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

centuries

canopy

landscape

region/biome

continent

Weather model

Biophyscial Model

EcosystemDynamics

λE, HRn

Species,Functional

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

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

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

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

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

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

e/σ2 ne

0.0001

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-

Cplant

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

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

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

heteorsoil

autoplant

][][max

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

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

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

Forest Evaporation

ET (mm/y)

500 1000 1500 2000

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

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

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

DenmarkTennesseeIndianaMichiganOntarioCaliforniaFranceMassachusettsGermanyItalyJapan

Harvard Forest

1990 1992 1994 1996 1998 2000 2002 2004 2006

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

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

16008 day means

r2 = 0.92

8 day means

r2 = 0.65

Single clear days

AM net CO2 flux (mmol m-2 hr-1)

0 20 40 60 80 100 120

800Single clear days

AM gross CO2 flux (mmol m-2 hr-1)

0 20 40 60 80 100 120 140

r2 = 0.88

Single clear days

AM gross 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

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-

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

days/seasons

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-

2ln( )y xG η

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-

-10-505101520

Rg (GJ m

-2 y-1 )

Tair (C)

FLUXNET Database

0 500 1000 1500 2000 2500 3000

20003000

40005000

Rg (GJ m

-2 y-1 )

ppt (mm y -1)

FLUXNET Database

0 1000 2000 3000 4000

20003000

40005000

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

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

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Documents

Web viewWe demonstrate the utility of our architecture in the context of the FLUXNET [12] Synthesis Dataset. FLUXNET is one of the largest ongoing biogeophysical experiments

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FLUXNET: A New Tool to Study the Temporal and Spatial

Fluxdata.org FLUXNET Dataset Synthesis Support

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FLUXNET: Measuring CO 2 and Water Vapor Fluxes Across a Global Network

User’s Guidestratus.ssec.wisc.edu/fluxnet/userman.pdfFluxNet User ’s Guide Key, J., E. Amano, J. Collins, and A. Schweiger, 2001, FluxNet User ’s Guide, Cooperative Institute

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EIONET – CIRCA Operation

Use of FLUXNET in the Community Land Model development

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FLUXNET: Database of Fluxes, Site Characteristics, and

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FLUXNET, Status and Future

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