FLUXNET after 10 Years: Synthesizing CO2 and Water Vapor Fluxes

From Across a Global Network

Dennis BaldocchiESPM/Ecosystem Science Div.

University of California, Berkeley

ILEAPS, Boulder, Jan 2006

FLUXNET: From Sea to Shining Sea379 Sites, circa 2006

http://www.fluxnet.ornl.gov/fluxnet/images/Fluxnet_Growth_Oct2005a.jpg

Global distribution of Flux Towers with Respect to Climate

Evolution of FLUXNET

• Measure Annual Cycle of NEE– Micromet issues of Detrending, Transfer Functions, Flux Sampling

and Measurements, Gap-filling, Error Assessment• Measure and Interpret Intra-annual Variation of NEE

– Flux partitioning (GPP & Reco); assessment of metadata,e.g. Vcmax, soil respiration, LAI, biomass inventories.

• Measure and Interpret Inter-annual variations of NEE• Measure NEE over multiple Land-Use Classes

– crops, grasslands, deciduous and evergreen broadleaf and conifer forests

– Disturbance, logging, biodiversity and fire• Manipulative Studies

– Nitrogen and H2O additions• Measure NEE over Representative Areas

– Scaling Flux Information of Footprint to MODIS pixel

Successes

• Mountains of data from a spectrum of canopy roughness conditions, functional types and climate spaces have been collected

• A Model for Data Sharing– FLUXNET Web Site, a venue for distributing Primary, Value-added and Meta-

Data products• Value-Added Products have been produced

– Development of Gap-Filling Techniques– Production of Gap-Filled Daily and Annual Sums

• Data for Validating and Improving SVAT models used for weather, climate, biogeochemistry and ecosystem dynamics

• Collaboration & Synthesis through Workshops and Hosting Visitors– Building a Collaborative, Cooperative, Multi-Disciplinary & International

Community of Researchers– Characterizing Annual C Fluxes– Environmental Controls on NEE

• Training New and Next Generation of Scientists, Postdocs, Students

‘Failures’/’Un-resolved’ Issues

• Not Measuring Night-time Fluxes Well• Not Measuring Fluxes over Complex terrain and during

Advection Well• ImPerfect U* correction

– New Gu Algorithm• ImPerfect Flux Partitioning

– Works Better on Longer Time Scales• ImPerfect Energy Balance Closure

– Could be ‘red-herring’ based on recent several talks at a SSSA workshop

• Need Better Outreach and Training

Visions with a Flux Measurement Network

• Processes– Canopy-Scale Response Functions

• Emergent Processes– Flux Partitioning, NEP=GPP-Reco

• Acclimation• Time

– Daily/Seasonal Dynamics– Pulses, Lags, Switches– Intra- + Interannual Variability– Stand Age/Disturbance

• Space– Climate/Structure/Function– Coherence/Gradients– Upscaling with Remote Sensing

• New Directions

Probability Statistics of NEE

Col 4 vs Col 5

NEE (gC m-2 y-1)

-1500 -1000 -500 0 500 1000

pdf

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16Published data (275 site years)Median: -229 gC m-2 y-1

Light and Photosynthesis:

Emergent Processes at Leaf and Canopy Scales

D208 Oak leaf, forest floorTleaf: 25o CCO2 : 360 ppm

Qpar (mol m-2 s-1)

0 200 400 600 800 1000 1200 1400 1600 1800

A ( m

ol m-2 s-1 )

0

2

4

6

8

10

12datamodel

model: clumped leaves

PPFD (mol m-2 s-1)

0 500 1000 1500 2000

F c (

mol

m-2

s-1)

-40

-30

-20

-10

0

10measured

(a)

0 500 1000 1500 2000-50

-45

-40

-35

-30

-25

-20

-15

-10

-5

0

absorbed PAR (µmol m-2 s-1)

WHEAT

F c (mg

m-2 s-1 )

Volcanoes, Aerosols + NEE

CO2 Flux and Diffuse Radiation

Niyogi et al., GRL 2004

Photosynthesis-Respiration

Processed by Falge

North America + Europe

Reco (gC m-2 yr-1)

0 200 400 600 800 1000 1200 1400 1600

GP

P (g

C m

-2 y

r-1)

0

200

400

600

800

1000

1200

1400

1600

b[1] 1.18r ² 0.893

Mean Summer Temperature (C)5 10 15 20 25 30

Tem

pera

ture

Opt

imum

fo

r Can

opy

CO 2 upt

ake

(C)

5

10

15

20

25

30

35

b[0] 3.192b[1] 0.923r ² 0.830

Analysis of E. Falge

NEE: Acclimation with TemperatureNEE: Acclimation with Temperature

Respiration: Temperature and acclimation

Analyst: Enquist et al. 2003, Nature

GPP RH/Ca (mol m-2s-1)

0.00 0.01 0.02 0.03 0.04 0.05 0.06

g c (m

ol m

-2s-1

)

0.0

0.2

0.4

0.6

0.8

Linking Water and Carbon:Potential to assess Gc with Remote Sensing

Xu + DDB

0

10

20

30

40Stomatal Conductance SurveyDennis Baldocchi; UC Berkeley

Bal

l-Ber

ry

0

10

20

30

40

FLUXNET Data"B

all-B

erry

"

Processed by M. Falk

An Example of Scale Invariance

Temporal Dynamics of C Fluxes

• Hour• Day• Month• Season• Year• Multiple Years • Pulses

• Lags• Switches

Decadal Plus Time Series of NEE:Flux version of the Keeling’s Mauna Loa Graph

Harvard Forest

Year

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

CO

2 Fl

ux D

ensi

ty (g

C m

-2 d

-1)

-12

-8

-4

0

4

8 NEERecoGEE

Data of Wofsy, Munger, Goulden et al.

Complicating Dynamical Factors

• Switches/Pulses– Rain– Phenology/Length of

Season– Frost/Freezing

• Emergent Processes– Clouds & LUE

• Acclimation• Lags • Stand Age/Disturbance

Temperate Broadleaved Deciduous Forest

Day

0 50 100 150 200 250 300 350

NE

E (g

C m

-2 d

-1)

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

LAI=0GPP=0;Litterfall (+)Reco=f(litterfall)(+)

snow:Tsoil(+)

GPP=0; Reco(+)

no snow

Tsoil (-)

Reco (-)

GPP=f(LAI, Vcm

ax )

late spring

early spring

Drought:(-)GPP(-); Re(-)

Clouds:PAR(-) GPP=f(PAR)(+)

Re vs GPPInterannual Variability in NEE

d GPP/dt

-300 -200 -100 0 100 200 300

d R

eco/

dt

-300

-200

-100

0

100

200

300

2002 2003 2004 2005

NE

E[g

C m

-2 w

eek-

1 ]

-80

-70

-60

-50

-40

-30

-20

-10

0

10

20

HainichLeinefelde

Knohl et al Max Planck, Jena

Lag Effects Due to Drought/Heat Stress

Soroe, DenmarkBeech Forest1997

day

0 50 100 150 200 250 300 350-10

-5

0

5

10

15

20

NEE, gC m-2 d-1

Tair, recursive filter, oC

Tsoil, oC

Data of Pilegaard et al.

Soil Temperature: An Objective Indicator of Phenology??

Data of: ddb, Wofsy, Pilegaard, Curtis, Black, Fuentes, Valentini, Knohl, Yamamoto. Granier, SchmidBaldocchi et al. Int J. Biomet, in press

Soil Temperature: An Objective Measure of Phenology, part 2

Temperate Deciduous Forests

Day, Tsoil >Tair

70 80 90 100 110 120 130 140 150 160

Day

NE

E=0

70

80

90

100

110

120

130

140

150

160

DenmarkTennesseeIndianaMichiganOntarioCaliforniaFranceMassachusettsGermanyItalyJapan

Spatial Gradients:NEE and Length of Growing Season

Broad-Leaved Forests

Length of Growing Season

100 150 200 250

NEE

(gC

m-2

yr-1

)

-800

-700

-600

-500

-400

-300

-200

-100

0

100Japan

DenmarkItaly

Massachusetts, USA

Belgium

Tennessee, USAPrince Albert, CANADA

Ontario

Indiana, USA

Michigan, USA

Spatial Variations in C Fluxes

8 day means

Dai

ly g

ross

CO

2 flu

x

(mm

ol m

-2 d

ay-1

)

0

200

400

600

800

1000

1200

1400

16008 day means

Dai

ly n

et C

O2 f

lux

(mm

ol m

-2 d

ay-1

)

-400

-200

0

200

400

600

800

r2 = 0.92

8 day means

Dai

ly g

ross

LU

E

0.00

0.01

0.02

0.03

r2 = 0.65

Single clear days

AM net CO2 flux (mmol m-2 hr-1)

0 20 40 60 80 100 120

Dai

ly n

et C

O2 f

lux

(mm

ol m

-2 d

ay-1

)

-400

-200

0

200

400

600

800Single clear days

AM gross CO2 flux (mmol m-2 hr-1)

0 20 40 60 80 100 120 140

Dai

ly g

ross

CO

2 flu

x

(mm

ol m

-2 d

ay-1

)

0

200

400

600

800

1000

1200

1400

1600

r2 = 0.88

Single clear days

AM gross LUE

0.00 0.01 0.02 0.03

Dai

ly g

ross

LU

E

0.00

0.01

0.02

0.03

r2 = 0.73

r2 = 0.64

r2 = 0.56

Evergreen needleleaf forestDeciduous broadleaf forestGrassland and woody savanna

a b c

d e f

Sims et al 2005 AgForMet

Heinsch et al. in press

Global MODIS Test

Testbed for Ecohydrological Theory

Miller et al, Adv. Water Research, submitted

Value of Flux Networks

• Produces Large and Long Data Sets– Reduced Sampling Error– Robust Dataset for Model Development

• Study Spectra of Time Scales– Capture Pulses and Lags

• Study Gradient of Climates, Structure and Function• Field of Dreams: ‘Build it and they will Come’

– Better Integrated Research Studies

Future Directions

• Administrative– ReOrganize FLUXNET with Multiple/International Funding Sources

• Scientific

– NEE in Urban and Suburban, Africa, India, Latin America and High Arctic Environments– Coupling CO2, Trace Gas Deposition/Emission (O3, voc) and Methane Fluxes– Adopting New Technology (TDL, wireless networks) to embellish flux measurements– Couple tower data with Real-time Data Assimilation Models.– Boundary Layer Budgets using Fluxes and High Precision CO2 measurements– Spectral reflectance measurements across the network– Spatial-Temporal Network-Scale Analysis– Real-time Data Assimilation– Matching Footprints of Tower and Pixels– Model Lags, Switches and Pulses– Using Fluxnet data to assess problems in

• Ecology, Ecohydrology, Biogeochemistry, Biogeography, Remote Sensing, Global Modeling, Biodiversity

Validating MODIS

Falk, Ma, Baldocchi, unpublished

Heinsch et al. submitted

Tower vs Satellite NDVI

0.00 0.25 0.50 0.75 1.000.00

0.25

0.50

0.75

1.00

NDVIMODIS

ND

VI TO

WE

R

conifers deciduous

crops grassland

Falk et al., to be submitted

Limits to Landscape Classification by Functional Type

• Stand Age/Disturbance• Biodiversity• Fire• Logging• Insects/Pathogens• Management/Plantations• Kyoto Forests

Effects of Stand Age:After Logging

Law et al. 2003 Global Change Biology

Biodiversity and Evaporation

Temperate/Boreal Broadleaved ForestsSummer Growing Season

Number of Dominant Tree Species (> 5% of area or biomass survey)

1 2 3 4 5 6 7 8

E/

Eeq

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

Baldocchi, 2004: Data from Black, Schmid, Wofsy, Baldocchi, Fuentes