Author Archive for mmaheigan – Page 8

Registration is open: Pathways Connecting Climate Changes to the Deep Ocean Workshop

Posted by mmaheigan 
· Friday, October 20th, 2023 

Pathways Connecting Climate Changes to the Deep Ocean: Tracing physical, biogeochemical, and ecological signals from the surface to the deep sea (OCB/US CLIVAR joint workshop)

April 23-25, 2024 (University of Delaware, Virden Center) 

Workshop website

This workshop will bring together observational oceanographers and modelers across physical, biogeochemical, and ecological communities to assess our understanding of pathways connecting the surface to the seafloor and to develop recommendations for improved detection and attribution of change in the global deep ocean system.
Workshop goals:

  1. Provide an updated comprehensive assessment of the deep ocean’s state and changes across disciplines, of key quantities in which these changes are expressed, and of  pathways and timescales connecting the surface to the seafloor.
  2. Review existing observation and modeling tools and their adequacy for constraining, understanding, and attributing changes in the deep ocean system. Identify critical knowledge and observational data gaps and model deficiencies.
  3. Develop a collective set of recommendations for improved detection and attribution of change in the global deep ocean system, with a focus on better serving and supporting deep ocean science across disciplines.
  4. Build an interdisciplinary network of ocean modelers and observers across disciplines. Our aim is to open communication channels and facilitate collaborative exchange of data, knowledge, and tools across communities.

The workshop welcomes the participation of scientists from fields across the ocean observing and modeling communities, spanning physics, biogeochemistry, and ecology. We welcome insights from highly localized to global-scale studies as well as efforts focused on individual disciplines, but we strongly encourage all participants to consider how their work can increase opportunities for other disciplines/communities to have a collective impact.

We also encourage participation from relevant oceanographic networks and observing campaigns (e.g., Deep Argo, BGC Argo, GO-SHIP (Global Ocean Shipboard Hydrographic Investigations Program) and Bio-GO-SHIP, JETZON (Joint Exploration of the Twilight Zone Ocean Network), ECCO and ECCO-Darwin (Estimating the Circulation and Climate of the Ocean), DOSI (Deep Ocean Stewardship Initiative), Challenger-150, PICES (North Pacific Marine Science Organization) to ensure broad disciplinary representation and connectivity to established programs.

Scientific Organizing Committee
Xinfeng Liang (Univ. Delaware), Monique Messié (MBARI), Leslie Smith (Your Ocean Consulting LLC, DOOS), Isabela Le Bras (WHOI), Patrick Heimbach (Univ. Texas, Austin), Helen Pillar (Univ. Texas, Austin), Zachary Erickson (NOAA/PMEL), Charlie Stock (NOAA/GFDL)

International FAIR Data Workshop POSTPONED

Posted by mmaheigan 
· Tuesday, October 10th, 2023 

International Workshop: FAIR Data Practices for Ship-based Ocean Time Series

DATES POSTPONED

OVERVIEW: Sustained ocean time series measurements are fundamental to distinguish between natural and human-induced variability in ecosystems and processes required to advance ecological forecasting. The last international ship-based ocean time series workshop, held in November 2012 (Bermuda), focused on recommendations to improve data comparability. Over the past decade (see Fig.) the ocean observing community has contributed to numerous efforts and activities in support of building a global network of ocean time series with the aims of:

  • Elevating the visibility and utility of these observing assets for understanding climate-ecosystem links
  • Improving coordination, communication, and scientific synthesis products across ocean time series programs/sites
  • Building consensus on foundational components such as methods and FAIR data practices

This workshop on FAIR Data Practices for Ship-based Ocean Time Series will bring together globally distributed ship-based ocean time series representatives who are interested and committed to FAIR data practices with data managers and experts in semantic web technologies with the following objectives:

  • Share and vet newly drafted biogeochemical and biological use cases for adoption by the broader METS community
  • Work with participating time series representatives to implement these use cases for their time series programs
  • Co-develop best practices for responsible use of ocean time series datasets (as a contribution to the Ocean Best Practices System repository)
  • Share new findings and update recommendations on sampling and analytical protocols from the 2012 Bermuda Time Series Methods Workshop
  • Explore mechanisms (and identify champions!) for engaging broader stakeholders (managers, educators, etc.) in the use of ocean time series data sets
  • Start planning (and identify champions!) for an Ocean HackWeek for ocean time series data

Get Involved with BECS – Benthic Ecosystem & Carbon Synthesis

Posted by mmaheigan 
· Friday, September 8th, 2023 

Get Involved in OCB Benthic Ecosystem & Carbon Synthesis!

The OCB Benthic Ecosystem and Carbon Synthesis (BECS) Working Group is aimed at understanding the carbon cycle and ecosystems within the land-to-ocean aquatic continuum by improving our understanding of related benthic processes and their representation in ocean and climate models.

The BECS working group is seeking
1) 15 new members - nominate or apply by October 6
2) Give input to guide the working group's activities and focus by October 6
3) Call for speakers for webinar series (ongoing)

Find details on the working group webpage.

MarChemSpec – tutorial and resources

Posted by mmaheigan 
· Tuesday, September 5th, 2023 

These easy-to-use models are for the calculation of:
• Acid-base equilibria, seawater state parameters, and CaCO3 saturation in natural waters containing the ions of seawater
• Inorganic complexation of trace metals Al, Cd, Co, Cu(II), Fe(II), Fe(III), Mn, Ni, Pb and Zn 
in natural waters

The natural waters of the world do not just consist of seawater of varying salinity. It is important to be able to estimate the influence of changing natural water composition on equilibria and to understand the effects of anthropogenic change in a range of environments. These models help us to do that.

Please visit marchemspec.org for more information about MarChemSpec, our published papers, and for software downloads.

Watch recorded lectures on the MarChemSpec YouTube playlist.

A suite of CO2 removal approaches modeled for the 1.5 ˚C future

Posted by mmaheigan 
· Thursday, August 31st, 2023 

Carbon dioxide removal (CDR) is “unavoidable” in efforts to limit end-of-century warming to below 1.5 °C. This is because some greenhouse gas emissions sources—non-CO2 from agriculture, and CO2 from shipping, aviation, and industrial processes—will be difficult to avoid, requiring CDR to offset their climate impacts. Policymakers are interested in a wide variety of ways to draw down CO2 from the atmosphere, but to date, the modeling scenarios that inform international climate policies have mostly used biomass energy with carbon capture and storage (BECCS) as a proxy for all CDR. It is critical to understand the potential of a full suite of CDR technologies, to understand their interactions with energy-water-land systems and to begin preparing for these impacts.

Figure caption: Each of the six carbon dioxide removal approaches identified in recent U.S. legislation and modeled for this study could bring unique benefits and tradeoffs to the energy-water-land system. This image depicts afforestation, direct ocean capture, direct air capture, biochar, enhanced weathering, and bioenergy with carbon capture and storage in clockwise order. Floating carbon dioxide molecules hover above the landscape (image credit: Nathan Johnson, PNNL).

A recent study published in the journal Nature Climate Change was the first to model six major CDR pathways in an integrated assessment model. The modeled pathways range from bioenergy with carbon storage and afforestation (already represented by most models), also direct air capture, biochar and crushed basalt spreading on global croplands, and electrochemical stripping of CO2 from seawater aka direct ocean capture. The removal potential contributed by each of the six pathways varies widely across different regions of the world. Direct ocean capture showed the smallest removal potential but has important potential synergies with water desalination. This method could help arid regions such as the Middle East meet their water needs in a warming world. Enhanced weathering has much larger (GtCO2-yr-1) removal potential and could potentially help ameliorate ocean acidification. Overall, similar total amounts of CO2 are removed compared to other modeling scenarios, but broader set of technologies lessens the risk that any one of them would become politically or environmentally untenable.

Authors:
Jay Fuhrman  (Joint Global Change Research Institute)
Candelaria Bergero (Joint Global Change Research Institute)
Maridee Weber (Joint Global Change Research Institute)
Seth Monteith (ClimateWorks Foundation)
Frances M. Wang (ClimateWorks Foundation)
Andres F. Clarens (University of Virginia)
Scott C. Doney (University of Virginia)
William Shobe (University of Virginia)
Haewon McJeon (Joint Global Change Research Institute )

Twitter: @pnnlab @climateworks @uva

Insights into Lagrangian phytoplankton variability from profiling floats

Posted by mmaheigan 
· Thursday, August 31st, 2023 

Phytoplankton are small, drifting photosynthetic organisms that form the base of marine food webs and play an important role in carbon and nutrient cycling. Analyses of how they vary in space and time (through variables like the concentration of pigment chlorophyll-a, a proxy for their biomass) are therefore important. Because phytoplankton drift with ocean currents, their variability and rates of change should be analyzed in a Lagrangian frame (observer moves with a water parcel) as opposed to an Eulerian frame (observer is fixed in space). However, Lagrangian observations are less available and it is difficult to separate the effects of physical and biological processes in Eulerian observations.

Figure 1: The decorrelation time and length scales of chlorophyll-a in the Lagrangian (Tl,Chl and Ll,Chl) and Eulerian (Te,Chl and Le,Chl) frames are related by the underlying mesoscale velocity field. (a) Ratio Tl,Chl / Te,Chl as a function of u’ / c*Chl computed from anomalies relative to a climatology, where u’ is a scale for the mesoscale eddy velocities and c*Chl = Le,Chl / Te,Chl is an evolution speed for the chlorophyll field. (b) As in (a) but for ratio of length scales, Ll,Chl / Le,Chl. In (a) and (b), hollow (filled) circles come from all surface drifters (all BGC-Argo floats) in a 5º x 5º bin and crosses weight the float-derived scales by the inverse square of a Quasi-Planktonic Index (so that float segments more similar to a surface trajectory count more; see below). Triangles come from two floats that profiled frequently. Solid line is an empirical curve: as u’ / c*Chl → ∞, Lagrangian decorrelation is entirely determined by Fickian diffusion and the ratio of length scales approaches qChl = π/2. (c) Example demonstrating the Quasi-Planktonic Index (QPI), which quantifies the average distance between a float (squares) and the best-fit synthetic trajectory generated from surface currents (circles). Altimetric geostrophic currents are shown as vectors, initial particle locations are black dots, and final forward (backward) particle locations are blue (orange) dots. A smaller QPI indicates a float segment more similar to a surface geostrophic trajectory.

A recent study used observations of chlorophyll-a concentration in the upper ocean from satellites and BGC-Argo profiling floats to quantify the statistics of phytoplankton (time and length scales obtained from autocorrelation functions) in the Lagrangian and Eulerian frames and to understand how the two frames are related by the underlying velocity field. At the mesoscale (the size of swirling, balanced flows), the Eulerian scales of chlorophyll-a anomalies relative to a seasonal cycle matched those of velocity, suggesting ocean dynamics play a role in setting phytoplankton scales. The ratio of Lagrangian to Eulerian length scales of chlorophyll depends on the magnitude of turbulent velocity fluctuations relative to how fast the chlorophyll field translates, following an empirical curve with an asymptotic limit consistent with stirring by mesoscale eddies (Figure 1a,b). They conclude that when velocity fluctuations are relatively large, turbulent diffusion drives decorrelation, but when they are relatively small, biological sources drive decorrelation.

Figure 2: A composite view of the physical and biological processes at mesoscale ocean fronts. (a) Example of a straining maximum in altimetric geostrophic currents and rotated coordinate system, along with a coincident float trajectory. Lagrangian time series from 18 floats in the North Atlantic are matched to all straining maxima and averaged in rotated coordinates, utilizing only profiles where the Quasi-Planktonic Index (QPI) is less than 5 km; (b) strain rate (color) and relative vorticity (contours); (c) time derivative of mixed layer depth; (d) phytoplankton accumulation rate; (e) chlorophyll-a accumulation rate. Fronts are characterized by a shoaling mixed layer and increasing chlorophyll-a over the front.

Finding that floats can sample a mixed layer tracer somewhat like a surface Lagrangian observer when their trajectory is similar to a surface trajectory (Figure 1c), the authors conducted a follow-up study where they used floats under those conditions to better understand the biological-physical interactions at straining fronts, which are regions between mesoscale eddies where lateral gradients are sharpened, force balances break down, and episodic vertical velocities may be important for mixed layer budgets of carbon and nutrients. By averaging rates of phytoplankton accumulation (from the along-track derivative of mixed-layer averaged chlorophyll-a) in coordinates aligned with ocean fronts, they found that these dynamical structures are characterized by a shoaling mixed layer and increasing phytoplankton carbon and chlorophyll (Figure 2). They conclude that the vertical motions at ocean fronts restratify the mixed layer which increases average light levels experienced by cells, accelerating division rates and causing their accumulation.

The results of these studies provide important insights into the space-time evolution of reactive tracers like chlorophyll-a in a mesoscale flow. The results also provide insight into how to interpret time series obtained from BGC-Argo floats, which are observing platforms that are neither Lagrangian nor Euleran, and highlight floats’ potential to address problems of biological-physical interactions under certain sampling conditions.

Authors:
Darren C. McKee (University of Virginia, USA)
Scott C. Doney (University of Virginia, USA)
Alice Della Penna (University of Auckland, NZ)
Emmanuel S. Boss (University of Maine, USA)
Peter Gaube (University of Washington, USA)
Michael J. Behrenfeld (Oregon State University, USA)
David M. Glover (Woods Hole Oceanographic Institution, USA)

Net primary production from daily cycles of biomass using Argo floats

Posted by mmaheigan 
· Thursday, August 31st, 2023 

Net primary productivity is a central metric in ocean biogeochemistry that is costly and time-consuming to estimate using traditional water sampling methods. As a result, it is difficult to detect large-scale trends in ocean productivity. While satellite remote-sensing has partially solved this issue, its observations are limited to the top 10 to 40 m of the ocean and require assumptions about the depth profile of productivity.

Figure 1. (A) A map of BGC-Argo float profiles of particle backscattering where only floats deemed to contain samples from all hours of the day over its lifetime were shown. (BE) The hourly median (black points) and standard error (black vertical lines) of carbon biomass, estimated from particle backscatter. Over a 24-hour period, carbon biomass shows a net accumulation during the day (white space) and net loss during the night (gray space). This daily rhythm in biomass was used to infer gross primary productivity from a sinusoidal model fit that assumes productivity scales with light, community respiration is constant, and net community production is zero. (FI) Profiles of net primary productivity, shown with one standard error (shaded region), can be inferred from these daily cycles available for each depth and region. To estimate net primary production, we estimate gross oxygen production from gross primary (carbon) productivity assuming a photosynthetic quotient of 1.4 and that dissolved primary production is a third of total primary production. We then used an empirical ratio (equal to 2.7) to convert gross oxygen production to net primary productivity.

Our study addresses this problem by using BGC-Argo floats to estimate the in situ vertical structure of net primary productivity inferred from daily cycles of carbon biomass (Fig. 1). Although typical floats collect profiles every 5 or 10 days, it is possible to reconstruct the daily cycle in biomass by combining profiles from many floats that measure non-integer profiling frequencies (e.g., 5.2 or 10.2 days). These floats collect each subsequent profile at a different hour of the day, such that all hours of the day are about equally represented over the floats’ lifetimes. Combining enough of these floats’ profiles, the small daily variations in carbon biomass can be detected and used to infer net primary productivity. We demonstrate this for various depths, regions, and seasons.

Our approach provides low-cost, ground-truthed information throughout the water column across large expanses of the ocean and under various conditions (e.g., clouds, sea ice, or polar night), addressing some of the limitations of satellite or ship observations. We argue that the combination of BGC-Argo with satellite imagery will provide an invaluable tool for assessing large-scale trends in net primary production that may arise from climate change and other environmental purturbations.

 

Authors
Adam Stoer and Katja Fennel (Dalhousie University)

Dalhousie’s Marine Environmental Modelling Group:  memg.ocean.dal.ca

Identifying the water mass composition of a sample has never been so easy!

Posted by mmaheigan 
· Thursday, August 31st, 2023 

When we collect seawater in any point of the ocean, we are collecting a mix of water masses from different origin that traveled until there keeping their salinity and temperature properties. The Atlantic Ocean is likely the most complex basin in term of water masses containing more than 15 in its depths. Some of them were “born” in the North Atlantic Ocean, others in the Southern Ocean, even in the Mediterranean Sea! And when we collect a seawater sample we can know which water masses are there, where they come from, what happened to each of them during their journey to us, what story can they tell us.

The variation of any non-conservative property (such as dissolved organic carbon or nutrients) in the deep open ocean depends on the mixing of those water masses and on the biogeochemical processes affecting it (such as heterotrophic respiration). But the effect of the water mass mixing is usually very high, so in order to study the biogeochemical processes, it is necessary to remove that effect.

On the other hand, estimating the contribution of the water masses composing a sample is useful to trace the distribution of each water mass identifying the depth of maximum water mass contribution or the depth-range where the water mass is dominant contributing > 50%. Ocean biogeochemists and microbiologists can get more out of their data estimating the impact of water mass mixing on the variability of any chemical (e.g. inorganic nutrients and dissolved organic carbon) or biological (e.g. prokaryotic heterotrophic abundance and production) property.

Knowing the contribution of each water mass to each sample was not an easy task and required expertise on the origin, circulation and mixing patterns of the water masses present in the study area. This could be even harder in very complex oceanic basin such as the deep Atlantic Ocean. The most commonly used methodology is the Optimum Multi-Parameter (OMP) analysis that was first applied by Tomczak in 1981. However, this methodology is time consuming and requires availability of a large set of quality-controlled chemical variables (e.g. nutrients, oxygen,..) together with a deep knowledge of the oceanography of the studied area. Those chemical variables are not always available or do not have the required quality by contrast to potential temperature and salinity that are high standard core variables in any cruise or database. In a recent research article, we applied multi-regression machine learning models to solve ocean water mass mixing. The models tested were trained using the solutions from OMP analyses previously applied to samples from cruises in the Atlantic Ocean. Extremely Randomized Trees algorithm yielded the highest score (R2 = 0.9931; mse = 0.000227). The model allows solving the mixing of water masses in the Atlantic Ocean using potential temperature, salinity, latitude, longitude and depth. Potential temperature and salinity are the most commonly collected and curated variables in oceanography both from oceanographic cruises and autonomous vehicles (e.g. ARGO) avoiding the use of less commonly measured chemical variables which also require longer and time-consuming analyses of both the water samples and the data.

Figure 1. A16 section for the contribution of the water masses (A) AAIW5, (B) ENACW12, (C) AAIW3, (D) MW, (E) LSW, (F) ISOW, (G) CDW and (H) WSDW obtained with the Extremely Randomized Trees algorithm. Ocean Data View software (Schlitzer, 2015).

We also provide the code with instructions where any user can easily introduce the required variables (latitude, longitude, depth, temperature and salinity) of the chosen Atlantic samples and obtain the water mass proportion of each one in a fast and easy way. Actually, it would allow the user to obtain this information in real time during a cruise.

New research using other methods like OMP and its variants can be incorporated to the existing model increasing its accuracy and prediction capacity. Help us to improve the model and increase its spatial resolution!

Ocean biogeochemists and microbiologists can benefit from this tool even if they do not have a deep knowledge of the oceanography of the studied area. Identifying the water masses composition of a sample has never been so easy!

Author
Cristina Romera-Castillo (Instituto de Ciencias del Mar-CSIC, Barcelona, Spain)

Twitter: @crisrcas

RPiAlk: Balancing Measurement Uncertainty and Accessibility

Posted by mmaheigan 
· Thursday, August 31st, 2023 

High-accuracy measurement of total alkalinity (TA) is crucial for our understanding of ocean acidification and the inorganic carbon complex. It is also particularly expensive in terms of labor and resources. These barriers limit its application in understudied settings such as inland waters and developing coastal regions.

To address this problem, the authors constructed an instrument using open-source and low-cost principles and wrote about it in an article published in Limnology & Oceanography: Methods. The instrument implements a standard oceanographic open-cell acidimetric titration method within Python software written to coordinate titration, data acquisition, and calculation on a Raspberry Pi platform called RPiAlk. Repeated analysis of reference materials demonstrated TA measurement precision of 3.0 μmol/kg and measurement uncertainty of 5.3 μmol/kg. This uncertainty qualifies as “weather” level uncertainty (GOA-ON 2015) and approaches “climate” level uncertainty.

We hope the accessibility of this design will aid its replication and improvement by other alkalinity-measuring laboratories, including researchers, regulators, and educators previously without access to such TA instrumentation. An expanded production of high-quality TA measurements may aid scientific understanding of understudied waters around the world.

 

Authors
Daniel Sandborn (University of Minnesota, Saint Paul)
Elizabeth Minor (University of Minnesota Duluth)
Craig Hill (University of Minnesota Duluth)

Mastodon: @DanielSandborn@sciencemastodon.com

Twitter: @DanielSandborn | @CraigHill_UMD

 

Backstory
RPiAlk came about as an artifact of instrument development in the Minor Lab at the Large Lakes Observatory. The author had been growing weary of the poor measurement repeatability of manual Gran titration (common in inland waters) and the many problems with comparison to non-linear titration curve fitting demonstrated in Dickson’s SOPs, so he decided to write a program to automate it. To the author’s delight, Dr. M. Humphreys had already written a fantastic TA calculation program, Calkulate. All that was needed was a simple wrapper and I/O function, right? Not quite. If only software and instrument development was that easy. Debugging became as tiresome as it was rewarding and educational.

 

Unveiling the Hidden Secrets of Ancient Carbon Burial

Posted by mmaheigan 
· Thursday, August 31st, 2023 

How much carbon has been buried in the depths of our ancient oceans, and how did it shape our planet’s climate? Unraveling this enigma has long eluded researchers, but a recent groundbreaking “bottom-up” study unveils the surprising history of organic carbon burial in marine sediments during the Neogene period.

Departing from conventional methods, this study presents an innovative approach to calculating organic carbon burial rates independently. Drawing from data collected from 81 globally distributed sites, the research covers the Neogene era (approximately 23 to 3 million years ago). The results reveal unprecedented spatiotemporal variability in organic carbon burial, challenging previous estimates. Notably, high burial rates were found during the early Miocene and Pliocene, contrasting with a significant decline during the mid-Miocene, marked by the lowest ratio of organic-to-carbonate burial rates. This finding disputes earlier interpretations of enriched carbonate 13C values during the mid-Miocene (so called “Monterey Period” or “Monterey Excursion”) as indicative of massive organic carbon burial.

Figure Caption: Neogene organic carbon (OC) burial in the global ocean. Burial rates calculated using different definitions of provinces, including three approaches: Longhurst (black curve with uncertainty envelope，± 1σ in purple and ± 2σ in pale lilac), Oceans (blue curve), and FAO Fishing (orange curve).

Understanding the complex carbon burial dynamics of ancient oceans holds profound implications for comprehending our planet’s climate evolution. The suppressed organic carbon burial during the warm mid-Miocene, likely driven by temperature-dependent bacterial degradation, suggests the organic carbon cycle acted as a positive feedback mechanism during past global warming events. These findings emphasize the vital role of ocean carbon sequestration, providing stark evidence for policymakers, funding agencies, citizens, and educators to acknowledge its significance in combating modern climate challenges.

Authors
Ziye Li (University of Bremen)
Yi Ge Zhang (Texas A&M University)
Mark Torres (Rice University)
Ben Mills (University of Leeds)

Twitter: @chemclimatology

Backstory
Dr. Zhang, a shipboard organic geochemist during International Ocean Discovery Program Expedition 363, embarked on the legendary drilling ship JOIDES Resolution. While on the journey, Yige spent hours and hours daily crushing samples to measure organic carbon until his palm grew calluses, but the TOC% numbers did not really change. Fueled by sheer determination, Yige’s former student Ziye Li and himself delved into 50 years of IODP data archives to uncover global trends, and with the help of carbon cycle modelers Mark Torres and Ben Mills, leading to the discovery of the history of organic carbon burial.

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