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“I’ve Looked at Plants from Both Sides Now”:Ecosystem‐Climate interactions
Dennis BaldocchiUniversity of California, Berkeley
KIT Environmental Lecture, July 2014
Outline• Big Questions and Open Problems• Ecosystem‐Atmosphere Interactions Principles
– What are Fluxes?; Why?– Roles of Models and Observations– Non‐Linear, Multi‐Scaled, Coupled Feedbacks and Forcings
• Observations– Eddy Covariance– Flux Networks
• Data, Modeling and Synthesis– Lessons Learned, Scale Emergent Processes, Feedbacks, Roles of Land Use
Physics Wins
ESPM 2 The Biosphere
‘Our bodies are stardust;Our lives are sunlight’
Oliver Morton, 2008 Eating the Sun: How Plants Power the Planet
ESPM 111 Ecosystem Ecology
Ecosystem Ecology, the Baldocchi‐Biometeorology Perspective
• Physics wins– Ecosystems function by capturing solar energy
• Only so much Solar Energy can be capture per unit are of ground
– Plants convert solar energy into high energy carbon compounds for work• growth and maintenance respiration
– Plants transfer nutrients and water down concentration/potential energy gradients between air, soil and plant pools to sustain their structure and function
– Ecosystems must maintain a Mass Balance• Plants can’t Use More Water or Carbon than has been acquired
• Biology is how it’s done– Species differentiation (via evolution and competition) produces the structure and
function of plants, invertebrates and vertebrates, which are nearly optimal for their conditions
– In turn, structure and function provides the mechanisms for competing for and capturing light energy and transferring matter
• Gases diffuse in and out of active ports on leaves, stomata
– Bacteria, fungi and other micro‐organisms re‐cycle material by exploiting differences in redox potential; they are adept at passing electrons and extracting energy
– Reproductive success passes genes for traits through the gene pool; less optimal plants can be excluded by natural selection
What are Fluxes?;and, Why are they Important?
Fluxes, as a Form of Currency:The Piggy‐Bank Analogy
The Change in $$$ in a Bank Account depends on the Differencesin the Fluxes of $$$ In and the Fluxes $$$ Out
Fluxes define Mass Balance of the Atmosphere and the underlying Ecosystem:
The Bath Tub Analogy
The Change in the Amount of Material in a Reservoir DependsOn the Difference between the Flux Entering and Leaving the Reservoir
Quantify Fluxes, rather than Dose Response to Pollutants:The Analogy of Being in a Bar, and Not Drinking—You Won’t get Drunk
Why Study Trace Gas and Energy Exchange?
• Flux Boundary Conditions of Weather, Climate, Biogeochemical, Air Pollution and Ecological Models– State of the Atmosphere is determined by Fluxes across the Boundary
• Information is Needed for Ecological Assessments of Environmental Change (climate, land use, disturbance)
• Base lines for Policy and Management (Carbon and Water markets; Pollution Abatement; Forest Management, REDD)
Ecosystem‐Atmosphere Interactions: Biogeophysical View
There Has Been A Revolution in Stable, Precise, Accurate and Low PowerFast Response Greenhouse Gas Sensors
The Composition of the Atmosphere depends onBiogenic and Biotrophic Fluxes
Flux‐Related Questions 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 Pollution (O3, NOx, SO2) Budgets at Landscape to Global Scales?
• How do These Balances Vary Seasonally?; Year to Year?; By Plant Functional Type/Traits?; By Climate Region?; By type of Disturbance? by Time Since Disturbance?; by Management?; by Land Use?
• Can We Scale Microbial Gas Emissions with Photosynthesis?
We First Need to Look Under the Hood And ConsiderUnderlying Biological, Ecological and Physiological Processes
To Interpret Land‐Atmosphere Flux Models and Measurements
To Develop a Scientifically Defensible Virtual World‘You Must get your boots dirty’, and Not Treat the Earth System
Science as a Video Game
Collecting 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
-90
-75
-60
-45
-30
-15
0
15
30
45
60
75
90
Latit
ude
FLUXNET 2007
ESPM 129 Biometeorology 17
BoundaryLayer
Resistance
CuticleResistance
StomatalResistance,
Top
StomatalResistance,
Bottom
MesophyllResistance
DC
Leaf Resistance Network for Trace Gas Fluxes
(z)r+(z)r)C-(C(z) a(z) -=z)S(C,
zF
sb
i
Quantifying Trace Gas Sources and Sinks
• Biology: – Leaf area density: a(z);– Internal Concentration: Ci;– Stomatal Resistance, rs
• Physics: – Boundary Layer Resistance, rb;– Atmospheric Concentration,
C(z)
Big Picture Question Regarding Predicting and Quantifying the ‘Breathing of the Biosphere’:
• How Do we Upscale Information from the Soil/Leaf/Plant Continuum to Canopy and Landscape scales, from hours to years?
Stomata: 10‐5 m
Leaf: 0.01‐0.1 m
Plant: 1‐10 m
Canopy: 100‐1000 m
Landscape: 1‐100 km
Continent: 1000 km (106 m)
Globe: 10,000 km (107 m)
Assessing Flux‐EcosystemAtmosphere Interactions isComplex
Components Spans > 14Orders of Magnitude inSpace
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
Original time series:
Decomposed time series:
‐ Nonlinear trend
‐ Annual cycle
‐ Intra‐annual cycle
‐ High frequency modes
Singular System Analysis: example application
Mahecha et al. (2007) Biogeosciences, 4, 743‐758
New developments allow application of SSA to fragmented time series
ESPM 228 Adv Topics Biometeorology and Micrometeorology
ESPM 129 Biometeorology 23
,l in aT T,l out aT T
( )hg f T
‐+
Effects of Feedbacks on Fluxes
:T
T T Q T g D PC g T e T g Pl a
a w
a p h a s a a w
4
3
0 6224 0 622
. /. ( )' /
0 1 2 3 4 5 6 7 8 9 101
1.5
2
2.5
3
3.5x 10-3
dT
g h m s
-1
Important Ecosystem‐Atmosphere Feedbacks
• Photosynthesis– As stomata close, diffusive supply does not meet demand– A draw‐down the substrate [C] occurs and down‐regulates the enzymatic flux
• Leaf Energy Balance– As stomata close, leaf temperature warms– convection is induced and radiative heat loss is increased– additional warming is hindered
• Leaf Transpiration– As stomata close, transpiration is restricted. – Leaf temperature increases, which increases saturation vapor pressure at the
leaf and drives the leaf to air gradient, which Up Regulates Transpiration – The reduction in transpiration reduces the vapor pressure deficit of the
surrounding air, which reduces stomatal conductance• Soil Respiration
– Recent photosynthesis is translocated to Rhizosphere and Up‐Regulates Microbial Decomposition
a mxC C V CFr K C
ESPM 129 Biometeorology
leaf-air vapor pressure deficit
0 1 2 3 4
Tra
nspi
rati
on
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 1 2 3 4
Sto
mat
al C
ondu
ctan
ce0
5
10
15
20
25
30
35
Constant ConductancesNegative FeedbackFeedforward
g e f el l
E e e f el l ( )2
Feedforward
E g e es l l ( )
Feedback
Feedbacks among Transpiration, Stomatal Conductance And Vapor Pressure Deficits
ESPM 129 BiometeorologyMonteith, 1981 QJRMS
Conflicting Controls on Evaporation, Supply of Water vs Demand by Available Energy
0 an p
H
T TE R G CR
0( )s a
w
q T qER
•Numerous and Coupled •Biophysical Processes, •Fast and Slow
•Numerous Feedbacks, •Positive and Negative
Biometeorological View ofEcosystem Ecology
Multiple Methods To Assess Terrestrial Trace Gas Budgets with Different Pros and Cons
Across Multiple Time and Space Scales
GCM InversionModeling
Remote Sensing/MODIS
Eddy Flux Measurements/Flux Networks, e.g.FLUXNET
Forest/Biomass /SoilInventories
Biogeochemical/Ecosystem DynamicsModeling
Physiological Measurements/Manipulation Expts.
F ws w sa ~ ' ' s c
a
( )
Eddy Covariance Technique
Mean
Fluctuation
•Direct•In situ•Quasi‐Continuous
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 Information Do Networks of Flux Towers Produce?
• Groups of towers at the landscape, regional, continental, and global scales allow scientists to study a greater range of climate and ecosystem conditions– Dominant plant functional type (Evergreen/Deciduous Forests,
Grasslands, Crops, Savanna, Conifer/Broadleaved, Tundra)– Biophysical attributes (C3/C4 Photosynthesis; Aerodynamic
Roughness; Albedo; Bowen Ratio)– Biodiversity– Time since the last disturbance from fire, logging, wind throw,
flooding, or insect infestation– The effect of management practices such as fertilization, irrigation, or
cultivation or air pollution• A global flux network has the potential to observe how ecosystems
are affected by, and recover from, low‐probability but high‐intensity disturbances associated with rare weather events.
Lessons Learned from Flux Studies
Emergent Scale Properties• Leads, Lags and Pulses
– Photosynthesis Drives Microbial Activity– Rain Induces Pulses in Soil Respiration
• Up Regulation– Light response curves, diffuse light– Photosynthesis Drives Microbial Activity– Acclimation of Photosynthetic Response to Temperature
• Feedbacks– Soil Temperature Drives Phenology, Phenology Drives Net Carbon Flux– Water Use Efficiency, Stomatal Conductance and Vapor Pressure Deficits– Land Use (Grass/Forest; Evergreen/Deciduous) on Surface Temperature and
Mass and Energy Exchange• Upscaling
– Footprints Define VOC Fluxes in Mixed Forest and for Microbially‐Mediated Fluxes (e.g. methane)
Effects of Meteorology on Fluxes
Classical View of Soil Respiration, F = f(T)Does Photosynthesis drive Microbial activity?
deForest et al 2006 Int J Biomet
Baldocchi et al. JGR Biogeoscience, 2006
Photosynthesis Enhances Soil Respiration
Translocation of C to the Rhizosphere Causes a 5 hour Lag in the Enhancement in Respiration
Baldocchi et al. JGR Biogeoscience, 2006
Hatala et al, GRL 2012
Photosynthesis leads Methane Fluxes, which lead TemperatureRecently Fixed and More Labile C feeds Microbes
Let’s Avoid Hand‐waving Arguments
Granger Causality, an Auto Regressive Tool for Quantifying Cause and Effect
Hatala et al, GRL 2012
Acclimation
Way and Yamori 2014 Atkin 2005 Func Plant Biol
ESPM 111 Ecosystem Ecology
Baldocchi et al. 2001 BAMS
Optimum Temperature for Canopy Photosynthesis Acclimateswith Summer Growing Season Temperature
Isotopes Infer Leaf Temperatures of Tree Leaves are Constrained, ~ 21 C
Helliker and Richter 2008 Nature
Leaf Temperature
Growing Season Temperature
CzechGlobe Seminar
Tleaf
0 10 20 30 40
0.00
0.02
0.04
0.06
0.08
0.10
0.12
1993198119821984199419971995
Temperate Broadleaved ForestDays 100 to 273
Leaf Temperature, Modeled with CANOAK, as a Central Tendency near 20 C
Canoak Model
CzechGlobe Seminar
? Plants Adjust Leaf Energy Balance to Operate near Optimal Temperature?
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
ESPM 228 Adv Topics Biometeorology and Micrometeorology
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??
ESPM 228 Adv Topics Biometeorology and Micrometeorology
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
ESPM 228 Adv Topics Biometeorology and Micrometeorology
Is Water Use Efficiency Trending with CO2?Conceptually, Stomatal Conductance Decreases with CO2,
Thereby Increasing WUEHas CO2 Increased Enough to Affect WUE?
Keenan et al. 2013 NatureJohn Norman
‘Watch for Confounding Factors’
vpd
0 1 2 3 4
A/T
0
5
10
15
20
25
30
Water Use Efficiency, A/T, Decreases with Increasing Vapor Pressure Deficits:Theoretically and Experimentally
soybeans
VPD, kPa
1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5
Fc/E
T (m
g/g)
-1
0
1
2
3
4
5
6
7
Coefficients: b[0] 7.94 b[1] -1.90
r ² 0.71
Baldocchi et al. 1984 AgForMetCanoakBaldocchi and Harley, 1995, PCE
A kT vpd
Ralph Slatyer
Baldocchi et al 1984, AgForMet
With Drought, a Reduction in Stomatal Conductance Reduces ET and WUE;Reduced ET produces an Increase in VPD
rs vs cwfr mg/g
soybeans, 1980
gs (m/s)
0.001 0.01 0.1
NE
E/E
T (m
g/g)
0
1
2
3
4
5
6
7
Coefficients: b[0] 13.60 b[1] 4.833
r ² 0.8288550481
What Happens over 20 years across a Wider Range of CO2?
WUE (A/ET) and CO2, CANOAKCANOAK, Oak Ridge, TN
CO2, ppm
340 345 350 355 360 365 370 375
A/E
T (g
C m
-2 y
-1/g
H2O
m-2
y-1
)
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
b[0] -1.5393250052 b[1] 0.0101515874
r ² 0.1362776487
Computations using Meteorological Data, 1981‐2001
CANOAK, 1982 Meteorology, Oak Ridge, TN
[CO2] ppm
260 280 300 320 340 360 380 400 420 440
WU
E (g
C m
-2 y
-1/k
g H
2O m
-2 y
-1)
1.7
1.8
1.9
2.0
2.1
2.2
2.3
2.4
2.5
Coefficients: b[0] 0.5290908731 b[1] 4.4226162465e-3
r ² 0.9984067229
Using Model to Isolate CO2 Forcing from Meteorology, Physiological Stress and Growth
Re-Interpreting WUE from Stable Isotopes:with Drought both VPD and Ci/Ca change
Ci/Ca
0.60 0.65 0.70 0.75 0.80 0.85
A/T
(mm
ol m
ol-1
)
0
5
10
15
20
25
CANOAKA/E=f(Ci/Ca, D)
AE
p pp
vpd
i
( )
.
1
16
Conventional theory
CzechGlobe Seminar
Effects of Surface Layer‐Boundary Layer Interactions
CzechGlobe 250 m ICOS tower
What Happens when you Warm the Surface?
Vapor Pressure
LongwaveEnergy
ShortwaveEnergy
Sensible HeatLatent Heat
PBL Height
Time 1
Time 2
Time 3
Temperature
To Understand Land-Atmosphere Interactions, We CANNOT forget PBL Feedbacks
Time (hrs)
6 8 10 12 14 16 18
pbl h
t (m
)
0
500
1000
1500
2000
2500
3000
Time (hrs)
6 8 10 12 14 16 18
e a (Pa
)
0
500
1000
1500
2000
2500
3000
Rain Induces of Pulses in Soil Respiration
Day after rain (d)-5 0 5 10 15 20
Rec
o (gC
m-2
d-1)
0
2
4
6
8
10
0 2 4 6 8 10 12 140
1
2
3d214 2003 understory
(, Max/e)
Xu, Baldocchi, Tang, 2004 Global Biogeochem Cycles
CzechGlobe Lecture
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
60
understory
grassland
Role of Subsequent Pulses and Labile C Role of Photodegradation
2001
Day-Hour
298 300 302 304 306 308 310 312 314 316 318
CO
2 (pp
m)
340
360
380
400
420
440Rain Event (12.7 mm) Rain Event (61 mm)
Rise In [CO2] in PBL Following Rain‐Induced Respiration Pulse
How Sky Conditions Affect Net Carbon Uptake?
Niyogi et al. 2004 GRL
ESPM 228 Adv Topics Biomet and Micromet
The Functioning of the Canopy is Different from that of Leaves
Leaf area index [m2 m-2]
0 2 4 6 8 10
Diff
use
light
effe
ct (s
lope
) [ -
]
0.2
0.3
0.4
0.5
Knohl and Baldocchi, 2008 JGR Biogeosci
ESPM 228 Adv Topics Biomet and Micromet
Diffuse Enhancement of Photosynthesis is function of LAI
Effects of Land Use on Fluxes
On the Differential Advantages of Evergreenness and Deciduousness in Mediterranean Oak Woodlands:
A Flux Perspective
Pros and Cons of Being Evergreen
• Advantages – Longer Photosynthesis period– Lower Amortization Cost for Leaf Construction– Has ecological advantage on nutrient poor soils– Lower hydraulic conductance
• Disadvantages– Must withstand herbivory by producing leaves with defense
compounds– Must withstand occasional frosts and freezing
• Adaptive Mechanisms– Leaves are constructed with less nitrogen, with a cost of lower
photosynthetic rates and lower rates of nitrogen losses– Xylem architecture withstands low water potentials and avoids
embolisms
Pros and Cons of being Deciduous
• Advantages– Avoids winter stress periods when it is cold and photosynthetic potential is low
• Disadvantages– Shorter photosynthetic period
• Adaptive Mechanisms– Produce leaves with more N and higher rates of photosynthesis
Puechabon, FR
Ione, CAEvora, PT
Roccaraspampani, IT
Evergreen Sites Deciduous Sites
Rg (1-exp(-k L)) (MJ m-2 d-1)
0 5 10 15 20 25
GP
P (g
C m
-2 d
-1)
0
2
4
6
8
10
12
14
deciduous oaksevergreen oaks
Light Use Efficiency is Greater over Deciduous Oaks, Regardless of Differences in
Leaf Area Index
Light Use Efficiency: deciduous: 17.5 +/‐ 0.85 gC MJ‐1 ; evergreen: 8.09 +/‐ 0.35 gC MJ‐1
a IGPPb I
Baldocchi et al. 2010 Ecological Applications
On Annual Time Scales Evergreen and Deciduous Oaks do the Same Thing, Differently
Duke, 2004
Day of Year
0 50 100 150 200 250 300 350
NE
E (g
C m
-2 d
-1)
-16
-14
-12
-10
-8
-6
-4
-2
0
2
4
6
Deciduous Forest, -896 gC m-2 y-1
Conifer Forest, -863 gC m-2 y-1
Evergreen Conifer vs Deciduous Broadleaved Forests
Deciduous: Higher Capacity, shorter Growing SeasonConifer: Lower Capacity, longer Growing Season
Net Difference in NEE is small; similar finding for oaksKatul data
Roles of Land Use on Temperature
Case StudyOak Savanna and Annual Grassland
Working Hypotheses
• H1: Forests have a negative feedback on Global Warming– Forests are effective and long‐term Carbon Sinks– Landuse change (more forests) can help offset greenhouse gas
emissions and mitigate global warming
• H2: Forests have a positive feedback on Global Warming– Forests are optically dark and Absorb more Energy– Forests have a relatively large Bowen ratio (H/LE) and convect more
sensible heat into the atmosphere– Landuse change (more forests) can help promote global warming
Daily Averages, 2001-2011
Potential Temperature Difference: Woodland - Grassland, C
-3 -2 -1 0 1 2 3 4
0.0
0.2
0.4
0.6
0.8
pdf non-parametric kernal smoothing method
Daily Averages, 2001-2011
Potential Temperature, Woodland, C
0 5 10 15 20 25 30 35 40
Pote
ntia
l Tem
erpa
ture
, Ann
ual G
rass
land
, C
0
10
20
30
40
Coefficients: b[0] -0.684 b[1] 1.007
r ² 0.988
Mean: 0.558Median: 0.510Std Deviation: 0.713Skewness: 0.217Kurtosis: 2.617P(0.025, 0.975): [-0.718, 2.012]
b)
a)
On Average, mean Daily AveragedPotential Temperature over savanna is warmer than over grassland, = 0.558 C
Baldocchi and Ma, 2013 Tellus
Averaged by Day, 2001-2011
Day
0 50 100 150 200 250 300 350
Pot
entia
l Tem
pera
ture
Diff
eren
ce, C
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Day
0 50 100 150 200 250 300 350
Volu
met
ric S
oil M
oist
ure
(m3 m
-3)
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
savannaannual grassland
b)
a)
Baldocchi and Ma, 2013 Tellus
Air above the oak woodland is Warmer because it:
• is darker, so it absorbs more radiation.
• is aerodynamically rougher, so it is able to inject more sensible heat into the atmosphere.
The magnitude of the temperature differences were conditional on time of year, phenology, biophysical conditions of the surface and the depth of the planetary boundary layer.
Future Directions
• Fluxes over Non‐Ideal Landscapes with Spatially Varying Sources and Sinks
• Roles of Flux Footprint Models and Satellite Remote Sensing
C5H8
NO NO2 O3
O3
OH
NO
RO2 NO2
h
ROOH
f(PAR, TL)
Measuring and Modeling Biogenic Hydrocarbon Fluxes
ESPM 228 Adv Biomet & Micromet
Roles of Appropriate Environmental Drivers, Species Diversity, and Flux Footprints
Aspen: BoreasD207, 215,216,219,243, 1994
Time (hours)
0 4 8 12 16 20 24
F isop
rene
(nm
ol m
-2 s
-1)
0
4
8
12
16
20
24
28
measured
calculated
Baldocchi et al 1999 JAM
ESPM 228 Adv Biomet & Micromet
Isoprene from Mono‐Specific Aspen Forest
500m
N
Walker Branch 1999Species Composition
(each plot has a radius of 10m,distance between plot centers on one transect is 100m)
Plo t 5 (NNE500)
T u lip Po p la r
5 %
o th e r d e c id .
6 %O a k8 9 %
Plo t 4 (NNE400)
Hicko ry9 %
O a k5 8 %
o th e r d e cid .1 8 %
T u lip P o p la r
1 3 %
M a p le2 %
Plo t 3 (NNE300 )
M a p le1 0 % T u lip
P o p la r1 4 %
o th e r d e c id .
7 %
O a k3 %
P in e6 6 %
Plo t 2 (NNE200 )
P in e7 8 %
o th e r d e c id .
9 %
T u lip P o p la r
1 0 %
M a p le3 %
Plo t 1 (NNE100 )
T u lip Po p la r
2 3 %
M a p le1 0 %
o th e r d e c id .
2 %
P in e6 5 %
Plo t 21 (NEE100 )
T u lip P o p la r
3 %
M a p le5 0 %
o th e r d e c id .
9 %
P in e1 9 %
Hicko ry1 9 %
Plo t 22 (NEE200 )
T u lip P o p la r
4 % o th e r d e cid .1 3 %
Ma p le6 %Hicko ry
7 6 %
P in e1 % Plo t 23 (NEE300 )
o th e r d e c id .
2 %
M a p le1 4 %
T u lip Po p la r
2 0 %
O a k6 4 %
Plo t 24 (NEE400 )
T u lip P o p la r
1 8 % Ma p le3 6 %
o th e r d e c id .4 6 %
Plo t 25 (NEE500 )
O a k7 9 %
o th e r d e c id .
4 %
M a p le7 %
T u lip P o p la r
1 0 %
Plo t 4 1 (SEE100)
O a k7 8 %
o th e r d e c id .
9 %
M a p le1 3 %
Plo t 4 2 (SEE200)
M a p le1 0 %
o th e r d e c id .1 4 %
Hicko ry1 5 %
O a k6 1 %
Plo t 4 3 (SEE300)
M a p le1 7 %
o th e r d e c id .1 0 %
T u lip P o p la r
1 %
O a k7 2 %
Plo t 4 4 (SEE400)
M a p le1 %
o th e r d e c id .1 1 %
O a k8 8 %
Plo t 4 5 (SEE500)
M a p le1 3 %
T u lip Po p la r
7 0 %
O a k1 6 %
o th e r d e c id .
1 %
P lo t 7 (S SW 20 0 )
o th e r d ec id .
7 %
Ma p le4 6%
O a k47 %
Plo t 8 (S SW 30 0 )
Ma p le3 %
o th e r d e c id .
4%
O a k8 8 %
Hick o ry5 %
Plo t 9 (S SW 40 0 )
o th e r d e c id .
5 %
Ma p le5 %
Pin e1 1%
O a k79 %
Plo t 1 0 ( SSW 5 00 )
Ma p le5 %
o th e r d e c id .
6 %O a k8 9 %
Plo t 2 6 (NEE1 00 )
o th e r d e c id .2 1 %
O a k7 0 %
Pin e7 %
Hicko ry2 %
Plo t 2 7 ( NEE2 00 )
M a p le1 1 %
T u lip Po p la r
1%
o the r de c id .12 %
P in e7 5 %
O a k1%
Plo t 2 8 ( NEE3 00 )
T u lip P op la r
9 %Ma p le32 %
o th e r d e c id .
7 %
O a k2 %
P in e5 0 %
Plo t 2 9 ( NEE4 00 )
M a p le2 7 %
o the r de c id .35 %
T u lip P op la r
3 4 %Pin e4 %
Plo t 3 0 ( NEE5 00 )
T u lip Po p la r
2 3 %
M a p le4 6 %
o th e r d e c id .1 4 %
P in e1 7 %
P lot 6 (S SW 1 0 0 )
Hic ko ry5 %
Pin e17 %
O ak1 8 %T u lip
Po p la r9 %
M ap le3 3 %
o th e r d e c id .1 8 %
Plo t 46 (NW W 1 00)
M ap le1 7 %
T u lip P op la r
5 3 %
o th e r d ec id .1 6 %
O ak1 %
Pin e9 %
Hicko ry4%
Plo t 47 (NW W 2 00)
M ap le2 5 %
o th e r d e cid .38 %
O a k3 7%
Plo t 48 (NW W 300 )
M ap le4 %
o the r d e cid .10 %O ak
8 6 %Plo t 49 (NW W 400)
M ap le16 %
o the r dec id .14%
H icko ry
7%
O ak63 %
Plo t 5 0 (NW W 500)
T u lip Po p la r
9 %
M ap le1 9 %
oth e r d e c id .1 6%
O ak5 5%
Hicko ry1%
Data of Eva Falge
Mixed Forests Contain Isoprene Emitters and non Emitters
ESPM 228 Adv Biomet & Micromet
Fetch (m)
0 200 400 600 800 1000
Flux
Foo
tprin
t pro
babi
lity
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
Mixed Oak-Maple Forest
Isop
rene
Em
ittin
g B
iom
ass
(g m
-2)
0
50
100
150
200
250
300footprint-weighted biomass:146 g m-2
0
dx)x(p)x(bb II
isoprene emitting biomass (bI), sensed by a micrometeorological flux measurement system, along the wind‐blown axis (x) is a function of the flux footprint, defined by the probability distribution p(x)
ESPM 228 Adv Biomet & Micromet
Footprint Weighted Biomass
Model in Mixed Forest with and without Flux Footprint
Baldocchi et al 1999 JAM ESPM 228 Adv Biomet & Micromet
Emerging Mystery:
Strong, Unexpected Diurnal Pattern in Methane Efflux with a Nocturnal Efflux Maximum…
Why?
Time
0 4 8 12 16 20 24
CH
4 Flu
x (n
mol
m-2
s-1)
0
10
20
30
40
50
60
70
Days 100-300 Days < 100 or > 300
Baldocchi et al AgForMet, 2012
Even Over Perfect Flat Sites with Extensive Fetch Advection can/does Occur with Methane:
Source Strength of Hot spots and Cold Spots can Differ by 1 to 2 orders of Magnitude (10x to 100x)
Such Advection is Less Pronounced for Water Vapor and CO2 Fluxes BecauseFlux Differences Emanating from the Different Land Forms are Smaller
10 nmol m‐2 s‐1 100 ‐ 1000 nmol m‐2 s‐1
ESPM Adv Topics Micromet & Biomet
100 200 300 400 500 600 700 800 900
-100
-50
0
50
100
distance from the tower [m]
late
ral s
prea
d [m
]
0 200 400 600 800 10000
0.5
1
1.5x 10
-3
2D‐Footprint Model of Detto‐Hsieh
Daytime Footprint, drained ditches and paddockNight Footprint, wetter fields and ditches
Night‐time Flux Footprint Does Not Extend to the Wetlands
Concluding Remarks
• Flux Networks remain fundamental tool for enriching our ability to study the breathing of the biosphere with satellite remote sensing, new orbital CO2 sensors, and coupled climate‐ecosystem models
• Plants are Coupled to the Atmosphere, and Vice Versa• Soil Microbes are Coupled to Plants, and Vice Versa• Physics Wins, Biology is How it is Done