The ability of general circulation models (GCMs) to correctly simulate precipitation is usually assessed by comparing simulated mean precipitation with observed climatologies. However, to what extent the skill in simulating average precipitation indicates how well the models represent temporal changes is unclear. A direct assessment of the latter is hampered by the fact that freely evolving climate simulations for past periods are not set up to reproduce the specific evolution of internal atmospheric variability. Therefore, model-toreal-world comparisons of time series of daily, monthly, or annual precipitation are not meaningful. Here, for the first time, the authors quantify GCM skill in simulating precipitation variability using simulations in which the temporal evolution of the large-scale atmospheric state closely matches that of the real world. This is achieved by nudging the atmospheric states in the ECHAM5 GCM, but crucially not the precipitation field itself, toward the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40). Global correlation maps between observed and simulated seasonal precipitation allow areas in which simulated future precipitation changes are likely to be meaningful to be identified. In many areas, correlations higher than 0.8 are found.This means also that in these regions the simulated precipitation is a very good predictor for the true precipitation, and thus a statistical correction of the simulated precipitation, which can include a downscaling component, can provide useful estimates for local-scale precipitation. The authors show that a simple scaling of the simulated precipitation performs well in a cross validation and thus appears to be a promising alternative to standard statistical downscaling approaches.

Urban areas have well-documented effects on climate, such as the urban heat island (UHI) effect, reduction of wind speeds, enhanced turbulence and boundary layer heights, and changes in cloud cover and precipitation. The aim of this study is to quantify the impact of the urban area of London on local and regional climate. This is achieved through the coupling of the non-hydrostatic mesoscale model METRAS with the sophisticated urban canopy scheme BEP. The model is configured for case studies of the London region, for typical UHI conditions, and the model results are evaluated using data from meteorological monitoring sites. This study develops a methodology to quantify the regional impact of urbanisation from numerical model results. The urban area, in its current form, is found to affect near surface temperature, the diurnal temperature range, the UHI, and the near surface wind speed and direction. For the selected cases, peak UHI intensities of up to 2.5 K are found during night time hours, with the timing and magnitude of the peak showing good agreement with previous experimental studies for London. The timing of the UHI peak intensity for the current urban land cover for London shows a good agreement with the results of measurements. A significant reduction in wind speed over the urban area was also simulated during both daytime and night time, due to the higher roughness of the city compared to the rural domain. The effect is shown to have a regional character, with both urban and surrounding rural areas demonstrating a significant impact. Thus, the UHI can not only be understood when focussing on local data, but the interaction with the surrounding needs to be considered.

Abstract:In cities, social well-being faces obstacles posed by globalization, demographic and climate change, new forms of social organization, and the fragmentation of lifestyles. These changes affect the vulnerability of city societies and impact their health-related urban well-being (UrbWellth). The conceptual model introduced in this paper systematizes the relevant variables while considering previous research, and establishes the target value UrbWellth. The model differs from existing approaches mainly in the analytical distinctions it suggests. These allow us to group the relevant urban influence variables into four sectors and enable a more general and abstract consideration of health-related urban relations. The introduction of vulnerability as a filter and transfer function acts as an effect modifier between UrbWellth and the various urban variables.