factors influencing co 2 exchange in northern ecosystems - a synthesis (kind of) anders lindroth...

Factors influencing CO2 exchange innorthern ecosystems - a synthesis (kind of)

Anders LindrothLund University

Geobiosphere Science CentrePhysical Geography and Ecosystems Analysis

Sölvegatan 12, 223 62 Lund, SwedenAnders.Lindroth@nateko.lu.se

Part of synthesis work within the Nordic Centre for Ecosystem Carbon Exchange and Its Interactions With the Climate System, NECC (and partly from LUSTRA1)23 papers to appear in two coming issues of Tellus B

Co- authors:

Mika Aurela, Brynhildur Bjarnadottir, Torben Röjle Christensen,Ebba Dellwik, Achim Grelle, Andreas Ibrom, Torbjörn Johansson, Leif Klemedtsson, Fredrik Lagergren,Harry Lankreijer, Ola Langvall,Samuli Launiainen, Tuomas Laurila, Magnus Lund, Eero Nikinmma,Mats Nilsson, Kim Pilegaard, Janne Rinne, Jörgen Sagerfors,Bjarni Sigurdsson, Lena Ström, Juha-Pekka Tuovinen, Timo Vesalaand Per Weslien

1 LUSTRA; A swedish research programme on developing land-use strategies for reducing emissions in forestry

• Part A1 - understanding differences in CO2 exchange between forests of different species, age, climate and soils

• Part B2 - Productivity and respiration in the forest of similar species but growing in different climates

• Part C3 - Factors controling CO2 exchange in peatlands

1Lindroth, A., Lagergren, F., Aurela, M., Bjarnadottir, B., Christensen, T., Dellwik, E., Grelle, A., Ibrom, A., Johansson, T., Lankreijer, H., Launiainen, S., Laurila, T., Mölder, T., Nikinmaa, T., Pilegaard, K., Sigurdsson B. and Vesala, T. 2007. Leaf area index is primary scaling parameter for both gross photosynthesis and ecosystem respiration of Northern deciduous and coniferous forests. Tellus B (accepted)

2Lindroth, A., Klemedtsson, L., Grelle, A., Weslien, P. and Langvall, O. 2007. Net ecosystem exchange, productivity and respiration in three spruce forests in Sweden. Biogeochemistry (in press).

3Lindroth, A., Lund, M., Nilsson, M., Aurela, M., Christensen, T.R., Laurila, T., Rinne, J., Sagerfors, J., Ström, L., Tuovinen, P. and Vesla, T. 2007. Environmental controls on CO2 exchange of boreal mires in northern Europe. Tellus B doi: 10.1111/j.1600-0889.2007.00310.x

Part A - Eight different forests

T = 8.3°CP = 730 mmBeech

T = 5.5°CP = 527 mmPine/Spruce

T = 3.0°CP = 700 mmPine

T = -1.0°CP = 429 mmPine

T = 1.2°CP = 523 mmSpruce

T = -0.9°CP = 305 mmBirch

T = 3.4°CP = 738 mmLarch

Method:

• Day and night separately• One ’normal’ year from all sites• Fco2 only for u*>threshold• Two-week means

Light response curve

Q (mol m-2 s-1)

0 500 1000 1500 2000 2500 3000

Fc

(m

ol m

-2 s

-1)

-15

-10

-5

0

5

ddsatc

dsatcc RRFQ

RFF

exp1

= initial slope of line

Rd

Fcsat

GPP

Daytime analysis

13.227

1

02.56

1308.56

10 e Tnight RNEE

Nighttime analysis

(Lloyd & Taylor, 1994)

Half-month no.

0 2 4 6 8 10 12 14 16 18 20 22 24

NE

En

igh

t ( m

ol m

-2s-1

)

0

2

4

6

8

10

NorundaSkyttorpFlakalidenAbiskoHyytiäläSoroeSodankyläVallanes

Lloyd & Taylor equation

Mean nighttime air temperature (°C)

-15 -10 -5 0 5 10 15 20

NE

Eni

ght (

mol

m-2

s-1)

0

2

4

6

8

10

NorundaSkyttorpFlakalidenAbiskoHyytiäläSoroeSodankyläVallanes

Mean nighttime air temperature (°C)

-20 -15 -10 -5 0 5 10 15 20

Res

idua

l

-2

-1

0

1

2

r2 = 0.75

-20 -15 -10 -5 0 5 10 15 20

0.0

0.5

1.0

1.5

2.0

2.5

NorundaSkyttorpFlakalidenAbiskoHyytiäläSoroeSodankyläVallanes

NE

En

igh

t rel

ativ

e to

the

resp

irat

ion

rate

at

10 °

C

0 2 4 6 8 10 12 14 16 18 20 22 24

Fcs

at (

mol

m-2

s-1)

0

5

10

15

20

25NorundaSkyttorpFlakalidenAbiskoHyytiäläSoroeSodankyläVallanes

0 2 4 6 8 10 12 14 16 18 20 22 24

(

mm

ol

mol

-1)

-40

-20

0

20

40

60

80

100

120

Half-month no.

0 2 4 6 8 10 12 14 16 18 20 22 24

Rd

(m

ol m

-2s-1

)

-2

0

2

4

6

8

10

12

Two-week means ofparameter values

• air temperature• PAR• VPD• Soil water content• Leaf area index• Species• Age• (Latitude)

LAI

1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

GP

Pm

ax

(m

ol m

-2s-1

)

0

5

10

15

20

25

30

y= 0.588 + 4.326xr ² = 0.807

LAI

1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

Fcs

at ( m

ol m

-2s-1

)

0

5

10

15

20

25

y= -1.607 + 3.840xr ² = 0.769

LAI

1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

m

mol

m

ol-1

)

10

20

30

40

50

60

y = 15.728 + 6.669xr2 = 0.69

LAI

1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

Rd

(m

ol m

-2s-1

)

1

2

3

4

5

6

7

8

9

Y = 0.826 + 1.204xr ² = 0.861

54 56 58 60 62 64 66 68 70

Fcs

at (

mol

m-2

s-1)

468

10121416182022

54 56 58 60 62 64 66 68 70G

PP

max

( m

ol m

-2s-1

)

468

10121416182022

Latitude

54 56 58 60 62 64 66 68 70

(

mol

mol

-1)

20

25

30

35

40

45

50

55

60

54 56 58 60 62 64 66 68 70

Rd

(m

ol m

-2s-1

)

2

3

4

5

6

7

8

54 56 58 60 62 64 66 68 70

Nor

m G

PP

max

0.4

0.6

0.8

1.0

1.2

1.4

54 56 58 60 62 64 66 68 70

Nor

m F

csat

0.4

0.6

0.8

1.0

1.2

1.4

54 56 58 60 62 64 66 68 70

Nor

m

0.4

0.6

0.8

1.0

1.2

1.4

54 56 58 60 62 64 66 68 70

Nor

m R

d

0.4

0.6

0.8

1.0

1.2

1.4

r2 = 0.932 r2 = 0.932

r2 = 0.002r2 = 0.058

r2 = 0.054r2 = 0.046

r2 = 0.814r2 = 0.814

.and after normalizing for LAI-dependence

What about correlation with latitude?(reminding about Valentini et al., 2000)

54 56 58 60 62 64 66 68 70

Fcs

at (

mol

m-2

s-1)

468

10121416182022

54 56 58 60 62 64 66 68 70G

PP

max

( m

ol m

-2s-1

)

468

10121416182022

Latitude

54 56 58 60 62 64 66 68 70

(

mol

mol

-1)

20

25

30

35

40

45

50

55

60

54 56 58 60 62 64 66 68 70

Rd

(m

ol m

-2s-1

)

2

3

4

5

6

7

8

54 56 58 60 62 64 66 68 70

Nor

m G

PP

max

0.4

0.6

0.8

1.0

1.2

1.4

54 56 58 60 62 64 66 68 70

Nor

m F

csat

0.4

0.6

0.8

1.0

1.2

1.4

54 56 58 60 62 64 66 68 70

Nor

m

0.4

0.6

0.8

1.0

1.2

1.4

54 56 58 60 62 64 66 68 70

Nor

m R

d

0.4

0.6

0.8

1.0

1.2

1.4

r2 = 0.932 r2 = 0.932

r2 = 0.002r2 = 0.058

r2 = 0.054r2 = 0.046

r2 = 0.814r2 = 0.814

.and after normalizing for LAI-dependence

54 56 58 60 62 64 66 68 70

Fcs

at (

mol

m-2

s-1)

468

10121416182022

54 56 58 60 62 64 66 68 70G

PP

max

( m

ol m

-2s-1

)

468

10121416182022

Latitude

54 56 58 60 62 64 66 68 70

(

mol

mol

-1)

20

25

30

35

40

45

50

55

60

54 56 58 60 62 64 66 68 70

Rd

(m

ol m

-2s-1

)

2

3

4

5

6

7

8

54 56 58 60 62 64 66 68 70

Nor

m G

PP

max

0.4

0.6

0.8

1.0

1.2

1.4

54 56 58 60 62 64 66 68 70

Nor

m F

csat

0.4

0.6

0.8

1.0

1.2

1.4

54 56 58 60 62 64 66 68 70

Nor

m

0.4

0.6

0.8

1.0

1.2

1.4

54 56 58 60 62 64 66 68 70

Nor

m R

d

0.4

0.6

0.8

1.0

1.2

1.4

r2 = 0.932 r2 = 0.932

r2 = 0.002r2 = 0.058

r2 = 0.054r2 = 0.046

r2 = 0.814r2 = 0.814

After normalization for the LAI-dependency - no correlation

with latitude!

Stand age (yrs)

0 20 40 60 80 100 120

Nor

mal

ized

Rd

0.8

0.9

1.0

1.1

1.2

1.3

y = 0.877 + 0.002214xr ² = 0.700

..but stand age does matter

Conclusions:

• Ecosystem respiration is well determined by the Lloyd & Taylor equation with only one fitting parameter, the respiration rate at 10°C.

• Leaf area index is the parameter that best explaines between stand variations in parametes controling respiration as well as gross photosynthesis

• After correction for leaf area, stand respiration shows a weak dependency on stand age

Part B - Three similar forests(all are ca. 40 yrs old spruce stands)

T = 5.5°C; P = 688 mmGley podzolSoil0-100 C = 23 kg m-2

Basal area = 32.3 m2 ha-1

T = 3.4°C; P = 613 mmPodzolSoil0-100 C = 5.9 kg m-2

Basal area = 14.7 m2 ha-1

T = 1.2°C; P = 523 mmPodzolSoil0-100 C = 7.2 kg m-2

Basal area = 20.7 m2 ha-1

Method:• Grouping into bi-weekly periods• Filled when u*<threshold- light response fkn for daytime- exp fkn for nighttime• Components separation: Pg = Fcmeas- modelled Reco

• Biomass increment from empirical functions within foot- print area• Litterfall & fine root turnover measured in nearby plots

Pn a constant fraction of Pg?

Pg(g C m-2yr-1)

700 800 900 1000 1100 1200 1300 1400

Pn

(g C

m-2

yr.1

)

100

200

300

400

500

600

y = 0.358 x(0.43 - 0.31)

Conclusions:

• Norunda is not unique in being a ’looser’!

• Pn is probably not a constant fraction of Pg but varies in the range 30-45%.

• Unexpected very large losses of soil carbon

• Large difference in NEP between forests of the same species and age

Part C - Factors controling CO2 exchange in peatlands

T = 6.2°C; P = 700 mmTemperate ombrotrophic bog

T = 3.0°C; P = 713 mmBoreal oligotrophic fen

T = 1.2°C; P = 523 mmBoreal oligotrophicminerotrophic mire

T = -1.1°C; P = 474 mmSub-arctic mesotrophic fen

Fäje myrThe source area is dominated by a mosaic of

hummocks, lawns and carpets

SiikanevaThe source area is dominated by sedges and moss carpet

Degerö Stormyr The source area is dominated by a lawn

plant community

KaamanenThe source area is dominated by the hummock-

hollow microstructure

Questions asked:

• Similarities/differences in seasonal dynamics of NEE, GPP & Reco • Similarities/differences in responses of respiration and photosynthesis to enviromental parameters among different types of Nordic peatlands?

• What are the major controls of CO2 exchanges?

Methods

• De-trending of seasonal effects were made using dummy variable

• Total ecosystem respiration during daytime was estimated as the difference between measured NEE and estimated GPP

• GPP was estimated using fitted bi-weekly light-response functions

• Datasets were further divided into 14-days periods for parameter estimations

• One year of data separated into DAYTIME and NIGHTTIME periods

• The same method was used at all sites (i.e., Euroflux methodology)

• Half hourly fluxes of CO2 net exchange between peatland surface and atmosphere measured by eddy covariance under well-mixed conditions (u*>0.1)

Eddy Covariance Method

F

'' cwF

-1.00.01.0

0 5 10 15 20

w

348350352

0 5 10 15 20

CO

2

-1.5

0.0

1.5

0 5 10 15 20

w'C

O2'

w

CO2

'' 2COw

w'C'1

N(w w)(C C)i

i 1

N

i

Weather during growing season

Apr May Jun Jul Aug Sep Oct Nov

Q ( m

ol m

-2s-1

)

0

200

400

600

800

1000

FäjeDegeröKaamanenSiikaneva

Apr May Jun Jul Aug Sep Oct Nov

Ta

da

y (°

C)

0

5

10

15

20

25

Apr May Jun Jul Aug Sep Oct Nov

VP

D (

hPa)

0

2

4

6

8

10

Apr May Jun Jul Aug Sep Oct Nov

WT

D (

cm)

-30

-20

-10

0

10

20

30

May

Hour2 4 6 8 10 12 14 16 18 20 22 24

NE

E (

mg

m-2

s-1)

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15June

Hour2 4 6 8 10 12 14 16 18 20 22 24

NE

E (

mg

m-2

s-1)

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

July

Hour2 4 6 8 10 12 14 16 18 20 22 24

NE

E (

mg

m-2

s-1)

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

August

Hour2 4 6 8 10 12 14 16 18 20 22 24

NE

E (

mg

m-2

s-1)

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

September

Hour2 4 6 8 10 12 14 16 18 20 22 24

NE

E (

mg

m-2

s-1)

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

FäjeDegeröKaamanenSiikaneva

Mean diurnalvariation in NEE during summer

Seasonal variationof components

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

NE

E (

g m-2

)

-100

-80

-60

-40

-20

0

20

FäjeDegeröKaamanenSiikaneva

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecG

PP

(g

m-2)

-225

-200

-175

-150

-125

-100

-75

-50

-25

0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Re

co (

g m

-2)

0

25

50

75

100

125

150

175

Seasonal variationof components

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

NE

E (

g m-2

)

-100

-80

-60

-40

-20

0

20

FäjeDegeröKaamanenSiikaneva

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecG

PP

(g

m-2)

-225

-200

-175

-150

-125

-100

-75

-50

-25

0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Re

co (

g m

-2)

0

25

50

75

100

125

150

175

Seasonal variationof components

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

NE

E (

g m-2

)

-100

-80

-60

-40

-20

0

20

FäjeDegeröKaamanenSiikaneva

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecG

PP

(g

m-2)

-225

-200

-175

-150

-125

-100

-75

-50

-25

0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Re

co (

g m

-2)

0

25

50

75

100

125

150

175

Relationship between GPP, Reco and environmental variables

Independent variables:

- GPP- Reco- GPP_res- Reco_res- GPP_res_norm- Reco_res_norm

Dependent variables:

- Air temperature- Photon flux density (PPFD)- Vapour pressure deficit (VPD)- Water table depth WTDa (whole season) WTDb (period of decreasing WTD)

GP

P (

g C

m-2

)

-250

-200

-150

-100

-50

0

Period

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

GP

P_r

es (

g C

m-2

)

-80

-40

0

40

80

120

New de-trended GPP variable

GPP dummy

Measured GPP

De-trending for seasonal effects

Normalizing for temperature dependencies

Temperature

6 8 10 12 14 16 18 20 22 24

GP

P_r

es

(g C

m-2

)

-80

-60

-40

-20

0

20

40

60

80

100

y = 105.32 - 6.55*T

New seasonally de-trended and temperature normalized variable:

GPP_res_norm = GPP_res/f(T)

Fäje

-5 0 5 10 15 20 25

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

Kaamanen

-15 -10 -5 0 5 10 15

0.00

0.02

0.04

0.06

0.08

0.10

Siikaneva

-15 -10 -5 0 5 10 15 20

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

Degerö

-20 -15 -10 -5 0 5 10 15 20

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

Reco = f(Tan)

Tannight eRNEE 0

Fäje Degerö Kaamanen Siikaneva All sites

r2 0.9454 0.9518 0.9324 0.9507 0.8601

R0 0.0262 0.0124 0.0151 0.0097 0.0223

0.0849 0.1860 0.1343 0.1646 0.0980

• differences in base respiration and temperature sensitivity due to litter quality?

Ta PPFD VPD WTDa WTDb GPP Reco

0.0

0.2

0.4

0.6

0.8

1.0

Ta PPFD VPD WTDa WTDb GPP Reco

0.0

0.2

0.4

0.6

0.8

1.0

GPP GPP_res GPP_res_norm Reco Reco_res Reco_res_norm

Ta PPFD VPD WTDa WTDb GPP Reco

0.0

0.2

0.4

0.6

0.8

1.0

Ta PPFD VPD WTDa WTDb GPP Reco

De

term

ina

tio

n c

oe

ffic

ien

t (-

)

0.0

0.2

0.4

0.6

0.8

1.0

Fäje myr

Degerö

Kaamanen

Siikaneva

Regression analysis

Ta PPFD VPD WTDa WTDb GPP Reco

0.0

0.2

0.4

0.6

0.8

1.0

Ta PPFD VPD WTDa WTDb GPP Reco

0.0

0.2

0.4

0.6

0.8

1.0

GPP GPP_res GPP_res_norm Reco Reco_res Reco_res_norm

Ta PPFD VPD WTDa WTDb GPP Reco

0.0

0.2

0.4

0.6

0.8

1.0

Ta PPFD VPD WTDa WTDb GPP Reco

De

term

ina

tio

n c

oe

ffic

ien

t (-

)

0.0

0.2

0.4

0.6

0.8

1.0

Fäje myr

Degerö

Kaamanen

Siikaneva

GPP (g C m-2)

-200 -150 -100 -50 0

Rec

o (g

C m

-2)

0

50

100

150

200

y = 15.39 - 0.756xr ² = 0.9653

Fäje Degerö

-200 -150 -100 -50 0

0

50

100

150

200

y = 8.44 - 0.643xr ² = 0.9270

GPP (g C m-2)

Rec

o (g

C m

-2)

Kaamanen

-200 -150 -100 -50 0

0

50

100

150

200

y = 12.67 - 0.458xr ² = 0.8934

Rec

o (g

C m

-2)

GPP (g C m-2)

Siikaneva

-200 -150 -100 -50 0

0

50

100

150

200

y = 10.72 - 0.555xr ² = 0.9098

Rec

o (g

C m

-2)

GPP (g C m-2)

Today Reco is 50 - 80% of GPP - how sustainable is this relationship?

Fäje

Siikaneva

Degerö

Kaamanen

-120

-100

-80

-60

-40

-20

0

-10 -5 0 5 10 15Ta (° C)

NE

E (

g C

O2

m-2

)

R2 = 0.41

NE

E (

g C

m-2)

What about other peatlandsin northern hemisphere?

Conclusions

• Apart from a high correlation between the two main components themselves, i.e., respiration and photosynthesis, temperature was the single most important variable in explaining the variation in the component fluxes

• Surprisingly, gross primary productivity was, also after de-trending the inherent seasonal variation, found to be more sensitive to temperature than respiration for the actual sites

• Water table depth explained variations in respiration and photsynthesis only during consistent drying up phases

• Wetlands seems to be small but consistent sinks

Thanks for your attention!