Using a Global Flux Network—FLUXNET— to Study the Breathing of the Terrestrial Biosphere

Dennis BaldocchiESPM/Ecosystem Science Div.

University of California, Berkeley

AsiaFlux, Seoul, Korea Nov. 17, 2008

Contemporary CO2 Record

Mauna LoaKeeling data

year

1950 1960 1970 1980 1990 2000 2010

CO

2 (pp

m)

300

310

320

330

340

350

360

370

380

Methods To Assess Terrestrial Carbon Fluxes at Landscape to Continental Scales, and Across

Multiple Time Scales

GCM InversionModeling

Remote Sensing/MODIS

Eddy Flux Measurements/FLUXNET

Forest/Biomass Inventories

Biogeochemical/Ecosystem Dynamics Modeling

Physiological Measurements/Manipulation Expts.

Objectives

• Time– Annual Integration– Seasonal Dynamics– Inter-Annual Variability– Disturbance/Chronosequence

• Processes– Photosynthesis = f(Q,T,functional type)– Respiration = f(T, growth, ppt, q)

• Space• Other Uses and Application

– Ecosystem Modeling

Temporal Dynamics of C Fluxes

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

• Lags• Switches

FN (gC m-2 y-1)

-1500 -1000 -500 0 500 1000 1500

p(x)

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

mean: -182.9 gC m-2 y-1

std dev: 269.5n: 506

Baldocchi, Austral J Botany, 2008

Probability Distribution of Published NEE Measurements, Integrated Annually

Baldocchi, Austral J Botany, 2008

Does Net Ecosystem Carbon Exchange Scale with Photosynthesis?

FA (gC m-2 y-1)

0 500 1000 1500 2000 2500 3000 3500 4000

F N (g

C m

-2 y

-1)

-1000

-750

-500

-250

0

250

500

750

1000

Ecosystems with greatest GPP don’t necessarily experience greatest NEE

FA (gC m-2 y-1)

0 500 1000 1500 2000 2500 3000 3500 4000

F R (g

C m

-2 y

-1)

0

500

1000

1500

2000

2500

3000

3500

4000

UndisturbedDisturbed by Logging, Fire, Drainage, Mowing

Baldocchi, Austral J Botany, 2008

Ecosystem Respiration Scales Tightly with Ecosystem Photosynthesis, But Is with Offset by Disturbance

Length of Growing Season, days

50 100 150 200 250 300 350

F N (g

C m

-2 y

r-1)

-1000

-800

-600

-400

-200

0

200

Temperate and Boreal Deciduous Forests Deciduous and Evergreen Savanna

Baldocchi, Austral J Botany, 2008

Net Ecosystem Carbon Exchange Scales with Length of Growing Season

Harvard Forest, 1991-2004

Year

1990 1992 1994 1996 1998 2000 2002 2004 2006

NE

E (g

C m

-2 d

-1)

-10

-8

-6

-4

-2

0

2

4

6

8

10

Data of Wofsy, Munger, Goulden, et al.

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

Harvard Forest

Year

1990 1992 1994 1996 1998 2000 2002 2004 2006

Car

bon

Flux

(gC

m-2

y-1

)

-600

-400

-200

01000

1200

1400

1600

1800

FNFAFR

Interannual Variation and Long Term Trends in Net Ecosystem Carbon Exchange (FN), Photosynthesis (FA) and Respiration (FR)

Urbanski et al 2007 JGR

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 2003 European Drought/Heat Stress

Interannual Variability in FN

d FA/dt (gC m-2 y-2)

-750 -500 -250 0 250 500 750 1000

d F R

/dt (

gC m

-2 y

-2)

-750

-500

-250

0

250

500

750

1000Coefficients:b[0] -4.496b[1] 0.704r ² 0.607n =164

Baldocchi, Austral J Botany, 2008

Interannual Variations in Photosynthesis and Respiration are Coupled

Interannual Variability in NEE is tiny across the Global Network

FLUXNET Network, 75 sites

NEE (gC m-2 y-1)

-1000 -800 -600 -400 -200 0 200 400 600

p(N

EE

)

0.00

0.05

0.10

0.15

0.20

0.25

2002: -220 +/- 35.2 gC m-2 y-1

2003: -238 +/- 39.92004: -243 +/- 39.7 2005: -237 +/- 38.7

Interannual Variability in GPP is tiny across the Global Network, too

FLUXNET Network, 75 sites

GPP (gC m-2 y-1)

0 500 1000 1500 2000 2500 3000 3500

p(G

PP

)

0.00

0.05

0.10

0.15

0.20

0.25

2002: 1117 +/- 74.0gC m-2 y-1

2003: 1103 +/- 67.82004: 1162 +/- 77.02005: 1133 +/- 70.1

FLUXNET database

precipitation

0 500 1000 1500 2000 2500

pdf

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

2003: 721 +/- 59 mm2004: 855 +/- 542005: 806 +/- 46

Rg (GJ m-2 y-1)

1 2 3 4 5 6 7 8

pdf

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

2003: 4.70 +/- 0.129 GJ m-2 y-1

2004: 4.67 +/- 0.1322005: 4.59 +/- 0.135

Little Change in Abiotic Drivers--annual Rg, ppt --across Network

FLUXNET, 75 sites

Tair, C

-10 -5 0 5 10 15 20 25 30

pdf

0.00

0.05

0.10

0.15

0.20

0.25

2003: 8.86 +/- 0.82 C2004: 8.2 +/- 0.86 2005: 8.84 +/- 0.78

Complicating Dynamical Factors

• Switches– Phenology– Drought– Frost/Freeze

• Pulses– Rain– Litterfall

• Emergent Processes– Diffuse Light/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:q(-)GPP(-); Re(-)

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

June, 2003

Time (hour)

0 5 10 15 20 25 30

Flux

Den

sity

-10

-8

-6

-4

-2

0

6

7

soil respirationcanopy photosynthesis

Soil Respiration Lags Photosynthesis onHourly Scale

Tang et al. 2006, GCB

July, Rsoil-Ps lag

lag (30 min)

-30 -20 -10 0 10 20 30la

g co

rrel

atio

n-1.0

-0.8

-0.6

-0.4

-0.2

0.0

Emergent Scale Process:CO2 Flux and Diffuse Radiation

Niyogi et al., GRL 2004

• We are poised to see effects of Cleaner/Dirtier Skies and Next Volcano

FLUXNET 2007 Database

GPP at 2% efficiency and 365 day Growing Season

Potential and Real Rates of Gross Carbon Uptake by Vegetation:Most Locations Never Reach Upper Potential

tropics

GPP at 2% efficiency and 182.5 day Growing Season

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

E. Falge et al 2002 AgForMet; Baldocchi et al 2001 BAMS

Optimal NEE: Acclimation with Temperature

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??

Baldocchi et al. Int J. Biomet, 2005

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

8 day means

Daily g

ross

CO

2 flux

(m

mol m

-2 d

ay-1

)

0

200

400

600

800

1000

1200

1400

16008 day meansDaily n

et C

O2 flux

(m

mol m

-2 d

ay-1

)

-400

-200

0

200

400

600

800

r2 = 0.92

8 day means

Daily g

ross

LUE

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

Daily n

et C

O2 flux

(m

mol 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

Daily g

ross

CO

2 flux

(m

mol 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 0.03Daily g

ross

LUE

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

Do Snap-Shot C Fluxes, inferred from Remote Sensing, Relate to Daily C Flux Integrals?

Spatial Variations in C Fluxes

Xiao et al. 2008, AgForMet

Xiao and Baldocchi, unpublished

Upscaling Flux Data with Remote Sensing DataPublished, Global: -182 gC m-2 y-1

Map, US: -189 gC m-2 y-1

Baldocchi, White, Schwartz, unpublished

Spatialize Phenology with Transformation Using Climate Map

Mean Air Temperature, C

4 6 8 10 12 14 16 18

Day

of N

EE =

060

80

100

120

140

160

Coefficients:b[0]: 169.3b[1]: -4.84r ²: 0.691

White, Baldocchi and Schwartz, unpublished

Flux Based Phenology Patterns with Match well with data from Phenology Network

Limits to Landscape Classification by Functional Type

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

Conifer Forests, Canada and Pacific Northwest

Stand Age After Disturbance

1 10 100 1000

F N (g

C m

-2 y

-1)

-600

-400

-200

0

200

400

600

800

1000

Time Since Disturbance Affects Net Ecosystem Carbon Exchange

Baldocchi, Austral J Botany, 2008 Data of teams lead by Amiro, Dunn, Paw U, Goulden

Other Activities and Uses of Fluxnet Data

• Landuse• Ecosystem Modeling• EcoHydrology• Biodiversity• Climate

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

Kucharik et al., 2006 Ecol Modeling

Ecosystem Model Testing and Development

Seasonality of Photosynthetic Capacity

Wang et al, 2007 GCB

Optimizing Seasonality of Vcmax improves Prediction of Fluxes

Wang et al, 2007 GCB

‘A continuing challenge to long-term Earth observations is the prejudice against science that is not directly aimed at hypothesis testing. At a time when the planet is being propelled by human action into another climate regime with incalculable social and environmental costs, we cannot afford such a rigid view of the scientific enterprise. The only way to figure out what is happening to our planet is to measure it, and this means tracking changes decade after decade and poring over the records. A point of diminishing scientific returns has never been realized in what is now known as the "Keeling Curve," the Mauna Loa CO2 record’.

Ralph Keeling, Science March 28, 2008

A Continuing Challenge and Task:Measure Ecosystem Processes on Ecosystem Timescales

Acknowledgements

• Leadership– Riccardo Valentini, Steve Running, Bev Law

• Data Preparation: FLUXNET-2007– Dario Papale, Markus Reichstein, Catharine Van Ingen,

Deb Agarwal, Tom Boden, Bob Cook, Susan Holliday, Bruce Wilson, +++

• FLUXNET Office @ Berkeley– Eva Falge, Lianhong Gu, Matthias Falk, Rodrigo Vargas,

Youngryel Ryu• Networks

– AmeriFlux, CarboEurope, AsiaFlux, ChinaFlux, Fluxnet Canada, OzFlux, +++

• Agencies– NSF/RCN, ILEAPS, DOE/TCP, NASA, Microsoft, ++++