Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes

Kelder, Timo; Müller, Malte; Slater, Louise; Marjoribanks, Timothy I.; Wilby, Robert L.; Prudhomme, Christel; Bohlinger, Patrik; Ferranti, Laura; Nipen, Thomas N.

doi:10.1038/s41612-020-00149-4

Cited by 63 publications

(117 citation statements)

References 78 publications

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“…Furthermore, convection‐permitting models are sensitive to boundary conditions from ESMs (Keller et al., 2018; Shepherd, 2014); thus understanding the mechanisms behind observed changes requires sensitivity analyses, which are still computationally unfeasible. Strategies to exploit the available model runs are thus needed to reach even a minimal level of accuracy for, say, 100‐year events (e.g., Kelder et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Toward Narrowing Uncertainty in Future Projections of Local Extreme Precipitation

Marra

Armon

Adam

et al. 2021

Geophysical Research Letters

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Projections of extreme precipitation based on modern climate models suffer from large uncertainties. Specifically, unresolved physics and natural variability limit the ability of climate models to provide actionable information on impacts and risks at the regional, watershed and city scales relevant for practical applications. Here, we show that the interaction of precipitating systems with local features can constrain the statistical description of extreme precipitation. These observational constraints can be used to project local extremes of low yearly exceedance probability (e.g., 100‐year events) using synoptic‐scale information from climate models, which is generally represented more accurately than the local scales, and without requiring climate models to explicitly resolve extremes. The novel approach, demonstrated here over the south‐eastern Mediterranean, offers a path for improving the predictability of local statistics of extremes in a changing climate, independent of pending improvements in climate models at regional and local scales.

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Section: Introductionmentioning

confidence: 99%

Toward Narrowing Uncertainty in Future Projections of Local Extreme Precipitation

Marra

Armon

Adam

et al. 2021

Geophysical Research Letters

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“…precipitation) such as 100-year events from short (e.g. 30 year) climate model records (Kelder et al, 2020). The UNSEEN trends approach has the potential to detect nonstationarities in a range of climate extremes and can be applied to centurylong seasonal hindcasts (e.g.…”

Section: Pooled Methods For Detecting Changes In Extremesmentioning

confidence: 99%

“…Pooled approaches -both using model ensemble members (e.g. Thompson et al, 2017;Kelder et al, 2020;Massey et al, 2015) and spatial pooling of observations (e.g. Prosdocimi et al, 2019;Blum et al, 2020) -hold considerable promise for enhanced detection and attribution.…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management

Slater

Anderson

Buechel

et al. 2021

Hydrol. Earth Syst. Sci.

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145

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Abstract. Hydroclimatic extremes such as intense rainfall, floods, droughts, heatwaves, and wind or storms have devastating effects each year. One of the key challenges for society is understanding how these extremes are evolving and likely to unfold beyond their historical distributions under the influence of multiple drivers such as changes in climate, land cover, and other human factors. Methods for analysing hydroclimatic extremes have advanced considerably in recent decades. Here we provide a review of the drivers, metrics, and methods for the detection, attribution, management, and projection of nonstationary hydroclimatic extremes. We discuss issues and uncertainty associated with these approaches (e.g. arising from insufficient record length, spurious nonstationarities, or incomplete representation of nonstationary sources in modelling frameworks), examine empirical and simulation-based frameworks for analysis of nonstationary extremes, and identify gaps for future research.

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“…S5 and S6). Furthermore, such a comparison makes it clear that the compared setups are climate model setups and not weather model setups, despite the high spatial resolution (Kelder et al, 2020). While the simulation of individual extremes can differ greatly, the 10-year return levels as a climatic indicator for extreme precipitation show a high degree of agreement (see Fig.…”

Section: Uncertainties Of the Rcm Datasetsmentioning

confidence: 99%

“…Alexandru et al (2007) have shown that a 20-member RCM ensemble of the CRCM driven by the same lateral boundary conditions with slightly perturbed starting conditions leads to a reasonable spread of simulated precipitation. Even seasonal weather model forecast simulations, which are initialized every month, still show variability, especially for precipitation extremes (Kelder et al, 2020;Thompson et al, 2017). Hence, internal variability cannot be excluded as uncertainty source.…”

Section: Uncertainties Of the Rcm Datasetsmentioning

confidence: 99%

Using high-resolution regional climate models to estimate return levels of daily extreme precipitation over Bavaria

Poschlod

2021

Preprint

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Abstract. Extreme daily rainfall is an important trigger for floods in Bavaria. The dimensioning of water management structures as well as building codes are based on observational rainfall return levels. In this study, three high-resolution regional climate models (RCMs) are employed to produce 10-year daily rainfall return levels and their performance is evaluated by comparison to observational return levels. The study area is governed by different types of precipitation (stratiform, orographic, convectional) and a complex terrain, with convective precipitation also contributing to daily rainfall levels. The Canadian Regional Climate Model version 5 (CRCM5) at 12 km spatial resolution and the Weather and Forecasting Research model (WRF) at 5 km resolution both driven by ERA-Interim reanalysis data use parametrization schemes to simulate convection. The WRF at 1.5 km resolution driven by ERA5 reanalysis data explicitly resolves convectional processes. Applying the Generalized Extreme Value (GEV) distribution, all three model setups can reproduce the observational return levels with an areal average bias of +6.6 % or less and a spatial Spearman rank correlation of ρ > 0.72. The increase of spatial resolution between the 12 km CRCM5 and the 5 km WRF setup is found to improve the performance in terms of bias (+6.6 % and +3.2 %) and spatial correlation (ρ = 0.72 and ρ = 0.82). However, the finer topographic details of the WRF-ERA5 return levels cannot be evaluated with the observation data because their spatial resolution is too low. Hence, this comparison shows no great further improvement (bias = +1.1 %, ρ = 0.82) of the overall performance compared to the 5 km resolution setup. Uncertainties due to extreme value theory are explored by employing three different approaches for the highest-resolution WRF-ERA5 setup. The GEV distribution with fixed shape parameter (bias = +0.9 %, ρ = 0.79) and the Generalized Pareto (GP: bias = +1.3 %, ρ = 0.81) show almost equivalent results for the 10-year return period, whereas the Metastatistical Extreme Value (MEV) distribution leads to a slight underestimation (bias = -6.2 %, ρ = 0.86). From these results, it follows that high-resolution regional climate models are suitable for generating spatially homogeneous rainfall return level products. In regions with a sparse rain gauge density or low spatial representativeness of the stations due to complex topography, RCMs can support the observational data. Further, RCMs driven by global climate models with emission scenarios can project climate change-induced alterations in rainfall return levels at regional to local scales. This would allow adjustment of structural design and, therefore, adaption to future precipitation conditions.

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Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes

Cited by 63 publications

References 78 publications

Toward Narrowing Uncertainty in Future Projections of Local Extreme Precipitation

Toward Narrowing Uncertainty in Future Projections of Local Extreme Precipitation

Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management

Using high-resolution regional climate models to estimate return levels of daily extreme precipitation over Bavaria

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