Physics Wins, Biology is How its Done · living things and atmospheric ... Study the physical,...

Physics Wins, Biology is How it’s Done: Biometeorology@BerkeleyBiometeorology@Berkeley

Dennis Baldocchi

Department of Environmental Science, Policy and Management

University of California, BerkeleyUniversity of California, Berkeley

B S LAWR At S i 1977B.S., LAWR, Atmos. Sci, 1977

What Is Biometeorology?What Is Biometeorology?

• It is a science that deals with the

l ti hi b trelationship between living things and atmosphericatmospheric phenomena.

Has Applications to Biogeochemistry, Ecosystem Ecology, Agriculture, Weather and Climate Prediction etcWeather and Climate Prediction, etc

Biometeorology in a Bottle

Wine, the Perfect Integrator of Climate, Terroir and Biology

Goals of Biomet Research @ UCB

‘Breathing of the Biosphere’Study the physical biological and chemical process that controlStudy the physical, biological, and chemical process that control

trace gas fluxes between the biosphere and atmosphere

Physics Wins—Biology is How it’s Done

• Physics wins– Ecosystems function by capturing solar energy

• Only so much Solar Energy can be captured per unit are of ground

– Plants convert solar energy into high energy carbon compounds for work• growth and maintenance respiration

– Ecosystems must maintain a Mass Balance• Plants can’t Use More Water or Carbon than has been acquiredq

– Plants transfer nutrients and water between air, soil and plant pools to sustain their structure and function.

• Biology is how it’s doneS i diff i i ( i l i d i i ) d h d– Species differentiation (via evolution and competition) produces the structure and function of plants, invertebrates and vertebrates

– In turn, structure and function provides the mechanisms for competing for and capturing light energy and transferring matter

• Stomata open and close to regulate gas exchange through leaves

– Bacteria, fungi and other micro‐organisms re‐cycle material by exploiting differences in redox, passing electrons and extracting energy

– Reproductive success passes genes through the gene pool.

ESPM 111 Ecosystem Ecology

Size vs Density transcends 9‐12 orders of magnitudey g

Physics Wins:You can only be so big and sustainYou can only be so big and sustain so many individualswith the resources available

Corollary 1: You can only grow so fast; an Ecological lessonfor the stock market and the Federal Reserve.

Corollary 2: Don’t Eat anything

Enquist et al. 1998. Nature, 395

Bigger than your Head


Metabolic Scaling of Populations of Organisms

Energy flux of a population per unit area (Bt) is invariant with mass of the system (M):

B N B M M MT i i i i 3 4 3 4 0/ /

Allen et al. (2002)


Enquist et al 1998 Nature

Biometeorology: Represents Multidisciplinary Integration of Atmospheric Science, Plant Physiology and Ecosystems

Intellectual InfluencesDavis, 1973‐1977

Jerry Hatfield:BiometeorologyTed Hsiao:

Plant Water Relations

John Carroll:Atmospheric Science

Bill Pruitt:Evapotranspiration

pBob Loomis:Crop Ecology

Leonard Myrup:Micrometeorology

OutlineOutline

• Water and Energy• Carbon Dioxide• Policy Implications:

– Pros/Cons of Afforestation to os/Co s o o estat o toRemedy Global Warming

Energy Exchange: Classical View

Energy PartitioningNet RadiationBudgetBudget

TerrestrialRadiation

Latent heat

Solar Radiation Sensible heat

Soil HeatConduction

Biogeophysical-Ecohydrological ViewBiogeophysical Ecohydrological View

PBL ht

Transpiration/Evaporation

AvailableEnergy

LAI Sensible Heat

Water

SurfaceConductance

p

Photosynthesis/RespirationRespiration

Carbon

NutrientsLitterSoil Moisture

Controlling Processes and Linkages:Roles of Time and Space ScalesRoles of Time and Space Scales

Cli t

continentWeather:ppt, T, u,

Ecosystem Dynamics:species, functional

type

Climate

landscape

region/biome

pp , , ,Rg

type

Biogeo-chemistry:

LAI, C/N, VcmaxBiophysics:

canopy

ap y

E, H, A, Gc

seconds

days

years

centuriess

Energy Exchange

PBL:1500 m

PBL:1000 m 0 15 R1000 m

G = 0 02 R

H = 0.3 Rn

LE = 0.6 Rn

= 0.15 Rg

Rn = 0.65 Rg

= 0.25 Rg

Rn = 0.85 Rg

S = 0.08 Rn

G = 0.02 RnLEn = 0.8 Rn

H = 0.05 Rn

S = 0.15 Rn

•How does Evaporation vary with Climate and Ecosystem?

•How much Solar Energy is Available?gy•Pros/Cons of biofuels vs solar cells

•How does Land Use Change affect Climate, Weather and Water Availability?•Tropical Deforestation•Tropical Deforestation•Large-scale Biofuels Plantations•Re-Forestation

Crop Evaporation is Strongly Coupled to Solar Radiation

600

Wheat

400

500

b[1]: 1.09r ²: 0.84

LE (W

m-2

)

300

100

200

LEequilibrium (W m-2)

0 50 100 150 200 250 300 350 4000

Baldocchi, 1994, AgForMetBoardman, OR; 1991

( )eq nsE R G

s

But Corn (C4) Doesn’t Evaporate like Wheat (C3)!3

600

400

500 cornwheat

LE (W

m-2

)

300

400

L

100

200

0 100 200 300 400 5000

100

LEequilibrium (W m-2)Baldocchi, 1994, AgForMetBoardman, OR; 1991

Plus Forests Do not Evaporate Like CropsPlus Forests Do not Evaporate Like Crops

600

400

500 cornwheat boreal jack pine forest

E (W

m-2

)

300

400

LE

100

200

0 100 200 300 400 5000

100

LEequilibrium (W m-2)

And Evaporation of a California Oak Savanna is weakly related to Available Energy

oak savanna 2006

600

500

600

Coefficients:b[0 15.3b[1] 0.24r ² 0.41

LE (W

m-2

)

300

400

L

100

200

LEeq (W m-2)

0 100 200 300 400 500 6000

Classic View Meteorological View to Evaporation:g pBudyko Evaporation Index


Biometeorological View to EvaporationBiometeorological View to EvaporationPenman Monteith Equation

E

s R S C G D

s Gn p H

H

( )

Function of:

sGs

•Available Energy (Rn‐S)•Vapor Pressure Deficit (D)•Aerodynamic Conductance (Gh)•Surface Conductance (Gs)Surface Conductance (Gs)

Effects of Functional Types and Rsfc on Normalized Evaporation

1.75

2.00

q

1.25

1.50

WheatCornBoreal jackpine forestTemperate deciduous forestMediterranean oak-grass savanna

LE/L

E eq

0.75

1.00

0.25

0.50

Rcanopy (s m-1)10 100 1000 10000

0.00

Rc is a f(LAI, N, soil moisture, Ps Pathway)

You Need Water to Grow Trees, Maintain high LAI and Achieve a Low Surface Resistance!Achieve a Low Surface Resistance!

10

various functional types:Baldocchi and Meyers (1998)savanna:Eamus et al. 2001

LAI

1b[0] -0.785b[1] 0.925r ² 0.722

[N]ppt/E

0.1 1 10 1000.1

[N]ppt/Eeq

Stomatal Conductance Scales with N, via Photosynthesis

Stomatal Conductance Scales with Photosynthesis

80

Photosynthetic Capacity Scales with Nitrogen and Ps

Pathway

Stomatal Conductance scales with Nitrogen

mol

m-2 s

-1)

0.10

0.12

0.14

0.16

0.18

0.20

oak, varying lightCa: 360 ppmTa: 25 C

mol

m-2

s-1

)

40

60after Schulze et al (1994)

al c

ondu

ctan

ce (

mm

s-1)

6

8

10

12

14

A ( mol m-2 s-1)

0 1 2 3 4 5 6 7 8

g s (

mm

0.00

0.02

0.04

0.06

0.08

V cmax

(m

0

20

leaf nitrogen (mg g-`1)

5 10 15 20 25 30 35 40

max

imum

sto

mat

a

0

2

4

6

A (mol m s )

Na (g m-2)

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Wilson et al. 2001, Tree PhysiologySchulze et al 1994. Annual Rev Ecology

Eco‐hydrology:ET, Functional Type, Physiological Capacity and Drought

?

Eeq

?

E/

?

?

ET and Soil Water Deficits:Root‐Weighted Soil Moisture

Grassland1.0

eq

1.00

1.25

summer rain

Oak Savanna

eq

0.6

0.8

E/

Ee

0.25

0.50

0.75

E/

Ee

0.2

0.4

weighted by roots (cm3 cm-3)

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.400.00

weighted by roots(cm3 cm-3)

0.00 0.05 0.10 0.15 0.20 0.25 0.300.0

Baldocchi et al., 2004 AgForMet

Use Appropriate and Root‐Weighted Soil Moisture

0

z

z

z dP z

dP z

0

dP z

Soil Moisture, arthimetic average

Soil Moisture root weighted

Chen et al, 2008 WRR

Soil Moisture, root‐weighted

G f LAI G Nc s v~ ( , , , )max

Boreal Forest

c s vmax

1.2

1.3

k=8.0

k=10

k=7.0

QE/

QE,

eq 1.0

1.1

Q

0.8

0.9

V *LAI

0 20 40 60 80 100 120 140 160 180 2000.6

0.7

ESPM 129 Biometeorology

Vcmax*LAI

Stand Age also affects differences between ET of forest vs grassland

Plynlimon, Wales) 700

800

900

grassland conifer forest

ratio

n (m

m y

-1)

500

600

700

Evap

or

300

400

500

Year

1970 1975 1980 1985 1990 1995 2000 2005 2010200

Marc and Robinson, 2007 HESS

Forest Biodiversity is Negatively Correlated with Normalized Evaporation

Temperate/Boreal Broadleaved ForestsSummer Growing Season

1.2

1.0

1.1

E/

Eeq

0.8

0.9

0.6

0.7

Number of Dominant Tree Species (> 5% of area or biomass survey)

0 1 2 3 4 5 6 7 80.5

Baldocchi, 2005 In: Forest Diversity and Function: Temperate and Boreal Systems.

Photosynthesis >Respiration

CO2 160

180

200

Limited Lear Area andSparse Canopy Reduce ET,

too

20

40

60

80

100

120

140

100

120

140

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

20 40 60 80 100 120 140 160 180

0.5

0.6

0.05 mSh t i

DOY100 150 200 250 300 350

Vcm

ax

0

20

40

60

80Ps Capacity must be Great,

For Short Period v (%

)

0.0

0.1

0.2

0.3

0.4

0.50.05 m0.50 mShort growing

season with available moisture

DOY

Leaf N and Leaf Thickness

Broadleaved, Deciduous Trees

mol

g-1

s-1

)

200

250

300Savanna, 2005

Flux 400

500

600

700

StomatalClosure so

Evaporation >Precipitation

Leaf N and Leaf Thicknessmust support Ps

Machinery

Specific Leaf Area (m2 g-1)

60 80 100 120 140 160 180 200

Am

ass

(nm

0

50

100

150 Quercus douglasii

data of Reich et al and Xu and Baldocchi

Day

0 50 100 150 200 250 300 350

Wat

er

0

100

200

300

ET ppt

•What types of landscapes are better or worse sinks for yp pCarbon?•How does Carbon Assimilation and Respiration respond to Environmental stresses, like drought, heatspells, pollution?p•Are Forests Effective Mitigators for stalling Global Warming?

Carbon Uptake of Crops is a Linear Function of Sunlight:A E t P t f th E S tAn Emergent Property of the EcoSystem

0

W H E A TD 2 0 8 O a k le a f , f o r e s t f lo o rT le a f : 2 5 o CC O 2 : 3 6 0 p p m

-2 5

-2 0

-1 5

-1 0

-5

mg

m-2

s-1

)

s-1) 8

1 0

1 2d a t am o d e l

(a )

-5 0

-4 5

-4 0

-3 5

-3 0F c (m

A (

mol

m-2

2

4

6

0 5 0 0 1 0 0 0 1 5 0 0 2 0 0 0a b s o rb e d P A R (µ m o l m -2 s -1)

Q p a r ( m o l m - 2 s - 1 )

0 2 0 0 4 0 0 6 0 0 8 0 0 1 0 0 0 1 2 0 0 1 4 0 0 1 6 0 0 1 8 0 00

What is the Upper Bound of GPP?

Bottom‐Up:Counting Productivity on leaves, plant by plant species by species

Top‐Down:Energy Transfer

Bounding Global Primary Productivity

S* = 1365 W m-2

plant by plant, species by species

Global GPP is ~ 120 * 1015 gC y‐1

gy

S*ave = 1365/ W m-2transmission ~ 1-0.17=0.83

ontransmissicatmospherit

ygCmLGDkfSGPP

)8301701(~:

10139 115*

molegCmmareaLandL

efficiencyuselightadaysseasongrowingG

hrsxhrdaylengthDcanopybyquantaabsorbedoffractionf

quantavisiblemolestoenergyshortwavefactorconversionkontransmissicatmospherit

12:)10100(:

:180:

)/360012(:)9.0(:

)106.45.0(,:)83.017.01(:

212

6


Upper‐Bound on Global Gross Primary Productivity

• Global GPP is ~ 120 * 1015 gC y‐1

• Solar Constant, S* (1366 W m‐2)– Ave across disk of Earth S*/4

• Transmission of sunlight through the atmosphere (1‐0.17=0.83)• Conversion of shortwave to visible sunlight (0.5)• Conversion of visible light from energy to photon flux density in moles of quanta

(4.6/106)– Mean photosynthetic photon flux density, Qp

• Fraction of absorbed Qp (1‐0.1=0.9)• Photosynthetic efficiency a (0 015)• Photosynthetic efficiency, a (0.015)• Arable Land area (~ 133 * 1012 m2)• Length of daylight (12 hours * 60 minutes * 60 seconds = 43200 s/day)• Length of growing season (188 days)• Gram of carbon per mole (12)• Gram of carbon per mole (12)

GPP = 1366*0.83*0.5*4.6*0.9*0.015*133 1012*43200*188*12/(4* 106)=114* 1015 gC y‐1


GPP 1366 0.83 0.5 4.6 0.9 0.015 133 10 43200 188 12/(4 10 ) 114 10 gC y

Canopy Light Response Curves: Effect of Diffuse Light

10Sunny days

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

-5

0

5Sunny daysdiffuse/total <= 0.3

Cloudy daysdiffuse/total >= 0.7

E (

mol

m-2

s-1

)

20

-15

-10

NEE

-30

-25

-20

PPFD ( mol m-2 s-1)

0 500 1000 1500 2000-40

-35

ESPM 129 Biometeorology

PPFD (mol m s )

More Diffuse Light is Intercepted that Direct Radiation, at High Solar Elevation Angles

0

1

2

2

02/

diffuse dsincosPP

LAI

3

4

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

0

4

5

P0

0.0 0.2 0.4 0.6 0.8 1.06

Knohl and Baldocchi, 2008 JGR Biogeosciences

Net Ecosystem Carbon Exchange Scales with Length of Growing Season

200

Temperate and Boreal Deciduous Forests Deciduous and Evergreen Savanna

0

200

gC m

-2 y

r-1)

-400

-200

F N (g

-800

-600

Length of Growing Season, days

50 100 150 200 250 300 350-1000

g g , y

Baldocchi, Austral J Botany, 2008

Length of Growing Season, in Temperate Ecosystems, is associated with the timing between soil temperature and mean

annual air temperature

Oak Ridge, TNMixed Oak/Maple Forest1996

30

2 1

20

NEE, gC m-2 d-1

Tair, recursive filter: oC

Tsoil, oC

10

10

0

Day

0 50 100 150 200 250 300 350-10

Soil Temperature:An Objective Measure of Phenology part 2An Objective Measure of Phenology, part 2

Temperate Deciduous ForestsTemperate Deciduous Forests

140

150

160

ay N

EE

=0

110

120

130

DenmarkTennesseeIndianaMichigan

Da

80

90

100OntarioCaliforniaFranceMassachusettsGermanyItalyJ

Day, Tsoil >Tair

70 80 90 100 110 120 130 140 150 16070

Japan

Baldocchi et al. Int J. Biomet, 2005

If Papal Indulgences Saved Them from burning in Hell:C C b I d l f Gl b l W i ?Can Carbon Indulgences save us from Global Warming?

Alexander VI

Sixtus IV

Innocent VIII

Julius II

Leo X

Working HypothesesWorking Hypotheses

• H1: Forests have a negative feedback on Global Warmingg g– Forests are effective and long‐term Carbon Sinks

– Landuse change (more forests) can help offset greenhouse gas emissions and mitigate global warmingemissions 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

Ecosystem Respiration Scales Tightly with Ecosystem Photosynthesis,And Is with Offset Positively by Disturbance

4000

3000

3500 UndisturbedDisturbed by Logging, Fire, Drainage, Mowing

R (g

C m

-2 y

-1)

1500

2000

2500

F R

500

1000

1500

FA (gC m-2 y-1)

0 500 1000 1500 2000 2500 3000 3500 40000

500

FA (gC m y )

Baldocchi, Austral J Botany, 2008

Case Study:

Energetics of a Grassland and Oak Savanna

Measurements and Model

Case Study:Savanna Woodland adjacent to Grassland

35

Solar RadiationNet Radiation, GrasslandNet Radiation, Savanna

Savanna Woodland adjacent to Grassland

2006

m-2

d-1

)

25

30

ux D

ensi

ty (M

J

15

20

Ene

rgy

Flu

5

10

Day

0 50 100 150 200 250 300 3500

1. Savanna absorbs much more Radiation (3.18 GJ m‐2 y‐1) than the Grassland (2.28 GJ m‐2 y‐1) ; Rn: 28.4 W m‐2

Landscape DifferencesOn Short Time Scales, Grass ET > Forest ET

1.0

Monthly Averages

,

0.8

LE/L

Eeq

0.6

L

0.2

0.4

Savanna Woodland

0 2 4 6 8 10 12 14 160.0

Savanna WoodlandAnnual Grassland

Gs (mm s-1)

Ryu, Baldocchi, Ma and Hehn, JGR‐Atmos, 2008

Role of Land Use on ET:On Annual Time Scale Forest ET > Grass ET

California Savanna

440

On Annual Time Scale, Forest ET > Grass ET

) 380

400

420

440or

atio

n (m

m y

-1

320

340

360

380

Eva

po

260

280

300

320

Oak WoodlandAnnual Grassland

Hydrological Year

02_03 03_04 04_05 05_06 06_07240

260

Ryu, Baldocchi, Ma and Hehn, JGR‐Atmos, 2008

4a. U* of tall, rough Savanna > short,

0.5

2002

smooth Grassland

d, d

aily

ave

rage

0.3

0.4 4b. Savanna injects more Sensible Heat into the atmosphere because it has more Available Energy and it is Aerodynamically Rougher

u*, g

rass

land

0.1

0.2is Aerodynamically Rougher

14

u*, oak woodland, daily average

0.0 0.2 0.4 0.6 0.8 1.00.0

2006

nsity

(MJ

m-2

d-1)

8

10

12

14GrasslandSavanna

ensi

ble

Hea

t Flu

x D

en

2

4

6

8

Day

0 50 100 150 200 250 300 350

Se

0

2

2006

40GrasslandSavanna

2006, Ione, CA

anna 310

315

Tem

pera

ture

(C)

20

30

mpe

ratu

re, O

ak S

ava

290

295

300

305

0 50 100 150 200 250 300 350

Air

0

10

275 280 285 290 295 300 305 310 315

Pote

ntia

l Tem

275

280

285

290

b[0] -2.67b[1] 1.012r ² 0.953

Day

0 50 100 150 200 250 300 350

Potential Temperature, Grassland

275 280 285 290 295 300 305 310 315

5. Mean Potential Temperature differences are relatively small (0.84 C; grass: 290.72 vs savanna: 291.56 K); despite large differences in Energy Fluxes‐‐albeit the Darker vegetation is Warmer

Compare to Greenhouse Sensitivity ~2 4 K/(4 W m‐2)Compare to Greenhouse Sensitivity 2‐4 K/(4 W m 2)

Conceptual Diagram of PBL Interactions

ShortwaveEnergy

p g

2500

3000

LongwaveEnergy

PBL Height

pbl h

t (m

)

500

1000

1500

2000

Time 2

Time 3

Time (hrs)

6 8 10 12 14 16 180

Vapor Pressure Time 1Temperature

e a (Pa

)

1500

2000

2500

3000

Sensible HeatLatent Heat

Time (hrs)

6 8 10 12 14 16 18

e

0

500

1000

Time (hrs)

H and LE: Analytical/Quadratic version of Penman‐Monteith Equation

ET-PBL O ak-G rass Savanna Land U se

ace 300

310

Th E i f

T su

rfa

290albedo = 0.3; R c=2560 s /malbedo = 0.15; R c = 320 s /m

•The Energetics of afforestation/deforestation is complicated

T im e

6 8 10 12 14 16 18 20280

800

•Forests have a low albedo, are darker and absorb more energy

et (W

m-2

)

400

600

energy

•But, Ironically the darkerforest maybe cooler (Tsfc)

Rne

0

200

albedo = 0 .3; R c=2560 s /malbedo = 0 .15; R c = 320 s/m

y ( sfc)than a bright grassland due to evaporative cooling

T im e

6 8 10 12 14 16 18 20

250ET-PBL Oak-Grass Savanna Land Use

W m

-2) 150

200

LE (W

50

100 albedo = 0.3; Rc=2560 s/malbedo = 0.15; Rc = 320 s/m

•Forests Transpire effectively,

Time

6 8 10 12 14 16 18 200

500

causing evaporative cooling, which in humid regions may form clouds and reduce

m-2

) 300

400

planetary albedoH

(W m

100

200

albedo = 0.3; Rc=2560 s/malbedo = 0.15; Rc = 320 s/m

Time

6 8 10 12 14 16 18 200

Small, but Positive, Temperature Differences Stem from interactions among PBL, Ra and albedo….!!

298

lb d 0 30 R 2560 / R 40 /

u* savanna = 2 u* grassland

294

296grass, albedo = 0.30; Rc = 2560 s/m; Ra = 40 s/msavanna, albedo=0.15; Rc = 320 s/m; Ra= 10 s/m

Tair

(K)

290

292

286

288

Time (hours)

4 6 8 10 12 14 16 18 20

Summer Conditions

What about Forests and other Vegetation as a source Biofuels?g

Energy Drives Metabolism: Only a small fraction of Solar

FLUXNET database

gyHow Much Energy is Available and Where

Only a small fraction of Solar Energy is converted to Biofuels

8000

Rg

(MJ

m-2

y-1

)

4000

6000

0 10 20 30 40 50 60 70 80 900

2000

Latitude

0 10 20 30 40 50 60 70 80 90

Adler et al 2007 Ecol Appl

Plant systems are Low Efficiency Solar Cells that Operate only Part of the Year!Plant systems are Low Efficiency Solar Cells that Operate only Part of the Year!

Potential and Real Rates of Gross Carbon Uptake by Vegetation:Most Locations Never Reach Upper Potential

GPP at 2% efficiency and 365 day Growing Season

tropics

GPP at 2% efficiency and 182.5 day Growing Season

FLUXNET 2007 Database

How Does Energy Availability Compare with Energy Use?gy y p gy

• US Energy Use: 105 EJ/year– 1018J per EJ– US Population: 300 106

– 3.5 1011 J/capita/year• US Land Area: 9 8 106 km2=9 8 1012 m2 = 9 8 108 ha• US Land Area: 9.8 106 km2=9.8 1012 m2 = 9.8 108 ha• Energy Use per unit area: 1.07 107 J m‐2

• Potential, Incident Solar Energy: 6.47 109 J m‐2

– Ione, CAIone, CA• Assuming 20% efficient solar system

– 8.11 1010 m2 of Land Area Needed (8.11 105 km2, the size of South Carolina)

Concluding Issues to Consider

i l h ½ f h d i l ll i h l h• Vegetation operates less than ½ of the year and is a solar collector with less than 2% efficiency

– Solar panels work 365 days per year and have an efficiency of 20%+• Ecological Scaling Laws are associated with Planting Trees

– Mass scales with the ‐4/3 power of tree density• Available Land and Water

– Best Land is Vegetated and New Land needs to take up More Carbon than current land– You need more than 500 mm of rain per year to grow Trees

• The ability of Forests to sequester Carbon declines with stand age• There are Energetics and Environmental Costs to soil, water, air and land use

change– Changes in Albedo and surface energy fluxes g gy– Emission of volatile organic carbon compounds, ozone precursors– Changes in Watershed Runoff and Soil Erosion

• Societal/Ethical Costs and Issues– Food for Carbon and Energygy– Energy is needed to produce, transport and transform biomass into energy– Role of forests for habitat and resources– Fostering natural Carbon Sinks may be a Band‐Aid compared to ‘natural’ growth

attributed to population and economy

How much is C in the Air?

• Mass of Atmosphere– F=Pressure x Area=g x Mass P R4 2F=Pressure x Area=g x Mass– Surface Area of the Globe = 4 R2

– Matmos = 101325 Pa 4(6378 103 m)2/9.8 m2 s‐1=– 5.3 1021 g air

M P Rgatmos

4

g

• Compute C in Atmosphere @ 380 ppm

M M pP

mm

mm

gCc atmosc co c 2 15833 10

P m ma co2

/ ( ) 2.19cc

pMP


c P

CO2 in 50/100 years Business as Usual?CO2 in 50/100 years Business as Usual?

• Current Anthropogenic C EmissionsCurrent Anthropogenic C Emissions– 7 GtC/yr, (1 GtC = 1015 g=1Pg)– 45% retention in Atmosphere

• Net Atmospheric Efflux over 50 yearsNet Atmospheric Efflux over 50 years– 7 * 50 * 0.45 = 157 GtC

• Atmospheric Burden over 50 years– 833 (@380 ppm) + 157 = 990 GtC– 833 (@380 ppm) + 157 = 990 GtC,

• Conversion back to mixing ratio– 451 ppm ( 2.19 Pg/ppm) or 1.6 x pre‐industrial level of 280 ppm

• To keep atmospheric CO2 below 450 ppm the world must• To keep atmospheric CO2 below 450 ppm the world must add less than157 GtC into the atmosphere over the next 100 to 200 years.


Atmospheric Chemistry

OHh

C5H8O3

NO

RO2 NO2ROOH

NO + O3 = NO2

f(PAR, TL)

NO2 + h =NO + O

How effective is Vegetation as a sink for Pollution?•How effective is Vegetation as a sink for Pollution?•To what extent are forests sources of precursors for pollution?•What is the Feedback between aerosols and LUE?What is the Feedback between aerosols and LUE?•How do the sources and sinks vary with weather and climate?

CANVEG Schematic

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

LAI, h, d,l, z o

LongwaveRadiative

Radiative Transfer:Q par ,R nir

f( )

StomatalConductace=

f(A Ci Tl

LongwaveRadiativeTransfer:

f(T l,IR up ,IR dn ,

Boundary LayerConductace=

f(u l f(A,Ci,Tl,

Leaf EnergyBalance:H, E, T l

Leaf Photosynthesisand Respiration:

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

f(u,l

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

ScalarSca aProfiles:

T,q,C

ESPM 228 Adv Topics Micromet & Biomet

Aspen: BoreasD207 215 216 219 243 1994

measuredD207, 215,216,219,243, 1994

28

calculated

m-2

s-1

) 20

24

F isop

rene

(nm

ol m

12

16

F

4

8

Time (hours)

0 4 8 12 16 20 240

Baldocchi et al 1999 JAM

ESPM 228 Adv Biomet & Micromet

Walker Branch 1999Species Composition

Mixed Forests Contain Isoprene Emitters and non Emitters

(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 Po p la r

1 3 %

M a p le2 %

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 %

NPlo t 3 (NNE300 )

7 %

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 %

T u lip P o p la r

2 3 %

M a p le1 0 %

o th e r d e c id .

2 %

Pin e6 5 %

T u lip P o p la r

3 %

Hicko ry1 9 %

T u lip P o p la r

4 % o th e r d e cid .1 3 %

Ma p le6 %Hicko ry

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 (NEE40 0)

T u lip P o p la r

1 8 %M a p le3 6 %

o th e r d e cid .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 %

T u lip P op la r

o th e r d ec id

O ak1 %

o th e r d ec id .3 8 %

O ak

M ap le4 %

oth e r de c id .1 0%O ak

8 6%Plo t 49 (NW W 4 00)

M a p le16%

othe r de cid .1 4%

H icko ry

7%

O a k6 3%

Plo t 50 (NW W 500)

T u lip Po p la r

9%

Ma p le1 9 %

o the r d ec id .16 %

O ak5 5 %

Hicko ry1 %

Plo t 1 (NNE100 )

P lo t 21 (NEE100)

3 %

M a p le5 0 %

o th e r d e c id .

9 %

P in e1 9 % Plo t 22 (NEE200 )

7 6 %

Plo t 41 (SEE100)

O a k7 8 %

o th e r d e c id .

9 %

M a p le1 3 %

Plo t 42 (SEE200)

Ma p le1 0 %

o th e r d e c id .1 4 %

Hicko ry1 5 %

O a k6 1 %

Plo t 43 (SEE3 00)

M a p le1 7 %


T u lip Po p la r

1 %

O a k7 2 %

Ma p le1 %


O a k8 8 % O a k

1 6 %o th e r d e c id

O a k4 7 %

P lo t 26 (NEE1 00 )

o th e r d ec id .2 1%

O ak70 %

P in e7%

Hic ko ry2 %

Plo t 2 7 (NEE2 00 )

M ap le1 1 %

T u lip Po p la r

1 %


Pin e75 %

O a k1 %

T u lip Po p la r

9 %M a p le3 2 %

o the r de c id .

7 %

O a k2%

Pin e5 0%


T u lip

P lot 6 (SS W 1 0 0 )

Hicko ry5 %

P in e1 7 %

O a k1 8 %T u lip

Po p la r9%

M a p le3 3 %

o the r de c id .18 %

Plo t 46 (NW W 1 00)

M ap le1 7 %

P op la r5 3 %

d ec id .1 6 %

Pin e9 %

Hicko ry4%

Plo t 47 (NW W 2 00)

M a p le2 5%

3 7%Plo t 48 (NW W 30 0)

500m Plot 44 (SEE4 00)

Plo t 4 5 (SEE500)

M a p le1 3 %

T u lip Po p la r

7 0 %

d e c id .1 %

Plo t 7 ( SSW 2 00 )

o th e r d e c id .

7%

M a p le4 6 %

4 7 %

Plo t 8 ( SSW 3 00 )

Ma p le3 %

oth er d ec id .

4 %

O ak88 %

Hicko ry5 %

o th e r d e c id .

5 %

Pin e

O a k79 %

P lo t 28 (NEE300 )

Plo t 2 9 (NEE4 00 )

Ma p le2 7%

P o p la r34 %

Pin e4 %

Plo t 3 0 (NEE5 00 )

T u lip P o p la r

2 3 %

M a p le4 6 %

oth e r d ec id .1 4 %

Pin e1 7%

Plo t 9 (S SW 40 0 )

Ma p le5 %

1 1%

Plo t 1 0 ( SSW 5 00 )

Ma p le5 %

o th e r d e c id .

6 %O a k8 9 %

Data of Eva Falge ESPM 228 Adv Biomet & Micromet

Mixed Oak-Maple Forest

mas

s (g

m-2

)

250

300footprint-weighted biomass:146 g m-2

0

dx)x(p)x(bb II

rene

Em

ittin

g B

iom

50

100

150

200

isoprene emitting biomass (bI), sensed by a micrometeorological

obab

ility

0.10

0.12

0.14

Isop

r

0flux measurement system, along the wind‐blown axis (x) is a function of the flux footprint, defined by the probability

Flux

Foo

tprin

t pro

0.02

0.04

0.06

0.08defined by the probability distribution p(x)

Fetch (m)

0 200 400 600 800 10000.00

ESPM 228 Adv Biomet & Micromet

Model in Mixed Forest with and without Flux Footprint:For Atmospheric Chemistry Species Composition MATTERS as well as the biophysicsFor Atmospheric Chemistry Species Composition MATTERS as well as the biophysics

Baldocchi et al 1999 JAM ESPM 228 Adv Biomet & Micromet

Physics Wins, Biology is How its Done · living things and atmospheric ... Study the physical,...

Documents

Transcript of Physics Wins, Biology is How its Done · living things and atmospheric ... Study the physical,...