- 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
- Hideo Shiogama Noriko N. Ishizaki Naota Hanasaki Kiyoshi Takahashi Seita Emori Rui Ito Toshiyuki Nakaegawa Izuru Takayabu Yasuaki Hijioka Yukari N. Takayabu Ryosuke Shibuya
- 出版者
- Meteorological Society of Japan
- 雑誌
- SOLA (ISSN:13496476)
- 巻号頁・発行日
- vol.17, pp.57-62, 2021 (Released:2021-04-03)
- 参考文献数
- 20
- 被引用文献数
- 1 16
Climate change impact assessment studies often use future projections of only a few global climate models (GCMs) due to limited research resources. Here we develop a novel method to select a small subset of GCMs that widely capture the uncertainty range of large ensemble. By applying this method, we select a subset of five GCM projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble for impact and adaptation studies in Japan. At first, we omit GCMs whose global warming projections have been evaluated to be overestimated in the recent literature. Then, we select a subset of five GCMs that widely captures the uncertainty ranges for 8 climate variables and have good performances in present-climate simulations. These selected GCM simulations will be used to provide better climate scenarios for impact and adaptation studies than those in the previous impact assessment project.
- 著者
- Hideo Shiogama Noriko N. Ishizaki Naota Hanasaki Kiyoshi Takahashi Seita Emori Rui Ito Toshiyuki Nakaegawa Izuru Takayabu Yasuaki Hijioka Yukari N. Takayabu Ryosuke Shibuya
- 出版者
- Meteorological Society of Japan
- 雑誌
- SOLA (ISSN:13496476)
- 巻号頁・発行日
- pp.2021-009, (Released:2021-02-16)
- 被引用文献数
- 16
Climate change impact assessment studies often use future projections of only a few global climate models (GCMs) due to limited research resources. Here we develop a novel method to select a small subset of GCMs that widely capture the uncertainty range of large ensemble. By applying this method, we select a subset of five GCM projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble for impact and adaptation studies in Japan. At first, we omit GCMs whose global warming projections have been evaluated to be overestimated in the recent literature. Then, we select a subset of five GCMs that widely captures the uncertainty ranges for 8 climate variables and have good performances in present-climate simulations. These selected GCM simulations will be used to provide better climate scenarios for impact and adaptation studies than those in the previous impact assessment project.
- 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.