Representativeness Errors in Comparing Chemistry Transport Models with Satellite UV/Vis Tropospheric Column Retrievals

Abstract:

UV/Vis satellite retrievals of trace gas columns of nitrogen dioxide (NO₂), sulphur dioxide (SO₂), and formaldehyde (HCHO) are useful to test and improve models of atmospheric composition, for data assimilation, and to provide top-down constraints on emissions. However, because models and satellite measurements do not represent the exact same geophysical quantities, the process of confronting model fields with satellite measurements is complicated by representativeness errors, which degrade the quality of the comparison beyond contributions from modelling and measurement errors alone. Here we discuss representativeness errors that arise from the act of carrying out a model-satellite comparison: (1) horizontal representativeness errors due to imperfect collocation of the model grid cell and an ensemble of satellite pixels called superobservation, (2) temporal representativeness errors originating mostly from differences in cloud cover between the modelled and observed state, and (3) vertical representativeness errors because of reduced satellite sensitivity towards the surface. To minimize the impact of these representativeness errors, models and satellite measurements should be sampled as consistent as possible, and we provide recipes to do so. A practical confrontation of tropospheric NO₂ columns simulated by the TM5 CTM with OMI tropospheric NO₂ retrievals suggests that horizontal representativeness errors are <5-10% in most cases and of random nature. These errors should be included along with the individual retrieval errors in the overall superobservation error. Temporal sampling errors from mismatches in cloud cover, and in photolysis rates, are on the order of 10% for NO₂ and HCHO, and systematic, but partly avoidable. In the case of air pollution applications where sensitivity down to the ground is required, models should be sampled on the same mostly cloud-free days as the satellite retrievals. The most relevant representativeness error is associated with the vertical sensitivity of UV/Vis satellite retrievals. Simple vertical integration of modelled profiles leads to systematically different model columns compared to application of the appropriate averaging kernel. In comparing OMI NO₂ to GEOS-Chem NO₂ simulations, these systematic differences are as large as 15-20%.