Using a Global Flux NetworkFLUXNET to Study the

Using a Global Flux NetworkFLUXNET to Study the

Using a Global Flux NetworkFLUXNET to Study the Breathing of the Terrestrial Biosphere Dennis Baldocchi ESPM/Ecosystem Science Div. University of California, Berkeley CEFE/CNRS- Montpellier, Dec. 7, 2007 Contemporary CO2 Record Mauna Loa Keeling data 380 370 360 CO2 (ppm) 350 340 330 320 310 300 1950

1960 1970 1980 year 1990 2000 2010 Methods To Assess Terrestrial Carbon Budgets at Landscape to Continental Scales, and Across Multiple Time Scales GCM Inversion Modeling Remote Sensing/ MODIS Physiological Measurements/ Manipulation Expts. Eddy Flux Measurements/ FLUXNET Forest/Biomass

Inventories Biogeochemical/ Ecosystem Dynamics Modeling Eddy Covariance Technique F ws ~ a w' s' c s ( ) a Fluctuation Mean Objectives Network Background Time Daily and Annual Integration Seasonal Dynamics

Inter-Annual Variability Disturbance/Chronosequence Processes Photosynthesis = f(Q,T,functional type) Respiration = f(T, growth, ppt, Space Other Uses and Application Ecosystem Modeling FLUXNET: From Sea to Shining Sea 400+ Sites, circa 2007 FLU XN ET 2007 90 75 60 45 L a titu d e 30 15 0 -1 5 -3 0 -4 5

-6 0 -7 5 -9 0 -1 8 0 -1 5 0 -1 2 0 -9 0 -6 0 -3 0 0 L o n g itu d e 30 60 90 120 150

180 Network Representativeness Sundareshwar et al, 2007 Science Global distribution of Flux Towers with Respect to Climate Global Climate 1961-1990 30 Annual Air Temperature ( C) 25 20 15 10 5 0 -5 -10 -15 -20 1 10

100 Annual Precipitation ( mm) Climate FLUXNET 1000 Institutional Memory: Evolution of FLUXNET Measure Annual Cycle of NEE Micromet issues of Detrending, Transfer Functions, Flux Sampling and Measurements, Gap-filling, Error Assessment Measure and Interpret Intra-annual Variation of NEE Flux partitioning (GPP & Reco); assessment of

metadata,e.g. Vcmax, soil respiration, LAI, biomass inventories. Quantifying Biophysical Controls on Fluxes Measure and Interpret Inter-annual variations of NEE Measure NEE over multiple Land-Use Classes crops, grasslands, deciduous and evergreen broadleaf and conifer forests Disturbance, logging, biodiversity and fire Manipulative Studies Nitrogen and H2O additions Measure NEE over Representative Areas Scaling Flux Information of Footprint to MODIS pixel Workshops LaThuile Italy, 1995 Flathead Lake MT, 1997 Marconi CA, 2000 Orvieto Italy, 2002

Lake Tahoe CA, 2003 Firenze Italy, 2004 LaThuile, 2007 FLUXNET Successes Mountains of data from a spectrum of canopy roughness and stability conditions, functional types and climate spaces have been collected A Model for Data Sharing Value-Added Products have been produced

Development of Gap-Filling Techniques Production of Gap-Filled Daily and Annual Sums Many New Findings on Emergent Processes, Environmental Controls and Seasonality and Annual C fluxes Data for Validating and Improving SVAT models used for weather, climate, biogeochemistry and ecosystem dynamics Collaboration & Synthesis through Workshops and Hosting Visitors FLUXNET Web Site, a venue for distributing Primary, Value-added and MetaData products Building a Collaborative, Cooperative, Multi-Disciplinary & International Community of Researchers Training New and Next Generation of Scientists, Postdocs, Students LaThuile Fluxnet Workshop, Feb. 2007 New Gap-Filled, Qa/Qc Dataset 250 Sites; 930 Site-years of Data

www.fluxdata.org Failures/Un-resolved Issues Not Measuring Night-time Fluxes Well ImPerfect U* correction Not Measuring Fluxes over Complex terrain and during Advection Well ImPerfect Flux Partitioning Works Better on Longer Time Scales ImPerfect Energy Balance Closure Could be red-herring based limited Rn and G fetch Need Better Outreach and Training Being Rectified at LaThuile with Participation of New Generation of Fluxnet Scientists Temporal Dynamics of C Fluxes

Hour Day Month Season Year Multiple Years Pulses Lags Switches Annual Time Series of Trace Gas Exchange Vaira Grassland 2001 15 10 Fc (mol m-2 s-1) 5 0 -5 -10 -15 -20 -25 0

50 100 150 200 Day/Hour Xu and Baldocchi, AgForMet, 2004 250 300 350 Complicating Dynamical Factors Temperate Broadleaved Deciduous Forest Switches Phenology

Drought Frost/Freeze -2 ) now no s -) ( il T so (-) R eco -1 x NEE (gC m-2 d-1) 0 ma Acclimation Lags

Stand Age/Disturbance I, V c LA =f( g ri n sp Diffuse Light/LUE 1 Drought: (-) GPP(-); Re(-) -3 in g Emergent Processes

2 pr ys 3 rl ea Rain Litterfall LAI=0 GPP=0; Litterfall (+) Reco=f(litterfall)(+) : snow ) + ( (+) T soil

o =0; R ec P P G P GP Pulses 4 e lat 5 -4 -5 Clouds: PAR(-) GPP=f(PAR)(+)

-6 -7 0 50 100 150 200 Day 250 300 350 Probability Distribution of Published NEE Measurements, Integrated Annually 0.07 0.06 0.05 mean: -182.9 gC m-2 y-1 std dev: 269.5

n: 506 p(x) 0.04 0.03 0.02 0.01 0.00 -1500 -1000 -500 0 FN (gC m-2 y-1) Baldocchi, Austral J Botany, submitted 500 1000 1500

Does pdf change with Time and/or as the Network Grows? FLUXNET Database: n ~ 300 in 2003; n ~ 430 in 2005 Does Net Ecosystem Carbon Exchange Scale with Photosynthesis? 1000 750 FN (gC m-2 y-1) 500 250 0 -250 -500 -750 -1000 0 500 1000 1500 2000

2500 3000 3500 4000 FA (gC m-2 y-1) Ecosystems with greatest GPP dont necessarily experience greatest NEE Baldocchi, Austral J Botany, submitted Ecosystem Respiration Scales Tightly with Ecosystem Photosynthesis, But Is with Offset by Disturbance 4000 Undisturbed Disturbed by Logging, Fire, Drainage, Mowing 3500 FR (gC m-2 y-1) 3000 2500

2000 1500 1000 500 0 0 500 1000 1500 2000 2500 FA (gC m-2 y-1) Baldocchi, Austral J Botany, submitted 3000 3500 4000 Net Ecosystem Carbon Exchange Scales with Length of Growing Season

Temperate and Boreal Deciduous Forests Deciduous and Evergreen Savanna 200 FN (gC m-2 yr-1) 0 -200 -400 -600 -800 -1000 50 100 150 200 250

300 Length of Growing Season, days Baldocchi, Austral J Botany, submitted 350 Decadal Plus Time Series of NEE: Flux version of the Keelings Mauna Loa Graph 10 8 Harvard Forest, 1991-2004 6 -2 -1 NEE (gC m d ) 4 2 0 -2

-4 -6 -8 -10 1990 1992 1994 1996 1998 Year Data of Wofsy, Munger, Goulden, et al. 2000 2002 2004 2006 Interannual Variation and Long Term Trends

in Net Ecosystem Carbon Exchange (FN), Photosynthesis (FA) and Respiration (FR) Harvard Forest 1800 Carbon Flux (gC m-2 y-1) 1600 1400 1200 FN FA 1000 0 FR -200 -400 -600 1990 1992 1994

1996 1998 Year Urbanski et al 2007 JGR 2000 2002 2004 2006 Interannual Variations in Photosynthesis and Respiration are Coupled Interannual Variability in FN 1000 Coefficients: b[0] -4.496 b[1] 0.704 r 0.607 n =164 500

-2 -2 d FR/dt (gC m y ) 750 250 0 -250 -500 -750 -750 -500 -250 0 250 d FA/dt (gC m-2 y-2) Baldocchi, Austral J Botany, submitted 500

750 1000 Lag Effects Due to 2003 European Drought/Heat Stress 20 10 NEE [g C m-2 week-1] 0 -10 -20 -30 -40 -50 -60 -70 Hainich Leinefelde -80

2002 Knohl et al Max Planck, Jena 2003 2004 2005 Emerging Processes WHEAT 0 -10 -15 Fc (m g m-2 s-1) Light and Photosynthesis: Emergent Processes at Leaf and Canopy Scales -5

-20 -25 -30 -35 -40 (a) -45 -50 0 500 1000 1500 absorbed PAR (mol m -2 2000 -1

s ) D208 Oak leaf, forest floor Tleaf: 25o C CO2 : 360 ppm 12 data model -40 A ( m o l -m2 s- 1) 8 6 4 2 0 0 200

400 600 800 1000 1200 1400 1600 1800 Qpar (mol m-2 s-1) Fc (mol m-2 s-1) 10 -30 -20 -10 0 measured model: clumped leaves 10 0 500

1000 1500 PPFD (mol m-2 s-1) 2000 Emergent Scale Process: CO2 Flux and Diffuse Radiation We are poised to see effects of Cleaner/Dirtier Skies and Next Volcano Niyogi et al., GRL 2004 Potential and Real Rates of Gross Carbon Uptake by Vegetation: Most Locations Never Reach Upper Potential GPP at 2% efficiency and 365 day Growing Season tropics GPP at 2% efficiency and 182.5 day Growing Season FLUXNET 2007 Database

Optimal NEE: Acclimation with Temperature T e m p e ra t u re O p t im u m fo r C a n o p y C2Ou p ta k e (C ) 35 30 25 20 15 b[0] 3.192 b[1] 0.923 r 0.830 10 5 5

10 15 20 25 Mean Summer T emperature (C) E. Falge et al 2002 AgForMet; Baldocchi et al 2001 BAMS 30 Linking Water and Carbon: Potential to assess Gc with Remote Sensing gc (mol m-2s-1) 0.8 0.6 0.4 0.2

0.0 0.00 Xu + DDB, 2003 AgForMet 0.01 0.02 0.03 0.04 0.05 GPP RH/Ca (mol m-2s-1) 0.06 Gc Scale Invariance? Task to Expand with New Database "Ball-Berry" 40 FLUXNET Data 30 20

10 0 Ball-Berry 40 30 Stomatal Conductance Survey Dennis Baldocchi; UC Berkeley 20 10 0 Processed by M. Falk Environmental Controls on Respiration 2.0 Fast growth period data Reco/Rref 1.5

Rain pulse 1.0 0.5 0.0 0.0 0.1 0.2 0.3 Soil volumetric water content (m3 m-3) Xu + Baldocchi, AgForMet 2004 0.4 Quantifying the impact of rain pulses on respiration 10 d214 2003 understory 3 Reco (gC m-2d-1)

8 2 1 6 0 4 (, Max/e) 0 2 4 6 8 10 12 14 2 0 -5 0 5 10

15 Day after rain (d) Xu, Baldocchi, Tang, 2004 Global Biogeochem Cycles 20 Soil Respiration Lags Photosynthesis on Hourly Scale July, Rsoil-Ps lag June, 2003 7 0.0 -0.2 lag correlation Flux Density soil respiration canopy photosynthesis

6 0 -2 -0.4 -0.6 -4 -0.8 -6 -8 -10 -1.0 0 5 10 15 Time (hour)

Tang et al. 2006, GCB 20 25 30 -30 -20 -10 0 lag (30 min) 10 20 30 Soil Temperature: An Objective Indicator of Phenology??

Soroe, Denmark Beech Forest 1997 20 NEE, gC m-2 d-1 Tair, recursive filter, oC Tsoil, oC 15 10 5 0 -5 -10 0 50 100 150

200 day Data of Pilegaard et al. 250 300 350 Soil Temperature: An Objective Measure of Phenology, part 2 Temperate Deciduous Forests 160 150 140 Day NEE=0 130 Denmark Tennessee Indiana

Michigan Ontario California France Massachusetts Germany Italy Japan 120 110 100 90 80 70 70 80 90 100 110 120

Day, Tsoil >Tair Baldocchi et al. Int J. Biomet, 2005 130 140 150 160 Spatial Variations in C Fluxes Spatialize Phenology with Transformation Using Climate Map 160 Day of NEE = 0 140 120 100 Coefficients: b[0]: 169.3

b[1]: -4.84 r : 0.691 80 Baldocchi, unpublished 60 4 6 8 10 12 Mean Air Temperature, C 14 16 18 Flux Based

Phenology Patterns with Match well with data from Phenology Network White, Baldocchi and Schwartz, GRL, submitted Do Snap-Shot C Fluxes, inferred from Remote Sensing, Relate to Daily C Flux Integrals? Sims et al 2005 AgForMet MODIS GPP Algorithm Test Heinsch et al. 2006 RSE Limits to Landscape Classification by Functional Type

Stand Age/Disturbance Biodiversity Fire Logging Insects/Pathogens Management/Plantations Kyoto Forests Time Since Disturbance Affects Net Ecosystem Carbon Exchange Conifer Forests, Canada and Pacific Northwest 1000 800 FN (gC m-2 y-1) 600 400 200 0 -200 -400 -600 1 10

100 1000 Stand Age After Disturbance Baldocchi, Austral J Botany, submitted Data of teams lead by Amiro, Dunn, Paw U, Goulden Effects of Stand Age: After Logging Law et al. 2003 Global Change Biology Northern Manitoba: Black Spruce chronosequence (Goulden et al.) SINK SOURCE Other Activities and Uses of Fluxnet Data

Ecosystem Modeling EcoHydrology Biodiversity Climate Ecosystem Model Testing and Development Kucharik et al., 2006 Ecol Modeling Net ecosystem exchange of CO2 (NEE) predicted by different terrestrial biosphere models compares favourably with FLUXNET observations at diurnal and seasonal timescales. Friend et al 2007, GCB Seasonality of Photosynthetic Capacity Wang et al, 2007 GCB Optimizing Seasonality of Vcmax improves Prediction of Fluxes Wang et al, 2007 GCB Biodiversity and Evaporation Temperate/Boreal Broadleaved Forests Summer Growing Season 1.3

1.2 1.1 E/E eq 1.0 0.9 0.8 0.7 0.6 0.5 1 2 3 4 5 6 7

8 Number of Dominant Tree Species (> 5% of area or biomass survey) Baldocchi, 2004: Data from Black, Schmid, Wofsy, Baldocchi, Fuentes Mean Soil Temperature Scales with Mean Air Temperature FLUXNET Database 18 16 b[0] 0.6243257655 b[1] 0.9216133206 r 0.9342107631 T soil: annual (C) 14 12 10 8 6 4 2 2

4 6 8 10 12 T air:annual (C) 14 16 18 Current and Future Scientific Directions NEE in Urban and Suburban, Africa, India, Latin America and High Arctic Environments Quantifying and Understanding the controls on Interannual Variability of C and energy Fluxes Monitoring the Metabolism of Ecosystems as we undergo Global Change Coupling CO2, Trace Gas Deposition/Emission (O3, voc) and Methane Fluxes Adopting New Technology (TDL, wireless networks) to embellish flux

measurements Couple tower data with Real-time Data Assimilation Models. Boundary Layer Budgets using Fluxes and High Precision CO2 measurements Spectral reflectance measurements and Digital Photos across the network for phenology and dynamics of structure and function Spatial-Temporal Network-Scale Analysis Real-time Data Assimilation Matching Footprints of Tower and Pixels Model Lags, Switches and Pulses Using Fluxnet data to assess problems in Ecology, Ecohydrology, Biogeochemistry, Biogeography, Remote Sensing, Global Modeling, Biodiversity Testing Maximum Entropy, Ecosystem Ecology, Biogeography and EcoHydrology Theories Acknowledgements Data Preparation Dario Papale, Markus Reichstein, Catharine Van Ingen, Deb Agarwal, Tom Boden, Bob Cook, Susan Holliday, Bruce Wilson, +++ Networks AmeriFlux, CarboEurope, AsiaFlux, ChinaFlux, Fluxnet Canada, OzFlux, +++ Agencies

NSF/RCN, ILEAPS, DOE/TCP, NASA, Microsoft, ++++ FLUXNET 2007++ New Issues/Questions Raised Production of New, Expanded DataBase Use of New Software Tools to Facilitate DataBase Navigation & Exploration Broader representation of vegetation types and climates on NEE, GPP and Reco. Roles of natural and human induced disturbance on C Fluxes Impacts of climate and ecosystem factors on inter-annual variations of carbon, water and energy fluxes. Use FLUXNET data to provide ground-truth information to validate and anchor NPP and fpar products being produced by MODIS LAND Perform geostatistical analyses with the FLUXNET database to examine the scales of spatial coherence of net carbon, water and

energy fluxes across landscapes, regions and continents and to quantify the network connectivity among groups of sites. Revisit many basic tenets of bio- & micrometeorology Data are being collected from a spectrum of land surface types (short grasses and crops, through open heterogeneous canopies to tall, closed forests) on flat to moderately undulating terrain over a wide range of atmospheric stability conditions Intermittent Turbulence FLUXNET database Rg (MJ m-2 y-1) 8000 6000 4000 2000 0 0 10 20

30 40 50 Latitude 60 70 80 90 Testbed for Ecohydrological Theory Miller et al, Adv. Water Research, on-line

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