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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/nhess-2020-352
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2020-352
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  04 Nov 2020

04 Nov 2020

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This preprint is currently under review for the journal NHESS.

Improving snowfall representation in climate simulations via statistical models informed by air temperature and total precipitation

Flavio Maria Emanuele Pons1 and Davide Faranda1,2 Flavio Maria Emanuele Pons and Davide Faranda
  • 1LSCE-IPSL, CEA Saclay l’Orme des Merisiers, CNRS UMR 8212 CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
  • 2London Mathematical Laboratory, 14 Buckingham Street, London, WC2N 6DF, UK

Abstract. The description and analysis of compound extremes affecting mid and high latitudes in the winter requires an accurate estimation of snowfall. Such variable is often missing for in-situ observations, and biased in climate model outputs, both in magnitude and number of events. While climate models can be adjusted using bias correction (BC), snowfall presents additional challenges compared to other variables, preventing from applying traditional univariate BC methods. We extend the existing literature on the estimation of the snowfall fraction from near-surface temperature, which usually involves binary thresholds or fitting parametric nonlinear functions. We show that, combining breakpoint search algorithms to define threshold temperatures and segmented regression models, it is possible to obtain accurate out-of-sample estimates of snowfall over Europe in ERA5 reanalysis, and to perform effective BC on the IPSL-WRF high resolution EURO-CORDEX climate model only relying on bias adjusted temperature and precipitation. This method offers a feasible way to reconstruct or adjust snowfall observations without requiring multivariate or conditional bias correction and stochastic generation of unobserved events.

Flavio Maria Emanuele Pons and Davide Faranda

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Flavio Maria Emanuele Pons and Davide Faranda

Flavio Maria Emanuele Pons and Davide Faranda

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Latest update: 02 Dec 2020
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Short summary
The objective motivating this study is the assessment of the impacts of winter climate extremes, which requires accurate simulation of snowfall. However, climate simulation models contain physical approximations, which result in biases that must be corrected using past data as a reference. We show how to exploit simulated temperature and precipitation to estimate snowfall from already bias-corrected variables, without requiring the elaboration of complex, multivariate bias adjustment techniques.
The objective motivating this study is the assessment of the impacts of winter climate extremes,...
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