2006gl026162-txts01.txt

Parameterization of vegetation emissions

We have used a very simple procedure to estimate the spatial and temporal variation of vegetation emissions, given the limited information that is available to date. It can be formulated as follows: E(x,y,t)=(LAI(x,y,t) x A(x,y,t) x L(x,y,t))/sum(x,y,t) x E_glob
(1) L(x,y,t)=1.+4.*JNO2(x,y,t)/5.e-3
(2) The emission E (kg) of grid box 'x,y' at month 't' equals the local leaf area index LAI (m^2/m^2) times the geometric area A (m^2) times a local light dependent emission factor L (kg/m^2). This product is normalized by its integral over the whole domain (sum(x,y,t)) and multiplied by the desired global total E_glob.  For a description of our LAI data see Ganzeveld et al. [2002]. For L we assume a 5 times increased emission going from dark to sunlit conditions as reported by Keppler et al. [2006]. In the model, we use the photolysis of NO2 (JNO2) as a proxy for the presence of sunlight. JNO2 values were calculated in TM5 [Krol et al., 2005] accounting for solar zenith angle, surface albedo and cloudiness. We used monthly mean values as calculated for the surface layer of the model. In equation 2, L increases linearly with JNO2 so that a NO2 photolysis of 5.e-3 s^-1 (a typical value for spring time mid latitudes around noon) leads to a 5 fold increase of L compared to darkness.

We have tested if the diurnal cycle of vegetation emissions is important when comparing the model to SCIAMACHY measurements, given the equator crossing time of SCIAMACHY at 10:00 am. As a first assumption we have distributed vegetation emissions over the day using the diurnal cycle of CO2 uptake derived from the TURC model [Lafont et al., 2002]. Since SCIAMACHY CH4 represents in fact CH4/CO2 and the diurnal cycle of CO2 has an opposite sign compared with CH4, the diurnal cycle of CH4/CO2 emissions varies with the square of the diurnal cycle of CO2 uptake. The effect of accounting for this diurnal variation in the model turned out to be negligible when comparing SCIAMACHY to model output sampled at the appropriate local overpass time. (note that this outcome might well be different for a sensor with a different overpass time)

References

Ganzeveld, L. N., J. Lelieveld, F. J. Dentener, M. C. Krol, A. J. Bouwman, and G.-J. Roelofs (2002), Global soil-biogenic NOx emissions and the role of canopy processes, J. Geophys. Res., 107(D16), 4298, doi:10.1029/2001JD001289.

Keppler, F., J. T. G. Hamilton, M. Brass, and T. Roeckmann (2006), Methane emissions from terrestrial plants under aerobic conditions, Nature, 439, 187-191, doi:10.1038/nature04420.

Krol, M., S. Houweling, B. Bregman, M. van den Broek, A. Segers, P. van Velthoven, W. Peters, F. Dentener, and P. Bergamaschi (2005), The two-way nested global chemistry-transport zoom model TM5: algorithm and applications, Atmos. Chem. Phys., 5, 417-432.

Lafont, S., L. Kergoat, G. Dedieu, A. Chevillard, U. Karstens, and O. Kolle (2002), Spatial and temporal variability of land CO2 fluxes estimated with remote sensing and analysis data over western Eurasia, Tellus, Ser. B, 54, 820-833.