1. Carbon cycle model-data integration: The role of collaboratories
Carbon Data-Model Assimilation (C-DAS)
Carbon cycle model-data integration:
The role of collaboratories

2. Biogeosciences data systems: carbon cycle modeling and data assimilation
CO2 is the most important greenhouse gas and a model for studying others
Sinks currently account for ~1/2 of emissions and must be understood and possibly managed in the future
Sinks, especially land sinks, are of immediate value to society and individuals
The mechanisms, permanence and longevity of sinks is not well known
A unique interdisciplinary challenge for scientists

3. The North American Carbon Question
State of knowledge ca 1997: Large sink in Northern Hemisphere, distribution unknown,
ca 1998: Large sink in North America proposed based on modeling (Fan et al) sufficient to balance US fossil emissions
1999 Carbon Cycle Science Plan
ca 2000, 2001: Efforts to replicate Fan et al suggest a sink of ~1/3 the size
2002 North American Carbon Plan document (NACP)

4. The North American Carbon Question
Carbon Cycle Science Plan and NACP propose a measurement strategy based on airborne and surface concentration measurements and eddy covariance flux measurements
Airborne measurements are costly and controversial
Estimating fluxes and surface concentrations can (as of 2002) only be done meaningfully in simple (flat) landscapes
Sept. 2001: NCAR charged to develop a network design approach and convene the community in Summer 2002

6. Eg., Carbon uptake modeled using satellite inputs checked against eddy correlation data from the Niwot Ridge LTER site

7. Point observations are characteristic of bioregions but must link to regional management history data for extrapolation to grid scale

8. Designing observational networks
Rarely begun to test specific hypotheses!
Sites usually chosen by teams of scientists based on the interest and logistics of that site, and not mainly on the marginal return of the site to the integrated analyses
Large-scale networks have rarely been designed an integrated fashion, and are evaluated post-hoc.
With today’s statistical, modeling and remote sensing tools we can and (given the cost of network based research) must do better than this.

9. Designing observational networks - Solutions
Begin with a hypothesis or well-posed question (a network is really just like any other experiment) and follow up with assessments of the design against the hypothesis (could the network disprove the hypothesis?)
Treat the network design problem as a research project (resources in, technology infrastructure developed and applied, publications out)
Networks are intrinsically collaborative: the “human engineering” can’t be left out of the research project

10. Designing observational networks - Solutions
Carbon Data-Model Assimilation (C-DAS)
Designing observational networks - Solutions

11. Carbon Data-Model Assimilation (C-DAS)
Overview of CDAS
Users
http-Based
Interface
DODS
Aggregation
Server
Simulated
Observing
System
GrADS-
DODS
Server
Simulated
CO2
Observations
Reference Global
Atmospheric CO2
4D VAR
Assimilation
System

12. Biogeoscience data systems must:
Be user-driven
Be knowledge-based
Be discovery-oriented
Enhance collaboration

13. Carbon Data-Model Assimilation (C-DAS)
CDAS Application: Participation
Users
NCAR: SCD, Unidata;
GRADS
http-Based
Interface
NCAR:
CGD, ESIG, SCD, ATD, SOARS
DODS
Aggregation
Server
Simulated
Observing
System
Participants:
11 Nations, 17 Universities,
4 Federal Agencies, IGBP, SOARS
GrADS-
DODS
Server
Simulated
CO2
Observations
Reference Global
Atmospheric CO2
4D VAR
Assimilation
System
NCAR: CGD, SCD
NASA
CSU

14. Carbon Data-Model Assimilation (C-DAS)
Overview of CDAS: Production of Reference Atmospheric CO2
Users
http-Based
Interface
Annual
Land Model
Fluxes
(0.5o)
DODS
Aggregation
Server
Diurnal & Seasonal Cycle Model
Simulated
Observing
System
GrADS-
DODS
Server
Ocean
Model
Fluxes
(2o )
Atmospheric Transport
Model
Reference
Global
Atmospheric
CO2
Simulated
CO2
Observations
Reference Global
Atmospheric CO2
2.5o, resolution
25 vertical levels, 1 hour Dt, & 365 days = 2.6TB
Industrial
Fluxes
(1o )
4D VAR
Assimilation
System

15. Carbon Data-Model Assimilation (C-DAS)
CDAS Application: Data System User Interface
Users
http-Based
Interface
DODS
Aggregation
Server
Simulated
Observing
System
GrADS-
DODS
Server
Simulated
CO2
Observations
Reference Global
Atmospheric CO2
4D VAR
Assimilation
System

16. Carbon Data-Model Assimilation (C-DAS)
CDAS Application: Data System User Interface

17. Carbon Data-Model Assimilation (C-DAS)

18. Carbon Data-Model Assimilation (C-DAS)

19. Carbon Data-Model Assimilation (C-DAS)
Overview of CDAS: retrieval of fluxes using data assimilation
Users
http-Based
Interface
4D VAR
Assimilation
System
Atmospheric
Transport
Model
Estimated
Annual Fluxes
(Bioregional)
DODS
Aggregation
Server
Retrieved
CO2
Observations
Simulated
Observing
System
GrADS-
DODS
Server
Adjoint of
Atmospheric
Transport
Model
Compare
1st Guess fluxes
Simulated
CO2
Observations
Reference Global
Atmospheric CO2
Input Global
Atmospheric
CO2 fluxes
Optimizer
4D VAR
Assimilation
System

20. Carbon Data-Model Assimilation (C-DAS)
CDAS Application: Data Volumes
Users
2.6 TB
http-Based
Interface
DODS
Aggregation
Server
Simulated
Observing
System
GrADS-
DODS
Server
Simulated
CO2
Observations
Reference Global
Atmospheric CO2
200 MB
4D VAR
Assimilation
System
Global Estimate,
11 North American
Bioregions

21. Carbon Data-Model Assimilation (C-DAS)
CDAS Application: Hardware & Software Infrastructure
Users
SCD Data Portal,
NetCDF
http-Based
Interface
Perl/CGI-Based -> JSP/Servlet-Based
mySQL
DODS
Aggregation
Server
Simulated
Observing
System
GrADS-
DODS
Server
Open Source
Simulated
CO2
Observations
Reference Global
Atmospheric CO2
4D VAR
Assimilation
System
Fortran,
SCD “Blue Dawn” (IBM SP3)
NASA Model

22. Carbon Data-Model Assimilation (C-DAS)
CDAS Application: Latency & Performance
Users
http-Based
Interface
Requirements for User Interface:
2.6TB source data; Typical query = 106 records;
Latency < 1+ hours
DODS
Aggregation
Server
Simulated
Observing
System
GrADS-
DODS
Server
Simulated
CO2
Observations
Reference Global
Atmospheric CO2
Requirements for Assimilation System:
Global, 365 day forward and adjoint runs; Hourly time step;
1-10 iterations required; Latency < 24 hours
(8 SP3 dedicated processors per run; Typical latency = 2 hours per iteration)
4D VAR
Assimilation
System

23. Carbon Data-Model Assimilation (C-DAS)

24. Carbon Data-Model Assimilation (C-DAS)
“True” Concentrations
from “True” Fluxes
Unspecified land biosphere model
Unspecified ocean carbon model
“Prior” Concentrations
from “Prior” Fluxes
CASA land biosphere
Takahashi ocean

25. Carbon Data-Model Assimilation (C-DAS)
Adjoint Flux
October
June
March
January

26. In Situ Aircraft Network
Carbon Data-Model Assimilation (C-DAS)
Existing Network
Adjoint fields for July, inputting 1/s for each measurement and plotting lowest model layer
ppm-1
Blue Network
Red Network
Manual Flask Network
In Situ Aircraft Network

27. Designing observational networks – Analyses
Carbon Data-Model Assimilation (C-DAS)
Designing observational networks – Analyses
The SOARS program

28. Designing observational networks – Analyses
Carbon Data-Model Assimilation (C-DAS)
Designing observational networks – Analyses
Surface millibars

29. Designing observational networks – Analyses
Carbon Data-Model Assimilation (C-DAS)
Designing observational networks – Analyses
Surface Tropopause (10-15km)

30. Designing observational networks – Analyses
Carbon Data-Model Assimilation (C-DAS)
Designing observational networks – Analyses
Surface Tropopause (10-15km)

31. All of this should be more true in the real world…..
The North American Carbon Question
Even with 2.5o resolution (C-DAS) the lower atmosphere is rich in spatial-temporal structure in carbon dioxide concentrations resulting from both flux and transport processes
As the sampled altitude increases, carbon dioxide concentrations become smoother in time and space and reflect large-area time averages
Retrieving regional fluxes at scales meaningful to managers and decisionmakers requires being near the ground
Measurements near the ground, over land, must be continuous because of rapid variations of concentrations in time
Checking measurements made near the ground (where variability is high and sums therefore uncertain) requires complementary measurements aloft where the atmosphere records these sums
All of this should be more true in the real world…..

32. Information Technology for Biogeosciences
Developing and testing theory and models requires integration of complex process data with gridded data sets.
Required data are multi-scale, many formats, originating in multiple disciplines.
Rapid prototyping and development cycle to maximize user control of information systems, implies incorporating existing state-of-the-art components rather than de novo development
Data systems must allow user-driven, knowledge-based querying of multiple data types

33. Biogeoscience data systems must:
Be user-driven
Be knowledge-based
Be discovery-oriented
Enhance collaboration