Toronto climate data for building simulations with urban heat island effects and nature-based solutions

As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Toronto airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions. This dataset contains hourly historical and future weather files for use in building simulations for the city of Toronto, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned. The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R. The historical and future global warming thresholds and their corresponding time periods are as follows: Global Warming Scenario Time Period Historical 1991-2021 Global Warming 0.5ºC 2003-2033 Global Warming 1.0ºC 2014-2044 Global Warming 1.5ºC 2024-2054 Global Warming 2.0ºC 2034-2064 Global Warming 2.5ºC 2042-2072 Global Warming 3.0ºC 2051-2081 Global Warming 3.5ºC 2064-2094 The following variables are included in the files: Variable Description RUN Run number (R1-R15) of Canadian Regional Climate Model, CanRCM4 large ensemble associated with the selected reference year data YEAR Year associated with the record MONTH Month associated with the record DAY Day of the month associated with the record HOUR Hour associated with the record YDAY Day of the year associated with the record DRI_kJPerM2 Direct horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated) DHI_kJperM2 Diffused horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated) DNI_kJperM2 Direct normal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated) GHI_kJperM2 Global horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated) TCC_Percent Instantaneous total cloud cover at the HOUR in % (range: 0-100) RAIN_Mm Total rainfall in mm (total from previous HOUR to the HOUR indicated) WDIR_ClockwiseDegFromNorth Instantaneous wind direction at the HOUR in degrees (measured clockwise from the North) WSP_MPerSec Instantaneous wind speed at the HOUR in meters/sec RHUM_Percent Instantaneous relative humidity at the HOUR in % TEMP_K Instantaneous temperature at the HOUR in Kelvin ATMPR_Pa Instantaneous atmospheric pressure at the HOUR in Pascal SnowC_Yes1No0 Instantaneous snow-cover at the HOUR (1 - snow; 0 - no snow) SNWD_Cm Instantaneous snow depth at the HOUR in cm随着城市面临气温攀升、极端天气事件频次增加以及降水格局改变的挑战，建筑正承受着日益增长的能源需求、热应激、热舒适问题以及服役寿命缩短的压力。因此，在气候变化条件下评估建筑性能，对于构建可持续且韧性的社区至关重要。城市独特的气候特征（如城市热岛效应）难以通过全球或区域气候模型实现精准模拟，因此典型建筑分析往往未纳入此类影响因素。为此，研究人员采用了一种计算高效的方法，基于区域气候模型生成“城市化”气候数据，以制备纳入城市效应的建筑模拟气候数据集。本研究以多伦多机场气象站的现有气候数据为例演示了该流程，并将其扩展至实施了基于自然的解决方案（如增加绿化面积和地表反照率）的情景预测。研究发现，在城市化气候数据中，城市热岛效应的表征以及基于自然的解决方案的后续降温效果得到了显著改善。本数据集可帮助建筑从业者在考虑城市冠层内的复杂相互作用以及基于自然的解决方案等减排措施的前提下，评估历史及潜在未来气候变化下的建筑性能。 本数据集包含用于加拿大多伦多市建筑模拟的逐小时历史与未来气象文件。尽管同类气象文件通常基于城市附近机场的观测数据，但本数据集采用了一种新颖的统计-动力降尺度技术：结合动力天气研究与预报（Weather Research and Forecasting, WRF）模型与统计方法，以及来自15个加拿大区域气候模型4（Canadian Regional Climate Model 4, CanRCM4）集合的气候预测数据，生成包含城市热岛效应以及不同基于自然的解决方案（Nature-based Solutions, NBS）减排策略（如提高地表反照率和绿化覆盖率）影响的城市气候数据。此外，本数据集还生成了不同减排措施实施强度的情景：例如将反照率提升至0.40（ALBD40）和0.80（ALBD80），以及绿化情景（GRN40、GRN80）和综合情景（COMB40、COMB80）。URBAN情景作为对照案例，该情景已纳入城市热岛效应，但尚未实施基于自然的解决方案。 数据以大型CSV文件存储，文件的行对应加拿大区域气候模型4集合的15个集合成员，列则为各类气象变量。例如，每个31年时段会重复15次，对应每个RCM集合成员。因此，每个文件包含4,073,400行数据（15×31×8760）。我们推荐使用Python或R语言的相关包查看该数据。 全球增温情景与对应时段如下： - 历史：1991-2021 - 全球增温0.5℃：2003-2033 - 全球增温1.0℃：2014-2044 - 全球增温1.5℃：2024-2054 - 全球增温2.0℃：2034-2064 - 全球增温2.5℃：2042-2072 - 全球增温3.0℃：2051-2081 - 全球增温3.5℃：2064-2094 文件中包含以下变量： | 变量 | 变量说明 | | --- | --- | | RUN | 加拿大区域气候模型4（CanRCM4）大集合的运行编号（R1-R15），对应所选参考年份的数据 | | YEAR | 记录对应的年份 | | MONTH | 记录对应的月份 | | DAY | 记录对应的当月日期 | | HOUR | 记录对应的小时 | | YDAY | 记录对应的年内日序 | | DRI_kJPerM2 | 水平直接辐射照度，单位kJ/m²（为当前小时与前一小时时段内的总辐照度） | | DHI_kJperM2 | 水平散射辐射照度，单位kJ/m²（为当前小时与前一小时时段内的总辐照度） | | DNI_kJperM2 | 法向直接辐射照度，单位kJ/m²（为当前小时与前一小时时段内的总辐照度） | | GHI_kJperM2 | 水平总辐射照度，单位kJ/m²（为当前小时与前一小时时段内的总辐照度） | | TCC_Percent | 当前小时的瞬时总云量，单位%（取值范围0-100） | | RAIN_Mm | 总降雨量，单位mm（为当前小时与前一小时时段内的总降雨量） | | WDIR_ClockwiseDegFromNorth | 当前小时的瞬时风向，单位°（以正北为基准顺时针计量） | | WSP_MPerSec | 当前小时的瞬时风速，单位m/s | | RHUM_Percent | 当前小时的瞬时相对湿度，单位% | | TEMP_K | 当前小时的瞬时气温，单位开尔文（K） | | ATMPR_Pa | 当前小时的瞬时大气压强，单位帕斯卡（Pa） | | SnowC_Yes1No0 | 当前小时的瞬时积雪状态（1表示有积雪，0表示无积雪） | | SNWD_Cm | 当前小时的瞬时积雪深度，单位cm