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Motivation

A challenge to the flux-tower upscaling hypothesis? A multi-tower comparison from the Chequamegon Ecosystem-Atmosphere Study.

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Motivation

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  1. A challenge to the flux-tower upscaling hypothesis? A multi-tower comparison from the Chequamegon Ecosystem-Atmosphere Study K.J. Davis1, D.R. Ricciuto1, B.D. Cook2, M.P. Butler1, A.R. Desai1, W. Wang1, C. Yi3, P.S. Bakwin4, P.V. Bolstad2, J. Martin2, E. Carey2, D.S. Mackay5, B.E. Ewers6, J. Chen7, A. Noormets7, F.A. Heinsch8, A.S. Denning9, R. Teclaw10 1Penn State, 2U.Minnesota, 3U.Colorado, 4NOAA-CMDL, 5U.Buffalo, 6U.Wyoming, 7U.Toledo, 8U.Montana, 9Colorado State, 10USDA Forest Service With support from: DoE Terrestrial Carbon Processes Program, DoE National Institutes for Global Environmental Change, NOAA, NSF Division of Environmental Biology

  2. Motivation What is and what governs ecosystem-atmosphere exchange of CO2 on spatial scales of geopolitical and bioclimatological relevance?

  3. Outline • What is the “flux tower upscaling hypothesis?” • Method: How do we test this hypothesis? • Results: Flux magnitudes and flux variability. • Simultaneous up-scaling and down-scaling.

  4. Flux tower upscaling hypothesis Fluxes of CO2 (NEE, R, GEP) = f (climate variables, ecosystem characteristics) Climate and ecosystem variables can be mapped, functions determined, fluxes interpolated and integrated across space. NEE = net ecosystem-atmosphere exchange, R = ecosystem respiration, GEP = gross ecosystem productivity, NEP = net ecosystem productivity NEE = R – GEP = - NEP

  5. Flux: R, NEE, GEP Climate variables (x, y) Flux tower upscaling hypothesis Each point ~ (1 km)2 Flux = ax + by + c, interpolate fluxes over ~ (1000 km)2 Segregate further by ecosystem characteristics? Stand type (conifer, deciduous, grass, crop) Stand age (young, mature, old)

  6. Flux tower upscaling hypothesis Continent: Map biomes and climate, model fluxes Upper Midwest, N. America Stand: Eddy covariance flux towers representing key biomes and climate regions Tower sites Plots at towers Within stand: biometric data, chamber fluxes

  7. Flux tower upscaling hypothesis with simultaneous constraints Continent: Map biomes and climate, model fluxes N. American [CO2] Region: Map ecosystem variables, model fluxes N. Wisconsin [CO2] WLEF tower Forest: Clusters of flux towers Stand: Eddy covariance flux towers Within stand: biometric data, chamber fluxes

  8. Chequamegon Ecosystem-Atmosphere Study (ChEAS) region

  9. Flux: R, NEE, GEP Climate variables (x, y) Testing the upscaling hypothesis ChEAS

  10. Testing the upscaling hypothesis: Regional clusters of flux towers • Can fluxes be up-scaled from stand to forest or region? • Clusters can isolate the role of ecosystem characteristics via identical climate across sites. • What must be measured and mapped for flux upscaling?

  11. Forest-scale evaluation of the upscaling hypothesis: WLEF flux tower Photo credit: UND Citation crew, COBRA WLEF tall tower (447m) CO2 flux measurements at: 30, 122 and 396 m CO2 mixing ratio measurements at: 11, 30, 76, 122, 244 and 396 m

  12. ChEAS vegetation

  13. ChEAS flux tower array Yi et al, 00 Berger et al, 01 Davis et al, 03 Ricciuto et al, B51 Mackay et al, 02 Mackay et al, H29 Ewers et al, 02 Ewers et al, H30 Forest-scale flux: WLEF tower, 1997-present Dominant stand types and flux towers: Northern Aspen Forested Conifer hardwood wetland young old mature Willow Creek (UMBS) Lost Creek Chen B 2000-present 1999-present 2001-present 2002-present Bolstad et al, in press Cook et al, in prep Chen A 2002–present Sylvania 2002-present Desai et al, in prep Desai et al, B52D-04 Chen mobile Chen mobile 2003 2002

  14. ChEAS upscaling test results • Climate alone does not explain ChEAS CO2 fluxes. • The WLEF region is a source of CO2 to the atmosphere. • drying wetlands? • disturbance/management?

  15. ChEAS upscaling test results • Climate alone does not explain ChEAS CO2 fluxes. • The WLEF footprint is a source of CO2 to the atmosphere. • drying wetlands? • disturbance/management? • WLEF fluxes cannot be explained as a linear combination of Lost Creek and Willow Creek fluxes. • aspen? conifers? WLEF footprint dissimilar?

  16. NEE and gross fluxes at ChEAS sites: 1997-2002

  17. ChEAS upscaling test results • Climate alone does not explain ChEAS CO2 fluxes. • The WLEF footprint is a source of CO2 to the atmosphere. • drying wetlands? • disturbance/management? • WLEF fluxes cannot be explained as a linear combination of Lost Creek and Willow Creek fluxes. • aspen? conifers? WLEF footprint dissimilar? systematic errors that differ among flux towers? • Soil + leaf + stem respiration is similar in aspen and northern hardwoods in the Willow Creek area. • WLEF high respiration rate due to coarse woody debris?

  18. Chamber respiration fluxes Table 4. Estimated annual respiration for the whole ecosystems and components, 1999-2002. All rates are reported in Mg C ha-1 yr-1. Bolstad et al, in press.

  19. ChEAS upscaling test results • Climate alone does not explain ChEAS CO2 fluxes. • The WLEF footprint is a source of CO2 to the atmosphere. • drying wetlands? • disturbance/management? • WLEF fluxes cannot be explained as a linear combination of Lost Creek and Willow Creek fluxes. • aspen? conifers? WLEF footprint dissimilar? systematic errors that differ among flux towers? • Soil + leaf + stem respiration is similar in aspen and northern hardwoods in the Willow Creek area. • WLEF high respiration rate due to coarse woody debris? • Chamber R >> W Creek R implies error? • Sylvania (old growth) fluxes differ from Willow Creek (mature) fluxes as expected due to stand age (similar GEP, old R > mature R). • But soil respiration from chambers contradicts this result.

  20. GEP, old growth (red) vs. mature (blue) forest R, old growth (red) vs. mature (blue) Sylvania – Willow Creek flux tower comparison

  21. Summary • Simple tower upscaling hypothesis, WLEF = a*W Creek + b*L Creek, fails. • Means of reconciliation is not clear. • Upscaling the magnitude of R, GEP, NEE is challenging.

  22. Motivation II What is and what governsthe interannual variability in ecosystem-atmosphere exchange of CO2 on spatial scales of geopolitical and bioclimatological relevance?

  23. Interannual variability in the rate of accumulation of atmospheric CO2

  24. s(flux) = flux – mean flux Climate variables (x, y) Flux tower upscaling hypothesis II – interannual variability Each point ~ (1 km)2 s(flux) = ax + by + c, interpolate interannual variability in fluxes over ~ (1000 km)2 Ecosystem fluxes respond similarly to climate variability across a wide range of forest types and ages(?)

  25. Testing the interannual variability upscaling hypothesis Flux tower clusters deployed for multiple years test the hypothesis that various forest stands respond similarly to climate variability.

  26. Interannual variability upscaling results • ChEAS annual fluxes (R, GEP, NEE) are moderately coherent across ChEAS sites, 2000-2001. (Caterpillars, not climate?). • ChEAS chamber and tower R fluxes show similar variability, 2001-2002, across sites. (2001 high flux, 2002 low flux). s(WLEF) = a*s(W Creek) + b*s(L Creek)? 3. Continental scale fluxes are very coherent, spring 1998, and linked to [CO2]! (Butler et al, this session) An extreme climatic event.

  27. Joint constraints! Complementary methods

  28. Flux tower upscaling hypothesis with simultaneous constraints Continent: Map biomes and climate, model fluxes N. American [CO2] Region: Map ecosystem variables, model fluxes N. Wisconsin [CO2] WLEF tower Forest: Clusters of flux towers Stand: Eddy covariance flux towers Within stand: biometric data, chamber fluxes

  29. ChEAS regional flux experiment domain = LI-820 sampling from 75m above ground on a communications tower. = 40m Sylvania flux tower with high-quality standard gases. = 447m WLEF tower. LI-820, CMDL in situ and flask measurements.

  30. Potential VTT network:Selection of new sites to be based on optimization study, Skidmore et al, and plans for a Midwest regional intensive

  31. Complementary nature of inversion downscaling and flux tower upscaling Inversion downscalingFlux tower upscaling Excellent spatial Intrinsically local integration measurements. Strong constraint on Difficult to upscale flux flux magnitude magnitudes. Variability easier. Poor temporal Excellent temporal resolution resolution Limited mechanistic Strong mechanistic understanding. understanding

  32. Conclusions • It is relatively difficult to upscale stand level fluxes to a region. • Upscaling interannual variability may be more tractable than absolute flux magnitudes. • Clustered flux towers provide upscaling methods testbeds. • Flux tower up-scaling and inversion down-scaling are very complementary.

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