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Arsenic is present at nanomolar levels throughout the surface ocean, and microbes assimilate this toxic element due to its similarity to inorganic phosphorus. Although the concentration and characterization of dissolved arsenic has been a focus of ocean studies, the size of the particulate arsenic pool and its partitioning into organic molecules within the microbial community is not known. We measured the particulate pool of arsenic in five surface samples from the open ocean and determined the contribution of arsenic-containing lipids to this pool. Here we show that the accumulation of arsenic into lipids is a widespread phenomenon in the surface ocean. Particulate arsenic concentrations were 15 to 42 pmol L−1 with 7–20% of the particulate arsenic pool in the form of arsenolipids. We characterized these arsenolipids and found that arsenosugar phospholipids dominated the arsenolipid pools in our samples with a minor component of arsenohydrocarbons and other unidentified lipids. A significant portion of the arsenosugar phospholipids (up to 35%) were present as previously undescribed mixed acyl ether lipids, suggesting a bacterial source. Scientific significance statement Marine microbes experience constant exposure to the toxic element arsenic. However, there are no baseline measurements of how much arsenic accumulates in microbial communities nor do we know the full spectrum of arsenic containing biomolecules produced in marine systems. In culture-based studies there is strong evidence that phytoplankton synthesize arsenic-containing lipids, but these lipids have not been observed in natural communities of marine microbes. Here we make measurements of bulk particulate arsenic at five sites in the surface open ocean and show that a significant portion of this particulate arsenic is present as complex arsenic-containing lipids. We characterize this arsenolipid pool chemically and quantitatively to show a variety of chemically distinct and quantitatively significant lipids that expand our understanding of marine arsenic biogeochemistry. Data availability statement Mass spectrometry data and its associated metadata will be available on Dryad with publication. Scripts for data processing and figure generation found on github at https://github.com/kheal/particulate_As_data_analysis.

The carbon isotopic fractionation during photosynthesis (Ɛp) from sedimentary alkenone biomarkers produced by coccolithophores is a widely used proxy for concentrations of past dissolved CO2 in seawater (CO2[aq]). Currently, Ɛp records covering the last 1 Myr exist only for low-latitudes regions, which are characterized by stratified waters and nutrient (NO3− and PO4−) limitation. Higher latitudes are affected by more variable hydrographic conditions which are expected to produce larger changes in factors which are important for Ɛp, such as light, temperature, CO2 and growth rate. Understanding these processes at high latitudes is important in order to derive correct estimates of past CO2 concentrations. Here we present new Ɛp and alkenone-based SST records and a review of previously published paleoceanographic multi-proxy datasets, from sites across a latitudinal transect in the North Atlantic (61°37°N) covering the interval between ∼800 and 400 ka. During this period, the subpolar hydrographic fronts shifted latitudinally, following orbital and sub-orbital climate variability, leading to large zonal and meridional environmental gradients in the North Atlantic. We observe that Ɛp and climate-state relationships (depicted by benthic δ18O) are similar across different latitudes. Ɛp is lower at mid-latitude regions (Sites U1385 and U1313), due to a higher coccolithophore growth rate during colder intervals. In the high-latitude sites, a longitudinal gradient is observed, with higher Ɛp during glacial intervals eastwards (Site 982) compared to interglacials. The opposite is observed on the sites located westwards (Sites U1314 and 984), where Ɛp is higher during interglacials compared to glacials. We suggest that the depressed interglacial Ɛp values at Site 982 is the result of the uninterrupted northward flow of warm Atlantic waters towards the east, which sustained high coccolithophore productivity and growth rates. Combining high-latitude planktonic foraminifera species and calcium carbonate records, we reconstructed the effect of non-CO2 factors on Ɛp., Quaternary Science Reviews, 309, ISSN:0277-3791

Phosphorus (P) is essential for life, but most of the global surface ocean is P depleted, which can limit marine productivity and affect ecosystem structure. Over recent decades, a wealth of new knowledge has revolutionized our understanding of the marine P cycle. With a revised residence time (~10–20 kyr) that is similar to nitrate and a growing awareness that P transformations are under tight and elaborate microbial control, the classic textbook version of a tectonically slow and biogeochemically simple marine P cycle has become outdated. P moves throughout the world’s oceans with a higher level of complexity than has ever been appreciated before, including a vast, yet poorly understood, P redox cycle. Here, we illustrate an oceanographically integral view of marine P by reviewing recent advances in the coupled cycles of P with carbon, nitrogen and metals in marine systems. Through this lens, P takes on a more dynamic and connected role in marine biogeochemistry than previously acknowledged, which points to unclear yet profound potential consequences for marine ecosystems, particularly under anthropogenic influence. Phosphorus plays a dynamic and complex role in marine biogeochemistry, which is closely connected to carbon, nitrogen and metal cycling, according to a literature synthesis on recent advances in understandings of the marine phosphorus cycle.

We developed a simple method to refine existing open-ocean maps and extend them towards different coastal seas. Using a multi-linear regression we produced monthly maps of surface ocean fCO2 in the northern European coastal seas (the North Sea, the Baltic Sea, the Norwegian Coast and the Barents Sea) covering a time period from 1998 to 2016. A comparison with gridded Surface Ocean CO2 Atlas (SOCAT) v5 data revealed mean biases and standard deviations of 0 ± 26 µatm in the North Sea, 0 ± 16 µatm along the Norwegian Coast, 0 ± 19 µatm in the Barents Sea and 2 ± 42 µatm in the Baltic Sea. We used these maps to investigate trends in fCO2, pH and air–sea CO2 flux. The surface ocean fCO2 trends are smaller than the atmospheric trend in most of the studied regions. The only exception to this is the western part of the North Sea, where sea surface fCO2 increases by 2 µatm yr−1, which is similar to the atmospheric trend. The Baltic Sea does not show a significant trend. Here, the variability was much larger than the expected trends. Consistently, the pH trends were smaller than expected for an increase in fCO2 in pace with the rise of atmospheric CO2 levels. The calculated air–sea CO2 fluxes revealed that most regions were net sinks for CO2. Only the southern North Sea and the Baltic Sea emitted CO2 to the atmosphere. Especially in the northern regions the sink strength increased during the studied period.

Oceanic tracers are transported by oceanic motions of all scales, but only the large-scale motions are resolved by the present-day Earth system models. In these models, the unresolved lateral sub-gridscale tracer transport is generally parameterized through diffusive closures with a scale-independent diffusion coefficient. However, evidence from observations and theory suggests that diffusivity varies spatially and is length-scale dependent. Here we provide new scale-dependent quantification of the global surface diffusivities. To this end we use a recently developed statistical inversion method, MicroInverse, to diagnose horizontal surface diffusivities from observed sea surface temperature and idealized model simulation. We compare the results to theoretical estimates of mixing by the large-scale shear and by the sub-gridscale velocity fluctuations. The diagnosed diffusivity magnitude peaks in the tropics and western boundary currents with minima in the subtropical gyres (~3000 and ~100 m2 s−1) at ~40-km scale, respectively. Focusing on the 40–200-km length scale range, we find that the diffusivity magnitude scales with the length scale to a power n that is between 1.22 and 1.54 (90% confidence) in the tropics and also peaks at values above 1 in the boundary currents. In the midlatitudes we find that 0.58 < n < 0.87 (90% confidence). Comparison to the theory suggests that in regions with n > 1 the horizontal mixing is dominated by large-scale shear, whereas in regions where n < 1 the horizontal mixing is due to processes that are small compared to the 40–200-km length scale range considered in this study.

Massive green tides caused by Ulva prolifera have annually occurred in the Yellow Sea since 2007, which has attracted much attention from the government and society. There are associations between the green tides in the Yellow Sea and social response in the social media (i.e., Sina Weibo), which are bidirectional and could be captured by the bidirectional neural network. For instance, how to detect daily U. prolifera green tides by fusing remote sensing data with social media data, and how to use the observed U. prolifera green tides to infer the social response are two challenges of State Oceanic Administration, China. This article first illustrated that there are bidirectional associations between green tides and Sina Weibo data. Then, this article introduced a bidirectional spatio-temporal associative memory neural network (BSAMNN) model for modeling this bidirectional association from the spatio-temporal perspective. BSAMNN first extracted six characteristics from green tides and nine characteristics from the social responses in 2016–2019. Second, these characteristics were split by year, and the characteristics in 2016–2018 were, respectively, put into the bidirectional associative memory neural network (BAM), which is a two-layer artificial neural network. Based on the BAM results and the observed data, the residual network was constructed. Third, BSAMNN extracted the spatio-temporal rules from the characteristics in 2016–2018 as the constraints through the mining rule algorithm and modify the results via sea surface wind and ocean surface current. Last, BSAMNN put the characteristics in 2019 into BAM and used the residual network to modify the results, which was constrained by the spatio-temporal rules. The feasibility and reliability of our approach were demonstrated by using the U. prolifera green tides in 2019. The average accuracy, false alarm rate, and missing alarm rate of BSAMNN results were 0.69, 0.25, and 0.31, respectively, which was 0.11 higher, 0.10 lower, and 0.11 lower than that of the traditional BAM. The results indicated that our method is an effective alternative of linking the U. prolifera green tides and its public sentiments on social media.

Oceans have remained the least well-researched reservoirs of persistent organic pollutants (POPs) globally, due to their vast scale, difficulty of access, and challenging (trace) analysis. Little data on POPs exists along South America and the effect of different currents and river plumes on aqueous concentrations. Research cruise KN210-04 (R/V Knorr) offered a unique opportunity to determine POP gradients in air, water, and their air-water exchange along South America, covering both hemispheres. Compounds of interest included polychlorinated biphenyls (PCBs), organochlorine pesticides (OCPs), polybrominated diphenylethers (PBDEs), and polycyclic aromatic hydrocarbons (PAHs). Remote tropical Atlantic Ocean atmospheric concentrations varied little between both hemispheres; for HCB, BDEs 47 and 99, they were ∼5 pg/m3, PCBs were ∼1 pg/m3, α-HCH was ∼0.2 pg/m3, and phenanthrene and other PAHs were in the low 100s pg/m3. Aqueous concentrations were dominated by PCB 52 (mean 4.1 pg/L), HCB (1.6 pg/L), and β-HCH (1.9 pg/L), with other compounds

In this study, we present the first combined open and coastal ocean pCO2 mapped monthly climatology (Landschützer et al. (2020), doi:10.25921/qb25-f418, https://www.nodc.noaa.gov/ocads/oceans/MPI-ULB-SOM_FFN_clim.html) constructed from observations collected between 1998 and 2015 extracted from the Surface Ocean CO2 Atlas (SOCAT) database. We combine two neural network-based pCO2 products, one from the open ocean and the other from the coastal ocean, and investigate their consistency along their common overlap areas. While the difference between open and coastal ocean estimates along the overlap area increases with latitude, it remains close to 0 μatm globally. Stronger discrepancies, however, exist on the regional level resulting in differences that exceed 10 % of the climatological mean pCO2, or an order of magnitude larger than the uncertainty from state of the art measurements. This also illustrates the potential of such analysis to inform the measurement community about the locations where additional measurements are essential to better represent the aquatic continuum and improve our understanding of the carbon exchange at the air water interface. A regional analysis further shows that the seasonal carbon dynamics at the coast-open interface are well represented in our climatology. While our combined product is only a first step towards a true representation of both the open ocean and the coastal ocean air-sea CO2 flux in marine carbon budgets, we show it is a feasible task and the present data product already constitutes a valuable tool to investigate and quantify the dynamics of the air-sea CO2 exchange consistently for oceanic regions regardless of its distance to the coast.

A range of future climate scenarios are projected for high atmospheric CO2 concentrations, given uncertainties over future human actions as well as potential environmental and climatic feedbacks. The geological record offers an opportunity to understand climate system response to a range of forcings and feedbacks which operate over multiple temporal and spatial scales. Here, we examine a single interglacial during the late Pliocene (KM5c, ca. 3.205±0.01 Ma) when atmospheric CO2 exceeded pre-industrial concentrations, but were similar to today and to the lowest emission scenarios for this century. As orbital forcing and continental configurations were almost identical to today, we are able to focus on equilibrium climate system response to modern and near-future CO2. Using proxy data from 32 sites, we demonstrate that global mean sea-surface temperatures were warmer than pre-industrial values, by ∼2.3 ∘C for the combined proxy data (foraminifera Mg∕Ca and alkenones), or by ∼3.2–3.4 ∘C (alkenones only). Compared to the pre-industrial period, reduced meridional gradients and enhanced warming in the North Atlantic are consistently reconstructed. There is broad agreement between data and models at the global scale, with regional differences reflecting ocean circulation and/or proxy signals. An uneven distribution of proxy data in time and space does, however, add uncertainty to our anomaly calculations. The reconstructed global mean sea-surface temperature anomaly for KM5c is warmer than all but three of the PlioMIP2 model outputs, and the reconstructed North Atlantic data tend to align with the warmest KM5c model values. Our results demonstrate that even under low-CO2 emission scenarios, surface ocean warming may be expected to exceed model projections and will be accentuated in the higher latitudes.

Satellite remote sensing and numerical models are widely used to estimate large-scale variations in ocean carbon export, but the relationship between export efficiency (e-ratio) of sinking organic carbon out of the surface ocean and its drivers remains poorly understood, especially in the Southern Ocean. Here, we assess the effects of temperature and primary productivity on e-ratio by combining particulate organic carbon export flux from in situ measurements during 1997–2013, environmental parameters from satellite products, and outputs from ocean biogeochemical models in the Southern Ocean. Results show that “High Productivity Low E-ratio” (HPLE) is a common phenomenon in the Subantarctic Zone and the Polar Frontal Zone, but not the Antarctic Zone. The e-ratio shows little dependence on temperature below 6 °C. Our results support the hypothesis that the HPLE phenomenon is due to the large contribution of non-sinking organic carbon. Both temperature and ballast minerals play less important roles in controlling e-ratio than ecosystem structure at low temperatures. These findings suggest that non-sinking organic carbon, ecosystem structure, and region-specific parameterizations of e-ratio are key factors to quantify the carbon export in the Southern Ocean.

Surface ocean currents are often of interest in environmental monitoring. These vectorial data can be reasonably treated as a finite realization of a complex- valued random field, where the decomposition in modulus (current speed) and direction (current direction) of the current field is natural. Moreover, when observations are also available for different time points (other than at several locations), it is useful to evaluate the evolution of their complex correlation over time (rather than in space) and the corresponding modeling which is required for estimation purposes. This paper illustrates a first approach where the temporal profile of surface ocean currents is considered. After introducing the fundamental aspects of the complex formalism of a random field indexed in time, a new class of models suitable for including the temporal component is proposed and applied to describe the time-varying complex covariance function of current data. The analysis concerns ocean current observations, taken hourly on 30 April 2016 through high frequency radar systems at some stations located in the Northeastern Caribbean Sea. The selected complex covariance model indexed in time is used for estimation purposes and its reliability is confirmed by a numerical analysis.

Wintertime surface ocean heat loss is the key process driving the formation of Subantarctic Mode Water (SAMW), but there are few direct observations of heat fluxes, particularly during winter. The Ocean Observatories Initiative (OOI) Southern Ocean mooring in the southeast Pacific Ocean and the Southern Ocean Flux Station (SOFS) in the southeast Indian Ocean provide the first concurrent, multiyear time series of air–sea fluxes in the Southern Ocean from two key SAMW formation regions. In this work we compare drivers of wintertime heat loss and SAMW formation by comparing air–sea fluxes and mixed layers at these two mooring locations. A gridded Argo product and the ERA5 reanalysis product provide temporal and spatial context for the mooring observations. Turbulent ocean heat loss is on average 1.5 times larger in the southeast Indian (SOFS) than in the southeast Pacific (OOI), with stronger extreme heat flux events in the southeast Indian leading to larger cumulative winter ocean heat loss. Turbulent heat loss events in the southeast Indian (SOFS) occur in two atmospheric regimes (cold air from the south or dry air circulating via the north), while heat loss events in the southeast Pacific (OOI) occur in a single atmospheric regime (cold air from the south). On interannual time scales, wintertime anomalies in net heat flux and mixed layer depth (MLD) are often correlated at the two sites, particularly when wintertime MLDs are anomalously deep. This relationship is part of a larger basin-scale zonal dipole in heat flux and MLD anomalies present in both the Indian and Pacific basins, associated with anomalous meridional atmospheric circulation.

The Mg/Ca of planktic foraminifera is widely used to determine past surface ocean temperatures but temperature is not the only factor that controls test Mg/Ca. Here we quantify the combined influence of seawater temperature, carbon chemistry, and cation chemistry on Orbulina universa Mg/Ca, based on experimental cultures where these factors were varied independently and simultaneously. We fit a new empirical multi-parameter model that quantifies the effects of each of these variables on the Mg/Ca of O. universa: Mg/Ca O. universa = Mg/Ca sw A · DIC sw B · exp C · C a sw + D · T + E We extend our approach to published Mg/Ca from cultured and sediment trap collected Globigerinoides ruber, and show that a similar equation can be used to describe test Mg/Ca, using [CO32−]sw or pH in place of DIC. The cause of this difference is unknown, but the sensitivity of these two ecophysiologically similar species to different carbon chemistry parameters highlights the uncertainty inherent in applying modern Mg/Ca calibrations to uncalibrated and/or extinct species. In both O. universa and G. ruber, Mg/Casw, [Ca]sw and carbon chemistry (DIC, [CO32−]sw or pH) each modulate the sensitivity of Mg/Ca to temperature. These variables are not constant in the ocean over time-scales relevant to palaeotemperature reconstructions, and must be accounted for when deriving temperature from foraminiferal Mg/Ca. Over time-scales longer than the residence times of Mg and Ca, secular changes in Mg/Casw and [Ca]sw will exert a primary influence on foraminiferal Mg/Ca and its sensitivity to temperature. On shorter timescales, co-variance between temperature and seawater carbon chemistry will influence Mg/Ca-derived temperature estimates. This is particularly relevant to transient hyperthermal events, where the Mg/Ca palaeothermometer is used to evaluate the relationship between atmospheric CO2 and ocean temperature. We explore the potential impact of these effects in an illustrative re-interpretation of Mg/Ca records across the Paleocene-Eocene Thermal Maximum.

Nearshore coastal waters are among the most dynamic regions on the planet and difficult to sample from conventional oceanographic platforms. It has been suggested that environmental sampling of the nearshore could be improved by mobilising vast numbers of citizens who partake in marine recreational sports, like surfing. In this paper, we compared two approaches for measuring sea surface temperature (SST), an Essential Climate Variable, when surfing. One technique involved attaching a commercially-available miniature temperature logger (Onset UTBI-001 TidbiT v2) to the leash of the surfboard (tether connecting surfer and surfboard) and the second, attaching a surfboard fin (Smartfin) that contained an environmental sensor package. Between July 2017 and July 2018, 148 surfing sessions took place, 90 in the southwest UK and 58 in San Diego, California, USA. During these sessions, both Smartfin and leash sensors were deployed simultaneously. On the leash, two TidbiT v2 sensors were attached, one with (denoted LP) and one without (denoted LU) a protective boot, designed to shield the sensor from sunlight. The median temperature from each technique, during each surfing session, was extracted and compared along with independent water temperature data from a nearby pier and benthic logger, and matched with photosynthetically available radiation (PAR) data from satellite observations (used as a proxy for solar radiation during each surf). Results indicate a mean difference ( &delta, ) of 0.13 ∘ C and mean absolute difference ( ϵ ) of 0.14 ∘ C between Smartfin and LU, and a &delta, of 0.04 ∘ C and an ϵ of 0.06 ∘ C between Smartfin and LP. For UK measurements, we observed better agreement between methods ( &delta, = 0 . 07 ∘ C and ϵ = 0 . 08 ∘ C between Smartfin and LU, and &delta, = 0 . 00 ∘ C and ϵ = 0 . 03 ∘ C between Smartfin and LP) when compared with measurements in San Diego ( &delta, = 0 . 22 ∘ C and ϵ = 0 . 23 ∘ C between Smartfin and LU, and &delta, = 0 . 08 ∘ C and ϵ = 0 . 11 ∘ C between Smartfin and LP). Surfing SST data were found to agree well, in general, with independent temperature data from a nearby pier and benthic logger. Differences in SST between leash and Smartfin were found to correlate with PAR, both for the unprotected (LU) and protected (LP) TidbiT v2 sensors, explaining the regional differences in the comparison (PAR generally higher during US surfing sessions than UK sessions). Considering that the Smartfin is sheltered from ambient light by the surfboard, unlike the leash, results indicate the leash TidbiT v2 sensors warm with exposure to sunlight biasing the SST data positively, a result consistent with published tests on similar sensors in shallow waters. We matched all LU data collected prior to this study with satellite PAR products and corrected for solar heating. Results highlight the need to design temperature sensor packages that minimise exposure from solar heating when towed in the surface ocean.

Phytoplankton subsumes the great variety of unicellular photoautotrophs that perform roughly half of Earth’s primary production. They achieve this despite their challenging oceanic habitat, with opposing vertical gradients of nutrients (which often limit their growth near the surface) and light (which becomes limiting with increasing depth). Most phytoplankton species are commonly assumed to be incapable of moving actively between the zones of light and nutrient availability, which are separated vertically by from 30–120 m. Here we propose that a considerable fraction of phytoplankton vertically traverse these gradients over time scales from hours to weeks, employing variations of a common migration strategy to acquire multiple resources. We present a mechanistic Lagrangian model resolving phytoplankton growth linked to optimal migration behaviour and demonstrate unprecedented agreement of its calculated vertical CHL-a distributions with 773 profiles observed at five prominent marine time-series stations. Our simulations reveal that vertically cycling phytoplankton can pump up enough nutrient to sustain as much as half of oceanic Net Primary Production (NPP). Active locomotion is therefore a plausible mechanism enabling relatively high NPP in the oligotrophic surface ocean. Our simulations also predict similar fitness for a variety of very different migration strategies, which helps to explain the puzzling diversity of phytoplankton observed in the ocean.

Over the last decade, advanced statistical inference and machine learning have been used to fill the gaps in sparse surface ocean CO2 measurements (Rödenbeck et al., 2015). The estimates from these methods have been used to constrain seasonal, interannual and decadal variability in sea–air CO2 fluxes and the drivers of these changes (Landschützer et al., 2015, 2016; Gregor et al., 2018). However, it is also becoming clear that these methods are converging towards a common bias and root mean square error (RMSE) boundary: “the wall”, which suggests that pCO2 estimates are now limited by both data gaps and scale-sensitive observations. Here, we analyse this problem by introducing a new gap-filling method, an ensemble average of six machine-learning models (CSIR-ML6 version 2019a, Council for Scientific and Industrial Research – Machine Learning ensemble with Six members), where each model is constructed with a two-step clustering-regression approach. The ensemble average is then statistically compared to well-established methods. The ensemble average, CSIR-ML6, has an RMSE of 17.16 µatm and bias of 0.89 µatm when compared to a test dataset kept separate from training procedures. However, when validating our estimates with independent datasets, we find that our method improves only incrementally on other gap-filling methods. We investigate the differences between the methods to understand the extent of the limitations of gap-filling estimates of pCO2. We show that disagreement between methods in the South Atlantic, southeastern Pacific and parts of the Southern Ocean is too large to interpret the interannual variability with confidence. We conclude that improvements in surface ocean pCO2 estimates will likely be incremental with the optimisation of gap-filling methods by (1) the inclusion of additional clustering and regression variables (e.g. eddy kinetic energy), (2) increasing the sampling resolution and (3) successfully incorporating pCO2 estimates from alternate platforms (e.g. floats, gliders) into existing machine-learning approaches.

We present here a global oceanic compilation of 234Th measurements that collects results from researchers and laboratories over a period exceeding 50 years. The origin of the 234Th sampling in the ocean goes back to 1967, when Bhat et al. (1969) initially studied 234Th distribution relative to its parent 238U in the Indian Ocean. However, it was the seminal work of Buesseler et al. (1992) – in which it was proposed that particulate organic carbon (POC) flux could be calculated from 234Th distributions if the ratio of POC to 234Th measured on sinking particles (POC : 234Th) at the desired depth was known – that drove the extensive use of the 234Th-238U radioactive pair to evaluate the efficiency with which photosynthetically fixed carbon is exported from surface ocean by means of the biological pump. Since then, a large number of 234Th depth profiles have been collected using a variety of sampling instruments and strategies that have changed the past 50 years. The present compilation is made of a total 223 datasets: 214 from studies published either in articles in referred journals, PhD thesis or repositories, and 9 unpublished datasets. The data were compiled from over 5000 locations spanning all the oceans for total 234Th profiles, dissolved and particulate 234Th concentrations, and POC : 234Th ratios (both sediment traps and filtration methods that include two sizes classes; 1–53 µm and

International audience; The future wide-swath satellite altimeters, such as the upcoming Surface Water Ocean Topography (SWOT) mission, will provide instantaneous 2D measurements of sea level down to the spatial scale of O(10 km) for the first time. However, the validity of the geostrophic assumption for estimating surface currents from these instantaneous maps is not known a priori. In this study, we quantify the accuracy of geostrophy for the estimation of surface currents from a knowledge of instantaneous sea level using the hourly snapshots from a tide- and eddy-resolving global numerical simulation. Geostrophic balance is found to be the leading-order balance in frontal regions characterized by large kinetic energy, such as the western boundary currents and the Antarctic Circumpolar Current. Everywhere else, geostrophic approximation ceases to be a useful predictor of ocean velocity, which may result in significant high-frequency contamination of geostrophically computed velocities by fast variability (e.g., inertial and higher). As expected, the validity of geostrophy is shown to improve at low frequencies (typically urn:x-wiley:21699275:media:jgrc24720:jgrc24720-math-0001 0.5 cpd). Global estimates of the horizontal momentum budget reveal that the tropical and mid-latitude regions where geostrophic balance fails are dominated by fast variability and turbulent stress divergence terms rather than higher-order geostrophic terms. These findings indicate that the estimation of velocity from geostrophy applied on SWOT instantaneous sea level maps may be challenging away from energetic areas.

Various machine learning methods were attempted in the global mapping of surface ocean partial pressure of CO2 (pCO2) to reduce the uncertainty of global ocean CO2 sink estimate due to undersampling of pCO2. In previous researches the predicators of pCO2 were usually selected empirically based on theoretic drivers of surface ocean pCO2 and same combination of predictors were applied in all areas unless lack of coverage. However, the differences between the drivers of surface ocean pCO2 in different regions were not considered. In this work, we combined the stepwise regression algorithm and a Feed Forward Neural Network (FFNN) to selected predicators of pCO2 based on mean absolute error in each of the 11 biogeochemical provinces defined by Self-Organizing Map (SOM) method. Based on the predicators selected, a monthly global 1° × 1° surface ocean pCO2 product from January 1992 to August 2019 was constructed. Validation of different combination of predicators based on the SOCAT dataset version 2020 and independent observations from time series stations was carried out. The prediction of pCO2 based on region-specific predicators selected by the stepwise FFNN algorithm were more precise than that based on predicators from previous researches. Appling of a FFNN size improving algorithm in each province decreased the mean absolute error (MAE) of global estimate to 11.32 μatm and the root mean square error (RMSE) to 17.99 μatm. The script file of the stepwise FFNN algorithm and pCO2 product are distributed through the Institute of Oceanology of the Chinese Academy of Sciences Marine Science Data Center (IOCAS; http://dx.doi.org/10.12157/iocas.2021.0022, Zhong et al., 2021).

Oceans have absorbed approximately 30% of anthropogenic CO₂ emissions, causing a phenomenon known as ‘ocean acidification’. With surface ocean pH changing at a rapid pace, continued uptake of CO₂ is expected to decrease ocean pH by 0.3 pH units as early as 2081, accompanied by a decrease in the saturation of calcium carbonate minerals needed to produce skeletons and shells (RCP 8.5 scenario, IPCC 2019). Natural marine CO₂ vent systems provide ocean acidification proxies, offering a glimpse into what a future ocean may look like.

Marine ecosystems are experiencing unprecedented warming and acidification caused by anthropogenic carbon dioxide. For the global sea surface, we quantified the degree that present climates are disappearing and novel climates (without recent analogs) are emerging, spanning from 1800 through different emission scenarios to 2100. We quantified the sea surface environment based on model estimates of carbonate chemistry and temperature. Between 1800 and 2000, no gridpoints on the ocean surface were estimated to have experienced an extreme degree of global disappearance or novelty. In other words, the majority of environmental shifts since 1800 were not novel, which is consistent with evidence that marine species have been able to track shifting environments via dispersal. However, between 2000 and 2100 under Representative Concentrations Pathway (RCP) 4.5 and 8.5 projections, 10–82% of the surface ocean is estimated to experience an extreme degree of global novelty. Additionally, 35–95% of the surface ocean is estimated to experience an extreme degree of global disappearance. These upward estimates of climate novelty and disappearance are larger than those predicted for terrestrial systems. Without mitigation, many species will face rapidly disappearing or novel climates that cannot be outpaced by dispersal and may require evolutionary adaptation to keep pace.

Dissolved organic phosphorus (DOP) has a dual role in the surface ocean as both a product of primary production and as an organic nutrient fueling primary production and nitrogen fixation, especially in oligotrophic gyres. Though poorly constrained, understanding the geographic distribution and environmental controls of surface ocean DOP concentration is critical to estimating distributions and rates of primary production and nitrogen fixation in the global ocean. Here we pair DOP concentration measurements with a metric of phosphate (PO43−) stress (P*), and satellite-based chlorophyll a concentrations and iron stress estimates to explore their relationship with upper 50 m DOP stocks. Our results show that PO43− and iron stress work together to control surface DOP concentrations at basin scales. Specifically, upper 50 m DOP stocks decrease with increasing phosphate stress, while alleviated iron stress leads to either surface DOP accumulation or loss depending on PO43− availability. Our work suggests an interdependence between DOP concentration, inorganic nutrient ratios, and iron availability, and establishes a predictive framework for DOP distributions in the global surface ocean.

To derive an optimal observation system for surface ocean pCO2 in the Atlantic Ocean and the Atlantic sector of the Southern Ocean eleven Observation System Simulation Experiments (OSSEs) were completed. Each OSSE is a Feed-Forward Neural Network (FFNN) that is based on a different data distribution and provides ocean surface pCO2 for the period 2008–2010 with a 5 day time interval. Based on the geographical and time positions from three observational platforms, volunteering observing ships (VOS), Argo floats and OceanSITES moorings, pseudo-observations were constructed using the outputs from an online-coupled physical-biogeochemical global ocean model with 0.25° nominal resolution. The aim of this work was to find an optimal spatial distribution of observations to supplement the widely used Surface Ocean CO2 Atlas (SOCAT) and to improve the accuracy of ocean surface pCO2 reconstructions. OSSEs showed that the additional data from mooring stations and an improved coverage of the Southern Hemisphere with biogeochemical ARGO floats corresponding to least 25 % of the density of active floats (2008–2010) (OSSE 10) would significantly improve the pCO2 reconstruction and reduce the bias of derived estimates of sea-air CO2 fluxes by 74 % compared to ocean model outputs.

Sea surface salinity is retrieved for more than 11 years from the Soil Moisture and Ocean Salinity (SMOS) satellite mission. This data set provides a unique monitoring of the Sea Surface Salinity (SSS) spatio-temporal variability at global scale. It is particularly useful to follow the surface ocean pathway of fresh river plumes water as illustrated here in the Bay of Bengal. A revised adjustment of the whole SMOS SSS time series (CATDS Expertise Center version 5, 2010–2020) leads to clear reduction of local biases in very variable regions and in very noisy regions. The robust std difference between SMOS CEC v5 (18-day, ~70km SSS) and Argo in situ SSS is 0.17 in regions warmer than 5°C. We will discuss how future CATDS products will be improved in view of two ongoing reprocessings, the CATDS L1/L2 v7 reprocessing and the ESA CCI+SSS L2 SMOS reprocessing.

The abstract is available here: https://uscholar.univie.ac.at/o:1083742

The distribution of dissolved Mn (dMn) was studied in the Tropical and Equatorial Pacific along a meridional transect beginning at station ALOHA (22.45°N, 158°W) extending southwards to 7°S along 158°W. Results show that in the surface ocean ( 6000 km) from the continents, which suggests that dMn in the deep open ocean is strongly affected by remineralization. The dMn:AOU linear relationship (n = 37, R = 0.87) that we found in this work is consistent with culture studies and can be applied to Mn models to reproduce the observed open-ocean Mn concentrations in deep waters.

The development of coupled atmosphere-ocean prediction systems with utility on the short-range Numerical Weather Prediction (NWP) and ocean forecasting timescales has accelerated over the last decade. This builds on a body of evidence showing the benefit, particularly for weather forecasting, of more correctly representing the feedbacks between surface ocean and atmosphere. It prepares the way for more unified prediction systems with the capability of providing consistent surface meteorology, wave and surface ocean products to users for whom this is important. Here we describe a coupled ocean-atmosphere system, with weakly coupled data assimilation, which was operationalised at the Met Office as part of the Copernicus Marine Environment Service (CMEMS). We compare the ocean performance to that of an equivalent ocean-only system run at the Met Office, and other CMEMS products. Sea surface temperatures in particular are shown to verify better than in the ocean-only systems, although other aspects including temperature profiles and surface currents are slightly degraded. We then discuss the plans to improve the current system in future as part of the development of a coupled NWP system at the Met Office.

19 pages, 7 tables, 11 figures.-- Open access, Global climatologies of the seawater CO2 chemistry variables are necessary to assess the marine carbon cycle in depth. The climatologies should adequately capture seasonal variability to properly address ocean acidification and similar issues related to the carbon cycle. Total alkalinity (AT) is one variable of the seawater CO2 chemistry system involved in ocean acidification and frequently measured.We used the Global Ocean Data Analysis Project version 2.2019 (GLODAPv2) to extract relationships among the drivers of the AT variability and AT concentration using a neural network (NNGv2) to generate a monthly climatology. The GLODAPv2 qualitycontrolled dataset used was modeled by the NNGv2 with a root-mean-squared error (RMSE) of 5.3 μmol kg1. Validation tests with independent datasets revealed the good generalization of the network. Data from five ocean time-series stations showed an acceptable RMSE range of 3–6.2 μmol kg1. Successful modeling of the monthly AT variability in the time series suggests that the NNGv2 is a good candidate to generate a monthly climatology. The climatological fields of AT were obtained passing through the NNGv2 the World Ocean Atlas 2013 (WOA13) monthly climatologies of temperature, salinity, and oxygen and the computed climatologies of nutrients from the previous ones with a neural network. The spatiotemporal resolution is set by WOA13: 1 1 in the horizontal, 102 depth levels (0–5500 m) in the vertical and monthly (0–1500 m) to annual (1550–5500 m) temporal resolution. The product is distributed through the data repository of the Spanish National Research Council (CSIC; https://doi.org/10.20350/digitalCSIC/8644, Broullón et al., 2019), This research has been supported by the H2020 Food (AtlantOS, grant no. 633211); the Ministerio de Educación, Cultura y Deporte (grant no. FPU15/06026); the Ministerio de Economía y Competitividad, Consejo Superior de Investigaciones Científicas (grant no. CTM2016-76146-C3-1-R); and the Ministerio de Economía y Competitividad, Salvador de Madariaga (grant no. PRX18/00312)

Previous work has not led to a clear understanding of the causes of spatial pattern in global surface ocean dissolved inorganic carbon (DIC), which generally increases polewards. Here, we revisit this question by investigating the drivers of observed latitudinal gradients in surface salinity-normalized DIC (nDIC) using the Global Ocean Data Analysis Project version 2 (GLODAPv2) database. We used the database to test three different hypotheses for the driver producing the observed increase in surface nDIC from low to high latitudes. These are (1) sea surface temperature, through its effect on the CO2 system equilibrium constants, (2) salinity-related total alkalinity (TA), and (3) high-latitude upwelling of DIC- and TA-rich deep waters. We find that temperature and upwelling are the two major drivers. TA effects generally oppose the observed gradient, except where higher values are introduced in upwelled waters. Temperature-driven effects explain the majority of the surface nDIC latitudinal gradient (182 of the 223 µmol kg−1 increase from the tropics to the high-latitude Southern Ocean). Upwelling, which has not previously been considered as a major driver, additionally drives a substantial latitudinal gradient. Its immediate impact, prior to any induced air–sea CO2 exchange, is to raise Southern Ocean nDIC by 220 µmol kg−1 above the average low-latitude value. However, this immediate effect is transitory. The long-term impact of upwelling (brought about by increasing TA), which would persist even if gas exchange were to return the surface ocean to the same CO2 as without upwelling, is to increase nDIC by 74 µmol kg−1 above the low-latitude average.

Knowledge of the surface ocean dynamics and the underlying controlling mechanisms is critical to understand the natural variability of the ocean and to predict its future response to climate change. In this paper, we highlight the potential use of Volunteer Observing Ship (VOS), as carrier for automatic underway measuring system and as platform for sample collection, to enhance the observing capacity for the surface ocean. We review the concept, history, present status and future development of the VOS-based in situ surface ocean observation. The successes of various VOS projects demonstrate that, along with the rapid advancing sensor techniques, VOS is able to improve the temporal resolution and spatial coverage of the surface ocean observation in a highly cost-effective manner. A sustained and efficient marine monitoring system in the future should integrate the advantages of various observing platforms including VOS.