H. Kikumoto, R. Ooka, Y. Arima and T. Yamanaka
Bibliographic info:
Proceedings of the 34th AIVC - 3rd TightVent - 2nd Cool Roofs' - 1st venticool Conference , 25-26 September, Athens 2013

Climate change phenomena such as global warming and urban heat island effects cause serious problems for the development of building technology. Therefore, it is imperative that architects and designers consider the effects of climate change on long-term building performance. At present, energy simulations are often used to evaluate the indoor thermal environment and energy consumption of buildings. In these simulations, it is common to use regional weather data that are usually based on current or past weather conditions. However, most buildings have a lifespan of several decades, during which climate can gradually change. Therefore, the design of energy conservation systems such as ventilative cooling strategies and energy simulations should incorporate climate change predictions in order to ensure that buildings are adaptable to future climatic conditions. As a result, future weather scenarios are very important for simulating building performance. 

The purpose of this study is to construct future standard weather data using numerical meteorological models, for use in architectural designs. At present, the climatic data used for this purpose are obtained from a Global Climate Model (GCM). Although a GCM can predict long-term global warming, its coarse grid resolution (~100 km) cannot describe the details of local phenomena. Therefore, we employ a downscaling method. We input GCM data into a Regional Climate Model (RCM) as initial and boundary conditions, and physically downscale the data using the RCM. RCM uses nested regional climate modeling and can analyze the local climate at fine grid resolutions (~1 km). The climatic scenarios obtained via this method are expected to accurately predict local phenomena such as the urban heat island effect. 

The results confirm that the weather data generated via the dynamical downscaling method can predict local climate. We subsequently constructed a prototype of the future standard weather data based on the Model for Interdisciplinary Research On Climate (MIROC) and the Weather Research and Forecasting (WRF) Model, and simulated building energy consumption using regional climate data. By comparing present and future energy simulations, we estimated the impact of climate change on the energy performance of a building.