Uncertainties in climate model ensembles are still relatively large. Besides scenario and model response uncertainty, natural variability is another important source of uncertainty. To study regional natural variability on timescales of several decades and more, observations are often too sparse and short. Regional Climate Models (RCMs) can be used to overcome this lack of useful data at high spatial resolutions. In this study, we compare a new 50-member single RCM large ensemble (CRCM5-LE) with an ensemble of 22 EURO-CORDEX models for seasonal temperature and precipitation at 0.11° grid size over Europe-all driven by the RCP 8.5 scenario. This setup allows us to quantify the contribution of natural/modelinternal variability on the total uncertainty of a multi-model ensemble. The variability of climate change signals within the two ensembles is compared for three future periods (2020-2049, 2040-069 and 2070-2099). Results show that the single model spread is usually smaller than the multi-model spread for temperature. Similar variabilities can mostly be found in the near future (and to a lesser extent in the mid future) during winter and spring, especially for northern and central parts of Europe. The contribution of internal variability is generally higher for precipitation. In the near future almost all seasons and regions show similar variabilities. In the mid and far future only fall, summer and spring still show similar variabilites. There is a significant decrease of the contribution of internal variability over time for both variables. However, even in the far future for most regions and seasons 25-75% of the overall variability can be explained by internal variability.

Abstract. For sectors like agriculture, hydrology and ecology, increasing interannual variability (IAV) can have larger impacts than changes in the mean state, whereas decreasing IAV in winter implies that the coldest seasons warm more than the mean. IAV is difficult to reliably quantify in single realizations of climate (observations and single-model realizations) as they are too short, and represent a combination of external forcing and IAV. Single-model initial-condition large ensembles (SMILEs) are powerful tools to overcome this problem, as they provide many realizations of past and future climate and thus a larger sample size to robustly evaluate and quantify changes in IAV. We use three SMILE-based regional climate models (CanESM-CRCM, ECEARTH-RACMO and CESM-CCLM) to investigate downscaled changes in IAV of summer and winter temperature and precipitation, the number of heat waves, and the maximum length of dry periods over Europe. An evaluation against the observational data set E-OBS reveals that all models reproduce observational IAV reasonably well, although both under- and overestimation of observational IAV occur in all models in a few cases. We further demonstrate that SMILEs are essential to robustly quantify changes in IAV since some individual realizations show significant IAV changes, whereas others do not. Thus, a large sample size, i.e., information from all members of SMILEs, is needed to robustly quantify the significance of IAV changes. Projected IAV changes in temperature over Europe are in line with existing literature: increasing variability in summer and stable to decreasing variability in winter. Here, we further show that summer and winter precipitation, as well as the two summer extreme indicators mostly also show these seasonal changes.

This study introduces a holistic approach for the hydrological modelling of peak flows for the major Bavarian river basins, referred to as Hydrological Bavaria. This approach, intended to develop a robust modelling framework to support water resources management under climate change conditions, comprises a regionalized parameterization of the water balance simulation model (WaSiM) for 98 catchments in high temporal (3 h) and spatial (500 m) resolution using spatially coherent information and an automatized calibration (dynamically dimensioned search–simulated annealing, DDS-SA) for storage components. The performance of the model was examined using common metrics (Nash & Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE)). The simulations provided the means for the calculation of a level of trust (LOT) by comparing observed and simulated high flows with a five, ten, and 20-year return period. These estimates were derived by the Generalized Pareto Distribution (GPD) applying the peak over threshold (POT) sampling method. Results show that the model overall performs well with regard to the selected objective measures, but also exhibits regional disparities mainly due to the availability of meteorological inputs or water management data. For most catchments, the LOT shows moderate to high confidence in the estimation of return periods with the hydrological model. Therefore, we consider the holistic modelling approach applicable for climate change impact studies concerned with dynamic alterations in peak flows.

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