The five stable noble gases are sensitive unambiguous tracers of glacial meltwater

Author Posting. © American Geophysical Union, 2014. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 41 (2014): 2835–2841, doi:10.1002/2013GL058804.The five inert noble gases—He, Ne, Ar, Kr, and Xe—exhibit a unique dissolved gas saturation pattern resulting from the formation and addition of glacial meltwater to seawater. He and Ne become oversaturated, and Ar, Kr, and Xe become undersaturated to varying percentages. For example, addition of 10‰ glacial meltwater to seawater results in a saturation anomaly of ΔHe = 12.8%, ΔNe = 8.9%, ΔAr = −0.5%, ΔKr = −2.2%, and ΔXe = −3.3%. This pattern in noble gas saturation reflects a unique meltwater signature that is distinct from the other major physical processes that modify the gas concentration and saturation, namely, seasonal changes in temperature at the ocean surface and bubble mediated gas exchange. We use Optimum Multiparameter analysis to illustrate how all five noble gases can help distinguish glacial meltwater from wind-driven bubble injection, making them a potentially valuable suite of tracers for glacial melt and its concentration in the deep waters of the world ocean.We are grateful to the National Science Foundation (OCE825394 and OCE0752980) for support of this research.2014-10-1

Instrument Bias Correction With Machine Learning Algorithms: Application to Field-Portable Mass Spectrometry

In situ sensors for environmental chemistry promise more thorough observations, which are necessary for high confidence predictions in earth systems science. However, these can be a challenge to interpret because the sensors are strongly influenced by temperature, humidity, pressure, or other secondary environmental conditions that are not of direct interest. We present a comparison of two statistical learning methods—a generalized additive model and a long short-term memory neural network model for bias correction of in situ sensor data. We discuss their performance and tradeoffs when the two bias correction methods are applied to data from submersible and shipboard mass spectrometers. Both instruments measure the most abundant gases dissolved in water and can be used to reconstruct biochemical metabolisms, including those that regulate atmospheric carbon dioxide. Both models demonstrate a high degree of skill at correcting for instrument bias using correlated environmental measurements; the difference in their respective performance is less than 1% in terms of root mean squared error. Overall, the long short-term memory bias correction produced an error of 5% for O2 and 8.5% for CO2 when compared against independent membrane DO and laser spectrometer instruments. This represents a predictive accuracy of 92–95% for both gases. It is apparent that the most important factor in a skillful bias correction is the measurement of the secondary environmental conditions that are likely to correlate with the instrument bias. These statistical learning methods are extremely flexible and permit the inclusion of nearly an infinite number of correlates in finding the best bias correction solution

Upper ocean distribution of glacial meltwater in the Amundsen Sea, Antarctica

Pine Island Ice Shelf, in the Amundsen Sea, is losing mass due to increased heat transport by warm ocean water penetrating beneath the ice shelf and causing basal melt. Tracing this warm deep water and the resulting glacial meltwater can identify changes in melt rate and the regions most affected by the increased input of this freshwater. Here, optimum multi‐parameter analysis is used to deduce glacial meltwater fractions from independent water mass characteristics (standard hydrographic observations, noble gases and oxygen isotopes), collected during a ship‐based campaign in the eastern Amundsen Sea in February‐March 2014. Noble gases (neon, argon, krypton and xenon) and oxygen isotopes are used to trace the glacial melt and meteoric water found in seawater and we demonstrate how their signatures can be used to rectify the hydrographic trace of glacial meltwater, which provides a much higher resolution picture. The presence of glacial meltwater is shown to mask the Winter Water properties, resulting in differences between the water mass analyses of up to 4 g kg−1 glacial meltwater content. This discrepancy can be accounted for by redefining the ”pure” Winter Water endpoint in the hydrographic glacial meltwater calculation. The corrected glacial meltwater content values show a persistent signature between 150 ‐ 400 m of the water column across all of the sample locations (up to 535 km from Pine Island Ice Shelf), with increased concentration towards the west along the coastline. It also shows, for the first time, the signature of glacial meltwater flowing off‐shelf in the eastern channel

Sea Ice Formation, Glacial Melt and the Solubility Pump Boundary Conditions in the Ross Sea

Seasonal formation of Dense Shelf Water (DSW) in the Ross Sea is a direct precursor to Antarctic Bottom Water, which fills the deep ocean with atmospheric gases in what composes the southern limb of the solubility pump. Measurements of seawater noble gas concentrations during katabatic wind events in two Ross Sea polynyas reveal the physical processes that determine the boundary value properties for DSW. This decomposition reveals 5–6 g kg−1 of glacial meltwater in DSW and sea-ice production rates of up to 14 m yr−1 within the Terra Nova Bay polynya. Despite winds upwards of 35 m s−1 during the observations, air bubble injection had a minimal contribution to gas exchange, accounting for less than 0.01 μmols kg−1 of argon in seawater. This suggests the slurry of frazil ice and seawater at the polynya surface inhibits air-sea exchange. Most noteworthy is the revelation that sea-ice formation and glacial melt contribute significantly to the ventilation of DSW, restoring 10% of the gas deficit for krypton, 24% for argon, and 131% for neon, while diffusive gas exchange contributes the remainder. These measurements reveal a cryogenic component to the solubility pump and demonstrate that while sea ice blocks air-sea exchange, sea ice formation and glacial melt partially offset this effect via addition of gases. While polynyas are a small surface area, they represent an important ventilation site within the southern-overturning cell, suggesting that ice processes both enhance and hinder the solubility pump

Sea ice biogeochemistry and material transport across the frozen interface

Author Posting. © Oceanography Society, 2011. This article is posted here by permission of Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 24 no. 3 (2011): 202–218, doi:10.5670/oceanog.2011.72.The porous nature of sea ice not only provides a habitat for ice algae but also opens a pathway for exchanges of organic matter, nutrients, and gases with the seawater below and the atmosphere above. These constituents permeate the ice cover through air-ice gas exchange, brine drainage, seawater entrainment into the ice, and air-sea gas exchange within leads and polynyas. The central goal in sea ice biogeochemistry since the 1980s has been to discover the physical, biological, and chemical rates and pathways by which sea ice affects the distribution and storage of biogenic gases (namely CO2, O2, and dimethyl sulfide) between the ocean and the atmosphere. Historically, sea ice held the fascination of scientists for its role in the ocean heat budget, and the resulting view of sea ice as a barrier to heat and mass transport became its canonical representation. However, the recognition that sea ice contains a vibrant community of ice-tolerant organisms and strategic reserves of carbon has brought forward a more nuanced view of the "barrier" as an active participant in polar biogeochemical cycles. In this context, the organisms and their habitat of brine and salt crystals drive material fluxes into and out of the ice, regulated by liquid and gas permeability. Today, scientists who study sea ice are acutely focused on determining the flux pathways of inorganic carbon, particulate organics, climate-active gases, excess carbonate alkalinity, and ultimately, the role of all of these constituents in the climate system. Thomas and Dieckmann (2010) recently reviewed sea ice biogeochemistry, and so we do not attempt a comprehensive review here. Instead, our goal is to provide a historical perspective, along with some recent discoveries and observations to highlight the most outstanding questions and possibly useful avenues for future research

Numerical investigation of the Arctic ice–ocean boundary layer and implications for air–sea gas fluxes

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ocean Science 13 (2017): 61-75, doi:10.5194/os-13-61-2017.In ice-covered regions it is challenging to determine constituent budgets – for heat and momentum, but also for biologically and climatically active gases like carbon dioxide and methane. The harsh environment and relative data scarcity make it difficult to characterize even the physical properties of the ocean surface. Here, we sought to evaluate if numerical model output helps us to better estimate the physical forcing that drives the air–sea gas exchange rate (k) in sea ice zones. We used the budget of radioactive 222Rn in the mixed layer to illustrate the effect that sea ice forcing has on gas budgets and air–sea gas exchange. Appropriate constraint of the 222Rn budget requires estimates of sea ice velocity, concentration, mixed-layer depth, and water velocities, as well as their evolution in time and space along the Lagrangian drift track of a mixed-layer water parcel. We used 36, 9 and 2 km horizontal resolution of regional Massachusetts Institute of Technology general circulation model (MITgcm) configuration with fine vertical spacing to evaluate the capability of the model to reproduce these parameters. We then compared the model results to existing field data including satellite, moorings and ice-tethered profilers. We found that mode sea ice coverage agrees with satellite-derived observation 88 to 98 % of the time when averaged over the Beaufort Gyre, and model sea ice speeds have 82 % correlation with observations. The model demonstrated the capacity to capture the broad trends in the mixed layer, although with a significant bias. Model water velocities showed only 29 % correlation with point-wise in situ data. This correlation remained low in all three model resolution simulations and we argued that is largely due to the quality of the input atmospheric forcing. Overall, we found that even the coarse-resolution model can make a modest contribution to gas exchange parameterization, by resolving the time variation of parameters that drive the 222Rn budget, including rate of mixed-layer change and sea ice forcings.Funding for this research was provided by the NSF Arctic Natural Sciences program through Award # 1203558

Methane-Oxidizing Seawater Microbial Communities from an Arctic Shelf

Marine microbial communities can consume dissolved methane before it can escape to the atmosphere and contribute to global warming. Seawater over the shallow Arctic shelf is characterized by excess methane compared to atmospheric equilibrium. This methane originates in sediment, permafrost, and hydrate. Particularly high concentrations are found beneath sea ice. We studied the structure and methane oxidation potential of the microbial communities from seawater collected close to Utqiagvik, Alaska, in April 2016. The in situ methane concentrations were 16.3 ± 7.2 nmol L−1 , approximately 4.8 times oversaturated relative to atmospheric equilibrium. The group of methaneoxidizing bacteria (MOB) in the natural seawater and incubated seawater was \u3e 97 % dominated by Methylococcales (γ -Proteobacteria). Incubations of seawater under a range of methane concentrations led to loss of diversity in the bacterial community. The abundance of MOB was low with maximal fractions of 2.5 % at 200 times elevated methane concentration, while sequence reads of non-MOB methylotrophs were 4 times more abundant than MOB in most incubations. The abundances of MOB as well as non-MOB methylotroph sequences correlated tightly with the rate constant (kox) for methane oxidation, indicating that non-MOB methylotrophs might be coupled to MOB and involved in community methane oxidation. In sea ice, where methane concentrations of 82 ± 35.8 nmol kg−1 were found, Methylobacterium (α-Proteobacteria) was the dominant MOB with a relative abundance of 80 %. Total MOB abundances were very low in sea ice, with maximal fractions found at the ice– snow interface (0.1 %), while non-MOB methylotrophs were present in abundances similar to natural seawater communities. The dissimilarities in MOB taxa, methane concentrations, and stable isotope ratios between the sea ice and water column point toward different methane dynamics in the two environments

Frazil ice Growth and Production during Katabatic Wind Events in the Ross Sea, Antarctica

Katabatic winds in coastal polynyas expose the ocean to extreme heat loss, causing intense sea ice production and dense water formation around Antarctica throughout autumn and winter. The advancing sea ice pack, combined with high winds and low temperatures, has limited surface ocean observations of polynyas in winter, thereby impeding new insights into the evolution of these ice factories through the dark austral months. Here, we describe oceanic observations during multiple katabatic wind events during May 2017 in the Terra Nova Bay and Ross Sea polynyas. Wind speeds regularly exceeded 20 m s−1, air temperatures were below −25 ∘C, and the oceanic mixed layer extended to 600 m. During these events, conductivity–temperature–depth (CTD) profiles revealed bulges of warm, salty water directly beneath the ocean surface and extending downwards tens of meters. These profiles reflect latent heat and salt release during unconsolidated frazil ice production, driven by atmospheric heat loss, a process that has rarely if ever been observed outside the laboratory. A simple salt budget suggests these anomalies reflect in situ frazil ice concentration that ranges from 13 to 266×10-3 role= presentation style= box-sizing: border-box; display: inline; line-height: normal; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative; \u3e266×10−3 kg m−3. Contemporaneous estimates of vertical mixing reveal rapid convection in these unstable density profiles and mixing lifetimes from 7 to 12 min. The individual estimates of ice production from the salt budget reveal the intensity of short-term ice production, up to 110 cm d−1 during the windiest events, and a seasonal average of 29 cm d−1. We further found that frazil ice production rates covary with wind speed and with location along the upstream–downstream length of the polynya. These measurements reveal that it is possible to indirectly observe and estimate the process of unconsolidated ice production in polynyas by measuring upper-ocean water column profiles. These vigorous ice production rates suggest frazil ice may be an important component in total polynya ice production

A Parameter Model of Gas Exchange for the Seasonal Sea Ice Zone

Carbon budgets for the polar oceans require better constraint on air–sea gas exchange in the sea ice zone (SIZ). Here, we utilize advances in the theory of turbulence, mixing and air–sea flux in the ice–ocean boundary layer (IOBL) to formulate a simple model for gas exchange when the surface ocean is partially covered by sea ice. The gas transfer velocity (k) is related to shear-driven and convection-driven turbulence in the aqueous mass boundary layer, and to the mean-squared wave slope at the air–sea interface. We use the model to estimate k along the drift track of ice-tethered profilers (ITPs) in the Arctic. Individual estimates of daily-averaged k from ITP drifts ranged between 1.1 and 22 m d−1, and the fraction of open water (f) ranged from 0 to 0.83. Converted to area-weighted effective transfer velocities (keff), the minimum value of keff was 10−55 m d−1 near f = 0 with values exceeding keff = 5 m d−1 at f = 0.4. The model indicates that effects from shear and convection in the sea ice zone contribute an additional 40% to the magnitude of keff, beyond what would be predicted from an estimate of keff based solely upon a wind speed parameterization. Although the ultimate scaling relationship for gas exchange in the sea ice zone will require validation in laboratory and field studies, the basic parameter model described here demonstrates that it is feasible to formulate estimates of k based upon properties of the IOBL using data sources that presently exist

Winter seal-based observations reveal glacial meltwater surfacing in the southeastern Amundsen Sea

Funding: This work is funded by the UK Natural Environment Research Council under the iSTAR Programme through grants NE/J005703/1 (K.J.H., D.P.S., B.G.M.W.); European Research Council (under H2020-EU.1.1.) under research grant COMPASS (Climate-relevant Ocean Measurements and Processes on the Antarctic continental Shelf and Slope, grant agreement ID: 741120, K.J.H., Y.Z.); National Science Foundation Division of Polar Programs and Natural Environment Research Council under the research grant TARSAN (Thwaites-Amundsen Regional Survey and Network, NSF PLR 1738992 and NE/S006419/1, K.J.H.).Y.Z. is supported by China Scholarship Council and University of East Anglia. L.C.B. is supported by a Wallenberg Academy Fellowship (WAF 2015.0186) and Swedish Research Council grant (VR2019-04400) of S. Swart.Determining the injection of glacial meltwater into the polar oceans is crucial for quantifying the response of the climate system to ice sheet mass loss. However, meltwater is poorly observed and its pathways poorly known, especially in winter. Here we present winter meltwater distribution in the eastern Amundsen Sea near Pine Island Glacier, revealing a highly variable meltwater distribution with two meltwater-rich layers in the upper 250 m and at around 450 m, connected by scattered meltwater-rich columns. We show that the hydrographic signature of meltwater is clearest in winter, when its presence can be unambiguously mapped throughout the water column. We argue that the buoyant meltwater provides near-surface nutrient that enhances productivity and heat that helps maintain polynyas, close to ice shelves across the Amundsen Sea. Therefore, although the processes determining the distribution of meltwater are challenging, they are important to represent in Earth system models.Publisher PDFPeer reviewe