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From inferential statistics to climate knowledge


Maia, AHN and Meinke, HB, From inferential statistics to climate knowledge, Advances in Geosciences, 6, (2006) pp. 211-216. ISSN 1680-7340 (2006) [Refereed Article]


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Copernicus Publications 2006

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DOI: doi:10.5194/adgeo-6-211-2006


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, runoff) 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 Details

Item Type:Refereed Article
Research Division:Mathematical Sciences
Research Group:Statistics
Research Field:Applied statistics
Objective Division:Environmental Policy, Climate Change and Natural Hazards
Objective Group:Understanding climate change
Objective Field:Climate variability (excl. social impacts)
UTAS Author:Meinke, HB (Professor Holger Meinke)
ID Code:71504
Year Published:2006
Deposited By:Research Division
Deposited On:2011-07-20
Last Modified:2012-03-06
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