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Extreme precipitation is highly variable in space and time. It is therefore important to characterize precipitation intensity distributions at several temporal and spatial scales. This is a key issue in infrastructure design and risk analysis, for which Intensity-Duration-Frequency (IDF) curves are the standard tools used for describing the relationships among extreme rainfall intensities, their frequencies, and their durations. Simple Scaling (SS) models, characterizing the relationships among extreme probability distributions at several durations, represent a powerful means for improving IDF estimates. This study tested SS models for approximately 2700 stations in North America. Annual Maxima Series (AMS) over various duration intervals from 15 h to 7 days were considered. The range of validity, magnitude, and spatial variability of the estimated scaling exponents were investigated. Results provide additional guidance for the influence of both local geographical characteristics, such as topography, and regional climatic features on precipitation scaling. Generalized Extreme Value (GEV) distributions based on SS models were also examined. Results demonstrate an improvement of GEV parameter estimates, especially for the shape parameter, when data from different durations were pooled under the SS hypothesis. [ABSTRACT FROM AUTHOR]

Extratropical cyclones (ETCs) play a crucial role in determining the climate in a large part of the world. For this reason, climate models' ability to reproduce ETCs' characteristics (such as track location and development and decay regions) is of great importance. ETC tracking algorithms are sophisticated diagnostic tools that have been used extensively to evaluate models' ability to simulate ETCs. In general, these algorithms have been developed and applied to coarse resolution global datasets such as global climate models and reanalyses. As regional climate models (RCMs) mature, the potential for tracking ETCs at considerably higher resolutions has naturally become attractive. Application of ETC tracking algorithms to RCM-produced datasets, however, introduces additional challenges. In order to investigate these challenges, we compare ETC tracking results of a simulation produced by the Canadian Regional Climate Model (CRCM) over North America at a 45-km resolution driven by ECMWF ERA-Interim, with the results obtained directly from the driving reanalyses. The ERA-Interim data is treated in three different ways applying spatial smoothing, the tracking algorithm, and the extraction of the regional grid in a different order, so that individual sources of error can be identified. It is shown that the mere existence of boundaries in the regional domain of the CRCM affects tracking results not only near the boundaries but also well within the domain. To a lesser extent, the use of different spatial smoothing techniques also affects the number of tracked ETCs. However, even after accounting for these artifacts, the CRCM produces a lower ETC count than the driving dataset. This underestimation of the tracking statistics might be related to known CRCM biases, or to some problems with the surface representation at 45-km resolution. Nevertheless, it is concluded that RCMs are a suitable choice for ETC trajectory studies, but only when handled carefully and used over large domains targeting the cyclone track of interest. [ABSTRACT FROM AUTHOR]

Anthropogenic greenhouse gas emissions that induce changes in the Earth's climate affect particular variables and locations differently. A key part of this difference is the timescale at which this change takes place, which will eventually have important consequences for adaptation requirements. This idea of timescale associated with climate change has been used several times in the past to estimate the urgency of adaptation in particular regions. The definition of climate-change timescale is, however, not unique. For example, we can think of it in terms of an expected trend (e.g. in temperature) reaching a given threshold, or think of it in terms of the time it may take this trend to become statistically significant. We may also wonder about the validity of this speculation given that, due to natural variability, the expected trend may in fact not be realized. In this article we explore alternative ways of defining the timescale of climate-change, compare their properties, and illustrate them with an example for the case of projected surface temperature over North America. It is shown that these timescales are analytically related but may differ substantially in magnitude under certain conditions. In particular, it is shown that climate change impact on vulnerable systems may arrive before statistical detection of the variable's trend takes place. This fact may have implications on how climate change impacts are seen by those with diverging interests. [ABSTRACT FROM AUTHOR]

This work is a first step in the analysis of uncertainty sources in the RCM-simulated climate over North America. Three main sets of sensitivity studies were carried out: the first estimates the magnitude of internal variability, which is needed to evaluate the significance of changes in the simulated climate induced by any model modification. The second is devoted to the role of CRCM configuration as a source of uncertainty, in particular the sensitivity to nesting technique, domain size, and driving reanalysis. The third study aims to assess the relative importance of the previously estimated sensitivities by performing two additional sensitivity experiments: one, in which the reanalysis driving data is replaced by data generated by the second generation Coupled Global Climate Model (CGCM2), and another, in which a different CRCM version is used. Results show that the internal variability, triggered by differences in initial conditions, is much smaller than the sensitivity to any other source. Results also show that levels of uncertainty originating from liberty of choices in the definition of configuration parameters are comparable among themselves and are smaller than those due to the choice of CGCM or CRCM version used. These results suggest that uncertainty originated by the CRCM configuration latitude (freedom of choice among domain sizes, nesting techniques and reanalysis dataset), although important, does not seem to be a major obstacle to climate downscaling. Finally, with the aim of evaluating the combined effect of the different uncertainties, the ensemble spread is estimated for a subset of the analysed simulations. Results show that downscaled surface temperature is in general more uncertain in the northern regions, while precipitation is more uncertain in the central and eastern US. [ABSTRACT FROM AUTHOR]

The recent accumulation of extreme hydrological events over Eastern North America and Central Europe has stimulated scientific and also societal interest. In addition to the challenges of an improved prediction of such situations and the implications for the associated risk management, there is as yet no confirmed knowledge whether and how climate change contributes to the magnitude and frequency of hydrological extreme events and how regional water management could adapt to the corresponding risks. The ClimEx project focuses on the effects of climate change on hydro-meteorological extreme events and their implications for water management in Bavaria and Québec. Therefore, a hydro-meteorological model chain is applied. It employs HPC capacity of LRZ's SuperMUC to dynamically downscale 50 members of the Global Circulation Model CanESM2 over European and Eastern North American domains using the Canadian Regional Climate Model (RCM) CRCM5 (0.11° resolution - 1951-2100 - 15.000 model years - 7.500 years in each domain). The unique single model ensemble is conjointly analyzed and compared with the latest information provided through the CORDEX-initiative, to better assess the influence of natural climate variability and climatic change on the dynamics of extreme events.Hydrological assessment is performed using the process-based and spatially explicit hydrological model WaSiM in high temporal (3h) and spatial (500m) resolution. The simulations form the basis for in depth analysis of hydrological extreme events based on the inputs from the large climate model dataset. The specific data situation enables to establish a new method for 'virtual perfect prediction', which assesses climate change impacts on flood risk and water resources management by identifying patterns in the data which reveal preferential triggers of hydrological extreme events.The presentation highlights results from the analysis of the large scale ClimEx model ensemble over Eastern North America and Europe, showing the current and future ratio of natural variability and climate change impacts on meteorological extreme events. Hydrological application of this data set is exemplified for catchments in Bavaria, Germany, where hydrological modeling illustrates the capacity to better determine the recurrence periods of hydrological extreme events (e.g. HF100) under conditions of climate change (based on 1.500 model years for the reference period 50x 1981-2010 and 50x three scenario periods 2011-2040, 2041-2070 and 2071-2100, respectively). The regionally specific and non-linear impact of natural climate variability and climate change on extreme stream flows for the Bavarian catchments is discussed.AcknowledgementFunding of the ClimEx project by the Bavarian State Ministry for the Environment and Consumer Protection is gratefully acknowledged. [ABSTRACT FROM AUTHOR]