Oceanography | Vol. 38, No. 3
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TABLE 1. Abridged summary of priorities for addressing needs and challenges related to biogeochemical observations, models, data management, and col-
laboration for each discussion topic.
OBSERVATIONS
MODELS
DATA MANAGEMENT
COLLABORATION
Biological Carbon Pump (BCP)
The BCP is a key process in the ocean carbon cycle involving transfer and remineralization of organic carbon to depth. Biologically mediated biogeochemical (BGC)
transformations occurring against a backdrop of complex physical dynamics make the BCP a uniquely challenging process to measure and model.
• Prioritize deep (mesopelagic and deeper)
measurements and time series
• Observing gaps: physicochemical particle
properties, key nutrients like iron and
ammonium, community structure and
function, trophic interactions, vertical
migration, biological rates (grazing, viral lysis)
• Uncertainty quantification
• Gridded climatology of particulate organic
carbon (POC) flux
• Data rescue and digitization
• Integrate diverse multi-platform
datasets over space and time
• Perform quantitative evaluation of
observation and model mismatch
• Standardized guidelines for data
collection, metadata reporting, and
data processing (data aggregation
and error propagation)
• Centralized repository or aggregator
of metadata for BCP-relevant
measurements
• Conduct moderately sized BCP
process studies that integrate
sampling and modeling activities
from the outset
• Hackathons and community activities
that build capacity and facilitate idea
and knowledge exchange
Episodic and Extreme Events (EEEs)
EEEs such as storms and wildfires may generate large BGC fluxes over short periods of time, thus serving as major players in BGC cycles and marine ecosystem health.
However, EEEs pose safety and logistical challenges, and observations and models require high spatiotemporal resolution to understand their impacts.
• Develop and deploy robust (able to
withstand EEEs) platforms and technologies
to fill spatial and temporal gaps (including
satellite remote sensing, e.g., geostationary
missions like NASA GLIMR)
• Leverage existing observatories (e.g., OOI,
LTERs) to conduct event-based sampling
• Establish sentinel sites where a dynamic
range of EEE impacts occur (storms,
cyclones, wildfires) for sustained data
collection
• Regional models and/or dynamical
or statistical downscaling of global
outputs to constrain event-scale
dynamics
• Organize early collaboration to
ensure sampling resolution is
adequate for models
• Adopt common definition of
“extreme” (% departure from
baseline)
• Create metadata fields and/or flags
for EEEs
• Funding mechanisms and community
activities (model intercomparison,
data synthesis, comparative
analysis) that require integration
of observations and models for
knowledge sharing and capacity
building
• Mechanisms to support collaborative
international EEE research
Machine Learning (ML)
Models used to predict ocean BGC cycling encode a host of relationships between environmental variables. A key question is whether these mathematical relationships
realistically combine to produce the emergent behavior of ocean BGC systems to allow predictions to be made in areas with sparse observations.
• Increase spatiotemporal resolution of BGC
(especially nutrients like iron, ammonium)
and biology (plankton biomass) observations
to improve ML algorithms
• Increase availability of model outputs
for ML reanalysis
• Standardization of model outputs
of phytoplankton community
composition
• Improve data standardization, quality
control, and open access to facilitate
synthesis and ML training on large
datasets
• Make ML tools available to the
community
• Workshops for ML training
Marine Carbon Dioxide Removal (mCDR)
Anthropogenic CO2 emissions have been the leading driver of climate change over the past century. To limit warming and associated climate and ecosystem impacts, multi-
sector efforts are underway to explore human intervention strategies to remove CO2 from the atmosphere and sequester it long-term in the ocean (NASEM, 2022). Well-
integrated modeling and observing efforts are vital to rigorous assessment of these approaches.
• Strategic BGC observing system
deployments (water column and benthos)
for mCDR projects to assess efficacy and
impacts
• Work with industry to produce and refine
BGC sensors and autonomous platforms
(e.g., AUVs, ASVs, moorings) that specifically
focus on relevant carbonate chemistry
parameters
• Optimize sampling strategies using
OSSEs
• Improve representation of particulate
inorganic carbon distributions within
models
• Prioritize development of models that
simulate regional and mesoscale
dynamics
• Transparency and public availability
of data, methods, and software
emerging from mCDR research
• Adopt common vocabularies and
data/metadata reporting standards
• Create mCDR data flags to note
datasets that contain results from
experiments that modify natural
ocean conditions, and/or novel data
assembly centers for mCDR projects
• Integration of observations and
models starting in early stages of
mCDR projects to build common
vocabularies and understanding
• Research funding must keep pace
with venture capital investment to
ensure rigorous scientific evaluation
of emerging technologies
• Efficient data archiving and peer
review to make information available
more quickly
Table continued on next page…
regions due to sea ice coverage and adverse sea conditions (Heimdal
et al., 2024). Several regions (e.g., polar, tropical Pacific, Indian
Ocean) and depths below the surface layer of the ocean are rela-
tively undersampled (Abrahamsen, 2014; Levin et al., 2019; Smith
et al., 2019). Across biogeochemistry disciplines, specific variables
and processes are necessary to measure but remain undersampled.
Measurement, monitoring, reporting, and verification of mCDR
projects require information about baseline ocean biogeochemis-
try (Fay et al., 2024; Ho et al., 2023). The biological pump observing
community requires the constraint of high trophic level processes
in addition to measurements of physicochemical particle proper-
ties, including sinking velocity, porosity, and chemical composition,