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Volume 17, issue 7
Nat. Hazards Earth Syst. Sci., 17, 993–1001, 2017
https://doi.org/10.5194/nhess-17-993-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Risk and uncertainty estimation in natural hazards

Nat. Hazards Earth Syst. Sci., 17, 993–1001, 2017
https://doi.org/10.5194/nhess-17-993-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 03 Jul 2017

Research article | 03 Jul 2017

Simple and approximate estimations of future precipitation return values

Rasmus E. Benestad et al.

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Cited articles

Arkin, P. A., Joyce, R., and Janowiak, J. E.: The estimation of global monthly mean rainfall using infrared satellite data: The GOES precipitation index (GPI), Remote Sensing Reviews, 11, 107–124, https://doi.org/10.1080/02757259409532261, 1994.
Benestad, R.: Novel Methods for Inferring Future Changes in Extreme Rainfall over Northern Europe, Clim. Res., 34, 195–210, 2007.
Benestad, R. E.: Association between trends in daily rainfall percentiles and the global mean temperature, J. Geophys. Res.-Atmos., 118, 10802–10810, https://doi.org/10.1002/jgrd.50814, 2013.
Benestad, R. E.: A Mental Picture of the Greenhouse Effect: A Pedagogic Explanation, Theor. Appl. Climatol., 128, 679–688, https://doi.org/10.1007/s00704-016-1732-y, 2016.
Benestad, R. E.: Simple and approximate estimation of future precipitation return-values, https://doi.org/10.6084/m9.figshare.5047789.v1, 2017.
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We propose a strategy for quantifying the maximum effect a temperature change has on heavy precipitation amounts, making use of the limited available sources of information: laws of physics, seasonal variations, mathematical estimation of probability, and s large number of climate model results. An upper bound is estimated rather than the most likely value.
We propose a strategy for quantifying the maximum effect a temperature change has on heavy...
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