A challenge to the flux-tower upscaling hypothesis? A multi-tower comparison from the Chequamegon

Ecosystem-Atmosphere StudyK.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

What is and what governs ecosystem-atmosphere exchange of CO2 on spatial

scales of geopolitical and bioclimatological relevance?

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

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 hypothesis

Flux:R, NEE, GEP

Climate variables (x, y)

Flux = ax + by + c,interpolate fluxes over ~ (1000 km)2

Each point~ (1 km)2

Segregate further by ecosystem characteristics?Stand type (conifer, deciduous, grass, crop)Stand age (young, mature, old)

Flux tower upscaling hypothesis

Plots at towers Within stand: biometric data,chamber fluxes

Tower sites Stand: Eddy covariance flux towers representing key biomes and climate regions

Continent: Map biomes and climate, model fluxes

Upper Midwest,N. America

Flux tower upscaling hypothesis with simultaneous constraints

Within stand: biometric data,chamber fluxes

Stand: Eddy covariance flux towers

Forest: Clusters of flux towersWLEF tower

Continent: Map biomes and climate, model fluxes

Region: Map ecosystem variables, model fluxes

N. Wisconsin [CO2]

N. American [CO2]

Chequamegon Ecosystem-Atmosphere Study (ChEAS) region

Testing the upscaling hypothesis

Flux:R, NEE, GEP

Climate variables (x, y)

ChEAS

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?

Pho

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WLEF tall tower (447m)CO2 flux measurements at: 30, 122 and 396 mCO2 mixing ratio measurements at: 11, 30, 76, 122, 244 and 396 m

Forest-scale evaluation of the upscalinghypothesis: WLEF flux tower

ChEAS vegetation

ChEAS flux tower arrayForest-scale flux: WLEF tower, 1997-present

Dominant stand types and flux towers:

Northern Aspen Forested Coniferhardwood wetland

youn

g

old

m

atur

e

Willow Creek (UMBS) Lost Creek Chen B2000-present 1999-present 2001-present 2002-presentBolstad et al, in pressCook et al, in prep

Chen A2002–present

Sylvania2002-presentDesai et al, in prepDesai et al, B52D-04

Chen mobile Chen mobile2003 2002

Yi et al, 00Berger et al, 01Davis et al, 03Ricciuto et al, B51

Mackay et al, 02Mackay et al, H29Ewers et al, 02Ewers et al, H30

ChEAS upscaling test results1. Climate alone does not explain

ChEAS CO2 fluxes.

2. The WLEF region is a source of CO2 to the atmosphere.

• drying wetlands?• disturbance/management?

ChEAS upscaling test results1. Climate alone does not explain ChEAS CO2

fluxes.2. The WLEF footprint is a source of CO2 to

the atmosphere.• drying wetlands?• disturbance/management?

3. WLEF fluxes cannot be explained as a linear combination of Lost Creek and Willow Creek fluxes.

• aspen? conifers? WLEF footprint dissimilar?

 

NEE (gC m-2)

Respiration (gC m-2)

Photosynthesis (gC m-2)

WLEF 1997 27 991 964

WLEF 1998 48 986 938

WLEF 1999 100 1054 954

WLEF 2000 74 1005 931

WLEF 2001 141 1067 926

WLEF average 78 1021 942

Willow Creek 2000 -347 762 1109

Willow Creek 2001 -108 741 849

Willow Creek 2002 -437 648 1085

Willow Creek average -297 717 1014

Lost creek 2001 1 759 758

Lost Creek 2002 -58 631 689

Lost Creek average -30 695 724

NEE and gross fluxes at ChEAS sites: 1997-2002

ChEAS upscaling test results1. Climate alone does not explain ChEAS CO2 fluxes.2. The WLEF footprint is a source of CO2 to the

atmosphere.• drying wetlands? • disturbance/management?

3. 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?

4. 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

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.

Forest type andrespiration (soil + leaf + stem)

1999 2000 2001 2002

Northern Hardwoods

11.55 11.92 12.71 10.89

MatureAspen

13.57 13.96 14.69 12.95

Intermediate Aspen

9.93 10.24 10.76 9.49

ChEAS upscaling test results1. Climate alone does not explain ChEAS CO2 fluxes.2. The WLEF footprint is a source of CO2 to the

atmosphere.• drying wetlands? • disturbance/management?

3. 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?

4. 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?

5. 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.

Sylvania – Willow Creek flux tower comparison

GEP, old growth (red) vs. mature (blue) forest

R, old growth (red) vs. mature (blue)

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

What is and what governs the interannual variability in ecosystem-atmosphere exchange of CO2 on spatial scales of

geopolitical and bioclimatological relevance?

Interannualvariabilityin the rate ofaccumulationof atmosphericCO2

Flux tower upscaling hypothesis II – interannual variability

flux) = flux – mean flux

Climate variables (x, y)

(flux) = ax + by + c,interpolate interannual variability in fluxesover ~ (1000 km)2

Each point~ (1 km)2

Ecosystem fluxes respond similarly to climate variabilityacross a wide range of forest types and ages(?)

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

1. ChEAS annual fluxes (R, GEP, NEE) are moderately coherent across ChEAS sites, 2000-2001. (Caterpillars, not climate?).

2. ChEAS chamber and tower R fluxes show similar variability, 2001-2002, across sites. (2001 high flux, 2002 low flux).

(WLEF) = a*(W Creek) + b*(L Creek)?

3. Continental scale fluxes are very coherent, spring 1998, and linked to [CO2]! (Butler et al, this session) An extreme climatic event.

Joint constraints! Complementary methods

Upscaling

Downscaling

Ch

amb

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flux

Tower flux

Airborne flux

Forest inventory Inverse study

year

month

hour

day

Tim

e S

cale

Spatial Scale

(1m)2 = 10-4ha

(1000km)2 = 108ha

(100km)2 = 106ha

(10km)2 = 104ha

(1km)2 = 102ha

Rearth

Flux tower upscaling hypothesis with simultaneous constraints

Within stand: biometric data,chamber fluxes

Stand: Eddy covariance flux towers

Forest: Clusters of flux towersWLEF tower

Continent: Map biomes and climate, model fluxes

Region: Map ecosystem variables, model fluxes

N. Wisconsin [CO2]

N. American [CO2]

ChEAS regional flux experiment domain

= LI-820 sampling from 75m above ground on a communications tower.

= 40m Sylvania flux towerwith high-quality standardgases.

= 447m WLEF tower. LI-820, CMDLin situ and flaskmeasurements.

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

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ARM-CARTregion

Poker Flats, AK(aircraft profile + flux tower)

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LegendExisting VTTProposed VTTTall towerProfiling aircraftCO2 mesonet

Complementary nature of inversion downscaling and flux tower upscalingInversion downscaling Flux 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

• 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.