Climate and Dynamics
The relative importance of random error and observation frequency in detecting trends in upper tropospheric water vapor
Article first published online: 12 NOV 2011
DOI: 10.1029/2011JD016610
Copyright 2011 by the American Geophysical Union.
Issue
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Journal of Geophysical Research: Atmospheres (1984–2012)
Volume 116, Issue D21, 16 November 2011
Additional Information
How to Cite
, , , and (2011), The relative importance of random error and observation frequency in detecting trends in upper tropospheric water vapor, J. Geophys. Res., 116, D21118, doi:10.1029/2011JD016610.
Publication History
- Issue published online: 12 NOV 2011
- Article first published online: 12 NOV 2011
- Manuscript Accepted: 13 SEP 2011
- Manuscript Revised: 7 SEP 2011
- Manuscript Received: 27 JUL 2011
- Abstract
- Article
- References
- Cited By
Keywords:
- lidar;
- measurement frequency;
- radiosonde;
- trend detection;
- tropospheric water vapor
[1] Recent published work assessed the amount of time to detect trends in atmospheric water vapor over the coming century. We address the same question and conclude that under the most optimistic scenarios and assuming perfect data (i.e., observations with no measurement uncertainty) the time to detect trends will be at least 12 years at approximately 200 hPa in the upper troposphere. Our times to detect trends are therefore shorter than those recently reported and this difference is affected by data sources used, method of processing the data, geographic location and pressure level in the atmosphere where the analyses were performed. We then consider the question of how instrumental uncertainty plays into the assessment of time to detect trends. We conclude that due to the high natural variability in atmospheric water vapor, the amount of time to detect trends in the upper troposphere is relatively insensitive to instrumental random uncertainty and that it is much more important to increase the frequency of measurement than to decrease the random error in the measurement. This is put in the context of international networks such as the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) and the Network for the Detection of Atmospheric Composition Change (NDACC) that are tasked with developing time series of climate quality water vapor data.
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