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1 Division of Atmospheric Sciences, Department of Physical Sciences, University of Helsinki, POBox 64, FIN-00014, Helsinki, Finland (e mail: Andrei.Sogachev@helsinki.fi). 2 Institute of Bioclimatology, University Göttingen, Büsgenweg 2, D-37077, Göttingen, Germany (e mail: opanfyo@gwdg.de) Sogachev Andrey 1 and Panferov Oleg 2 Gennady Menzhulin, St. Petersburg Jon Lloyd, Jena Gode Gravenhorst, Göttingen Timo Vesala, Helsinki Contributors / Discussants:

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The world-wide network of carbon flux measuring sites (total of 216 towers as of July 2003)

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(Schmid, 2002, AFM) What part of the ecosystem does the flux sensor ‘see’ ? Interpretation of measurement data

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(Schmid, 2002, AFM) Source weight function or flux footprint Definition In a simple form «footprint» or «source weight function» f (x,y,z m ) is the transfer function between the measured value at a certain point F(0,0,z m ) and the set of forcings on the surface-atmosphere interface F(x,y,0) (Schuepp et al., 1990, Schmid, 2002).

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Eulerian analytic models (Schuepp et al.,1990; Horst and Weil, 1992) (z m, z 0, d, u *, L) Lagrangian stochastic models (z m, z 0, d, u *, L, T L, σ u, σ v, σ w ) Forward (pre-defined sources) (Leclerc et al., 1990; Wilson and Flesch, 1993)). Backward (pre-defined sensor location) (Flesch et al.,1995; Kljun et al., 1999). Model approaches for flux footprint estimation (Schmid, 2002) High towerLow tower

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Approaches based on Navier-Stokes equations Large-eddy simulation (Hadfield, 1994; Leclerc et al., 1997) Ensemble-averaged model (K-theory) (Sogachev et al., 2002) Most ecosystems are not spatially homogeneous. Linking the patch patterns to the carbon cycle is a serious challenge.

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Methods of estimation of source weight function in ensemble-averaged model i = 1, 2, 3, k-2, k-1, k, I Wind F (CO 2 ) Z 2m Z 1m F (CO 2 ) Z 2m Z 1m i = 1, 2, 3, k-2, k-1, k, I Wind I i = 1, 2, 3, k-2, k-1, k, I Wind F (CO 2 ) Z 2m Z 1m II i = 1, 2, 3, k-2, k-1, k, I Wind F (CO 2 ) Z 2m Z 1m III i indicates a model grid cell within a domain of I grid cells. k is the investigated grid cell (measurement point). Z 1 and Z 2 are the heights for which the footprint is estimated. The dashed areas depict high intensity areas of vertical scalar flux. (Sogachev and Lloyd, 2004)

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q(t),T(t),C(t), V(t), U(t) Clouds (t ) T (soil), q ( soil), F CO2 (soil), V = 0, U = 0 Q 0 (t), l o w e r b o u n d a r y c o n d i t i o n s 3 km 1 - 10 km Upper boundary conditions Scheme of the SCADIS (scalar distribution) model F CO2 ERH G y f x f , 10 - 100 m f = U, V, T, q, C,forx = ±X,y = ±Y 10 - 100 km f(x,y,z,t) -X -Y +X +Y c o n d i t i o n s o n lateral borders SCADIS is high resolution 3-D numerical model capable of computing the physical processes within both plant canopy and atmospheric boundary layer simultaneously. advection (Sogachev et al., 2002)

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Terrain-following coordinate system Basic equations: momentum, heat, moisture, scalars (CO 2, SO 2, O 3 ), turbulent kinetic energy (E) One-and-a-half-order turbulence closure based on equations of E and ε or ω: E-l, E-ε, E-ω.) Structure of vegetation presented by type of vegetation (vertical profile of LAD, leaf or needle size, optical properties, aerodynamic drag coefficients …) Vertical resolution 75 - 110 model levels from 0 to 3025 m, 42 of it between 0 and 35 m. Basic characteristics of the SCADIS model (Sogachev et al., 2002; Sogachev et al., 2004, TAC)

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Footprint and contribution function predicted by different methods by different methods Spatially homogeneous source located at a fixed height Effect of the vertical distribution of sources within a plant canopy Effect of φ=|S c /S 0 | S 0 is the soil respiration intensity; S c is the photosynthetic activity of the canopy. (Sogachev and Lloyd, 2004)

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Generalized effect of different disturbances on the airflow, scalar flux fields and the footprint (Sogachev et al., 2004, TAC)

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b 1. Effect of natural complex terrain on footprint Tver region, Russia a (Sogachev and Lloyd., 2004) b N 1 km

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2. Effect of natural complex terrain on footprint Hyytiälä, Finland (Sogachev et al., 2004, AFM)

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3. Effect of natural complex terrain on footprint Solling, Germany 100 m N (Sogachev et al., 2004, TAC)

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Effect of forest edge on footprint Modelling aspects (Klaassen et al., 2002)

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To improve our understanding of the carbon cycle… Valkea-Kotinen Lake, Finland Tower

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Eddy covariance measuring system provide a piece of the C balance puzzle The footprint of a turbulent flux measurement defines its spatial context. That is required for correct interpretation of experimental data. Footprint models should produce realistic results in real-world situations There are several methods to describe airflow (transported signal) in such situations by economical computing way with accuracy sufficient enough for practical tasks (K-l, K-ε, K-ω). It has been demonstrated that they are suitable for footprint estimation. Summary Problems of airflow parameterization to be solved: -wake turbulence description within vegetation canopy remain uncertain. (1-D verification is insufficient. It can lead to wrong conclusions). -soil (surface) flux description under condition of weak turbulence -flow separation within vegetation canopy: both for a dence forest on a flat surface and for topography variations

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