Archive for deoxygenation

Quantifying uncertainties in future projections of Chesapeake Bay Hypoxia

Posted by mmaheigan 
· Wednesday, December 4th, 2024 

Climate change is expected to especially impact coastal zones, worsening deoxygenation in the Chesapeake Bay by reducing oxygen solubility and increasing remineralization rates of organic matter. However, simulated responses of this often fail to account for uncertainties embedded within the application of future climate scenarios.

Recent research published in Biogeosciences and in Scientific Reports sought to tackle multiple sources of uncertainty in future impacts to dissolved oxygen levels by simulating multiple climate scenarios within the Chesapeake Bay region using a coupled hydrodynamic-biogeochemical model. In Hinson et al. (2023), researchers showed that a multitude of climate scenarios projected a slight increase in hypoxia levels due solely to watershed impacts, although the choice of global earth system model, downscaling methodology, and watershed model equally contributed to the relative uncertainty in future hypoxia estimates. In Hinson et al. (2024), researchers also found that the application of climate change scenario forcings itself can have an outsized impact on Chesapeake Bay hypoxia projections. Despite using the same inputs for a set of three experiments (continuous, time slice, and delta), the more commonly applied delta method projected an increase in levels of hypoxia nearly double that of the other experiments. The findings demonstrate the importance of ecosystem model memory, and fundamental limitations of the delta approach in capturing long-term changes to both the watershed and estuary. Together these multiple sources of uncertainty interact in unanticipated ways to alter estimates of future discharge and nutrient loadings to the coastal environment.

Figure 1: Chesapeake Bay hypoxia is sensitive to multiple sources of uncertainty related to the type of climate projection applied and the effect of management actions. Percent contribution to uncertainty from Earth System Model (ESM), downscaling methodology (DSC), and watershed model (WSM) for estimates of (a) freshwater streamflow, (b) organic nitrogen loading, (c) nitrate loading, and (d) change in annual hypoxic volume (ΔAHV). (e) Summary of all experiment results for ΔAHV, expressed as a cumulative distribution function. The Multi-Factor experiment (blue line) used a combination of multiple ESMs, DSCs, and WSMs, the All ESMs experiment (pink line) simulated 20 ESMs while holding the DSC and WSM constant, and the Management experiment (green line) only simulated 5 ESMs with a single DSC and WSM but incorporated reductions in nutrient inputs to the watershed. The vertical dashed black line marks no change in AHV.

Understanding the relative sources of uncertainty and impacts of environmental management actions can improve our confidence in mitigating negative climate impacts on coastal ecosystems. Better quantifying contributions of model uncertainty, that is often unaccounted for in projections, can constrain the range of outcomes and improve confidence in future simulations for environmental managers.

Figure 2: A schematic of differences between the Continuous and Delta experiments. In the Delta experiment a combination of altered distributions in future precipitation and changes to long-term soil nitrogen stores eventually result in increased levels of hypoxia (right panel).

 

Authors
Kyle E. Hinson (Virginia Institute of Marine Science, William & Mary)
Marjorie A. M. Friedrichs (Virginia Institute of Marine Science, William & Mary)
Raymond G. Najjar (The Pennsylvania State University)
Maria Herrmann (The Pennsylvania State University)
Zihao Bian (Auburn University)
Gopal Bhatt (The Pennsylvania State University, USEPA Chesapeake Bay Program Office)
Pierre St-Laurent (Virginia Institute of Marine Science, William & Mary)
Hanqin Tian (Boston College)
Gary Shenk (USGS Virginia/West Virginia Water Science Center)

Powerful new tools for working with Argo data

Posted by mmaheigan 
· Thursday, June 9th, 2022 

No single program has been as transformative for ocean science over the past two decades as Argo: the fleet of robotic instruments that collect measurements of temperature and salinity in the upper 2 km of the ocean around the globe. The Argo program has been instrumental in revealing changes to ocean heat content, global sea level, and patterns of ice melt and precipitation. In addition, Biogeochemical Argo—the branch of the Argo program focused on floats with additional biological and chemical sensors—has recently shed light on topics such as regional patterns of carbon production and export, the magnitude of carbon dioxide air-sea flux in the Southern Ocean (thanks to the SOCCOM project), and the dynamics modulating ocean oxygen concentrations and oxygen minimum zones. While Argo data are publicly available in near-real-time via two Global Data Assembly Centers, there tends to be a steep learning curve for new users seeking to access and utilize the data.

To address this issue, a team led by scientists at NOAA’s Pacific Marine Environmental Laboratory developed a software toolbox available in two programming languages for accessing and visualizing Argo data— OneArgo-Mat for MATLAB and OneArgo-R for R. The toolbox includes functions to identify and download float data that adhere to user-defined time and space constraints, and other optional requirements like sensor type and data mode; plot float trajectories and their current positions; filter and manipulate float data based on quality flags and additional metadata; and create figures (profiles, time series, and sections) displaying physical, biological, and chemical properties measured by floats. Examples of figures created using the OneArgo-Mat toolbox are given below (Figure 1).

Figure 1. Example figures created using the OneArgo-Mat toolbox: (A) the trajectory of a float deployed in the North Atlantic from the R/V Johan Hjort in May of 2019, (B) a time series of dissolved oxygen at 80 dbars from that float, and (C) a vertical section plot of nitrate concentrations along the float track from the surface to 300 dbars. The black contour line in panel C denotes the mixed layer depth (MLD) based on a temperature criterion and the red line denotes the depth of the time series shown in panel B. The effects of seasonal phytoplankton blooms are evident in panel C, with mixed layer shoaling in the spring followed by drawdown of nitrate in the surface ocean. Panel B shows that, as the mixed layer deepens through the winter, the oxygen concentration at 80 dbars increases as a result of the oxygenated surface waters reaching that depth. The MATLAB code to download the required data and create all of these plots is shown (D).

The OneArgo-Mat and OneArgo-R toolboxes are intended for newcomers to Argo data, seasoned users, data managers, and everyone in between. For this reason, toolbox functions are equipped with options to streamline float selection, data processing, and figure creation with minimal user coding, if desired. Alternatively, the toolbox also provides rapid and straightforward access to the entire Argo database for experienced users who simply want to download up-to-date profile data for further processing and analysis. The authors hope these new tools will empower current Argo data users and entrain new users, especially as the US GO-BGC Project and US and international Argo partners move toward a global biogeochemical Argo fleet, which will create myriad new opportunities for novel studies of ocean biogeochemistry.

 

Authors
Jonathan Sharp – Cooperative Institute for Climate, Ocean, and Ecosystem Studies (CICOES) & NOAA Pacific Marine Environmental Laboratory (PMEL)
Hartmut Frenzel – CICOES & NOAA PMEL
Marin Cornec – University of Washington & NOAA PMEL
Yibin Huang – University of California Santa Cruz & NOAA PMEL
Andrea Fassbender – NOAA PMEL

Air-sea gas disequilibrium drove deoxygenation of the deep ice-age ocean

Posted by mmaheigan 
· Thursday, March 18th, 2021 

During the Last Glacial Maximum (~20,000 years ago, LGM) sediment data show that the deep ocean had lower dissolved oxygen (O2) concentrations than the preindustrial ocean, despite cooler temperatures of this period increasing O2 solubility in sea water.

Figure 1. a) Whole ocean inventory of the O2 components in the preindustrial control (PIC): total O2 (O2); the preformed components equilibrium O2 (O2 equilibrium), physical disequilibrium O2 (O2 diseq phys) and biologically-mediated disequilibrium (O2 diseq bio); and O2 respired from soft-tissue (O2 soft). b) The difference in whole ocean inventory of O2 components between the LGM and PIC simulations.

In a study published in Nature Geoscience, the authors provide one of the first explanations for glacial deoxygenation. The authors combined a data-constrained model of the preindustrial (PIC) and LGM ocean with a novel decomposition of O2 to assess the processes affecting the oceanic distribution of oxygen. The decomposition allowed for the preformed disequilibrium O2—the amount of oxygen that deviates from its solubility equilibrium value when at the surface—to be tracked, along with other contributions such as the O2 consumed by bacterial respiration of organic matter. In the preindustrial ocean, a third of the subsurface oxygen deficit was a result of disequilibrium rather than oxygen consumed by bacteria. This contradicts previous assumptions (Figure 1a). Nearly 80% of the disequilibrium resulted from upwelling waters, depleted in O2 due to respiration, not fully equilibrating before re-subduction into the ocean interior. This effect was even greater during the LGM (Figure 1b). The authors attributed this largely to the widespread presence of sea ice—which acts as a cap on the surface preventing the water from gaining oxygen from the atmosphere—in the ocean around Antarctica, with a smaller contribution from iron fertilization.

This study provides one of the first mechanistic explanations for LGM deep ocean deoxygenation. As the ocean is currently losing oxygen due to warming, the effect of other processes, including sea ice changes, could prove important for understanding long-term ocean oxygenation changes.

Authors
Ellen Cliff (University of Oxford)
Samar Khatiwala (University of Oxford)
Andreas Schmittner (Oregon State University)

Joint highlight with GEOTRACES International Project Office

Modern OMZ copepod dynamics provide analog for future oceans

Posted by mmaheigan 
· Thursday, July 23rd, 2020 

Global warming increases ocean deoxygenation and expands the oxygen minimum zone (OMZ), which has implications for major zooplankton groups like copepods. Reduced oxygen levels may impact individual copepod species abundance, vertical distribution, and life history strategy, which is likely to perturb intricate oceanic food webs and export processes. In a study recently published in Biogeosciences, authors conducted vertically-stratified day and night MOCNESS tows (0-1000 m) during four cruises (2007-2017) in the Eastern Tropical North Pacific, sampling hydrography and copepod distributions in four locations with different water column oxygen profiles and OMZ intensity (i.e. lowest oxygen concentration and its vertical extent in a profile). Each copepod species exhibited a different vertical distribution strategy and physiology associated with oxygen profile variability. The study identified sets of species that (1) changed their vertical distributions and maximum abundance depth associated with the depth and intensity of the OMZ and its oxycline inflection points, (2) shifted their diapause depth, (3) adjusted their diel vertical migration, especially the nighttime upper depth, or (4) expanded or contracted their depth range within the mixed layer and upper part of the thermocline in association with the thickness of the aerobic epipelagic zone (habitat compression concept) (Figure 1). Distribution depths for some species shifted by 10’s to 100’s of meters in different situations, which also had metabolic (and carbon flow) implications because temperature decreased with depth.  This observed present-day variability may provide an important window into how future marine ecosystems will respond to deoxygenation.

Figure caption: Schematic diagram showing how future OMZ expansion may affect zooplankton distributions, based on present-day responses to OMZ variability. The dashed line indicates diel vertical migration (DVM) and highlights the shoaling of the nighttime depth as the aerobic habitat is compressed. The lower oxycline community and the diapause layer for some species, associated with a specific oxygen concentration, may deepen as the OMZ expands.

 

Authors:
Karen F. Wishner (University of Rhode Island)
Brad Seibel (University of South Florida)
Dawn Outram (University of Rhode Island)

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