Modelling representation errors of atmospheric CO2 mixing ratios at a regional scale L. F. Tolk1, A. G. C. A. Meesters1, A. J. Dolman1, and W. Peters2 1VU University Amsterdam, Amsterdam, The Netherlands 2Wageningen University and Research Centre, Wageningen, The Netherlands
Abstract. Inverse modelling of carbon sources and sinks requires an accurate quality
estimate of the modelling framework to obtain a realistic estimate of the
inferred fluxes and their uncertainties. So-called "representation errors"
result from our inability to correctly represent point observations with
simulated average values of model grid cells. They may add substantial
uncertainty to the interpretation of atmospheric CO2 mixing ratio data.
We simulated detailed variations in the CO2 mixing ratios with a high
resolution (2 km) mesoscale model (RAMS) to estimate the representation
errors introduced at larger model grid sizes of 10–100 km. We found that
meteorology is the main driver of representation errors in our study causing
spatial and temporal variations in the error estimate. Within the nocturnal
boundary layer, the representation errors are relatively large and mainly
caused by unresolved topography at lower model resolutions. During the day,
convective structures, mesoscale circulations, and surface CO2 flux
variability were found to be the main sources of representation errors.
Interpreting observations near a mesoscale circulation as representative for
air with the correct footprint relative to the front can reduce the
representation error substantially. The remaining representation error is
0.5–1.5 ppm at 20–100 km resolution.
Citation: Tolk, L. F., Meesters, A. G. C. A., Dolman, A. J., and Peters, W.: Modelling representation errors of atmospheric CO2 mixing ratios at a regional scale, Atmos. Chem. Phys., 8, 6587-6596, doi:10.5194/acp-8-6587-2008, 2008.