Modelling representation errors of atmospheric CO2 concentrations at a regional scale

L.F. Tolk, A.G.C.A. Meesters, A.J. Dolman, W. Peters

Research output: Contribution to journalArticleAcademicpeer-review

26 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)6587-6596
JournalAtmospheric Chemistry and Physics
Volume8
Issue number1
DOIs
Publication statusPublished - 2008

Keywords

  • greenhouse gases
  • carbon dioxide
  • climatic change
  • models
  • emission reduction
  • mass conservation
  • transport models
  • system rams
  • inversion
  • fluxes
  • assimilation
  • variability
  • continent
  • forest

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