In order to understand how North Pacific (NP) marine ecosystems have varied, 120 marine biological time series for both the western (29 time series) and eastern (91 time series) NP were analyzed with a Principal Component Analysis (PCA) for the period 1965-2006. This is the first attempt to conduct a multivariate analysis for a large number of marine biological data in the western and eastern NP combined. We used Monte-Carlo simulation to evaluate confidence levels of correlations and explained variance ratio of PCA modes while accounting for auto-correlation within the analyzed time series. All first mode principal components (PC1s), which are the time coefficients of the first PCA modes, calculated for the data in the whole, western, and eastern NP exhibit a long-term trend. The PC1s were associated with an overall increase of Alaskan and Japanese/Russian salmon, and decreases of groundfish across the basin. This mode was closely related to the warming of sea-surface temperature over the NP and over the global oceans, thereby suggesting that the strongest mode of the NP marine ecosystem was already influenced by global warming. The eastern NP PC2, characterized by multidecadal variability, was correlated positively with salmon and negatively with groundfish. On the other hand, the western NP PC2 exhibited slightly shorter timescale interdecadal variability than the eastern NP PC2 and was negatively correlated with zooplankton and two small pelagic fish time series around Japan. The eastern NP PC2 was most strongly related to the Pacific (inter-)Decadal Oscillation index, while the western NP PC2 was most closely related to the North Pacific Gyre Oscillation index. Consequently, the present analysis provides a new and unified view of climate change and marine ecosystem variations across the western and eastern NP. In particular, it is suggested that global warming has already substantially influenced the NP marine ecosystem, and that groundfish may suffer more than pelagic fish in response to future global warming.

Blue carbon ecosystems are key for successful global climate change mitigation; however, they are one of the most threatened ecosystems on Earth. Thus, this study mapped the climatic and human pressures on the blue carbon ecosystems in Indonesia using multi-source spatial datasets. Data on moderate resolution imaging spectroradiometer (MODIS) ocean color standard mapped images, VIIRS (visible, infrared imaging radiometer suite) boat detection (VBD), global artificial impervious area (GAIA), MODIS surface reflectance (MOD09GA), MODIS land surface temperature (MOD11A2), and MODIS vegetation indices (MOD13A2) were combined using remote sensing and spatial analysis techniques to identify potential stresses. La Niña and El Niño phenomena caused sea surface temperature deviations to reach −0.5 to +1.2 °C. In contrast, chlorophyll-a deviations reached 22,121 to +0.5 mg m−3. Regarding fishing activities, most areas were under exploitation and relatively sustained. Concerning land activities, mangrove deforestation occurred in 560.69 km2 of the area during 2007–2016, as confirmed by a decrease of 84.9% in risk-screening environmental indicators. Overall, the potential pressures on Indonesia’s blue carbon ecosystems are varied geographically. The framework of this study can be efficiently adopted to support coastal and small islands zonation planning, conservation prioritization, and marine fisheries enhancement.

Sea surface temperature (SST) prediction based on the multi-model seasonal forecast with numerous ensemble members have more useful skills to estimate the possibility of climate events than individual models.Hence, we assessed SST predictability in the North Pacific (NP) from multi-model seasonal forecasts. We used 23 years of hindcast data from three seasonal forecasting systems in the Copernicus Climate Change Service to estimate the prediction skill based on temporal correlation. We evaluated the predictability of the SST from the ensemble members' width spread, and co-variability between the ensemble mean and observation. Our analysis revealed that areas with low prediction skills were related to either the large spread of ensemble members or the ensemble members not capturing the observation within their spread. The large spread of ensemble members reflected the high forecast uncertainty, as exemplified in the Kuroshio-Oyashio Extension region in July. The ensemble members not capturing the observation indicates the model bias; thus, there is room for improvements in model prediction. On the other hand, the high prediction skills of the multi-model were related to the small spread of ensemble members that captures the observation, as in the central NP in January. Such high predictability is linked to El Niño Southern Oscillation (ENSO) via teleconnection.