Britton Stephens National Center for Atmospheric Research Atmospheric Technology Division
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Transcript of Britton Stephens National Center for Atmospheric Research Atmospheric Technology Division
Britton StephensNational Center for Atmospheric Research
Atmospheric Technology Division
CDAS Team: Dave Schimel, Britt Stephens, David Baker, Steve Aulenbach, Jennifer Oxelson, Dave Brown, Roger Dargaville
Carbon Model-Data Fusion
• Are we presently model or data limited?– Data are sparse but models can’t handle high variability
• Model-data fusion– Synthesis inversion
– Data assimilation
– Parameter estimation
– “Introduction of observations into a modeling framework, to
provide:
Estimates of model parameters
Uncertainties on parameters and model output
Ability to reject a model” (Michael Raupach)
Challenges to carbon model-data fusion
• Limited concentration data, far from sources
• Vertical and horizontal model coarseness
• “Representativeness” or model-data mismatch
- Boundary-layer stable-layer height errors
- Spatial flux heterogeneities
must weight measurements appropriately
360 m
120 m
800 m
S
Regional and smaller scales are critical for linking to underlying processes
(NRCS/USDA, 1997)
(NRCS/USDA, 1997) (SeaWIFS, 2002)
CHLOROPHYLL
TEMPERATURE (C)
(IPCC, 2001)
Unresolved variance presently contains most of the information on regional- and smaller-scale fluxes
Even biased high-frequency measurements do better than long-term means. . .
(Rachel Law, submitted to Tellus, 2001)
. . . but to use on a global scale requires a new approach.
Data-assimilation+ Ingests data at the time of observations+ Can handle very large data streams
• Used extensively in weather prediction and satellite analysis
+ Can assimilate multiple data types• In situ concentrations• Satellite concentrations• Satellite environmental data (e.g. standing water)• Direct flux measurements• Inventory data
- Methods are relatively complex- Error statistics are not produced as easily
Differences between CH4 and CO2
Assimilation of CH4 may be easier because:
• Fluxes are much more unidimensional– Diurnal rectification of sources not an issue
• Ocean fluxes are much less significant• Satellite measurements may be more feasible
However. . .
• Spatial structure of sources are highly local• In situ measurements are more challenging
Carbon Data-Model Assimilation (C-DAS)
http://dataportal.ucar.edu/CDAS/
DODS Aggregation
Server
GrADS-DODSServer
Reference GlobalAtmospheric CO2
Overview of CDAS
Users
http-BasedInterface
Simulated Observing
System
SimulatedCO2
Observations
4D VARAssimilation
System
Carbon Data-Model Assimilation (C-DAS)
http://dataportal.ucar.edu/CDAS/
DODS Aggregation
Server
GrADS-DODSServer
Reference GlobalAtmospheric CO2
Overview of CDAS: Production of Reference Atmospheric CO2
Users
Simulated Observing
System
SimulatedCO2
Observations
http-BasedInterface
4D VARAssimilation
System
2.5o, resolution25 vertical levels, 1 hour t, & 365 days = 2.6TB
Annual Land Model
Fluxes(0.5o)
IndustrialFluxes
(1o )
Ocean Model Fluxes
(2o )
Diurnal & Seasonal Cycle
Model
Reference Global
Atmospheric CO2
Atmospheric Transport
Model
Carbon Data-Model Assimilation (C-DAS)
http://dataportal.ucar.edu/CDAS/
Carbon Data-Model Assimilation (C-DAS)
http://dataportal.ucar.edu/CDAS/
Reference GlobalAtmospheric CO2
CDAS Application: Data Volumes
2.6 TB
200 MB
Users
4D VARAssimilation
System
Simulated Observing
System
SimulatedCO2
Observations
http-BasedInterface
DODS Aggregation
Server
GrADS-DODSServer
Global Estimate, 11 North American
Bioregions
Carbon Data-Model Assimilation (C-DAS)
http://dataportal.ucar.edu/CDAS/
DODS Aggregation
Server
GrADS-DODSServer
Reference GlobalAtmospheric CO2
Overview of CDAS: retrieval of fluxes using data assimilation
Users
4D VARAssimilation
System
Compare
Estimated Annual Fluxes(Bioregional)
Simulated Observing
System
SimulatedCO2
Observations
http-BasedInterface
4D VAR Assimilation
System
AtmosphericTransport
Model
Optimizer
Adjoint ofAtmosphericTransport
Model
RetrievedCO2
Observations
1st Guess fluxes
Input Global Atmospheric
CO2 fluxes
Carbon Data-Model Assimilation (C-DAS)
http://dataportal.ucar.edu/CDAS/
Hour = 1Hour = 2Hour = 3Hour = 4Hour = 5Hour = 6Hour = 7
Flux corrections using existing CO2 network
Month = 1Month = 2Month = 3Month = 4Month = 5Month = 6Month = 7Month = 8Month = 9Month = 10Month = 11Month = 12
Flux corrections constrained by regional patterns
Potential applications for CH4
• What is the optimal network expansion?– Continuous vs. flask measurements– Value of satellite concentrations for various sensors– Proximity of measurements to sources– Accuracy and resolution vs. density of measurements
• What other types of data can we assimilate?– Satellite water distributions– Direct flux measurements– Inventory data
• Can we assimilate CO2 and CH4 together?
Primary requirement is people