1. Integrating ecological data and models to enable understanding and forecasting Mathew Williams, University of Edinburgh

2. 2 Source: CD Keeling, NOAA/ESRL Sampling at 3397 meters, well mixed free troposphere ppm Changes in atmospheric carbon dioxide Mauna Loa, Hawaii Atmospheric carbon dioxide is changing ?

3. Source: Sarmiento & Gruber 2002 There is a strong terrestrial sink for carbon Where is the sink located, what are its determinants and its resilience?

4. Models of climate-carbon coupling are inconsistent Data from IPCC AR4 comparing 11 coupled models NPP=net primary production, i.e. plant growth

5. Projections of the terrestrial sink differ significantly Friedlingstein et al. 2006 11 different model runs

6. Space (km) time s hr day month yr dec 0.11.010100100010000 Flask Site Time and space scales in ecological processes Physiology Climate change Succession Growth and phenology Adaptation Disturbance Photosynthesis and respiration Climate variability Nutrient cycling Models struggle to represent time and space scales in ecological processes

7. MODELS OBSERVATIONS FUSION ANALYSIS MODELS + Capable of interpolation & forecasts - Subjective & inaccurate? OBSERVATIONS +Clear confidence limits -Incomplete, patchy -net fluxes -indirect measures ANALYSIS + Complete + Clear confidence limits + Capable of forecasts Improving understanding of C dynamics

8. TALK OUTLINE 1.What is the information content of eddy flux time series for model calibration? 2.What biometric measurements are most valuable for model calibration? 3.How can problems of model equifinality (i.e. improbable parameters produce reasonable estimates of fluxes) be dealt with? 4.How can models upscale observations? 5.How can earth observations be used to constrain regional modelling? 6.GREENHOUSE – a UK GHG upscaling activity

9. 1. REFLEX To what degree are model parameters constrained by 2 years of daily flux data? What is the confidence interval on assessments of parameters and on projections? How do different calibration processes compare? CarbonFusion

10. Data Assimilation Linked Ecosystem Carbon (DALEC) model NPP Foliage Wood Roots SOM Litter Labile R auto R het Canopy processes Biomass Dead Organic Matter GPP Climate

11. Fox et al. 2009 AFM All algorithms seem to calibrate a model from time series of fluxes But disagree on the associated uncertainty calibrateforecast [Time, days]

12. Fox et al. 2009 AFM Net fluxes can reveal little about internal C dynamics [Time, days] Flux data provide little information on pool dynamics of SOM and wood

13. Parameter constraint indicates limited information content for key processes Analyse: Consistency among methods Confidence intervals constrained by the data Consistent with known “truth” “truth” Fox et al. 2009 A synthetic retrieval from noisy data – ‘true’ parameters are known,

14. Source: Howlandforest.org how do combinations of different data streams influence parameter estimates? how do these different parameter sets propagate into uncertainty in model predictions? Used multiple years of flux and biometric data from Howland, Maine, USA CarbonFusion 2. Biometric constraints

15. calibrationvalidation Richardson et al. 2010 obs model Wood increment data provide an orthogonal constraint on processes

16. 1.Daytime NEE fluxes only 2.Daytime and nighttime NEE fluxes 3.(2) plus “reality constraint” on C som and C w 4.(3) plus soil respiration 5.(3) plus LAI 6.(3) plus litterfall 7.(3) plus cumulative woody biomass increment 8.All constraints simultaneously CONSTRAINTS Richardson et al. 2010 NPP:GPP Root allocation Biomass increment data have significant information content for parameter determination

17. Constraints on predictions Richardson et al. 2010 Predictions of DALEC model, at end of calibration (1997-2000, open circles) and validation (2001-2004, closed circles) periods Multi-constraint parameter estimation generates predictions with narrower confidence intervals

18. 3. Dynamical and Ecological Constraints NPP Foliage Wood Roots SOM Litter Labile R auto R het f fol f root T dec T fol T wood T root T litter T SOM D temp C eff B day f lab T lab F day LMA C labrelease Canopy processes Biomass Dead Organic Matter f auto GPP Climate

19. Reality Check – DALEC Parameter Relationships Pool Magnitude and Dynamics Turnover Rates Pools - Input: Output Allocation Fractions & Phenology C POOL GPP Foliage Roots LitterWood SOMRoots Foliar Links to plant trait databases

20. Reality Check - Parameter Links f fol f root T dec T fol T wood T root T litter T SOM D temp C eff B day f lab T lab F day LMA C-lab C labrelease C-foliage C-root C-SOM C-litter C-wood f auto Canopy processes Biomass Dead Organic Matter

21. Source: Tim Hill and Tom Wade 4. Spatial heterogeneity and upscaling

22. Leaf area index and photosynthesis are strongly correlated Street et al, 2007

23. Carbon fluxes can be estimated from LAI Shaver et al, 2007 Net Ecosystem C exchange = f(LAI, temp, PPFD) R 2 =0.80 n=1400

24. LAI can be determined over landscapes from remote sensing Van Wijk & Williams 2005

25. Coarser scale landscape data produce altered NDVI distributions Stoy et al 2009

26. Altered NDVI distributions have implications for LAI and thus C uptake Stoy et al. 2009

27. Large-scale carbon cycle How can we use the currently available satellite data to improve our understanding of the Net Ecosystem Exchange (NEE) across the globe? Saatchi et al. 2011 Above-ground Biomass (AGB) NASA MODIS Leaf Area Index (LAI) 5. Earth observation assists upscaling

28. LAI assimilation constrains NEE at 206 FLUXNET Sites Woody Savana Deciduous Broadleaf Forest Grassland International comparison - This study has been performed within the FLUXCOM project framework DALEC NEE FLUXNET EC data 208 FLUXNET Sites

29. Ryan et al. (2012) GCB Biomass is a function of growth, mortality, disturbance

30. MODIS LAI only GPP - gC m -2 day NEE - gC m -2 day AGB- tC ha -1 864864 -0.1 -0.3 -0.5 -0.7 50 30 10 DALEC C cycle retrieval, 2007-2010 Sofala Province, Mozambique – 1160km 2

31. MODIS LAI only GPP - gC m -2 dayNEE - gC m -2 dayAGB- tC ha -1 864864 -0.1 -0.3 -0.5 -0.7 50 30 10 LAI & ALOS AGB DALEC C cycle retrieval, 2007-2010

32. MODIS LAI only GPP - gC m -2 day DA: MODIS LAI DA: MODIS LAI & ALOS AGB NEE - gC m -2 day GPP - gC m -2 day NEE- gC m -2 day AGB- tC ha -1 864864 -0.1 -0.3 -0.5 -0.7 50 30 10 LAI & ALOS PALSAR derived AGB DALEC C cycle retrieval, 2007-2010

33. NERC Greenhouse Gas Programme Deliverable C

34. Aims Over-arching goal: – To develop spatio-temporal upscaling frameworks for GHG fluxes over dominant UK land-covers and land-uses Address process uncertainties: – How do terrestrial biogenic fluxes of GHGs in the UK vary in response to meteorological drivers, land use and management? – How does uncertainty of regional biogenic GHG flux estimates change as model complexity and scale are varied?

35. Upscaling based around long-term measurement sites

36. Satellite and airborne data for spatial upscaling Flask sampling open path IRGA Picarro CH 4 Camera LST (K) Leaf area index Land surface temperature

37. Multi-scale observational campaigns Forestry Intensive grazing Arable Flight paths ★ ★ ★ Airborne 2015 Airborne 2014 Auto and manual chambers Eddy covariance towers Airborne fluxes & sampling

38. Upscaling Inputs Linked to multi-scale environmental, ecological and management data... Upscaling Soil sensor networks aerial photos ecological surveys farm surveys weather stations Satellite imagery Land cover maps Climate reanalyses

39. We use data to calibrate models... State data (traits, physics, inputs) Calibration Model development

40. ...to assess model error... State data (traits, physics, inputs) Calibration Model development Model error assessment Model simulation GHG fluxes, EO LST, LAI, Biometry Met. data, land cover, management

41. and to improve process representation State data (traits, physics, inputs) Model-data fusion Model development Model error assessment Model simulation GHG fluxes, EO LST, LAI, Biometry Met. data, land cover, management Compare for JULES and C-TESSEL

42. Summary and Conclusions (i) Constraining models with multiple independent data sets provides improve parameter estimation (ii) Ecological and dynamical constraints provide important relationships between parameters and carbon pools. (iii) NEE simulations can be improved by assimilating optical and radar satellite data. (iv) Satellite based estimates of AGB are important in determination of of large-scale C fluxes. (v) An opportunity for GREENHOUSE links to NEON

43. Acknowledgements: Andy Fox, Andrew Richardson, REFLEX team Casey Ryan,Tim Hill, Ed Ryan Anthony Bloom GREENHOUSE team