Fujimori Shinichiro, Abe Manabu, Kinoshita Tsuguki, Hasegawa Tomoko, Kawase Hiroaki, Kushida Kazuhide, Masui Toshihiko, Oka Kazutaka, Shiogama Hideo, Takahashi Kiyoshi, Tatebe Hiroaki, Yoshikawa Minoru
Center for Social and Environmental Systems Research, National Institute for Environmental Studies (NIES), 16-2, Onogawa, Tsukuba, Japan.
Department of Integrated Climate Change Projection Research, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 3173-25 Showa-machi, Kanazawa-ku, Yokohama, Japan.
PLoS One. 2017 Jan 11;12(1):e0169733. doi: 10.1371/journal.pone.0169733. eCollection 2017.
In climate change research, future scenarios of greenhouse gas and air pollutant emissions generated by integrated assessment models (IAMs) are used in climate models (CMs) and earth system models to analyze future interactions and feedback between human activities and climate. However, the spatial resolutions of IAMs and CMs differ. IAMs usually disaggregate the world into 10-30 aggregated regions, whereas CMs require a grid-based spatial resolution. Therefore, downscaling emissions data from IAMs into a finer scale is necessary to input the emissions into CMs. In this study, we examined whether differences in downscaling methods significantly affect climate variables such as temperature and precipitation. We tested two downscaling methods using the same regionally aggregated sulfur emissions scenario obtained from the Asian-Pacific Integrated Model/Computable General Equilibrium (AIM/CGE) model. The downscaled emissions were fed into the Model for Interdisciplinary Research on Climate (MIROC). One of the methods assumed a strong convergence of national emissions intensity (e.g., emissions per gross domestic product), while the other was based on inertia (i.e., the base-year remained unchanged). The emissions intensities in the downscaled spatial emissions generated from the two methods markedly differed, whereas the emissions densities (emissions per area) were similar. We investigated whether the climate change projections of temperature and precipitation would significantly differ between the two methods by applying a field significance test, and found little evidence of a significant difference between the two methods. Moreover, there was no clear evidence of a difference between the climate simulations based on these two downscaling methods.
在气候变化研究中，综合评估模型（IAMs）生成的温室气体和空气污染物排放的未来情景被用于气候模型（CMs）和地球系统模型，以分析人类活动与气候之间未来的相互作用和反馈。然而，IAMs和CMs的空间分辨率不同。IAMs通常将世界划分为10 - 30个聚合区域，而CMs需要基于网格的空间分辨率。因此，将IAMs的排放数据降尺度到更精细的尺度，以便将排放数据输入到CMs中是必要的。在本研究中，我们检验了降尺度方法的差异是否会显著影响温度和降水等气候变量。我们使用从亚太综合模型/可计算一般均衡（AIM/CGE）模型获得的相同区域聚合硫排放情景，测试了两种降尺度方法。降尺度后的排放数据被输入到气候跨学科研究模型（MIROC）中。其中一种方法假设国家排放强度（例如，国内生产总值排放）有很强的收敛性，而另一种方法基于惯性（即基年保持不变）。两种方法生成的降尺度空间排放中的排放强度明显不同，而排放密度（单位面积排放）相似。我们通过应用场显著性检验，研究了两种方法在温度和降水的气候变化预测方面是否会有显著差异，结果发现几乎没有证据表明两种方法之间存在显著差异。此外，基于这两种降尺度方法的气候模拟之间也没有明显的差异证据。
