Iizumi Toshichika, Takimoto Takahiro, Masaki Yoshimitsu, Maruyama Atsushi, Kayaba Nobuyuki, Takaya Yuhei, Masutomi Yuji
Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization, 3-1-3 Kannondai, Tsukuba, Ibaraki, 305-8604, Japan.
Japan Meteorological Agency, 3-6-9 Toranomon, Minato City, Tokyo, 105-8431, Japan.
Sci Data. 2024 Aug 8;11(1):849. doi: 10.1038/s41597-024-03702-5.
Climate variability in the growing season is well suited for testing adaptation measures. Adaptation to adverse events, such as heatwaves and droughts, increases the capacity of players in agri-food systems, not only producers but also transporters and food manufacturers, to prepare for production disruptions due to seasonal extremes and climate change. Climate impact models (e.g., crop models) can be used to develop adaptation responses. To run these models, historical records and climate forecasts need to be combined as a single daily time series. We introduce the daily 0.5° global hybrid reanalysis-forecast meteorological forcing dataset from 2010 to 2021. The dataset consists of the Japanese 55-yr Reanalysis (JRA55) and the Japan Meteorological Agency/Meteorological Research Institute Coupled Prediction System version 2 (JMA/MRI-CPS2) 5-member ensemble forecast. Both are bias-corrected using the Delta method and integrated with a baseline climatology derived from the Environmental Research and Technology Development Fund's Strategic Research 14 Meteorological Forcing Dataset (S14FD). The dataset is called JCDS (JRA55-CPS2-Delta-S14FD) and offers a framework for monitoring and forecasting applications towards adaptation.
生长季节的气候变异性非常适合测试适应措施。适应诸如热浪和干旱等不利事件，不仅能提高农业食品系统中的生产者，还能提高运输商和食品制造商应对季节性极端情况和气候变化导致的生产中断的能力。气候影响模型（如作物模型）可用于制定适应对策。要运行这些模型，需要将历史记录和气候预测结合成一个单一的每日时间序列。我们引入了2010年至2021年的每日0.5°全球混合再分析-预测气象强迫数据集。该数据集由日本55年再分析（JRA55）和日本气象厅/气象研究所耦合预测系统第2版（JMA/MRI-CPS2）的5成员集合预报组成。两者均使用Delta方法进行偏差校正，并与源自环境研究与技术开发基金战略研究14气象强迫数据集（S14FD）的基线气候学相结合。该数据集称为JCDS（JRA55-CPS2-Delta-S14FD），为适应的监测和预测应用提供了一个框架。
