the sensors maintained nearly 100 % operational uptime. Post-recovery inspection revealed only minor corrosion on external metallic components, while internal sensor modules remained intact and free of corrosion, further confirming their robustness and good mechanical strength in the harsh marine environment. The successful detection of daily variations in the CO2 stable periods further demonstrates the method's potential for deployment in open ocean observations. To achieve the goal of significantly increasing the number of marine observations, low-cost sensors must be deployed on small drifting buoys in the following studies. The current short-term deployment does not allow for full assessment of long-term sensor drift, which will require correction in extended observations. Long-term operation of NDIR sensors is expected to introduce non-linear temporal drift associated with light source aging and detector degradation. Since such drift exhibits no consistent linear relationship with time, a linear correction coefficient was not adopted in this study. Instead, periodic re-calibration every 3–6 months using stable background CO2 observations under quiescent atmospheric conditions is recommended for future long-term deployments. While the current short-term deployment confirms satisfactory real-world performance, a comprehensive assessment of long-term operational durability and lifespan requires extended multi-month to multi-year deployments, which will be conducted in future work with periodic recalibration and intercomparison. Future work will also aim at long-term buoy deployments to capture seasonal variability and further validate sensor performance under varying environmental conditions. In the future, if large-scale deployment of buoys for observation can be realized to obtain extensive regional oceanic CO2 observational data, these data could be utilized for “top-down” atmospheric inversions. This would provide new perspectives and methodologies for estimating air–sea CO2 fluxes, representing a groundbreaking endeavor. It holds significant importance for accurately estimating oceanic carbon sinks and quantifying the dynamics of the carbon cycle.