Archive for global ocean

Swirling Currents: How Ocean Mesoscale Affects Air-Sea CO2 Exchange

Posted by mmaheigan 
· Friday, October 25th, 2024 

Due to a sparsity of in‐situ observations and the computational burden of eddy‐resolving global simulations, there has been little analysis on how mesoscale processes (e.g., eddies, meanders—lateral scales of 10s to 100s km) influence air‐sea CO2 fluxes from a global perspective. Recently, it became computationally feasible to implement global eddy‐resolving [O (10) km] ocean biogeochemical models. Many questions related to the influence of mesoscale motions on CO2 fluxes remain open, including whether ocean eddies serve as hotspots for CO2 sink or source in specific dynamic regions.

A recent study in Geophysical Research Letters investigated the contribution of ocean mesoscale variability to air-sea CO2 fluxes by analyzing the CO2 flux anomaly within the mesoscale band using a coarse-graining approach in a global eddy-resolving biogeochemical simulation. We found that in eddy-rich mid-latitude regions, ocean mesoscale variability can contribute to over 30% of the total CO2 flux variability. The cumulative net CO2 flux associated with mesoscale motions is on the order of 105 tC per year. The global pattern of cumulative mesoscale-related CO2 flux exhibits significant spatial heterogeneity, with the highest values in western boundary currents, the Antarctic Circumpolar Current, and the equatorial Pacific. The local distribution of cumulative mesoscale-related CO2 flux displays zonal bands alternate between positive (a net source) and negative (a net sink) due to the meandering nature of ocean mesoscale currents, which is related to local relative vorticity and the background cross-stream pCO2 gradient.

Figure caption. Mesoscale (<nominal 2 degree) contribution to air‐sea CO2 flux (F<2°CO2)in the model. (a)–(d) Monthly time series of F<2°CO2 (black lines) and cumulative F<2°CO2 (green/red solid lines) in four locations marked in (e). Dashed lines are the least squares regression of cumulative flux for the period 1982–2000; slopes are indicated in the bottom left; (e) Blue colors imply a CO₂ sink, and red colors represent a source. The figure shows the global distribution of the regressed slopes of cumulative F<2°CO2. Units are converted from mol m-2 per year to kg of CO2 per year using the atomic mass of CO2. This figure shows significant spatial heterogeneity of mesoscale-modulated CO2 flux, showing contributions to both CO₂ sources and sinks across different regions of the ocean, with a magnitude on the order of 105 tC per year.

 

Authors
Yiming Guo (Yale University; now at Woods Hole Oceanographic Institution)
Mary-Louise Timmermans (Yale University)

Fishing Vessel Ocean Observing Network (FVON) reimagines the global data collection paradigm

Posted by mmaheigan 
· Friday, December 1st, 2023 

With an increasingly wide variety of technology and innovations, from buoys to satellites, we now understand the open ocea n better than ever. Yet, existing technologies cannot cost-effectively provide accurate, up-to-date data on coastal and shelf ocean environments, especially beneath the surface. These dynamic regions impact billions of people in profound and varied ways.

Figure caption: Alongside other major global ocean observing technologies and networks, the Fishing Vessel Ocean Observing Network is built around the concept of “fishing for data” to collect high-quality ocean data such as temperature and salinity profiles. These measurements inform critical policy decisions, are integrated into sustainability efforts for fishers, scientists, and other relevant stakeholders, and can improve climate resiliency while protecting the health, well-being, and livelihoods of coastal communities and participants in the blue economy.

As described in a recent publication, the Fishing Vessel Ocean Observing Network (FVON) is reimagining the global data collection paradigm of coastal and shelf oceans by partnering with fishers and regional observation networks around the world. With more than four million fishing vessels worldwide, fishers cover much of the data-sparse nearshore ocean environments, vitally important regions of the ocean. By outfitting sensors onto vessels and on fishing gear, programs from New Zealand to Japan to New England, including researchers at WHOI, demonstrate that fishers can participate actively in the ongoing data revolution and eliminate critical oceanic data gaps without changing their standard fishing activities. Exponentially increasing the scale of data collection through fishing vessel and gear-based observations in nearshore marine environments has and will continue to democratize ocean observation, improve weather forecasting and ocean monitoring, and promote sustainable fishing while safeguarding lives and livelihoods. Already a proven concept regionally, FVON, alongside fishers and regional observation networks, will expand fishing-based observation to a global initiative.

 

Authors
Cooper Van Vranken (Ocean Data Network)
Julie Jakoboski (MetOcean Solutions, New Zealand)
John W. Carroll (Ocean Data Network)
Christopher Cusack (Environmental Defense Fund)
Patrick Gorringe (Swedish Meteorological and Hydrological Institute)
Naoki Hirose (Kyushu University, Japan)
James Manning (NOAA Northeast Fisheries Science Center (retired))
Michela Martinelli (National Research Council−Institute of Marine Biological Resources and Biotechnologies, Italy)
Pierluigi Penna (National Research Council−Institute of Marine Biological Resources and Biotechnologies, Italy)
Mathew Pickering (Environmental Defense Fund)
A. Miguel Piecho-Santos (Portuguese Institute for Sea and Atmosphere)
Moninya Roughan (University of New South Wales, Australia)
João de Souza (MetOcean Solutions, New Zealand)
Hassan Moustahfid (NOAA Integrated Ocean Observing System (IOOS))

Integrated analysis of carbon dioxide and oxygen concentrations as a quality control of ocean float data

Posted by mmaheigan 
· Friday, August 26th, 2022 

A recent study in Communications Earth & Environment, examined spatiotemporal patterns of the two dissolved gases CO2 and O2 in the surface ocean, using the high-quality global dataset GLODAPv2.2020. We used surface ocean data from GLODAP to make plots of carbon dioxide and oxygen relative to saturation (CORS plots). These plots of CO2 deviations from saturation (ΔCO2) against oxygen deviations from saturation (ΔO2) (Figure 1) provide detailed insight into the identity and intensity of biogeochemical processes operating in different basins.

Figure 1: Relationships between ΔCO2 and ΔO2 in the global ocean basins based on surface data in the GLODAPv2.2020 database. The black dashed lines are the least-squares best-fit lines to the data; unc denotes the uncertainty in the y-intercept value with 95% confidence; r is the associated Pearson correlation coefficient; n is the number of data points.

In addition, data in all basins and all seasons shares some common behaviors: (1) negative slopes of best fit lines to the data, and (2) near-zero y-intercepts of those lines. We utilized these findings to compare patterns in CORS plots from GLODAP with those from BGC-Argo float data from the Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) program. Given that the float O2 data is likely to be more accurate than the float pH data (from which the float CO2 is calculated), CORS plots are useful for detecting questionable float CO2 data, by comparing trends in float CORS plots (e.g. Figure 2) to trends in GLODAP CORS plots (Figure 1). As well as the immediately detected erroneous data, we discovered significant discrepancies in ΔCO2-ΔO2 y-intercepts compared to the global reference (i.e., GLODAPv2.2020 y-intercepts, Figure 1). The y-intercepts of 48 floats with QCed O2 and CO2 data (at regions south of 55°S) were on average greater by 0.36 μmol kg−1 than the GLODAP-derived ones, implying the overestimations of float-based CO2 release in the Southern Ocean.

Figure 2. CORS plots from data collected by SOCCOM floats F9096 and F9099 in the high-latitude Southern Ocean. Circles with solid edges denote data flagged as ‘good’, whereas crosses denote data flagged as ‘questionable’.

Our study demonstrates CORS plots’ ability to identify questionable data (data shown to be questionable by other QC methods) and to reveal issues with supposed ‘good’ data (i.e., quality issues not picked up by other QC methods). CORS plots use only surface data, hence this QC method complements existing methods based on analysis of deep data. As the oceanographic community becomes increasingly reliant on data collected from autonomous platforms, techniques like CORS will help diagnose data quality, and immediately detect questionable data.

 

Authors:
Yingxu Wu (Polar and Marine Research Institute, Jimei University, Xiamen, China; University of Southampton)
Dorothee C.E. Bakker (University of East Anglia)
Eric P. Achterberg (GEOMAR Helmholtz Centre for Ocean Research Kiel)
Amavi N. Silva (University of Southampton)
Daisy D. Pickup (University of Southampton)
Xiang Li (George Washington University)
Sue Hartman (National Oceanography Centre, Southampton)
David Stappard (University of Southampton)
Di Qi (Polar and Marine Research Institute, Jimei University, Xiamen, China)
Toby Tyrrell (University of Southampton)

New Data Standard for Oceanographic Research

Posted by mmaheigan 
· Friday, February 18th, 2022 

Effective data management is paramount in oceanographic research. The ocean is a global system, and research to understand regional and global oceanographic processes often involves compiling cruise-based data from different laboratories and expeditions.

The new international data standard covers column header abbreviations, quality control flags, missing value indicators, and standardized calculation of numerous parameters. Released alongside this paper are newly developed tools to calculate some oceanographic properties, and recommendations for dissociation constants of the seawater carbon system calculations. In addition, the use of “content” instead of “concentration” is recommended for mass-based properties.

Image of CTD alongside ship held by two people with ropes

The column header abbreviation standards presented here are based on the 30-year-old Exchange format of the World Ocean Circulation Experiment (WOCE) Hydrographic Program (Joyce and Corry, 1994; Swift and Diggs, 2008) with updates and refinements by the Climate and Ocean-Variability, Predictability, and Change (CLIVAR) and the Carbon Hydrographic Data Office (CCHDO) of the Scripps Institution of Oceanography. This format has been used as a data file standard for discrete chemical oceanographic observations for several decades.

The new international data standards will facilitate data sharing, quality control, and synthesis efforts to promote climate change and ocean acidification research at regional to global scales. This product is a significant step forward in terms of (a) creating common data standards for the international oceanographic research community to streamline data management, quality control, and data product developments; and (b) bringing the subject matter expertise from the research community to the data management world.

 

Authors (partial, see full list on publication)
Li-Qing Jiang (Univ Maryland, NOAA/NCEI)
Denis Pierrot (NOAA/AOML)
Rik Wanninkhof (NOAA/AOML)
Richard A. Feely (NOAA/PMEL)
Bronte Tilbrook (CSIRO Oceans and Atmosphere and Australian Antarctic Program Partnership)
Simone Alin (NOAA/AOML)
Leticia Barbero (Univ Miami; NOAA/AOML),
Robert H. Byrne (Univ South Florida),
Brendan R. Carter (Univ Washington, NOAA/PMEL)
Andrew G. Dickson (Scripps Institution of Oceanography)
Jean-Pierre Gattuso (CNRS, Laboratoire d’Océanographie de Villefranche, Sorbonne Univ; Institute for Sustainable Development and International Relations, Sciences Po, France)
Dana Greeley (NOAA/PMEL)
Mario Hoppema (Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Sciences Po,)
Matthew P. Humphreys (NIOZ Royal Netherlands Institute for Sea Research, Netherlands)
Johannes Karstensen (GEOMAR Helmholtz Centre for Ocean Research Kiel, Germany)
et al.

 

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