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  • Author or Editor: William E. Easterling x
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William E. Easterling

The identity and characteristics of users of existing climate predictions (monthly and seasonal) as inputs to decision making are described. Subscribers to the NOAA Climate Analysis Center's Monthly and Seasonal Weather Outlook (MSWO) are surveyed by questionnaire to identify their industry types, general levels of climate-information use, and geographic locations. Characteristics of subscribers who have indicated that they do use the predictions in decision making, as opposed to those who do not, are determined using stepwise discriminant analysis. It is found that agricultural activities represent the largest group of subscribers, whereas energy producers and distributors represent the largest group of systematic users of the climate predictions. Maps showing the distribution of the three leading categories of respondents (agriculture, energy, and government and education) are presented to show where certain types of subscribers are located and where they most often apply the predictions. The analysis suggests that subscriber/respondents' firm size, level of familiarity with atmospheric science, and judgments of the usefulness of predictions given current accuracies, lead time, and skill in anticipating extreme weather events had the most bearing on whether or not they use the MWSO in decision making. Moreover, the fact that the MSWO has essentially no lead time was three times more important than any of the other parameters in discriminating between users and nonusers.

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Gabriele C. Hegerl, Emily Black, Richard P. Allan, William J. Ingram, Debbie Polson, Kevin E. Trenberth, Robin S. Chadwick, Phillip A. Arkin, Beena Balan Sarojini, Andreas Becker, Aiguo Dai, Paul J. Durack, David Easterling, Hayley J. Fowler, Elizabeth J. Kendon, George J. Huffman, Chunlei Liu, Robert Marsh, Mark New, Timothy J. Osborn, Nikolaos Skliris, Peter A. Stott, Pier-Luigi Vidale, Susan E. Wijffels, Laura J. Wilcox, Kate M. Willett, and Xuebin Zhang

Abstract

Understanding observed changes to the global water cycle is key to predicting future climate changes and their impacts. While many datasets document crucial variables such as precipitation, ocean salinity, runoff, and humidity, most are uncertain for determining long-term changes. In situ networks provide long time series over land, but are sparse in many regions, particularly the tropics. Satellite and reanalysis datasets provide global coverage, but their long-term stability is lacking. However, comparisons of changes among related variables can give insights into the robustness of observed changes. For example, ocean salinity, interpreted with an understanding of ocean processes, can help cross-validate precipitation. Observational evidence for human influences on the water cycle is emerging, but uncertainties resulting from internal variability and observational errors are too large to determine whether the observed and simulated changes are consistent. Improvements to the in situ and satellite observing networks that monitor the changing water cycle are required, yet continued data coverage is threatened by funding reductions. Uncertainty both in the role of anthropogenic aerosols and because of the large climate variability presently limits confidence in attribution of observed changes.

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