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Statistical methods for assessing and analysing the building performance in respect to the future climate

Nik, Vahid M. LU ; Sasic Kalagasidis, Angela and Kjellström, Erik (2012) In Building and Environment 53. p.107-118
Abstract

Global warming and its effects on climate are of great concern. Climate change can affect buildings in different ways. Increased structural loads from wind and water, changes in energy need and decreased moisture durability of materials are some examples of the consequences. Future climate conditions are simulated by global climate models (GCMs). Downscaling by regional climate models (RCMs) provides weather data with suitable temporal and spatial resolutions for direct use in building simulations.There are two major challenges when the future climate data are used in building simulations. The first is to handle and analyse the huge amount of data. The second challenge is to assess the uncertainties in building simulations as a... (More)

Global warming and its effects on climate are of great concern. Climate change can affect buildings in different ways. Increased structural loads from wind and water, changes in energy need and decreased moisture durability of materials are some examples of the consequences. Future climate conditions are simulated by global climate models (GCMs). Downscaling by regional climate models (RCMs) provides weather data with suitable temporal and spatial resolutions for direct use in building simulations.There are two major challenges when the future climate data are used in building simulations. The first is to handle and analyse the huge amount of data. The second challenge is to assess the uncertainties in building simulations as a consequence of uncertainties in the future climate data. In this paper two statistical methods, which have been adopted from climatology, are introduced. Applications of the methods are illustrated by looking into two uncertainty factors of the future climate; operating RCMs at different spatial resolutions and with boundary data from different GCMs. The Ferro hypothesis is introduced as a nonparametric method for comparing data at different spatial resolutions. The method is quick and subtle enough to make the comparison. The parametric method of decomposition of variabilities is described and its application in data assessment is shown by considering RCM data forced by different GCMs. The method enables to study data and its variations in different time scales. It provides a useful summary about data and its variations which makes the comparison between several data sets easier.

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author
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Building simulation, Climate change, Climate uncertainties, Decomposition of variabilities, Statistical methods
in
Building and Environment
volume
53
pages
12 pages
publisher
Elsevier
external identifiers
  • scopus:84856859378
ISSN
0360-1323
DOI
10.1016/j.buildenv.2012.01.015
language
English
LU publication?
no
id
9b15dcd5-90c8-4d4a-ab56-9ca893264f8f
date added to LUP
2017-03-02 18:56:46
date last changed
2017-10-08 04:59:34
@article{9b15dcd5-90c8-4d4a-ab56-9ca893264f8f,
  abstract     = {<p>Global warming and its effects on climate are of great concern. Climate change can affect buildings in different ways. Increased structural loads from wind and water, changes in energy need and decreased moisture durability of materials are some examples of the consequences. Future climate conditions are simulated by global climate models (GCMs). Downscaling by regional climate models (RCMs) provides weather data with suitable temporal and spatial resolutions for direct use in building simulations.There are two major challenges when the future climate data are used in building simulations. The first is to handle and analyse the huge amount of data. The second challenge is to assess the uncertainties in building simulations as a consequence of uncertainties in the future climate data. In this paper two statistical methods, which have been adopted from climatology, are introduced. Applications of the methods are illustrated by looking into two uncertainty factors of the future climate; operating RCMs at different spatial resolutions and with boundary data from different GCMs. The Ferro hypothesis is introduced as a nonparametric method for comparing data at different spatial resolutions. The method is quick and subtle enough to make the comparison. The parametric method of decomposition of variabilities is described and its application in data assessment is shown by considering RCM data forced by different GCMs. The method enables to study data and its variations in different time scales. It provides a useful summary about data and its variations which makes the comparison between several data sets easier.</p>},
  author       = {Nik, Vahid M. and Sasic Kalagasidis, Angela and Kjellström, Erik},
  issn         = {0360-1323},
  keyword      = {Building simulation,Climate change,Climate uncertainties,Decomposition of variabilities,Statistical methods},
  language     = {eng},
  pages        = {107--118},
  publisher    = {Elsevier},
  series       = {Building and Environment},
  title        = {Statistical methods for assessing and analysing the building performance in respect to the future climate},
  url          = {http://dx.doi.org/10.1016/j.buildenv.2012.01.015},
  volume       = {53},
  year         = {2012},
}