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Bomin Sun, Melissa Free, Hye Lim Yoo, Michael J. Foster, Andrew Heidinger, and Karl-Göran Karlsson


Variability and trends in total cloud cover for 1982–2009 across the contiguous United States from the International Satellite Cloud Climatology Project (ISCCP), AVHRR Pathfinder Atmospheres–Extended (PATMOS-x), and EUMETSAT Satellite Application Facility on Climate Monitoring Clouds, Albedo and Radiation from AVHRR Data Edition 1 (CLARA-A1) satellite datasets are assessed using homogeneity-adjusted weather station data. The station data, considered as “ground truth” in the evaluation, are generally well correlated with the ISCCP and PATMOS-x data and with the physically related variables diurnal temperature range, precipitation, and surface solar radiation. Among the satellite products, overall, the PATMOS-x data have the highest interannual correlations with the weather station cloud data and those other physically related variables. The CLARA-A1 daytime dataset generally shows the lowest correlations, even after trends are removed. For the U.S. mean, the station dataset shows a negative but not statistically significant trend of −0.40% decade−1, and satellite products show larger downward trends ranging from −0.55% to −5.00% decade−1 for 1984–2007. PATMOS-x 1330 local time trends for U.S. mean cloud cover are closest to those in the station data, followed by the PATMOS-x diurnally corrected dataset and ISCCP, with CLARA-A1 having a large negative trend contrasting strongly with the station data. These results tend to validate the usefulness of weather station cloud data for monitoring changes in cloud cover, and they show that the long-term stability of satellite cloud datasets can be assessed by comparison to homogeneity-adjusted station data and other physically related variables.

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CARDS Workshop on Adjusting Radiosonde Temperature Data for Climate Monitoring

Melissa Free, Imke Durre, Enric Aguilar, Dian Seidel, Thomas C. Peterson, Robert E. Eskridge, James K. Luers, David Parker, Margaret Gordon, John Lanzante, Stephen Klein, John Christy, Steven Schroeder, Brian Soden, Larry M. McMillin, and Elizabeth Weatherhead

Homogeneous upper-air temperature time series are necessary for climate change detection and attribution. About 20 participants met at the National Climatic Data Center in Asheville, North Carolina on 11–12 October 2000 to discuss methods of adjusting radiosonde data for inhomogeneities arising from instrument and other changes. Representatives of several research groups described their methods for identifying change points and adjusting temperature time series and compared the results of applying these methods to data from 12 radiosonde stations. The limited agreement among these results and the potential impact of these adjustments on upper-air trends estimates indicate a need for further work in this area and for greater attention to homogeneity issues in planning future changes in radiosonde observations.

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