Biosphere)Atmosphere-Interac2ons:- Issues,-Observa2ons-and …€¦ · Some Towers are Not Active,...

Biosphere)Atmosphere-Interac2ons:-Issues,-Observa2ons-and-Technology-

Dennis-Baldocchi-UC-Berkeley-

Land-Atmosphere-Observa2on-Facility-Workshop--Boulder,-CO,-June,-2012-

-Outline-

•  Big-Ques2ons-and-Open-Problems-•  Biosphere)Atmosphere-Interac2ons-Principles-

–  Roles-of-Models-and-Observa2ons-– Non)Linear,-Coupled-Feedbacks-and-Forcings-– Mul2)Scaled-

•  Designing-Experiments-•  Observa2ons-

–  Flux-Networks-•  Modeling-and-Satellites--

Why-Study-Trace-Gas-and-Energy-Exchange?-

•  Flux-Boundary-Condi2ons-of-Weather,-Climate,-Biogeochemical-and-Ecological-Models-– State-of-the-Biosphere-is-determined-by-Fluxes-

•  Informa2on-is-Needed-for-Ecological-Assessments-of-Environmental-Change-(climate,-land-use,-disturbance)-

•  Base-lines-for-Policy-and-Management-(Carbon-and-Water-markets;-Pollu2on-Abatement;-Forest-Management,-REDD)-

Ques2ons-Facing-Earth-System-Science-

•  What-is-the-Carbon-and-Water-Balance-at-Landscapes-to-Global-Scales?-

•  What-are-the-Greenhouse-Gas-(CH4,-N2O,-C5H8)-and-Pollu2on-(O3,-NOx,-SO2)-Budgets-at-Landscape-to-Global-Scales?-

•  How-do-These-Balances-Vary-Seasonally?;-Year-to-Year?;-By-Plant-Func2onal-Type?;-By-Climate-Region?;-By-Disturbance-and-Time-Since-Disturbance?-

Big$Picture$Ques-on$Regarding$Predic-ng$and$Quan-fying$the$‘Breathing$of$the$Biosphere’:$

•  How$can$We$Compute$Biosphere?Atmosphere$Trace$Fluxes$‘Everywhere,$All$of$the$Time?’$

We-First-Need-to-Look-Under-the-Hood-

Designing-Earth-System-Flux--Experiments-and-Measurements-

Stomata:$10?5$m$

Leaf:$0.01?0.1$m$

Plant:$1?10$m$

Canopy:$100?1000$m$

Landscape:$1?100$km$

Con-nent:$1000$km$(106$m)$$

Globe:$10,000$km$(107$m)$The$Biosphere$Spans$>$14$Orders$of$Magnitude$in$Space$-

Bacteria/Chloroplast:$10?6$m$

The-Breathing-of-an-Ecosystem-is-Defined-by--the-Sum-of-an-Array-of-Coupled,-Non)Linear,-Biophysical-Processes-that-Operate-across-a-Hierarchy/Spectrum-of-

Fast-to-Slow-Time-Scales-

A aIb cI

dCe fC

aA bA cA d

~ ;+ +

+ + + =3 2 0Seconds,-Hours-

Days,-Seasons-

Years,-Decades-

Centuries,-Millennia-

Biogeo-chemistry

seconds

days

years

centuries

canopy

landscape

region/biome

continent

Weather model

Biophyscial Model

EcosystemDynamics

λE, HRn

Species,Functional

Type

Leaf Area, N/C,Ps capacity

Biophysical model

ppt, Ta, P, ea,u,Rg,L Ac,Gc

Biophysical Processes and their Linkages in Time and Space

Multiple Methods To Assess Terrestrial Trace Gas Budgets with Different Pros and Cons

Across Multiple Time and Space Scales

GCM-Inversion-Modeling-

Remote-Sensing/--MODIS-

Eddy-Flux--Measurements/-Flux-Networks,-e.g.-FLUXNET-

Forest/Biomass-/Soil-Inventories-

Biogeochemical/-Ecosystem-Dynamics--Modeling-

Physiological-Measurements/-Manipula2on-Expts.-

Remote--sensing--and--Earth--system--science--model--user--community-

Eddy--covariance--flux--system- Global--network--of--flux--towers-

Database-

Role-of-Flux-Networks-in-Biogeosciences-

What%Informa,on%Do%Networks%of%Flux%Towers%Produce?-

•  Groups-of-towers-at-the-landscape,-regional,-con2nental,-and-global-scales-allow-scien2sts-to-study-a-greater-range-of-climate-and-ecosystem-condi2ons-–  Dominant-plant-func2onal-type-(Evergreen/Deciduous-Forests,-

Grasslands,-Crops,-Savanna,-Conifer/Broadleaved,-Tundra)-–  Biophysical-adributes-(C3/C4-Photosynthesis;-Aerodynamic-

Roughness;-Albedo;-Bowen-Ra2o)-–  Biodiversity-–  Time-since-the-last-disturbance-from-fire,-logging,-wind-throw,-

flooding,-or-insect-infesta2on-–  The-effect-of-management-prac2ces-such-as-fer2liza2on,-irriga2on,-or-

cul2va2on-or-air-pollu2on-•  -A-global-flux-network-has-the-poten2al-to-observe-how-ecosystems-

are-affected-by,-and-recover-from,-low)probability-but-high)intensity-disturbances-associated-with-rare-weather-events.-

Global Flux Networks—FLUXNET—Offer opportunities to Parameterize, Test and Validate

Models across a Spectrum of Climates and Biomes

-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180Longitude

-90

-75

-60

-45

-30

-15

0

15

30

45

60

75

90

Latit

ude

FLUXNET 2007

www.fluxdata.org-

Ques2ons-Regarding-Networks-

•  How-Many-Sta2ons-Are-Enough?-•  Where-Should-there-be-Flux-Measurements-Sta2ons?--

•  How-long-Should-we-Con2nue-to-Measure-Fluxes?-

Global distribution of Flux Towers Covers Climate Space Well

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

How-many-Towers-are-needed-to-es2mate-mean-NEE,-GPP-and-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-Sta2s2cs-

We-Need-to-be-in-the-Right-Places-Bias-in-Sampling-Pdf-from-Network-vs-Globe-

FLUXNET-is-Undersampling-Tropics,-Tundra-and-Semi)Arid-Regions-It-is-Oversampling-Temperate-Zones-

FLUXNET Database

GPP (gC m-2 y-1)

0 1000 2000 3000 4000

pdf

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

FLUXNET (254 sites), <GPP> = 1033 +/- 1033 gC m-2 y-1 Cartesian-Gridded, <GPP> =1139 g C m-2 y-1

Area-Weighted, <GPP> = 1281 gC m-2 y-1

1980s 1995s 2000s 2010s

30 hours

One Site-Year

1990s

Ten Site-Years

100 Site-Years

1000 Site-Years

How-Long-Should-We-Measure?-Time-Line-of-Exis2ng-Flux-Data-

Marconi--Workshop-

La-Thuile-II-Workshop-

Some Towers are Not Active, nor Submitting data, circa La Thuile dataset

FLUXNET Data Archive

Year

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

Site

-Yea

rs o

f Dat

a

0

20

40

60

80

100

120

140

160

180

200

Few-Loca2ons-with-Long-Records-Many-with-2-to-3-Year-Long-Records-

Delays-in-Data-Submission-and-Network-Processing-

Original-2me-series:-

-

-

Decomposed-2me-series:-

) Nonlinear$trend$

) Annual-cycle-

)  Intra)annual-cycle-

) High-frequency-modes-

-

Singular$System$Analysis:-example-applica2on-

$

Mahecha-et-al.-(2007)-Biogeosciences,-4,-743)758-

New-developments-allow-applica2on-of-SSA-to-fragmented-2me-series--

Decadal-Scales-of-Variability,--Informa2on-exists-at-Long-2me-scales-

Walker Branch Watershed, TN: 1981-2001CANOAK

Frequency (1/day)

0.0001 0.001 0.01 0.1 1

nSne

e/σ2 ne

e

0.0001

0.001

0.01

0.1

1

7 years

year 130 days

ESPM-228-Adv-Topics-Biomet-and-Micromet-

ESPM-111-Ecosystem-Ecology-

Dynamics-of-a-Coupled-3)Pool-Model-with-different-Turnover-Times-

Cair

Cplant

Csoil

dCair/dt = (-P(Cair) + Reco(Ceco))/pbl

dCplant/dt = (P(Cair) -Rauto(Cplant))/ht

Csoil/dt = +dCplant/dt - Rhetero(Csoil)/zsoil

RC

heteorsoil

soil

=τ

RC

autoplant

plant

=τ

][][max

airm

air

CKCV

P+

=

Coupled-Exchange-Across-Big-and-Small-Pools-with-Short-and-Long-Time-Scales-

Fluxdata.org – A Common, Shared Database

Individual-Sites-

Regional-Networks-

Flux/Met-Data-

www.Fluxdata.org-

BADM-Data-

Flux/Met-Data-

BADM-Data-+-Flux/Met-Gap)Filled/QA/Products-

Guidelines-for-Effec2ve-Networks,-1.0-

•  Data-are-best-when-there-are-standards-and-protocols-for-instrument-performance,-data-quality,-and-calibra2on;-data-gaps-are-minimized-if-redundant-or-replacement-sensors-are-available;-data-gaps-are-filled-with-veded-methods-


•  Data-are-converted-into-informa2on-and-knowledge-when-there-is-a-shared-and-integrated-database-with-which-researchers-can-merge-flux-measurements-with-a-cohort-of-meteorological,-ecological,-and-soil-variables.--

•  A-centralized-database-can-harmonize-data-processing-and-gap)filling-to-produce-value)added-products-such-as-daily-or-annual-sums-or-averages,-establish-version-control-and-sharing-policies,-and-archive-data.--

•  Databases-can-be-queried-to-pull-data-for-specific-2mes,-loca2ons,-or-variables.--


•  The-success-of-a-scien2fic-flux-network-relies-on-crea2ng-a-human-network,-too.--

•  Data-sharing-depends-upon-fostering-trust-among-colleagues,-crossing-cultural-and-poli2cal-obstacles-and-devising-a-fair)-use-data-sharing-policy.--

•  Shared-leadership-and-frequent-communica2on-through-workshops,-internet-forums,-and-newsleders-can-also-help-to-build-trust.--

To$Develop$a$Scien-fically$Defensible$Virtual$World$‘You$Must$get$your$boots$dirty’,$too$

$Collec-ng$Real$Data$Gives$you$Insights$on$What$is$Important$&$

Data$to$Parameterize$and$Validate$Models$

-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180Longitude

-90

-75

-60

-45

-30

-15

0

15

30

45

60

75

90

La

titu

de

FLUXNET 2007

Acquire-Metadata-on-Leaf,-Soil-and-Ecosystems-Structure-and-Func2on,-too-

Assess-Leaf-Photosynthe2c-Capacity--and-Stomatal-Control-

Par22on-Ecosystem-Fluxes--according-to-Soil-and-Vegeta2on-

-Components-

Acquire-Informa2on-on-Canopy-Structure-with-Ac2ve-(LIDAR)-and-Passive-Remote-Sensing-

Canopy-Structure-

Leaf)Scale- Soil-System-

The-New-Millennium:-Op2mism-and-Opportunity-

Data-Sets-are-Gekng-Longer-and-Longer-Plethora-of-New-Data-(Satellite,-Flux,-Ecological)-and-Scaling-Rules-


Plethora-of-EcoPhysiological-Scaling-Rules:---

Metabolism-vs-Life-Span,-Specific-Leaf-Area-and-Nitrogen-

hdp://www.try)db.org-

There-Has-Been-A-Revolu2on-in-Fast-Response-Greenhouse-Gas-Sensors-

New-Technology-•  Tunable-Diode-Laser-Spectrometers-

–  CH4,-N2O,-13C,-COS,-18O-•  Proton-Transfer-Reac2on-Mass-Spectrometers-

– Hydrocarbons,-Terpenes,-Sequeterpenes-•  Wireless-Networks-•  Ground-Penetra2ng-Radar,-LIDAR-•  Electromagne2c-Induc2on-(EMI)-•  Satellite-Remote-Sensing-•  Flux-Footprint-Modeling-•  Digital-Cameras-for-Phenology-and-Leaf-Area-

4th-Paradigm-

•  What-to-Do-with-This-Mass-Quan2ty-of-Data?-– Plot-Histograms-and-Time-Series-– Plot-Spectral-Transforms-

•  Fourier-or-Wavelet-Transforms-– Plot-X-vs-Y-

•  Look-for-Non)Lineari2es,-Lags,-Hysteresis,-Condi2onal-Switches-and-Pulses;-Test-Granger-Causality-

– Plot-Y-in-Mul2)Variate-Space-•  Look-for-Covariance-and-Confounding-Effects-

– Data)Model-Fusion-

Published Data, April, 2011

NEE (gC m-2 y-1)

-1000 -500 0 500 1000

pdf

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

n = 973mean = -165 +/- 253 gC m-2 y-1

Forest Evaporation

ET (mm/y)

500 1000 1500 2000

pdf

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

Histograms-of-Annual-Integrated-Carbon-and-Water-Fluxes-

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-Respira2on-Scales-Tightly-with-Ecosystem-Photosynthesis,--But-Is-with-Offset-by-Disturbance-

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 ≤ 0.607n =164

Baldocchi,-Austral-J-Botany,-2008-

Interannual-Varia2ons-in-Photosynthesis-and-Respira2on-are-Coupled-

Baldocchi-et-al.-Int-J.-Biomet,-2005-

Soil-Temperature:--An-Objec2ve-Measure-of-Phenology,-part-2-

Temperate Deciduous Forests

Day, Tsoil >Tair

70 80 90 100 110 120 130 140 150 160

Day

NEE

=0

70

80

90

100

110

120

130

140

150

160

DenmarkTennesseeIndianaMichiganOntarioCaliforniaFranceMassachusettsGermanyItalyJapan

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-Varia2on-and-Long-Term-Trends--in-Net-Ecosystem-Carbon-Exchange-(FN),-Photosynthesis-(FA)-and-Respira2on-(FR)-

Urbanski-et-al-2007-JGR-

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-

Challenge$for$Landscape$to$Global$Upscaling$$

Conver2ng-Virtual-‘Cubism’-back-to-Virtual-‘Reality’-

Realis2c-Spa2aliza2on-of-Flux-Data-Requires-the-Merging-Numerous-Data-Layers-with-varying-Time-Stamps-(hourly,-daily,-weekly),-Spa2al-Resolu2on-(1-km-to-0.5-degree)-and-Data-Sources-

(Satellites,-Flux-Networks,-Climate-Sta2ons)-


‘Seven)stage-simula2on-models-by-means-of-which-ecosystems-may-be-explained-on-basis-of-the-molecular-sciences-are-impossible-large-and-detailed-and-it-is-naïve-to-pursue-their-construc2on’-

Biophysical-Modeling,-Circa-1969:--A-Pessimis2c-Legacy-

Cornelius-T.-deWit-

Sources-of-Model-Complexity-and-Uncertainty-

•  Representa2on-of-System-Complexity-–  Geometrical-–  Processes-–  Non)Lineari2es-

•  Model-Parameters-•  Driving,-or-Input,-Variables-and-their-Transforma2on-

•  Spa2al-and-Temporal-Resolu2on-•  Dura2on-of-the-Record-•  Accuracy-of-Test)Bed-Flux-Data-


MODIS-on-Terra-and-Aqua-

Satellite-Remote-Sensing-Gives-Us-the-Ability-to-S2tch-this-Informa2on-Together-

Model-Data-Fusion-Using-Flux-Networks-and-Satellite-Data-

Williams-et-al-2009-Biogeosciences-

Key-Ques2ons-

•  How-Complex-or-Simple-Should-the-Structure-of-the-Model-be?-

•  Should-Model-Parameters-be-Held-Constant-or-Vary-with-Season-and-Plant-Func2onal-Type?-

•  How-Best-to-Couple-Fast-Biophysical-Algorithms,-with-Slower-Biogeochemical-Algorithms-and-Slow-Dynamic-Vegeta2on/Ecological-Algorithms?-

Other-Ques2ons-

•  How-Can-We-Upscale-and-Integrate-Satellite-Remote-Sensing-Products-in-Time-that-

•  -May-only-Sample-the-Surface-Once-a-Day?-•  Whose-Vision-of-the-Surface-May-be-Obscured-by-Clouds?-

•  With-Sensors-that-Measure-Reflected-Light,-which-are-Proxies,-and-Infer-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-

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

Heinsch-et-al.-2006-RSE-

MODIS GPP Algorithm Test: How Good is Good Enough?

Deriving-and-Using-Model-Parameters-in-Land-Surface-Models-

Groenendijk-et-al.-2011-AgForMet-

Seasonality$of$Photosynthe-c$Capacity$

Wang-et-al,-2007-GCB-

Op2mizing-Seasonality-of-Vcmax-improves-Predic2on-of-Fluxes-

Wang-et-al,-2007-GCB-

Spa2al-Upscaling-of-Carbon-Fluxes-with-Flux-Networks,-Remote-Sensing-and-Machine-Learning-Models-

Beer-et-al.,-2010-Science-

Global$Primary$Produc-vity$

GPP-=-123-+/)-8-PgC-y)1--

GPP)CLM-~-160-PgC-y)1-

Ryu-et-al-2012-GBC-

Global-ET:-63,000-+/)-13000-km3/y--Global-ET:-Trenberth~-73,000-km3/y-

Global-Terrestrial-Evapora2on-

BESS-vs-Machine-Learning-Upscaling-Method-

Ryu et al (2012) Global Biogeochemical Cycles

Conclusions-•  New-Opportuni2es-Exist-to-Expand-and-Sustain-Flux-Networks,-which-are-Cri2cal-Partners-in-Upscaling-Fluxes-in-Time-and-Space-with-Models-and-Remote-Sensing-

•  Long)Term-Networks-are-Required-to-Study-Changes-in-the-Global-Environment-

•  Revolu2on-in-Sensors-Enables-Use-to-Expand-to-Other-Trace-Gases-

•  New-Data-Products-are-Refining-Past-Global-Budgets-that-were-Poorly-Constrained-or-Based-on-Residuals-

Necessary-Adributes-of-Global-Biophysical-Model:--Applying-Lessons-from-the-Berkeley-Biomet/Ecosystem-Ecology-

Classes-and-CANVeg-•  Treat-Canopy-as-Dual-Source-(Sun/Shade),-Two)Layer-(Vegeta2on/Soil)-system-

–  Treat-Non)Linear-Processes-with-Sta2s2cal-Rigor-(Norman,-1980s)-•  Requires-Informa2on-on-Direct-and-Diffuse-Por2ons-of-Sunlight-

–  Monte-Carlo-Atmospheric-Radia2ve-Transfer-model-(Kobayashi-+-Iwabuchi,,-2008)-•  Couple-Carbon)Water-Fluxes-for-Constrained-Stomatal-Conductance-Simula2ons-

–  Photosynthesis-and-Transpira2on-on-Sun/Shade-Leaf-Frac2ons-(dePury-and-Farquhar,-1996)-

–  Compute-Leaf-Energy-Balance-to-compute-Leaf-Satura2on-Vapor-Pressure-and-Respira2on-Correctly-

–  Photosynthesis-of-C3-and-C4-vegeta2on-Must-be-considered-Separately-•  Light-transfer-through-canopies-MUST-consider-Leaf-Clumping-to-Compute-Photosynthesis/

Stomatal-Conductance-correctly-(Baldocchi-and-Harley,-1995)-–  Apply-New-Global-Clumping-Maps-of-Chen-et-al./Pisek-et-al.-

•  Use-Emerging-Ecosystem-Scaling-Rules-to-parameterize-models,-based-on-remote-sensing-spa2o)temporal-inputs-

–  Vcmax=f(N)=f(albedo)-(Ollinger-et-al;-Hollinger-et-al;-Wright-et-al.)-–  Seasonality-in-Vcmax-is-considered-(Wang-et-al.,-2008)-–  Vcmax-scales-with-Jmax-(Wullschleger,-1993-)-

Spa2al-Paderns-in-Net-Carbon-Exchange-

Xiao-et-al-2011-AgForMet-

Atmospheric-radia2ve-transfer-

Canopy--photosynthesis,-Evapora2on,--Radia2ve-transfer-

Soil-evapora2on-

Beam-PAR-------------NIR-

Diffuse-PAR---------------NIR-

Albdeo)>Nitrogen-)>-Vcmax,-Jmax-

LAI,-Clumping)>-canopy-radia2ve-transfer-

dePury-&-Farquhar-two-leaf--Photosynthesis-model-

Rnet-

Surface-conductance-

Penman)Monteith-evapora2on-model-

Radia2on-at-understory-

Soil-evapora2on-

shade- sunlit-

BESS,$Breathing?Earth$Science$Simulator$

Posi2ve-and-Nega2ve-Feedbacks-affec2ng-Surface-Temperature-

Soil-Moisture)Rainfall)Evapora2on-Feedbacks-


Biometeorology/Ecosystem-Ecology,-v2,-the-Processes-

Physiology:Photosynthesis,

Respiration, Transpiration

Weather:Light Energy, Temperature,

Rainfall, Humidity, WindVelocity, CO2, soil

moisture

Growth and Allocation:Leaves, Stems, Roots,

Light Interception, Water andNutrient Uptake

Biogeochemistry:Decomposition,

Mineralization, Nitrification,Denitrification

Ecosystem Dynamics:Reproduction, Disperal, Recruitment,Competition, Facilitation, Mortality,

Disturbance, Sucession

Soil:Texture, DEM, C/

N,bulk density,Hydraulic Properties

EcoPhysiology:Leaf area index, plant

functional type,photosynthetic capacity,canopy height, albedo

hours

days/seasons

years

hours/days

• Numerous-and-Coupled--• Biophysical-Processes,--• Fast-and-Slow-

• Numerous-Feedbacks,--• Posi2ve-and-Nega2ve-

Discerning-Cause-and-Effect-

•  Issues-– Complexity-– Mul2ple-Scales-– Mul2ple-Feedbacks-– Mul2ple,-Coupled-Processes-

Granger-Causality-

0t k t kX a b X η−= + +∑

0 1, 2,t k t k k t kX a a X a Y ε− −= + + +∑ ∑

Compute-Lagged-Auto)Regressive-Func2on-on-a-2me-series,-Xt,-Determine--if-the-Variance-is-Reduced-if-the-other-Variable,-Yt,--

is-included-

2

2ln( )y xG η

ε

σ

σ→ =

Univariate-Model-

Mul2)Variate-Model-

Test-If-Muli)variate-model-is-beder-than-Uni)Variate-

2 2ε ησ σ<

0 1, 2,t k t k k t kY b b X b Y ξ− −= + + +∑ ∑

Dedo-et-al.-2012-Am-Nat-

Does-Photosynthesis-‘Granger)Cause’-Respira2on?-

Hatala-et-al-2012-GRL-

Photosynthesis-Causes-Methane-Produc2on-to-Lead-Temperature-

Try-pbl-model-and-Granger-Causality--Try-Vostock-Ice-core,-CO2,-CH4-and-T-and-Causality-

Transfer-Entropy-

Data$processing,$Value$Added$Products$and$Uncertainty$Es-ma-on$

Half$hourly$data$

u*$threshold$selec-on$?%3/5%different%methods%

u*$filtering$$?%3/4%possibility%

Gapfilling$?%x%methods%

Par--oning$?%3%methods%

3-methods,-bootstraping…-

Also-day2me,-also-data-axer-low-turb.,-…-

Neural-networks,-Mean-Diural-Average,--Lookup-Tables-or-Non)Linear,--Mul2)Variable-Fit-

Convert-NEE-into-GPP-and-Reco:--Reichstein,-Lasslop,-van-Gorsel-

Mobile-Methane-Flux-System-

0

500

1000

1500

2000

2500

3000

12

34

56

78

-10-505101520

GPP

(gC

m-2

y-1

)

Rg (GJ m

-2 y-1 )

Tair (C)

FLUXNET Database

0 500 1000 1500 2000 2500 3000

0

1000

2000

3000

4000

12

34

56

78

01000

20003000

40005000

6000

GPP

(gC

m-2

y-1

)

Rg (GJ m

-2 y-1 )

ppt (mm y -1)

FLUXNET Database

0 1000 2000 3000 4000

0

1000

2000

3000

4000

-10-5

05

1015

2025

01000

20003000

40005000

6000

GPP

(gC

m-2

y-1

)

T air (C

)

ppt (mm/y)

FLUXNET Database

0 1000 2000 3000 4000

E[GPP(Rg,-T)]=-1237-gC-m)2-y)1~136-PgC/y-

E[GPP(Rg,-ppt)]=-1901-gC-m)2-y)1--

E[GPP(ppt,T)]=-1753-gC-m)2-y)1--

Expected-Values-of-GPP-from-joint)pdfs-

Apply Bayes Theorem to FLUXNET?

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

p flux p fluxp Flux

p=

Es2mate-Global-flux-by-Integra2ng-p(Flux|climate)-across--Globally)gridded-Climate-space-

p(flux)-from-FLUXNET-p(climate|flux)-prior-from-FLUXNET-p(climate)-from-climate-database-

Global Flux Networks—FLUXNET—Offer opportunities to Parameterize, Test and Validate

Models across a Spectrum of Climates and Biomes

-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180Longitude

-90

-75

-60

-45

-30

-15

0

15

30

45

60

75

90

Latit

ude

FLUXNET 2007

www.fluxdata.org-

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