1. CarbonFusion meeting, 4 or 5 June 2008 Building from the bottom-up and learning as we go: data requirements for upscaling ecosystem function Paul Stoy 1*, Mathew Williams 1 1 School of GeoSciences, University of Edinburgh, UK Jon Evans 2, Colin Lloyd 2 2 Center for Ecology and Hydrology, Wallingford, UK Ana Prieto-Blanco 3, Mathias Disney 3 3 Department of Geography, University College London, London, UK Gaby Katul 4, Mario Siqueira 4, Kim Novick 4, Jehn-Yih Juang 4, Ram Oren 4 4 Nicholas School of the Environment and Earth Sciences, Duke University, USA 1)Intro a)Motivation 2) Examples a)Duke sites b)Tundra site c)IC 3) Summary a)What models need

2. Motivation ‘The Leuning 7’ [after Liu and Gupta (2007)] LSMs consist of 7 components: 1) the system boundary, B 2) inputs, u 3) initial states, x 0 4) parameters, θ 5) model structure, M 6) model states, x and 7) outputs, y 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 1)Intro a)Motivation 2) Examples a)Duke sites b)Tundra site c)IC 3) Summary a)What models need

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

4. The challenge (continued): Oishi et al., (in press) AFM The adjacent DBF ecosystem (HW) has: wet & dry subplots, multiple species, LAI variability 95% peak s.w.f. 50% peak s.w.f. sapflux Litter baskets 1)Intro a)Motivation 2) Examples a)Duke sites b)Tundra site c)IC 3) Summary a)What models need

5. A small part of a complicated landscape Juang et al., (2007) WRR Stoy et al., (2007) GCB 1)Intro a)Motivation 2) Examples a)Duke sites b)Tundra site c)IC 3) Summary a)What models need

6. MODIS GPP algorithm for PP Heinsch et al., (2006) IEEE-TGRS ENF or MF? Savanna? Observational bias (remote sensing) plays a central role for modelling & measurement 1)Intro a)Motivation 2) Examples a)Duke sites b)Tundra site c)IC 3) Summary a)What models need

7. Sources of bias (tundra) Burba et al., (2008) GCB Asner et al., (2003) GEB Flux observation bias is an additional challenge 1)Intro a)Motivation 2) Examples a)Duke sites b)Tundra site c)IC 3) Summary a)What models need

8. ‘De-biasing (?)’ using a footprint model Left: LAI map of Abisko Tundra (AT) With ½ hr. footprint Right: pdf of tower- measured (daily, black) vs. footprint NDVI 1)Intro a)Motivation 2) Examples a)Duke sites b)Tundra site c)IC 3) Summary a)What models need

9. ‘De-biasing (?)’ using a footprint model 1)Intro a)Motivation 2) Examples a)Duke sites b)Tundra site c)IC 3) Summary a)What models need

10. Upscaling = preserving information? 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? 1)Intro a)Motivation 2) Examples a)Duke sites b)Tundra site c)IC 3) Summary a)What models need

11. NEE Residual Spectrum (mg C m -2 s -1 ) 2 2000 2000.5 2001 2001.5 2002 2002.5 2003 2003.5 Year Time Scale (y) 10 -4 10 -3 10 -2 10 -1 10 0 10 -4 10 -3 10 -2 10 -1 10 0 2000 2000.5 2001 2001.5 2002 2002.5 2003 2003.5 HDWMYHDWMY HDWMYHDWMY Wavelet half plane model residual analysis: Duke PP and HW Color = residual energy

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? 1)Intro a)Motivation 2) Examples a)Duke sites b)Tundra site c)IC 3) Summary a)What models need

14. The important time scales of variability are long 1)Intro a)Motivation 2) Examples a)Duke sites b)Tundra site c)IC 3) Summary a)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? Funding: NERC (IPY) 1)Intro a)Motivation 2) Examples a)Duke sites b)Tundra site c)IC 3) Summary a)What models need

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