Extreme and near-extreme climate change data in relation to building and plant design

CHOW, D., LEVERMORE, G., JONES, P., LISTER, D., LAYCOCK, P. J. and PAGE, J. (2002). Extreme and near-extreme climate change data in relation to building and plant design. Building service engineering research and technology, 23 (4), 233-242.

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Link to published version:: 10.1191/0143624402bt046oa

Abstract

Buildings and plant are designed utilizing near-extreme weather data. The present data used are briefly discussed, including manual near-extreme percentiles for manual design and hourly data for simulation on a PC (test reference years and design summer years, and near-extreme periods). However, with climate change occurring, designs based on current data will produce uncomfortable summer thermal conditions within and around buildings in the future. This expected change is especially relevant now, as buildings have to last typically from 50 to 100 years. Climate change data for the future are needed to assess the performance of buildings and plant in the future. The Hadley Centre climate change models could provide such data. In this paper analysis of extreme data from one model, the HadCM3 model (south-east England grid box) with an appropriate climate change scenario, are considered in relation to their use for design assessment. Dry bulb temperature and solar irradiance extreme values are considered in this paper. The expected trend in both minimum and maximum temperature is for both to increase with time, but the maxima are found to rise faster than the minima. There are two factors influencing the solar radiation estimates, the basic clarity of the atmosphere and the seasonal amount of cloud. The latter is predicted to increase slightly in winter and decrease slightly in summer. The variations in the predicted short-wave radiation values reflect the expected combined impacts of these two factors. The implications of these results are briefly discussed.

Item Type: Article
Research Institute, Centre or Group: Built Environment Division Research Group
Identification Number: 10.1191/0143624402bt046oa
Depositing User: Ann Betterton
Date Deposited: 29 Jan 2009
Last Modified: 09 Dec 2009 18:23
URI: http://shura.shu.ac.uk/id/eprint/440

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