Meta-analysis of eddy covariance carbon fluxes data

Dario Papale, Markus Reichstein, Riccardo Valentini

Marc Aubinet, Christian Bernhofer, Alessandro Cescatti, Alexander Knohl, Tuomas Laurila, Anders Lindroth, Eddy Moors, Kim Pilegaard, Günther Seufert

Climate, soil

+ Disturbances ?

Schulze et al. 2000

Code Site Years Lat Lon Land cover#

T (°C) P (mm)

BE1 Vielsalm 1997-2003 50° 18’ 5° 59’ MF 7.5 1000 BE2 Braschaat 1998-2002 51° 18’ 4° 31’ MF 9.8 750 DE1 Bayreuth 1997-1999 50° 09’ 11° 52’ ENF 5.8 885 DE2 Tharandt 1997-2003 50° 58’ 13° 34’ ENF 7.7 820 DE3 Hainich 2000-2003 51° 04’ 10° 27’ DBF 6.8 775 DK1 Soroe 1997-2003 55° 29’ 11° 39’ MF 8.3 730 ES1 El Saler 1999-2002 39° 21’ -0° 19’ ENF 17.8 551 FI1 Hyytiala 1997-2002 61° 51’ 24° 18’ ENF 3.8 709 FI2 Sodankyla 2000-2003 67° 22’ 26° 38’ ENF -1.0 499 FI3 Kaamanen 2000-2003 69° 08’ 27° 18’ W -1.3 395 FR1 Hesse 1997-2002 48° 40’ 7° 04’ DBF 9.9 975 FR2 Le Bray 1997

2001-2002 44° 43’ -0° 46’ ENF 13.2 972

FR4 Puechabon 2001-2002 43° 44’ 3° 35’ EBF 13.5 872 IL1 Yatir 2002 31° 21’ 35° 03’ ENF 18.2 280 IT1 Collelongo 1998

2000-2001 41° 51’ 13° 35’ DBF 7.4 1140

IT2 Castelporziano 1997-1998 2000-2003

41° 42’ 12° 23’ EBF 15.6 767

IT3 Roccarespampani 1

2001-2003 42° 24’ 11° 56’ DBF 15.15 876

IT5 Nonantola 2001-2002 44° 41’ 11° 05’ MF 12.7 583 IT6 San Rossore 2000-2003 43° 44’ 10° 18’ ENF 14.2 920 IT7 Zerbolò 2002 45° 12’ 9° 03’ DBF 11.8 980 IT8 Lavarone 2001-2002 45° 57’ 11° 17’ MF 7.8 1150 NL1 Loobos 1997-1999

2001-2003 52° 10’ 5° 45’ ENF 9.8 786

SE1 Norunda 2001-2003 60° 05’ 17° 28’ ENF 5.5 527 UK1 Aberfeldy 1998

2000-2001 56° 37’ -3° 48’ ENF 8 1400

Euroflux and Carboeuroflux datasets used

IWA (Index Water Availability): ratio of actual and potential evapotranspiration

MAT: Mean annual temperature

The data set was split into two populations by a threshold of potentially available radiation energy of 8.8 TJ m-2 yr-1 (52°N)

Pot. Rad. > 8.8 TJ m-2 y-1 Pot. Rad. < 8.8 TJ m-2 y-1

Pot. Rad. > 8.8 TJ m-2 y-1 Pot. Rad. < 8.8 TJ m-2 y-1

12

12

8.8

8.8

yrmTJRbIWAa

yrmTJRbMATay

potIWAIWA

potMATMAT

Best-fit modeled versus observed annual (a) GPP, (b) TER, (c) NEP

6 7 8 9 10 11

Potential annual radiation threshold [TJ m-2 yr-1]

0.0

0.2

0.4

0.6

0.8

1.0

Exp

l. va

rianc

e by

Eq.

1

(B) TER

6 7 8 9 10 11

Potential annual radiation threshold [TJ m-2 yr-1]

0.0

0.2

0.4

0.6

0.8

1.0Exp

l. va

rianc

e by

Eq.

1(A) GPP

6 7 8 9 10 11

Potential annual radiation threshold [TJ m-2 yr-1]

0.00

0.10

0.20

0.30

0.40

0.50

0.60

Exp

l. va

rianc

e by

Eq.

1

(D) Average

6 7 8 9 10 11

Potential annual radiation threshold [TJ m-2 yr-1]

0.0

0.2

0.4

0.6

0.8

1.0

Exp

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Eq.

1

(C) NEP

How do we chose the radiation threshold?

Performance of the simple regression model

Are the results robust against errors?(advection, footprint, quality, gapfilling, partitioning…)

Results from 500 Monte-Carlo simulations where randomly plus or minus 200 gC m-2 were added to GPP, TER and NEP

At high [S], Km = insignificant

and Q10 of R = Q10 of Vmax (Arrhenius kinetics)

At low [S] : Km becomes important

Also Km increases with Temp

At low [S]: Q10 of R << Q10 of Vmax

Davidson et al., Global Change Biology, in press Thanks Ivan for the slide!

][

][*max

SK

SVR

m

Decomposition = enzymatic process that follows Michaelis-Menten kinetics (1913)

0

0.5

1

1.5

2

2.5

3

3.5

4

0 0.2 0.4 0.6 0.8 1

Relative soil water content (fraction of FC)

Eff

ec

tiv

e Q

10

0

0.5

1

1.5

2

2.5

3

3.5

4

0 0.2 0.4 0.6 0.8 1

Relative soil water content (fraction of FC)

Eff

ec

tiv

e Q

10

A: Observed from eddy covariance data B: Modelled with BGC-model

5°C

15°C

25°C

5°C

15°C

25°C

Apparent Q10 depends on soil moisture

Under ‘standard’ conditions (15°C; RSWC=0.6) the emergent Q10 of model and data are similar

Along decreasing/increasing water availability data and model behave completely differently with respect to how Q10 changes.

In opposite direction of what models predict

Valentini et al. 2000

Latuitude - NEE relation

Conclusions

• GPP and TER compensate each other canceling out single climate factor effects

• Water availability plays an important role in GPP and TER not only in the Mediterranean region but also in central Europe

• Ecosystem carbon balance modeling approaches should abandon the convenient climate-NPP analogy and better account for carbon-water cycle interactions and non-climatic factors affecting respiration

• Flux tower data are a unique source of information that play an important role in process understanding and model development