Integrating Data Management into Climate Change Science Research

Bruce WilsonGroup Leader,Client and Collaboration TechnologiesInformation Technology Services Division

Adjunct Professor of Information SciencesUniversity of Tennessee

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My background and perspective

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• Carbon Cycle and Carbon Sequestration• Climatic Change Research• Atmospheric Radiation Measurements• Biogeochemistry of Terrestrial Ecosystems• Biodiversity

We provide value-added data and analysis tools used to: • Enable an objective assessment of the potential for, and

consequences of, global change• Improve the treatment of cloud and radiation physics in global climate

models in order to improve the climate simulation capabilities of these models

• Understand the complexity of the carbon cycle and the linkages to physical, biogeochemical and ecological processes and human influences

• Harvest, index, and search metadata for the discovery and delivery of spatiotemporal data

Objective: Advance environmental research and policy by developing and providing leading-edge, integrated environmental data management and delivery systems, particularly in the areas of:

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Data Centers at a GlanceARM Archive DAAC CDIAC

Sponsor DOE BER NASA SMD DOE BER

Type of dataAtmospheric

processes, cloud dynamics

Biogeochemical dynamics, carbon cycle, FLUXNET

Atmospheric gasses, emissions

data, Ameriflux

Archive Size > 200 TB~ 1 TB + 30 TB of MODIS data

~ 400 GB

Users/year ~1500 ~15000 ~300,000

Year Started 1991 1993 1982

• Broad range of users: modelers, field scientists, educators, NGO’s, general public

• Broad range of atmospheric, land process, and ocean data

• Additional projects within and between data centers

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Climate Data Explosion (hundreds of XB by 2020)

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Climate Data Integration Challenges

• Complexities of field research data

• Volume of remote sensing and model data

• Challenges of spatial projections and scale– Earth System Grid vs biodiversity data

• Tremendous range of length and time scales– What scale is relevant depends on what question you ask

• Widely varying cultures and data sharing expectations

• Semantics– What is the sign for NPP when carbon is sequestered

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Cost is a huge challenge

• How good is good enough?

• Should we close down 4 flux towers to improve data management at the remaining 90?

• What kind of mortgage can we afford?

• How do we train scientists in data, when they already have to learn so much “in domain”?

• How do data centers adopt new technologies while maintaining legacy operations?

• How do we move tools from proof-of-concept to production quality?

• How do we deal with data that has commercial value and governments that want to recoup investment?

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