Search for: All records

Dissolved gas concentrations in surface waters can have sharp gradients across marine and freshwater environments, which often prove challenging to capture with analytical measurement. Collecting discrete samples for laboratory analysis provides accurate results, but suffers from poor spatial resolution. To overcome this limitation, water equilibrators and gas membrane contactors (GMCs) have been used for the automated underway measurement of dissolved gas concentrations in surface water. However, while water equilibrators can provide continuous measurements, their analytical response times to changes in surface water concentration can be slow, lasting tens of minutes. This leads to spatial imprecisions in the dissolved gas concentration data. Conversely, while GMCs have proven to have much faster analytical response times, often lasting only a few minutes or less, they suffer from poor accuracy and thus require routine calibration. Here we present an analytical system for the high accuracy and high precision spatial mapping of dissolved methane concentration in surface waters. The system integrates a GMC with a cavity ringdown spectrometer for fast analytical response times, with a calibration method involving two Weiss‐style equilibrators and discrete measurements in vials. Data from both the GMC and equilibrators are collected simultaneously, with discrete vial samples collected periodically throughout data collection. We also present a mathematical algorithm integrating all data collected for the routine calibration of the GMC dataset. The algorithm facilitates comparison between the GMC and equilibrator datasets despite the substantial differences in response times (0.7–2.1 and 4.1–17.6 min, respectively). This measurement system was tested with both systematic laboratory experiments and field data collected on a research cruise along the US Atlantic margin. Once calibrated, this system identified numerous sharp peaks of dissolved methane concentration in the US Atlantic margin dataset that would be poorly resolved, or outright missed with previous measurement techniques. Overall, the precision and accuracy for the technique presented here were determined to be 11.2% and 10.4%, respectively, the operating range was 0–1000 ppm methane, and thee‐folding response time to changes in dissolved methane concentration was 0.7–2.1 min.

 

Ocean biogeochemical models have become critical tools for interpreting trace element and isotope (TEI) distributions observed during the GEOTRACES program and understanding their driving processes. Models stimulate new research questions that cannot be addressed with observations alone, for instance, concerning processes that occur over vast spatial scales and linkages between TEIs and other elemental cycles. A spectrum of modeling approaches has been applied to date, including (1) fully prognostic models that couple TEIs to broader biogeochemical frameworks, (2) simpler element-specific mechanistic models that allow for assimilation of observations, and (3) machine learning models that have no mechanistic underpinning but allow for skillful extrapolation of sparse data. Here, we evaluate the strengths and weaknesses of these approaches and review three sets of novel insights they have facilitated. First, models have advanced our understanding of global-scale micronutrient distributions, and their deviations from macronutrients, in terms of a “ventilation-regeneration-scavenging” balance. Second, models have yielded global-scale estimates of TEI inputs to and losses from the ocean, revealing, for instance, a rapid iron (Fe) cycle with an oceanic residence time on the order of decades. Third, models have identified novel links among various TEI cycling processes and the global ocean carbon cycle, such as tracing the supply of hydrothermally sourced Fe to iron-starved microbial communities in the Southern Ocean. We foresee additional important roles for modeling work in the next stages of trace element research, including synthesizing understanding from the GEOTRACES program in the form of TEI state estimates, and projecting the responses of TEI cycles to global climate change.

 

Abstract. Recent earth system models predict a 10 %–20 % decrease in particulate organic carbon export from the surface ocean by the end of the21st century due to global climate change. This decline is mainly caused by increased stratification of the upper ocean, resulting in reducedshallow subsurface nutrient concentrations and a slower supply of nutrients to the surface euphotic zone in low latitudes. These predictions,however, do not typically account for associated changes in remineralization depths driven by sinking-particle size. Here we combinesatellite-derived export and particle size maps with a simple 3-D global biogeochemical model that resolves dynamic particle size distributions toinvestigate how shifts in particle size may buffer or amplify predicted changes in surface nutrient supply and therefore export production. We showthat higher export rates are empirically correlated with larger sinking particles and presumably larger phytoplankton, particularly in tropical andsubtropical regions. Incorporating these empirical relationships into our global model shows that as circulation slows, a decrease in export isassociated with a shift towards smaller particles, which sink more slowly and are thus remineralized shallower. This shift towards shallowerremineralization in turn leads to greater recycling of nutrients in the upper water column and thus faster nutrient recirculation into the euphoticzone. The end result is a boost in productivity and export that counteracts the initial circulation-driven decreases. This negative feedbackmechanism (termed the particle-size–remineralization feedback) slows export decline over the next century by ∼ 14 % globally (from −0.29to −0.25 GtC yr−1) and by ∼ 20 % in the tropical and subtropical oceans, where export decreases are currently predicted tobe greatest. Our findings suggest that to more accurately predict changes in biological pump strength under a warming climate, earth system modelsshould include dynamic particle-size-dependent remineralization depths. 

Methane is a powerful greenhouse gas and a key player in atmospheric chemistry. Important uncertainties remain in the global atmospheric methane budget, with natural geologic emissions being one of the particularly uncertain terms. In recent bottom-up studies, geologic emissions have been estimated to comprise up to 10% of the global budget (40–60 Teragrams of methane per year, Tg CH4 yr–1). In contrast, top-down constraints from 14C of methane in preindustrial air extracted from ice cores indicate that the geologic methane source is approximately an order of magnitude lower. Recent bottom-up inventories propose microseepage (diffuse low-level flux of methane through soils over large areas) as the largest single component of the geologic methane flux. In this study, we present new measurements of methane microseepage from the Appalachian Basin (Western New York State) and compare these with prior microseepage measurements from other regions and with predicted values from the most recent bottom-up inventory. Our results show lower microseepage values than most prior data sets and indicate that positive microseepage fluxes in this region are not as widespread as previously assumed. A statistical analysis of our results indicates that mean microseepage flux in this region has very likely been overestimated by the bottom-up inventory, even though our measurements more likely than not underestimate the true mean flux. However, this is a small data set from a single region and as such cannot be used to evaluate the validity of the microseepage emissions inventory as a whole. Instead, the results demonstrate the need for a more extensive network of direct geologic emission measurements in support of improved bottom-up inventories. 

Aluminum (Al) is delivered to surface ocean waters by aeolian dust, making it a promising tracer to constrain dust deposition rates and the atmospheric supply of trace metal micronutrients. Over recent years, dissolved Al has been mapped along the GEOTRACES transects, providing unparalleled coverage of the world ocean. However, inferring atmospheric input rates from these observations is complicated by a suite of additional processes that influence the Al distribution, including reversible particle scavenging, biological uptake by diatoms, hydrothermal sources, sediment resuspension. Here we employ a data‐assimilation model of the oceanic Al cycle that explicitly accounts for these processes, allowing the atmospheric signal to be extracted. We conduct an ensemble of model optimizations that test different dust deposition distributions and consider spatial variations in Al solubility, thereby inferring the atmospheric Al supply that is most consistent with GEOTRACES observations. We find that 37.2 ± 11.0 Gmol/yr of soluble Al is added to the global ocean, dominated in the Atlantic Ocean, and that Al fractional solubility varies strongly as a function of atmospheric dust concentration. Our model also suggests that 6.1 ± 2.4 Gmol Al/yr is injected from hydrothermal vents, and that vertical Al redistribution through the water column is dominated by abiotic reversible scavenging rather than uptake by diatoms. Our results have important implications for the oceanic iron (Fe) budget: based on the soluble Fe:Al ratio of dust, we infer that aeolian Fe inputs lie between 3.82 and 9.25 Gmol/yr globally, and fall short of the biological Fe demand in most ocean regions.

 

The iron (Fe) supply to phytoplankton communities in the Southern Ocean surface exerts a strong control on oceanic carbon storage and global climate. Hydrothermal vents are one potential Fe source to this region, but it is not known whether hydrothermal Fe persists in seawater long enough to reach the surface before it is removed by particle scavenging. A new study (Jenkins, 2020,https://doi.org/10.1029/2020GL087266) fills an important gap in this puzzle: a helium‐3 mass balance model is used to show that it takes ~100 yr for deep hydrothermally influenced waters to upwell to the surface around Antarctica. However, estimates of Fe scavenging time scales range from tens to hundreds of years and must be more narrowly constrained to fully resolve the role of hydrothermal Fe in the ocean's biological pump.