Dennis Baldocchi University of California, Berkeley Brazil Flux Eddy Covariance Meeting
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Transcript of Dennis Baldocchi University of California, Berkeley Brazil Flux Eddy Covariance Meeting
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Dennis BaldocchiUniversity of California, Berkeley
Brazil Flux Eddy Covariance MeetingSanta Maria Rio Grande do Sul, Brazil
November, 2011
Micrometeorological Methods Used to Measure Greenhouse Gas Fluxes
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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 and or Calm Winds• 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
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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
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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
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Flux Methods Appropriate for Slower Sensors, e.g. FTIR
• Relaxed Eddy Accumulation
• Modified Gradient Approach
• Integrated Profile
• Disjunct Sampling
)(' dnupw cccwF '
Fxu dzc background
z
z10
( )
F F csc sz
z
~
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0 1 2 3 4 5 6 7 8 9
x 104
-5
0
5
W (
m s
-1)
24 hours
4.3 4.35 4.4 4.45 4.5 4.55 4.6 4.65 4.7
x 104
-4
-2
0
2
4
W (
m s
-1)
hour
4.32 4.321 4.322 4.323 4.324 4.325 4.326 4.327 4.328 4.329 4.33
x 104
-2
-1
0
1
2
seconds
W (
m s
-1)
100 seconds
Vaira Ranch, d110, 2008
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ESPM 228 Adv Topics Micromet & Biomet
Vaira Ranch, d110, 2008
0 1 2 3 4 5 6 7 8 9
x 104
14
16
18
20
CO
2 (mm
ol m
-3)
24 hours
4.3 4.35 4.4 4.45 4.5 4.55 4.6 4.65 4.7
x 104
15
15.5
16
CO
2 (m
mol
m-3
)
hour
4.32 4.321 4.322 4.323 4.324 4.325 4.326 4.327 4.328 4.329 4.33
x 104
15
15.5
16
seconds
CO
2 (m
mol
m-3
)
100 seconds
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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
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ESPM 228 Adv Topics Micromet & Biomet
0
)('' dSww ww
0
)('' dScwF wc
Co-Spectrum
Power and Co- Spectrum defines the Range of Frequencies to be Sampled
Power Spectrum
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Frequency
Spec
tral
Den
sity
Fourier Transform of Signal from Temporal to Frequency Space Illustrates the Role of
Filtering Functions and Spectra0 1 2 3 4 5 6 7 8 9
x 104
14
16
18
20
CO
2 (mm
ol m
-3)
24 hours
4.3 4.35 4.4 4.45 4.5 4.55 4.6 4.65 4.7
x 104
15
15.5
16
CO
2 (m
mol
m-3
)
hour
4.32 4.321 4.322 4.323 4.324 4.325 4.326 4.327 4.328 4.329 4.33
x 104
15
15.5
16
seconds
CO
2 (m
mol
m-3
)
100 seconds
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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
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Detto et al 2012 AgForestMet
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
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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 2010
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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
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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
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ESPM 228 Adv Topics Micromet & Biomet
dead annual grassland
Day
180 181 182 183 184 185
F (
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
See new Theory by Gu et al concerning da/dt
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ESPM 228 Adv Topics Micromet & Biomet
Hanslwanter et al 2009 AgForMet
Annual Time Scale, Open vs Closed sensors
Ope
n Pa
th
Closed path
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Towards Annual SumsAccounting for Systematic and Random Bias Errors
• Advection/Flux Divergence• U* correction, or some alternative, 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
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Oak Ridge, TNTemperate Deciduous Forest1997
Day-Hour
0 50 100 150 200 250 300 350
F wpl
(m
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 (
mol
m-2
s-1
)
-25
-20
-15
-10
-5
0
5
10
15
Tall Vegetation, Undulating Terrain
Short Vegetation, Flat Terrain
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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
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Random Errors Diminish as We Measure Fluxes Annually and Increase the Sample Size, n
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Systematic Biases and Flux Resolution: A Perspective
• FCO2: +/- 0.3 mol 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
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The Real World is Not Kansas, which is Flatter than a Pancake
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ESPM 228 Adv Topics Micromet & Biomet
Cloud
Cloud
Eddy Covariance in the Real World
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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
Our Master ---To Design and Conduct Eddy Flux Measurements under Non-ideal Conditions
Conservation Equation for C for Turbulent Flow
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Test Representativeness:
Sampling Error with Two Towers
Hollinger et al GCB, 2004
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Estimating Flux Uncertainties:Two Towers over Rice
Detto, Anderson, Verfaillie, Baldocchi unpublished
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Test for AdvectionDaytime and Nightime Footprints over an Ideal, Flat Paddock
Detto et al. 2010 Boundary Layer Meteorology
0Fz
FF dz Constz
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Examine Flux Divergence
Detto, Baldocchi and Katul, Boundary Layer Meteorology 2010
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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-2
0
2
4
6
8
10
12NEE at Night, Peatland Pasture
CO2 [
mol
m-2
s-1
]
u* m/s
Losses of CO2 Flux at Night: u* correction
U* Corrections are not the Best, But we have Few Better Alternatives
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time (hours)
0 4 8 12 16 20 24
CO
2 Flu
x D
ensi
ty 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
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Van Gorsel et al 2007, Tellus
Consider Storage under Low, Winds
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Systematic Biases are an Artifact of Low Nocturnal Wind Velocity
Friction Velocity, m/s
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Moffat et al., 2007, AgForMet
Gap-Filling Inter-comparison Bias Errors
ESPM 228 Adv Topic Micromet & Biomet
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ESPM 228 Adv Topics Micromet & Biomet
Annual Sums comparing Open and Closed Path Irgas
Hanslwanter et al 2009 AgForMet
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Biometric and Eddy Covariance C Balances Converge after Multiple Years
Gough et al. 2008, AgForMet
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Is There an Energy Balance Closure Problem?:
Evidence from FLUXNET
Wilson et al, 2002 AgForMet
Timing/SeasonInstrument/Canopy Roughness
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-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
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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
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Methane Measurements in California
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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
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Closed Path Systems Need access to AC powerTo power 1000 W pumps and sensors
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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
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Open Path Sensors Work off the Grid
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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 mol 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
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Detto et al 2012 AgForestMet
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 mol m-2 s-1 and 3.78 nmol m-2 s-1 for water vapour, carbon dioxide and methane flux,
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Lags are Introduced when Sampling through a Tube
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Closed Path Methane Sensors Attenuate Fluctuations
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Detto et al. 2012 AgForMet
Spectral Losses Scale with Wind Speed
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Open Path Sensors are Sensitive toWPL Density Correction
Detto et al. 2012 AgForMet
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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
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Open-Closed Methane Flux Comparison
Detto et al. 2012 AgForMet
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Time
0 4 8 12 16 20 24
CH
4 (pp
b)
1900
2000
2100
2200
2300
2400
2500
Days 100-300Days < 100 or > 300
Time
0 4 8 12 16 20 24
CH
4 Fl
ux (n
mol
m-2
s-1)
0
10
20
30
40
50
60
70
Days 100-300 Days < 100 or > 300
Sherman Island, 2007-2010, Westerly Winds
Baldocchi et al. 2012 AgForMet
Interpreting Methane Flux Measurements
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8:15 9:15 10:4510:15
8:15
11:15 11:45 12:15 13:4513:15
14:15 14:45 15:15 15:45 16:45
8:45
12:45
16:15
9:45
Watch Out for Vacche
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UpScaling Tower Based C Fluxes with Remote Sensing
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LIDAR derived map of Tree location and Height
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Hemispherical Camera Upward Looking Camera
Web Camera
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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
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Ground Based, Time Series of Hyper-Spectral Reflectance Measurements, in Conjunction with Flux Measurements Can be Used to Design Future Satellites
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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
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Spectrally-Selective Vegetation Indices Track Seasonality of C Fluxes Well
Ryu et al. Agricultural and Forest Meteorology, 2010
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Ryu et al. Agricultural and Forest Meteorology, 2010
Vegetation Indices can be Used to Predict GPP with Light Use Efficiency Models
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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
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Moffat et al., 2007, AgForMet
ESPM 228 Adv Topic Micromet & Biomet
Root Mean Square Errors with Different Gap Filling Methods
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Non-Dispersive Infrared Spectrometer, CO2 and H2O
LI 7500
Measures mole density, not mixing ratio
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