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Spatio-temporal precipitation is modelled for urban application at 1 h temporal resolution on a 2 km grid using a Spatio-Temporal Neyman--Scott Rectangular Pulses weather generator (WG). Precipitation time series for fitting the model are obtained from a network of 60 tipping-bucket rain gauges irregularly placed in a 40 by 60 km model domain. The model simulates precipitation time series that are comparable to the observations with respect to extreme precipitation statistics. The WG is used for downscaling climate change signals from Regional Climate Models (RCMs) with spatial resolutions of 25 and 8 km respectively. Six diferent RCM simulations are used to perturb the WG with climate change signals resulting in six very different perturbation schemes. All perturbed WGs result in more extreme precipitation at the sub-daily to multi-daily level and these extremes exhibit a much more realistic spatial pattern than what is observed in RCM precipitation output. The WG seems to correlate increased extreme intensities with an increased spatial extent of the extremes meaning that the climate-change-perturbed extremes have a larger spatial extent than those of the present climate. Overall, the WG produces robust results and is seen as a reliable procedure for downscaling RCM precipitation output for use in urban hydrology. [ABSTRACT FROM AUTHOR]

A major challenge in the emerging research field of coupling of existing regional climate models (RCMs) and hydrology/land-surface models is the computational interaction between the models. Here we present results from a full two-way coupling of the HIRHAM RCM over a 4000 km x 2800 km domain at 11 km resolution and the combined MIKE SHE-SWET hydrology and land-surface models over the 2500 km² Skjern River catchment. A total of 26 one-year runs were performed to assess the influence of the data transfer interval (DTI) between the two models and the internal HIRHAM model variability of 10 variables. DTI frequencies between 12 and 120 min were assessed, where the computational overhead was found to increase substantially with increasing exchange frequency. In terms of hourly and daily performance statistics the coupled model simulations performed less accurately than the uncoupled simulations, whereas for longer-term cumulative precipitation the opposite was found, especially for more frequent DTI rates. Four of six output variables from HIRHAM, precipitation, relative humidity, wind speed and air temperature, showed statistically significant improvements in root-mean-square error (RMSE) by reducing the DTI. For these four variables, the HIRHAM RMSE variability corresponded to approximately half of the influence from the DTI frequency and the variability resulted in a large spread in simulated precipitation. Conversely, DTI was found to have only a limited impact on the energy fluxes and discharge simulated by MIKE SHE. [ABSTRACT FROM AUTHOR]

EURO-CORDEX is an international climate downscaling initiative that aims to provide high-resolution climate scenarios for Europe. Here an evaluation of the ERA-Interim- driven EURO-CORDEX regional climate model (RCM) ensemble is presented. The study documents the performance of the individual models in representing the basic spatiotemporal patterns of the European climate for the period 1989–2008. Model evaluation focuses on near-surface air temperature and precipitation, and uses the E-OBS data set as observational reference. The ensemble consists of 17 simulations carried out by seven different models at grid resolutions of 12 km (nine experiments) and 50 km (eight experiments). Several performance metrics computed from monthly and seasonal mean values are used to assess model performance over eight subdomains of the European continent. Results are compared to those for the ERA40-driven ENSEMBLES simulations. The analysis confirms the ability of RCMs to capture the basic features of the European climate, including its variability in space and time. But it also identifies nonnegligible deficiencies of the simulations for selected metrics, regions and seasons. Seasonally and regionally averaged temperature biases are mostly smaller than 1.5 _C, while precipitation biases are typically located in the ±40% range. Some bias characteristics, such as a predominant cold and wet bias in most seasons and over most parts of Europe and a warm and dry summer bias over southern and southeastern Europe reflect common model biases. For seasonal mean quantities averaged over large European subdomains, no clear benefit of an increased spatial resolution (12 vs. 50 km) can be identified. The bias ranges of the EURO-CORDEX ensemble mostly correspond to those of the ENSEMBLES simulations, but some improvements in model performance can be identified (e.g., a less pronounced southern European warm summer bias). The temperature bias spread across different configurations of one individual model can be of a similar magnitude as the spread across different models, demonstrating a strong influence of the specific choices in physical parameterizations and experimental setup on model performance. Based on a number of simply reproducible metrics, the present study quantifies the currently achievable accuracy of RCMs used for regional climate simulations over Europe and provides a quality standard for future model developments. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increase in the number of climate studies addressing changes in extreme precipitation. A common step in these studies involves the assessment of the climate model performance. This is often measured by comparing climate model output with observational data. In the majority of such studies the characteristics and uncertainties of the observational data are neglected. This study addresses the influence of using different observational data sets to assess the climate model performance. Four different data sets covering Denmark using different gauge systems and comprising both networks of point measurements and gridded data sets are considered. Additionally, the influence of using different performance indices and metrics is addressed. A set of indices ranging from mean to extreme precipitation properties is calculated for all the data sets. For each of the observational data sets, the regional climate models (RCMs) are ranked according to their performance using two different metrics. These are based on the error in representing the indices and the spatial pattern. In comparison to the mean, extreme precipitation indices are highly dependent on the spatial resolution of the observations. The spatial pattern also shows differences between the observational data sets. These differences have a clear impact on the ranking of the climate models, which is highly dependent on the observational data set, the index and the metric used. The results highlight the need to be aware of the properties of observational data chosen in order to avoid overconfident and misleading conclusions with respect to climate model performance. [ABSTRACT FROM AUTHOR]

In this study the potential increase of extreme precipitation in a future warmer European climate has been examined. Output from the regional climate model (RCM) HIRHAM4 covering Europe has been analysed for two periods, a control period 1961-1990 and a scenario 2071-2100, the latter following the IPCC scenario A2. The model has a resolution of about 12 km, which is unique compared with existing RCM studies that typically operate at 25-50 km scale, and make the results relevant to hydrological phenomena occurring at the spatial scale of the infrastructure designed to drain off rainfall in large urban areas. Extreme events with one- and 24-hour duration were extracted using the Partial Duration Series approach, a Generalized Pareto Distribution was fitted to the data and T-year events for return periods from 2 to 100 years were calculated for the control and scenario period in model cells across Europe. The analysis shows that there will be an increase of the intensity of extreme events generally in Europe; Scandinavia will experience the highest increase and southern Europe the lowest. A 20 year 1-hour precipitation event will for example become a 4 year event in Sweden and a 10 year event in Spain. Intensities for short durations and high return periods will increase the most, which implies that European urban drainage systems will be challenged in the future. [ABSTRACT FROM AUTHOR]

We investigated the effect of changing the horizontal resolution of a regional climate model (RCM) on the simulation of hydrological extremes. We employed the results of three experiments of the RCM HIRHAM using a grid size of approximately 12, 25 and 50 km. These simulations were used to drive the hydrological model LISFLOOD, developed for flood forecasting at European scale. The discharge simulations of LISFLOOD were compared with statistics of observed river runoff at 209 gauging stations across Europe. The largest discrepancies in peak flow occurred in climates with a seasonal snow cover, which may be explained by inaccuracies in the simulated precipitation that accumulate over winter. Although previous studies have found that high resolution climate simulations result in more realistic patterns of extreme precipitation, especially in mountainous regions, we did not find conclusive evidence that the 12-km HIRHAM run generally yields a better simulation of peak discharges. At some gauging stations the model performance is increasing with increasing horizontal resolution of the RCM, while at other stations it is decreasing. However, the differences between the three experiments become less important in larger river basins. Above about 30 000km[sup2] and 120 000km[sup2], respectively, the 25- and 50-km runs generally provided a good approximation of the simulations based on the 12-km climatology. Under the A2 scenario of climate change, the changes in extreme discharge levels were similar between the three experiments at continental scale. At the scale of individual river basins, however, there were occasionally important differences. If we assume the 12-km HIRHAM simulation to be more realistic, the use of lower-resolution climate simulations may lead to an underestimation of future flood hazard. This means that results obtained with lower-resolution RCM simulations should be interpreted with care, as the grid scale of the climate model adds to the uncertainty. [ABSTRACT FROM AUTHOR]

A deforestation experiment is performed over the western Mediterranean, applying two different RCMs with differing domains and an ensemble technique to obtain a measure of their internal variability. The internal variability is used to assign statistical significance to the results, and also to discuss whether the models are sufficiently free to develop internal mesoscale processes. Considerable internal variability values found for hydrological variables even in autumn and winter seem to support the assumption that the models are free enough to be applied to such a sensitivity study. The combined use of two models, with strongly differing domains, and significance assigned through the use of internal variability should highlight responses to deforestation which are of physical origin and not a result dependent on one particular model. The overall significant response from both RCMs to deforestation is a reduction of evaporation (spring and summer, extending over the whole deforested zone) and a decrease in precipitation (late spring and summer, over some regions). A detailed analysis over subzones shows remarkable agreement between the two models over some of these subzones, showing non-local effects in precipitation response. [ABSTRACT FROM AUTHOR]

Two regional climate models have been applied to the task of generating an ensemble of realizations of the year 1982 with observed boundary conditions in areas covering parts of the Mediterranean countries. These realizations were generated by applying boundary conditions from the ECMWF ERA reanalysis project consecutively, carrying over the soil variables from the regional models from one iteration to the next. Monthly mean fields for six iterations of each model have been used as statistical ensembles in order to investigate the internal variability of the regional model dynamics. This internal variability is a necessary consequence of the non-linear physical feedback mechanisms of the RCM being active. A small value of internal variability will give better statistics for climate sensitivity signals, but will make these results less credible. The internal variability is important for the quantitative assessment of a climate sensitivity signal. With the present choice of models and integration domains the internal variabilities of surface fields and precipitation do reach levels that are less than, but in summer of comparable order of magnitude to, corresponding atmospheric variabilities of an atmospheric general circulation model. [ABSTRACT FROM AUTHOR]