using a global flux network—fluxnet— to study the breathing of the terrestrial biosphere

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

Dennis BaldocchiESPM/Ecosystem Science Div.

University of California, Berkeley

CollaboratorsRodrigo Vargas, Youngryel Ryu, Markus Reichstein,

Dario Papale, Andrew Richardson, Deb Agarwal Catharine van Ingen, Bob Cook and Regional

Networks

ILEAPS 2009Melbourne, Australia

Objectives

• Network Background• Statistical Representativeness

– Global Statistics on NEE, GPP, Reco– Network Size– Is the Statistical Distribution of Carbon Fluxes Invariant at

the Global Scale?• Emergent Processes

– Coupling between Photosynthesis and Respiration– PhotoDegradation, a New Process to Consider– Fluxes and Phenology

• Space– Regional Flux Maps– Phenology Greenwave

Network of Regional Flux Networks

FLUXNET: From Sea to Shining Sea500+ Sites, circa 2009

F ws w sa ~ ' ' s c

a

( )

Eddy Covariance Technique

Mean

Fluctuation

• Direct• In situ• Quasi-Continuous• Extended Footprint

FLUXNET DataBase

• Web sites– www.fluxnet.ornl.gov/fluxnet/index.cfm– www.fluxdata.org

• QA/QC– Data Standardization (names, units)– Version Control

• Derived Flux Quantities– Gap-Filling– U* corrections for periods of low turbulence– Daily, Monthly, Annual Integrals

• Flux Partitioning– Converting NEE into GPP and Reco

• Compiling Multifaceted Site MetaData• Data Distribution

– Search/Manipulate/Archive

Standard Processing Procedure Vs non-standard, but Expert Based

Pub_Reco (gC m-2 yr-1)

0 1000 2000 3000 4000

LaTh

uile

_Rec

o (g

C m

-2 y

r-1)

0

1000

2000

3000

4000

Pub_GPP (gC m-2 yr-1)

0 1000 2000 3000 4000La

Thui

le_G

PP (g

C m

-2 y

r-1)

0

1000

2000

3000

4000

Results and Discussion

Over-Arching Questions relating to Statistical Representativeness

• As the sparse Network has grown, can it provide a Statistically-Representative sample of NEE, GPP and Reco to infer Global Behavior?, e.g. Polls sample only a small fraction of the population to generate political opinion

• If mean Solar inputs and Climate conditions are invariant, on an annual and global basis,are NEE, GPP and Reco constant, too?; e.g. global GPP scales with solar radiation which is constant

• Can Processes derived from a Sparse-Network be Upscaled with Remote Sensing and Climate Maps?; e.g. We don’t need to be everywhere all the time; We can use Bayes Theorem and climate records to upscale.

Global distribution of Flux Towers Covers Climate Space Well

Can we Integrate Fluxes across Climate Space, Rather than Cartesian Space?

Apply Bayes Theorem to FLUXNET?

(climate | ) ( )( | climate)(climate)

p flux p fluxp Fluxp

Estimate Global flux by Integrating p(Flux|climate) across Globally-gridded Climate space

p(flux) from FLUXNETp(climate|flux) prior from FLUXNETp(climate) from climate database

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

Published FLUXNET Data, 2009

NEE (gC m-2 y-1)

-1000 -500 0 500 1000

pdf

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

<NEE> = -176 +/- 260 gC m-2 y-1

670 Site-Years

Updated PDF with 164 extra site-years yields an insignificant change in the mean (P= 0.68)

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

How many Towers are needed to estimate mean NEE,And assess Interannual Variability, at the Global Scale?

We Need about 75 towers to produce robust Statistics

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

Interannual Variability in NEE is small (between -220 to -243 gC m-2 y-1) 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 small, too, across the Global Network (1103 to 1162 gC m-2 y-1)

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 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

max (4.6 )0.022gRGpp PAR LUE

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

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

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

What Happens to the Grass?

OctoberJune

PhotoDegradation is a New and Emerging Process Affecting C Cycle in Semi-arid Grasslands

200 400 600 800 1000-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Rglobal W m-2

F CO

2 m

ole

m-2

s-1

2007

soil CO2 flux-gradienteddy covariance

Baldocchi and Ma, unpublished

PhotoDegradation: A Process Not in Coupled Carbon-Climate Models

Baldocchi, Ma, Rutledge, unpublished

More Carbon is Lost with Rain Pulse on Dry Matter that has Been PhotoDegraded than Dry Matter in the Shade

Xu, Baldocchi, Tang, 2004 Global Biogeochem Cycles

Amount of precipitation (mm)0 10 20 30 40 50 60 70

Tota

l car

bon

resp

ired

(g C

m-2

)

0

20

40

60Grasslandintercept=5.84slope=0.96r ²=0.86

understoryintercept=3.66slope=0.47r ²=0.91

Phenology

Day of year, 2007

50 100 150 200 250 300 350

LED

ND

VI

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Gro

ss p

rimar

y pr

oduc

tivity

(gC

m-2

day

-1)

0

2

4

6

8

10

12LED NDVIGross primary productivity

LED NDVI

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Gro

ss p

rimar

y pr

oduc

tivity

(gC

m-2

day

-1)

0

2

4

6

8

10

2006200720082009

(a)

(b)

y=-1.59+0.81exp(3.02x)r2=0.91

Ryu et al. in prep

Seasonality in GPP follows NDVI and Defines Growing Season

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

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

Baldocchi, 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

Spatial Variations in C Fluxes with Remote Sensing

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

springsummer

autumn winter

Conclusions

• A sparse Global Network of Flux Towers produces statistically robust findings that are representative of broad climate and ecological zones that may integrated to validate models at grid/landscape/regional scales

• Any Statistical Scaling Approach based on Climate must consider Disturbance and Stand Age, too.

• A global Network of Flux Towers is discovering a number of emergent scale processes relating to the controls in inter-annual variability, controls by light, and phenology

Acknowledgments

• NSF, Research Coordinated Networks• Microsoft, Regional Networks, ILEAPS• World-wide Collection of Collaborators

and Colleagues, Technicians, Postdocs and Students

Filtered: annual NPP_GPP_fqc > 0.5, minimum 2 years of data per site

GPP Reco NEENMean LS Mean Mean LS Mean Mean LS Mean

1997 25 1247 1178 982 989 -265 -1901998 25 1262 1095 1048 965 -214 -1311999 36 1267 1163 987 953 -280 -2112000 44 1298 1128 1041 945 -257 -1842001 60 1262 1178 1022 975 -241 -2052002 84 1279 1177 1066 963 -214 -2152003 98 1178 1185 971 933 -206 -2532004 131 1176 1213 934 952 -244 -2632005 133 1144 1216 899 951 -245 -2652006 96 1202 1198 922 948 -280 -251

1 S.D. 52 37 57 16 27 42

A. Richardson & D. Baldocchi, unpublished

ANOVA analysis of the annual sums