1. Benefit of ASP, not generally being subjected to this:

2. What the flux? Constraining ecosystem models with flux tower mesonets Ankur Desai National Center for Atmospheric Research ASP Research Review, 7 Mar 2007 Boulder, CO USA

3. Carbon Dioxide Carbon dioxide and climate are closely linked in our atmospheric system Atmospheric mixing ratios of CO 2 exceed anything seen in last 650,000 yr

4. Carbon Dioxide

6. Atmospheric CO 2 growth rate is not constant –more variable than rate of increase in fossil fuel use Land and ocean sources/sinks –complex internal feedbacks –also affected by external episodic (e.g., volcano) and oscillatory (e.g., ENSO) events Basic mechanisms understood –specific processes in land and ocean are not –regional scale evaluation is critically needed

7. Pools and Fluxes

8. The Terrestrial Ecosystem Responses between land and atmospheric CO 2 are highly variable and functions of: –geography (e.g., N.H. land sink) –land cover –management (e.g., tropical deforestation) –land-atmosphere feedbacks of carbon, water and energy Latest atmospheric data inversions and biogeochemical models converge on terrestrial carbon cycle as primary control on atmospheric CO 2 growth rate variability (Peylin et al, 2005, GBC) Measurements of atmospheric CO 2 over land have, until recently, been limited

9. Peylin et al, 2005, GBC

10. Terrestrial Ecosystem Regional biosphere flux variability is complex Source: NOAA/ESRL (Carbon Tracker), units Mg Ha -1 yr -1

11. Terrestrial Terminology The terrestrial CO 2 cycle: –Plants uptake CO 2 by photosynthesis = Gross Primary Production (GPP) = function of light, CO 2, water, temperature, humidity [Farquhar, Ball, Berry, Cook, Collatz, Sharkey] –Plants respire some of this CO 2 during carbohydarate conversion and utilization = Autotrophic Respiration (Ra) = function of temperature and substrate availability –Soil bacteria decompose organic carbon (dead plants) and release CO 2 back to the atmosphere = Heterotrophic Respiration (Rh) = function of temperature, soil moisture, substrate availability, bacterial community kinetics –Total Ecosystem Respiration = Rh + Ra –Lots of non-linear interactions –Disturbance, land use, competitions are larger scale effects

12. Terrestrial Terminology Most important term: –NEE = Net Ecosystem Exchange = Net CO 2 flux = ER – GPP Negative = sink from atmosphere to biosphere Positive = source from biosphere to atmosphere Modeling NEE, GPP, ER is hard because: –Functions are empirical, typically enzyme kinetics –Parameters are unknown, hard to measure –Works well for a single leaf, simple soil but not always for entire forests and realistic soils What are we trying to do –Upscaling fluxes from leaf to forest stand, ecosystem, biome is current heart of research enterprise called the “bottom-up” approach –Downscaling tracers/satellites from globe to continent to region is heart of the “top-down” approach –Convergence = we can measure/predict/test hypotheses with regional fluxes –At least 98 grad students agree and want to learn more

13. Measuring Stand Scale Flux We can measure ecosystem land-atmosphere flux (NEE) at spatial length scales of 1-10 km with the Eddy Covariance technique –How? Use the ensemble-averaged turbulent scalar conservation equation

14. Measuring Stand Scale Flux We have instruments to be able to do this

15. Measuring Stand Scale Flux

16. Respiration Respiration and Photosynthesis Measuring Stand Scale Flux

17. Top: Daily NEE, Bottom: Cumulative NEE

18. Measuring Stand Scale Flux

19. Lots of folks are now doing this (first in early 90s)

20. Pitfalls With Eddy Covariance Major assumptions for using time-averaged flux as stand-in for ensemble average (Reynolds’ “frozen field” hypothesis) –flow is turbulent, above roughness sublayer, stationary –signal spectral attenuation and instrument lags are minimal and can be empirically corrected –time period captures major scales of turbulence Berger et al, 2001, JAOT

21. Pitfalls With Eddy Covariance Nocturnal stable boundary layer provides most challenging conditions: –nighttime NEE decline with u* suggests primary flow is not 1-D (e.g., advection) intermittent turbulence –non-homogenous cover/terrain effects Cook et al, 2004, Ag. For. Met. Desai et al, 2005, Ag. For. Met

22. Upscaling Goals Upscaling fluxes from sites (e.g., measured with eddy covarinace) to regions is a pressing research issue –Helps understand land-atmosphere interaction at scales relevant to global models, decisions support –Emergent properties of land-atmosphere interaction may appear –But: upscaling is hard when landcover or terrain is complex Hypotheses: –Inversion of NEE from multiple tower sites can lead to regional scale ecosystem parameters that reproduce regional flux –Parameters are significantly different across major ecosystem type boundaries –Wetlands are more sensitive to precipitation variability than uplands Several regions have dense flux tower networks that could be used to constrain a regional ecosystem model Northern Wisconsin is one of these regions –Plus we can evaluate this flux with the 447-m tall flux tower, tall tower ABL budgets, forest inventory, and a regional mesoscale CO2 inversion

23. Upscale This!

24. Already upscaled

25. Dense Mesonet

26. Tall Tower Cumulative NEE Net annual source since 1997

27. Complex Landcover

28. Regional Flux?

29. Stand Scale Flux Variability

30. Method We can use models constrained with data to get regional flux Ecosystem models do generally well at simulating daily and seasonal cycle –Poor at interannual variability, long term trends –Also, parameters are unknown Parameter estimation using well established method – Markov Chain Monte Carlo (MCMC) Ecosystem Model to be used is SipNET SipNET parameter estimation was designed from the get-go to be “spatial” –Multiple sites can be assimilated at once –Some parameters vary spatially, others are fixed –Cost function reflects this by summing RMS model-data error across sites and modifying parameter walk

31. Method MCMC is an optimizing method to minimize model-data mismatch –Quasi-random walk through parameter space (Metropolis) Prior parameters distribution needed Start at many places (random) in prior parameter space –Move “downhill” to minima in model-data RMS –Avoid local minima by occasionally performing “uphill” moves –Requires ~100,000 model iterations –End result – “best” parameter set and confidence intervals (from all the iterations) –NEE, Latent Heat Flux (LE) and Sensible Heat Flux (H) can all be used Nighttime NEE good measure of respiration, maybe H? Daytime NEE, LE good measures of photosynthesis SipNET is fast (~100 ms year -1 ), so good for MCMC (hours) –Based on PNET ecosystem model –Tested at several sites –Driven by climate, parameters and initial carbon pools –Trivially parallelizable (needs to be done, though)

32. Simple Test of SipNET & MCMC

33. The Next Test Region is 70% upland, 30% wetland Combine the 3 hardwood sites together to estimate upland NEE Combine the 3 wetland sites to estimate wetland NEE Use remote sensing to add hardwood+wetland Compare to using only 1 hardwood tower, 1 wetland tower, 1 hardwood+wetland tower Compare to the independent regional flux estimates (tall tower, FIA driven model, ABL budgets, regional inverse methods) See if parameters can predict interannual variability over next several years at tall tower

34. Progress Not much, ACME07 and RBGC07 take all my time. Need a catchy acronym to get more work done! Test assimilation with tall tower done SipNET probably not a good wetland model, proposal funded to fix that Number of parameters one can constrain with flux data is relatively small (4-10), other data (transpiration, vegetation indices, …) could help –Meteorologists are better at this kind of data assimilation but goal is different (forecast, equations are known, model is slower, [3,4]DVAR or EKF better suited) Could regional tracer mesonets also be used here? Another oversampled test case this summer is the North American Carbon Program (NACP) Mid-Continent Intensive (MCI) over Iowa

35. Conclusions Atmospheric CO 2 growth rates are mediated by land fluxes –Problem is nonlinear - land fluxes are also functions of CO 2 and temperature There’s lots to learn about land-atmosphere trace gas exchange and interaction –Regional scales are key in terms of understanding whole ecosystems, emergent responses, regional impacts, decision support and global model evaluation We can measure fluxes with the eddy covariance technique Scaling up and down is hard Ecosystem models can be constrained with eddy covariance flux data Ecologists, meteorologists, foresters, and hydrologists will one day live in perfect harmony

36. Thanks Collaborators: Dave Schimel (CGD), Dave Moore (CIRES), Steve Aulenbach (CGD), Ken Davis (PSU), Bill Sacks (UWI) Funding: NSF, DOE, NASA, USDA Thanks: Land owners, technicians, students

37. Lots of Fluxes WLEF tall tower Lost Creek wetland Willow Creek hardwood Sylvania old-growth

38. Fluxes and Age

39. ABL Budget Equation