Climate departure refers to the time after which climate parameters shift beyond historical values. Because climate variability and changes increase the frequency, intensity, and duration of heat waves, this study focuses on summer climate departures leading to heat-related urban mortality. Heat waves are especially deadly in large cities owing to the heat island effect, the production of anthropogenic heat, and the aging population. Twenty-seven Earth System Models of the Coupled Model Inter-comparison Project phase 5 were applied to different Representative Concentration Pathways (RCPs 2.6, 4.5, 8.5), using historical and modeled data from 1950 to 2100, including the following variables: daily maximum, minimum, and mean near-surface air temperatures; relative humidity; and wind speed. The predicted times at which the temperatures of recent heat waves will become normal summer temperatures depend on the scenarios for greenhouse gas emission, from stabilization to unconstrained growth. The tropics will be affected first, but summer climate departures will be more lethal in mid-latitude cities. Nine mega-cities were selected from Western Europe, North America, and Eastern Asia, based on a literature review of high temperatures and mortality from 1980 to 2010. Climate departure times and potential health impact were analyzed as a function of population characteristics, urban surface properties and heat island effects, anthropogenic emissions, temperature thresholds, and risk exposure. Summer climate departures in mid-latitude cities prompt for a substantial reduction of greenhouse gas emissions and implementation of mitigation strategies, such as increasing urban surfaces reflectance and vegetation, and for strengthening adaptation measures in public health actions to prevent the lethal impact of extreme heat events.

The urban heat island effect, in which air temperatures tend to be higher in urban environments than in rural areas, is a well-known and widely studied phenomenon. During heat waves, the urban heat island is known to exacerbate the impact on population health. Including urban heat island effects in the formulation of heat warnings and climate change adaptation plans is therefore essential and part of a sustainable urban development in general.

An important difficulty often encountered with typical numerical climate models is the limited resolution and long integration time, making them difficult to use when studying urban and intra-urban variations especially in the context of climate change. In this contribution, we will present a new urban climate model, further referred to as UrbClim, designed to cover agglomeration-scale domains at a spatial resolution of a few hundred metres. Despite its simplicity, UrbClim is found to be of the same level of accuracy as more sophisticated models. At the same time, the urban boundary layer climate model is faster than high-resolution mesoscale climate models by at least two orders of magnitude. Because of that, the model is well suited for long time integrations, in particular for applications in urban climate projections.

Within the EU RAMSES and NACLIM projects, the UrbClim model has been set up for a large number of cities : Antwerp, Lisbon, London, Bilbao, Berlin, New York, Rio De Janeiro and Skopje. We will present results and comparisons for these cities as well as detailed validations against air temperature measurements. Furthermore, a coupling was established between UrbClim and CMIP5 ensemble climate projections employed by the IPCC allowing the assessment of the urban heat island effects under future climate conditions, both for the near (2025 - 2045) and far (2081 - 2100 future).

Regional climate models are sufficient tools for estimating future climate change of an area in detail, although most of them cannot represent the urban climate characteristics, because their spatial resolution is too coarse (to date 10-50 km) and they characterize the urbanized areas as natural surfaces. At the Hungarian Meteorological Service (HMS) the externalised SURFEX land surface scheme including the TEB urban canopy model is used to describe the interactions between the urban surface and atmosphere on few km spatial scale.

In our study, the atmospheric condition is provided to the SURFEX by the ALADIN-Climate regional climate model adapted at the HMS. SURFEX is applied in a stand alone mode, thus the urban surface processes do not affect the atmospheric model. In the first step of our research a detailed validation is achieved to understand how the urban scheme modifies the climate model's results, and what the sensitivity of the SURFEX is to the biases of ALADIN. Model experiments are conducted for a 10-year period in the past for two Hungarian cities: Budapest is the capital with 2 millions inhabitants and the smaller Szeged is located at the flat southern part of the country. Temperature, wind results and certain fluxes provided by SURFEX and ALADIN are investigated and compared with measurements.

The focus of the presentation will be put on these results. However, the overall aim of the research is to provide climate change scenarios for Hungarian cities to help decision makers in preparing scientifically reasoned adaptation and mitigation strategies.

Climate change phenomena, such as global warming and urban heat island, is proceeding. Mitigation and adaptation measures are the two approaches used when we deal with the global warming. For the mitigation in the construction sector, buildings are required to improve their energy efficiency to reduce CO2 emissions, which is the main cause of the global warming. Further, the adaptation of building designs to the climate change is needed to keep building indoor environment comfortable in the future. During the design processes, energy simulations are often used to calculate energy consumption of buildings and evaluate indoor environment. In these simulations, it is common to use regional weather data made from observations, which is based on current or past weather events. However, most buildings have been used for several decades during which climate conditions have gradually changed. Therefore, the development of weather data for the future is very important for the both of the mitigation and adaptation measures for the climate change.

In this study, we attempt to construct a near-future weather data for the building design using numerical meteorological models. Future climate data projected by Global Climate Models (GCMs) is available. Although GCMs can predict the long-term global warming, they cannot illustrate the details of local phenomena due to their coarse grid resolution (~100 km). Therefore, we input GCM data to a Regional Climate Model (RCM) as initial and boundary conditions and downscale the GCM data based on physical modeling with the RCM. This process is called dynamical downscaling. The RCM uses a nested regional climate modeling to analyze local climate in high resolution (~1 km). The future weather data produced by this method is expected to present the global climate change and local phenomena such as urban heat islands.

In this paper, we employ the Model for Interdisciplinary Research on Climate version 4 (MIROC4h) as GCM and the Weather Research and Forecasting (WRF) model as RCM. MIROC4h has a relatively high grid resolution among GCMs and its atmospheric horizontal grid size is about 60 km. In the WRF simulations, the target area was the Kanto region, or Tokyo and its surrounding area. We used four levels of nested regional climate modeling: where the first and fourth levels have horizontal spatial resolutions of 54 km and 2 km, respectively. We first dynamically downscaled current weather data projected by MIROC4h in August and January for a 10-year period (2001-2010) and compared results to observations to confirm the accuracy of dynamically downscaled MIROC4h data. Next, we downscaled near-future weather data projected by MIROC4h in August and January for a near-future period of 10 years (2026-2035) to confirm the climate change information of the weather data. The weather data, output of the climate models, included statistical error, or bias. Because the bias can become problematic when the obtained weather data is directly used for building energy simulations, we corrected the bias of the weather data by a statistical manipulation using statistical values of observations and results of current WRF simulation. We then constructed prototypes of near-future standard weather data for building energy simulations in summer/winter seasons in Tokyo by selecting average monthly weather data over multiple years. The weather data represents the near-future weather conditions and is expected to present both of the global climate change and local phenomena.

The expected global climate changes are supposed to increase the heat load in urban areas. In order to plan and undertake the mitigation actions in particular cities, it is necessary to recognize the possible range of heat load increase, in terms of both its magnitude and spatial extent. Therefore, not only land use but also land form influences should be included. The present study shows preliminary results of an international project aimed to evaluate the expected heat load increase in four Central European cities (Krakow, Poland; Bratislava, Slovakia; Brno, Czech Republic and Szeged, Hungary) using the non-hydrostatic MUKLIMO_3 model developed by DWD (Deutscher Wetterdienst) for micro-scale urban climate and planning applications (Sievers 2012, 2014). All four cities have the spatial structure typical for post-communistic urban areas. Additionally, Krakow, Bratislava and Brno are located in large river valleys, in concave land forms, while Szeged is located in a flat area. In order to allow comparison of modeling results between the cities, the model setup uses standardize classification of land use properties based on local climate zones (Stewart and Oke, 2012) derived from remote sensing images (Bechtel and Daneke, 2012). The comparative analysis allows to study spatial patterns in urban heat distribution. The climatological changes in urban heat load are evaluated in terms of expected increase in the number of days with maximum air temperature exceeding 25 centigrade. The 30-year climatological indices are calculated using the cuboid method based on meteorological data from a local reference station for the recent climatic period and regional climate projections for the future climate signal.

Acknowledgement

The project “Urban climate in Central European cities and global climate change” is being realized within the framework of the International Visegrad Fund’s Standard Grant No. 21410222 in the years 2014-2015.

References:

Bechtel, B. and C. Daneke, 2012. Classification of local Climate Zones based on multiple Earth Observation Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5(4): 1191-1202.

Sievers, U., 2012. Das kleinskalige Strömungsmodell MUKLIMO_3 Teil 1: Theoretische Grundlagen, PC-Basisversion und Validierung. Berichte des Deutschen Wetterdienstes 240, Offenbach am Main, Germany (in German).

Sievers, U., 2014. Das kleinskalige Strömungsmodell MUKLIMO_3 Teil 2: Thermodynamische Erweiterungen. Berichte des Deutschen Wetterdienstes Entwurf, Offenbach am Main, Germany (in German).

Stewart, I. D. and T. R. Oke, 2012: Local Climate Zones for Urban Temperature Studies. Bull. Amer. Meteor. Soc., 93, 1879–1900.

Although tourism in Southern Europe is often assumed to be synonymous with beach tourism, cities are far from being just the gateway to a given region or country. In fact, city tourism is one of the rising markets of international tourism and, in association with market trends such as an aging world population and a growing interest in culture, it is expected to increase even further. Urban climate is a non-negligible asset that these regions have in common.

It is, thus, important to provide a comprehensive interpretation of the weather conditions experienced by tourists in some of the most visited southern European capitals during the summer. Besancenot’s weather type method was selected and applied to Barcelona, Rome, Athens, Lisbon and Zagreb in order to assess these cities climate suitability for tourism activities. The weather type method was preferred on behalf of some of its advantages, namely the possibility to integrate a measure of comfort (using a thermophysiological index - PET) and to incorporate nuances, taking into consideration: a) tourists stated preferences; b) tourists revealed preferences; and c) the possibility of adjusting thresholds to the predominant tourist activities taking place in urban areas. The frequency of occurrence of the different established classes (eight different classes, numbered in descent, from the most favourable to the least favourable), each defined by the combination of 5 different variables (number of sunshine hours, cloud cover (at 12.00h), maximum daily air temperature, precipitation, wind speed) and a comfort index provides a weather-type pattern, which will be crossed with the seasonal tourist demand, in order to understand whether it influences visitors’ arrival.

Following the assessment of the climate suitability of these cities during a baseline period (2000-2010), the same methodology will be applied to 2020 and 2050, making use of projections of temperature and precipitation drawn from an ensemble of 9 regional climate model (RCM) simulations based on the International Panel on Climate Change (IPCC) – Synthesis Report on Emission Scenarios (SRES) A1B emission scenario. This will allow to assess whether these destinations will still be adequate for the practice of tourism activities by mid XXI century. With this research, we expect to question claims of southern Europe’s cities declining attractiveness during the summer, under climate change scenarios.