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Building trust in climate science: data products for the 21st century

Authors


  • This article is published in Environmetrics as a special issue on Advances in Statistical Methods for Climate Analysis, edited by Peter Guttorp, University of Washington, Norwegian Computing Center, Stephan R. Sain, National Center for Atmospheric Research, Christopher K. Wikle, University of Missouri.

Richard E. Chandler, Department of Statistical Science, University College London, Gower Street, London WC1E 6BT, U.K. E-mail: richard@stats.ucl.ac.uk

Abstract

Climate science has a key role to play in informing strategies for adapting to and mitigating the effects of climate change. However, given the magnitude of the issues involved and their implications, it is imperative that the scientific process is–and is seen to be—rigorous, defensible, and transparent so as to ensure trust in the results. A key element in building such trust is to provide access to underlying data, so that interested parties can check published results and compare with their own analyses. A further priority is to provide data at the fine space and time scales that are relevant for user needs. Until recently, the ability to meet these requirements has been constrained by data-sharing agreements and limitations on digital storage and processing. This is now changing however, thanks to improved global collaboration, communication and computing capability. This article describes current efforts to exploit these opportunities via the International Surface Temperature Initiative, an international and multidisciplinary effort that aims: firstly, to create a single comprehensive global databank of surface meteorological observations at monthly, daily, and sub-daily resolutions; and secondly, to encourage the contribution of multiple independent data products, subject to common performance assessment and benchmarking criteria, thus providing the opportunity for a detailed assessment of uncertainties. The rationale for the initiative is discussed, along with logistical and technical challenges, as well as opportunities for involvement from the statistical and wider scientific and user communities. Copyright © 2012 John Wiley & Sons, Ltd.

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