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