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

My Geography and Uncertainty X X

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Hour Day Month Year Decade Leaf ~cm 2 Small Chamber ~m 2 Flux Tower ~1 km 2 Regional ~100s km 2 My spatio-temporal pattern and uncertainty Landscape ~ 100s m 2 Plant 10s cm 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 Naithani et al., in prep. Less data More data (95% CI / Mean)

Uncertainty increases with increasing spatial coverage of flux tower data record Naithani et al., in prep. Mean 95 % CI Multiple Towers One Tower

Time 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 Tg C yr % 33 %

16 Eddy Flux Towers e1….e17 16 Eddy Flux Towers e1, e2,….e17 17 Eddy Flux Towers e1, e2, ….e17    a) Common riska) Independentc) 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 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

Thank you! 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.