Human activities have exponentially increased the atmospheric concentration of carbon dioxide (CO₂) since the Industrial Revolution. As a consequence of the oceanic uptake of about 20–30% of those CO₂ emissions (Gruber et al., 2019; Friedlingstein et al., 2022), ocean surface pH has decreased by 0.1 to 0.2 units (Doney et al., 2009; Rhein et al., 2013; Bates et al., 2014; Ríos et al., 2015), a phenomenon known as ocean acidification. In addition to the pH decrease, the dissolution of anthropogenic CO₂ in seawater leads to an increase in the partial pressure of CO₂ (pCO₂) and total dissolved inorganic carbon (DIC), and a reduction in the carbonate ion levels (Doney et al., 2009), thus reducing the saturation states of calcium carbonate minerals, such as aragonite (Ω_Ar) and calcite. Such changes in ocean chemistry may have direct and indirect consequences for marine life (particularly calcifying organisms), ecosystems, and reliant human communities (Riebesell et al., 2000; Kroeker et al., 2013; Mostofa et al., 2016; Doney et al., 2020).

Although ocean acidification is a global problem, it does not occur uniformly worldwide (Gattuso et al., 2015), presenting a wide range of rates, especially in coastal zones (Hofmann et al., 2011; Duarte et al., 2013a), where natural spatiotemporal variability is an important source of uncertainty when detecting anthropogenic acidification (Duarte et al., 2013a; Carstensen and Duarte, 2019). While the phenomenon of ocean acidification has garnered considerable scientific attention (Doney et al., 2009; Gattuso et al., 2015), the processes and consequences associated with coastal acidification have been less explored.

The ocean carbon cycle community is increasingly recognizing coastal and nearshore areas as hotspots for carbon and biogeochemical variability (Cai et al., 1998; Cai et al., 2020; Cai et al., 2021; Dai et al., 2022), especially vulnerable to global change, likely sites for marine CO₂ removal interventions, and regions of great importance, as coastal ecosystems provide invaluable resources and services, including various for climate change mitigation (Duarte et al., 2013b). The increased CO₂ uptake of coastal waters is not only contributing to their acidification but also exacerbating existing global challenges such as eutrophication, pollution, and habitat degradation (Andersson and Gledhill, 2013; Cai et al., 2023). Therefore, a robust understanding of carbonate chemistry is key to properly assessing habitat vulnerability to ocean acidification.

The dynamics of the seawater carbonate system are intricately shaped by the interplay between physicochemical (such as ocean circulation and mixing, and heat, carbon, and freshwater exchanges with the atmosphere) and biological (such as photosynthesis and calcification) factors. The temporal and spatial dimensions of these interactions add further complexity, emphasizing the need for continuous time-series to disentangle these complex interactions, providing a detailed understanding of how the seawater carbonate system responds to evolving environmental conditions (e.g., Doney et al., 2009; Benway et al., 2019). Long-term observations allow for the identification of trends, patterns, and potential tipping points, enabling to distinguish between natural variability and anthropogenic influences. Continuous datasets are, therefore, essential for advancing our knowledge and facilitating informed management strategies in the context of ongoing global environmental changes.

The Mediterranean Sea has been recognized as one of the most prominent climate-change hotspots (Giorgi, 2006) and is considered a “miniature ocean” (Bethoux et al., 1999), serving as a model to anticipate the responses of the global ocean to diverse pressures (Lejeusne et al., 2010). The relatively rapid overturning circulation of the Mediterranean Sea and the high total alkalinity (TA) of its waters lead to a naturally high capacity of the Mediterranean Sea to absorb and buffer anthropogenic CO₂ (Schneider et al., 2010; Lee et al., 2011; Álvarez et al., 2014; Palmiéri et al., 2015). Despite its importance, the seawater carbonate system of the Mediterranean Sea is still poorly quantified, especially in coastal zones (understood here as those within the continental shelf), and long-term time-series are still sparse (Hassoun et al., 2022).

In this context, we present here the seawater carbonate system measurements of two coastal time-series in the NW Mediterranean Sea: L’Estartit Oceanographic Station (EOS; 42.05°N 3.2542°E) and Blanes Bay Microbial Observatory (BBMO; 41.665°N 2.805°E) ( Figure 1 ). At these two coastal time-series, surface sampling for TA, pH, and associated variables, such as dissolved inorganic nutrients (nitrate, NO₃ ^-, silicate, SiO₂, and phosphate, PO₄ ^3-), temperature, and salinity, have been performed monthly since 2010. Using these newly released datasets, we evaluate the seasonality and assess the long-term pH changes in surface waters and explore the physical and chemical drivers causing them. Variations in these drivers are the result of changes in ocean circulation and mixing, biological processes, as well as exchanges of heat, freshwater, and carbon with the atmosphere.

The two presented coastal time-series in the NW Mediterranean Sea, with continuous monthly data, allowed us to unravel the intricate feedbacks inherent to the carbon cycle, despite the considerable fluctuations often exhibited in coastal regions. This study, therefore, highlights the importance of sustaining continuous time-series observations to help us distinguish natural from human-induced changes, such as rising ocean temperatures or deoxygenation (e.g., Benway et al., 2019).

The two studied time-series exhibit similar seasonal dynamics and ocean acidification trends and drivers. Summer stratification (May to October; Guadayol et al., 2009; Aparicio et al., 2017; Zamanillo et al., 2021) leads to decreases in dissolved inorganic nutrients ( Figure 2H ) and DIC ( Figure 2D ), the latter exacerbated by CO₂ outgassing associated with summer warming. Notably, BBMO experiences a more pronounced decrease in DIC and dissolved inorganic nutrients compared to EOS, likely attributable to wind patterns. The occurrence of “Garbins” (south-westerlies) triggers episodic upwelling events that transport deep waters from outside the platform (enriched in DIC and dissolved inorganic nutrients) to the time-series locations (Aparicio et al., 2017). Guadayol and Peters (2006) found that Garbins are more frequent and intense at Roses (nearby EOS) than at Malgrat (nearby BBMO), propitiating a greater impact of upwelling events replenishing the DIC and dissolved inorganic nutrient pools at EOS compared to BBMO. This would explain the more pronounced decrease in DIC and dissolved inorganic nutrients during summer at BBMO compared to EOS. The decrease in DIC observed during summer months would lead to an increase in pH_{T in situ} and a decrease in fCO₂, but the increase in temperature during the stratification season leads to a net decrease in pH_{T in situ} ( Figure 2E ) and a net increase in fCO₂ ( Figure 2G ). Once the summer stratification is over, surface waters are replenished in DIC ( Figure 2D ) and dissolved inorganic nutrients ( Figure 2H ) from deeper layers.

Both time-series present quite homogeneous values of salinity and TA all year round ( Figures 2B, C , 3B, C ), with higher variability in March and December, where extreme precipitation events can sporadically lower salinity to values below 36.5 ( Figure 3B ). These extremes in salinity are not accompanied by extremes in TA ( Figures 3B, C ), most likely because the freshwater endmember in this region may have a relatively high TA content due to the limestone draining in part of the courses of the rivers and groundwaters in the area (Hartmann et al., 2015). This precludes the detection of relationships between TA and salinity, at least with the frequency of sampling of our dataset.

The decreasing trends in sea surface pH_{T in situ} found at BBMO and EOS ( Table 1 ) agree with other published time-series in the Mediterranean Sea. Specifically, the observed pH_{T in situ} trends at EOS are very similar to those reported for the coastal time-series Point B (-0.0028 ± 0.0003 yr^-1 for 2007–2015; Kapsenberg et al., 2017) and the open-ocean time-series DYFAMED (-0.003 ± 0.001 yr^-1 for 1995–2001; Marcellin Yao et al., 2016), both in the NW Mediterranean Sea. Our sea surface pH_{T in situ} trends also agree with trends computed for pH_T at 25 °C in the Mediterranean Sea, such as those reported for the northern Adriatic Sea (-0.0025 yr^-1 for 1983–2008; Luchetta et al., 2010) and the Strait of Gibraltar (-0.0030 ± 0.0003 yr^-1 for 2005–2021; Amaya-Vías et al., 2023). These pH trends reported at a constant temperature would most likely be larger if the trends were assessed at in situ conditions, due to the warming trends observed in the Mediterranean Sea (e.g., Salat et al., 2019) that contribute to ocean acidification (as shown in Kapsenberg et al. (2017)). The sea surface pH_{T in situ} trends reported here also agree with other sea surface pH trends reported in open ocean waters elsewhere (Bates et al., 2014; Lauvset et al., 2015; González-Dávila and Santana-Casiano, 2023), thus indicating that EOS and BBMO time-series may reflect regional as well as global ocean acidification signals despite being coastal sites.

The rapid warming of the Mediterranean Sea contributes to the acidification of its waters, increasing the ocean acidification signal derived from the CO₂ uptake. This is corroborated by our results ( Figure 5 ; Table 2 ), which go in line with those reported for the coastal time-series Point B (Kapsenberg et al., 2017), being changes in DIC and temperature the main drivers of the observed sea surface pH changes at these three coastal time-series in the NW Mediterranean Sea (EOS, BBMO, and Point B). However, the trends in each of the drivers do not completely coincide. While the sea surface temperature trends observed at EOS and BBMO ( Figure 5 ; Table 1 ) agree with those observed at Point B (0.072 ± 0.022 °C yr^-1), the trends in DIC and TA at Point B are twice as high as those observed at our coastal time-series sites. Additionally, there are contrasting trends in sea surface salinity, where Point B shows a non-statistically significant decrease while BBMO and EOS exhibit a statistically-significant increase. Regarding TA, Kapsenberg et al. (2017) remarked that their changes were faster than those attributable to direct effects of seawater CO₂ uptake, and they suggested that this could be related to increasing limestone weathering. Within our dataset, some TA rises could potentially link to increases in salinity (as shown in Table 1 ). This suggests that the observed TA increases at EOS and BBMO might relate to increased evaporation and/or reduced river and precipitation.

The ability to remove seasonal patterns from the datasets depends on how comprehensively the dataset covers the entire seasonal cycle. Essentially, the duration of the time-series needed to identify a human-induced trend depends on the level of natural variability present within the signal. In the case of pH in coastal systems, this requires nearly a decade or longer of data before a trend emerges from the noise (Sutton et al., 2019; Turk et al., 2019). To investigate how representative our observed trends in sea surface pH are at EOS and BBMO, we compared trends with those derived from the OceanSODA-ETHZ product (Gregor and Gruber, 2021), which is an observation-based, global gridded product with monthly surface data for all parameters of the seawater carbonate system at a resolution of 1° × 1° derived from the pCO₂ observations from the Surface Ocean CO₂ ATlas (SOCAT; Bakker et al., 2016) and the TA observations from the Global Ocean Data Analysis Product (GLODAP; Olsen et al., 2016). This product produced rates of pH change in agreement with those reported in time-series around the globe (Ma et al., 2023). The version used here was v2023, which expands from 1982 to 2022. The grids selected for our study were those centered at 42.50°N 3.50°E (for EOS) and 41.50°N 2.50°E (for BBMO). The OceanSODA-ETHZ product reproduces the observed seasonal cycle at our sites, but does not reproduce the seasonal amplitude of our observations ( Supplementary Figure 1 ). The pH_{T in situ} trends from the de-seasoned OceanSODA-ETHZ product are similar for both sites (-0.0020 ± 0.0001 yr^-1 for BBMO and -0.0021 ± 0.0001 yr^-1 for EOS; Supplementary Figures 2A, B black symbols). These pH_{T in situ} trends do not change if we shorten the time period to match the length of our time-series ( Supplementary Figures 2A, B red symbols). The pH_{T in situ} trend from the de-seasoned OceanSODA-ETHZ product agrees with that obtained with the BBMO dataset, while for EOS it is slightly lower. Ma et al. (2023) previously noted that pH trends from OceanSODA-ETHZ cannot reproduce those obtained in short (< 30 years) time-series. However, it was already reported that the stronger ocean acidification trends observed toward the coast are not captured by the synthesis and modeling products (McGovern et al., 2022).

To test the capacity of our EOS dataset to resolve long-term trends, we compared our sea surface temperature trends for 2010–2019 at EOS with those resulting from the EOS sea surface temperature dataset for 1974–2018 (Pascual and Salat, 2019). For 1974–2018, sea surface temperature at EOS increased at a rate of 0.032°C yr^-1 ( Supplementary Figure 2C black symbols), which is the same trend that Salat et al. (2019) reported after de-seasoning the dataset using the Bates et al. (2014) approach. When we use the information from the entire dataset (1974–2018) to de-season the dataset and then compute the sea surface temperature trend for 2010–2018, there is no difference from the trend for 1974–2018 (details not shown). However, if we reduce the period to de-season the dataset to 2010–2018, the trend changes to 0.13 ± 0.03°C yr^-1 ( Supplementary Figure 2C red symbols), which agrees with the trend in sea surface temperature found in this study. This therefore indicates that our studied time period for EOS may be too short to produce robust long-term trends.

We characterized the seasonality and long-term changes of the seawater carbonate system in the surface waters of two coastal time-series in the NW Mediterranean Sea, EOS and BBMO. Despite EOS being more offshore and near a natural reserve and BBMO being close to a harbor area, the two sites present similar seasonal dynamics and ocean acidification trends and drivers, suggesting that these changes are mainly determined by changes in temperature and the interplay between seasonal stratification/mixing that are common to both sites, and not by more local processes that could have differing effects at the two locations.

The observed sea surface pH_{T in situ} trends at BBMO (-0.0021 ± 0.0003 yr^-1; 22/12/2009–02/08/2022) and EOS (-0.0028 ± 0.0005 yr^-1; 22/01/2010–23/08/2019) agree with other ocean acidification trends reported for coastal and open ocean time-series in the Mediterranean Sea and open ocean waters of the global ocean, therefore indicating that these coastal time-series are representative of global ocean acidification signals.

The observed decreases in sea surface pH_{T in situ} are caused by increases in DIC (related to anthropogenic CO₂ uptake) and sea surface temperature. Once accounted for the neutralizing effect of the observed increase in TA (counteracting 60% and 72% of the influence of increasing DIC at EOS and BBMO, respectively), the rapid sea surface warming plays a larger role in the observed pH_{T in situ} decreases (43% at EOS and 62% at BBMO) than the DIC increase (36% at EOS and 33% at BBMO, when the TA effect is removed).

Future climate-related changes, such as rising temperatures or deoxygenation (Gruber, 2011), are expected to complicate the detection of future changes in seawater carbonate chemistry attributable to the atmospheric CO₂ increase. Maintaining time-series of ocean carbon data is therefore crucial for assessing seasonal dynamics, annual budgets, and interannual and climatic variability (Tanhua et al., 2013; Vance et al., 2022), as they provide essential information to disentangle the complex interplay of climate-related physical, chemical, and biological feedbacks within the carbon cycle. Emergent climate trends can generally be identified earlier with more frequent measurements, particularly for systems that are highly variable on shorter timescales, like coastal regions. Despite the considerable spatiotemporal fluctuations in coastal regions, our dataset derived from the two presented coastal time-series, covering monthly data collected over 9.6–12.7 years, already reveals robust trends in ocean acidification comparable to those reported in open ocean time-series, offering valuable insights that allow unravelling the complex interplay of climate-induced physical and chemical feedbacks inherent to the carbon cycle. Although our dataset may not provide a complete overview due to the influence of short-term variability on long-term signals, our study nevertheless emphasizes the importance of long-term time-series for evaluating anthropogenic changes.

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.20350/digitalCSIC/16070.

MIG-I: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing. EG: Data curation, Formal analysis, Writing – review & editing. AL: Data curation, Formal analysis, Writing – review & editing. JP: Project administration, Resources, Writing – review & editing. JG: Funding acquisition, Project administration, Resources, Writing – review & editing. CM: Funding acquisition, Project administration, Resources, Writing – review & editing. EC: Funding acquisition, Project administration, Resources, Writing – review & editing. CP: Funding acquisition, Project administration, Resources, Writing – review & editing.