Advances in Geosciences www.adv-geosci.net 1680-7340 1680-7359 6 1st Alexander von Humboldt International Conference 2006 10.5194/adgeo-6-211-2006 http://www.adv-geosci.net/6/211/2006/ http://www.adv-geosci.net/6/211/2006/adgeo-6-211-2006.html http://www.adv-geosci.net/6/211/2006/adgeo-6-211-2006.pdf 211 216 2006-02-14 From inferential statistics to climate knowledge A. de H. N. Maia H. Meinke Embrapa Meio Ambiente, PO Box 69, Jaguariúna, SP, Brazil Queensland Department of Primary Industries and Fisheries, Emerging Technologies, PO Box 102, Toowoomba, Qld 4350, Australia 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.