Sampling bias adjustment for sparsely sampled satellite measurements applied to ACE-FTS carbonyl sulfide observations

Abstract

Abstract. When computing climatological averages of atmospheric trace-gas mixing ratios obtained from satellite-based measurements, sampling biases arise if data coverage is not uniform in space and time. Homogeneous spatiotemporal coverage is essentially impossible to achieve. Solar occultation measurements, by virtue of satellite orbit and the requirement of direct observation of the sun through the atmosphere, result in particularly sparse spatial coverage. In this proof-of-concept study, a method is presented to adjust for such sampling biases when calculating climatological means. The method is demonstrated using carbonyl sulfide (OCS) measurements at 16 km altitude from the ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer). At this altitude, OCS mixing ratios show a steep gradient between the poles and Equator. ACE-FTS measurements, which are provided as vertically resolved profiles, and integrated stratospheric OCS columns are used in this study. The bias adjustment procedure requires no additional information other than the satellite data product itself. In particular, the method does not rely on atmospheric models with potentially unreliable transport or chemistry parameterizations, and the results can be used uncompromised to test and validate such models. It is expected to be generally applicable when constructing climatologies of long-lived tracers from sparsely and heterogeneously sampled satellite measurements. In the first step of the adjustment procedure, a regression model is used to fit a 2-D surface to all available ACE-FTS OCS measurements as a function of day-of-year and latitude. The regression model fit is used to calculate an adjustment factor that is then used to adjust each measurement individually. The mean of the adjusted measurement points of a chosen latitude range and season is then used as the bias-free climatological value. When applying the adjustment factor to seasonal averages in 30∘ zones, the maximum spatiotemporal sampling bias adjustment was 11 % for OCS mixing ratios at 16 km and 5 % for the stratospheric OCS column. The adjustments were validated against the much denser and more homogeneous OCS data product from the limb-sounding MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) instrument, and both the direction and magnitude of the adjustments were in agreement with the adjustment of the ACE-FTS data.

Carbonyl Sulfide (OCS) in the Upper Troposphere/Lowermost Stratosphere (UT/LMS) Region: Estimates of Lifetimes and Fluxes

“…The global coverage afforded by this measurement program allows several aspects of the atmospheric chemistry of OCS to be assessed. The observed OCS MRs are typically within the range 301–552 pptv which is in good agreement with ACE measurement (Kloss et al., 2019). The observed variability can be driven by the cycle of tropopause height, OCS seasonal uptake (Barkley et al., 2008), altitudes of sampling locations (note 90% of the samples were collected at 10–11 km altitude), and local emissions and sinks.…”

“…Carbonyl sulfide (OCS or COS) is the most abundant sulfur‐containing compound in the atmosphere. Atmospheric OCS distributions are generally derived from for example, tropospheric surface observations (Montzka et al., 2007), satellite retrievals (Barkley et al., 2008; Glatthor et al., 2015, 2017; Kloss et al., 2019) or with models (Brühl et al., 2012, 2015; Kjellström, 1998). Direct oceanic emission of OCS and oxidation of carbon disulfide (CS 2 ) are its major sources in the atmosphere (Stoy et al., 2019).…”

Overview and update of the SPARC Data Initiative: comparison of stratospheric composition measurements from satellite limb sounders

“…Limb observations, and merged products thereof, are also becoming increasingly important for the detection and attribution of climate change and potential feedback mechanisms, including the role of stratospheric water vapor and aerosol trends and variability in radiative forcing of climate (e.g., Solomon et al, , 2011Gilford et al, 2016;Schmidt et al, 2018). More generally, limb observations are used for the study of stratospheric dynamics and transport (e.g., Gray and Pyle, 1986;Solomon et al, 1986;Holton and Choi, 1988;Funke et al, 2005a;Manney et al, 2009), empirical studies of stratospheric climate and variability (e.g., Randel et al, 2006Randel et al, , 2010Randel and Thompson, 2011;Manney et al, 2008;Bourassa et al, 2010;Stiller et al, 2012;Gille et al, 2014), data merging, and trend evaluation activities (e.g., Randel and Wu, 1999;Hegglin et al, 2014;Shepherd et al, 2014;Froidevaux et al, 2015;Harris et al, 2015;Davis et al, 2016;Arosio et al, 2019;SPARC, 2019), with merged datasets also being used as forcing databases in climate models (e.g., Cionni et al, 2011, for ozone; Thomason et al, 2018, for aerosol) and for the validation of the representation of transport and chemistry in numerical models (e.g., Eyring et al, 2006;Gettelman et al, 2010;Hegglin et al, 2010;Strahan et al, 2011;Kolonjari et al, 2018;Froidevaux et al, 2019).…”

Intercomparison of Atmospheric Carbonyl Sulfide (TransCom‐COS): 2. Evaluation of Optimized Fluxes Using Ground‐Based and Aircraft Observations

“…Finally, it would be instructive to compare the results also to the Fourier‐transform infrared spectroscopy (FTIR) network (Hannigan et al., 2022; Wang et al., 2016) and satellite observations, that is, MIPAS (Glatthor et al., 2017; Ma et al., 2021; Remaud et al., 2022), TES (Kuai et al., 2014, 2015; Ma et al., 2021) and ACE‐FTS observations (Kloss et al., 2019; Yousefi et al., 2019). However, applying the averaging kernel without decaying profiles in the stratosphere hampers a straightforward evaluation of the current model results.…”