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From inferential statistics to climate knowledge

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journal contribution
posted on 2023-05-17, 06:56 authored by Maia, AHN, Holger MeinkeHolger Meinke
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.

History

Publication title

Advances in Geosciences

Volume

6

Issue

2006

Pagination

211-216

ISSN

1680-7340

Department/School

Tasmanian Institute of Agriculture (TIA)

Publisher

Copernicus GmbH

Place of publication

Goettingen, Germany

Rights statement

Copernicus Publications 2006

Repository Status

  • Open

Socio-economic Objectives

Climate variability (excl. social impacts)

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