Iron is a key micronutrient controlling phytoplankton growth in vast regions of the global ocean. Despite its importance, uncertainties remain high regarding external iron source fluxes and internal cycling on a global scale. In this study, we used a global dissolved iron dataset, including GEOTRACES measurements, to constrain source and scavenging fluxes in the marine iron component of a global ocean biogeochemical model. Our model simulations tested three key uncertainties: source inputs of atmospheric soluble iron deposition (varying from 1.4–3.4 Gmol/yr), reductive sedimentary iron release (14–117 Gmol/yr), and compared a variable ligand parameterization to a constant distribution. In each simulation, scavenging rates were tuned to reproduce the observed global mean iron inventory for consistency. The variable ligand parameterization improved the global model-data misfit the most, suggesting that heterotrophic bacteria are an important source of ligands to the ocean. Model simulations containing high source fluxes of atmospheric soluble iron deposition (3.4 Gmol/yr) and reductive sedimentary iron release (114 Gmol/yr) further improved the model most notably in the surface ocean. High scavenging rates were then required to maintain the iron inventory resulting in relatively short surface and global ocean residence times of 0.83 and 7.5 years, respectively. The model simulates a tight spatial coupling between source inputs and scavenging rates, which may be too strong due to underrepresented ligands near source inputs, contributing to large uncertainties when constraining individual fluxes with dissolved iron concentrations. Model biases remain high and are discussed to help improve global marine iron cycle models.

Waves and water level setup during storms can create overwashing flows across barrier islands. Overwashing flows can cause erosion, barrier breaching, and inlet formation, but their sediments can also be deposited and form washover fans. These widely different outcomes remain difficult to predict. Here we suggest that a breach develops when the sediment volume transported by overwashing flows exceeds the barrier subaerial volume. We form a simple analytical theory that estimates overwashing flows from storm characteristics, barrier morphology, and dune vegetation, and which can be used to assess washover deposition and breaching likelihood. Our theory suggests that barrier width and storm surge height are two important controls on barrier breaching. We test our theory with the hydrodynamic and morphodynamic model Delft3D as well as with field observations of 21 washover fans and 6 breaches that formed during hurricane Sandy. There is reasonable correspondence for natural but not for developed barrier coasts, where traditional sediment transport equations do not readily apply. Our analytical formulations for breach formation and overwash deposition can be used to improve long-term barrier island models.

The paper presents a combined numerical - deep learning (DL) approach for improving wind and wave forecasting. First, a DL model is trained to improve wind velocity forecasts by using past reanalysis data. The improved wind forecasts are used as forcing in a numerical wave forecasting model. This novel approach, used to combine physics-based and data-driven models, was tested over the Mediterranean. It resulted in ∼10% RMSE improvement in both wind velocity and wave height forecasts over operational models. This significant improvement is even more substantial at the Aegean Sea from May to September, when Etesian winds are dominant, improving wave height forecasts by over 35%. The additional computational costs of the DL model are negligible compared to the costs of either numerical models. This work has the potential to greatly improve the wind and wave forecasting models used nowadays by tailoring models to localized seasonal conditions, at negligible additional computational costs.

The effects of instrument noise on estimating the spectral attenuation rates of ocean waves in sea ice are explored using synthetic observations in which the true attenuation rates are known explicitly. The spectral shape of the energy added by noise, relative to the spectral shape of the true wave energy, is the critical aspect of the investigation. A negative bias in attenuation that grows in frequency is found across a range of realistic parameters. This negative bias decreases the observed attenuation rates at high frequencies, such that it can explain the rollover effect commonly reported in field studies of wave attenuation in sea ice. The published results from four field experiments are evaluated in terms of the noise bias, and a spurious rollover (or flattening) of attenuation is found in all cases. Remarkably, the wave heights are unaffected by the noise bias, because the noise bias occurs at frequencies that contain only a small fraction of the total energy.

The advocacy activities necessary to sustain healthy watersheds and improve impaired ones ultimately rely on the democratic process, and therefore depend on a public that values our coastal resources and understands the role that water quality plays in maintaining that value. We contend that an opportunity exists to improve the temporal and spatial density of monitoring by reducing the cost of collecting measurements, while simultaneously fostering an informed and invested public. We envision a distributed water quality monitoring sensor network, composed of low-cost ($1000-$2000) profiling devices we call TideRiders, built and operated by private citizens and local educational organizations and supported by an institution-hosted centralized data and control portal. The TideRider concept engages the public not just in the collection of data but also in the building, deployment, operation, and recovery of these robot sensors. TideRiders will carry a suite of basic water quality instrumentation (temperature, conductivity, and dissolved oxygen), transmit data and accept commands over the cellular network, and can sample surface and bottom waters by surfacing and submerging on a programmable schedule. Operators will harness tidal currents to move their TideRiders deliberately around an embayment, essentially by surfacing in a favorable tide and anchoring on the bottom in an adverse tide. A network of TideRiders deployed in tidally-dominated estuaries like Buzzards Bay and Narragansett Bay could provide basic water quality data at several-hour intervals for weeks at a time by “virtually mooring” in center-bay locations that are otherwise only accessible by boat and therefore typically sampled less frequently than shore stations. We present preliminary field results from a series of prototypes designed and built by students. The prototype devices utilize a novel low-cost semi-passive shallow-water buoyancy engine and were constructed for less than $1000 in parts.

Ocean phytoplankton play a critical role in the global carbon cycle, contributing ~50% of global photosynthesis. As planktonic organisms, phytoplankton encounter significant environmental variability as they are advected throughout the ocean. How this variability impacts phytoplankton growth rates and population dynamics remains unclear. Here, we systematically investigated the impact of different rates and magnitudes of sea surface temperature (SST) variability on phytoplankton community growth rates using surface drifter observations from the Southern Ocean (> 30oS) and a phenotype-based ecosystem model. Short-term SST variability (<7 days) had a minimal impact on phytoplankton community growth rates. Moderate SST changes of 3-5oC over 7-21 days produced a large time lag between the temperature change and the biological response. The impact of SST variability on community growth rates was nonlinear and a function of the rate and magnitude of change. Additionally, the nature of variability generated in a Lagrangian reference frame (following trajectories of surface water parcels) was larger than that within an Eulerian reference frame (fixed point), which initiated different phytoplankton responses between the two reference frames. Finally, we found that these dynamics were not captured by the Eppley growth model commonly used in global biogeochemical models and resulted in an overestimation of community growth rates, particularly in dynamic, strong frontal regions of the Southern Ocean. This work demonstrates that the timescale for environmental selection (community replacement) is a critical factor in determining community composition and takes a first step towards including the impact of variability and biological response times into biogeochemical models.

Astronomical variations in tidal magnitude can strongly modulate the severity of coastal flooding on daily, monthly, and interannual timescales. Here, we present a new quasi-nonstationary skew surge joint probability method (qn-SSJPM) that estimates interannual fluctuations in flood hazard caused by the 18.6 and quasi 4.4-year modulations of tides. We demonstrate that qn-SSJPM-derived storm tide frequency estimates are more precise and stable compared with the standard practice of fitting an extreme value distribution to measured storm tides, which is often biased by the largest few events within the observational period. Applying the qn-SSJPM in the Gulf of Maine, we find significant tidal forcing of winter storm season flood hazard by the 18.6-year nodal cycle, whereas 4.4-year modulations and a secular trend in tides are small compared to interannual variation and long-term trends in sea-level. The nodal cycle forces decadal oscillations in the 1% annual chance storm tide at an average rate of ±13.5 mm/y in Eastport, ME; ±4.0 mm/y in Portland, ME; and ±5.9 mm/y in Boston, MA. Currently (in 2020), nodal forcing is counteracting the sea-level rise-induced increase in flood hazard; however, in 2025, the nodal cycle will reach a minimum and then begin to accelerate flood hazard increase as it moves toward its maximum phase over the subsequent decade. Along the world’s meso-to-macrotidal coastlines, it is therefore critical to consider both sea-level rise and tidal non-stationarity in planning for the transition to chronic flooding that will be driven by sea-level rise in many regions over the next century.

Reducing uncertainty in the global carbon budget requires better quantification of ocean CO2 uptake and its temporal variability. Several methodologies for reconstructing air-sea CO2 exchange from sparse pCO2 observations indicate larger decadal variability than estimated using ocean models. We develop a new application of multiple Large Ensemble Earth system models to assess these reconstructions’ ability to estimate spatiotemporal variability. With our Large Ensemble Testbed, pCO2 fields from 25 ensemble members each of four independent Earth system models are subsampled as the observations and the reconstruction is performed as it would be with real- world observations. The power of a testbed is that the perfect reconstruction is known for each of the 100 original model fields; thus, reconstruction skill can be comprehensively assessed. We find that a commonly used neural-network approach can skillfully reconstruct air-sea CO2 fluxes when and where it is trained with sufficient data. Flux bias is low for the global mean and Northern Hemisphere, but can be regionally high in the Southern Hemisphere. The phase and amplitude of the seasonal cycle are accurately reconstructed outside of the tropics, but longer-term variations are reconstructed with only moderate skill. For Southern Ocean decadal variability, insufficient sampling leads to a 39% [15%:58%, interquartile range] overestimation of amplitude, and phasing is only moderately correlated with known truth (r=0.54 [0.46:0.63]). Globally, the amplitude of decadal variability is overestimated by 21% [3%:34%]. Machine learning, when supplied with sufficient data, can skillfully reconstruct ocean properties. However, data sparsity remains a fundamental limitation to quantification of decadal variability in the ocean carbon sink.

The significant wave height (SWH) is of great importance in industries such as ocean engineering, marine resource development, shipping and transportation. Haiyang-2C (HY-2C), the 2nd operational satellite of China’s marine dynamic exploration series, can provide all-weather, all-day, global observations of wave height, wind, and temperature. The altimeter can only measure the nadir wave height and other information, and the scatterometer can obtain the wind field with a wide swath. In this paper, a deep learning approach is applied to produce a wide swath SWH data through the wind field from the scatterometer and the nadir wave height from altimeter. Two validation sets, 1-month data at 6-minute intervals and 1-day data with an interval of 10 s, are fed into the trained model. Experiments indicate that the extending nadir SWH yields a real-time wide swath grid product along track, which can be offered as support for oceanographic study, and it is superior to take the swell characteristics of ERA5 into account as the input of wide swath SWH model. In conclusion, the verification results demonstrate the effectiveness and feasibility of the wide swath SWH model.

Projecting fluid systems onto coordinates defined by fluid properties (e.g., pressure, temperature, tracer concentration) can reveal deep insights, for example into the thermodynamics and energetics of the ocean and atmosphere. We present a mathematical formalism for fluid flow in such coordinates. We formulate mass conservation, streamfunction, tracer conservation, and tracer angular momentum within fluid property space (q-space) defined by an arbitrary number of continuous fluid properties. Points in geometric position space (x-space) do not generally correspond in a 1-to-1 manner to points in q-space. We therefore formulate q-space as a differentiable manifold, which allows differential and integral calculus but lacks a metric, thus requiring exterior algebra and exterior calculus. The Jacobian, as the ratio of volumes in x-space and q-space, is central to our theory. When x-space is not 1-to-1 with q-space, we define a generalized Jacobian either by patching x-space regions that are 1-to-1 with q-space, or by integrating a Dirac delta to select all x-space points corresponding to a given q value. The latter method discretises to a binning algorithm, providing a practical framework for analysis of fluid motion in arbitrary coordinates. Considering q-space defined by tracers, we show that tracer diffusion and tracer sources drive motion in q-space, analogously to how internal stresses and external forces drive motion in x-space. Just as the classical angular momentum of a body is unaffected by internal stresses, the globally integrated tracer angular momentum is unaffected by tracer diffusion — unless different tracers are diffused differently, as in double diffusion.

This study demonstrates a link between coastal downwelling and tropical cyclone (TC) intensification. We show that coastal downwelling increases air-sea enthalpy (heat, moisture) fluxes ahead of TCs as they approach land, creating conditions conducive to intensification even in the presence of typically inhibiting factors like strong vertical wind shear. The study uses a coupled TC model (HWRF-B) and buoy observations to demonstrate that coastal downwelling developed as three TCs in 2020 approached land. Results show downwelling maintained warmer sea-surface temperatures over the ocean shelf, enhancing air-sea temperature/humidity contrasts. We found that in such cases resulting air-sea enthalpy fluxes can replenish the boundary-layer even when cool, dry air intrudes, as in sheared storms and storms approaching continental land-masses. The resulting warm, moist air is advected into the TC inner core, enhancing convective development, thus providing energy for TC intensification. These results indicate coastal downwelling can be important in forecasting TC intensity change before landfall.

Coastal nitrogen (N) enrichment is a global environmental problem that can influence acidification, deoxygenation, and subsequent habitat loss in ways that can be synergistic with global climate change impacts. In the Southern California Bight, an eastern boundary upwelling system, modeling of wastewater discharged through ocean outfalls has shown that it effectively doubles N loading to urban coastal waters. However, effects of wastewater outfalls on biogeochemical rates of primary production and respiration, key processes through which coastal acidification and deoxygenation are manifested, have not been directly linked to observed trends in ambient chlorophyll a, oxygen and pH. In this paper, we compare observations of nutrient concentrations and forms, as well as rates of biogeochemical cycling, in areas within treated wastewater effluent plumes compared to areas spatially distant from ocean outfalls where we expected minimum influence of the plume. We document that wastewater nutrient inputs have an immediate, local effect on nutrient stoichiometry, elevating ammonium and nitrite concentrations by a mean of 4 µM and 0.2 µM, respectively, increasing dissolved nitrogen: phosphorus ratios by a mean of 7 and slightly increasing chlorophyll a by a mean of 1 µg L-1 in the upper 60 m of the watercolumn, as well as increasing rates of nitrification within the plume by a mean of 17 nmol L-1 day-1 and increasing δ13C and δ15N of suspended particulate matter, an integrated measure of primary production, by a mean of 1.3 ‰ and 1 ‰, respectively. We did not observe a significant near plume effect on δ18O and δ15N of the dissolved nitrate+nitrite, an indicator of nitrate+nitrite assimilation into the biomass, instantaneous rates of primary production and respiration, or dissolved oxygen concentration, suggesting any potential impact from wastewater on these is moderated by other factors, notably mixing of water masses. These results indicate that a “reference-area” approach, wherein stations within or near the zone of initial dilution (ZID) from the wastewater outfall are compared to stations farther afield (reference areas) to assess contaminant impacts, may be insufficient to document regional scale impacts of nutrients.

The core tools of science (data, software, and computers) are undergoing a rapid and historic evolution, changing what questions scientists ask and how they find answers. Earth science data are being transformed into new formats optimized for cloud storage that enable rapid analysis of multi-petabyte datasets. Datasets are moving from archive centers to vast cloud data storage, adjacent to massive server farms. Open source cloud-based data science platforms, accessed through a web-browser window, are enabling advanced, collaborative, interdisciplinary science to be performed wherever scientists can connect to the internet. Specialized software and hardware for machine learning and artificial intelligence (AI/ML) are being integrated into data science platforms, making them more accessible to average scientists. Increasing amounts of data and computational power in the cloud are unlocking new approaches for data-driven discovery. For the first time, it is truly feasible for scientists to bring their analysis to data in the cloud without specialized cloud computing knowledge. This shift in paradigm has the potential to lower the threshold for entry, expand the science community, and increase opportunities for collaboration while promoting scientific innovation, transparency, and reproducibility. Yet, we have all witnessed promising new tools which seem harmless and beneficial at the outset become damaging or limiting. What do we need to consider as this new way of doing science is evolving?

For nearly a century, the functional response curves, which describe how predation rates vary with prey density, have been a mainstay of ecological modelling. While originally derived to describe terrestrial interactions, they have been adopted to characterize aquatic systems in marine biogeochemical, size-spectrum, and population models. However, marine ecological modellers disagree over the qualitative shape of the curve (e.g. Type II vs. III), whether its parameters should be mechanistically or empirically defined (e.g. disk vs. Michaelis-Menten scheme), and the most representative value of those parameters. As a case study, we focus on marine biogeochemical models, providing a comprehensive theoretical, empirical, and numerical road-map for interpreting, formulating, and parameterizing the functional response when used to prescribe zooplankton specific grazing rates on a single prey source. After providing a detailed derivation of each of the canonical functional response types explicitly for aquatic systems, we review the literature describing their parameterization. Empirical estimates of each parameter vary by over three orders of magnitude across 10 orders of magnitude in zooplankton size. However, the strength and direction of the allometric relationship between each parameter and size differs depending on the range of sizes being considered. In models, which must represent the mean state of different functional groups, size spectra or in many cases the entire ocean’s zooplankton population, the range of parameter values is smaller, but still varies by two to three orders of magnitude. Next, we conduct a suite of 0-D NPZ simulations to isolate the sensitivity of phytoplankton population size and stability to the grazing formulation. We find that the disk parameterizations scheme is much less sensitive to it parameterization than the Michaelis-Menten scheme, and quantify the range of parameters over which the Type II response, long known to have destabilizing properties, introduces dynamic instabilities. Finally, we use a simple theoretical model to show how the mean apparent functional response, averaged across sufficient sub-grid scale heterogeneity diverges from the local response. Collectively, we recommend using a type II disk response for models with smaller scales and finer resolutions but suggest that a type III Michaelis-Menten response may do a better job of capturing the complexity of all processes being averaged across in larger scale and coarser resolution modal, not just local consumption and capture rates. While we focus specifically on the grazing formulation in marine biogeochemical models, we believe these recommendations are robust across a much broader range of ecosystem models.

High-resolution underwater glider data collected in the Gulf of Oman (2015-16), combined with reanalysis datasets, describe the spatial and temporal variability of the mixed layer during winter and spring. We assess the effect of surface forcing and submesoscale processes on upper ocean buoyancy and their effects on mixed layer stratification. Episodic strong and dry wind events from the northwest (Shamals) drive rapid latent heat loss events which lead to intraseasonal deepening of the mixed layer. Comparatively, the prevailing southeasterly winds in the region are more humid, and do not lead to significant heat loss, thereby reducing intraseasonal upper ocean variability in stratification. We use this unique dataset to investigate the presence and strength of submesoscale flows, particularly in winter, during deep mixed layers. These submesoscale instabilities act mainly to restratify the upper ocean during winter through mixed layer eddies. The timing of the spring restratification differs by three weeks between 2015 and 2016 and matches the sign change of the net heat flux entering the ocean and the presence of restratifying submesoscale fluxes. These findings describe key high temporal and spatial resolution drivers of upper ocean variability, with downstream effects on phytoplankton bloom dynamics and ventilation of the oxygen minimum zone.

Idealized models can reveal insights into Earth’s climate system by reducing its complexities. However, their potential is undermined by the scarcity of fully coupled idealized models with components comparable to contemporary, comprehensive Earth System Models. To fill this gap, we compare and contrast the climates of two idealized planets which build on the Simpler Models initiative of the Community Earth System Model (CESM). Using the fully coupled CESM, the Aqua configuration is ocean-covered except for two polar land caps, and the Ridge configuration has an additional pole-to-pole grid-cell-wide continent. Contrary to most sea surface temperature profiles assumed for atmosphere-only aquaplanet experiments with the thermal maximum on the equator, the coupled Aqua configuration is characterized by a global cold belt of wind-driven equatorial upwelling, analogous to the eastern Pacific cold tongue. The presence of the meridional boundary on Ridge introduces zonal asymmetry in thermal and circulation features, similar to the contrast between western and eastern Pacific. This zonal asymmetry leads to a distinct climate state from Aqua, cooled by ~2{degree sign}C via the radiative feedback of clouds and water vapor. The meridional boundary of Ridge is also crucial for producing a more Earth-like climate state compared to Aqua, including features of atmospheric and ocean circulation, the seasonal cycle of the Intertropical Convergence Zone, and the meridional heat transport. The mean climates of these two basic configurations provide a baseline for exploring other idealized ocean geometries, and their application for investigating various features and scale interactions in the coupled climate system.

Melt ponds have a strong impact on the Arctic surface energy balance and the ice-associated ecosystem because they transmit more solar radiation compared to bare ice. In the existing literature, melt ponds are considered as bright windows to the ocean, even during freeze-up in autumn. In the central Arctic during the summer-autumn transition in 2018, we encountered a situation where more snow accumulated on refrozen melt ponds compared to the adjacent bare ice, leading to a reduction in light transmittance of the ponds even below that of bare ice. Supporting results from a radiative transfer model suggest that melt ponds with a snow cover >0.04 m lead to lower light transmittance than adjacent bare ice. This scenario has not been described in the literature before, but has potentially strong implications for example on autumn ecosystem activity, oceanic heat budget and thermodynamic ice growth.

Alkalinity, the excess of proton acceptors over donors, plays a major role in ocean chemistry, in buffering and in calcium carbonate precipitation and dissolution. Understanding alkalinity dynamics is pivotal to quantify ocean carbon dioxide uptake during times of global change. Here we review ocean alkalinity and its role in ocean buffering as well as the biogeochemical processes governing alkalinity and pH in the ocean. We show that it is important to distinguish between measurable titration alkalinity and charge-balance alkalinity that is used to quantify calcification and carbonate dissolution and needed to understand the impact of biogeochemical processes on components of the carbon dioxide system. A general treatment of ocean buffering and quantification via sensitivity factors is presented and used to link existing buffer and sensitivity factors. The impact of individual biogeochemical processes on ocean alkalinity and pH is discussed and quantified using these sensitivity factors. Processes governing ocean alkalinity on longer time scales such as carbonate compensation, (reversed) silicate weathering and anaerobic mineralization are discussed and used to derive a close-to-balance ocean alkalinity budget for the modern ocean.

Observations of the spatio-temporal structure of turbulent mixing in a large, energetic strait were used to examine the interactions between wind- and tidally-forced boundary layers in a coastal environment. Te Moana-o-Raukawa (Cook Strait) of Aotearoa (New Zealand) is a relatively wide, energetic strait, known to experience substantial tidal currents and wind stress. A turbulence-enabled ocean glider mission measured O(40,000) turbulence samples that passed QAQC including the use of a vehicle-mounted speed through water sensor. The observations were compared to one-dimensional models of turbulence to understand the mechanisms that regulates the vertical structure of mixing. Tidal flows of O(1 m/s) and wind speeds of O(10 m/s) enhance dissipation to ε=O(10^{-5} W/kg) through boundary drag, shear-driven production of turbulent kinetic energy (P) and to a minor extent buoyancy flux (G). The benthic and wind-driven boundary layers behaved reasonably predictably when considering a 1D perspective. The interaction between the two boundary layers depended on mid-water column stratification which is to a large degree an externally-prescribed condition. Transient stratification can stabilize the mean flow (median Ri_g=0.6(>1/4)) and reduce both turbulence intensity (Re_b) and diapycnal diffusivity (K_z) by up to two orders of magnitude in the middle of the water column, insulating bottom and surface mixing-layers. Mid-water dissipation rate levels tend to be associated with marginal dynamical stability (median Ri_g=0.22(~1/4)) and canonical mixing efficiency (median R_f=0.17), while elevated levels are connected to unstable mean flow conditions (median Ri_g=0.14(<1/4)) and reduced mixing efficiency (median R_f=0.1(<0.17)) that promotes turbulence growth.

We investigate if the commonly neglected riverine detrital carbonate fluxes might balance several chemical mass balances of the global ocean. Particulate inorganic carbon (PIC) concentrations in riverine suspended sediments, i.e., carbon contained by these detrital carbonate minerals, was quantified at the basin and global scale. Our approach is based on globally representative datasets of riverine suspended sediment composition, catchment properties and a two-step regression procedure. The present day global riverine PIC flux is estimated at 3.1 ± 0.3 Tmol C/y (13% of total inorganic carbon export and 4 % of total carbon export), with a flux-weighted mean concentration of 0.26 ± 0.03 wt%. The flux prior to damming was 4.1 ± 0.5 Tmol C/y. PIC fluxes are concentrated in limestone-rich, rather dry and mountainous catchments of large rivers in Arabia, South East Asia and Europe with 2.2 Tmol C/y (67.6 %) discharged between 15 °N and 45 °N. Greenlandic and Antarctic meltwater discharge and ice-rafting additionally contribute 0.8 ± 0.3 Tmol C/y. This amount of detrital carbonate minerals annually discharged into the ocean implies a significant contribution of calcium (~ 4.75 Tmol Ca/y) and alkalinity fluxes (~ 10 Tmol(eq)/y) to marine mass balances and moderate inputs of strontium (~ 5 Gmol Sr/y), based on undisturbed riverine and cryospheric inputs and a dolomite/calcite ratio of 0.1. Magnesium fluxes (~ 0.25 Tmol Mg/y), mostly hosted by less-soluble dolomite, are rather negligible. These unaccounted fluxes help elucidating respective marine mass balances and potentially alter conclusions based on these budgets.

Studies on the molecular mechanisms of microbial adaptation in chaotropic and low water activity (aw) environments are poorly understood. Chaotropic environments are characterized as salt rich, MgCl2 and CaCl2, which lowers the availability of water for biological processes. PATRIC, an integrated genomic browsing tool containing vast libraries of sequenced genomes, can help us identify unique genetic markers in chaophilic and xerophilic microbes. Halophilic microbes are characterized as obligate hypersaline with the ability to tolerate exposure to chaotropic agents. Microbes with the greatest tolerance in these extreme environments must have advanced adaptive methods. Halobacterium salinarum and Haloquadratum walsbyi are chaotolerant and well adapted to low water activity. Haloquadratum walsbyi is unique among the halophilics as having the highest tolerance for chaotropes and its square shape. Performing comparative genomics using fully sequenced halophilic archaea such as Halobacterium salinarum NRC-1, a model halophile, and Haloquadratum walsbyi C23, we were able to identify genes that confer adaptation to chaotropic and low aw environments, as well as individual adaptations that may be responsible for the varying levels of tolerance in chaotropic environments . Characterizing genes associated with chaotolerance and low aw adaptations can help elucidate the cellular functions that make these microbes unique. Chaotropic brines may be used as analogs to study the origin of life and the possibility of suitable environments hosting extremophilic microbes on other planets like the Martian brines and the icy moons of Europa; therefore, studying the microbiome of chaotropic environments are essential in the field of astrobiology.

Poleward ocean heat transport is a key process in the earth system. We detail and review the northward Atlantic Water (AW) flow, Arctic Ocean heat transport, and heat loss to the atmosphere since 1900 in relation to sea ice cover. Our synthesis is largely based on a sea ice-ocean model forced by a reanalysis atmosphere (1900-2018) corroborated by a comprehensive hydrographic database (1950-), AW inflow observations (1996-), and other long-term time series of sea ice extent (1900-), glacier retreat (1984-) and Barents Sea hydrography (1900-). The Arctic Ocean, including the Nordic and Barents Seas, has warmed since the 1970s. This warming is congruent with increased ocean heat transport and sea ice loss and has contributed to the retreat of marine-terminating glaciers on Greenland. Heat loss to the atmosphere is largest in the Nordic Seas (60% of total) with large variability linked to the frequency of Cold Air Outbreaks and cyclones in the region, but there is no long-term statistically significant trend. Heat loss from the Barents Sea (~30%) and Arctic seas farther north (~10%) is overall smaller, but exhibit large positive trends. The AW inflow, total heat loss to the atmosphere, and dense outflow have all increased since 1900. These are consistently related through theoretical scaling, but the AW inflow increase is also wind-driven. The Arctic Ocean CO2 uptake has increased by ~30% over the last century - consistent with Arctic sea ice loss allowing stronger air-sea interaction and is ~8% of the global uptake.

Large Antarctic ice volume changes characterized the middle to Late Oligocene and the first million years of climate evolution during the Miocene. However, the sea surface temperature (SST) evolution over this period remains poorly constrained, as only a few records from contrasting proxies are available. In this study, we present a long-term alkenone-derived SST record from sediments drilled by the Ocean Drilling Program (ODP) at Site 1168 in the west Tasmanian Sea spanning 29.8 Ma to 16.7 Ma. The SST record highlight that the long-term warming in the Late Oligocene linked to the end of the Middle Oligocene Glacial Interval can be recognized also at mid-to-high latitudes of the Southern Hemisphere. Warmer average temperatures (25.5°C) characterize the period from 24.6 to 22 Ma; average temperatures then decrease by 1 to 2°C into the Miocene and stabilize by 20.1 Ma. The reconstructed temperatures are highly variable in the warm Late Oligocene waters, and more stable and slightly colder in the Early to Middle Miocene. We confirm that this temperature trend is not an artefact of the latitudinal drift of the site, as the temperature anomaly relative to the modern water temperature at the paleolocation confirms the SST trends of the Oligocene. This is the first alkenone-derived record to reproduce the long-term Oligocene climate trend previously interpreted from the benthic δ18O, which recorded a warming and/or reduction in ice volume from the Middle Oligocene Glacial Interval through the latest Oligocene.

Marine sedimentary records are a key archive when reconstructing past climate; however, mixing at the seabed (bioturbation) can strongly influence climate records, especially when sedimentation rates are low. By commingling the climate signal from different time periods, bioturbation both smooths climate records, by damping fast climate variations, and creates noise when measurements are made on samples containing small numbers of individual proxy carriers, such as foraminifera. Bioturbation also influences radiocarbon-based age-depth models, as sample ages may not represent the true ages of the sediment layers from which they were picked. While these effects were first described several decades ago, the advent of ultra-small-sample 14C dating now allows samples containing very small numbers of foraminifera to be measured, thus enabling us to directly measure the age-heterogeneity of sediment for the first time. Here, we use radiocarbon dates measured on replicated samples of 3-30 foraminifera to estimate age-heterogeneity for five marine sediment cores with sedimentation rates ranging from 2-30 cm / kyr. From their age-heterogeneities and sedimentation rates we infer mixing depths of 10-20 cm for our core sites. Our results show that when accounting for age-heterogeneity, the true error of radiocarbon dating can be several times larger than the reported measurement. We present estimates of this uncertainty as a function of sedimentation rate and the number of individuals per radiocarbon date. A better understanding of this uncertainty will help us to optimise radiocarbon measurements, construct age models with appropriate uncertainties and better interpret marine paleo records.