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RISS 인기검색어
- 검색키워드
- 저자 : Anthony Rosati (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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An OSSE Study for Deep Argo Array using the GFDL Ensemble Coupled Data Assimilation System
장유순,Shaoqing Zhang,Anthony Rosati,Gabriel A. Vecchi,Xiaosong Yang 한국해양과학기술원 2018 Ocean science journal Vol.53 No.2
An observing system simulation experiment (OSSE) using an ensemble coupled data assimilation system was designed to investigate the impact of deep ocean Argo profile assimilation in a biased numerical climate system. Based on the modern Argo observational array and an artificial extension to full depth, “observations” drawn from one coupled general circulation model (CM2.0) were assimilated into another model (CM2.1). Our results showed that coupled data assimilation with simultaneous atmospheric and oceanic constraints plays a significant role in preventing deep ocean drift. However, the extension of the Argo array to full depth did not significantly improve the quality of the oceanic climate estimation within the bias magnitude in the twin experiment. Even in the “identical” twin experiment for the deep Argo array from the same model (CM2.1) with the assimilation model, no significant changes were shown in the deep ocean, such as in the Atlantic meridional overturning circulation and the Antarctic bottom water cell. The small ensemble spread and corresponding weak constraints by the deep Argo profiles with medium spatial and temporal resolution may explain why the deep Argo profiles did not improve the deep ocean features in the assimilation system. Additional studies using different assimilation methods with improved spatial and temporal resolution of the deep Argo array are necessary in order to more thoroughly understand the impact of the deep Argo array on the assimilation system.
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An OSSE Study for Deep Argo Array using the GFDL Ensemble Coupled Data Assimilation System
Chang, You-Soon,Zhang, Shaoqing,Rosati, Anthony,Vecchi, Gabriel A.,Yang, Xiaosong Korean Ocean Research & Development Institute and 2018 OCEAN SCIENCE JOURNAL Vol.53 No.2
An observing system simulation experiment (OSSE) using an ensemble coupled data assimilation system was designed to investigate the impact of deep ocean Argo profile assimilation in a biased numerical climate system. Based on the modern Argo observational array and an artificial extension to full depth, 'observations' drawn from one coupled general circulation model (CM2.0) were assimilated into another model (CM2.1). Our results showed that coupled data assimilation with simultaneous atmospheric and oceanic constraints plays a significant role in preventing deep ocean drift. However, the extension of the Argo array to full depth did not significantly improve the quality of the oceanic climate estimation within the bias magnitude in the twin experiment. Even in the 'identical' twin experiment for the deep Argo array from the same model (CM2.1) with the assimilation model, no significant changes were shown in the deep ocean, such as in the Atlantic meridional overturning circulation and the Antarctic bottom water cell. The small ensemble spread and corresponding weak constraints by the deep Argo profiles with medium spatial and temporal resolution may explain why the deep Argo profiles did not improve the deep ocean features in the assimilation system. Additional studies using different assimilation methods with improved spatial and temporal resolution of the deep Argo array are necessary in order to more thoroughly understand the impact of the deep Argo array on the assimilation system.
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Seasonal to multiannual marine ecosystem prediction with a global Earth system model
Park, Jong-Yeon,Stock, Charles A.,Dunne, John P.,Yang, Xiaosong,Rosati, Anthony American Association for the Advancement of Scienc 2019 Science Vol.365 No.6450
<P><B>Predicting marine futures</B></P><P>The ability to predict how climate variations will affect marine ecosystems would allow better economic and ecosystem planning and management. Park <I>et al.</I> found that a global Earth system model skillfully predicted seasonal to multiannual ocean chlorophyll fluctuations in many regions. This could allow annual fish catches in some regions to be forecast 2 to 3 years in advance.</P><P><I>Science</I>, this issue p. 284</P><P>Climate variations have a profound impact on marine ecosystems and the communities that depend upon them. Anticipating ecosystem shifts using global Earth system models (ESMs) could enable communities to adapt to climate fluctuations and contribute to long-term ecosystem resilience. We show that newly developed ESM-based marine biogeochemical predictions can skillfully predict satellite-derived seasonal to multiannual chlorophyll fluctuations in many regions. Prediction skill arises primarily from successfully simulating the chlorophyll response to the El Niño–Southern Oscillation and capturing the winter reemergence of subsurface nutrient anomalies in the extratropics, which subsequently affect spring and summer chlorophyll concentrations. Further investigations suggest that interannual fish-catch variations in selected large marine ecosystems can be anticipated from predicted chlorophyll and sea surface temperature anomalies. This result, together with high predictability for other marine-resource–relevant biogeochemical properties (e.g., oxygen, primary production), suggests a role for ESM-based marine biogeochemical predictions in dynamic marine resource management efforts.</P>
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