Exploring Alternatives to the "Typical Meteorological Year" for Incorporating Climate Change into Building Design

Typical climate conditions of the 20 century may not provide adequate design parameters for the built environment of the 21 century due to changing climate. The conventional practice in the engineering community is to use past climate observations to provide climate input for building design to function into the middle of the 21 century. Recent studies have used global climate models together with statistical downscaling techniques to develop site-specific climates for future energy demands on buildings. An alternative method is to use “dynamical” downscaling by use of regional climate models as is being done under the North American Regional Climate Change Assessment Program (NARCCAP). This technique uses results of global climate models to drive regional climate models which give higher resolution results over specific locations, thereby more robustly representing features such as mountains, coastal areas, and fine scale dynamical processes of the atmosphere that create regionally unique climates. We use seven global/regional climate model combinations in conjunction with the solar radiation analyses method of Wilcox and Marion to produce scenarios of future typical meteorological years for the middle of the 21 century for Mason City, IA. Our method goes beyond previous results in that (1) we use dynamical downscaling rather than statistical downscaling, (2) our results are applicable to all US locations available in the TMY3 database, and (3) our use of multiple global and multiple regional models enables us to present strong evidence that, for our test location, the magnitude of climate change in meteorological variables of high importance to building energy considerations by the middle of the 21 century will be of greater magnitude than both the natural variation in these variables during last three decades of the 20 century and the inter-model variation of the model combinations used to project this change. In future work these results will be used in building design and building energy modeling, starting with the U.S. DOE Commercial Reference Buildings. INTRODUCTION Typical climate conditions for the 20 century may not provide adequate design parameters for the built environment of the 21 century. Huang (2006) (as reported by Xu et al., 2009) used results of four global climate model (GCM) future climate scenarios to estimate that net energy use by residential and commercial buildings in Los Angeles will increase by 25 28% by 2100 due to increase in atmospheric greenhouse gases. Furthermore, he noted that the frequency, duration and intensity of heat waves will increase peak energy demand significantly under these climate scenarios. The conventional practice in the engineering community for determining normal climate is to use the time-honored method developed by the World Meteorological Organization (WMO) and the National Oceanic and Atmospheric Administration (NOAA). This method uses the most recent three completed decades as the definition of “normal climate”. CH-12-C049 384 ASHRAE Transactions Currently the observed weather conditions are averaged from 1981-2010 to produce a location’s “normal climate”. However, Livezey et al. (2007) assert that the WMO-recommended 30-yr normals are no longer useful for many applications such as building design. Use of such data for estimating future natural gas send-out by utility firms, for example, leads to serious over-estimates of consumer demand in locations such as Chicago where winters are becoming much milder. Alternative methods such as those explored by Huang (2006) and Xu et al. (2009) are needed to better represent continuing climate trends that are outside the range of means of past observations, while concurrently allowing for high levels of interannual variability and extreme events as suggested by Huang (2006). Wilcox and Marion (2008) have developed and described the current version of the typical meteorological year (TMY3) for use by building designers and others for modeling renewable energy conversion systems at a wide range of locations across the US. The database uses observed conditions from the National Solar Radiation Data Base (NSRDB) and from 1020 locations in the U.S. and its territories and meteorological data from the National Climatic Data Center. This TMY3 database enjoys wide use in building design and alternative energy applications, although other weather databases also are used, such as the Weather Year for Energy Calculations (WYEC), the Test Reference Year (TRY), Canadian Weather for Energy Calculations (CWEC) for Canada, and the California Thermal Zones (CTZ2) for California. Crawley and Huang (1997) provide discussion on characteristics and uses such of alternative climate databases. The currently accepted method for assessing impacts of climate change is to “downscale” climate change information produced by GCMs for particular locations and add these “changes” to the current (20 century) climate to produced a refined estimate of future climate. This downscaling can be performed with statistical methods as was done in the most recent assessment of impacts for the U.S. (Karl et al., 2009) and by Xu et al. (2009) for California. Crawley (2008) used GCMs with statistical downscaling to represent four scenarios of climate change and two cases of urban heat islands for 25 locations worldwide. Overall, the impacts of climate change were projected to reduce energy use for cold climates by around 10%, increase energy use in tropical climates by more than 20%, and change energy use from heating to cooling for the midlatitudes. The study states that unless significant changes are made to buildings, “building owners will experience substantial operating cost increases and possible disruptions in an already strained energy supply system.” Guan (2009) reviewed a variety of methods including the extrapolating statistics method, the imposed offset method, the stochastic weather model and GCMs. The study declared that the extrapolating statistics method was too simple, and the stochastic weather model was too complex. GCMs were said to be useful for generating average changes but perhaps not local changes. That left the imposed offset method as the best overall method of those considered but left open the prospect of improving GCMs to better represent regional changes. An alternative method to that used by Karl et al. (2009), Xu et al. (2009), Crawley (2008), and Guan (2009) is to use “dynamical” downscaling by use of regional climate models (RCMs) as is discussed in Chapter 11 of IPCC (2007) and as is being done under the North American Regional Climate Change Assessment Program (NARCCAP, 2010). This technique uses results of GCMs to drive RCMs, which give higher resolution results over specific locations, thereby more robustly representing features such as mountains, coastal areas, and fine scale dynamical processes of the atmosphere that create regionally unique climates. Xu et al. (2009) recommended that this method be used as a refinement to their statistically downscaled results when the dynamically downscaled results become available. Our study combined the solar radiation analyses method as described by Wilcox and Marion (2008) together with dynamically downscaled climate change information generated under NARCCAP to produce scenarios of future typical meteorological years for the middle of the 21 century. Dynamical downscaling consists of using a high-resolution RCM driven at lateral boundaries (in our case place well beyond the borders and coasts of the US) by results of a GCM. The GCM therefore provides the global climate conditions but the RCM provides finer scale regional refinements by dynamical methods based on the laws of motion and thermodynamics. Under this method the GCM/RCM combined models determine the changes in monthly climate, which are then added to the TMY3 values, so that the advantages of the TMY3 cumulative frequency distributions are preserved in the future climate datasets. It is important to note that this method preserves the method to account for daily extremes that has been used in the TMY3. RCMs produce regional climate refinements not simulated in global models. Our method builds on, but also extends, previous efforts to incorporate future climate