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

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

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)

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

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

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

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.

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

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

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 http://www.cdas.ucar.edu/

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

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 http://www.cdas.ucar.edu/

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 http://www.cdas.ucar.edu/

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 http://www.cdas.ucar.edu/

Carbon Data-Model Assimilation (C-DAS) CDAS Application: Data System User Interface http://www.cdas.ucar.edu/

Carbon Data-Model Assimilation (C-DAS) http://www.cdas.ucar.edu/

Carbon Data-Model Assimilation (C-DAS) http://www.cdas.ucar.edu/

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 http://www.cdas.ucar.edu/

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 http://www.cdas.ucar.edu/

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 http://www.cdas.ucar.edu/

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 http://www.cdas.ucar.edu/

Carbon Data-Model Assimilation (C-DAS) http://www.cdas.ucar.edu/

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 http://www.cdas.ucar.edu/

Carbon Data-Model Assimilation (C-DAS) Adjoint Flux October June March January http://www.cdas.ucar.edu/

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

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

Designing observational networks – Analyses Carbon Data-Model Assimilation (C-DAS) Designing observational networks – Analyses Surface 600 millibars

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

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

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…..

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

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