“I’ve Looked at Plants from Both Sides Now”: Ecosystem ... · ESPM 111 Ecosystem Ecology...

“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

pdf

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


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


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

pdf

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


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 %


Hicko ry1 5 %

O a k6 1 %

Plo t 4 3 (SEE300)

M a p le1 7 %


T u lip P o p la r

1 %

O a k7 2 %

Plo t 4 4 (SEE400)

M a p le1 %


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 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 %


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 %


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


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)


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

“I’ve Looked at Plants from Both Sides Now”: Ecosystem ... · ESPM 111 Ecosystem Ecology...

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Transcript of “I’ve Looked at Plants from Both Sides Now”: Ecosystem ... · ESPM 111 Ecosystem Ecology...