Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (West and Midwest)

As global emissions and temperatures continue to rise, global climate models offer projections as to how the climate will change in years to come. These model projections can be used for a variety of end-uses to better understand how current systems will be affected by the changing climate. While climate models predict every individual year, using a single year may not be representative as there may be outlier years. It can also be useful to represent a multi-year period with a single year of data. Both items are currently addressed when working with past weather data by a using Typical Meteorological Year (TMY)methodology. This methodology works by statistically selecting representative months from a number of years and appending these months to achieve a single representative year for a given period. In this analysis, the TMY methodology is used to develop Future Typical Meteorological Year (fTMY) using climate model projections. The resulting set of fTMY data is then formatted into EnergyPlus weather (epw) fi les that can be used for building simulation to estimate the impact of climate scenarios on the built environment. This dataset contains fTMY fi les for 3281 US Counties in the continental United States. The data for each county is derived from six different global climate models (GCMs) from the 6th Phase of Coupled Models Intercomparison Project CMIP6-ACCESSCM2, BCC-CSM2-MR, CNRM-ESM2-1, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. The six climate models were statistically downscaled for 1980–2014 in the historical period and 2015–2059 in the future period under the SSP585 scenario using the methodology described in Rastogi et al. (2022). Additionally, hourly data was derived from the daily downscaled output using the Mountain Microclimate Simulation Model (MTCLIM; Thornton and Running, 1999). The shared socioeconomic pathway (SSP) used for this analysis was SSP 5 and the representative concentration pathway (RCP) used was RCP 8.5. More information about SSP and RCP can be referred to O'Neill et al. (2020). More information about the six selected CMIP6 GCMs: ACCESS-CM2 -<br> http://dx.doi.org/10.1071/ES19040<br> BCC-CSM2-MR -<br> https://doi.org/10.5194/gmd-14-2977-2021<br> CNRM-ESM2-1-<br> https://doi.org/10.1029/2019MS001791<br> MPI-ESM1-2-HR -<br> https://doi.org/10.5194/gmd-12-3241-2019<br> MRI-ESM2-0 -<br> https://doi.org/10.2151/jmsj.2019-051<br> NorESM2-MM -<br> https://doi.org/10.5194/gmd-13-6165-2020 Additional references:<br> O'Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). Achievements and Needs for the Climate Change Scenario Framework.<br> Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0<br> Rastogi, D., Kao, S.-C., and Ashfaq, M. (2022). How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections? Earth's Future, 10, e2022EF002734. https://doi.org/10.1029/2022EF002734<br> Thornton, P. E. and Running, S. W. (1999). An Improved Algorithm for Estimating Incident Daily Solar Radiation from Measurements of Temperature, Humidity and Precipitation, Agricultural and Forest Meteorology, 93, 211-228. Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (<strong><em>West and Midwest</em></strong>)." ORNL internal Scientific and Technical Information (STI) report, doi:10.5281/zenodo.8338549, Sept 2023. [Data] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (<strong><em>East and South</em></strong>)." ORNL internal Scientific and Technical Information (STI) report, doi:10.5281/zenodo.8335815, Sept 2023. [Data] Bass, Brett, New, Joshua R., Rastogi, Deeksha and Kao, Shih-Chieh (2022). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation (1.0) [Data set]." Zenodo, doi.org/10.5281/zenodo.6939750, Aug. 2022. [Data]随着全球碳排放与气温持续攀升，全球气候模型可对未来数十年的气候变化趋势作出预测。这些模型预测结果可应用于多种场景，以助力我们更好地明晰当前系统将如何受气候变化的影响。不过，气候模型虽可逐年进行预测，但单一年份的数据未必具有代表性，因为可能存在异常年份；用单一年份的数据表征多年时段同样具有实用价值。目前，处理历史气象数据时，典型气象年（Typical Meteorological Year, TMY）方法已可解决上述两类问题：该方法通过统计手段从多年数据中筛选具有代表性的月份，将其拼接后即可得到对应时段内单一年份的代表性气象数据集。 本分析采用典型气象年方法，基于气候模型预测结果构建未来典型气象年（Future Typical Meteorological Year, fTMY）数据集。随后，生成的fTMY数据将被格式化为能源Plus天气（EnergyPlus weather, EPW）文件，可用于建筑能耗模拟，以评估气候情景对建成环境的影响。 本数据集包含美国本土3281个县的fTMY文件。每个县的数据均源自耦合模型比较计划第六阶段（Coupled Models Intercomparison Project Phase 6, CMIP6）的6个全球气候模型（Global Climate Models, GCMs），分别为ACCESS-CM2、BCC-CSM2-MR、CNRM-ESM2-1、MPI-ESM1-2-HR、MRI-ESM2-0及NorESM2-MM。 这6个气候模型的结果已基于Rastogi等人（2022）提出的方法进行统计降尺度处理：历史时段为1980–2014年，未来时段为2015–2059年，所用情景为SSP585。此外，研究团队利用山地微气候模拟模型（Mountain Microclimate Simulation Model, MTCLIM; Thornton and Running, 1999），将每日降尺度输出结果转换为逐小时气象数据。本分析所用的共享社会经济路径（Shared Socioeconomic Pathway, SSP）为SSP5，代表性浓度路径（Representative Concentration Pathway, RCP）为RCP8.5。关于SSP与RCP的更多信息可参考O'Neill等人（2020）的研究。 关于所选6个CMIP6全球气候模型的更多信息如下： ACCESS-CM2 - http://dx.doi.org/10.1071/ES19040 BCC-CSM2-MR - https://doi.org/10.5194/gmd-14-2977-2021 CNRM-ESM2-1 - https://doi.org/10.1029/2019MS001791 MPI-ESM1-2-HR - https://doi.org/10.5194/gmd-12-3241-2019 MRI-ESM2-0 - https://doi.org/10.2151/jmsj.2019-051 NorESM2-MM - https://doi.org/10.5194/gmd-13-6165-2020 附加参考文献： 1. O'Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). 气候变化情景框架的成就与需求. 《自然·气候变化》, 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0 2. Rastogi, D., Kao, S.-C., and Ashfaq, M. (2022). 降尺度技术与气象参考观测的选择如何影响未来水文气候预测？《地球的未来》, 10, e2022EF002734. https://doi.org/10.1029/2022EF002734 3. Thornton, P. E. and Running, S. W. (1999). 基于温度、湿度与降水观测估算入射日太阳辐射的改进算法, 《农业与森林气象学》, 93, 211-228. 相关数据集文献： Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "适用于全美各县建筑模拟的未来典型气象年（fTMY）美国天气文件（<strong><em>西部与中西部</em></strong>）". 橡树岭国家实验室（Oak Ridge National Laboratory, ORNL）内部科学与技术信息（Scientific and Technical Information, STI）报告, doi:10.5281/zenodo.8338549, 2023年9月. [数据集] Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "适用于全美各县建筑模拟的未来典型气象年（fTMY）美国天气文件（<strong><em>东部与南部</em></strong>）". 橡树岭国家实验室（Oak Ridge National Laboratory, ORNL）内部科学与技术信息（Scientific and Technical Information, STI）报告, doi:10.5281/zenodo.8335815, 2023年9月. [数据集] Bass, Brett, New, Joshua R., Rastogi, Deeksha and Kao, Shih-Chieh (2022). "适用于建筑模拟的未来典型气象年（fTMY）美国天气文件（1.0版）[数据集]". Zenodo, doi.org/10.5281/zenodo.6939750, 2022年8月. [数据集]