The Amazon River has the largest volume on earth, making up 15–20% of the annual fluvial discharge into oceans. The neighboring Pará River mixes with the Amazon River waters in the Amazon Estuary before forming a plume that extends into the Atlantic. Despite the global importance of these rivers, dissolved trace metal fluxes from this estuary remain unknown. Here we present data for dissolved (<0.2 µm) trace metals (Al, Mn, Fe, Co, Ni, Cu, Zn, Cd, Pb and U) in the Amazon Estuary during the high discharge season (April–May 2018). We observed distinct trace metal signatures for the Amazon and Pará Rivers, reflecting different catchment areas. Concentrations of the particle-reactive elements (Mn, Fe and Pb) decreased rapidly at low-salinity (S≤2), resulting in the highest estuarine removal (86–94% in the Amazon; 61–70% for the Pará). Co, Ni and Cu removal was comparatively low in both river transects (6–39%), while Cd was the only element with a consistent net input. Chemical fluxes were estimated using (a) endmember concentrations and estuarine removal and (b) combining trace element concentrations with 228Ra fluxes. Relative to global total river fluxes, the Amazon and Pará Rivers combined contribute 21% of dissolved Cu and 18% of dissolved Ni during the high discharge season, but account for comparatively low fractions of Mn, Fe, Co and Zn. These data quantify, for the first time, the trace metal output from the world’s largest and 5th largest river into the Atlantic Ocean, filling a critical gap in knowledge of this globally-important region.

A supermodel connects different models interactively so that their systematic errors compensate and achieve a model with superior performance. It differs from the standard non-interactive multi-model ensembles (NI), which combines model outputs a-posteriori. We formulate the first supermodel framework for Earth System Models (ESMs) and use data assimilation to synchronise models. The ocean of three ESMs is synchronised every month by assimilating pseudo sea surface temperature (SST) observations generated from them. Discrepancies in grid and resolution are handled by constructing the synthetic pseudo-observations on a common grid. We compare the performance of two supermodel approaches to that of the NI for 1980—2006. In the first (EW), the models are connected to the equal-weight multi-model mean, while in the second (SINGLE), they are connected to a single model. Both versions achieve synchronisation in locations where the ocean drives the climate variability. The time variability of the supermodel multi-model mean SST is reduced compared to the observed variability; most where synchronisation is not achieved and is bounded by NI. The damping is larger in EW than in SINGLE because EW yields additional damping of the variability in the individual models. Hence, under partial synchronisation, the part of variability that is not synchronised gets damped in the multi-model average pseudo-observations, causing a deflation during the assimilation. The SST bias in individual models of EW is reduced compared to that of NI, and so is its multi-model mean in the synchronised regions. The performance of a trained supermodel remains to be tested.

Interaction between ocean waves and sea ice may play an important role in sea ice retreat in the Arctic. However, it is difficult to quantify the change of ocean waves propagating in ice as nearly no available measurements. Although SAR has shown the capability of imaging ocean waves in ice-covered areas, there are few attempts to retrieve two-dimensional ocean wave spectra (OWS) by SAR. In this study, we applied the previously developed nonlinear inversion scheme, i.e., the MPI scheme, to retrieve OWS by the Sentinel-1 SAR data acquired in the Barents Sea, where waves penetrate deeply in ice. We compared the retrieved spectra by different combinations of modulation transfer functions (MTFs) used in the MPI scheme, i.e., the same MTFs as those used in retrievals in open water, neglecting both the hydrodynamic and tilt modulations in the MTFs, and neglecting the hydrodynamic modulation but remaining the tilt modulation (a new one fitted in this study for HH-polarized SAR data over ice) in the MTFs. As no in situ measurements (e.g., by directional buoys) available, we compared the simulated SAR image spectra based on the retrievals with the observational SAR image spectra to quantify their respective performances. The comparisons suggest that neglecting hydrodynamic modulation can significantly improve the retrievals. Remaining tilt modulation can further improve the retrievals, particularly for range-travelling waves. The study enhances the understanding of principles of SAR imaging waves in ice and provides basics for retrievals of ocean wave spectra by SAR data in ice-covered areas.

We argue that ocean general circulation models and observations based on Ekman or geostrophic balance provide estimates of the Lagrangian-mean ocean velocity field averaged over surface waves — the total time-averaged velocity that advects oceanic tracers, particles, and water parcels. This interpretation contradicts an assumption often made in ocean transport studies that numerical models and observations based on dynamical balances estimate the Eulerian-mean velocity — the velocity time-averaged at a fixed position and only _part_ of the total ocean velocity. Our argument uses the similarity between the wave-averaged Lagrangian-mean momentum equations appropriate at large oceanic scales, and the momentum equations solved by “wave-agnostic” general circulation models that neglect surface wave effects. We further our case by comparing a realistic, global, “wave-agnostic” general circulation ocean model to a wave-averaged Lagrangian-mean general circulation ocean model at eddy-permitting 1/4-degree resolution, and find that the wave-agnostic velocity field is almost identical to the wave-averaged Lagrangian-mean velocity.

The water mass assembly of Nares Strait is variable, owing to fluctuating wind forcings over the Arctic Basins, and irregular northward flows from the West Greenland Current (WGC) in Baffin Bay. Here we characterize the physico-chemical properties of the water masses entering Nares Strait in August 2014, and we employ an extended optimum multi-parameter (OMP) water mass analysis to estimate the mixing fractions of predefined source water masses, and to distinguish the role of physical and biological processes in governing the distribution of dissolved inorganic carbon (DIC) in Nares Strait. We show the first documented evidence of Siberian shelf waters in Nares Strait, along with a diluted upper halocline layer of partial Pacific-origin. These mixed-origin water masses appear to play an important role in driving a modest phytoplankton bloom in Kane Basin, leading to decreased surface pCO2 concentrations in Nares Strait. Although inorganic nitrogen was already limited in the surface mixed layer in northern Nares Strait, the unique properties of mixed Atlantic-Pacific water facilitated upwelling and nutrient supply to the surface. These observations suggest that the positioning of the Transpolar Drift, and hence the balance of Atlantic and Pacific water delivered to Nares Strait, is likely to play an important role in regional biological productivity and carbon uptake from the atmosphere. We also observed water masses from the WGC transported as far north as Kane Basin, contributing to relatively high pCO2 and low pH in the intermediate and deep water column of southern Nares Strait and northern Baffin Bay.

The Late Miocene Biogenic Bloom (LMBB) is a late Miocene to early Pliocene oceanographic event characterized by high accumulation rates of opal from diatoms and calcite from calcareous nannofossils and planktic foraminifera. This multi-million year event has been recognized in sediment cores from the Pacific, Atlantic and Indian Oceans. The numerous studies discussing the LMBB lead us to believe that this event is omnipresent in all oceans, although this hypothesis need to be tested. Moreover, the origin of this event is still widely discussed. In this study we aim to provide a comprehensive overview of the geographical and temporal aspects of the LMBB by compiling published ocean drilling (DSDP, ODP and IODP) records of sedimentation rates, CaCO\textsubscript{3} and opal and terrigenous accumulation rates that cover the late Miocene and early Pliocene interval. Our data compilation shows that traces of the LMBB are present in many different locations but in a very heterogeneous way, highlighting that the LMBB is not a pervasive event. The compilation in addition shows that the sites where the LMBB is recorded are mainly located in areas with a high productivity regime (i.e. upwelling systems). We suggest that the most likely hypothesis to explain the LMBB is a global increase in upwelling intensity due to an increase in wind strength or an increase in deep water formation, ramping up global thermohaline circulation.

Protists represent the majority of the eukaryotic diversity in the oceans. They have different functions in the marine food web, playing essential roles in the biogeochemical cycles. Meanwhile the available data is rich in horizontal and temporal coverage, little is known on their vertical structuring, particularly below the photic zone. The present study applies DNA metabarcoding to samples collected over three years in conjunction with the BATS time-series to assess marine protist communities in the epipelagic and mesopelagic zones. The protist community showed a dynamic seasonality in the epipelagic, responding to hydrographic yearly cycles. Mixotrophic lineages dominated throughout the year; however, autotrophs bloomed during the rapid transition between the winter mixing and the stratified summer, and heterotrophs had their peak at the end of summer, when the base of the thermocline reaches its deepest depth. Below the photic zone, the community, dominated by Rhizaria, is depth-stratified and relatively constant throughout the year, mirroring local hydrographic and biological features such as the oxygen minimum zone. The results suggest a dynamic partitioning of the water column, where the niche vertical position for each community changes throughout the year, likely depending on nutrient availability, the mixed layer depth, and other hydrographic features. Finally, the protist community closely followed mesoscale events (eddies), where the communities mirrored the hydrographic uplift, raising the deeper communities for hundreds of meters, and compressing the communities above.

This manuscript reports on a robot called Clio that we developed to facilitate basin-scale studies of ocean microbial communities and their biochemistry, to better understand how marine microorganisms regulate ocean and Earth system environmental cycles. Clio is designed to facilitate global-scale studies of ocean biochemistry, to move vertically through the water column with high precision, and specifically to return sensor data and samples from large swaths of the ocean ranging in depths from the surface to 6,000 m. Clio is capable of flexible, precise vertical motion that few other ocean robots can perform, and none to our knowledge over this depth range. We tested Clio extensively over several years, six cruises, and 26 dives, it is now fully operational and this manuscript describes all that we did to convince ourselves this was so. In June 2019, it completed its first large-scale ocean survey, and for which this manuscript will be the first data presentation.