Percentile indices for assessing changes in heavy precipitation events

Many climate studies assess trends and projections in heavy precipitation events using precipitation percentile (or quantile) indices. Here we investigate three different percentile indices that are commonly used. We demonstrate that these may produce very different results and thus require great care with interpretation. More specifically, consideration is given to two intensity-based indices and one frequency-based index, namely (a) all-day percentiles, (b) wet-day percentiles, and (c) frequency indices based on the exceedance of a percentile threshold. Wet-day percentiles are conditionally computed for the subset of wet events (with precipitation exceeding some threshold, e.g. 1 mm/d for daily precipitation). We present evidence that this commonly used methodology can lead to artifacts and misleading results if significant changes in the wet-day frequency are not accounted for. Percentile threshold indices measure the frequency of exceedance with respect to a percentile-based threshold. We show that these indices yield an assessment of changes in heavy precipitation events that is qualitatively consistent with all-day percentiles, but there are substantial differences in quantitative terms. We discuss the reasons for these effects, present a theoretical assessment, and provide a series of examples using global and regional climate models to quantify the effects in typical applications. Application to climate model output shows that these considerations are relevant to a wide range of typical climate-change applications. In particular, wet-day percentiles generally yield different results, and in most instances should not be used for the impact-oriented assessment of changes in heavy precipitation events

Climate scenarios for Switzerland CH2018 - approach and implications

To make sound decisions in the face of climate change, government agencies, policymakers and private stakeholders require suitable climate information on local to regional scales. In Switzerland, the development of climate change scenarios is strongly linked to the climate adaptation strategy of the Confederation. The current climate scenarios for Switzerland CH2018 - released in form of six user-oriented products - were the result of an intensive collaboration between academia and administration under the umbrella of the National Centre for Climate Services (NCCS), accounting for user needs and stakeholder dialogues from the beginning. A rigorous scientific concept ensured consistency throughout the various analysis steps of the EURO-CORDEX projections and a common procedure on how to extract robust results and deal with associated uncertainties. The main results show that Switzerland?s climate will face dry summers, heavy precipitation, more hot days and snow-scarce winters. Approximately half of these changes could be alleviated by mid-century through strong global mitigation efforts. A comprehensive communication concept ensured that the results were rolled out and distilled in specific user-oriented communication measures to increase their uptake and to make them actionable. A narrative approach with four fictitious persons was used to communicate the key messages to the general public. Three years after the release, the climate scenarios have proven to be an indispensable information basis for users in climate adaptation and for downstream applications. Potential for extensions and updates has been identified since then and will shape the concept and planning of the next scenario generation in Switzerland

Convection in future winter storms over Northern Europe

International audiencePrecipitation within extratropical cyclones is very likely to increase towards the end of the century in a business-as-usual scenario. We investigate hourly precipitation changes in end-of-century winter storms with the first km-scale model ensemble covering northwest Europe and the Baltic region. This is an ensemble that explicitly represents convection (convection permitting models (CPMs)). Models agree that future winter storms will bring 10%–50% more precipitation, with the same level of light precipitation but more moderate and heavy precipitation, together with less frequent frozen precipitation. The warm sector precipitation rates will get closer (up to similar) to those in present-day autumn storms, along with higher convective available potential energy and convective inhibition, suggesting more convection embedded in storms. To the first order, mean hourly precipitation changes in winter storms are driven by temperature increase (with little relative humidity changes) and storm dynamical intensity (more uncertain), both captured by regional climate models (RCMs). The CPMs agree with this, and in addition, most CPMs show more increase in intense precipitation in the warm sector of storms compared to their parent RCM

The first multi-model ensemble of regional climate simulations at kilometer-scale resolution, part I: Evaluation of precipitation

Here we present the first multi-model ensemble of regional climate simulations at kilometer-scale horizontal grid spacing over a decade long period. A total of 23 simulations run with a horizontal grid spacing of ∼ 3 km, driven by ERA-Interim reanalysis, and performed by 22 European research groups are analysed. Six different regional climate models (RCMs) are represented in the ensemble. The simulations are compared against available high-resolution precipitation observations and coarse resolution (∼ 12 km) RCMs with parameterized convection. The model simulations and observations are compared with respect to mean precipitation, precipitation intensity and frequency, and heavy precipitation on daily and hourly timescales in different seasons. The results show that kilometer-scale models produce a more realistic representation of precipitation than the coarse resolution RCMs. The most significant improvements are found for heavy precipitation and precipitation frequency on both daily and hourly time scales in the summer season. In general, kilometer-scale models tend to produce more intense precipitation and reduced wet-hour frequency compared to coarse resolution models. On average, the multi-model mean shows a reduction of bias from ∼ −40% at 12 km to ∼ −3% at 3 km for heavy hourly precipitation in summer. Furthermore, the uncertainty ranges i.e. the variability between the models for wet hour frequency is reduced by half with the use of kilometer-scale models. Although differences between the model simulations at the kilometer-scale and observations still exist, it is evident that these simulations are superior to the coarse-resolution RCM simulations in the representing precipitation in the present-day climate, and thus offer a promising way forward for investigations of climate and climate change at local to regional scales.Fil: Ban, Nikolina. Universidad de Innsbruck; AustriaFil: Caillaud, Cécile. Université de Toulouse; FranciaFil: Coppola, Erika. The Abdus Salam. International Centre for Theoretical Physics; Italia. The Abdus Salam; ItaliaFil: Pichelli, Emanuela. The Abdus Salam; Italia. The Abdus Salam. International Centre for Theoretical Physics; ItaliaFil: Sobolowski, Stefan. Norwegian Research Centre; NoruegaFil: Adinolfi, Marianna. Fondazione Centro Euro-Mediterraneo sui cambiamenti climatici; ItaliaFil: Ahrens, Bodo. Goethe Universitat Frankfurt; AlemaniaFil: Alias, Antoinette. Université de Toulouse; FranciaFil: Anders, Ivonne. German Climate Computing Center; AlemaniaFil: Bastin, Sophie. Universite Paris-Saclay;Fil: Belušić, Danijel. Swedish Meteorological and Hydrological Institute; SuizaFil: Berthou, Ségolène. Met Office Hadley Centre; Reino UnidoFil: Brisson, Erwan. Université de Toulouse; FranciaFil: Cardoso, Rita M.. Universidade Nova de Lisboa; PortugalFil: Chan, Steven C.. University of Newcastle; Reino UnidoFil: Christensen, Ole Bøssing. Danish Meteorological Institute; DinamarcaFil: Fernández, Jesús. Universidad de Cantabria; EspañaFil: Fita Borrell, Lluís. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Instituto Franco-Argentino sobre Estudios del Clima y sus Impactos; ArgentinaFil: Frisius, Thomas. Helmholtz Gemeinschaft; AlemaniaFil: Gaparac, Goran. Croatia Control Ltd.; CroaciaFil: Giorgi, Filippo. The Abdus Salam. International Centre for Theoretical Physics; Italia. The Abdus Salam; ItaliaFil: Goergen, Klaus. Centre for High-Performance Scientific Computing in Terrestrial Systems; Alemania. Helmholtz Gemeinschaft. Forschungszentrum Jülich; AlemaniaFil: Haugen, Jan Erik. Norwegian Meteorological Institute; NoruegaFil: Hodnebrog, Øivind. Center for International Climate and Environmental Research-Oslo; NoruegaFil: Kartsios, Stergios. Aristotle University Of Thessaloniki; GreciaFil: Katragkou, Eleni. Aristotle University Of Thessaloniki; GreciaFil: Kendon, Elizabeth J.. Met Office Hadley Centre; Reino UnidoFil: Keuler, Klaus. Brandenburg University of Technology Cottbus-Senftenberg; AlemaniaFil: Lavin Gullon, Alvaro. Universidad de Cantabria; EspañaFil: Lenderink, Geert. Royal Netherlands Meteorological Institute; Países Bajo

Proceedings of the 12th International Conference on Kinanthropology

Proceedings of the 12th Conference of Sport and Quality of Life 2019 gatheres submissions of participants of the conference. Every submission is the result of positive evaluation by reviewers from the corresponding field. Conference is divided into sections – Analysis of human movement; Sport training, nutrition and regeneration; Sport and social sciences; Active ageing and sarcopenia; Strength and conditioning training; section for PhD students

Towards advancing scientific knowledge of climate change impacts on short-duration rainfall extremes

A large number of recent studies have aimed at understanding short-duration rainfall extremes, due to their impacts on flash floods, landslides and debris flows and potential for these to worsen with global warming. This has been led in a concerted international effort by the INTENSE Crosscutting Project of the GEWEX (Global Energy and Water Exchanges) Hydroclimatology Panel. Here, we summarize the main findings so far and suggest future directions for research, including: the benefits of convection-permitting climate modelling; towards understanding mechanisms of change; the usefulness of temperature-scaling relations; towards detecting and attributing extreme rainfall change; and the need for international coordination and collaboration. Evidence suggests that the intensity of long-duration (1 day+) heavy precipitation increases with climate warming close to the Clausius–Clapeyron (CC) rate (6–7% K−1), although large-scale circulation changes affect this response regionally. However, rare events can scale at higher rates, and localized heavy short-duration (hourly and sub-hourly) intensities can respond more strongly (e.g. 2 × CC instead of CC). Day-to-day scaling of short-duration intensities supports a higher scaling, with mechanisms proposed for this related to local-scale dynamics of convective storms, but its relevance to climate change is not clear. Uncertainty in changes to precipitation extremes remains and is influenced by many factors, including large-scale circulation, convective storm dynamics andstratification. Despite this, recent research has increased confidence in both the detectability and understanding of changes in various aspects of intense short-duration rainfall. To make further progress, the international coordination of datasets, model experiments and evaluations will be required, with consistent and standardized comparison methods and metrics, and recommendations are made for these frameworks

Spatio-Temporal Downscaling of Climate Data Using Convolutional and Error-Predicting Neural Networks

Numerical weather and climate simulations nowadays produce terabytes of data, and the data volume continues to increase rapidly since an increase in resolution greatly benefits the simulation of weather and climate. In practice, however, data is often available at lower resolution only, for which there are many practical reasons, such as data coarsening to meet memory constraints, limited computational resources, favoring multiple low-resolution ensemble simulations over few high-resolution simulations, as well as limits of sensing instruments in observations. In order to enable a more insightful analysis, we investigate the capabilities of neural networks to reconstruct high-resolution data from given low-resolution simulations. For this, we phrase the data reconstruction as a super-resolution problem from multiple data sources, tailored toward meteorological and climatological data. We therefore investigate supervised machine learning using multiple deep convolutional neural network architectures to test the limits of data reconstruction for various spatial and temporal resolutions, low-frequent and high-frequent input data, and the generalization to numerical and observed data. Once such downscaling networks are trained, they serve two purposes: First, legacy low-resolution simulations can be downscaled to reconstruct high-resolution detail. Second, past observations that have been taken at lower resolutions can be increased to higher resolutions, opening new analysis possibilities. For the downscaling of high-frequent fields like precipitation, we show that error-predicting networks are far less suitable than deconvolutional neural networks due to the poor learning performance. We demonstrate that deep convolutional downscaling has the potential to become a building block of modern weather and climate analysis in both research and operational forecasting, and show that the ideal choice of the network architecture depends on the type of data to predict, i.e., there is no single best architecture for all variables.ISSN:2624-955