using a global flux network—fluxnet— to study the breathing of the terrestrial biosphere
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Using a Global Flux Network—FLUXNET— to Study the Breathing of the Terrestrial Biosphere. Dennis Baldocchi ESPM/Ecosystem Science Div. University of California, Berkeley Collaborators - PowerPoint PPT PresentationTRANSCRIPT
Using a Global Flux Network—FLUXNET— to Study the Breathing of the Terrestrial Biosphere
Dennis BaldocchiESPM/Ecosystem Science Div.
University of California, Berkeley
CollaboratorsRodrigo Vargas, Youngryel Ryu, Markus Reichstein,
Dario Papale, Andrew Richardson, Deb Agarwal Catharine van Ingen, Bob Cook and Regional
Networks
ILEAPS 2009Melbourne, Australia
Objectives
• Network Background• Statistical Representativeness
– Global Statistics on NEE, GPP, Reco– Network Size– Is the Statistical Distribution of Carbon Fluxes Invariant at
the Global Scale?• Emergent Processes
– Coupling between Photosynthesis and Respiration– PhotoDegradation, a New Process to Consider– Fluxes and Phenology
• Space– Regional Flux Maps– Phenology Greenwave
Network of Regional Flux Networks
FLUXNET: From Sea to Shining Sea500+ Sites, circa 2009
F ws w sa ~ ' ' s c
a
( )
Eddy Covariance Technique
Mean
Fluctuation
• Direct• In situ• Quasi-Continuous• Extended Footprint
FLUXNET DataBase
• Web sites– www.fluxnet.ornl.gov/fluxnet/index.cfm– www.fluxdata.org
• QA/QC– Data Standardization (names, units)– Version Control
• Derived Flux Quantities– Gap-Filling– U* corrections for periods of low turbulence– Daily, Monthly, Annual Integrals
• Flux Partitioning– Converting NEE into GPP and Reco
• Compiling Multifaceted Site MetaData• Data Distribution
– Search/Manipulate/Archive
Standard Processing Procedure Vs non-standard, but Expert Based
Pub_Reco (gC m-2 yr-1)
0 1000 2000 3000 4000
LaTh
uile
_Rec
o (g
C m
-2 y
r-1)
0
1000
2000
3000
4000
Pub_GPP (gC m-2 yr-1)
0 1000 2000 3000 4000La
Thui
le_G
PP (g
C m
-2 y
r-1)
0
1000
2000
3000
4000
Results and Discussion
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
• If mean Solar inputs and Climate conditions are invariant, on an annual and global basis,are NEE, GPP and Reco constant, too?; e.g. global GPP scales with solar radiation which is constant
• 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.
Global distribution of Flux Towers Covers Climate Space Well
Can we Integrate Fluxes across Climate Space, Rather than Cartesian Space?
Apply Bayes Theorem to FLUXNET?
(climate | ) ( )( | climate)(climate)
p flux p fluxp Fluxp
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
FN (gC m-2 y-1)
-1500 -1000 -500 0 500 1000 1500
p(x)
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
mean: -182.9 gC m-2 y-1
std dev: 269.5n: 506
Baldocchi, Austral J Botany, 2008
Probability Distribution of Published NEE Measurements, Integrated Annually
Published FLUXNET Data, 2009
NEE (gC m-2 y-1)
-1000 -500 0 500 1000
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
<NEE> = -176 +/- 260 gC m-2 y-1
670 Site-Years
Updated PDF with 164 extra site-years yields an insignificant change in the mean (P= 0.68)
Xiao and Baldocchi, unpublished
Upscaling Flux Data with Remote Sensing DataPublished, Global: -182 gC m-2 y-1
Map, US: -189 gC m-2 y-1
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
FLUXNET database
precipitation
0 500 1000 1500 2000 2500
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
2003: 721 +/- 59 mm2004: 855 +/- 542005: 806 +/- 46
Rg (GJ m-2 y-1)
1 2 3 4 5 6 7 8
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
2003: 4.70 +/- 0.129 GJ m-2 y-1
2004: 4.67 +/- 0.1322005: 4.59 +/- 0.135
Little Change in Abiotic Drivers--annual Rg, ppt --across Network
FLUXNET, 75 sites
Tair, C
-10 -5 0 5 10 15 20 25 30
0.00
0.05
0.10
0.15
0.20
0.25
2003: 8.86 +/- 0.82 C2004: 8.2 +/- 0.86 2005: 8.84 +/- 0.78
Interannual Variability in NEE is small (between -220 to -243 gC m-2 y-1) across the Global Network
FLUXNET Network, 75 sites
NEE (gC m-2 y-1)
-1000 -800 -600 -400 -200 0 200 400 600
p(N
EE
)
0.00
0.05
0.10
0.15
0.20
0.25
2002: -220 +/- 35.2 gC m-2 y-1
2003: -238 +/- 39.92004: -243 +/- 39.7 2005: -237 +/- 38.7
Interannual Variability in GPP is small, too, across the Global Network (1103 to 1162 gC m-2 y-1)
FLUXNET Network, 75 sites
GPP (gC m-2 y-1)
0 500 1000 1500 2000 2500 3000 3500
p(G
PP
)
0.00
0.05
0.10
0.15
0.20
0.25
2002: 1117 +/- 74.0gC m-2 y-1
2003: 1103 +/- 67.82004: 1162 +/- 77.02005: 1133 +/- 70.1
FLUXNET 2007 Database
GPP at 2% efficiency and 365 day Growing Season
Potential and Real Rates of Gross Carbon Uptake by Vegetation:Most Locations Never Reach Upper Potential
tropics
GPP at 2% efficiency and 182.5 day Growing Season
max (4.6 )0.022gRGpp PAR LUE
Baldocchi, Austral J Botany, 2008
Does Net Ecosystem Carbon Exchange Scale with Photosynthesis?
FA (gC m-2 y-1)
0 500 1000 1500 2000 2500 3000 3500 4000
F N (g
C m
-2 y
-1)
-1000
-750
-500
-250
0
250
500
750
1000
Ecosystems with greatest GPP don’t necessarily experience greatest NEE
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 Respiration Scales Tightly with Ecosystem Photosynthesis, But Is with Offset by Disturbance
Harvard Forest, 1991-2004
Year
1990 1992 1994 1996 1998 2000 2002 2004 2006
NE
E (g
C m
-2 d
-1)
-10
-8
-6
-4
-2
0
2
4
6
8
10
Data of Wofsy, Munger, Goulden, et al.
Decadal Plus Time Series of NEE:Flux version of the Keeling’s Mauna Loa Graph
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 Variation and Long Term Trends in Net Ecosystem Carbon Exchange (FN), Photosynthesis (FA) and Respiration (FR)
Urbanski et al 2007 JGR
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 ² 0.607n =164
Baldocchi, Austral J Botany, 2008
Interannual Variations in Photosynthesis and Respiration are Coupled
What Happens to the Grass?
OctoberJune
PhotoDegradation is a New and Emerging Process Affecting C Cycle in Semi-arid Grasslands
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
F CO
2 m
ole
m-2
s-1
2007
soil CO2 flux-gradienteddy covariance
Baldocchi and Ma, unpublished
PhotoDegradation: A Process Not in Coupled Carbon-Climate Models
Baldocchi, Ma, Rutledge, unpublished
More Carbon is Lost with Rain Pulse on Dry Matter that has Been PhotoDegraded than Dry Matter in the Shade
Xu, Baldocchi, Tang, 2004 Global Biogeochem Cycles
Amount of precipitation (mm)0 10 20 30 40 50 60 70
Tota
l car
bon
resp
ired
(g C
m-2
)
0
20
40
60Grasslandintercept=5.84slope=0.96r ²=0.86
understoryintercept=3.66slope=0.47r ²=0.91
Phenology
Day of year, 2007
50 100 150 200 250 300 350
LED
ND
VI
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Gro
ss p
rimar
y pr
oduc
tivity
(gC
m-2
day
-1)
0
2
4
6
8
10
12LED NDVIGross primary productivity
LED NDVI
0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Gro
ss p
rimar
y pr
oduc
tivity
(gC
m-2
day
-1)
0
2
4
6
8
10
2006200720082009
(a)
(b)
y=-1.59+0.81exp(3.02x)r2=0.91
Ryu et al. in prep
Seasonality in GPP follows NDVI and Defines Growing Season
Length of Growing Season, days
50 100 150 200 250 300 350
F N (g
C m
-2 y
r-1)
-1000
-800
-600
-400
-200
0
200
Temperate and Boreal Deciduous Forests Deciduous and Evergreen Savanna
Baldocchi, Austral J Botany, 2008
Net Ecosystem Carbon Exchange Scales with Length of Growing Season
Soroe, DenmarkBeech Forest1997
day
0 50 100 150 200 250 300 350-10
-5
0
5
10
15
20
NEE, gC m-2 d-1
Tair, recursive filter, oC
Tsoil, oC
Data of Pilegaard et al.
Soil Temperature: An Objective Indicator of Phenology??
Baldocchi et al. Int J. Biomet, 2005
Soil Temperature: An Objective Measure of Phenology, part 2
Temperate Deciduous Forests
Day, Tsoil >Tair
70 80 90 100 110 120 130 140 150 160
Day
NE
E=0
70
80
90
100
110
120
130
140
150
160
DenmarkTennesseeIndianaMichiganOntarioCaliforniaFranceMassachusettsGermanyItalyJapan
Baldocchi, unpublished
Spatialize Phenology with Transformation Using Climate Map
Mean Air Temperature, C
4 6 8 10 12 14 16 18
Day
of N
EE =
060
80
100
120
140
160
Coefficients:b[0]: 169.3b[1]: -4.84r ²: 0.691
White, Baldocchi and Schwartz, unpublished
Flux Based Phenology Patterns with Match well with data from Phenology Network
Spatial Variations in C Fluxes with Remote Sensing
8 day means
Daily g
ross
CO
2 flux
(m
mol m
-2 d
ay-1
)
0
200
400
600
800
1000
1200
1400
16008 day meansDaily n
et C
O2 flux
(m
mol m
-2 d
ay-1
)
-400
-200
0
200
400
600
800
r2 = 0.92
8 day means
Daily g
ross
LUE
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
Daily n
et C
O2 flux
(m
mol 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
Daily g
ross
CO
2 flux
(m
mol 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 0.03Daily g
ross
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
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?
Spatial Variations in C Fluxes
Xiao et al. 2008, AgForMet
springsummer
autumn winter
Conclusions
• A sparse Global Network of Flux Towers produces statistically robust findings that are representative of broad climate and ecological zones that may integrated to validate models at grid/landscape/regional scales
• Any Statistical Scaling Approach based on Climate must consider Disturbance and Stand Age, too.
• A global Network of Flux Towers is discovering a number of emergent scale processes relating to the controls in inter-annual variability, controls by light, and phenology
Acknowledgments
• NSF, Research Coordinated Networks• Microsoft, Regional Networks, ILEAPS• World-wide Collection of Collaborators
and Colleagues, Technicians, Postdocs and Students
Filtered: annual NPP_GPP_fqc > 0.5, minimum 2 years of data per site
GPP Reco NEENMean LS Mean Mean LS Mean Mean LS Mean
1997 25 1247 1178 982 989 -265 -1901998 25 1262 1095 1048 965 -214 -1311999 36 1267 1163 987 953 -280 -2112000 44 1298 1128 1041 945 -257 -1842001 60 1262 1178 1022 975 -241 -2052002 84 1279 1177 1066 963 -214 -2152003 98 1178 1185 971 933 -206 -2532004 131 1176 1213 934 952 -244 -2632005 133 1144 1216 899 951 -245 -2652006 96 1202 1198 922 948 -280 -251
1 S.D. 52 37 57 16 27 42
A. Richardson & D. Baldocchi, unpublished
ANOVA analysis of the annual sums