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Ensemble climate model simulations used for the Intergovernmental Panel onClimate Change (IPCC) assessments have become important tools for exploringthe response of the Earth System to changes in anthropogenic and naturalforcings. The systematic evaluation of these models through global satelliteobservations is a critical step in assessing the uncertainty of climatechange projections. This paper presents the technical steps required forusing nadir sun-synchronous infrared satellite observations for multi-modelevaluation and the uncertainties associated with each step. This is motivatedby need to use satellite observations to evaluate climate models. Wequantified the implications of the effect of satellite orbit and spatialcoverage, the effect of variations in vertical sensitivity as quantified bythe observation operator and the impact of averaging the operators for usewith monthly-mean model output. We calculated these biases in ozone, carbonmonoxide, atmospheric temperature and water vapour by using the output fromtwo global chemistry climate models (ECHAM5-MOZ and GISS-PUCCINI) and theobservations from the Tropospheric Emission Spectrometer (TES) instrument onboard the NASA-Aura satellite from January 2005 to December 2008.  The results show that sampling and monthly averaging of the observationoperators produce zonal-mean biases of less than ±3 % for ozone andcarbon monoxide throughout the entire troposphere in both models. Watervapour sampling zonal-mean biases were also within the insignificant range of±3 % (that is ±0.14 g kg<sup>−1</sup>) in both models. Sampling led to atemperature zonal-mean bias of ±0.3 K over the tropical and mid-latitudesin both models, and up to −1.4 K over the boundary layer in the higherlatitudes. Using the monthly average of temperature and water vapouroperators lead to large biases over the boundary layer in thesouthern-hemispheric higher latitudes and in the upper troposphere,respectively. Up to 8 % bias was calculated in the upper troposphere watervapour due to monthly-mean operators, which may impact the detection of watervapour feedback in response to global warming. Our results reveal theimportance of using the averaging kernel and the a priori profiles to accountfor the limited vertical resolution and clouds of a nadir observation duringmodel application. Neglecting the observation operators resulted in largebiases, which are more than 60 % for ozone, ±30 % for carbon monoxide,and range between −1.5 K and 5 K for atmospheric temperature, and between−60 % and 100 % for water vapour

The impact of orbital sampling, monthly averaging and vertical resolution on climate chemistry model evaluation with satellite observations