The effect of LAI on the representativeness of eddy covariance estimates of ecosystem respiration during turbulent conditions at night across a range of sites

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The effect of LAI on the representativeness of eddy covariance estimates of ecosystem respiration during

turbulent conditions at night across a range of sites

May Myklebust, G. Wohlfahrt, Laurent Misson, Roland Huc, N. Delpierre, L.E. Hipps, R.J. Ryel, M.A. Arain, Michael Bahn, C. Bernhofer, et al.

To cite this version:

May Myklebust, G. Wohlfahrt, Laurent Misson, Roland Huc, N. Delpierre, et al.. The effect of

LAI on the representativeness of eddy covariance estimates of ecosystem respiration during turbulent

conditions at night across a range of sites. European Geosciences Union General Assembly 2009, Apr

2009, Vienne, Austria. 1 p., 2009. �hal-02820244�

M.C. Myklebust ^a *, G. Wohlfahrt ^b , L. Misson ^c , R. Huc ^a , N. Delpierre ^d , L.E. Hipps ^e , R.J. Ryel ^f , M.A. Arain ^g , M. Bahn ^b , C. Bernhofer ^h , B. Chojnicki ⁱ , P. Curtis ^j , S. Frokling ^k , P. Lafleur ^l , B. Longdoz ^m , E. van Gorsel ⁿ M. Aurela ^o , P., M. Cavaleri ^p , A.R. Desai ^q , A. Ito ^r , H.W. Loescher ^s , S. Oberbauer ^t , J. Pumpanen ^u , M.G. Ryan ^v , N. Saigusa ^w , T. Vesala ^x , C. Yi ^y

Objectives Introduction

The effect of LAI on the representativeness of eddy covariance estimates of ecosystem respiration during turbulent conditions at night across a range of sites

Calm conditions at night cause eddy covariance (EC) systems to underestimate ecosystem respiration (R _ECO ) due to poor mixing of the canopy air. However, turbulent conditions do not always result in reliable nighttime measurements either. Decoupling at night in turbulent conditions can be explained by the difference between the adjustment lengths for momentum and heat transfer in the canopy (Belcher et al., 2008) or by drag imposed on moving air by canopy elements (Yi, 2008). Both explanations lead to storage, the development of horizontal and vertical gradients, and advective processes in the air within and below the canopy. Overall, these processes can cause EC to underestimate R _ECO and suggests that the potential for underestimating may depend on leaf area index (LAI). This study investigated the effect of LAI on the representativeness of EC measurements of R _ECO at night in turbulent conditions (R _EC ) in a wide range of vegetation types, climates, and topographies.

EC-independent estimates of R _ECO (R _ALT ) often have very large uncertainties and therefore, are difficult to use for the evaluation of R _EC . But R _ALT

estimates can be assumed to have random errors that cancel when pooled. In contrast, errors in R EC due to an effect of LAI are assumed to be systematic.

We used R _ALT to test R _EC representativeness (R _ACC ) by RE _C – R _ALT = R _ACC over a range of LAI from sites around the world.

Assess whether LAI effects R _ACC by:

1.. Test for a common trend in R _ACC over LAI at sites with seasonal or annual changes in LAI.

2. Test for a trend in R _ACC with LAI across multiple sites.

Methods

R _EC : Turbulence fluxes of CO ₂ were determined from high frequency measurements of CO ₂ density and wind speed in 3 dimensions. Only data from nights when turbulence intensity (u*) was above a site-specific threshold were used.

c

y = 0.43x - 0.28 R2 = 0.17

-8 -6 -4 -2 0 2 4 6 8 10

0 2 4 6 8 10

RACC (CO2 μmol m-2 s-1)

Results

While R _EC underestimates at some sites in turbulent conditions at night, we found no evidence for a general underestimation in these conditions when results are pooled across sites. This implies that EC generates realistic to slightly underestimated approximations of R _ECO when multiple ecosystems are considered.

The nighttime problem with EC estimates is seen at certain sites. We showed that these sites are not confined to certain vegetation structure or topography classes but may predominate in situations where vapor pressure deficit and transpiration rate are both high. This suggests that water vapor may play a role in canopy air movement at night.

R _ALT : Several methods were used to estimate R _ALT . The only criteria was that they needed to be completely independent of R _EC . Scaled up measurements of leaf and soil respiration, modeled R ECO and a combination of the two methods were used (table 1).

1. Mainly insignificant relationships between R _ACC and LAI across 6 of 8 sites of various vegetation structure and topography suggest that LAI does not directly effect R _EC . Two sites, US-MAL (a) and US_BLO (h) indicate LAI correlation with R _EC underestimation (p≤0.1). These sites differ in vegetation structure and topography but share a similar climate (semi-arid) and both species transpire at night.

Conclusion

n

Finnish Meteorological Institute, Climate and Global Change Research, P.O. Box 503, Helsinki, FIN 00101 Finland

o

CSIRO Marine and Atmospheric Research, Pye Laboratory, GPO Box 3023, Canberra ACT 2601, Austrailia

p

Botany Department, University of Hawaii, 3190 Mali Way, Honolulu, HI 96822 USA

q

Atmospheric and Oceanic Sciences Department, University of Wisconsin, AOSS 1549, 1225 W Dayton St., Madison, WI 53706 USA

r

Frontier Research Center for Global Change, JAMSTEC, Yokohama, Japan, National Institute for Environmental Studies, Tsukuba, Japan

s

The National Ecological Observatory Network (NEON), Science Office, Boulder CO 80303, and Institute for Arctic and Alpine Research,

University of Colorado, Boulder, CO, USA

t

Department of Biological Sciences, Florida International University, Miami, FL 33199 USA

u

Department of Forest Ecology, P.O. Box 27, University of Helsinki, FIN 00014, Finland

v

USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO 80526-2098 USA

w

National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba 305-8506, Japan

x

Department of Physics, P.O. Box 64, University of Helsinki, FIN 00014, Finland

y

School of Earth and Environmental Sciences, Queens College, City University of New York, NY 11367, USA

a

Institut National de la Recherche Agronomique, Ecologie des Forêts Méditerranéennes, UR 629, 84914 Avignon, France

b

Institute of Ecology, University of Innsbruck, Sternwartestr. 15, 6020 Innsbruck, Austria

c

CNRS-CEFE, 1919 Route de Mende, 34293 Montpellier Cedex 5, France

d

Université Paris-Sud, Département Ecophysiologie Végétale, Ecologie Systématique, Evolution (UMR 8079), 91405 Orsay, France

e

Department of Plants, Soils, and Climate, Utah State University, Logan, UT, USA 84322-4820

f

Department of Wildland Resources, Utah State University, Logan, UT, 84322-5230 USA

ef

Ecology Center, Utah State University, Logan UT, 84322-5205, USA

g

McMaster University, School of Geography and Earth Sciences, 1280 Main Street, West Hamilton, ON L8S 4K1 Canada

h

Institute of Hydrology and Meteorology, Technische Universität Dresden, D-01737 Tharandt, Germany

i

Poznan University of Life Sciences, 60-637 Poznan, Poland

j

Department of Evolution, Ecology, and Organismal Biology, Ohio State University, Columbus, OH 43210-1293 USA

k

Institute for the study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH USA

l

Department of Geography, Trent University, Peterborough, Ontario, Canada K9J 7B8

l

UMR INRA-UHP 1137 Ecologie et Ecophysiologie Forestières, 54280 Champenoux, France

Grasslands

Wetlands

Broadleaf/mixed Forests

Needleleaf Forests

a

*y = 14.52x - 5.93 R2 = 0.61

-8 -6 -4 -2 0 2 4 6 8 10

0 2 4 6 8 10

RACC (CO2 μmol m-2 s-1)

e

y = -0.10x - 0.21 R2 = 0.15

-8 -6 -4 -2 0 2 4 6 8 10

0 2 4 6 8 10

RACC (CO2 μmol m-2 s-1)

g

y = -0.20x + 1.59 R2 = 0.18

-8 -6 -4 -2 0 2 4 6 8 10

0 2 4 6 8 10

PLAI RACC (CO2 μmol m-2 s-1)

b

y = 0.25x - 1.33 R2 = 0.13

-8 -6 -4 -2 0 2 4 6 8 10

0 2 4 6 8 10

d

y = 2.41x - 1.86 R2 = 0.49

-8 -6 -4 -2 0 2 4 6 8 10

0 2 4 6 8 10

f

y = 0.92x - 1.47 R2 = 0.60

-8 -6 -4 -2 0 2 4 6 8 10

0 2 4 6 8 10

h

y = 1.27x - 3.80 R2 = 0.73

-8 -6 -4 -2 0 2 4 6 8 10

0 2 4 6 8 10

PLAI RACC (μmol m2 m-2)

y = 0.00x + 0.86 R

²

= 0.00

-8 -6 -4 -2 0 2 4 6 8

0 5 10 15 20 25

TLAI (m

²

m

^-2

)

RACC (CO2μmol m-2 s-1)

2. Across 15 sites around the world, we found no indication of bias in R EC relative to R _ALT (0.90 ± 0.99 µmol m-2 s-1 underestimate) and no significant trend with LAI (p >> 0.10).

Belcher, S.E., Finnigan, J.J., Harman, I.N., 2008. Flows through forest canopies in complex terrain. Ecol. Applic. 18, 1436-1543.

Yi, C. 2008. Momentum transfer within canopies. J. Appl. Meteorol. Clim. 47, 262-275 References

site ID country name climate elevation (m) IGBP vegetation class topography R

_ACC

PLAI TLAI u* R

_ALT

reference

AU-Tum Australia Tumbarumba Temperate 1200 evergreen broadleaf forest hilly -0.38 2.4 4.8 0.85 measured Van Gorsel et al., 2007

AT-Neu Austria Neustift/Stubai Valley Temperate 970 grassland flat valley bottom -0.79 2.4 4.8 0.20 measured/modeled Wohlfahrt et al., 2005

CA-MER Canada Mer Bleue Temperate 70 open shrubland flat 0.42 1.3 2.6 0.10 PCARS model Lafleur et al., 2003, Frokling et al., 2002

CA-TP4 Canada Turkey Point Temperate 184 evergreen needleleaf forest flat -1.07 8.0 19.7 0.25 CN-CLASS model Peichl and Arain, 2007; Arain et al. 2006; Arain and Retrepo-Coupe, 2005

CR-LAS Costa Rica La Selva Biological Station Tropical-wet 37 - 150 evergreen broadleaf forest variable 1-35% 6.33 6.0 12.0 0.20 measured/modeled Cavaleri, et al., 2008; Loescher et al., 2003

DE-Tha Germany Tharandt Temperate 380 evergreen needleleaf forest gentle slope (3.5%) 0.19 7.6 18.7 0.45 Castanea model Grunwald and Bernhofer, 2007

FI-HYY Finland Hyytiälä Boreal 181 evergreen needleleaf forest flat - modest height variability 0.08 2.6 8.0 0.30 Castanea model Rannick et al., 2006; Launiainen et al., 2005

FI-SII Finland Siikaneva Fen Boreal 180 grassland flat 0.00 3.1 8.0 N/A measured Aurela et al., 2007

FR-Hesse France Hesse Forest-Sarrebourg Temperate 300 deciduous broadleaf forest <5% -0.20 6.7 13.4 1.10 Castanea model Longdoz et al., 2008

JP-Tak Japan Takayama Temperate 1420 deciduous broadleaf forest variable < 20% 1.80 5.5 11.0 0.20 modeled Ito et al., 2007; Saigusa et al., 2005, 2002

PL-WET Poland Polwet Temperate-wet 54 permanent wetland flat 1.15 4.0 8.0 0.15 measured

US-BLO USA Blodgett Forest Meditteranean 1315 evergreen needleleaf forest variable < 58% -0.02 3.0 9.9 0.30 measured/modeled Misson et al., 2006; Tang et al., 2005; Goldstein et al., 2000

US-MAL USA Malta Semi-arid 1370 grassland slope<2% 1.65 0.5 1.0 0.30 measured/modeled Myklebust et al., 2008, submitted

US-UMB USA University of Michigan Biological Station Temperate 234 deciduous broadleaf forest gentle slope 0.33 3.6 7.2 0.35 measured Curtis et al., 2005

US-WCR USA Willow Creek Temperate 520 deciduous broadleaf forest slope<1% 4.04 5.3 10.6 0.30 measured Bolstad et al., 2004; Cook et al., 2004

Castanea model Davi et al., 2005; Dufrêne et al., 2005