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

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slide1
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

motivation
Motivation

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

outline
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.
flux tower upscaling hypothesis
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

flux tower upscaling hypothesis1

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)

flux tower upscaling hypothesis2
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

flux tower upscaling hypothesis with simultaneous constraints
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

testing the upscaling hypothesis

Flux:

R, NEE, GEP

Climate variables (x, y)

Testing the upscaling hypothesis

ChEAS

testing the upscaling hypothesis regional clusters of flux towers
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?
slide12

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

cheas flux tower array
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

cheas upscaling test results
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?
cheas upscaling test results1
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?
cheas upscaling test results2
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 respiration fluxes
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.

cheas upscaling test results3
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.
summary
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.
motivation ii
Motivation II

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

slide26

Interannual

variability

in the rate of

accumulation

of atmospheric

CO2

flux tower upscaling hypothesis ii interannual variability

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(?)

testing the interannual variability upscaling hypothesis
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.

interannual variability upscaling results
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.

flux tower upscaling hypothesis with simultaneous constraints1
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

cheas regional flux experiment domain
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.

slide33
Potential VTT network:Selection of new sites to be based on optimization study, Skidmore et al, and plans for a Midwest regional intensive
complementary nature of inversion downscaling and flux tower upscaling
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

conclusions
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.