GENERAL REQUIREMENTS FOR AN mCDR
MONITORING SYSTEM
Guidelines for monitoring were set for the Global Ocean
Observing system (GOOS) in the context of the Framework
for Ocean Observation (FOO; Tanhua et al., 2019). A key
element of the FOO is its organization and coordination
around essential ocean variables (EOVs) rather than spe-
cific observing systems. The mCDR operational system
described here differs from the GOOS observations, but
also focuses on EOVs.
We advocate that “essential mCDR variables” would
include most current EOVs (see GOOS, 2021), along with
more detailed data on lower atmosphere CO2 concentra-
tion and oceanic DIC used to estimate air- sea CO2 flux.
Variables would also include wind speed, which has a
strong influence on air- sea gas exchange.
The effects of mCDR deployments on carbon capture and
sequestration will accumulate over time. Consequently,
meeting the three objectives discussed above will require
long- term monitoring.
Given the remote nature and carbon- sequestration tar-
get of open- ocean mCDR deployments, monitoring their
effects will require systems with at least the following char-
acteristics to efficiently address the objectives of detection,
attribution, and determination of side effects:
• Calibrated sensors on autonomous platforms, that is,
satellites (Figure 2) and in situ robots (see Box 1)
• Sampling over large surface areas to address horizontal
eddy diffusion and transport
• Recurrent long- term measurements, commensurate
with the duration of mCDR deployments
• Quasi- simultaneous estimates of air- sea CO2 exchange
and concentrations of DIC, particulate inorganic carbon
(PIC), DOC, and POC from surface to depth to monitor
the fate of the additional carbon
Air- sea CO2 flux cannot be measured directly over large
areas; it would be estimated by modeling. To do so, at
least two parameters of the carbonate system should be
measured in the water column. These parameters include
pH, total DIC, total alkalinity (TA), and CO2 partial pressure
(pCO2). Detecting changes in the carbonate system is chal-
lenging, and detecting a superimposed mCDR effect would
be very difficult.
The monitoring system would combine satellite remote
sensing (Figure 2) and long- term regional in situ measure-
ments. The latter would be performed with autonomous
FIGURE 2. One of the numerous satellites used for mCDR monitoring,
PACE (Plankton, Aerosol, Cloud, ocean Ecosystem, to be launched in
early 2024) will be equipped with a hyperspectral spectrometer that
could be used for assessing possible ecological side effects of mCDR
deployments (e.g., changes in phytoplankton community composi-
tion). This figure is a derivative of https://commons.wikimedia.org/wiki/
File:PACE_Spacecraft_beauty2.jpg by NASA, in the public domain.
robots (see Box 1), as described by Chai et al. (2020).
Biogeochemical data would subsequently be analyzed using
AI and assimilated in models. The integration of these plat-
forms, analyzing their data with AI, and combining the data
with models is already partly implemented in open- ocean
research (e.g., Claustre et al., 2021) and could be readily
applicable to monitor open- ocean mCDR deployments.
Ideally, the observational and modeling components of
the mCDR monitoring system should be in place prior to
an mCDR deployment. If this is impossible, data collected
by the global networks of Biogeochemical- Argo (BGC- Argo)
floats (Figure 3) and ocean color satellites (Figure 2) could
document natural variability and contribute to the valida-
tion/calibration of models required for attribution.
COMPONENTS OF AN mCDR
MONITORING SYSTEM
Satellites and underwater robots operate autonomously,
making relatively high- frequency measurements over sev-
eral years. Present- day BGC- Argo floats (Figure B1) can
achieve 300 profiles of up to 13 different variables2 during
their lifetimes, for example, profiling every five days over
four years. Horizontally, satellites (Figure 2) cover large
surfaces (with a spatial resolution of up to 4 × 4 km), and
2 Temperature, salinity, dissolved O2, pH, dissolved NO3, chlorophyll a, particulate backscattering coefficient (bbp), colored dissolved organic
matter (CDOM), downwelling irradiance, upwelling radiance, particle size spectra, particles and plankton 100–2,00 µm, optical sediment trap
(transmissometer).