並列タイトル等北太平洋における海洋生態系の変動と海洋表面水温の予測可能性
一般注記Monitoring climatic parameters and their biological effect across various time scales has important economic and ecological implications. This dissertation describes marine ecosystem variability and its relation to large-climate variability and change and sea surface temperature (SST) predictability over the North Pacific (NP) from the latest seasonal forecast systems. Time series data for 120 marine species of zooplankton, invertebrates, small-pelagic fish, groundfish, and salmon in both the eastern and western NP basins and eight physical (climate) indices were analyzed by a large multivariate analysis to identify dominant modes of marine ecosystem variability and their relation to physical climate in 1965–2006. An empirical orthogonal function (EOF) of marine biology was performed. The time series of the first EOF mode (PC1) of marine biology for the eastern, western and the whole NP are characterized by a long-term trend in 1978-1998, accompanied by a decrease in groundfish and the increase in salmon in both basins as well as an increase of most of small pelagic fishes in the western NP and decrease zooplankton in the eastern NP. The time series of the second mode (PC2) of eastern NP marine biology exhibited multi-decadal variability with two phase reversals, while the western NP marine biology PC2 exhibited interdecadal variability with three phase reversals. All of marine biology PC1s were correlated with the SST anomalies (SSTs) averaged over the NP and with the globally averaged SSTs, suggesting that the leading mode of the marine ecosystem variations may influence by global warming. The eastern NP PC2 was the most strongly correlated with the Pacific Decadal Oscillation (PDO), while the western NP marine biology PC2 was correlated with the North Pacific Gyre Oscillation (NPGO). In an investigation of SST predictability, prediction skills and their association with the relations among ensemble members and observation were analyzed for January and July forecasts with 3-month lead time by using 95 members of multi-model ensemble in 1994-2016. The seasonal forecast system which produced by the European Centre for Medium-Range Weather Forecasts(ECMWF), Deutscher Wetterdienst(DWD),and Centro-Euro-Mediterraneo Sui Cambiamenti Climatici(CMCC)were used and the data were downloaded from the Copernicus Climate Change Service(C3S). The prediction skill was estimated using the temporal correlation between the multi-model ensemble mean (MMEM)and observed SSTs at each grid point, referred to as point-wise correlation. These point-wise correlations were high in the eastern and central NP in January and in the eastern NP in July and were low in the Kuroshio-Oyashio Extensions (KOE) in both January and July and in the central NP in July. Further analysis revealed that areas with high prediction skill show small spreads among ensemble members and that an observation can be regarded as an ensemble member. Low prediction skill was associated with either a large ensemble spread as found for the July KOE,or the failure of ensemble members to capture observed variability,as found in the January KOE and in the July central NP. The former case was associated with predictable component relative to stochastic component, and the latter case is resulted from biased variations commonly occurred across ensemble members.
(主査) 教授 見延 庄士郎, 教授 稲津 將, 准教授 佐々木 克徳, 准教授 藤井 賢彦（環境科学院）
理学院（自然史科学専攻）
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受理日（W3CDTF）2020-11-10T19:58:22+09:00
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