the big picture to assess the global carbon budget we need information that is ‘everywhere, all of...

The Big Picture

• To assess the Global Carbon Budget we need information that is ‘Everywhere, All of the Time’

• Many Complementary Methods exist, Each with specific, scale-dependent Pros and Cons, so the Overall Premise is Impossible for any stand alone method

• Multi-Faceted, Integrative C Flux program is Needed• Statistics can be our ‘Friend’ and May Relieve us from Being ‘Everywhere’• To Address Phenology, Seasonaly Dynamics, Intermittency and Trends we still

nee to measure ‘All the Time’.

Dennis Baldocchi, University of California, Berkeley

Methods To Assess Terrestrial Carbon Budgets 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.

remote sensingof CO2

Tem

pora

l sca

le

Spatial scale [km]

hour

day

week

month

year

decade

century

local 0.1 1 10 100 1000 10 000 global

forestinventory

plot

Countries EUplot/site

talltowerobser-

vatories

Forest/soil inventories

Eddycovariance

towers

Landsurface remote sensing

From point to globe via integration with remote sensing (and gridded metorology)

From: Markus Reichstein, MPI

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

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

Green Plants Abhor a Vacuum, Most Use C3 Photosynthesis, so we May Not need to be Everywhere, All of the Time

We Need about 75 towers to produce Robust and Invariant Statistics

Year

1992 1994 1996 1998 2000 2002 2004 2006 2008

Num

ber

of s

ites

1

10

100

1000

FLUXNET

Year

1992 1994 1996 1998 2000 2002 2004 2006 2008C

Flu

x, g

C m

-2 y

-10

200

400

600

800

1000

1200

1400

GPPReco

Year

1992 1994 1996 1998 2000 2002 2004 2006 2008

C F

lux,

gC

m-2

y-1

-500

-400

-300

-200

-100

0

Ne

Statistical Samping of Interannual variability of C fluxes with a Network

Baldocchi et al., in prep

Flux-Derived Global GPP: 123 +/- 8 PgC/y

Beer et al, Science, 2010

Upscale NEP, Globally, Explicitly

1. Compute GPP = f(T, ppt)2. Compute Reco = f(GPP,

Disturbance)3. Compute NEP = GPP-Reco

Reco = 101 + 0.7468 * GPP

Reco, disturbed= 434.99 + 0.922 * GPP

FA (gC m-2 y-1)

0 500 1000 1500 2000 2500 3000 3500 4000

FR (

gC m

-2 y

-1)

0

500

1000

1500

2000

2500

3000

3500

4000

UndisturbedDisturbed by Logging, Fire, Drainage, Mowing FLUXNET Synthesis

Baldocchi, 2008, Aust J Botany

<NEE> = -129 gC m-2 y-1

S NEE = -17.5 PgC/y!!

Implies too Large NEE (|-700 gC m-2 y-1| Fluxes in TropicsIgnores C losses from Disturbance and Land Use Change

Disturbance GPP,PgC y-1

Reco NBP,PgC y-1

0% 113.6 96.1 -17

1% - 101.05 -13.1

2% - 103.6 -10.4

4% - 109.7 +/1 0.17

-4.02 +/- 0.16

Carbon Balance if Randomly Disturb C pools

Remote Sensing of NPP:Up and down PAR, LED, Pyranometer, 4 band Net Radiometer

Phenology, fpar, LAI, APAR, N, Vcmax

Hemispherical Camera Upward Looking Camera

Web Camera

2009.2 2009.4 2009.6 2009.8 2010.0 2010.2 2010.4

LA

I

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

DCP1DCP2DCP3

Year

2009.2 2009.4 2009.6 2009.8 2010.0 2010.2 2010.4

Lan

ds

cap

e L

AI

0.0

0.2

0.4

0.6

0.8

1.0

1.2

DCPLAI-2000

Phenology with Upward-Facing Digital Camera

Ryu et al, in prep

ESPM 111 Ecosystem Ecology

Annual Grassland, 2004-2005

Wavelength (nm)

400 500 600 700 800 900 1000

Ref

lect

ance

0.0

0.2

0.4

0.6

0.8

1.0

Oct 13, 2004Oct 27, 2004Nov 11, 2004Jan 5, 2005Feb 2, 2005Apr 1, 2005Mar 9, 2005May 11, 2005Dec 29, 2005

Falk, Ma and Baldocchi, unpublished

Ground Based, Time Series of Hyper-Spectral Reflectance Measurements, in Conjunction with Flux Measurements Can be Used to Design Future Satellites

Ryu et al. Agricultural and Forest Meteorology, in review

Spectrally-Selective Vegetation Indices Track Seasonality of C Fluxes Well

Ryu et al. Agricultural and Forest Meteorology, in review

Ryu et al. Agricultural and Forest Meteorology, in review

Vegetation Indices can be Used to Predict GPP with Light Use Efficiency Models

UpScaling of FluxNetworks

Xiao et al 2010, Global Change Biology

What We can Do:Is Precision Good Enough for Treaties?

Xiao et al 2010, Global Change Biology

Map of Gross Primary Productivity Derived from Regression Tree AlgorithmsDerived from Flux Network, Satellite Remote Sensing and Climate Data

Youngryel Ryu and D. Baldocchi, unpublished

Xiao et al 2010, Global Change Biology

Net Ecosystem C Exchange

Xiao et al. 2008, AgForMet

springsummer

autumn winter

Jingfeng Xiao and D Baldocchi

area-averaged fluxes of NEE and GPP were -150 and 932 gC m-2 y-1

net and gross carbon fluxes equal -8.6 and 53.8 TgC y-1

Upscale GPP and NEE to the Biome Scale

Youngryel Ryu and D. Baldocchi, unpublished

Coupled Energy Balance-Photosynthesis Sun/Shade Model driven by MODIS,Implemented with Cloud-Computing System

Youngryel Ryu and D. Baldocchi, unpublished

Coupled Energy Balance-Photosynthesis Sun/Shade Model driven by MODIS

Conclusion

• Much Promise in Coupling Remote Sensing and Flux Network Information to produce State, Continental and Global Maps of Net and Gross Carbon Fluxes

• How Good is Good Enough?– Errors are still on the Order of +/- 8 PgC/y so we may

not have the resolution to detect inter-annual variability and may need Decades to Detect Trends in this Noisy System

– Perspective, Drawdown during Glacial-Inter-Glacial was 20 TgC/y of the Land-Ocean System