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The ocean’s chemistry is changing due to the uptake of anthropogenic carbon dioxide (CO 2 ). This phenomenon, commonly referred to as “Ocean Acidification”, is endangering coral reefs and the broader marine ecosystems. In this study, we combine a recent observational seawater CO 2 data product, i.e., the 6 th version of the Surface Ocean CO 2 Atlas (1991–2018, ~23 million observations), with temporal trends at individual locations of the global ocean from a robust Earth System Model to provide a high-resolution regionally varying view of global surface ocean pH and the Revelle Factor. The climatology extends from the pre-Industrial era (1750 C.E.) to the end of this century under historical atmospheric CO 2 concentrations (pre-2005) and the Representative Concentrations Pathways (post-2005) of the Intergovernmental Panel on Climate Change (IPCC)’s 5 th Assessment Report. By linking the modeled pH trends to the observed modern pH distribution, the climatology benefits from recent improvements in both model design and observational data coverage, and is likely to provide improved regional OA trajectories than the model output could alone, therefore, will help guide the regional OA adaptation strategies. We show that air-sea CO 2 disequilibrium is the dominant mode of spatial variability for surface pH, and discuss why pH and calcium carbonate mineral saturation states, two important metrics for OA, show contrasting spatial variability.

Robust detection of anthropogenic climate change is crucial to: (i) improve our understanding of Earth system responses to external forcing, (ii) reduce uncertainty in future climate projections, and (iii) develop efficient mitigation and adaptation plans. Here, we use Earth system model projections to establish the detection timescales of anthropogenic signals in the global ocean through analyzing temperature, salinity, oxygen, and pH evolution from surface to 2000 m depths. For most variables, anthropogenic changes emerge earlier in the interior ocean than at the surface, due to the lower background variability at depth. Acidification is detectable earliest, followed by warming and oxygen changes in the subsurface tropical Atlantic. Temperature and salinity changes in the subsurface tropical and subtropical North Atlantic are shown to be early indicators for a slowdown of the Atlantic Meridional Overturning Circulation. Even under mitigated scenarios, inner ocean anthropogenic signals are projected to emerge within the next few decades. This is because they originate from existing surface changes that are now propagating into the interior. In addition to the tropical Atlantic, our study calls for establishment of long-term interior monitoring systems in the Southern Ocean and North Atlantic in order to elucidate how spatially heterogeneous anthropogenic signals propagate into the interior and impact marine ecosystems and biogeochemistry.

The North Atlantic is one of the major sinks for anthropogenic carbon in the global ocean. Improved understanding of the underlying mechanisms is vital for constraining future projections, which presently have high uncertainties. To identify some of the causes behind this uncertainty, this study investigates the North Atlantic’s anthropogenically altered carbon uptake and inventory, that is, changes in carbon uptake and inventory due to rising atmospheric CO₂ and climate change (abbreviated as C ant * -uptake and C ant * -inventory). Focus is set on an ensemble of 11 Earth system models and their simulations of a future with high atmospheric CO₂. Results show that the model spread in the C ant * -uptake originates in middle and high latitudes. Here, the annual cycle of oceanic pCO₂ reveals inherent model mechanisms that are responsible for different model behavior: while it is SST-dominated for models with a low future C ant * -uptake, it is dominated by deep winter mixing and biological production for models with a high future C ant * -uptake. Models with a high future C ant * -uptake show an efficient carbon sequestration and hence store a large fraction of their contemporary North Atlantic C ant * -inventory below 1000-m depth, while the opposite is true for models with a low future C ant * -uptake. Constraining the model ensemble with observation-based estimates of carbon sequestration and summer oceanic pCO₂ anomalies yields later flattening of the C ant * -uptake than previously estimated. This result highlights the need to depart from the concept of unconstrained model ensembles in order to reduce uncertainties associated with future projections.

We estimated monthly air–sea CO2 fluxes in the Arctic Ocean and its adjacent seas north of 60∘ N from 1997 to 2014. This was done by mapping partial pressure of CO2 in the surface water (pCO2w) using a self-organizing map (SOM) technique incorporating chlorophyll a concentration (Chl a), sea surface temperature, sea surface salinity, sea ice concentration, atmospheric CO2 mixing ratio, and geographical position. We applied new algorithms for extracting Chl a from satellite remote sensing reflectance with close examination of uncertainty of the obtained Chl a values. The overall relationship between pCO2w and Chl a was negative, whereas the relationship varied among seasons and regions. The addition of Chl a as a parameter in the SOM process enabled us to improve the estimate of pCO2w, particularly via better representation of its decline in spring, which resulted from biologically mediated pCO2w reduction. As a result of the inclusion of Chl a, the uncertainty in the CO2 flux estimate was reduced, with a net annual Arctic Ocean CO2 uptake of 180 ± 130 Tg C yr−1. Seasonal to interannual variation in the CO2 influx was also calculated.

The temporal and spatial dynamics of primary and secondary biomass/production in the Barents Sea since the late 1990s are examined using remote sensing data, observations and a coupled physical-biological model. Field observations of mesozooplankton biomass, and chlorophyll a data from transects (different seasons) and large-scale surveys (autumn) were used for validation of the remote sensing products and modeling results. The validation showed that satellite data are well suited to study temporal and spatial dynamics of chlorophyll a in the Barents Sea and that the model is an essential tool for secondary production estimates. Temperature, open water area, chlorophyll a, and zooplankton biomass show large interannual variations in the Barents Sea. The climatic variability is strongest in the northern and eastern parts. The moderate increase in net primary production evident in this study is likely an ecosystem response to changes in climate during the same period. Increased open water area and duration of open water season, which are related to elevated temperatures, appear to be the key drivers of the changes in annual net primary production that has occurred in the northern and eastern areas of this ecosystem. The temporal and spatial variability in zooplankton biomass appears to be controlled largely by predation pressure. In the southeastern Barents Sea, statistically significant linkages were observed between chlorophyll a and zooplankton biomass, as well as between net primary production and fish biomass, indicating bottom-up trophic interactions in this region.

Extending oceanographic data beyond the instrumental period is highly needed to better characterize and understand multi-decadal to centennial natural ocean variability. Here, a stable isotope record at unprecedented temporal resolution (1 to 2 years) from a new marine core retrieved off western North Iceland is presented. We aim to better constrain the variability of subsurface, Atlantic-derived Subpolar Mode Water (SPMW), using near surface-dwelling planktic foraminifera and Arctic Intermediate Water (AIW) mass changes using benthic foraminifera over the last ~165 years. The reconstruction overlaps in time with instrumental observations and a direct comparison reveals that the [delta].sup.18 O record of Neogloboquadrina pachyderma is reliably representing temperature fluctuations in the SPMWs. Trends in the N. pachyderma [delta].sup.13 C record match the measured phosphate concentration in the upper 200 m on the North Icelandic Shelf well. Near surface-dwelling foraminifera trace anthropogenic CO.sub.2 in the Iceland Sea by ~ 1950 ± 8, however, a reduced amplitude shift in the Marine Suess effect is identified. We argue that this is caused by a contemporary ongoing increase in marine primary productivity in the upper ocean due to enhanced Greenland's freshwater discharge that has contributed to a nutrient-driven fertilization since the 1940s/50s (Perner et al., 2019). Multi-decadal variability is detected. We find that the 16-year periodicity evident in SPMW and AIWs based on the [delta].sup.18 O of N. pachyderma and M. barleeanum is a signal of SST anomalies propagated into the Nordic Seas via the Atlantic inflow branches around Iceland. Spectral analyses of the planktic foraminiferal [delta].sup.13 C signal indicate intermittent 30-year cycles that are likely reflecting the ocean response to atmospheric variability, presumably the East Atlantic Pattern. A long-term trend in benthic [delta].sup.18 O suggests that Atlantic-derived waters are expanding their core within the water column from the subsurface into deeper intermediate depths towards the present day. This is a result of increased transport by the North Icelandic Irminger Current to the North Iceland Shelf over the historical era.

The Barents Sea is a highly biologically productive Arctic shelf sea with several commercially important fish stocks. Interannual-to-decadal predictions of its ecosystem would therefore be valuable for marine resource management. Here, we demonstrate that the abundance of phytoplankton, the base of the marine food web, can be predicted up to five years in advance in the Barents Sea with the Norwegian Climate Prediction Model. We identify two different mechanisms giving rise to this predictability; 1) in the southern ice-free Atlantic Domain, skillful prediction is a result of the advection of waters with anomalous nitrate concentrations from the Subpolar North Atlantic; 2) in the northern Polar Domain, phytoplankton predictability is a result of the skillful prediction of the summer ice concentration, which influences the light availability. The skillful prediction of the phytoplankton abundance is an important step forward in the development of numerical ecosystem predictions of the Barents Sea.

We estimate regional long-term surface ocean pCO 2 growth rates using all available underway and bottled biogeochemistry data collected over the past four decades. These observed regional trends are compared with those simulated by five state-of-the-art Earth system models over the historical period. Oceanic pCO 2 growth rates faster than the atmospheric growth rates indicate decreasing atmospheric CO 2 uptake, while ocean pCO 2 growth rates slower than the atmospheric growth rates indicate increasing atmospheric CO 2 uptake. Aside from the western subpolar North Pacific and the subtropical North Atlantic, our analysis indicates that the current observation-based basin-scale trends may be underestimated, indicating that more observations are needed to determine the trends in these regions. Encouragingly, good agreement between the simulated and observed pCO 2 trends is found when the simulated fields are subsampled with the observational coverage. In agreement with observations, we see that the simulated pCO 2 trends are primarily associated with the increase in surface dissolved inorganic carbon (DIC) associated with atmospheric carbon uptake, and in part by warming of the sea surface. Under the RCP8.5 future scenario, DIC continues to be the dominant driver of pCO 2 trends, with little change in the relative contribution of SST. However, the changes in the hydrological cycle play an increasingly important role. For the contemporary (1970-2011) period, the simulated regional pCO 2 trends are lower than the atmospheric growth rate over 90% of the ocean. However, by year 2100 more than 40% of the surface ocean area has a higher oceanic pCO 2 trend than the atmosphere, implying a reduction in the atmospheric CO 2 uptake rate. The fastest pCO 2 growth rates are projected for the subpolar North Atlantic, while the high-latitude Southern Ocean and eastern equatorial Pacific have the weakest growth rates, remaining below the atmospheric pCO 2 growth rate. Our work also highlights the importance and need for a sustained long-term observing strategy to continue monitoring the change in the ocean anthropogenic CO 2 sink and to better understand the potential carbon cycle feedbacks to climate that could arise from it.

Predictions of ocean biogeochemistry, such as primary productivity and CO$_2$ uptake, would help to understand the changing marine environment and the global climate. There is an emerging number of studies where initialization of ocean physics has lead to successful predictions of ocean biogeochemistry. It is, however, unclear how much these predictions could be improved by also assimilating biogeochemical data to reduce uncertainties of the initial conditions. Further, the mechanisms that lead to biogeochemical predictability is poorly understood. Here we perform a suite of idealized twin experiments with an Earth System Model (ESM) with the aim to i) investigate the role of biogeochemical tracer's initial conditions on their predictability, and ii) understand the physical processes that gives rise to, or limits, predictability of ocean carbon uptake and export production. Our results suggest that initialization of the biogeochemical state does not significantly improve interannual to decadal predictions, which we relate to the strong control ocean physics exerts on the biogeochemical variability on these time scales. The predictability of ocean carbon uptake generally agrees well with the predictability of the mixed layer depth (MLD), suggesting that the predictable signal comes from the exchange of dissolved inorganic carbon (DIC) with deep-waters. The longest predictability is found in winter in at high latitudes, as for sea surface temperature and salinity, but the predictability of the MLD and carbon exchange is lower as it is more directly influenced by the atmospheric variability, e.g. the wind. The predictability of the annual mean export production is, on the contrary, nearly non-existing at high latitudes, despite the strong predictive skill for annual mean nutrient concentrations in these regions. This is related to the low predictability of the physical state of the summer surface ocean. Due to the shallow mixed layer it is decoupled from the ocean below and therefore strongly influenced by the chaotic atmosphere. Our results show that future studies need to target the predictability of the mixed layer to get a better understanding of the real-world predictability of ocean biogeochemistry.