Characterizing observational and model uncertainty
Kusum Naithani
Department of Geography
The Pennsylvania State University
ChEAS 2012 Workshop

X
My Geography and Uncertainty
X

X
My Geography and Uncertainty
X

My spatio-temporal pattern and uncertainty
Decade
Year
Month
Day
Hour
Leaf
~cm 2
Plant
10s cm 2
Small Chamber
~m 2
Landscape
~ 100s m 2
Flux Tower
~1 km 2
Regional
~100s km 2

Goals/Issues
1. 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
X
X
X
X
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
X
X
X
X
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
3,0
2,5
2,0
1,5
1,0
0,5
0,0
0 250 500 750 1000 1250
Number of Days
More data
Less data
Naithani et al., in prep.

Uncertainty increases with increasing spatial coverage of flux tower data record
0,85
0,75
0,65
0,55
0,45
0
Mean
95 % CI
2 4
Number of flux Towers
6
One Tower
Multiple
Towers
Naithani et al., in prep.

Influence of spatial and temporal extent of flux tower data on parameter and prediction uncertainty
Spatial representativeness
Spatial uncertainty
Temporal uncertainty
Space

Uncertainty in land cover introduces considerable uncertainty to carbon flux estimates
 Wetlands (MODIS)
NEE ( MODIS )
0.01% -2.9 Tg C yr -1
 Wetlands(NLCD)
33 %
-9.8 Tg C yr -1
NEE ( NLCD )
Xiao et al 2012, in review

Choice of modeling approach introduces considerable uncertainty to carbon flux estimates a) Independent
16 Eddy Flux Towers e1, e2,….e17
q,t q, t q, t a) Common risk
16 Eddy Flux Towers e1….e17
c) Hierarchical
17 Eddy Flux Towers e1, e2, ….e17
q, t q,t q,t q,t
Q, t
Naithani et al., in prep.

Choice of a particular model introduces considerable uncertainty to carbon flux estimates
Representation of different processes
Residuals 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

Mentors
Ken Davis (PI-ChEAS)
Erica Smithwick (PI-ChEASII)
Collaborators/Contributors
Klaus Keller
Robert Kennedy
Jeff Masek
Jingfeng Xio
Nathan Urban
Paul Bolstad
Dong Hua
Data Contributors
Data was contributed by K.
Davis, C. Gough, P. Curtis,
A. Noormets, J. Chen, A.
Desai, B. Cook & K.
Cherrey.