characterizing observational and model uncertainty kusum naithani department of geography
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Characterizing observational and model uncertainty Kusum Naithani Department of Geography The Pennsylvania State University ChEAS 2012 Workshop. My Geography and Uncertainty. X. X. My Geography and Uncertainty. X. X. My spatio-temporal pattern and uncertainty. Decade. Year. Month. - PowerPoint PPT PresentationTRANSCRIPT
Characterizing observational and model uncertainty
Kusum NaithaniDepartment of Geography
The Pennsylvania State University
ChEAS 2012 Workshop
My Geography and Uncertainty
XX
My Geography and Uncertainty
XX
Hour
Day
Month
Year
Decade
Leaf~cm2
Small Chamber~m2
Flux Tower~1 km2
Regional~100s km2
My spatio-temporal pattern and uncertainty
Landscape~ 100s m2
Plant10s cm2
Goals/Issues1. Quantification of regional/global C fluxes and associated
uncertainty -Maps of C fluxes and uncertainty (daily, seasonal, annual, decadal)-Temporal uncertainty versus spatial uncertainty
2. Diagnosis of the sources of uncertainty in C fluxes-Input data (climate, land cover, disturbance, phenology, flux tower) -Modeling framework (e.g., complex process models vs. statistical models)-Model structure (5 algorithms at Penn State)-Spatial representativeness of flux towers
3. Benchmarking standards for model-intercomparisons focused on uncertainty
4. Visualization of C fluxes and associated uncertainty-Better ways to visualize mean and uncertainty-Customize it for the enduser (scientific community, forest service, policy makers, public etc.)
Accuracy and Uncertainty
Accuracy: measure of centrality
Uncertainty (precision): measure of spread
XX
XX
X
High accuracy and Low uncertainty
Accuracy and Uncertainty
Accuracy: measure of centrality
Uncertainty (precision): measure of spread
X
X
X
X
X
High accuracy and High uncertainty
Accuracy and Uncertainty
Accuracy: measure of centrality
Uncertainty (precision): measure of spread
XX
XX
X
Low accuracy and Low uncertainty
Accuracy and Uncertainty
Accuracy: measure of centrality
Uncertainty (precision): measure of spread
X
X
X
X
X
Low accuracy and High uncertainty
Uncertainty declines with increasing temporal coverage of flux tower data record
Naithani et al., in prep.
Less data
More data
(95%
CI /
Mea
n)
Uncertainty increases with increasing spatial coverage of flux tower data record
Naithani et al., in prep.
Mean
95 % CIMultipleTowers
One Tower
Tim
e
Space
Influence of spatial and temporal extent of flux tower data on parameter and prediction uncertainty
Spatial uncertainty
Temporal uncertainty
Spatial representativeness
Xiao et al 2012, in review
Uncertainty in land cover introduces considerable uncertainty to carbon flux estimates
NEE (MODIS)
NEE (NLCD)
Wetlands (MODIS)
Wetlands(NLCD)
-9.8 Tg C yr-1
-2.9 Tg C yr-10.01%
33 %
16 Eddy Flux Towers e1….e17
16 Eddy Flux Towers e1, e2,….e17
17 Eddy Flux Towers e1, e2, ….e17
a) Common riska) Independent c) Hierarchical
Choice of modeling approach introduces considerable uncertainty to carbon flux estimates
Naithani et al., in prep.
Choice of a particular model introduces considerable uncertainty to carbon flux estimates
Representation of different processesResiduals analysis and MIPs
Better communication of modeling outputs in terms of visualization of mean and uncertainty
Thinking about clever ways of communicating science to outside world
In summary there are multiple sources and a great deal of uncertainty waiting to be quantified, analyzed and visualized!
Workshop outcomes
Synthesis paper (s) on assessment and/or visualization of uncertainties in C flux upscaling.
Upscaling methodologies
Comparison of existing products
Thank you!MentorsKen Davis (PI-ChEAS)Erica Smithwick (PI-ChEASII)
Collaborators/Contributors Klaus Keller Robert Kennedy Jeff Masek
Jingfeng XioNathan UrbanPaul BolstadDong Hua
Data ContributorsData was contributed by K. Davis, C. Gough, P. Curtis, A. Noormets, J. Chen, A. Desai, B. Cook & K. Cherrey.