1. Spatial Processes and Land-atmosphere Flux Constraining ecosystem models with regional flux tower data assimilation Flux Measurements and Advanced Modeling, 22 July 2008 CU Mountain Research Station, “Ned”, Colorado Ankur Desai Atmospheric & Oceanic Sciences, University of Wisconsin-Madison

2. Let’s get spacey…

3. And regional

4. Why regional? Spatial interpolation/extrapolation Evaluation across scales Landscape level controls on biogeochem. Understand cause of spatial variability Emergent properties of landscapes

6. Why regional? Courtesy: Nic Saliendra

7. Why regional? NEP (=-NEE) at 13 sites Stand age matters Ecosystem type matters Is interannual variability coherent? Are we sampling sufficient land cover types”?

8. Why data assimilation? Meteorological, ecosystem, and parameter variability hard to observe/model Data assimilation can help isolate model mechanisms responsible for spatial variability Optimization across multiple types of data Optimization across space

9. Why data assimilation? Old way: –Make a model –Guess some parameters –Compare to data –Publish the best comparisons –Attribute discrepancies to error –Be happy

10. Why data assimilation? New way: –Constrain model(s) with observations –Find where model or parameters cannot explain observations –Learn something about fundamental interactions –Publish the discrepancies and knowledge gained –Work harder, be slightly less happy, but generate more knowledge

12. Back to those stats… [A|B] = [AB] / [B] [P|D] = ( [D|P] [P] ) / [D] (parameters given data) = [ (data given parameters)× (parameters) ] / (data) Posterior = (Likelihood x Prior) / Normalizing Constraint

13. For the visually minded D Nychka, NCAR

14. Some case studies Prediction Up and down scaling Regional evaluation Interannual variability Forest disturbance and succession

15. Regional Prediction

16. Our tower is bigger…

17. Is there a prediction signal?

18. Sipnet A “simplified” model of ecosystem carbon / water and land-atmosphere interaction –Minimal number of parameters –Driven by meteorological forcing Still has >60 parameters Braswell et al., 2005, GCB Sacks et al., 2006, GCB added snow Zobitz et al., 2008

19. Parameter estimation MCMC is an optimizing method to minimize model-data mismatch –Quasi-random walk through parameter space (Metropolis) Prior parameters distribution needed Start at many random places (Chains) in prior parameter space –Move “downhill” to minima in model-data RMS by randomly changing a parameter from current value to a nearby value –Avoid local minima by occasionally performing “uphill” moves in proportion to maximum likelihood of accepted point –Use simulated annealing to tune parameter space exploration –Pick best chain and continue space exploration –Requires ~500,000 model iterations (chain exploration, spin-up, sampling) –End result – “best” parameter set and confidence intervals (from all the iterations) –NEE, Latent Heat Flux (LE), Sensible Heat Flux (H), soil moisture can all be used Nighttime NEE good measure of respiration, maybe H? Daytime NEE, LE good measures of photosynthesis SipNET is fast (<10 ms year -1 ), so good for MCMC (4 hours for 7yr WLEF) –Based on PNET ecosystem model –Driven by climate, parameters and initial carbon pools –Trivially parallelizable (needs to be done, though)

20. Goldilocks effect…

21. 2 years = 7 years 199719981999200020012002200320042005

22. Regional futures

24. Upscaling and Downscaling

25. So many towers…

26. …so much variability

27. Simple comparisons… Desai et al, 2008, Ag For Met

28. …don’t work

29. We need to do better Lots of flux towers (how many?) Lots of cover types A very simple model Have to think about the tall tower flux, too –What does it sample?

30. Multi-tower synthesis aggregation with large number of towers (12) in same climate space –towers mapped to cover/age types –parameter optimization with minimal 2 equation model

32. Heterogeneous footprint

33. Tall tower downscaling Wang et al., 2006

34. Scaling evaluation Desai et al., 2008

35. Scaling sensitivity

36. Now we can wildly extrapolate Take 17 towers Fill the met data Use a simple model to estimate parameters for each tower using MCMC Apply parameters to other region meteorology data Scale to region by cover/age class

37. Another simple(r) model No carbon pools GPP model driven by LAI, PAR, Air temp, VPD, Precip LAI model driven by GDD (leaf on) and soil temp (leaf off) 3 pool ER, driven by Soil temp and GPP 19 parameters, fix 3

40. 20 yr regional NEE Cover types + Age structure + Parameters Forcing for a lake organic carbon input model

41. Regional scale evaluation

42. Top down and bottom up

43. IAV not modeled well

44. Region Interannual variability

45. Ricciuto et al.

48. IAV Does growing season start explain IAV? Can a very simple model be constructed to explain IAV? –Hypothesis: growing season length explains IAV Can we make a cost function more attuned to IAV? –Hypothesis: MCMC overfits to hourly data

49. New cost function Original log likelihood computes sum of squared difference at hourly What if we also added monthly and annual squared differences to this likelihood? Have to scale these less frequent values

55. Regional Succession

56. History of land use

57. Ecosystem Demography Moorcroft et al., 2001; Albani et al., 2006; Desai et al., 2007 Height and age structured statistical gap model Well suited to data assimilation of regional inventory data (e.g., USFS FIA) –Use multiple FIA observation periods - estimate carbon pools by allometry, segregate by type, age, height classes –Tune growth parameters until forest growth matches FIA growth

62. Enough?

63. What did we learn? Spatial prediction, scaling, parameterizing all benefit from data assimilation Interannual variability has interesting spatial attributes that are hard to model! Wetlands and land use history matter You can’t build infinite towers, or even a sufficient number –Use data assim to discover optimal design? Spatial covariate information needs a formal way to be used in data assimilation

64. Where is your research headed? What questions do you have? –Mechanisms, forcings, inference, evaluation, prediction, estimating error or uncertainty What kinds of data do you have, can get, can steal? –“Method-hopping” A model can mean many things… Data assimilation can be another tool in your toolbox to answer questions, discover new ones

65. Data assimilation uses Not just limited to ecosystem carbon flux models E.g. estimating surface or boundary layer values (e.g., z 0 ), advection, transpiration, data gaps, tracer transport Many kinds, for estimating state or parameters

66. TOMORROW Lab - 6 hour tour Sipnet at Niwot Ridge Parameter estimation with MCMC Sipnet group projects –Several ideas: parameter sensitivity across sites, gap filling, prediction, regional extrapolation

67. Enough! Time for a beer?