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Call for Papers: "Progress in Developing Uncertainty Estimates for Gridded Climate Data" at CMOS/CGU (Canada)

Last modified February 08, 2016 11:36

In order to address an often overlooked aspect of climate analysis, climatology development and process modelling, we are convening a session to address methods for developing and delivering uncertainty estimates in gridded climate data.

Dear Colleagues, In order to address an often overlooked aspect of climate analysis, climatology development and process modelling, we are convening a session to address methods for developing and delivering uncertainty estimates in gridded climate data. We would like to explore the full range of spatial and temporal scales ranging from low-resolution, global to high resolution, landscape, and at temporal scales from long-term means to hourly or shorter. Please consider submitting an abstract to this session at the upcoming CMOS/CGU Joint Congress in Fredericton, New Brunswick to be held on May 29 - June 2, 2016. We hope to develop a more common basis for understanding such uncertainty and the need to disseminate uncertainty estimates to improve the downstream utility of gridded data. Session details are given below.

Sincerely,

Faron Anslow

Session Title: Progress in Developing Uncertainty Estimates for Gridded Climate Data

High spatial resolution gridded climate and weather products such as PRISM, DayMet, ANUSPLIN, TopoWx as well as those developed as needed within specific research contexts are widely applied in the natural sciences. They are used for development of downscaling products, forcing for process models, or simply for directly informing engineers, resource managers and other practitioners about climate in locations distant from observing stations. Lower resolution gridded climate data such as HadCRUT4 and the 20th Century Reanalysis are also critical for assessment of regional and global climate change and variability. These data sets are subject to considerable uncertainty in regions with complex topography and sparse observational networks. Uncertainty is compounded for precipitation, which has very high and typically complicated spatial variability especially at shorter timescales such as daily and monthly totals. Among the available datasets, some attempts have been made to quantify uncertainty to accompany the gridded data, but many products are still provided without such estimates. This session aims to further explore the development of supporting data to better enable users to propagate error in their application. We seek submissions ranging from the theoretical backing of spatial uncertainty, to describing methods for developing uncertainty estimates, to progress in refining existing estimates. Presentations discussing the analyses of uncertainty in lower resolution global observational or model ensemble data products are also desired. This session will help inform the attendees of the state of uncertainty analysis in gridded climate products and potentially help to establish more standard practice in their delivery. Any such developments will help end-users make better informed choices when deciding among products for application in a region of interest.

-- Pacific Climate Impacts Consortium University House 1 PO Box 1700 Stn CSC University of Victoria Victoria, British Columbia Canada V8W 2Y2 W: 250 472 4476 M: 250 885 7994 H: 778 352 0022
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