Abstract

It has become possible to retrieve the global, long-term trends of trace gases that are important to atmospheric chemistry, climate, and air quality from satellite data records that span more than a decade. However, many of the satellite remote sensing techniques produce measurements that have va... Show moreIt has become possible to retrieve the global, long-term trends of trace gases that are important to atmospheric chemistry, climate, and air quality from satellite data records that span more than a decade. However, many of the satellite remote sensing techniques produce measurements that have variable sensitivity to the vertical profiles of atmospheric gases. In the case of constrained retrievals like optimal estimation, this leads to a varying amount of a priori information in the retrieval and is represented by an averaging kernel (AK). In this study, we investigate to what extent the estimation of trends from retrieved data can be biased by temporal changes of averaging kernels used in the retrieval algorithm. In particular, the surface carbon monoxide data retrieved from the Measurements Of Pollution In The Troposphere (MOPITT) instrument from 2001 to 2010 were analyzed. As a practical example based on the MOPITT data, we show that if the true atmospheric mixing ratio is continuously 50% higher or lower than the a priori state, the temporal change of the averaging kernel at the surface level gives rise to an artificial trend in retrieved surface carbon monoxide, ranging from −10.71 to +13.21 ppbv yr⁻¹ (-5.68 to +8.84 % yr⁻¹) depending on location. Therefore, in the case of surface (or near-surface level) CO derived from MOPITT, the AKs trends multiplied by the difference between true and a priori states must be quantified in order to estimate trend biases. Show less