The environmental significance of land-use/land-cover changes, of which urbanization is an extreme case, is well recognized but understood in a limited way. How urbanization affects regional and global climate and how urban areas respond to climate change at time scales from seasonal to decadal are critical areas of research. However, to date, most global climate models that are used to investigate impacts of land-use/land-cover changes on the climate do not include an urban representation. Moreover, the effect of urban expansion on climate of near-by areas has not been characterized in any existing urban representations. In order to answer these questions, the Geophysical Fluid Dynamics Laboratory (GFDL) is developing a high-resolution global climate model with an urban representation that can simulate interactively both changes in urban environments and feedbacks of urban changes. In this study, efforts towards urbanizing the GFDL land model LM3 are described. Historical simulations with the urbanized GFDL LM3 are presented to demonstrate how growth of urban areas has affected the near-surface climate in recent decades over the continental United States.

The contrasting climate between urban and rural areas is a serious consideration in the context of climate change. Differences in the local response to large-scale changes could have significant repercussions for most of the population. However, Global Climate Models are only able to provide very limited information on future urban climate. In this study, we investigate the combined effects of urban expansion and changes in greenhouse gas emissions on the future climate of Sydney, Australia. We use a regional climate model at very high resolution (2 km) to dynamically downscale a Global Climate Model and simulate the climate of the region for both present and future climate conditions. The model is coupled with an urban canopy model to incorporate the interactions between urban structures and the atmosphere while preserving the physical consistency of the model. Changes in temperature and vapor pressure are examined as a means of determining changes in the population exposure to heat stress due to the combination of urban expansion and greenhouse gas. Daily maximum and minimum human heat stress are estimated using hourly values of temperature and vapor pressure. Our results indicate that new urban areas are likely to experience higher heat stress conditions at night in the future, with changes that are substantially larger than in the rural counterparts. During the day, urban structures seem to compensate the climate change effects on heat stress through vapor pressure differences. Overall, urban areas tend to enhance the climate change signal during the cooler hours of the night by contributing to temperature increases, whereas vapor pressure deficits induced by urban surfaces dominate over temperature during the day. This work emphasizes the need to include explicit representation of urban effects and to consider variables other than temperature to assess climate change impacts on urban population.

It is known from global climate ensemble projections that both global warming and the increasing extreme weather conditions including heat waves and pluvial flooding will put increasing pressure on the livability of cities. At the same time, urban heat island intensities are expected to increase because of the unrestrained urban expansion, which makes cities the 'hotspots' of climate change. This not only implies severe health hazards for the many urban dwellers, it also leads to increased energy consumption, damage of city's infrastructure, and hypothecates the urban environmental health.

We investigate the relative impact of urban land-use change and global climate change on the increased temperatures and extreme precipitation in cities at the second half this century. Therefore, urban-climate simulations with COSMO-CLM coupled to TERRA_URB at 2.8km resolution over Belgium are cascade-nested in EC-EARTH (GCM) for the climate of the recent past (2000-2010) and for future climate projections (2060-2070). The regional climate model accounts for urban land-use change for Belgium towards 2060 based on projections with 'Ruimtemodel Vlaanderen'. We further address the synergy between urban heating and global climate change. Hereby, we investigate how the frequency and intensity of heat waves (+), the local changes in atmospheric radiative transfer by greenhouse gases (-), changes in precipitation (+/-), but also the urban expansion (+) and increased cooling demands (+) affect heat island intensities in Belgium. In order to account for urban climate with a broad-risk assessment approach, we investigate the relation between urban heat island intensities and the frequencies of circulation weather types or the percentiles from the temporal temperature distributions.

A new high-resolution dynamical downscaling strategy to examine how rural and urban areas respond to change in future climate, is presented. The regional climate simulations have been performed with a new version of the limited-area model of the ARPEGE-IFS system running at 4-km resolution coupled with the Town Energy Balance scheme (TEB). In order to downscale further the regional climate projections to a urban scale, at 1km resolution, a stand-alone surface scheme is employed in offline mode. We performed downscaling simulations according to three model set-ups: (i) reference run, where TEB is not activated neither in 4-km simulations nor in 1-km urban simulation, (ii) offline run, where TEB is activated only for 1-km urban simulation and (iii) inline run, where TEB is activated both for regional and urban simulations. The applicability of this method is demonstrated for Brussels Capital Region, Belgium. For present climate conditions, another set of simulations were performed using European Center for Medium-Range Weather Forecasts global reanalysis ERA40 data. Results from our simulations indicate that the reference and offline runs have comparable values of daytime and nocturnal urban heat island (UHI) and lower values than the inline run. The inline values are closer to observations. In the future climate, under and A1B emission scenario, the three downscaling methods project a decrease of daytime UHI between -0.24 °C and -0.20 °C, however, their responses are different for nocturnal UHI: (i) reference run values remains unaltered, (ii) for the offline runs, the frequency of present climate weak nocturnal UHI decreases to the benefit of negative UHIs leading to a significant decrease in the nocturnal UHI over the city, (iii) for the inline run, nocturnal UHIs stays always positive but the frequency of the strong UHI decreases significantly in the future by 1°C. The physical explanation is put forth.

Evaluating the effect of adaptation and mitigation measures is important for urban development strategies. This can be achieved using high resolution numerical models. However, they are computationally expensive, thus simulating a 30-year climate period is challenging. An approach can be to simulate only a subset of days from the 30 years. Identifying the number of days which are sufficient to represent the urban climate is the aim of this presentation.

The presented statistical dynamical downscaling method is applied to simulate the urban climate of Hamburg. It utilises 30-year time series from 27 weather stations in Northern Germany and The Netherlands. For some meteorological quantities measured at these stations, the frequency distributions have been analysed. These are compared with artificial frequency distributions built with bootstrapping and a lower number of days. For comparing these distributions, a skill score following Perkins et al. (2007) is further developed, now taking into account the relationship between the quantities. The results of this statistical dynamical downscaling method indicate that the statistics of the urban climate of Hamburg can be simulated with a much lower number of days than the 30-year time series.

Perkins, S. A., A. J. Pitman, N. J. Holbrook, J. McAneney (2007): Evaluation of the AR4 climate models simulated daily maximum temperature, minimum temperature and precipitation over Australia using probability density functions, Journal of climate, 20, 4356-4376

The effects of global warming and climate change would lead the future weather more severe in terms of temperature. As buildings are built by complying nowadays building energy conservation regulations, they are vulnerable in facing the future weather change, especially for those operating in natural ventilation mode. To this end, this paper firstly adopted a statistical downscaling method, i.e. morphing method, to construct local hourly future weather data. This method uses either by shifting or stretching on the certain weather elements of the existing hourly local typical meteorological year (TMY) via calculating the differences between nowadays and the future projected weather base on the selected general circulation models (GCMs). SRA1B scenario suggested in IPCC AR4 was used to study the impact of weather change on the effectiveness of passive building design strategies. Probabilities of each year’s thermal comfort achieved by means of natural ventilation were therefore identified by superimposing future hourly weather data onto the bioclimatic chart. Furthermore, to understand the indoor overheating problem of residential space, we simulated a typical natural ventilated four room’s apartment via EnergyPlus with 2000 to 2100 weather data to obtain hourly indoor thermal condition. CEN standard EN15251 thermal comfort model were used to assess each room’s overheating occurrence frequencies and their overheating severities. Due to different occupied hours, living room and bedrooms were discussed independently under three future time slices, which are 2020s, 2050s, and 2080s. In comparison with the baseline case (1998-2013), for the future three time slices, the overheating frequencies slightly increase from nowadays 0.03% to 0.48%, 2.23%, and 5.39%, respectively for living room; and from nowadays 0.68% to 0.5%, 1.86%, and 4.15% for bedrooms. However, the growth rate of overheating severities exponentially increase by 210%, 1213%, and 3360% for living room; and are -34%, 299%, and 877% for bedrooms. It indicates that spaces using during daytime are much more vulnerable to overheating problems both in terms of occurrence probability and the severity in the future climate context, which may cause natural ventilation ineffective and need mechanical cooling to maintain indoor thermal comfort.