Hiruta Yuki, Ishizaki Noriko N, Ashina Shuichi, Takahashi Kiyoshi
Social Systems Division, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan.
Center for Climate Change Adaptation, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan.
Data Brief. 2022 Mar 11;42:108047. doi: 10.1016/j.dib.2022.108047. eCollection 2022 Jun.
Assessing the impacts of climate change in multiple fields, such as energy, land and water resources, and human health and welfare is important to find effective strategies to adapt to a changing climate and to reduce greenhouse gases. Many phenomena influenced by climate change have diurnal fluctuations and are affected by simultaneous interactions among multiple meteorological factors. However, climate scenarios with detailed (at least hourly) resolutions are usually not available. To assess the impact of climate change on such phenomena while considering simultaneous interactions (e.g., synergies), climate scenarios with hourly fluctuations are indispensable. However, because meteorological indicators are not independent, the value of one indicator varies as a function of other indicators. Therefore, it is almost impossible to determine the functions that show all relationships among meteorological elements considering the geographical and temporal (both seasonal and time of a day) characteristics. Therefore, generating hourly scenarios that include possible combinations of meteorological indicators for each hourly observation unit is a challenging problem. In this study, we provide secondary future climate scenario datasets that have hourly fluctuations with reasonable combinations of meteorological indicator values that are likely to occur simultaneously, without losing the long-term climate change trend in the existing daily climate scenarios based on global climate models. Historical hourly weather datasets observed from 2017 to 2019 (the reference years) are used to retrieve short-term fluctuations. Bias-corrected daily future climate scenario datasets generated using four global climate models (GFDL CM3, HadGEM2-ES, MIROC5, and MRI-CGCM3) and two Representative Concentration Pathways (RCP8.5 and 2.6) are used to model long-term climate change. A total of 48 different types of hourly future scenario datasets for five meteorological indicators (temperature, solar radiation, humidity, rainfall, and wind speed) were acquired, targeting a projection period from 2020 to 2080, for 10 weather stations in Japan. The generated hourly climate scenario datasets can be used to project the quantitative impacts of climate change on targeted phenomena considering simultaneous interactions among multiple meteorological factors.
评估气候变化在能源、土地和水资源以及人类健康与福祉等多个领域的影响，对于找到适应气候变化和减少温室气体的有效策略至关重要。许多受气候变化影响的现象具有日波动特征，并且受到多种气象因素同时相互作用的影响。然而，通常无法获得具有详细（至少每小时）分辨率的气候情景。为了在考虑同时相互作用（例如协同效应）的情况下评估气候变化对这些现象的影响，具有每小时波动的气候情景是必不可少的。然而，由于气象指标并非相互独立，一个指标的值会随着其他指标的变化而变化。因此，考虑到地理和时间（季节和一天中的时间）特征，几乎不可能确定显示气象要素之间所有关系的函数。因此，为每个每小时观测单元生成包含气象指标可能组合的每小时情景是一个具有挑战性的问题。在本研究中，我们提供了二次未来气候情景数据集，这些数据集具有每小时波动，且气象指标值的组合合理，可能同时出现，同时不会丢失基于全球气候模型的现有每日气候情景中的长期气候变化趋势。利用2017年至2019年（参考年份）观测到的历史每小时天气数据集来获取短期波动。使用四个全球气候模型（GFDL CM3、HadGEM2-ES、MIROC5和MRI-CGCM3）和两个代表性浓度路径（RCP8.5和2.6）生成的偏差校正每日未来气候情景数据集用于模拟长期气候变化。针对日本的10个气象站，获取了总共48种不同类型的五个气象指标（温度、太阳辐射、湿度、降雨量和风速）的每小时未来情景数据集，预测期为2020年至2080年。生成的每小时气候情景数据集可用于预测气候变化对目标现象的定量影响，同时考虑多种气象因素之间的同时相互作用。
