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From inferential statistics to climate knowledge

Maia, A. de H.N. and Meinke, H. (2006) From inferential statistics to climate knowledge. Advances in Geosciences, 6 . pp. 211-216.

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Publisher URL: http://publications.copernicus.org
Organisation URL: http://www.egu.eu/home.html

Abstract

Climate variability and change are risk factors for climate sensitive activities such as agriculture. Managing these risks requires "climate knowledge", i.e. a sound understanding of causes and consequences of climate variability and knowledge of potential management options that are suitable in light of the climatic risks posed. Often such information about prognostic variables (e.g. yield, rainfall, run-off) is provided in probabilistic terms (e.g. via cumulative distribution functions, CDF), whereby the quantitative assessments of these alternative management options is based on such CDFs. Sound statistical approaches are needed in order to assess whether difference between such CDFs are intrinsic features of systems dynamics or chance events (i.e. quantifying evidences against an appropriate null hypothesis). Statistical procedures that rely on such a hypothesis testing framework are referred to as "inferential statistics" in contrast to descriptive statistics (e.g. mean, median, variance of population samples, skill scores). Here we report on the extension of some of the existing inferential techniques that provides more relevant and adequate information for decision making under uncertainty.

Item Type:Article
Corporate Creators:Emerging Technologies
Additional Information:© Author(s) 2006. This work is licensed under a Creative Commons License.
Keywords:Climate variability; climate change; climate knowledge; inferential statistics.
Subjects:Science > Statistics
Agriculture > Agriculture (General) > Agricultural meteorology. Crops and climate
Deposited On:16 Feb 2009 02:08
Last Modified:17 Nov 2010 00:40

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