CarbonFusion meeting, 4 or 5 June 2008

1. CarbonFusion meeting, 4 or 5 June 2008 • Intro • Motivation • 2) Examples • Duke sites • Tundra site • IC • 3) Summary • What • models • need Building from the bottom-up and learning as we go: data requirements for upscaling ecosystem function Paul Stoy1*, Mathew Williams1 1 School of GeoSciences, University of Edinburgh, UK Jon Evans2, Colin Lloyd2 2 Center for Ecology and Hydrology, Wallingford, UK Ana Prieto-Blanco3, Mathias Disney3 3 Department of Geography, University College London, London, UK Gaby Katul4, Mario Siqueira4, Kim Novick4, Jehn-Yih Juang4, Ram Oren4 4 Nicholas School of the Environment and Earth Sciences, Duke University, USA

2. Motivation • Intro • Motivation • 2) Examples • Duke sites • Tundra site • IC • 3) Summary • What • models • need 9) How should the (FLUXNET) flux data be processed? 10) What ancillary data (including EO) can and should be used? Motivate these q’s using the upscaling challenge ‘The Leuning 7’[after Liu and Gupta (2007)] LSMs consist of 7 components: 1) the system boundary, B 2) inputs, u 3) initial states, x0 4) parameters, θ 5) model structure, M 6) model states, x and 7) outputs,y

3. The challenge: • Intro • Motivation • 2) Examples • Duke sites • Tundra site • IC • 3) Summary • What • models • need …interpreting ecosystem function from dynamic EC measurements. N gradient Example: The Duke FACE Site (PP) measures a footprint with relatively low LAI. NEEA would be ca. 50 g C m-2 y-1 if the tower was located centrally LAI 3.5 0.0 0 200m Oren et al., (2006) GCB How do we move from leaf to tree to tower to region?

4. The challenge (continued): • Intro • Motivation • 2) Examples • Duke sites • Tundra site • IC • 3) Summary • What • models • need The adjacent DBF ecosystem (HW) has: wet & dry subplots, multiple species, LAI variability 95% peak s.w.f. sapflux 50% peak s.w.f. Litter baskets Oishi et al., (in press) AFM

5. A small part of a complicated landscape • Intro • Motivation • 2) Examples • Duke sites • Tundra site • IC • 3) Summary • What • models • need Juang et al., (2007) WRR Stoy et al., (2007) GCB

6. MODIS GPP algorithm for PP • Intro • Motivation • 2) Examples • Duke sites • Tundra site • IC • 3) Summary • What • models • need ENF or MF? Savanna? Observational bias (remote sensing) plays a central role for modelling & measurement Heinsch et al., (2006) IEEE-TGRS

7. Sources of bias (tundra) Burba et al., (2008) GCB • Intro • Motivation • 2) Examples • Duke sites • Tundra site • IC • 3) Summary • What • models • need Asner et al., (2003) GEB Flux observation bias is an additional challenge

8. ‘De-biasing (?)’ using a footprint model • Intro • Motivation • 2) Examples • Duke sites • Tundra site • IC • 3) Summary • What • models • need Left: LAI map of Abisko Tundra (AT) With ½ hr. footprint Right: pdf of tower-measured (daily, black) vs. footprint NDVI

9. ‘De-biasing (?)’ using a footprint model • Intro • Motivation • 2) Examples • Duke sites • Tundra site • IC • 3) Summary • What • models • need

10. Upscaling = preserving information? • Intro • Motivation • 2) Examples • Duke sites • Tundra site • IC • 3) Summary • What • models • need Stoy et al. (in review) Ecosystems Finding spatial averaging operator(s) that preserve fine-scale information content (IC) [via Shannon Entropy, Kullback-Liebler divergence] IC for parameter space analysis?

11. Wavelet half plane model residual analysis: Duke PP and HW 10-4 10-3 10-2 10-1 100 H D W M Y Time Scale (y) Color = residual energy NEE Residual Spectrum (mg C m-2 s-1)2 • 2000.5 2001 2001.5 2002 2002.5 2003 2003.5 10-4 10-3 10-2 10-1 100 H D W M Y Time Scale (y) • 2000.5 2001 2001.5 2002 2002.5 2003 2003.5 • Year

12. Suggestions for LSMs • Problems for upscaling and models • Observational bias Measurement bias (and random error) • Potential for de-biasing using additional ecological information • Future directions / needs for FLUXNET • The ‘super site’ concept (e.g. IMECC) • Ray’s 20 ecosystems? • - We need temporal and spatial data for: • Ecosystem structure and • (with parameters), function • - How much? • - Probably just enough to describe • ecosystem change over time.

13. How does flux ‘resonate’ with climate? • Intro • Motivation • 2) Examples • Duke sites • Tundra site • IC • 3) Summary • What • models • need

14. The important time scales of variability are long • Intro • Motivation • 2) Examples • Duke sites • Tundra site • IC • 3) Summary • What • models • need Few high frequency (bi-monthly or less) Differences among Veg/climate types We need PFTs after The bi-monthly t.s.

15. Questions? • Intro • Motivation • 2) Examples • Duke sites • Tundra site • IC • 3) Summary • What • models • need Funding: NERC (IPY)

16. Reducing uncertainty with data assimilation Adding data increases confidence • Intro • Motivation • Model • 2) Methods • Site • Meas • Movie • 3) Results • Model • Data • assimilation • c) FLUXNET Obs (t+1) Initial Forecast model (PLIRT) State (t) Forecast (t+1) Assimilation State (t+1) (Ensemble Kalman Filter) (Shaver et al. Parameters) Early Season Improvement g C m-2 77±3 PLIRT gapfilling model (Burba GCB ’08?) 127±2 Cumulative 140±3 168±13