Preparation of future weather data to study the impact of climate change on buildings

Building and Environment 44 (2009) 793–800

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Building and Environment journal homepage: www.elsevier.com/locate/buildenv

Preparation of future weather data to study the impact of climate change on buildings Lisa Guan* School of Engineering Systems, Queensland University of Technology, 2 George Street, GPO Box 2434, Brisbane, QLD 4001, Australia

a r t i c l e i n f o

a b s t r a c t

Article history: Received 25 February 2008 Received in revised form 20 May 2008 Accepted 20 May 2008

The dynamic interaction between building systems and external climate is extremely complex, involving a large number of difficult-to-predict variables. In order to study the impact of climate change on the built environment, the use of building simulation techniques together with forecast weather data are often necessary. Since most of building simulation programs require hourly meteorological input data for their thermal comfort and energy evaluation, the provision of suitable weather data becomes critical. In this paper, the methods used to prepare future weather data for the study of the impact of climate change are reviewed. The advantages and disadvantages of each method are discussed. The inherent relationship between these methods is also illustrated. Based on these discussions and the analysis of Australian historic climatic data, an effective framework and procedure to generate future hourly weather data is presented. It is shown that this method is not only able to deal with different levels of available information regarding the climate change, but also can retain the key characters of a ‘‘typical’’ year weather data for a desired period. Ó 2008 Elsevier Ltd. All rights reserved.

Keywords: Climate change Building simulation Future weather data

1. Introduction The climate change induced by the emissions of greenhouse gases is considered as one of the most important global environmental issues facing the world today. It is now widely recognized as having significant potential to seriously affect the integrity of our ecosystems and human welfare. Buildings as part of the infrastructure will need to withstand changing climatic conditions for a long time span (50–100 years). This requires current and future building stocks to perform satisfactorily under changing climatic conditions. Since the dynamic interaction between building systems and external climate is extremely complex, involving a large number of difficult-to-predict variables, in order to study the impact of climate change on the built environment, the use of building simulation techniques together with forecast weather data are often necessary. Because building simulation programs require hourly meteorological input data for their thermal comfort and energy evaluation, the provision of suitable weather data becomes critical. In order to project and generate future weather data for the impact study of climate change on building performance, several different approaches have been used in previous studies. From simple to complex, they may be classified as: the extrapolating statistical method, the imposed offset method, stochastic weather

* Tel.: þ61 7 3138 2484: fax:þ61 7 3138 1469 E-mail address: [email protected] 0360-1323/$ – see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.buildenv.2008.05.021

model and global climate models. In this paper, these four methods of future weather data generation are reviewed. The advantages and disadvantages of each method are discussed. The inherent relationship between these four methods is also illustrated. An effective framework to generate future hourly weather data is then presented. The projection method of key individual climate variables, including air temperature, air humidity, solar radiation and wind characteristics are also discussed. 2. Methods of generating future weather data 2.1. Extrapolating statistical method (degree-day method) This method uses the approach of extrapolating statistical historical weather data to predict future weather conditions. Examples of the application of this method include the prediction of building energy consumption trends using degree-day, so that different levels of impact can be compared between different cities [1–3]. The degree-day method is essentially a single-measure steadystate method used for the estimation of building energy consumption. It is also a common approach adopted by the building industries to relate the trends of building energy consumption with local climate conditions. The assumption behind the degree-day method is that to maintain a specific indoor temperature, there should exist a balance point for outdoor temperature (or called base temperature). When outdoor temperature is higher than the balance point temperature, cooling load will be required. By

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contrast, when outdoor temperature is lower than the balance point temperature, heating load will be needed. Such a relationship between building energy use and the outdoor temperature is illustrated in Fig. 1. The advantage of this method is that it is simple and fast. However, the energy estimates from the degree-day calculations can be fairly coarse, because the degree-day method is appropriate only if the building use and the efficiency of the HVAC equipment are constant [4]. Furthermore, because this method only considers the effect of dry bulb temperature, its application (in energy predictions) is also limited. It has been found that the use of annual wet bulb temperature cooling degree days could be a more suitable indicator for the cooling load than that based on the dry bulb temperature [5]. This is because the cooling load is closely related to the air enthalpy, which means that the cooling load is dependent on both dry bulb temperature and humidity or both sensible and latent heats. From the psychrometric chart, it can be found that the wet bulb temperature generally follows the same change pattern as the enthalpy, so it is more closely related to both the dry bulb temperature and humidity. This finding is particularly relevant for humid locations, such as Sydney and Brisbane in Australia. 2.2. Imposed offset method

Building energy use

This method imposes the predicted future climate information due to global warming from the more complex climate models on top of the recorded current reference year weather data. This method has been used to generate future weather data for building simulation in the USA, UK, Australia and New Zealand [6–12]. It is also further divided into two approaches. The first approach only considers the potential change of individual weather parameters due to climate change, while the potential cross-correlation between different weather variables as shown in Fig. 2 [14] has been ignored. A program has been developed by Crawley [10] to create weather files for climate change and urbanization impacts analyses. This program requires both the input of the existing weather file and predicted monthly delta values generated from the General Circulation Models (GCM). It then produces a new weather file with recalculated hourly dry bulb temperature, humidity ratio, and global, direct normal and diffuse horizontal solar radiation based on the change in hourly temperature and diurnal temperature range, relative humidity and the cloud cover respectively, to represent climate change scenarios in 2100. Belcher et al. [13] have also summarized a set of common approaches/procedures, named as morphing, for the implementation of the imposed offset method by different researchers. The algorithms for morphing individual weather parameters may involve three generic operations: (1) shifting; (2) linear stretching (scaling factor); and (3) shifting and stretching.

Heating energy

Balance Point Temperatures

Cooling energy

When the heating degree days (HDD) value is calculated by using the following equation, it has been found that the HDD values calculated from the weather series morphed to future climates (i.e. Fig. 3 in Belcher et al.’s paper [13]) agree well with the results calculated directly from the climate model (i.e. Fig. 67 of UKCIP02 report [13]). This gives confidence that the morphing technique may have faithfully transformed the weather sequences.

HDD ¼

X 1 kt  to k 24 b

(1)

where tb is the baseline temperature and to is the hourly dry bulb temperature. The double vertical lines indicate that only positive values of the temperature difference are included in the sum. The second approach, which considers the possible close relationship between temperature and humidity, has also been employed by several researchers, including Scott et al. [6], Mullan [7], Degelman [9] and Guan et al. [11]. This approach is particularly suitable to deal with the situation of knowing only the projected potential temperature increase due to global warming, and having to use different assumptions to link the variation of absolute humidity with temperature. For instance, Scott et al. [6] and Guan et al. [11] have assumed that the relative humidity (RH) will maintain the same for both current and climate change scenarios. This is based on the assumption that with global warming, the earth temperature will increase which would lead to more water evaporating to atmosphere to maintain a constant RH. Based on the examination of available historical hourly statistic data, Mullan [7] however assumed that the relative humidity would be around 85% at the time of minimum temperature while the dew point temperature attained from this would remain unchanged for the rest of the day. In parallel, based on the statistic data analysis, Degelman [9] suggested a rise of one-half of the increase in the daily minimum temperatures in the daily dew point temperature. The advantages of the imposed offset method are:  The nature of the diurnal cycle is retained.  The projected temperature increase under climate change is often available from IPCC or relevant national research organizations.  The typical reference year weather data for the current climate is also normally available for building simulation.  The comparison between current and future weather conditions can be made on a consistent base. Because impact studies are more concerned with relative change, it is essential that all the comparisons are based on the same base. The limitations of this method are also quite obvious, as it is based on the following series of assumptions:  Although the possible close relationship between air temperature and air humidity are considered with different assumptions, a common approach will be needed to provide a systematic approach.  The daily hourly dry bulb temperature is assumed to increase ‘‘constantly’’ over a day. That is, the future increases in daily maximum and minimum temperature are assumed to be the same as the predicted increases in average temperatures.  The possible change in solar insolation and wind speed has been ignored.

The base energy which is insensitive to the weather

2.3. Stochastic weather model

Temperature Fig. 1. The relationship between building energy use and the outdoor temperature.

The stochastic weather models were proposed by van Paassen and Luo [15] and Adelard et al. [16] to generate future weather data. It was suggested that with inputs of only a few weather variables,

L. Guan / Building and Environment 44 (2009) 793–800

Water evaporation & Air saturation

Changes

795

Air Humidity

Controls Solar Radiation

Causes

Air Temperature variations Controls Air Pressure Difference

Drives

Wind Speed and Direction

Fig. 2. The relationship between different weather variables.

these models could generate an artificial meteorological database for a number of different purposes. However, because of the stochastic nature of this method, it will be necessary to generate weather data over many years, in order to be representative of the climatic patterns during the desired time periods [7]. Moreover, this method has difficulties in accurately modelling many climatic variables.

change in weather conditions between different years could be quite large, without finding a suitable year to represent the desirable period, a single random year approach (i.e. the impact study is based on the comparison between random current and future years) could produce serious errors or even misleading results (i.e. the hottest year of current climate is compared with the coldest year of future climate, or the coldest year of current climate with the hottest year of future climate).

2.4. Global climate models 2.5. Relationship between the above four methods A more complex and fundamental method of climate change models has also been developed by meteorologists [17]. This method takes into account the energy transfer mechanisms between a three-dimensional turbulent and radiation-active atmosphere and spatially heterogeneous land, ocean, and cryosphere surfaces [18]. However, because of the complex and chaotic nature of the global climate system, detailed modelling of complete atmospheric or surface processes using this method would be extremely difficult, if not impossible. Thus, various approximations and simplifications have been adopted. Among them, the general circulation model (GCM) appears to be the most reliable, particularly at continental scales and larger, where the influences of local geological factors may be ignored [19,20]. Running of the GCM model also requires numerical iteration and significant computer resources and accurate characterisation and representation of the atmospheric processes. At this stage, such models are often only available to meteorologic specialists. Similar to the stochastic weather model, this method also requires the generation of multiple years weather data to find a representative weather pattern for a desired time period. This will be an extremely expensive exercise. It is also noted that because the

Imposed offset method

Typical reference year

Historically observed weather data

The relationship between the above four methods is illustrated in Fig. 3 [11]. It can be seen that the first two methods (the extrapolating statistical method and the imposed offset method) are essentially modified from historically observed weather data. The extrapolating statistical method is based on the extrapolation and statistic treatment of historically observed weather data, while the imposed offset method is based on typical reference year weather data, which is used to represent the long term weather pattern, with the imposed offset technique applied (i.e. morphing approaches). The last two methods (the stochastic weather model and the global climate model) are based on fundamental physical models, using the historically observed weather data for the purpose of model calibration. For instance, the stochastic weather models are often based on the integration of the stochastic modelling of each individual climate variables, while the global climate models are based on the modelling of the whole climate systems, which considers the continuing change in atmospheric carbon dioxide concentration. Because of the requirement of understanding the nature of variation in weather variables and climate systems, and the complexity of mathematic modelling

Global climate models

Stochastic weather model for calibration for calibration Modelling climate variables

Extrapolating statistical method Fig. 3. The relationship between different methods.

Modelling climate systems

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involved, the last two methods often require specialists to operate the process. In particular, due to the complex nature and the special operation requirement, the global climate models are often used to project average change of climatic parameters, including temperature increase, which can then be used as input for other simpler methods to estimate local future weather conditions. However, it is also noted that instead of directly using data predicted by these climate models, a researcher holding 20–30 year data sets may also wish to use statistical methods to extract data from such archives to form some climate sets to support specific what-if scenarios. This approach might be acceptable. However, it is not recommended as climate change is a complicated issue, and temperature change, for example, may not vary ‘linearly’ each year. It also appears that except for the imposed offset method, the other methods are either too simplistic (i.e. extrapolating statistic method) or too complex (i.e. stochastic weather model or global climate models) to be adopted by building consultants for a building simulation study. Past experience has indicated that often the best and the most efficient approach to impact studies is to first assemble an ‘‘observed’’ daily data set for input to the impact study model. This can then be followed by necessary sensitivity studies with some adjustments to the model to accommodate the simulated changes from the climate model. 3. General projection of future weather variables Methods of predicting climate change have been the subject of intense research in recent years, and they are now able to yield reasonable estimates of generalised future values (such as annual means) together with the indication of likely future variability [21]. These predictions are typically based on models of global climate ‘‘forced’ by a presumed finite or continuing change in atmospheric carbon dioxide concentration. Estimates of trends over large areas are also generally considered more accurate than those over small areas. Based on detailed assessment of a number of possible emissions scenarios, the Intergovernmental Panel on Climate change (IPCC) has projected that by the year 2100, relative to 1990 [22]:  The average surface temperature of the Earth is likely to increase by 1.4–5.8  C (Fig. 4). This prediction has included the uncertainty in the climate system’s response to enhanced greenhouse gases as well as the uncertainty in the amount of future emissions.  Global mean sea level is likely to rise by 9–88 cm.  Precipitation (rainfall and snowfall) is likely to increase over the northern mid to high latitudes and Antarctica in winter.

 Extreme events (drought, tropical cyclone intensity) are also likely to increase over some areas.  There will be significant changes to snow cover and ice extent.  Large-scale density-driven circulation in the ocean may be weakened. Unlike most other scientific work, projection of climate change cannot be validated, since they do not relate to a currently replicable event [21]. Overall, it has been found that for different climate parameters, there are different levels of confidence in the prediction. A list of climate and associated scenario variables, ranked subjectively in decreasing order of confidence is shown in Table 1 [24]. It can be seen that the projection of change in dry bulb temperature has the highest confidence among all the climatic variables that are used in building simulation, including air temperature, air humidity, wind characteristic and solar radiation. This may explain why the projection of temperature change is the most common climatic parameter, and is often shown in so many climate change brochures. 4. Proposed framework for future weather data generation 4.1. Overview of the proposed framework Based on the analysis of Australian historic climatic data, together with a review of previous approaches used to generate future weather data, an effective procedure to generate future hourly weather data is proposed in Fig. 5 [11]. Although this procedure is evolved from the imposed offset method discussed previously, depending on the levels of information available in the prediction of future changes in weather variables, this framework allows either the current weather variable to remain unchanged, or the imposed offset method or diurnal modelling method be employed. It also allows the current reference year weather data contained in the test reference year (TRY)/typical meteorological year (TMY) to be either used directly, or used as the base for further modification, or used for the calibration of diurnal modelling of meteorological parameters. Only two sets of input data, i.e. the current reference year weather data and the projected change of weather variables due to climate change, are required for this method. Both of them are also often readily available or easily accessed. On the one hand, this avoids the limitations of the extrapolating statistical method, which ignores the effect of the weather variables, diurnal cycle and latent energy. On the other hand, it also removes the complexity associated with the stochastic weather models and global climate models, which often require meteorological specialists to operate these complicated models. This method also allows impact studies for comparison between current and future weather conditions to be made on a consistent base. An example

Table 1 List of climate and associated scenario variables, ranked subjectively in decreasing order of confidence.

Fig. 4. Ranges of possible global-average warming relative to 1990 [23].

Climate variable

Confidence

Atmospheric CO2 concentration Global mean sea-level Global mean temperature Regional seasonal temperature Regional temperature extremes Regional seasonal precipitation and cloud cover Regional potential evapotranspiration Changes in climatic variability (e.g. E1 Nino, daily precipitation regimes) Climate surprises (e.g. disintegration of the West Antarctic Ice Sheet)

High Y

Low Very low or unknown

L. Guan / Building and Environment 44 (2009) 793–800

TRY / TMY

Current weather variables

Prediction of future change in weather variables

No

797

Input Yes

Simple Retain to current condition

Imposed offset method

Detaile Diurnal modelling method

Output Predicted future hourly weather data Fig. 5. The flow chart of proposed framework to estimate the change of weather variables due to global warming.

of application of the proposed framework to the different global warming scenarios in Australia has been given in another paper by Guan et al. [11]. Therefore, this paper only focuses on the discussion of the formation and justification of the proposed framework.

4.2. Implementation of the proposed framework As shown in Fig. 5, if a reliable prediction for a specific key climatic parameter is not available, then the method of retaining current condition may be used, which implies that the data for that particular parameter from current weather conditions are still used (retained). To compensate for this, it is suggested that a sensitivity test of its impact on the building performance needs to be carried out. In particular, if there is a lack of the prediction of future change in humidity, then as a first approximation, the relative humidity, instead of absolute humidity, may be assumed to remain unchanged to allow for the possible increase of evaporation due to global warming [20]. If only the projected average value of change is available for a specific key climatic parameter, which could be in the scale of either annual, seasonal or monthly data, the morphing approach, including three generic operations of shifting, linear stretching, and shifting and stretching together, may then be adopted to generate future hourly data for that particular variable. This is achieved by modifying the typical reference year weather data for each site, either the test reference year (TRY) or typical meteorological year (TMY), through a constant value increase for every hour within the varied period, to reflect the estimated effect of potential global warming. However, if more detailed information of future change due to global warming is available for a specific key climatic parameter (i.e. projected temperature change in daily mean, maximum and minimum temperatures are available), then the diurnal modelling method for the weather variable should be applied to generate the future hourly data. In this case, the current TRY/TMY weather data will first be used to determine the coefficients for the modelled equations, and to validate the overall diurnal modelling. After the diurnal cycle model is constructed and calibrated, the new future project data (i.e. daily maximum and minimum temperature data) can then be used as an input to estimate future hourly weather data for the weather parameter. It is noted that since the idealization approach of diurnal cycle modelling often assumes the same diurnal pattern for a weather variable (i.e. places the daily maximum and/or minimum temperature at approximately the same time of day), which in the real data would vary from day to

day, a systematic bias may be introduced. Therefore, a stochastic term may also have to be used in the hourly variable simulation to reduce such bias [7]. 4.3. Projection of future hourly DBT data The projected mean temperature increase under climate change is often available from Intergovernmental Panel on Climate Change (IPCC) or relevant national research organizations. As shown in Table 1, compared with other weather parameters, the IPCC projection in temperature increase has the highest confidence. Therefore, DBT is the key information that should be used for an impact study of climate change on building performance. Depending on the nature and details of available future temperature change information due to global warming, either the morphing approach as discussed in the imposed offset method or the diurnal temperature modelling method may be employed to generate future hourly DBT data. Different researchers in the USA, UK, Australia, and New Zealand have used either the morphing approach or the diurnal modelling approach to project future hourly DBT data for their impact studies of global warming on building thermal performance [6–12]. Using the imposed offset method, a cumulative frequency analysis of hourly DBT for the different climate scenarios in Australia was presented by Guan et al. [11], which showed that all four future climate scenarios would have a similar distribution pattern with that of the current test reference year (TRY) weather data. The most common model to represent diurnal temperature cycle was discussed by Parton and Logan [25]. For an individual day, it was found that the diurnal patterns of air temperature change can be reasonably assumed as a sinusoidal variation during the daytime and an exponential decay during the nighttime. During daytime (between sunrise and sunset), the air temperature Ti ( C) can be expressed as:

Ti ¼ Tmin þ ðTmax  Tmin Þsin

h pm i Y þ 2a

(2)

During nighttime (from the sunset temperature to the early morning minimum), the air temperature Ti ( C) can then be expressed as:

 bn Zþc



Ti ¼ Tmin þ ðTset  Tmin Þexp

(3)

where Ti is the temperature at hour i ( C), Tmin is the daily minimum temperature ( C), Tmax is the daily maximum temperature ( C), Tset is the temperature at sunset ( C), Y is the day length (h), Z is the

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night length (h), m is the number of hours after the minimum temperature occurs until sunset, n is the number of hours after sunset until the time of the minimum temperature; a is the lag coefficient for the maximum temperature, b is the nighttime temperature coefficient, and c is the lag of the minimum temperature from the time of sunrise. However, it is noted that the idealization of sinusoidal variation in temperature may lead to a biased prediction, as the idealization method often places the daily minimum and maximum temperatures at approximately the same time of day, which would actually vary from day to day in the real data [7]. In order to reduce the range of such temperature bias, a stochastic term in hourly temperature simulation was introduced by Mullan [7], where a random number was drawn from a normal distribution of zero mean and was added to the interpolated hourly temperature.

increased dry bulb temperature, the air moisture content will also increase to maintain the same relative humidity, which reflects the possible increase of evaporation due to global warming. In this case, a sensitivity study on this assumption may also need to be carried out to show the potential impact of this assumption on the building performance. A range of 10–20% of variation in RH from the base value has been suggested by Guan et al. [11] for this purpose. However, if there is a reliable projection in air humidity change due to global warming, then similar to air temperature, either the imposed offset method or diurnal humidity modelling method should be applied to generate future hourly humidity data. There have been many papers published to discuss the modelling of diurnal cycle of relative humidity, including the work of Ephrath et al. [28]. 4.5. Projection of future hourly solar radiation data

4.4. Projection of future hourly air humidity data Different from DBT, air humidity can often be expressed in four different climatic terms, which include humidity ratio (or absolute humidity, W), relative humidity (RH), the wet bulb temperature (Twet) and the dew point temperature (Tdew). Given the dry bulb temperature (Tdry) and any one of the other four humidity relative climatic terms (W, RH, Twet, or Tdew), the other three variables can be deduced by standard meteorological formula. During a typical day, as the air warms up (Tdry increases), it is expected that Twet would also increase while the RH would generally decrease. Tdew and W would typically show little systematic change [7]. Associated with an increase in the Earth’s surface temperature, the rainfall pattern as well as the rate of water evaporation is also likely to change. These possible changes would potentially lead to a change in air humidity. However, unlike the projection of future dry bulb temperature change, which can often be seen in many climate change brochures, the projection of possible change in air humidity is much more variable and can also be inconsistent. For example, Sturman and Tapper [20] have suggested that the relative humidity of the troposphere may be assumed to remain constant, while Aguiar et al. [26] found that the absolute humidity will suffer only slight modifications under changed climates in Portugal, which means the atmospheric air will become dryer in the future. From the analysis of historic weather data in Australia, it has also been found that different from a cold year, a hot year may often have higher absolute moisture content but with lower relative humidity [27]. It is also found that there is a close correlation between hourly variations of Tdry and RH, with an increase of hourly temperature leading to a decrease of RH [14]. Therefore, it seems that in most cases, with an increase of air temperature, the humidity ratio will generally increase, although the amount of increased humidity ratio may not be enough to maintain a constant RH. This may explain the reason why the various assumptions were previously adopted by different researchers to project the possible change in air humidity. For Australia, it has been predicted that over most of the continent, the annual average relative humidity will decrease in the future, while in parts of New South Wales (NSW), southern Queensland, western Northern Territory and central Western Australia it will increase [12]. From the above discussion, as suggested in the proposed framework (Fig. 5), depending on the levels of available information on future humidity change due to global warming, either the constant RH method, morphing approach or diurnal humidity modelling method may be employed to generate hourly air humidity data. In particular, in the case of a lack of reliable predictions on the changes of humidity variables under global warming, it is suggested that as a first approximation, the local relative humidity may be assumed to remain unchanged for both current and global warming scenarios [20]. That is, with an

From the energy point of view, solar intensity would not change significantly as it passes through the ozone layer. However, associated with an increase in the Earth’s surface temperature, the sky cloudiness is likely to change, which could influence daylight and solar gains at ground level [29]. Clouds reflect incoming solar radiation and absorb long-wave radiation from the ground; the reflection produces cooling, and the absorption produces warming [20], so the net result of received solar irradiance may increase or decrease, depending on the dominant effect of either reflection or absorption of the cloud. Both cloud height and cloud radiative properties could also have an impact on the net result of received solar irradiance [20]. For instance, high clouds are colder, so they lose less energy to space than those at lower altitudes. In parallel, various cloud forms can also have different effects on solar and terrestrial long-wave radiation. Their absorption and reflective properties will depend on their microphysical characteristics. Therefore, the projection of possible change in solar irradiance is much more variable, and even a rough projection could be difficult. Croke et al. [30] conducted land-based observations of cloud cover, for the period 1900–1987 and over three geographical regions of the United States (coastal southwest, coastal northeast, and southern plains). It was found that there were strong positive correlations between cloud cover and global mean surface temperature, which was consistent with prior investigations, suggesting that cloud cover over land had increased during global warm periods relative to cold periods. However, the lack of a direct physical cause-and-effect explanation for the changes in regional cloud cover implies difficulty in the projection of cloud cover increase due to global warming. Degelman [9] believed that the horizontal insolation should generally be reduced by increased cloud cover due to global warming. However, the amount of increased cloud may not be the same at all latitudes and at all locations at the same latitude. By evaluating the KT value (clearness index) over 30 years of records (1961–1990), it was found that the clearness index was neither latitude nor season dependent. By contrast, Aguiar et al. [26] predicted higher solar radiation levels in future summers than those today. Based on projected climatic data provided by the Climate Impacts LINK project on behalf of the Hadley Centre and the UK Meteorological Office, it was found that the global horizontal daily irradiation is gradually increased from a baseline between April and September with the largest gap in July. In the other six months, it stays similar to the baseline. Through comparison between coldest and hottest year of ten years historical weather data in Australia, Guan [27] has found that global solar irradiance (GSI) on a horizontal plane remains a similar pattern. The total cloud cover, however, is different between hot and cold years, with the hot year often having a lower index of

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cloud cover. From the analysis of hourly variation between Tdry and GSI, it seems that there is a close correlation between them, with an increased hourly temperature having a higher GSI [14]. Although general trends are detected, a concrete model to relate change in solar radiation with global warming has not yet been obtained. However, it was predicted that for Australia, annual average radiation would decrease in the western half of Australia, with the possibility of increases or decreases in the east [12]. Decreases in radiation are also strongest in summer and cover most of western and southern Australia. In autumn, decreases in radiation affect most of the continent. In winter and spring, increases occur in southern and eastern areas, while decreases affect the northwest. From the above discussion, it may therefore be suggested that as shown in the proposed framework (Fig. 5), depending on the available information on future solar radiation change due to climate change, either remaining unchanged to current level, the morphing approach (or imposed offset method) or diurnal modelling method may be employed to generate hourly solar radiation data. In the case of lack of reliable predictions on the changes of solar radiation under climate change, an unchanged solar radiation may be assumed. This should also follow with a sensitivity test to understand the potential impact due to the possible change of sky cloudiness. As suggested by Scott et al. [6], solar insolation over the range of 80–120% from the base value may be appropriate for the sensitivity test. However, if there is a reliable projection in the change of global solar irradiation due to climate change, depending on the details of the projection, the imposed offset method or modelling of its diurnal pattern should be applied to generate the future hourly data. Hirschmann [31] used a simple cosine bell to mathematically express the daily incidence of solar radiation. 4.6. Projection of future hourly wind patterns Associated with an increase in the Earth’s surface temperature, the wind pattern is likely to change as well. From the analysis of 10 years historic weather data in Australia, it has been found that there are different patterns of wind speed and direction between a cold year and a hot year, and the variation is quite irregular [27]. Wind speed could be higher or lower in a hot year than in a cold year. There is also no clear correlation between hourly variations of Tdry and wind speed [14]. However, based on scenarios of average annual and seasonal wind-speed changes developed from 13 climate model simulations, it was found that there is a tendency of increase in annual average wind speed in Australia [12]. Up to 3% and 12% increases were projected for 2030 and 2070 respectively. In contrast, it was found by Aguiar et al. [26] that wind speed will suffer only slight modifications under changed climates in Portugal. On the other hand, it has also been difficult to define and quantify the effects of wind data on thermal designs and energy analysis of buildings due to the strong influences of local factors. However, it is generally believed that the influences of wind on thermal design load and building energy consumption are relatively insignificant, except in locations where severe wind conditions predominate [32]. It is also believed that wind direction would have little impact on a building’s annual energy performance [33]. Perhaps because of the above reasons, previous researchers often assumed an unchanged wind pattern for the future climate scenarios [6,7,9,11]. However, it was also suggested by Scott et al. [6] that to supplement this assumption, a sensitivity test may need to be carried out to show its impact on building thermal performance. They modified the TMY wind speed over the range of 80–120% of the base TMY condition to explore the sensitivity of the DOE-2 program to changes in this parameter. It was found that for the office building examined, even a 20% change in wind speed would

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only result in a change of overall building energy demand of less than 0.2%, which is very small. However, it is noted that the above result may be subject to the influence of the building size and type. It would normally be expected that smaller buildings, with a higher surface-to-volume ratio, would be more significantly affected by insolation and wind change. Based on the above discussion, it is therefore suggested that as shown in the proposed framework (Fig. 5), depending on the available information on future wind character change due to global warming, either remaining unchanged at the current level, or the imposed offset method (or morphing approach) or diurnal modelling method may be employed to generate hourly wind character data. In the case of lack of reliable predictions on the changes of wind character under climate change, an unchanged wind character may be assumed for the impact study of climate change, but a sensitivity test as suggested by Scott et al. [6] may then be needed to show the possible effect of wind speed on the building thermal behaviour. However, if there is a reliable projection in the change of wind character due to global warming, depending on the details of projection, the morphing approach (or imposed offset method) or a modelling of its diurnal pattern should be applied to generate the future hourly data. An example of diurnal model of wind speed was presented by Peterson and Parton [34]. 4.7. Summary Because the proposed framework allows either the method of retaining the current weather variable unchanged, or the imposed offset method, or diurnal modelling method to be employed, it therefore represents a more comprehensive and holistic approach than the previously imposed offset methods to covert available weather data climatic information to a format suitable for building simulation studies. Past experience has also indicated that often the best and the most efficient approach to impact studies is to first assemble an ‘‘observed’’ data set for input to the impact study model. This will then be followed by the necessary sensitivity studies with some adjustments to the model to accommodate the simulated changes from the climate model. 5. Conclusions The dynamic interaction between building systems and external climate is extremely complex, involving a large number of difficultto-predict variables. In order to study the impact of climate change on the built environment, the use of building simulation techniques together with forecast weather data are often necessary. Since all building simulation programs require hourly meteorological input data for their thermal comfort and energy evaluations, the provision of suitable weather data becomes critical. In this paper, the methods used to prepare future hourly weather data for the impact study of climate change have been reviewed. This includes, from simple to complex, the extrapolating statistic method, the imposed offset method, the stochastic weather model and global climate models. The advantages and disadvantages of each method have been discussed. The relationship between these four methods has also been illustrated. It has been found that due to the complexity and special operation requirements, global climate models may only be useful in generating average change of climatic parameters, including the temperature increase. They can then be used as input for other simpler methods to estimate local weather conditions. For use in building simulation studies, the extrapolating statistic method may be too simplistic while the stochastic weather model appears to be too complex. Therefore, it seems that the imposed offset method

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may be the most suitable method to be adopted by building consultants for the building simulation study. Based on the analysis of Australian historic climatic data, together with a review of previous approaches used to generate future weather data, an effective framework/procedure has been proposed to generate future hourly weather data. It has been shown that this framework is evolved from the previous imposed offset method, but is able to deal with different levels of available information regarding climate change, as well as to retain the character of a ‘‘typical’’ year, not a random year for a desired period. Because the proposed framework allows either the method of retaining current weather variable unchanged, or the imposed offset method, or diurnal modelling method to be employed, it therefore represents a more comprehensive and holistic approach than the previous imposed offset method to covert available weather data climatic information to a format suitable for building simulation study. Acknowledgements The author wishes to express great appreciation for the valuable comments/suggestions from both reviewers. They have made a great contribution to the improvement of the quality of this paper.

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