New OCB Research :: Ocean Carbon & Biogeochemistry

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Archive for New OCB Research – Page 4

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))

Want to improve the spatiotemporal coverage of coastal water clarity? This approach combines high-resolution satellite data with low-cost in situ methods

Posted by mmaheigan

· Friday, December 1st, 2023

To maintain marine ecosystem health and human well-being, it is important to understand coastal water quality changes. Water clarity is a key component of water quality, which can be measured in situ by tools such as Secchi disks or by satellites with high spatial and temporal coverage. Coastal environments pose unique challenges to remote sensing, sometimes resulting in inaccurate estimates of water clarity.

Figure caption: Maps of model-corrected Landsat-8 derived Secchi depths from monthly clear sky images (2019–2021).

In this study, we couple low-cost in situ methods (Secchi disk depths) with open-access, high-resolution satellite (Landsat-8 and Sentinel-2) data to improve estimates of water clarity in a shallow, turbid lagoon in Virginia, USA. Our model allows the retrieval of water clarity data across an entire water body and when field measurements are unavailable. This approach can be implemented in dynamic coastal water bodies with limited in situ measurements (e.g., as part of routine water quality monitoring). This can improve our understanding of water clarity changes and their drivers to better predict how water quality may change in the future. Improved water clarity predictions can lead to better coastal ecosystem management and human well-being.

Figure caption: Workflow for obtaining Secchi disk depth with l2gen in NASA SeaDAS, bio-optical algorithms, and empirical adjustments.

Authors
Sarah E. Lang (University of Rhode Island’s Graduate School of Oceanography)
Kelly M.A. Luis (Jet Propulsion Laboratory, California Institute of Technology)
Scott C. Doney (University of Virginia)
Olivia Cronin-Golomb (University of Virginia)
Max C.N. Castorani (University of Virginia)

Twitter / Mastodon
@sarah_langsat8 on Twitter
@kelly_luis1 on Twitter
@scottdoney@universeodon.com on Mastodon
@ocronin_golomb on Twitter
@MaxCastorani on Twitter

New evidence suggests that tiny zooplankton might be the biggest problem with carbon cycling in IPCC climate models

Posted by mmaheigan

· Friday, December 1st, 2023

The ocean is the most important sink of anthropogenic emissions and is being considered as a medium to manipulate to draw down even more. Essential in the ocean’s role as a natural carbon-sponge is the net production of organic matter by phytoplankton, some of which sinks and is stored for 100s-1000s of years. Successfully simulating this biological carbon pump is essential for projecting any climate scenario, but it appears that massive uncertainties in the way zooplankton consume phytoplankton are compromising predictions of future climate and our assessment of some strategies to deliberately engineer it.

Figure caption. Grazing pressure is largest source of uncertainty for marine carbon cycling in CMIP6 models a) The global and zonal median winter grazing pressure is shown for all models. b) the coefficient of variation across models (std/mean) is largest for grazing pressure compared 14 major terms in the marine carbon cycle.

A new publication in Communications Earth and Environment explains how our poor understanding of zooplankton biases our best projections of marine carbon sequestration. We compared 11 IPCC climate models and found zooplankton grazing is largest source uncertainty in marine carbon cycling. This uncertainty is over three times larger than that of net primary production and is driven by large differences in different models assumptions about the rate at which zooplankton can consume phytoplankton. Yet, very small changes in zooplankton grazing dynamics (roughly only 5% of the full range used across IPCC models) can increase carbon sequestrations by 2 PgC/yr, which is double the maximum theoretical potential of Southern Ocean Iron Fertilization! Moving forward, to move beyond merely treating zooplankton as a closure term, modelers must look towards novel observational constraints on grazing pressure.

Authors
Tyler Rohr, Anthony J. Richardson, Andrew Lenton, Matthew A. Chamberlain, and Elizabeth H. Shadwick

Size does matter: larger krill leads to more POC export in the West Antarctic Peninsula

Posted by mmaheigan

· Friday, December 1st, 2023

Despite the importance of particulate organic carbon (POC) export on carbon sequestration and marine ecology, there have been few multi-decade studies in the world’s oceans. A new analysis published in Nature analyzed two decades of POC export data in the West Antarctic Peninsula and found that export oscillates on a 5-year cycle.

Figure caption: A) Particulate organic carbon (POC) export oscillates on a 5-year timescale in sync with the oscillation in the body size of the krill Euphausia superba on the West Antarctic Peninsula. B) POC export is significantly correlated with krill body size (p = 0.01).

Using a unique combination of krill data from penguin diet samples and net tows over two decades, Trinh et al. found that the cycle of POC export is intimately tied to the Antarctic krill (Euphausia superba) life cycle, as the bulk of the POC in their sediment traps was krill fecal pellets. Surprisingly, more krill did not lead to more POC export. Instead, when the krill population size was smaller but dominated by larger, older adults, POC export increased.

E. superba is the longest-lived (5-6 years) and largest krill species. They exhibit continuous annual growth throughout their life cycle. After about five years a krill population reaches its end stage and the population size is at a minimum. This end-stage population is composed of large, 50-60 mm long individuals that produce large, fast-sinking fecal pellets, leading to increased POC export. Increasing temperatures and deterioration of sea ice cover during the winter season due to climate change will likely impact the recruitment of new cohorts of krill and their success in replenishing aging populations. It is unclear how changes in the krill population and life cycle will impact long-term carbon sequestration on the West Antarctic Peninsula and nutrients exported to the benthic ecosystem

Authors:
Rebecca Trinh (Lamont Doherty Earth Observatory, Columbia University)
Hugh Ducklow (Lamont Doherty Earth Observatory, Columbia University)
Deborah Steinberg (Virginia Institute of Marine Science, College of William and Mary)
William Fraser (Polar Oceans Research Group)

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.

Funding for the Ocean Carbon & Biogeochemistry Project Office is provided by the National Science Foundation (NSF) and the National Aeronautics and Space Administration (NASA). The OCB Project Office is housed at the Woods Hole Oceanographic Institution.

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