Simulation of 6-hourly rainfall and temperature by two resampling schemes

599

377

Abstract. Despite considerable progress in recent years, output of both global and regional circulation models is still afflicted with biases to a degree that precludes its direct use, especially in climate change impact studies. This is well known, and to overcome this problem, bias correction (BC; i.e. the correction of model output towards observations in a post-processing step) has now become a standard procedure in climate change impact studies. In this paper we argue that BC is currently often used in an invalid way: it is added to the GCM/RCM model chain without sufficient proof that the consistency of the latter (i.e. the agreement between model dynamics/model output and our judgement) as well as the generality of its applicability increases. BC methods often impair the advantages of circulation models by altering spatiotemporal field consistency, relations among variables and by violating conservation principles. Currently used BC methods largely neglect feedback mechanisms, and it is unclear whether they are time-invariant under climate change conditions. Applying BC increases agreement of climate model output with observations in hindcasts and hence narrows the uncertainty range of simulations and predictions without, however, providing a satisfactory physical justification. This is in most cases not transparent to the end user. We argue that this hides rather than reduces uncertainty, which may lead to avoidable forejudging of end users and decision makers.We present here a brief overview of state-of-the-art bias correction methods, discuss the related assumptions and implications, draw conclusions on the validity of bias correction and propose ways to cope with biased output of circulation models in the short term and how to reduce the bias in the long term. The most promising strategy for improved future global and regional circulation model simulations is the increase in model resolution to the convection-permitting scale in combination with ensemble predictions based on sophisticated approaches for ensemble perturbation.With this article, we advocate communicating the entire uncertainty range associated with climate change predictions openly and hope to stimulate a lively discussion on bias correction among the atmospheric and hydrological community and end users of climate change impact studies.

Section: U Ehret Et Al: Should We Apply Bias Correction To Global Amentioning

confidence: 99%

HESS Opinions "Should we apply bias correction to global and regional climate model data?"

Ehret

Zehe

Wulfmeyer

et al. 2012

599

377

“…Within empirical downscaling we distinguish methods which directly link the predictors to the surface variables in a basin of interest (e.g. Wilby et al, 1999), resampling methods (Wójcik and Buishand, 2003) or methods based on weather types. "Expanded Downscaling" (EDS) proposed by Bürger (2002) is a good example of a direct method.…”

Section: Introductionmentioning

confidence: 99%

Modelling of monsoon rainfall for a mesoscale catchment in North-West India I: assessment of objective circulation patterns

Zehe

Singh

Bàrdossy

2006

Abstract.Within the present study we shed light on the question whether objective circulation patterns (CP) classified from either the 500 HPa or the 700 HPa level may serve as predictors to explain the spatio-temporal variability of monsoon rainfall in the Anas catchment in North West India. To this end we employ a fuzzy ruled based classification approach in combination with a novel objective function as originally proposed by (Stehlik and Brdossy, 2002). After the optimisation we compare the obtained circulation classification schemes for the two pressure levels with respect to their conditional rainfall probabilities and amounts. The classification scheme for the 500 HPa level turns out to be much more suitable to separate dry from wet meteorological conditions during the monsoon season. As is shown during a bootstrap test, the CP conditional rainfall probabilities for the wet and the dry CPs for both pressure levels are highly significant at levels ranging from 95 to 99%. Furthermore, the monthly CP frequencies of the wettest CPs show a significant positive correlation with the variation of the total number of rainy days at the monthly scale. Consistently, the monthly frequencies of the dry CPs exhibit a negative correlation with the number of rainy days at the monthly scale. The present results give clear evidence that the circulation patterns from the 500 HPa level are suitable predictors for explaining spatio-temporal Monsoon variability. A companion paper shows that the CP time series obtained within this study are suitable input into a stochastical rainfall model.

“…A second version, GWEX_Disag, applies the same model, but at a 3-day scale. The 3-day simulated amounts are then disaggregated using an adaptation of the method of fragments (Wójcik and Buishand, 2003).…”

Section: Discussionmentioning

confidence: 99%

“…With GWEX_Disag, daily scenarios are first generated at a 3-day scale and then disaggregated at a daily scale using a method of fragments (e.g., Wójcik and Buishand, 2003). Simulated 3-day amounts are disaggregated using the temporal structures of the closest observed 3-day amounts, in terms of similarity of the spatial fields.…”

Section: Gwex_disagmentioning

confidence: 99%

Stochastic generation of multi-site daily precipitation focusing on extreme events

Evin

Favre

Hingray

2018

Abstract. Many multi-site stochastic models have been proposed for the generation of daily precipitation, but they generally focus on the reproduction of low to high precipitation amounts at the stations concerned. This paper proposes significant extensions to the multi-site daily precipitation model introduced by Wilks, with the aim of reproducing the statistical features of extremely rare events (in terms of frequency and magnitude) at different temporal and spatial scales. In particular, the first extended version integrates heavy-tailed distributions, spatial tail dependence, and temporal dependence in order to obtain a robust and appropriate representation of the most extreme precipitation fields. A second version enhances the first version using a disaggregation method. The performance of these models is compared at different temporal and spatial scales on a large region covering approximately half of Switzerland. While daily extremes are adequately reproduced at the stations by all models, including the benchmark Wilks version, extreme precipitation amounts at larger temporal scales (e.g., 3-day amounts) are clearly underestimated when temporal dependence is ignored.