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Remotely derived environmental variables, including sea surface height anomaly SSHA, sea surface temperature SST, chlorophyll-a chl-a, eddy kinetic energy EKE, mixed layer data set of argo float MLD, Nino 3.4 index, and bigeye tuna catch data for the period 1997–2008, were used to analyse ocean climate variability and how they relate to the hotspots of bigeye tuna catch in the eastern Indian Ocean off Java. The empirical orthogonal function EOF was performed to obtain a more detailed structure of the spatio-temporal ocean variability in the region. The results showed that the first EOF modes of chl-a, SSHA, and SST accounted for 42.8%, 36.5%, and 27.4% of total variance, respectively, and these corresponded to the interannual signal. The maps of spatial patterns of the first EOF modes of SSHA, SST, and chl-a gave very typical values for cold-water SSHA, low SST, and high chl-a concentration along the southern coast of the Indonesian archipelago; and warm-water SSHA, high SST, and low chl-a concentration in the offshore region to make frontal areas along the latitudinal line around 10–12o S. The EOF analysis further revealed a strong relationship between the El Nino event and favourable oceanographic conditions, resulting in a significant increase in bigeye tuna catch. The average hook rate of 0.71 0.43 was recorded during El Nino La Nina. Major hotspots were located away from the confluence region and frontal areas around 11–16° S and 110–118° E and were thus demonstrated as the most favourable oceanographic conditions for bigeye tuna fishing in the eastern Indian Ocean off Java.

A nutrient (N), phytoplankton (P), zooplankton (Z), and detritus (D) ecosystem model coupled to an ice-ocean model was applied to the Bering and Chukchi Seas for 2007–2008. The model reasonably reproduces the seasonal cycles of sea ice, phytoplankton, and zooplankton in the Bering–Chukchi Seas. The spatial variation of the phytoplankton bloom was predominantly controlled by the retreat of sea ice and the increased gradient of the water temperature from the south to the north. The model captures the basic structure of the measured nutrients and chl-a along the Bering shelf during 4–23 July 2008, and along the Chukchi shelf during 5–12 August 2007. In summer 2008, the Green Belt bloom was not observed by either the satellite measurements or the model. The model-data comparison and analysis reveal the complexity of the lower trophic dynamics in the Bering and Chukchi Seas. The complexity is due to the nature that the physical and biological components interact at different manners in time and space, even in response to a same climate forcing, over the physically distinct geographic settings such as in the Bering and North Aleutian Slopes, deep Bering basins, Bering shelf, and Chukchi Sea. Sensitivity studies were conducted to reveal the underlying mechanisms (i.e., the bottom-up effects) of the Bering–Chukchi ecosystem in response to changes in light intensity, nutrient input from open boundaries, and air temperature. It was found that (1) a 10% increase in solar radiation or light intensity for the entire year has a small impact on the intensity and timing of the bloom in the physical–biological system since the light is not a limiting factor in the study region; (2) a 20% increase in nutrients from all the open boundaries results in an overall 7% increase in phytoplankton, with the Slope region being the largest, and the Bering shelf and Chukchi being the smallest; and (3) an increase in air temperature by 2°C over the entire calculation period can result in an overall increase in phytoplankton by 11%.