Using Biophysical Models to Ask (and Answer) Questions About Biosphere-Atmosphere Interactions

Using Biophysical Models to Ask (and Answer) Questions About Biosphere-Atmosphere

Interactions

Dennis BaldocchiAlexander Knohl

James Dorsey

Biometeorology LabUniversity of California, Berkeley

ILEAP MeetingBoulder, COJan, 2006

Isotopicexchange

ILEAPS Paradigm

Sub-Grid Variability:What are Errors in ET Scaling?

Answering Questions with Models

• Diffuse Radiation– Light Use Efficiency– Isoprene emission– Water Use Efficiency– Stable Isotopes

• Sub-Grid Parameterization, Energy Balance Closure and Scaling– Insights from a 2-D, ‘Wet’ DaisyWorld

Physiology

Photosynthesis

Stomatal Conductance

Transpiration

Micrometeorology

Leaf/Soil Energy Balance

Radiative Transfer

Lagrangian Turbulent Transfer

Albedo

LEH

Gsoil

FCO2

CANVeg MODEL

Key Attributes of CanVeg

• Seasonality– Leaf Area Index– Photosynthetic Capacity (Vcmax)

• Model parameters based on Site Measurements and EcoPhysiological Rules and Scaling

– Stomatal Conductance scales with Photosynthesis– Jmax and Rd scale with Vcmax

• Multilayer Framework– Computes Fluxes (non-linear functions) on the basis of a leaf’s local environment– Considers

• Sun and Shade Leaf Fraction• Leaf Clumping• Leaf Inclination Angle• Non-local Turbulent Transport and Counter-Gradient Transfer

0 100 200 300 400 500 600 700

NE

E ( m

ol

m-2 s

-1)

-25

-20

-15

-10

-5

0

5

10

15

measuredcalculated

1997 Walker Branch Watershed

NEE measured (mol m-2 s -1)

-30 -25 -20 -15 -10 -5 0 5 10 15 20

NE

E c

om

pu

te

d (

mo

l m-2 s

-1)

-30

-20

-10

0

10

20

b[0] 0.908b[1] 1.085r ² 0.815

CO2 Flux Model Test: Hourly to Annual Time Scales

Another Form of Model Testing: Reproducing Spectral Fidelity

n, cycles per hour

0.0001 0.001 0.01 0.1 1

nS

wc(

n)/

w'c

'

0.0001

0.001

0.01

0.1

1

10

canoakdata

1997

Baldocchi et al, 2001 Ecological Modeling

Results and Discussion

PPFD (mol m-2 s-1)

0 500 1000 1500 2000

NE

E ( m

ol

m-2 s

-1)

-40

-35

-30

-25

-20

-15

-10

-5

0

5

10Sunny daysdiffuse/total <= 0.3

Cloudy daysdiffuse/total >= 0.7

Temperate Broad-leaved ForestSpring 1995 (days 130 to 170)

How Sky Conditions Affect Net Carbon Exchange (NEE)?: Data

Baldocchi, 1997 PCE

CO2 Flux and Diffuse Radiation:Data from AmeriFlux

Niyogi et al., GRL 2004

Volcanoes, Aerosols + NEE

How do Changes in Diffuse Radiation affect Canopy Fluxes?:Case: Mt Pinatubo Explosion, ~ 10% of beam -> diffuse

Gu et al, 2003, Science

dire

ct b

eam

diff

use

Sol

ar r

adia

tion

[W m

-2]

Solar elevation angle [°]

Year of Mt. Pinatuboeruption

Canopy Photosynthesis and Aerosols:Impact on Daily & Annual Scales, I

PAR (mol m-2 d-1)

0 10 20 30 40 50 60

Ac

(gC

m-2

d-1

)

2

4

6

8

10

12

base: 1478 gC m-2 y-1

Diffuse += 10% Beam: 1527 gC m-2 y-1

Canopy Photosynthesis may increase by +50 gC m-2 y-1

increase diffuse by 10% beam: 1527 gC m-2 y-1

Conventional Wisdom:More Light Absorption with Diffuse Radiation

Diffuse/Total

0.0 0.2 0.4 0.6 0.8 1.0

fAp

ar

0.90

0.91

0.92

0.93

0.94

0.95

WHY?

PPFD (mol m-2 s-1)

0 500 1000 1500 20000

5

10

15

20

Inte

gra

ted

ph

oto

sy

nth

es

is ( m

ol m

-2 s

-1)

Deciduous forest

shaded leavessunlit leavestotal

More Efficient Use of Light by Shade Leaves?

Oak Ridge, TN 1997 Growing Season, 10 to 16 hrs

Diffuse Fraction

0.0 0.2 0.4 0.6 0.8 1.0

vpd

(m

b)

0

5

10

15

20

25

30

vpd is Correlated with Diffuse Fraction:Less Physiological Stress (?)

Temperature Deciduous Forest

Day

0 50 100 150 200 250 300 350

Flu

x D

ensi

ty (

gC

m-2

d-1

)

0.01

0.1

1

10

Canopy Photosynthesis : 1478 gC m-2 y-1

Isoprene Efflux : 17.19 gC m-2 y-1

Isoprene and Diffuse Radiation

PAR (mol m-2 d-1)

0 10 20 30 40 50 60

Fis

o (g

C m

-2 d

-1)

0.1

0.2

0.3

0.4

base: 17.19 gC m-2 y-1

Diffuse += 10% Beam: 17.33 gC m-2 y-1

Stable Isotope Discrimination and Diffuse Light

Preference of photosynthesis for light 12CO2 vs. heavier 13CO2

Hainich, GermanyD160-290, 2002

Diffuse/Total PAR

0.2 0.4 0.6 0.8 1.0

p

er m

il d

aily

ave

rage

20

21

22

23

24

25

26

4 4 27 5 4 4000

000. ( . . )

C

Ci

a

Autocorrelations among Ci/Ca, vpd and diffuse/Total

Hainich, GermanyD160-290, 2002

vpd (hPa)

0 5 10 15 20

Ci/C

a

0.65

0.70

0.75

0.80

0.85

0.90

0.95

Hainich, GermanyD160 -290, 2002

Diffuse/Total PAR

0.2 0.4 0.6 0.8 1.0

vpd

(hP

a)

0

5

10

15

20

25

Hainich, GermanyD160-290, 2002

vpd (hPa)

0 5 10 15 20

p

er

mil

da

ily a

vera

ge

20

21

22

23

24

25

26

Water Use Efficiency (A/T) and diffuse light

PAR: diffuse/total

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

A/T

(m

mol

mo

l-1)

0

2

4

6

8

10

A

T

CCCD

ai

a

( )

.

1

16

Simple Model suggests A/T decreases with or Ci/Ca

)( fT

A

)))]((([( aiai CCDCCfT

A

But Complex feedbacks need to be considered!

e.g. A/T of C4 > C3

How Do Changes in vpd and Ci/Ca conspire to affect A/T?

A

T

CCCD

ai

a

( )

.

1

16

Ci/Ca

0.5 0.6 0.7 0.8

Fac

tor

0.01

0.1

1

10

100

1-Ci/Ca

1/D:(f(Ci/Ca))

A/T

A

T

C C C

C Ca i a

i a

( / )

. . /

1

2832 332

D a bC

Ci

a

19 20 21 22 23 24

A/T

(m

mo

l m

ol-1

)

0

5

10

15

20

25

30

In toto (considering coupled energy balance feedbacks) A/T increases with Ci/Ca

Ci/Ca

0.60 0.65 0.70 0.75 0.80 0.85

A/T

(m

mo

l m

ol-1

)

0

5

10

15

20

25

CANISOTOPEA/T=f(Ci/Ca, D)

A/T = f(Ci/Ca)

Sub-Grid Variability:Lessons Derived from Wet DaisyWorld

Latent Heat Exchange Map

Newly developed 3d Lagrangian stochastic footprint model was run for a 1 m canopy and 3 m measurement height. Half a million trajectories

were integrated to calculate the source probability density.

Footprint representation

Eddy covariance footprints and ecosystem representativeness

The footprint calculation was run using the same grid geometry as

the DaisyWorld simulation to allow convolution of the results.

The EC “tower” was placed in different locations in the

simulated ecosystem, and the EC system's view of the ecosystem

was calculated.

Each histogram shows 500 separate tower locations within the simulated ecosystem.

Eddy covariance footprints and ecosystem representativeness

Errors in ET Scaling

Conclusions

• Biophysical Model aids in understanding the impact of diffuse light on photosynthesis, isoprene emission, water use efficiency and stable isotope discrimination

• A cellular automata, energy balance model shows that spatial averaging of energy balance drivers can produce huge errors in grid-scale energy fluxes and can explain lack of energy balance closure

Acknowledgements

• Funding – NASA, DOE/TCP, NIGEC/WESTGEC

• Diffuse Light and Carbon– Lianhong Gu

• Isoprene– Peter Harley, Jose Fuentes, Dave Bowling, Russ Monson & Alex

Guenther

• 13C Isotopes– Dave Bowling, Russ Monson

• Footprints & DaisyWorld– Monique Leclerc, Tess Krebs, Joon Kim, Peter Levy, HaPe

Schmid, Brian Amiro

Canopy Photosynthesis and Aerosols:Impact on Daily & Annual Time Scales, II

PAR (mol m-2 d-1)

0 10 20 30 40 50 60

Ac

(gC

m-2

d-1

)

2

4

6

8

10

12

base: 1478 gC m-2 y-1

diffuse * 1.2: 1550 gC m-2 y-1

Role of Diffuse Light on Water Use Efficiency: A/E

PPFD (mol m-2 s-1)

0 500 1000 1500 2000

NE

E ( m

ol m-2

s-1)

-40

-35

-30

-25

-20

-15

-10

-5

0

5

10Sunny daysdiffuse/total <= 0.3

Cloudy daysdiffuse/total >= 0.7

Temperate Broad-leaved ForestSpring 1995 (days 130 to 170)

Tc (mmol m-2 s-1)

0 2 4 6 8 10

Ac

(m

ol m

-2 s

-1)

0

5

10

15

20

25

30

35

40

Temperate Forest, 1997

Rnet (W m-2)

-200 0 200 400 600 800T

c (m

mol

m-2

s-1

)

0

2

4

6

8

10

Daisy World

Window (m)

0.1 1 10 100 1000

T/<

Tsf

c>

0.00001

0.0001

0.001

0.01

0.1

Window (m)

0.1 1 10 100 1000

/<

E>

0.0001

0.001

0.01

0.1

1

Rwet =50 s m-1

Rdry = 1000 s m-1

Rsoil =2000 s m-1

Coefficients:b[0] -1.846b[1] -1.02r ² 0.996

Coefficients:b[0] -0.279b[1] -1.025r ² 0.982

(a)

(b)

Baldocchi et al, 2005 Tellus

E f x f xf x

xx[ ( )] ( )

( )( )

1

2

2

22

(z)r+(z)r

)C-(C(z) a(z) -=z)S(C,

z

F

sb

i

Quantifying Sources and Sinks

• Biology:– Leaf area density, a(z)– internal conc, Ci

– stomatal resistance, rs

• Physics: – Boundary layer resistance, rb

– Scalar conc, C(z)

Partial Explanation:Fiso is very sensitive to Leaf Temperature, which changed little in

response to the imposed direct to diffuse partitioning

Oak Ridge, TN, 1997

<Tleaf>, base

-20 -10 0 10 20 30

<T

leaf

>,

diff

use

+ 1

0% b

eam

-20

-10

0

10

20

30

b[0] 0.015b[1] 0.998r ² 0.999