Dennis BaldocchiUniversity of California, Berkeley
JASON GHG Emissions Monitoring Summer Study (June 16-18, 2010)
La Jolla, CA
Micrometeorological Methods Used to Measure Greenhouse Gas Fluxes:
The Challenges Associated with Them, at the Local to Global Scales
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
Challenges in Measuring Greenhouse Gas Fluxes
• Measuring/Interpreting greenhouse gas flux in a quasi-continuous manner for days, years and decades
• Measuring/Interpreting fluxes over Patchy, Microbially-mediated Sources (e.g. CH4, N2O)
• Measuring/Interpreting fluxes of Temporally Intermittent Sources (CH4, N2O, O3, C5H8)
• Measuring/Interpreting fluxes over Complex Terrain• Developing New Sensors for Routine Application of Eddy Covariance,
or Micrometeorological Theory, for trace gas Flux measurements and their isotopes (CH4, N2O,13CO2, C18O2)
• Measuring fluxes of greenhouse gases in Remote Areas without ac line power
Flux Methods Appropriate for Slower Sensors, e.g. FTIR
• Relaxed Eddy Accumulation
• Modified Gradient Approach
• Integrated Profile
• Disjunct Sampling
)(' dnupw cccwF '
Fx
u dzc background
z
z10
( )
F F csc sz
z
~
ESPM 228 Adv Topics Micromet & Biomet
Eddy Covariance
• Direct Measure of the Trace Gas Flux Density between the atmosphere and biosphere, mole m-2 s-1
• Introduces No Sampling artifacts, like chambers• Quasi-continuous• Integrative of a Broad Area, 100s m2
• In situ
ESPM 228 Adv Topics Micromet & Biomet
~ ' 'a aF ws w s
c c c
a a a
m psm P
Eddy Covariance, Flux Density: mol m-2 s-1 or J m-2 s-1
Eddy Covariance TowerSonic Anemometer, CO2/H2O IRGA,
inlet for CH4 Tunable diode laser spectrometer &Meteorological Sensors
0 1 2 3 4 5 6 7 8 9
x 105
-4
-2
0
2
4
W m
/s
0 1 2 3 4 5 6 7 8 9
x 105
1.5
2
2.5
3
3.5
4
4.5
5
seconds
CH
4 ppm
D164, 2008
24 Hour Time Series of 10 Hz Data, Vertical Velocity (w) and Methane (CH4) Concentration
Sherman Island, CA: data of Detto and Baldocchi
Non-Dispersive Infrared Spectrometer, CO2 and H2O
LI 7500
Open-path , 12.5 cmLow Power, 10 WLow noise, CO2: 0.16 ppm; H2O: 0.0047 ppthLow drift, stable calibrationLow temperature sensitivity: 0.02%/degree C
Measuring Methane with Off-Axis Infrared Laser Spectrometer
Los Gatos Research
Closed pathModerate Cell Volume, 400 ccLong path length, kilometersHigh power Use:Sensor, 80 WPump, 1000 W; 30-50 lpmLow noise: 1 ppb at 1 HzStable Calibration
LI-7700 Methane Sensor, variant of frequency modulation spectroscopy
Open path, 0.5 mShort optical path length, 30 mLow Power Use: 8 W, no pumpModerate Noise: 5 ppb at 10 HzStable Calibration
ESPM 228 Adv Topics Micromet & Biomet
0
)('' dSww ww
0
)('' dScwF wc
Co-Spectrum
Power Spectrum defines the Frequencies to be Sampled
Power Spectrum
ESPM 228 Adv Topics Micromet & Biomet
Signal Attenuation:The Role of Filtering Functions and Spectra
• High and Low-pass filtering via Mean Removal– Sampling Rate (1-10Hz) and Averaging Duration (30-60 min)
• Digital sampling and Aliasing• Sensor response time• Sensor Attenuation of signal
– Tubing length and Volumetric Flow Rate– Sensor Line or Volume averaging
• Sensor separation– Lag and Lead times between w and c
M.Detto and D. Baldocchi
Comparing Co-spectra of open-path CO2 & H2O sensor and closed-path CH4 sensor
Co-Spectra are More Forgiving of Inadequate Sensor Performance than Power SpectraBecause there is little w-c correlation in the inertial subrange
Co-Spectra is a Function of Atmospheric Stability:Shifts to Shorter Wavelengths under Stable ConditionsShifts to Longer Wavelengths under Unstable Conditions
Detto, Baldocchi and Katul, Boundary Layer Meteorology, accepted
ESPM 228 Adv Topics Micromet & Biomet
Zero-Flux Detection Limit, Detecting Signal from Noise
' ' wc w cF w c r rwc ~ 0.5ch4 ~ 0.84 ppbco2 ~ 0.11 ppm
Methane Lab Calibration
Time
260 280 300 320 340 360 380 400
Met
hane
Sen
sor
1880
1890
1900
1910
1920
Mean: 1897.4277StdDev: 0.8411Std Err 0.0219
U* w Fmin, CH4 Fmin, CO2m/s m/s nmol m-2 s-1 mmol m-2 s-1
0.1 0.125 2.1 0.2750.2 0.25 4.2 0.550.3 0.375 6.3 0.8250.4 0.5 8.4 1.10.5 0.625 10.5 1.375
Detto et al, in prep
Flux Detection Limit, v2Based on 95% CI that the Correlation between
W and C that is non-zero
0.035 mmol m-2 s-1, 0.31 mmol m-2 s-1 and 3.78 nmol m-2 s-1 for water vapour, carbon dioxide and methane flux,
ESPM 228 Adv Topics Micromet & Biomet
F ws w s w w wa a c c c ' ' ' '
Formal Definition of Eddy Covariance, V2
Most Sensors Measure Mole Density, Not Mixing Ratio
ESPM 228 Adv Topics Micromet & Biomet
F w mm
w mm T
w Tc ca
v
c
av
v a
a v
c ' ' ' ( ) ' ''
1
Webb, Pearman, Leuning Algorithm:‘Correction’ for Density Fluctuations when
using Open-Path Sensors
ESPM 228 Adv Topics Micromet & Biomet
dead annual grassland
Day
180 181 182 183 184 185
F (m
mol
m-2
s-1
)
-20
-15
-10
-5
0
5
10
Fwpl
<w'c'>
Raw <w’c’> signal, without density ‘corrections’, will infer Carbon Uptake when the system is Dead and Respiring
0 2 4 6 8 100
2
4
6
8
10
12
14
16
18
20
wq mmol m-2 s-1
met
hane
cor
rect
ion
term
for w
ater
vap
or, n
mol
m-2
s-1
Density ‘Corrections’ Are More Severe for CH4 and N2O:This Imposes a Need for Accurate and Concurrent Flux Measurements of H and LE
0 0.1 0.2 0.3 0.40
20
40
60
80
100
120
wt K m s-1
met
hane
cor
rect
ion
term
for <
wt>
, nm
ol m
-2 s
-1
ESPM 228 Adv Topics Micromet & BiometHanslwanter et al 2009 AgForMet
Annual Time Scale, Open vs Closed sensors
Towards Annual SumsAccounting for Systematic and Random Bias Errors
• Advection/Flux Divergence• U* correction for lack of adequate turbulent mixing at night• QA/QC for Improper Sensor Performance
– Calibration drift (slope and intercept), spikes/noise, a/d off-range– Signal Filtering
• Software Processing Errors• Lack of Fetch/Spatial Biases
– Sorting by Appropriate Flux Footprint
• Change in Storage• Gaps and Gap-Filling
ESPM 228 Adv Topic Micromet & Biomet
time 0 1 2 3 4 5 6 7
f(t)
-3 -2 -1 0 1 2 3
signal random error
time 0 1 2 3 4 5 6 7
f(t)
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
signal bias error
time 0 1 2 3 4 5 6 7
f(t)
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
signal systematic bias
Systematic and Random Errors
ESPM 228 Adv Topic Micromet & Biomet
Random Errors Diminish as We Measure Fluxes Annually and Increase the Sample Size, n
Oak Ridge, TNTemperate Deciduous Forest1997
Day-Hour
0 50 100 150 200 250 300 350
F wpl
(mm
ol m
-2 s
-1)
-40
-35
-30
-25
-20
-15
-10
-5
0
5
10
15
Vaira Grassland 2001
Day/Hour
0 50 100 150 200 250 300 350
Fc (m
mol
m-2
s-1
)
-25
-20
-15
-10
-5
0
5
10
15
Tall Vegetation, Undulating Terrain
Short Vegetation, Flat Terrain
Systematic Biases and Flux Resolution: A Perspective
• FCO2: +/- 0.3 mmol m-2 s-1 => +/- 113 gC m-2 y-1
• 1 sheet of Computer paper 1 m by 1 m: ~70 gC m-2 y-1
• Net Global Land Source/Sink of 1PgC (1015g y-1): 6.7 gC m-2 y-1
The Real World is Not Kansas, which is Flatter than a Pancake
ESPM 228 Adv Topics Micromet & Biomet
Cloud
Cloud
Eddy Covariance in the Real World
ESPM 228 Adv Topics Micromet & Biomet
' ' ' ' ' ' ' 'j
j
u cdc c c c c u c v c w cu v wdt t x y z x x y z
I: Time Rate of ChangeII: AdvectionIII: Flux Divergence
I II III
Diagnosis of the Conservation Equation for C for Turbulent Flow
Daytime and Nightime Footprints over an Ideal, Flat Paddock
Detto et al. Boundary Layer Meteorology, conditionally accepted
0Fz
FF dz Constz
Estimating Flux Uncertainties:Two Towers over Rice
Detto, Anderson, Verfaillie, Baldocchi, unpublished
Examine Flux Divergence
Detto, Baldocchi and Katul, Boundary Layer Meteorology, conditionally accepted
time (hours)
0 4 8 12 16 20 24
CO
2 Flu
x D
ensi
tym m
ol m
-2 s
-1
-25
-20
-15
-10
-5
0
5
10
Ne: measured (-4.84 gC m-2 day-1) Ne: computed (-5.09 gC m-2 day-1)
Fwpl+Storage: measured (-5.96 gC m-2 day-1) Fwpl: measured (-6.12 gC m-2 day-1)
Baldocchi et al., 2000 BLM
Underestimating C efflux at Night, Under Tall Forests, in Undulating Terrain
u* (m s-1)
0.0 0.2 0.4 0.6 0.8 1.0
F co2 (
m mol
m-2
s-1
)
0
1
2
3
4
5
6
7
8
r ² 0.325Canopy Respiration8 to 13 C
Wheat, Columbia River Valley, Oregon
Losses of CO2 Flux at Night: u* correction
ESPM 228 Adv Topic Micromet & Biomet
Systematic Biases are an Artifact of Low Nocturnal Wind Velocity
Friction Velocity, m/s
ESPM 228 Adv Topics Micromet & Biomet
Annual Sums comparing Open and Closed Path Irgas
Hanslwanter et al 2009 AgForMet
Biometric and Eddy Covariance C Balances Converge after Multiple Years
Gough et al. 2008, AgForMet
-100 0 100 200 300 400 500 600-100
0
100
200
300
400
500
600
slope=1.05r2=0.98
wheat
H+L
E (W
m-2
)
Rn-G (W m-2)
-100 0 100 200 300 400 500 600 700 800-100
0
100
200
300
400
500
600
700
800
r2=0.93slope=0.93
Boreas 1994Hourly averages
Old Jack Pine
LE+H
+S+G
(W
m-2
)
Rn (W m-2)
Rnet (W m-2)
0 200 400 600 800
E +
H +
G +
S +
Ps
(W m-2)
0
200
400
600
800
Coefficients:b[0] 3.474b[1] 1.005r ² 0.923
Temperate Deciduous Forest
Contrary Evidence from Personal Experience:Crops, Grasslands and Forests
Rnet (W m-2)
-100 0 100 200 300 400
LE+H
+G
-100
0
100
200
300
400coefficients:b[0] 5.55b[1] 0.94r ² 0.926
Vaira Grassland, D296-366, 2000
ESPM 228 Adv Topic Micromet & Biomet
Many Studies Don’t Consider Heat Storage of Forests Well, or at All, and Close Energy Balance when they Do
Lindroth et al 2010 Biogeoscience Haverd et al 2007 AgForMet
FLUXNET: From Sea to Shining Sea500+ Sites, circa 2009
Limits and Criteria for Network Design for Treaty Verification
• Can We Statistically-Sample or Augment Regional, Continental and Global Scale C Budgets with a Sparse Network of Flux Towers?
• If Yes:– How Many Towers are Enough?– Where Should the Towers Be?– How Good is Good Enough with regards to Fluxes?– How Long Should We Collect Data?
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
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
Interannual Variability of the Statistics of NEE is Small across a sub-network of 75 Sites
Mean NEE Ranges between -220 to -243 gC m-2 y-1
Standard Deviation Ranges between 35.2 and 39.9 gC m-2 y-1
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
Assuming Global Arable Land area is 110 106 km2, Mean Global GPP ranges between 121.3 and 127.8 PgC/y
Precision is about +/- 7 PgC/y
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
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
Net Carbon Exchange is a Function of Time Since Disturbance
Baldocchi, Austral J Botany, 2008
Walker Branch Watershed, TN: 1981-2001CANOAK
Frequency (1/day)
0.0001 0.001 0.01 0.1 1
nSne
e/ nee
0.0001
0.001
0.01
0.1
1
7 years
year 130 days
C Fluxes May Vary Interannual on 7 to 10 year Time Scales
mmk
Pk
mmMATaa
Caa
C eeGPP
eeGPP
PgMATfGPP
10001000
15
15 11,
11min
,min
21
21
Upscale NEP, Globally, Explicitly
1. Compute GPP = f(T, ppt)2. Compute Reco = f(GPP,
Disturbance)3. Compute NEP = GPP-Reco
Leith-Reichstein Model
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
F R (g
C 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> = -222 gC m-2 y-1
This Flux Density Matches FLUXNET (-225) well, but S NEE = -31 PgC/y!!
Implies too Large NEE (|-700 gC m-2 y-1| Fluxes in TropicsIgnores C losses from Disturbance and Land Use Change
FLUXNET Database
NEE (gC m-2 y-1)
-1400 -1200 -1000 -800 -600 -400 -200 0 200 400 600
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
FLUXNETGlobal Map
Statistically Sampling and Climate Upscaling Agree
<NEE: FLUXNET> = -225 +/- 164 gC m-2 y-1
<NEE 0% dist: sinusoidal> = -222 gC m-2 y-1
FLUXNET UnderSamples Tropics
Explicit Climate-Based Upscaling Under Represents Disturbance Effects
<NEE> = -4.5 gC m-2 y-1
S NEE = -1.58 PgC/y
To Balance Carbon Fluxes infers that Disturbance Effects May Be Greater than Presumed
UpScaling Tower Based C Fluxes with Remote Sensing
LIDAR derived map of Tree location and Height
Hemispherical Camera Upward Looking Camera
Web Camera
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
Remote Sensing of NPP:Up and down PAR, LED, Pyranometer, 4 band Net Radiometer
LED-based sensors are Cheap, Easy to Replicate and Can be Designed for a Number of Spectral Bands
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
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
Take-Home Message for Application of Eddy Covariance Method under Non-Ideal
Conditions
•Routine Flux Measurements Must Comply with Governing Principles of Conservation Equation•Design Experiment that measures Flux Divergence and Storage, in addition to Covariance•Networks need more Sites in Tropics and Distinguish C3/C4 crops•Networks need Sites that Cover a Range of Disturbance History•Network of Flux Towers, in conjunction with Remote Sensing, Climate Networks and Machine Learning Algorithms has Potential to Produce Carbon Flux Maps for Carbon Monitoring for Treaty, with Caveats and Accepted Errors
Additional Background Material
Sampling Error with Two Towers
Hollinger et al GCB, 2004
Moffat et al., 2007, AgForMet
Gap-Filling Inter-comparison Bias Errors
ESPM 228 Adv Topic Micromet & Biomet
Moffat et al., 2007, AgForMet
ESPM 228 Adv Topic Micromet & Biomet
Root Mean Square Errors with Different Gap Filling Methods
