- 著者
- Noriko N. Ishizaki Motoki Nishimori Toshichika Iizumi Hideo Shiogama Naota Hanasaki Kiyoshi Takahashi
- 出版者
- Meteorological Society of Japan
- 雑誌
- SOLA (ISSN:13496476)
- 巻号頁・発行日
- vol.16, pp.80-85, 2020 (Released:2020-05-13)
- 参考文献数
- 25
- 被引用文献数
- 24
Bias corrected climate scenarios over Japan were developed using two distinct methods, namely, the cumulative distribution function-based downscaling method (CDFDM) and Gaussian-type Scaling approach (GSA). We compared spatial distribution, monthly variation, and future trends. The seasonal distribution of bias-corrected data using CDFDM closely followed the original general circulation model (GCM) outputs. GSA overestimated the amount of precipitation by 12-18% in every season because of an unsuitable assumption on the probability distribution. We also examined the contributions of each source of the uncertainty in daily temperature and precipitation indices. For daily temperature indices, GCM selection was the main source of uncertainty in the near future (2026-2050), while different Representative Concentration Pathways (RCPs) resulted in large variability at the end of the 21st century (2076-2100). We found large uncertainty using the bias-correction (BC) methods for daily precipitation indices even in the near future. Our results indicated that BC methods are an important source of uncertainty in climate risk assessments, especially for sectors where precipitation plays a dominant role. An appropriate choice of BC, or use of different BC methods, is encouraged for local mitigation and adaptation planning in addition to the use of different GCMs and RCPs.
- 著者
- Noriko N. Ishizaki Motoki Nishimori Toshichika Iizumi Hideo Shiogama Naota Hanasaki Takahashi Kiyoshi
- 出版者
- Meteorological Society of Japan
- 雑誌
- SOLA (ISSN:13496476)
- 巻号頁・発行日
- pp.2020-014, (Released:2020-04-01)
- 被引用文献数
- 24
Bias corrected climate scenarios over Japan were developed using two distinct methods, namely, the cumulative distribution function-based downscaling method (CDFDM) and Gaussian-type Scaling approach (GSA). We compared spatial distribution, monthly variation, and future trends. The seasonal distribution of bias-corrected data using CDFDM closely followed the original general circulation model (GCM) outputs. GSA overestimated the amount of precipitation by 12-18% in every season because of an unsuitable assumption on the probability distribution. We also examined the contributions of each source of the uncertainty in daily temperature and precipitation indices. For daily temperature indices, GCM selection was the main source of uncertainty in the near future (2026-2050), while different Representative Concentration Pathways (RCPs) resulted in large variability at the end of the 21st century (2076-2100). We found large uncertainty using the bias-correction (BC) methods for daily precipitation indices even in the near future. Our results indicated that BC methods are an important source of uncertainty in climate risk assessments, especially for sectors where precipitation plays a dominant role. An appropriate choice of BC, or use of different BC methods, is encouraged for local mitigation and adaptation planning in addition to the use of different GCMs and RCPs.
- 著者
- Toshichika Iizumi Gen Sakurai Masayuki Yokozawa
- 出版者
- 日本農業気象学会
- 雑誌
- 農業気象 (ISSN:00218588)
- 巻号頁・発行日
- pp.D-13-00023, (Released:2014-04-03)
- 参考文献数
- 42
- 被引用文献数
- 3 10
The consequences of observed changes in climate and management on yield trends in major crop-producing regions have implications for future food availability and access. We presents an assessment of the impacts of historical changes in sowing date and climate to the maize yield trend in the United States (U.S.) Corn Belt from 1980 to 2006, using large-area crop modeling and data assimilation technique (the model optimization based on the Markov Chain Monte Carlo method). The model calibrated at a regional scale successfully captured the major characteristics of the reported changes in yield and the timing and length of maize growth periods over the Corn Belt. The simulation results using the calibrated model indicate that while the climate change observed for that period likely contributed to decrease the yield trend, the positive contribution from the reported earlier shift of sowing date offset the negative impacts. With given spread in the assessment results across previous studies and this study, the credence of the conclusion that the negative impacts of the climate change on the U.S. maize yield trend are more likely attributed to the decreasing growing-season precipitation trend than to the temperature trend increased. This study addressed an emerging use of large-area crop modeling and data assimilation to attribute observed change in crop yield trend to climate and management.