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| Titel |
Strategies for satellite-based monitoring of CO2 from distributed area and point sources |
| VerfasserIn |
Florian M. Schwandner, Charles E. Miller, Riley M. Duren, Vijay Natraj, Annmarie Eldering, Michael R. Gunson, David Crisp |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250098771
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| Publikation (Nr.) |
 EGU/EGU2014-14477.pdf |
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| Zusammenfassung |
| Atmospheric CO2 budgets are controlled by the strengths, as well as the spatial and temporal
variabilities of CO2 sources and sinks. Natural CO2 sources and sinks are dominated by the
vast areas of the oceans and the terrestrial biosphere. In contrast, anthropogenic and geogenic
CO2 sources are dominated by distributed area and point sources, which may constitute
as much as 70% of anthropogenic (e.g., Duren & Miller, 2012), and over 80% of
geogenic emissions (Burton et al., 2013). Comprehensive assessments of CO2 budgets
necessitate robust and highly accurate satellite remote sensing strategies that address the
competing and often conflicting requirements for sampling over disparate space and time
scales.
Spatial variability: The spatial distribution of anthropogenic sources is dominated by
patterns of production, storage, transport and use. In contrast, geogenic variability is almost
entirely controlled by endogenic geological processes, except where surface gas permeability
is modulated by soil moisture. Satellite remote sensing solutions will thus have to vary
greatly in spatial coverage and resolution to address distributed area sources and point
sources alike.
Temporal variability: While biogenic sources are dominated by diurnal and seasonal
patterns, anthropogenic sources fluctuate over a greater variety of time scales from diurnal,
weekly and seasonal cycles, driven by both economic and climatic factors. Geogenic sources
typically vary in time scales of days to months (geogenic sources sensu stricto are
not fossil fuels but volcanoes, hydrothermal and metamorphic sources). Current
ground-based monitoring networks for anthropogenic and geogenic sources record data
on minute- to weekly temporal scales. Satellite remote sensing solutions would
have to capture temporal variability through revisit frequency or point-and-stare
strategies.
Space-based remote sensing offers the potential of global coverage by a single sensor.
However, no single combination of orbit and sensor provides the full range of temporal
sampling needed to characterize distributed area and point source emissions. For instance,
point source emission patterns will vary with source strength, wind speed and direction.
Because wind speed, direction and other environmental factors change rapidly, short term
variabilities should be sampled. For detailed target selection and pointing verification,
important lessons have already been learned and strategies devised during JAXA’s GOSAT
mission (Schwandner et al, 2013).
The fact that competing spatial and temporal requirements drive satellite remote sensing
sampling strategies dictates a systematic, multi-factor consideration of potential
solutions. Factors to consider include vista, revisit frequency, integration times, spatial
resolution, and spatial coverage. No single satellite-based remote sensing solution can
address this problem for all scales. It is therefore of paramount importance for the
international community to develop and maintain a constellation of atmospheric CO2
monitoring satellites that complement each other in their temporal and spatial observation
capabilities:
Polar sun-synchronous orbits (fixed local solar time, no diurnal information) with
agile pointing allow global sampling of known distributed area and point sources
like megacities, power plants and volcanoes with daily to weekly temporal
revisits and moderate to high spatial resolution. Extensive targeting of distributed
area and point sources comes at the expense of reduced mapping or spatial
coverage, and the important contextual information that comes with large-scale
contiguous spatial sampling.
Polar sun-synchronous orbits with push-broom swath-mapping but limited
pointing agility may allow mapping of individual source plumes and their spatial
variability, but will depend on fortuitous environmental conditions during the
observing period. These solutions typically have longer times between revisits,
limiting their ability to resolve temporal variations.
Geostationary and non-sun-synchronous low-Earth-orbits (precessing local solar
time, diurnal information possible) with agile pointing have the potential to
provide, comprehensive mapping of distributed area sources such as megacities
with longer stare times and multiple revisits per day, at the expense of global
access and spatial coverage.
An ad hoc CO2 remote sensing constellation is emerging. NASA’s OCO-2 satellite
(launch July 2014) joins JAXA’s GOSAT satellite in orbit. These will be followed by
GOSAT-2 and NASA’s OCO-3 on the International Space Station as early as 2017. Additional
polar orbiting satellites (e.g., CarbonSat, under consideration at ESA) and geostationary
platforms may also become available. However, the individual assets have been designed with
independent science goals and requirements, and limited consideration of coordinated
observing strategies. Every effort must be made to maximize the science return from this
constellation. We discuss the opportunities to exploit the complementary spatial and temporal
coverage provided by these assets as well as the crucial gaps in the capabilities of this
constellation.
References
Burton, M.R., Sawyer, G.M., and Granieri, D. (2013). Deep carbon emissions from
volcanoes. Rev. Mineral. Geochem. 75: 323-354.
Duren, R.M., Miller, C.E. (2012). Measuring the carbon emissions of megacities. Nature
Climate Change 2, 560–562.
Schwandner, F.M., Oda, T., Duren, R., Carn, S.A., Maksyutov, S., Crisp, D., Miller, C.E.
(2013). Scientific Opportunities from Target-Mode Capabilities of GOSAT-2. NASA Jet
Propulsion Laboratory, California Institute of Technology, Pasadena CA, White Paper, 6p.,
March 2013.
© 2014 California Institute of Technology |
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