mmaheigan :: Ocean Carbon & Biogeochemistry

Studying marine ecosystems and biogeochemical cycles in the face of environmental change

Author Archive for mmaheigan – Page 10

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.

Unveiling the Past and Future of Ocean Acidification: A Novel Data Product covering 10 Global Surface OA Indicators

Posted by mmaheigan

· Wednesday, August 23rd, 2023

Accurately predicting future ocean acidification (OA) conditions is crucial for advancing research at regional and global scales, and guiding society’s mitigation and adaptation efforts.

As an update to Jiang et al. 2019, this new model-data fusion product:
1. Utilizes an ensemble of 14 distinct Earth System Models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) along with three recent observational ocean carbon data products –>instead of relying on just one model (i.e., the GFDL-ESM2M) this approach reduces potential projection biases in OA indicators.
2. Eliminates model biases using observational data, and model drift with pre-Industrial controls.
3. Covers 10 OA indicators, an expansion from the usual pH, acidity, and buffer capacity.
4. Incorporates the new Shared Socioeconomic Pathways (SSPs).

The use of the most recent observational datasets and a large Earth System Model ensemble is a major step forward in the projection of future surface ocean OA indicators and provides critical information to guide OA mitigation and adaptation efforts.

Figure X. Temporal changes of global average surface ocean OA indicators as reconstructed and projected from 14 CMIP6 Earth System Models after applying adjustments with observational data: (a) fugacity of carbon dioxide (fCO₂), (b) total hydrogen ion content ([H⁺]_total), (c) carbonate ion content ([CO₃^2-]), (d) total dissolved inorganic carbon content (DIC), (e) pH on total scale (pH_T), (f) aragonite saturation state (Ω_arag), (g) total alkalinity content (TA), (h) Revelle Factor (RF), and (i) calcite saturation state (Ω_calc). The asterisk signs on the left-side y-axes show the values in 1750. The numbers along right-side y-axes, i.e., 1-1.9, 1-2.6, 2-4.5, 3-7.0, and 5-8.5, indicate the shared socioeconomic pathway SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively. These are missing from panel g because the trajectories were more dependent on the model than the SSP.

Authors
Li-Qing Jiang (University Maryland)
John Dunne (NOAA/Geophysical Fluid Dynamics Laboratory)
Brendan R. Carter (University of Washington)
Jerry F. Tjiputra (NORCE Norwegian Research Centre Bjerknes)
Jens Terhaar (Woods Hole Oceanographic Institution)
Jonathan D. Sharp (University of Washington)
Are Olsen (University of Bergen and Bjerknes Centre for Climate Research)
Simone Alin (NOAA/Pacific Marine Environmental Laboratory)
Dorothee C. E. Bakker (University of East Anglia)
Richard A. Feely (NOAA/Pacific Marine Environmental Laboratory)
Jean-Pierre Gattuso (Sorbonne Université)
Patrick Hogan (NOAA/National Centers for Environmental Information)
Tatiana Ilyina (Max Planck Institute for Meteorology)
Nico Lange (GEOMAR Helmholtz Centre for Ocean Research)
Siv K. Lauvset (NORCE Norwegian Research Centre)
Ernie R. Lewis (Brookhaven National Laboratory)
Tomas Lovato (Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici)
Julien Palmieri (National Oceanography Centre)
Yeray Santana-Falcón (Université de Toulouse)
Jörg Schwinger (NORCE Norwegian Research Centre)
Roland Séférian (Université de Toulouse)
Gary Strand (US National Center for Atmospheric Research)
Neil Swart (Canadian Centre for Climate Modelling and Analysis)
Toste Tanhua (GEOMAR Helmholtz Centre for Ocean Research)
Hiroyuki Tsujino (JMA Meteorological Research Institute)
Rik Wanninkhof (NOAA/Atlantic Oceanographic Meteorological Laboratory)
Michio Watanabe (Japan Agency for Marine-Earth Science and Technology)
Akitomo Yamamoto (Japan Agency for Marine-Earth Science and Technology)
Tilo Ziehn (CSIRO Oceans and Atmosphere)

Twitter:
@JiangLiqing, @JensTerhaar, @jpGattuso, @j_d_sharp, @AreOlsen, @SimoneAlin, @Dorothee_Bakker, @RFeely, @ilitat, @sivlauvset, @yeraysf, @TosteTanhua,

OCB supports ECR participation in 2023 Cornell Satellite Remote Sensing course

Posted by mmaheigan

· Tuesday, August 15th, 2023

OCB supports ECR participation in 2023 Cornell Satellite Remote Sensing course

The Cornell Satellite Remote Sensing course, an intensive 2-week summer training course took place June 5-16 in Ithaca, NY. The goal of the course was to teach participants the basic skills needed to work independently to acquire, analyze and visualize data sets derived from a variety of satellite sensors. The course also covered image analysis methods to work with satellite imagery of 1) sea surface temperature, 2) ocean wind speed, and 3) sea surface height.

OCB supported six students to attend the 2023 course. Read about the students and their experiences below:

Ardian Rizal’s research interest is in the area of physical oceanography process and how it explains the interconnected system of the Earth. After graduation from his bachelor’s study in Bandung Institute of Technology, Indonesia, he became an academic assistant at the same institution. His work was to describe the wind-wave climate characteristics and its potential as a renewable energy in the Indonesian Seas. He utilized numerical simulation along with the observation from buoy and altimetry satellites to assess the model’s fidelity. Currently, he is in the 2nd year of his master’s study in the division of Marine Science at the University of Southern Mississippi. He is examining the tidal influences on the mechanism of circulation in the west Mississippi Sound as his theses project under the supervision of Dr. Jerry Wiggert.

On the course: It was a blast! The program serves as a comprehensive foundation for the acquisition, data analysis, and visualization of the imagery and altimetry satellite geophysical product. The course was delivered with a perfect balance of lectures and practical activities. I feel I can independently navigate myself to do my own project after the program. Outside of the course, there are a lot of fantastic things to do with amazing participants such as exploring the gorgeous gorges, visiting the renowned ornithology lab of Cornell, and many more. I am full of gratitude to Dr. Bruce for the amazing stories and guidance, the helpful and patient teaching assistants: Danielle and Nour, and OCB for the wonderful opportunity to participate in this course. This program is highly recommended to any marine scientists who want to do remote sensing studies or learn useful tools of Python and SeaDAS through UNIX environment for ocean studies.

Meg Yoder is a 4th year PhD student at Boston College. Her research focuses on the biological, chemical, and physical processes that govern the flux of carbon between the surface ocean, deep ocean, and atmosphere in the subpolar North Atlantic. Her current research in the Irminger Sea uses autonomous sensor data from Ocean Observatories Initiative including pH, pCO2, oxygen, and chlorophyll, as well as lab measurements.

On the course: The Cornell Ocean Satellite Remote Sensing course opened the door to satellite data for me, and not just for ocean color but sea surface temperature, wind, and altimetry data as well. I’m excited to integrate these new data sources and compare them to the in situ data I’m currently working with. The course was extremely well structured and well taught, I can’t thank Bruce, the TAs, and OCB for their support in participating enough!

James Lin is a second-year Ph.D. student in the Ocean Processes and Analysis Laboratory at the University of New Hampshire. Advised by Dr. Robert Letscher, his current project focuses on constraining the production, age, and biochemical fate of dissolved organic carbon (DOC) based on its sources (allochthonous vs autochthonous) and sinks (abiotic vs biotic) using isotope 13C and radiocarbon (14C) in the ocean biogeochemistry component (Marine Biogeochemistry Library- MARBL) of the Community Earth System Model V2 (CESM).

On the course: I am very happy to have taken the Cornell Satellite Remote Sensing 2023 workshop. A huge thank you to Dr. Bruce Monger and the supporting staff and TAs for their mentorship and patience in learning satellite imagery with Python. Learning how to access and process satellite data, such as sea surface temperature, altimetry, chlorophyll, and wind speed, is immensely valuable in my current carbon modeling research to provide long-term observations throughout the global ocean. I also appreciate meeting and getting to know other participants from different parts of the world working on Satellite-derived observations. This training workshop is a must to become a part of the Satellite Remote Sensing community. Thank you again to Dr. Bruce Monger, the Cornell workshop staff and participants, and Ocean Carbon & Biogeochemistry (OCB) for making this workshop experience unforgettable.

Mitch Torkelson is a 2nd year master’s student in Marine Science at the University of North Carolina-Wilmington. Under the guidance of Dr. Phil Bresnahan, his research primarily centers around assessing the accuracy of the SeaHawk CubeSat in measuring key oceanic water column constituents such as chlorophyll a and CDOM. His research interests lie in the field of water quality, where he utilizes bio-optical modeling and remote sensing techniques to conduct statistical analyses on the optically-detectable particles and sediments observed in satellite imagery captured near the Masonboro inlet, situated along the North Carolina coast.

On the course: I cannot praise the Ocean Color training program led by Bruce Monger enough. Prior to enrolling in the course, my knowledge of programming and ocean color satellite analysis was basic at best. However, after completing the 2 weeks, I emerged equipped with an entire new arsenal of skills and tools that not only will aid in the completion of my thesis research but also position me for success in my future endeavors!

Jessie Wynne is a 2nd year Master of Marine Science Student at the University of North Carolina Wilmington and is advised by Dr. Phil Bresnahan. Her research is focused on the development of low-cost water quality sensors as well as satellite water quality analysis. Jessie will be working with SeaHawk/HawkEye imagery and chlorophyll coastal analysis.

On the course: The Cornell Satellite Remote Sensing course was a fantastic experience. It provided me with so many tools to process satellite imagery, especially ocean dolor data. This course also equipped me with python programming skills. Dr. Bruce Monger was an excellent instructor, providing thorough explanations of satellite imagery analysis algorithms while being very patient with all questions directed his way. I really enjoyed this course and would recommend it to anyone pursuing satellite remote sensing and ocean color analysis. I would like to say thank you to Dr. Bruce Monger and OCB for this amazing experience!

Nick Baetge is a postdoctoral scholar in the laboratories of Dr. Michael Behrenfeld and Dr. Kimberly Halsey at Oregon State University. He has been examining variability in phytoplankton physiology and bio-optical properties over the day-night cycle using cultivation-based experiments and from publicly archived in situ data. He will soon be investigating the physiological and diversity-based responses of marine phytoplankton and bacterioplankton to wildfire ash deposition off the U.S. West Coast.

On the course: It can be hard for new ocean color data users to know how to approach remote sensing analyses, including coding in a different programming language. Dr. Monger breaks down the process and provides several resources that makes it less daunting for new users to start, giving them the confidence to use Python, retrieve level 1 satellite data, process it to level 3, and generate composite imagery from individual scenes. I left the Cornell Remote Sensing workshop with not only a new set of tools that I can continue to refine and use to view the oceans, but also some wonderful new colleagues and friends. Thank you so much to Dr. Monger, OCB, and all the course participants for the opportunity to connect and learn!

Are you interested in a primer on ocean biogeochemical modeling with hands-on examples?

Posted by mmaheigan

· Thursday, May 11th, 2023

Look no further. This primer article explains what an ocean biogeochemical model is, how such a model is designed and applied, and includes easily accessible code examples. Refresh your memory on commonly used metrics for model evaluation through model-data comparison. Get introduced to the underlying rationale, mechanics, applications, and pitfalls of data assimilation for parameter optimization, state estimation, and observing system design. Peruse overviews of available community code repositories and observational databases. And tour some of the important applications of ocean biogeochemical models for carbon accounting, ocean deoxygenation and acidification studies, and fisheries yield projections. The primer also includes recommendations for best practices in ocean biogeochemical modeling and discusses current limitations and anticipated future developments and challenges. First and foremost, the article is an invitation to get involved.

Figure caption: Schematic representation of the varying level of complexity in biogeochemical models. State variables are indicated by the boxes where different colors correspond to different elemental currencies. The black arrows indicate selected biogeochemical transformations. The simplest, the nutrient–phytoplankton– zooplankton–detritus (NPZD) model, includes four state variables and one nutrient currency, often nitrogen. A typical low-complexity model includes several nutrients and nutrient currencies. Chlorophyll is omitted in the schematic, although many models have a chlorophyll state variable for each phytoplankton group to account for photoacclimation.

Authors
Katja Fennel (Dalhousie University)
Jann Paul Mattern (University of California, Santa Cruz)
Scott C. Doney (University of Virginia)
Laurent Bopp (Institute Pierre Simon Laplace)
Andrew M. Moore (University of California, Santa Cruz)
Bin Wang (Dalhousie University)
Liuqian Yu (Hong Kong University of Science and Technology)

Twitter @katjafennel @ScottDoney1 @laurent_bopp @DalhousieU @uvaevsc

Adaptive emission pathways to stabilize global temperatures

Posted by mmaheigan

· Thursday, May 11th, 2023

Around the world, countries have agreed in the Paris Agreement to limit global warming well below 2°C and to pursue efforts to reduce global warming to 1.5°C. However, large uncertainties remain about which emission pathways will allow us to reach this goal. A recent paper presents a new adaptive approach to create emission pathways and estimate the necessary emission reductions every five years, following the stocktake process of the Paris Agreement. This Adaptive Emissions Reduction Approach (AERA) is solely based on past warming rates, and emissions of CO₂ and non-CO₂ radiative agents, and explicitly does not rely on projections by Earth System Models. Updating the emission pathways every five years, circumvents uncertainties in the climate system and its transient response to cumulative emissions (TCRE). Testing with the Bern3D-LPX Earth System Model of Intermediate Complexity shows that the approach works robustly across a wide range of TCREs, avoids large overshoots, and only small changes to the emission pathways are necessary every five years. This approach will allow policymakers to estimate emission pathways and create a base for international negotiations. Furthermore, it allows simulations with Earth System Models that all converge to the same temperature target to compare the climate at stabilized warming levels.

Figure caption: The three steps of the Adaptive Emission Reduction Approach: 1) Estimating the past anthropogenic warming, 2) estimating the remaining emission budget, and 3) redistributing it over the future years.

Authors
Jens Terhaar (University of Bern, now Woods Hole Oceanographic Institution)
Thomas L Frölicher (University of Bern)
Mathias T Aschwanden (University of Bern)
Pierre Friedlingstein (University of Exeter, Ecole Normale Superieure)
Fortunat Joos (University of Bern)

Twitter @JensTerhaar @froeltho @PFriedling @unibern @snsf_ch @4C_H2020 @ExeterUniMaths @Geosciences_ENS @IPSL_outreach

Hydrostatic pressure substantially reduces deep-sea microbial activity

Posted by mmaheigan

· Thursday, May 11th, 2023

Deep sea microbial communities are experiencing increasing hydrostatic pressure with depth. It is known that some deep sea microbes require high hydrostatic pressure for growth, but most measurements of deep-sea microbial activity have been performed under atmospheric pressure conditions.

In a recent paper published in Nature Geoscience, the authors used a new device coined ‘In Situ Microbial Incubator’ (ISMI) to determine prokaryotic heterotrophic activity under in situ conditions. They compared microbial activity in situ with activity under atmospheric pressure at 27 stations from 175 to 4000 m depths in the Atlantic, Pacific, and the Southern Ocean. The bulk of heterotrophic activity under in situ pressure is always lower than under atmospheric pressure conditions and is increasingly inhibited with increasing hydrostatic pressure. Single-cell analysis revealed that deep sea prokaryotic communities consist of a small fraction of pressure-loving (piezophilic) microbes while the vast majority is pressure-insensitive (piezotolerant). Surprisingly, the piezosensitive fraction (~10% of the total community) responds with a more than 100-fold increase of activity upon depressurization. In the microbe proteomes, the authors uncovered taxonomically characteristic survival strategies in meso- and bathypelagic waters. These findings indicate that the overall heterotrophic microbial activity in the deep sea is substantially lower than previously assumed, which implies major impacts on the carbon budget of the ocean’s interior.

Figure caption: Deep sea microbial activity under varying pressure. (a) In situ bulk leucine incorporation rates normalized to rates obtained at atmospheric pressure conditions. (b) A microscopic view of a 2000 m sample collected in the Atlantic and incubated under atmospheric pressure conditions. The black halos around the cells are silver grains corresponding to their activities. The highly active cells (indicated by arrows) were rarely found in in situ pressure incubations. (c) Depth-related changes in the metaproteome of three abundant deep sea bacterial taxa (Alteromonas, Bacteroidetes, and SAR202). The number indicates shared and unique up- and down-regulated proteins in different depth zones.

Authors
Chie Amano (University of Vienna, Austria)
Zihao Zhao (University of Vienna, Austria)
Eva Sintes (University of Vienna, IEO-CSIC, Spain)
Thomas Reinthaler (University of Vienna, Austria)
Julia Stefanschitz (University of Vienna, Austria)
Murat Kisadur (University of Vienna, Austria)
Motoo Utsumi (University of Tsukuba, Japan)
Gerhard J. Herndl (University of Vienna, Netherlands Institute for Sea Research)

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