| Project/Area Number |
17K05663
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Meteorology/Physical oceanography/Hydrology
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| Research Institution | Keio University (2022)
Japan Agency for Marine-Earth Science and Technology (2017-2021) |
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Principal Investigator |
KOMORI Nobumasa 慶應義塾大学, 自然科学研究教育センター(日吉), 研究員 (80359223)
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| Project Period (FY) |
2017-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2019: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2017: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | 海洋物理・陸水学 / 気象学 / 大気海洋相互作用 / データ同化 / 季節変動予測 / アンサンブル / 観測システムシミュレーション実験 |
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| Outline of Final Research Achievements |
We have constructed a system to assimilate atmospheric observational data into a global coupled atmosphere-ocean modeu using an ensemble-based method, and found the basin-scale structure of surface atmospheric variables over the tropical Pacific reconstructed from the ensemble correlation in the CGCM-based system but not in the AGCM-based system. We also constructed an experimental seasonal prediction system using a simple nudging method which assimilate only sea surface temperature data. The system exhibits a good prediction skill including sea-ice covered regions. In addition, we modified the atmospheric component to use the realistic depth of inland seas and large lakes, and revealed that biases in the large-scale circulation could be mitigated through the improved land-sea thermal contrast.
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| Academic Significance and Societal Importance of the Research Achievements |
大気海洋結合モデルを用いた季節変動予測自体はすでに実用化されているが，その精度を向上させるためには，初期条件を作成する際に大気と海洋を一体的に取り扱うことが重要であり，各国の現業数値予報センターでも精力的に開発が行われている分野である．一方，大気大循環モデルにおける内海や湖沼の取扱いは，これまであまり着目されてこなかったものの，その影響は無視できないことが本研究で明らかとなった．従って本研究の成果は，将来的な季節変動予測の精度向上に資するものであると考えている．
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