Search for: All records

Sediment cores from blue holes have emerged as a promising tool for extending the record of long‐term tropical cyclone (TC) activity. However, interpreting this archive is challenging because storm surge depends on many parameters including TC intensity, track, and size. In this study, we use climatological‐hydrodynamic modeling to interpret paleohurricane sediment records between 1851 and 2016 and assess the storm surge risk for Long Island in The Bahamas. As the historical TC data from 1988 to 2016 is too limited to estimate the surge risk for this area, we use historical event attribution in paleorecords paired with synthetic storm modeling to estimate TC parameters that are often lacking in earlier historical records (i.e., the radius of maximum wind for storms before 1988). We then reconstruct storm surges at the sediment site for a longer time period of 1851–2016 (the extent of hurricane Best Track records). The reconstructed surges are used to verify and bias‐correct the climatological‐hydrodynamic modeling results. The analysis reveals a significant risk for Long Island in The Bahamas, with an estimated 500‐year stormtide of around 1.63 ± 0.26 m, slightly exceeding the largest recorded level at site between 1988 and 2015. Finally, we apply the bias‐corrected climatological‐hydrodynamic modeling to quantify the surge risk under two carbon emission scenarios. Due to sea level rise and TC climatology change, the 500‐year stormtide would become 2.69 ± 0.50 and 3.29 ± 0.82 m for SSP2‐4.5 and SSP5‐8.5, respectively by the end of the 21st century. 

Abstract. Oxygen minimum zones (OMZs), due to their large volumes of perennially deoxygenated waters, are critical regions for understanding how the interplay between anaerobic and aerobic nitrogen (N) cycling microbial pathways affects the marine N budget. Here, we present a suite of measurements of the most significant OMZ N cycling rates, which all involve nitrite (NO2-) as a product, reactant, or intermediate, in the eastern tropical North Pacific (ETNP) OMZ. These measurements and comparisons to data from previously published OMZ cruisespresent additional evidence that NO3- reduction is the predominant OMZ N flux, followed by NO2- oxidation back to NO3-. The combined rates of both of these N recycling processes were observed to be much greater (up to nearly 200 times) thanthe combined rates of the N loss processes of anammox and denitrification, especially in waters near the anoxic–oxic interface. We also showthat NO2- oxidation can occur when O2 is maintained near 1 nM by a continuous-purge system, NO2-oxidation and O2 measurements that further strengthen the case for truly anaerobic NO2- oxidation. We also evaluate thepossibility that NO2- dismutation provides the oxidative power for anaerobic NO2- oxidation. The partitioning ofN loss between anammox and denitrification differed widely from stoichiometric predictions of at most 29 % anammox; in fact,N loss rates at many depths were entirely due to anammox. Our new NO3- reduction, NO2- oxidation, dismutation, andN loss data shed light on many open questions in OMZ N cycling research, especially the possibility of truly anaerobicNO2- oxidation.

 

Abstract The ocean is a net source of N 2 O, a potent greenhouse gas and ozone-depleting agent. However, the removal of N 2 O via microbial N 2 O consumption is poorly constrained and rate measurements have been restricted to anoxic waters. Here we expand N 2 O consumption measurements from anoxic zones to the sharp oxygen gradient above them, and experimentally determine kinetic parameters in both oxic and anoxic seawater for the first time. We find that the substrate affinity, O 2 tolerance, and community composition of N 2 O-consuming microbes in oxic waters differ from those in the underlying anoxic layers. Kinetic parameters determined here are used to model in situ N 2 O production and consumption rates. Estimated in situ rates differ from measured rates, confirming the necessity to consider kinetics when predicting N 2 O cycling. Microbes from the oxic layer consume N 2 O under anoxic conditions at a much faster rate than microbes from anoxic zones. These experimental results are in keeping with model results which indicate that N 2 O consumption likely takes place above the oxygen deficient zone (ODZ). Thus, the dynamic layer with steep O 2 and N 2 O gradients right above the ODZ is a previously ignored potential gatekeeper of N 2 O and should be accounted for in the marine N 2 O budget. 

Estuaries emit a large but highly uncertain amount of Nitrous oxide (N₂O) into the atmosphere. To better understand N₂O cycling processes in estuaries, we provide the first direct observations of N₂O consumption in the seasonally anoxic Chesapeake Bay, the largest estuary in the United States. N₂O consumption rates in anoxic waters reached up to 3.3 nmol L⁻¹ d⁻¹but were generally undetectable in oxygenated waters. However, N₂O consumption rates were substantially enhanced when the oxygen concentration was experimentally decreased in initially oxygenated samples, indicating the potential of N₂O consumption in oxygenated environments, for example, surface waters. These potential N₂O consumption rates followed Michaelis‐Menten kinetics as a function of increasing N₂O substrate concentration. N₂O‐consuming microbes that predominantly contained the clade II nitrous oxide reductase gene were detected throughout the water column. These new observations of environmental controls on N₂O consumption will benefit the modeling of N₂O cycling and help to constrain the estuarine N₂O flux.

 

Event‐based paleohurricane reconstructions of the last millennium indicate dramatic changes in the frequency of landfalling hurricanes on centennial timescales. It is difficult to assess whether the variability captured in these paleorecords is related to changing climate or randomness. We assess whether centennial‐scale active and quiet intervals of intense hurricane activity occur in a set of synthetic storms run with boundary conditions from an earth system model simulation of the last millennium. We generate 1,000 pseudo sedimentary records for a site on South Andros Island using a Poisson random draw from this synthetic storm data set. We find that any single pseudo sedimentary record contains active and quiet intervals of hurricane activity. The 1,000‐record ensemble average, which reflects the common signal of climate variability, does not. This suggests that the record of paleohurricane activity from The Bahamas reflects variability in hurricane frequency dominated by randomness and not variability in the climatic conditions.

 

Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data‐driven models are only as good as the data used for training, and this points to the importance of high‐quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time‐consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.