Fluxnet 2009 Progress

Dennis Baldocchi, Rodrigo Vargas, Youngryel Ryu, Markus Reichstein, Dario

Papale, Deb Agarwal, Catharine Van Ingen

AmeriFlux 2009

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

Global distribution of Flux Towers Covers Climate Space Well

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

FLUXNET Community Outreach

• NewsLetter, FluxLetter• Asilomar Workshop• Distributed Searchable Database,

www.fluxdata.org• Fluxnet Visitors

– Paul Stoy, Sebastiaan Luysaaert, Josep Penuelas, Bart Kruijt

Fluxnet Modeling and Data Workshop Asilomar Conference

Questions/Topics: What is the FLUXNET Measurement Community providing to the Modeling Community?What information and data products do modelers need from the FLUXNET measurement community?How can sensitivity runs from land surface models help us interpret flux data across climate gradients and plant functional types?Future composition of FLUXNET

Data Archive, Synthesis, Searchable and Manipulative Databasewww.fluxdata.org

Progress on the ‘LaThuile’ Synthesis Papers

Water Use Efficiency, Coupling Water and Carbon Fluxes

Beer et al. 2009. Global Biogeochemical Cycles

Scales of Flux Variance

Paul Stoy et al, Biogeosciences, Submitted

Vargas et al. New Phytologist, in press

Role of Mycorrhyzae and C Fluxes

Results and Discussion

Emerging Ideas, Science Beyond Routine Flux Measurements

• Continental/Global Upscaling in Time/Space• Flux Spectra across scales of Hours to Decade• PhotoDegradation• Site MetaData Syntheses

– Leaf clumping, albedo• Model Data Assimilation

Towards Continental and Global Representativeness

The Network is not like Acupuncture (credit M Reichstein). Fluxes from Towers represent far beyond their geographical domain.

But we are not Everywhere, All the Time, so We must rely on partnerships with Remote Sensing and Meteorological Data to Upscale

Spatial Variations in C Fluxes

Xiao et al. 2008, AgForMet

springsummer

autumn winter

Using Flux Data to produce Global ET maps, V1

No data

0 - 150

150 - 300

300 - 450

450 - 600

600 - 750

750 - 900

900 - 1,236

ET (mm H2O y-1)

180°

180°

135° E

135° E

90° E

90° E

45° E

45° E

0°

0°

45° W

45° W

90° W

90° W

135° W

135° W

180°

180°

60° N 60° N

30° N 30° N

0° 0°

30° S 30° S

60° S 60° S

Fig.9 Global Evapotranspiration (ET) driven by interpolated MERRA meteorological data and 0.5º×0.6º MODIS data averaged from 2000 to 2003.

Wenping Yuan

Martin Jung

Using Flux data to produce Global ET maps, v2

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

How Big Does the Network Need to Be?

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

• 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.

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

Apply Bayes Theorem to FLUXNET?

(climate | ) ( )( | climate)

(climate)

p flux p fluxp Flux

p

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

Probability Distribution of Published NEE Measurements, Integrated Annually

FLUXNET Database

NEE (gC m-2 y-1)

-1600 -1400 -1200 -1000 -800 -600 -400 -200 0 200 400 600

pd

f

0.00

0.02

0.04

0.06

0.08

0.10

mean= -225 +/- 227 gC m-2 y-1

n=254

Global GPP = 1033 * 110 1012 m2 = 113.6 PgC/y

Probability Distribution of Published GPP Measurements, Integrated Annually

FLUXNET Database

GPP (gC m-2 y-1)

0 1000 2000 3000 4000

pd

f

0.00

0.01

0.02

0.03

0.04

0.05

mean= 1033+/- 631 gC m-2 y-1

n=253

0

500

1000

1500

2000

2500

3000

12

34

56

78

-10-5

05

1015

20

GP

P (

gC m

-2 y

-1)

Rg

(GJ

m-2 y

-1 )

Tair (C)

FLUXNET Database

0 500 1000 1500 2000 2500 3000

Joint pdf GPP, Solar Radiation and Temperature

E[GPP]= 1237 gC m-2 y-1~136 PgC/y

What Happens to the Grass?

OctoberJune

Vaira Ranch, 2007

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

FC

O2

mol

e m

-2 s

-1

2007

soil CO2 flux-gradienteddy covariance

PhotoDegradation

Baldocchi, Ma, Rutledge

Remote Sensing of Canopy Structure and GPP

Remote Sensing and Ecosystem Metabolism

VI vs GPP when including all data.LED spectral region (white box) looks showing good correlation, but the high correlation region is large.

White rectangle box indicates LED spectral region

Williams et al. 2009. Biogeosciences

Incorporating Soil Evaporation Scheme in CABLE Improves Model Performance

Vargas et al New Phytologist, in press

Priestley-Taylor and Surface Conductance

Chris Williams

Testing Budyko

Chris Williams: EcoHydrology

Beer et al. 2009. Global Biogeochemical Cycles

And, WUE scales with LAI and Soil Moisture

Plant functional types

CRO DBF EBF ENF GRA MF OSH WSA

Clu

mp

ing

in

de

x

0.5

0.6

0.7

0.8

0.9

1.0

LAI-2000 (apparent clumping index)Literature (element clumping index)

2/

0

2/

0

sincos])(ln[2

sincos])([ln2

dP

dP

o

o

app

Apparent clumping index can constrain true clumping index

Ryu, Nilson, Kobayashi, Sonnentag, Baldocchi (to be submitted)

Hollinger et al 2009 Global Change Biology

Albedo and Nutrition

Annual Integrated Kin Departure (MJ m2)-15 -10 -5 0 5 10 15

ARM_SGP_MainBondville

Bondville_Companion_SiteFermi_Agricultural

Mead_IrrigatedMead_Irrigated_Rotation

Mead_RainfedWalnut_River

Chestnut_Ridge Duke_Forest_Hardwoods

Missouri_Ozark Morgan_Monroe_State_Forest

UMBS Walker_Branch

Willow_CreekBlack_Hills

Duke_Forest_Loblolly_Pine Flagstaff_Managed_Forest

Flagstaff_Unmanaged_Forest Niwot_Ridge

UCI_1850 UCI_1930 UCI_1964

UCI_1964wet UCI_1989 UCI_1998

Wind_River_Crane_SiteAudubon_Grasslands

Brookings Canaan_Valley

Duke_Forest_Open_Field Fort_Peck

Kendall_GrasslandIvotuk

Flagstaff_Wildfire Freeman_Ranch_Mesquite_Juniper

Santa_Rita_Mesquite_Savanna UCI_1981Vaira_Ranch

Lost_CreekFermi_Prairie

Heat absorbed Heat reflected

Croplands

Deciduous

Evergreen

Grasslands

Savannas

Integrated annual error, or departure in the shortwave energy budget, for each site as derived from the calculated biome mean albedo.

Albedo and Climate Forcing

Tom O’Hallaran

mean annual Tsoil, C

-20 -10 0 10 20 30 40

mea

n an

nual

Tai

r, C

-20

-10

0

10

20

30

40

Coefficients:b[0] -0.468b[1] 0.957r ² 0.884

FLUXNET Database

Optimizing Seasonality of Vcmax improves Prediction of Fluxes

Wang et al, 2007 GCB