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Preprints
https://doi.org/10.5194/nhess-2020-352
https://doi.org/10.5194/nhess-2020-352
04 Nov 2020
 | 04 Nov 2020
Status: this preprint has been withdrawn by the authors.

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

Flavio Maria Emanuele Pons and Davide Faranda

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.

This preprint has been withdrawn.

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This preprint has been withdrawn.

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The objective motivating this study is the assessment of the impacts of winter climate extremes,...
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