Canopy Modeling: Lessons from Models...Leaf/Soil Energy Balance Radiative Transfer Lagrangian Turbulent Transfer Albedo LE H G soil FCO2 CANOAK MODEL ESPM 228 Adv Topics Biomet and

Canopy Modeling:Lessons from Models

Dennis BaldocchiESPM 228

University of California, BerkeleySpring, 2016

ESPM 228 Advanced Topics in Biometeorology4/4/2016

PhysiologyPhotosynthesis

Stomatal Conductance

Transpiration

MicrometeorologyLeaf/Soil Energy BalanceRadiative TransferLagrangian Turbulent Transfer

Albedo

LEH

Gsoil

FCO2

CANOAK MODEL

ESPM 228 Adv Topics Biomet and Micromet

Meteorological and Plant inputsR g ,L in , T a , q a , [CO 2 ], u, P, ppt,

LAI, h, d,l, z o

StomatalConductace=

f(A,Ci,Tl,

LongwaveRadiativeTransfer:

f(T l,IR up ,IR dn ,

Leaf EnergyBalance:H, E, T l

Leaf Photosynthesisand Respiration:

f(g s , T l,C i, g b , Q par )

Source/Sinks:S T ,S q ,S C

ScalarProfiles:

T,q,C

Radiative Transfer:Q par ,R nir

f( )

Boundary LayerConductace=

f(u,l

CANOAK Schematic


Model Parameters

• Leaf Area Index• Photosynthetic Capacity, Jmax, Vcmax

• Kinetics• Basal Respiration, leaf/soil



Deciduous forest

Day of year

50 100 150 200 250 300 350

Leaf

Are

a In

dex

0

1

2

3

4

5

6

7

Seasonality in LAI, Deciduous Forests


Wullschleger, 1993 J Expt Bot

Jmax and Vcmax scale with one another


Data of KB Wilson

Amax

0 3 6 9 12 15 18 21 24

Vcm

ax

0

10

20

30

40

50

60

70

80

90

100

b[0] -2.6111018578b[1] 4.7224835061r ² 0.8172445191

Practical Assessment for Vcmax in sites with many species and spatial variability

Wohlfarht 1999mountain grassland species

Pml (mol m-2 s-1)

0 10 20 30 40 50 60

Vcm

ax (

mol

m-2

s-1

)

0

20

40

60

80

100

Vcmax=2.36+1.66 Pmxr2=0.87


Day of year

100 125 150 175 200 225 250 275 300 325

V cmax

(m

ol m

-2 s

-1)

0

10

20

30

40

50

60

70

White oak

Seasonality in Vcmax

Wilson et al. 2001 Tree Physiol

DOY100 150 200 250 300 350

Vcm

ax

0

20

40

60

80

100

120

140

Quercus alba (Wilson et al)Quercus douglasii (Xu and Baldocchi)

High Vcmaxmust be Achieved in Seasonally‐ DroughtedEcosystems to attain Positive Carbon Balance

Area under the Curves are Similar


Hollinger and Richardson 2005 Tree Physiol

ESPM 228 Adv Topics Micromet & Biomet

Today, We Know Parameters have Uncertainty

Monte Carlo Model parameterization

Verbeek et al 2006 Tree Physiol Medlyn et al 2005 Tree Physiol


Results and Discussion

P1. Validation and Testing


0 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0

NE

E (m

ol m

-2 s

-1)

- 2 5

-2 0

-1 5

-1 0

-5

0

5

1 0

1 5

m e a s u r e dc a lc u la te d

1 9 9 7 W a lk e r B r a n c h W a te r s h e d

N E E m e a s u r e d (m o l m -2 s -1 )

-3 0 -2 5 -2 0 -1 5 -1 0 -5 0 5 1 0 1 5 2 0

NE

E c

ompu

ted

( m

ol m

-2 s

-1)

- 3 0

-2 0

-1 0

0

1 0

2 0

b [0 ] 0 .9 0 8b [1 ] 1 .0 8 5r ² 0 .8 1 5

Model Test: Hourly to Annual Time Scale


W e e k

0 5 1 0 1 5 2 0 2 5

LE (

W m

-2)

0

1 0 0

2 0 0

3 0 0

4 0 0M e a s u r e dC a l c u l a t e d

T e m p e r a t e D e c i d u o u s F o r e s t , 1 9 9 7

L E m e a s u r e d ( W m - 2 )

0 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0

LE c

alcu

late

d (W

m-2)

0

1 0 0

2 0 0

3 0 0

4 0 0

5 0 0

C o e f f i c i e n t s :b [ 0 ] : 4 . 9 6b [ 1 ] : 1 . 1 4r ² : 0 . 8 3

Model Test: Hourly Data


Time Scales of Interannual Variabilityare recreated forcing model withi weather and seasonal

changes in LAI and Vcmax

n, cycles per hour

0.0001 0.001 0.01 0.1 1

nSw

c(n)/w

'c'

0.0001

0.001

0.01

0.1

1

10

canoakdata

1997



P2. Sensitivity and Science Questionsf(Time, space, Parameters & Processes)


What is Interannual Variability, beyond the measurement record?

Year

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000

Net

Eco

syst

em C

Exc

hang

e (g

C m

-2 y

r-1)

-650

-600

-550

-500

-450

-400

CANOAKMeasured and Gap-Filled

Temperate Deciduous Forest: Canoak


Decadal Scales of Variability, Information exists at Long time scales

W a lke r B ranch W a te rshed , T N : 1981 -2001C A N O A K

F req u en cy (1 /d ay)

0 .0001 0 .001 0 .01 0 .1 1

nSne

e/ ne

e

0 .0001

0 .001

0 .01

0 .1

1

7 years

year 130 d ays


T e m p e ra te D e c id u o u s F o re s ts

D a y s w ith N E E < 01 2 0 1 4 0 1 6 0 1 8 0 2 0 0 2 2 0 2 4 0

NEE

(g C

m-2

yea

r-1)

- 8 0 0

-7 0 0

-6 0 0

-5 0 0

-4 0 0

-3 0 0

-2 0 0

-1 0 0

0

C A N O A K , O a k R id g e , T NP u b lis h e d M e a s u re m e n ts , r 2 = 0 .8 9

NEE and Growing Season Length



Day

0 50 100 150 200 250 300 350

NEE

(gC

m-2

d-1

)

-10

-8

-6

-4

-2

0

2

4

Vcmax(73 mol m-2 s-1)

Vcmax (50 mol m-2 s-1)

Vcmax(73) Vcmax(50) % differenceNEE (gC m-2 a-1) -577 -454 -21.3E (MJ m-2 a-1) 1690 1584 -6.3H (MJ m-2 a-1) 1096 1199 9.3

Importance of Vcmax

Canoak, 1998

Day

0 100 200 300 400

Can

opy

Ps, g

C m

-2 d

-1

0

2

4

6

8

10

12

vcmax =f(z): 1521 gC m-2 y-1 vcmax =const: 1571 gC m-2 y-1

Vertical Variations in Vcmax, are they needed?


Light Use Efficiency and

Net Primary Productivity

Tree

TreeTree

Tree

NPP=f Qp


Emergent Processes: Impact of Leaf Clumping on Canopy Light Response Curves

D e c i d u o u s f o r e s t

m o d e l : c l u m p e d l e a v e s

P P F D ( m o l m - 2 s - 1 )

0 5 0 0 1 0 0 0 1 5 0 0 2 0 0 0 2 5 0 0

F c ( m

ol m

-2 s-1

)

- 4 0

- 3 0

- 2 0

- 1 0

0

1 0

m e a s u r e d

( b )

0 5 0 0 1 0 0 0 1 5 0 0 2 0 0 0 2 5 0 0

F c ( m

ol m

-2 s

-1)

- 4 0

- 3 0

- 2 0

- 1 0

0

1 0

( a )

m o d e l : s p h e r i c a l l e a v e s


P A R ( m o l m -2 s -1 )

0 5 0 0 1 0 0 0 1 5 0 0 2 0 0 0

P c (m

ol m

-1 s

-1)

- 1 0

0

1 0

2 0

3 0

4 0

5 0

c r o p c a n o p yV c m a x = 1 0 0 m o l m -2 s -1

L A I= 5

L A I= 3

L A I= 1

LUE and Leaf Area


P A R (m o l m -2 s -1 )

0 5 0 0 1 0 0 0 1 5 0 0 2 0 0 0

P c(m

ol m

-1 s

-1)

-1 0

0

1 0

2 0

3 0

4 0

5 0

c ro p c a n o p yL A I = 5

V c m a x = 1 0 0 m o l m -2 s -1

V c m a x = 5 0

V c m a x = 2 5

LUE and Ps Capacity


Vcmax LAI/fpar

0 100 200 300 400 500

GP

P (

gC m

-2 y

r-1)

400

600

800

1000

1200

1400

1600

1800

CANVEG

EUROFLUXWalker Branch WatershedDuke: Ellsworth/Katul Metolius Young: Law et alMetolius old: Law et alHarvard: Barford et al.

Developing Simple Model from Complex One



WBW 1997

Day

0 50 100 150 200 250 300 350

F c (gC

m-2

d-1

)

-10

-8

-6

-4

-2

0

2

erect leavesclumped plane leaves

clumped random spherical erectophile planophileNEE (gC m-2 a-1) -577 -354 -720 -1126 -224E (MJ m-2 a-1) 1690 1551 1774 2023 1473H (MJ m-2 a-1) 1096 1032 1095 1171 1008

Role of Leaf Angle Inclination and Clumping on Fluxes

Interaction between Clumping and Leaf Area

Temperate Deciduous Forest

LAI

0 1 2 3 4 5 6 7

Flux

sph/

Flux

clp

0.5

0.6

0.7

0.8

0.9

1.0

1.1

canopy photosynthesisENEE


P P F D (m o l m -2 s -1 )

0 5 0 0 1 0 0 0 1 5 0 0 2 0 0 0

NEE

(m

ol m

-2 s

-1)

-4 0

-3 5

-3 0

-2 5

-2 0

-1 5

-1 0

-5

0

5

1 0S u n n y d a y sd iffu s e /to ta l < = 0 .3

C lo u d y d a y sd iffu s e /to ta l > = 0 .7

T e m p e ra te B ro a d -le a v e d F o re s tS p r in g 1 9 9 5 (d a y s 1 3 0 to 1 7 0 )

How Sky Conditions Affect NEE?


P0

0.0 0.2 0.4 0.6 0.8 1.0

LAI

0

1

2

3

4

5

6

Diffuse RadiationBeam Radiation, = pi/2 Beam Radiation, =pi/3

2

002

/

diffuse dsincosPP

Conversion of direct to diffuse light increases light capture



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


CO

2 Flu

x [µ

mol

m-2

s-1

]

0

5

10

15

20

25

30

Canopy photosynthesisNet ecosystem exchange

Rd/Rs [ - ]

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Tran

spira

tion

[mm

ol m

-2 s

-1]

0

2

4

6

8

Wat

er u

se e

ffici

ency

[µm

ol C

O2 m

mol

-1 H

2O]

3.0

3.2

3.4

3.6

3.8

4.0

4.2

4.4

TranspirationWater use efficiency

A

B

Knohl and Baldocchi, 2008 JGR Biogeosci


A v e D a i ly L E , f ( z , w ) ( W m - 2 )

0 2 0 4 0 6 0 8 0 1 0 0 1 2 0 1 4 0 1 6 0 1 8 0 2 0 0

Ave

Dai

ly L

E, q

(z)=

qa; T

(z) =

Ta

0

5 0

1 0 0

1 5 0

2 0 0

b [ 0 ] : - 0 .7 5 3b [ 1 ] : 0 .9 8 3r ² : 0 .9 4 6 3

A v e D a i ly H , f ( z , w ) ( W m - 2 )

0 5 0 1 0 0 1 5 0 2 0 0

Ave

Dai

ly H

,q(

z)=

q a; T(z

) = T

a

0

5 0

1 0 0

1 5 0

2 0 0

b [ 0 ] 2 .7 2b [ 1 ] 0 .6 1 5r ² 0 .8 6 0

A v e D a i ly F c , f ( z , w ) ( W m - 2 )

- 8 - 6 - 4 - 2 0 2 4

Ave

Dai

ly F

c,

q(z)

= q a; T

(z) =

Ta; C

(z)=

Ca

- 8

- 6

- 4

- 2

0

2

4

b [ 0 ] 0 .0 1 1 9b [ 1 ] 0 .9 8 5r ² 0 .9 9 8

Do We Need to Consider Canopy Microclimate [C] Feedbacks on Fluxes?


Water Use Efficiency

A C Cr r r

a i

s m b

TP

e er rs a

b s

( )

AT

M p p r rM e T e r r

ke e

c c a c i a s

v s l a a c s c s a

( )( )( ( ) )( )

, ,

, ,

Complex Leaf Response to vpd

es(Tk)-ea

0 500 1000 1500 2000 2500 3000 3500

A/T

2

4

6

8

10

12

14

16

18

20

22

24

Isotope Models of WUE

AE

p pp

vpd

i

( )

.

1

16

a b a pp

pp

i

a

i

a

( ) . .0 0044 0 0256

Definitions of Isotopic Discrimination

a p

p1

13 12

13 12 1 1C CC C

reac ts

products

/ |/ |

tan

13 12

13 12 1C C

C Csample

s dard

/ |/ | tan

( )RR

air

plant

1 1000

Interpreting Stable Isotopes

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


19 20 21 22 23 24

A/T

(mm

ol m

ol-1

)

0

5

10

15

20

25

30

D (kPa)

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

Ci/C

a

0.6

0.7

0.8

0.9

1.0

AT

C C CC C

a i a

i a

( / ). . /

128 32 332

(1 )

1.6 ( ( ))

i

i

a

ppA p

pE vpd fp

Don’t Forget Feedbacks

DeConstructing WUE

Ci/Ca

0.5 0.6 0.7 0.8

Fact

or

0.01

0.1

1

10

100

1-Ci/Ca 1/D:(f(Ci/Ca))A/T

AT

C C CC C

a i a

i a

( / ). . /

128 32 332

Long Term Changes in WUE:Has CO2 Changed Enough to Matter?

Keenan et al. 2013 Nature

Water use Efficiency and CO2

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

Isotopes Infer Leaf Temperatures of Tree Leaves are Constrained, ~ 21 C

Helliker and Richter 2008 Nature

Leaf Temperature

Growing Season Temperature


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


Isotopes Evaluate a Flux-Weighted Temperature

Ponderosa Pine, Metolius, OR

Tleaf (C)

-20 -15 -10 -5 0 5 10 15 20 25 30 35

pdf

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

TleafTair

Flux-wted Tleaf = 23.6 C

leafleaf

ET dtT

E dt

Transpiration (E) WeightedLeaf Temperature


Transpiration Weighted Leaf Temperature for Oak Savanna

Canopy Temperature, Weighted by TranspirationOak Savanna, Ione, CA; CANOAK-3D

Tleaf

10 15 20 25 30 35

pdf

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

<Tleaf> = 25.2 C


s u n lit le a v e s , d a y tim eO a k R id g e , T N 1 9 9 7

T le a f

0 1 0 2 0 3 0 4 0

Pro

babi

lity

0 .0 0

0 .0 2

0 .0 4

0 .0 6

0 .0 8

p d f ts u n a m b ie n t C O 2 = 1 5 0 0 p p m , 1 0 0 m m le a fp d f ts u n s m a ll le a v e s

Leaf size, CO2 and Temperature: why oak leaves are small in CA and large in TN



Day

0 50 100 150 200 250 300 350

NEE

(gC

m-2

d-1

)

-10

-8

-6

-4

-2

0

2

4

100 mm10 mm

Temperate Deciduous Forest: 1997Role of Leaf length

0.1 m 0.01m 0.001 m

NEE (gC m-2 a-1) -577 -588 -586E (MJ m-2 a-1) 1690 1652 1615H (MJ m-2 a-1) 1096 1164 1202

T e m p e ra te D e c id u o u s F o re stS u n lit le a ve s , 1 9 9 7

T le a f (oC )

0 10 20 30 40

prob

abili

ty d

ensi

ty

0 .00

0 .02

0 .04

0 .06

0 .08

V c m ax = 7 3 m o l m -2 s -1

V cm a x = 10 m o l m -2 s -1

Physiological Capacity and Leaf Temperature: Why Low Capacity Leaves Can’t Be Sunlit::or don’t leave the

potted Laurel Tree in the Sun


Traverse radiometer system

ab

gfedc

1 2 3

Study area for test of CANOAK‐3d


(W m‐2) (W m‐2) (W m‐2)

Downward PAR Upward PAR Net radiation

Simulated understory (1m above the ground) radiations near the tram site

Kobayashi et al. 2011 AgForMet


8/5/2007

Simulation AVIRIS

5/12/2006

Simulation AVIRIS

Simulated images (RGB composite)

Kobayashi et al. 2011 AgForMetESPM 228 Adv Topics Biomet and Micromet

ESPM 228 Adv Topics Biomet and MicrometKobayashi et al

Importance of Model Hierarchy Testing

DOY 124 DOY 194 DOY 215

Comparison of simulated and tram‐measured PAR and net radiation

Hour Hour Hour

Hour Hour Hour

PAR (obs.)PAR (Sim.)



Net radiation Sensible heat Latent heat

Comparison of top of the tower net radiation, sensible heat and latent heat



P o n d e r o s a P i n eF o r e s t F l o o rD 1 8 7 - 2 0 5 , 1 9 9 6

Rnet

(W m

-2)

- 5 00

5 01 0 01 5 02 0 02 5 03 0 0

m e a s u r e d

c a l c u l a t e d

E (W

m-2

)

- 2 5

0

2 5

5 0

7 5

H (W

m-2

)

0

5 0

1 0 0

1 5 0

2 0 0

T i m e ( h o u r s )0 4 8 1 2 1 6 2 0 2 4

G (W

m-2

)

- 7 5- 5 0- 2 5

02 55 07 5

1 0 01 2 51 5 0

F i g u r e 1 5e n b m o d . s p w1 2 / 8 / 9 9 : l a i e f f = 1 . 8 , z l i t t e r = 0 . 0 8

Below Canopy Fluxes


Below Canopy Fluxes and Canopy Structure and Function

L A I * V c m a x

0 2 0 4 0 6 0 8 0 1 0 0 1 2 0 1 4 0 1 6 0 1 8 0 2 0 0

QE,

soil/Q

E

0 .0 0

0 .0 5

0 .1 0

0 .1 5

0 .2 0

0 .2 5

0 .3 0


Rn

(W m

-2)

- 5 00

5 01 0 01 5 02 0 02 5 03 0 0

E (W

m-2

)

01 02 03 04 05 06 07 0

R a = f ( s t a b i l i t y )R a : n e u t r a l

H (W

m-2

)

02 55 07 5

1 0 01 2 51 5 0

T i m e ( h o u r s )

0 4 8 1 2 1 6 2 0 2 4

G (W

m-2

)

- 5 00

5 01 0 01 5 02 0 0

P o n d e r o s P i n eF o r e s t F l o o r

F i g u r e 1 6e n m o d s t b . s p w1 2 / 8 / 9 9

Impact of Thermal Stratification


Rn

(W m

-2)

- 5 00

5 01 0 01 5 02 0 02 5 03 0 0

E (W

m-2

)

01 02 03 04 05 06 0

L i t t e r d e p t h , 0 . 0 1 ml i t t e r d e p t h , 0 . 0 2 m

H (W

m-2

)

02 55 07 5

1 0 01 2 51 5 0

T i m e ( h o u r s )

0 4 8 1 2 1 6 2 0 2 4

G (W

m-2

)

- 5 0

0

5 0

1 0 0

1 5 0

P o n d e r o s P i n eF o r e s t F l o o r

F i g u r e 1 7e n m o d l i t . s p w1 2 / 8 / 9 9

l i t t e r d e p t h , 0 . 0 5 m

Impact of Litter


Part 2, Upscaling from Landscapes to the Globe

‘Space: The final frontier … To boldly go where no man has gone before’

Captain James Kirk, Starship Enterprise


Big Picture Question Regarding Predicting and Quantifying the ‘Breathing of the Biosphere’:

• How can We Be ‘Everywhere All the Time?’


ESPM 111 Ecosystem Ecology

Big‐Leaf Model

VFR

Ohm’s Law Analog for Fluxes

• Motivation– Current Global-Scale Remote Sensing Products tend to rely

on• Highly-Tuned Light Use Efficiency Approach

– GPP=PAR*fPAR*LUE (since Monteith 1960’s)• Empirical, Data-Driven Approach (machine learning technique)• Some Forcings come from Satellite Remote Sensing Snap

Shots, at fine Spatial scale ( < 1 km)• Other Forcings come from coarse reanalysis data (several tens

of km resolution)– Hypothesis, We can do Better by:

• Applying the Principles taught in Biometeorology 129 and Ecosystem Ecology 111 which Reflect Intellectual Advances in these Fields over the past Decade

• Merging Vast Environmental Databases• Utilizing Microsoft Cloud Computational Resources


Lessons Learned from the CanOak Model

25+ years of Developing and Testing a Hierarchy of Scaling Models with Flux Measurements at Contrasting Oak Woodland Sites in

Tennessee and California

We Must:• Couple Carbon and Water Fluxes• Assess Non-Linear Biophysical Functions with Leaf-Level

Microclimate Conditions• Consider Sun and Shade fractions separately• Consider effects of Clumped Vegetation on Light Transfer• Consider Seasonal Variations in Physiological Capacity of Leaves

and Structure of the Canopy


Necessary Attributes of Global Biophysical ET Model:Applying Lessons from the Berkeley Biomet Class and CANOAK

• Treat Canopy as Dual Source (Sun/Shade), Two-Layer (Vegetation/Soil) system– Treat Non-Linear Processes with Statistical Rigor (Norman, 1980s)

• Requires Information on Direct and Diffuse Portions of Sunlight– Monte Carlo Atmospheric Radiative Transfer model (Kobayashi + Iwabuchi,,

2008)• Light transfer through canopies MUST consider Leaf Clumping

– Apply New Global Clumping Maps of Chen et al./Pisek et al.• Couple Carbon-Water Fluxes for Constrained Stomatal Conductance Simulations

– Photosynthesis and Transpiration on Sun/Shade Leaf Fractions (dePury and Farquhar, 1996)

– Compute Leaf Energy Balance to compute Leaf Saturation Vapor Pressure and Respiration Correctly

– Photosynthesis of C3 and C4 vegetation Must be considered Separately• Use Emerging Ecosystem Scaling Rules to parameterize models, based on remote

sensing spatio-temporal inputs– Vcmax=f(N)=f(albedo) (Ollinger et al; Hollinger et al;Schulze et al.; Wright et al.)– Seasonality in Vcmax is considered (Wang et al.)


Atmosphericradiativetransfer

Canopy photosynthesis,Evaporation, Radiative transfer

Soil evaporation

Beam PARNIR

Diffuse PARNIR

Albdeo‐>Nitrogen ‐> Vcmax, Jmax

LAI, Clumping‐> canopy radiative transfer

dePury & Farquhar two leaf Photosynthesis model

Rnet

Surface conductance

Penman‐Monteithevaporation model

Radiation at understory

Soil evaporation

shade sunlit

BESS, Breathing‐Earth Science Simulator


MOD04

MOD05

MOD06

MOD07

aerosol

Precipitable water

cloud

Temperature, ozone

MCD43 albedo

MOD11 Skin temperatureAtm

ospheric radiative transfer

Net radiation

MOD15 LAI

POLDER Foliage clumping

Canopy radiativetransfer

Challenge for a Computationally‐Challenged Biometeorology Lab:Extracting Data Drivers from Global Remote Sensing to Run the Model

Youngryel was lonely with 1 PC


Size and Number of Candidate Data Sets is Enormous

US: 15 tilesFluxTower: 32 tilesGlobal: 193 tiles

1. Global 1‐year source data: 2.4 TB (10 yr: 24 TB)2. How to know which source files are missed

among >0.1 million filesESPM 228 Adv Topics Biomet and Micromet

Barriers to Global Remote Sensing by the Berkeley Biometeorology Lab

• Data processing– Global 1‐year calculation: 9000 CPU hours– That is, 375 days.– 1‐year calculation takes 1 year!


Photosynthetic Capacity Leaf Area Index

Solar Radiation Humidity Deficits


Test of BESS Model with Flux Towers


Test of BESS model with Data‐Driven Model (Jung et al.) and Basin Water Balance


Ryu et al 2012 GBC

What is Globally Integrated GPP?


Ryu et al. unpublished

UpScaling GPP Regionally with Sun‐Shade Coupled Energy Balance Photosynthesis Model


Global Evaporation at 1 to 5 km scale

An Independent, Bottom‐Up Alternative to Residualsbased on the Global Water Balance, ET = Precipitation ‐ Runoff

<ET> = 503 mm/y == 6.5 1013 m3/y


Issues• How Good is Good Enough?• How Much Detail Is Enough?

– Where and When can we Simplify?• Assessing Errors and Variability in Model Parameters

• Constraining Model Parameters• Assessing Errors in Driving Meteorological Conditions

• Biases in Test Data used to validate Models


ESPM 228 Adv Topics Micromet & BiometHanson et al. 2004. Ecol Mono


Hansen et al, 2004 Ecol Monograph

Model Validation: Who is Right and Wrong, and Why?

How Good is Good Enough?

‘None of the models in this study match estimated GPP within the range of uncertainty of the observed fluxes’

Schaeffer et al 2012. JGR Biogeosciences

Our Ability to Model Global Gross Primary Productivity Remains Poor

Many C Cycles Model Don’t Simulate GPP‐Light Response, Well

Schaeffer et al 2012, JGR Biogeosciences

Conclusions

• Biophysical Models that Couple Aspects of Micrometeorology, Ecophysiology and Biogeochemistry Produce Accurate and Constrained Fluxes of C and Energy, across Multiple Time Scales

• Models can be used to Interpret Field Data – LUE is affected by LAI, Clumping, direct/diffuse

radiation, Ps capacity– NEE is affected by length of growing season– Interactions between leaf size, Ps capacity and position

help leaves avoid lethal temperatures– Below canopy fluxes are affected by T stratification and

litter








CO2 Microclimate

360 370 380 390 400 4100

20

40

60

80

100

120

CO2 ppm

laye

r

night

350 355 360 365 3700

20

40

60

80

100

120

CO2 ppm

laye

r

day


Temperature Microclimate

10 12 14 16 18 20 22 240

20

40

60

80

100

120

Tair C

laye

rday


Lesson/Exercise

• Vary CO2 and compute fluxes, ambient, +100, +300, +500 ppm

• Vary LAI and compute Fluxes, 1,2,4,8


Documents

Canopy Modeling: Lessons from Models...Leaf/Soil Energy Balance Radiative Transfer Lagrangian Turbulent Transfer Albedo LE H G soil FCO2 CANOAK MODEL ESPM 228 Adv Topics Biomet and