Benefit of ASP, not generally being subjected to this:

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 CO2 exceed anything seen in last 650,000 yr

4. Carbon Dioxide

5. Carbon Dioxide

6. Carbon Dioxide • Atmospheric CO2 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 CO2 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 CO2 growth rate variability (Peylin et al, 2005, GBC) • Measurements of atmospheric CO2 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 CO2 cycle: • Plants uptake CO2 by photosynthesis = Gross Primary Production (GPP) = function of light, CO2, water, temperature, humidity [Farquhar, Ball, Berry, Cook, Collatz, Sharkey] • Plants respire some of this CO2 during carbohydarate conversion and utilization = Autotrophic Respiration (Ra) = function of temperature and substrate availability • Soil bacteria decompose organic carbon (dead plants) and release CO2 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 CO2 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. Measuring Stand Scale Flux Respiration and Photosynthesis Respiration

17. Measuring Stand Scale Flux • Top: Daily NEE, Bottom: Cumulative NEE

18. Measuring Stand Scale Flux

19. Measuring Stand Scale Flux • 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 Desai et al, 2005, Ag. For. Met Cook et al, 2004, 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 CO2 growth rates are mediated by land fluxes • Problem is nonlinear - land fluxes are also functions of CO2 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 WLEFtall tower Lost Creekwetland Willow Creekhardwood Sylvaniaold-growth

38. Fluxes and Age

39. ABL Budget Equation