Characterizing sampling biases in the trace gas climatologies of the SPARC Data Initiative. Issue 20 (21st October 2013)

• Record Type: Journal Article
• Authors: Toohey, M.; Hegglin, M. I.; Tegtmeier, S.; Anderson, J.; Añel, J. A.; Bourassa, A.; Brohede, S.; Degenstein, D.; Froidevaux, L.; Fuller, R.; Funke, B.; Gille, J.; Jones, A.; Kasai, Y.; Krüger, K.; Kyrölä, E.; Neu, J. L.; Rozanov, A.; Smith, L.; Urban, J.; von, T.; Walker, K. A.; Wang, R. H. J.
• Is Part Of: Journal of geophysical research. Volume 118:Issue 20(2013)
• Page Start: 11, 847
• Page End: 11, 862
• Publication Date: 2013-10-21
• Subjects: Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/jgrd.50874
• Languages: English
• ISSNs: 2169-897X

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>[1] Monthly zonal mean climatologies of atmospheric measurements from satellite instruments can have biases due to the nonuniform sampling of the atmosphere by the instruments. We characterize potential sampling biases in stratospheric trace gas climatologies of the Stratospheric Processes and Their Role in Climate (SPARC) Data Initiative using chemical fields from a chemistry climate model simulation and sampling patterns from 16 satellite‐borne instruments. The exercise is performed for the long‐lived stratospheric trace gases O<sub>3</sub> and H<sub>2</sub>O. Monthly sampling biases for O<sub>3</sub> exceed 10% for many instruments in the high‐latitude stratosphere and in the upper troposphere/lower stratosphere, while annual mean sampling biases reach values of up to 20% in the same regions for some instruments. Sampling biases for H<sub>2</sub>O are generally smaller than for O<sub>3</sub>, although still notable in the upper troposphere/lower stratosphere and Southern Hemisphere high latitudes. The most important mechanism leading to monthly sampling bias is nonuniform temporal sampling, i.e., the fact that for many instruments, monthly means are produced from measurements which span less than the full month in question. Similarly, annual mean sampling biases are well explained by nonuniformity in the month‐to‐month sampling by different instruments. Nonuniform sampling in latitude and longitude are shown to also lead to nonnegligible sampling biases, which are most relevant for climatologies which are otherwise free of biases due to nonuniform temporal sampling.</p> </abstract>