Looking at Biosphere-Atmosphere Interactions Differently:

Roles of Switches, Lags and Pulses on Carbon, Water and Energy Fluxes with Data from a Global Network-Fluxnet

Dennis Baldocchi

ESPM/Ecosystem Sciences Div.

University of California, Berkeley

Toronto SeminarMarch 21, 2006

Why Pulses, Switches and Lags are Important?

• They are Features of Complex Dynamical Systems

• Biosphere is a Complex Dynamical System – Constituent Processes are Non-linear and Experience Non-

Gaussian Forcing– Possess Scale-Emergent Properties– Experiences Variability Across a Spectrum of Time and Space

Scales– Solutions are sensitive to initial conditions– Solutions are path dependent– Chaos or Self-Organization can Arise

C Dynamics of Pools and Fluxes:Leads, Lags and Oscillations

Day

10 100 1000 10000

C (

gC

m-3

)

0.1

1

10

100

1000

Csoil

Cplant

Cair

= 500 days

Examples: Non-Linear Biophysical Processes

L TsA~ 4

e T T

aLE bLE cs ( ) ~ exp( )

2 0

AaI

b cI

dC

e fC

aA bA cA d

~ ;

3 2 0

Leaf Temperature

Transpiration

Photosynthesis

Respiration R Td ~ exp( )

Scale Emergent Processes:Integrating Photosynthesis from Leaf to Canopy Scale

D208 Oak leaf, forest floorTleaf: 25o CCO2 : 360 ppm

Qpar (mol m-2 s-1)

0 200 400 600 800 1000 1200 1400 1600 1800

A (

mo

l m-2 s

-1)

0

2

4

6

8

10

12

data

model

(a)

0 500 1000 1500 2000-50

-45

-40

-35

-30

-25

-20

-15

-10

-5

0

absorbed PAR (µmol m-2 s-1)

WHEAT

Fc

(mg

m-2 s

-1)

C Fluxes occur across a Spectrum of Time Scales, with Different Forcings at Each Scale

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

Switches, Pulses and Lags are Evident in Annual Time Series of Trace Gas Exchange

Vaira Grassland 2001

Day/Hour

0 50 100 150 200 250 300 350

Fc

(m

ol m

-2 s

-1)

-25

-20

-15

-10

-5

0

5

10

15

Complicating Dynamical Factors: Annual Time Course

Grasslands

Day

0 50 100 150 200 250 300 350

NE

E (

gC

m-2 d

-1)

-6

-4

-2

0

2

4

Mediterranean GrasslandTemperate C4 grassland

Data sources: Valentini et al. 1996; Baldocchi + Xu, unpublished; Verma +Suyker

Spring/Summer Drought(-)GPP(-); Reco(-)

GPP > 0;AM Frost:GPP(-)

Tmin > 0 oC

GPP =f(LAI) (+)

Rain PulseReco(++)

GPP=0

Autumn Rains:T(-), (++)GPP(+), Reco(-)

snow covereddormant grassGPP=0, Reco > 0

• Pulses• Switches• Lags

Pulses:

• Volcanoes, Clouds and Aerosols– Photosynthesis

• Rain– Microbial Respiration– Evaporation

• Litterfall or Treefall– Respiration

• Fires– Carbon oxidation

Switches

Time• Phenological

– LAI– Canopy Conductance– Albedo and Radiation Balance – PBL Growth– Photosynthesis Duration and

Carbon Balance– Flowering and Respiration

• Climatic/Geophysical– Snow– Flood– Frost– Drought– Rain– day/night– Land Slide

Space• Land Use

– Age– Roughness– Disturbance– Functional Type

– evergreen vs deciduous

– Tree vs grass

Lags

• Transpiration:– Xylem transport and Bole Capacitance

• Microbial respiration– phloem transport of photosynthate to roots feeds associated

microbes– ‘acclimation’ as labile pools are depleted and populations grow and

change• Photosynthetic Capacity:

– Leaf chemistry depends on decomposition of previous year’s litter pool

• Soil respiration– soil temperature amplitude varies and phase angle shifts with depth

• Growth and Carbon Uptake– f(previous year’s growth)

Time (hours)

0 400 800 1200 1600 2000 2400

So

il T

em

peratu

re, C

10

15

20

25

30

35

40

45

Data Sources

• AmeriFlux– Oak Savanna, Ione, Ca– Annual Grassland, Ione, CA– Deciduous Forest, Oak Ridge,

TN

• FLUXNET – Grass and Crops– Conifer Forests– Evergreen and Deciduous

Broadleaved Forests

Oak-Grass Savanna: A Two Layer System and Model System for Studying Global Change

FLUXNET: From Sea to Shining Sea300+ Sites, circa 2006

Eddy Covariance

F w c ' '

Mean

Fluctuation

Outline

• Pulses– Volcanoes, Aerosols +

Photosynthesis– Rain + Respiration – Senescence +

Respiration• Switches

– Phenology• Growing Season Length• Albedo• PBL Development• Reproduction +

Respiration

– Direct/Diffuse• Light Use Efficiency

• Lags– Soil T and Respiration– VPD, Ps and Respiration– Interannual variability

• Interfaces

– Grassland/Savanna

PULSES

Volcanoes, Aerosols + Canopy Photosynthesis

•How does an array of biotic and abiotic factors interact to cause interannual variations in the breathing of the terrestrial biosphere?

Y ear

1950 1960 1970 1980 1990 2000 2010

Ch

an

ge

in

CO

2

(Gt

(10

15g

) p

er

ye

ar)

0

1

2

3

4

5

6

7

8

Rate of Change in Atmospheric CMauna Loa, data of KeelingFossil Fuel Emissionsdata of Marland et al.

average = 3.08 Gt C yr-1

std dev = 1.21 Gt C yr-1

Mt Pinatubo Eruption

Indonesia Fires

Pinatubo Altered Radiation Direct-Diffuse Radiation Partitioning more than Global Radiation

•Measurements at Mauna Loa, Ellsworth Dutton

direct -130 W m-2

diffuse +100 W m-2

Anomaly Global CO2 Trends after Pinatubo:

Reduction (--) in CO2 Growth Rate

Mauna Loa

Year

1988 1990 1992 1994 1996 1998 2000

CO

2 m

inim

um:

Mon

thly

Dat

a

348

350

352

354

356

358

360

362

364

366

year vs CO@ min

Mauna Loa

Year

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Res

idua

l Min

imum

[C

O2]

: D

etre

nded

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

post Pinatubo

Indonesian Fires

Data: Keeling and Whorf

•More Photosynthesis?•Or Less Respiration?

Global Impact of Mt Pinatubo Eruption

Rödenbeck, Houweling, Gloor and Heimann, 2003

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 NEE?

Baldocchi, 1997, Plant Cell Environ

CO2 Flux and Diffuse Radiation

Niyogi et al., GRL 2004; Gu et al, 2002, JGR; Hollinger et al, Xu et al; Flanagan et al

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

Impact on GPP

Gu et al., Science, 2003

•Theoretical Impact of 20% reduction in direct radiation is ~ +70 gC m-2 y-1 C uptake

Impact of rain pulses on ecosystem respiration:

Impact of rain pulse on ecosystem respiration: Fast response

Day

150 200 250 300 350

Fc

( m

ol m

-2 s

-1)

0

1

2

3

4

5

6

understoryopen grassland

Baldocchi et al, JGR, Biogeosciences, in press

Rains Pulse do not have Equal Impacts

Rec

o (g

C m

-2d-1

)

2

4

6

8

10

PP

T (

mm

d-1

)

10

20

30

40

50

60

DOY270 285 300 315

v (c

m3 cm

-3)

0.0

0.1

0.2

0.3

v (c

m3 c

m-3

)

0.0

0.1

0.2

0.3

Reco

PPT

Xu, Baldocchi Agri For Meteorol , 2004

Quantifying the impact of rain pulses on respiration: Assessing the Decay Time constant

Day after rain (d)-5 0 5 10 15 20

Rec

o (g

C m

-2d-1

)

0

2

4

6

8

10

0 2 4 6 8 10 12 140

1

2

3d214 2003 understory

(, Max/e)

Amount of the rain (mm)0 10 20 30 40 50 60

Tim

e co

nsta

nt (

d)0

2

4

6

8

10

Understoryb0=1.43b1=0.097r2=0.95

Grasslandb0=0.88b1=0.07r ²=0.98

Xu, Baldocchi, Tang, 2004 Global Biogeochem Cycles R b b

teco

0 1 exp( )

Respiration Enhancement Depends on Initial Condition

Reco pulse-background vs background. data from grassland and savanna

Reco_background (gC/m2d)

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Peak

of

pu

lse-b

ack

gro

ud

(g

C/m

2s)

0

2

4

6

8

Xu, Baldocchi, Tang Global Biogeochem Cycles 2004

Significance of rain pulse:Total C respired

Amount of precipitation (mm)0 20 40 60

To

tal

carb

on

res

pir

ed (

g C

m-2)

0

20

40

60

Grasslandintercept=4.7slope=0.96r ²=0.89

understoryintercept=2.4slope=0.5r ²=0.98

Xu, Baldocchi, Tang, 2004

Global Biogeochem Cycles

Impact of rain pulse on regional atmospheric CO2

2001

Day-Hour

298 300 302 304 306 308 310 312 314 316 318

CO

2 (

ppm

)

340

360

380

400

420

440Rain Event (12.7 mm) Rain Event (61 mm)

Coupled PBL-Sfc Energy CO2 Model:al a McNaughton-Spriggs

Time

6 8 10 12 14 16 18

CO

2 (

pp

m)

340

360

380

400

420

Ps=f(Rg)Resp after pptRs=5 s m-1Fc = 10 mol m-2 s-1

Litter Fall Respiration Pulse

1999 soil respiration

day of year

0 50 100 150 200 250 300 350

Da

ytim

e s

oil

res

pir

ati

on

(g

C m

-2 d

-1)

-1

0

1

2

3

4

5

Drought

Leaf Fall

•Wilson and Baldocchi, JGR, 2002; Granier et al. Functional Ecology, 2001

Switches

Ecohydrology:ET, Functional Type and Drought

?

?

?

E/

Eeq

Evaporation and Drought

GrasslandD70-300, 2001

weighted by roots (cm3 cm-3)

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

E/

Eeq

0.00

0.25

0.50

0.75

1.00

1.25

summer rain

Oak Savanna, 2002D105-270

weighted by roots(cm3 cm-3)

0.00 0.05 0.10 0.15 0.20 0.25 0.30 E

/E

eq

0.0

0.2

0.4

0.6

0.8

1.0

Baldocchi et al, 2004AgForMet

Environmental Controls on Respiration

Soil volumetric water content (m3 m-3)

0.0 0.1 0.2 0.3 0.4

Rec

o/R

ref

0.0

0.5

1.0

1.5

2.0Fast growth period data

Rain pulse

Xu + Baldocchi, AgForMet 2004

Phenology, Growing Season Length and annual NEE

Broad-Leaved Forests

Length of Growing Season

100 150 200 250

NE

E (

gC

m-2 y

r-1)

-800

-700

-600

-500

-400

-300

-200

-100

0

100Japan

Denmark

Italy

Massachusetts, USA

Belgium

Tennessee, USA

Prince Albert, CANADA

Ontario

Indiana, USA

Michigan, USA

Baldocchi et al, 2001, Bull Am Met Soc

Leaf Emergence and Onset of Photosynthesis

Oak Ridge, TNMixed Oak/Maple Forest1996

Day

0 50 100 150 200 250 300 350

-10

0

10

20

30

NEE, gC m-2 d-1

Tair, recursive filter: oC

Tsoil, oC

Predicting Phenology from Soil Temperature

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

Baldocchi et al. Int J Biomet, 2005

•Photosynthesis Switches Partitioning between dominance by Roots vs Microbes

Stimulation of Autotrophic is much delayed after onset of photosynthesis

2003, Nocturnal CO2 Efflux (0-5, 19-24)

Day

0 50 100 150 200 250 300 350

Fu

nd

ers

tory-F

gra

ssla

nd ( m

ol m

-2 s

-1)

-6

-4

-2

0

2

4

6Oak Savanna, 2003

Day

0 50 100 150 200 250 300 350 400

Ca

no

py

Ph

oto

syn

the

sis

(gC

m-2

d-1

)

-10

-8

-6

-4

-2

0

2

Baldocchi et al, JGR, Biogeosciences, in press

Lags-Leads

Barnola et alVostok Ice Core

Y ears before Present

0 100000 200000 300000 400000

CO

2 (

pp

m)

160

180

200

220

240

260

280

300

320

Vostok Ice CorePetit et al. 1999 Nature

Y ears Before Present

0 100000 200000 300000 400000

Te

mp

era

ture

Va

ria

tio

n

-12

-10

-8

-6

-4

-2

0

2

4

Soil respiration: False Lags

Tsoil (C)

10 15 20 25 30 35 40

Rso

il a

t 8

cm ( m

ol m

-2 s

-1)

2.0

2.5

3.0

3.5

4.0

4.5

2 cm4 cm8 cm16 cm32 cm

Time (hours)

0 400 800 1200 1600 2000 2400

So

il T

emp

erat

ure

, C

10

15

20

25

30

35

40

45

Soil tempreture (oC)

30 35 40 45 50

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

14:50h

6hFo=0.037e0.0525T, Q10=1.69, R2=0.95

Tonzi Open areas

Soil temperature (oC)

25 30 35

1.1

1.2

1.3

1.4

1.5

1.6

1.7

Under treesDOY 211

Fu=0.337e0.0479T, Q10=1.61, R2=0.80

20h

6h

12:50h12h

16h

Tonzi Under trees

10h

24h

Tang, Baldocchi, Xu, GCB, 2005

Respiration and Temperature

Lags and Leads in Ps and Resp: Diurnal

June

Time (hour)

0 4 8 12 16 20 24

Flu

x D

ensi

ty

-10

-8

-6

-4

-2

0

6

7

soil respirationcanopy photosynthesis

Tang et al, Global Change Biology 2005.

Soil Resp Lags Ps by about 5 to 6 hours

June, Rsoil-Ps lag

lag (30 min intervals)

-30 -20 -10 0 10 20 30

lag

corr

elat

ion

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

Other Ecosystem-Scale Lags:Residual Effects of Drought

2002 2003 2004 2005

NE

E

[g C

m-2

wee

k-1 ]

-80

-70

-60

-50

-40

-30

-20

-10

0

10

20

HainichLeinefelde

•Data of Alex Knohl, ED Schulze and MPI-Jena

Role of Switches, Pulses and Lags on Interannual Variability of NEE?

Harvard Forest

Year

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

CO

2 F

lux

Den

sity

(gC

m-2

d-1

)

-12

-8

-4

0

4

8 NEERecoGEE

Data of Wofsy, Munger, Goulden et al., Harvard Univ

Sources of Interannual Variability

Interannual Variability in NEE

d GPP/dt

-400 -300 -200 -100 0 100 200 300 400

d R

eco/

dt

-400

-300

-200

-100

0

100

200

300

400

Coefficients:b[0] -3.716b[1] 0.585r ² 0.469n = 78

Landscape Switches

Day

0 50 100 150 200 250 300 350

PA

R a

lbed

o

0.00

0.05

0.10

0.15

0.20

GrasslandOak Savanna

Albedo

Net Radiation

Day

0 50 100 150 200 250 300 350

En

ergy

Flu

x D

ensi

ty (

MJ

m-2

d-1

)

0

5

10

15

20

25

30

35

Rnet, grassland

Rnet, oak savanna

Rg

Conclusions

• Switches, Lags and Pulses are Components of Many Long Term Flux Records

• Toy Models Provide Insights on Complicated Dynamics and Help Interpret Field Data

• More Work is Needed to See if S/L/P induces Chaos and Unanticipated Consequences

• Papers:• Nature.berkeley.edu/biometlab

– Id: biomet_pubs– Password: cal_bears

Acknowledgements

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

• Students, Postdocs, Techs, LandownersLianhong Gu, Eva Falge, Jianwu Tang, Liukang Xu, Ted Hehn, Tess Krebs, Alex Knohl, Siyan Ma, Matthias Falk

• Fluxnet Community• Ranchers

– Russell Tonzi, Fran Vaira