characterizing observational and model uncertainty kusum naithani department of geography

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.