Effective generation mechanisms of tropical instability waves as represented by high-resolution coupled atmosphere-ocean prediction experiments.

Cusp-shaped fluctuations of the sea surface temperature (SST) front in the tropical Pacific, now known as tropical instability waves (TIWs), were discovered by remote sensing in the 1970s. Their discovery was followed by both theoretical and analytical studies, which, along with in situ observations, identified several possible generation mechanisms. Although modeling studies have shown that TIWs strongly influence the heat budget, their influence on local variations of realistically initialized predictions is not yet understood. We here evaluate a series of medium-range (up to ~ 10 days) coupled atmosphere-ocean predictions by a coupled model with different horizontal resolutions. Observational SST, surface wind stress, heat flux, and pressure data showed that representation of temporally and spatially local variations was improved by resolving fine-scale SST variations around the initialized coarse-scale SST front fluctuations of TIWs. Our study thus demonstrates the advantage of using high-resolution coupled models for medium-range predictions. In addition, analysis of TIW energetics showed two dominant sources of energy to anticyclonic eddies: barotropic instability between equatorial zonal currents and baroclinic instability due to intense density fronts. In turn, the eddy circulation strengthened both instabilities in the resolved simulations. This revealed feedback process refines our understanding of the generation mechanisms of TIWs.

Northward shift of the Kuroshio Extension during 1993-2021.

The Kuroshio Extension (KE) flows eastward at the northern boundary of the North Pacific subtropical gyre. By transporting large amounts of seawater with heat, the KE contributes significantly to the formation of sea surface temperature (SST) fields. Recently, poleward shifts of major ocean gyres in the world ocean, including the North Pacific subtropical gyre, have been highlighted based on basin-scale changes in SST and sea surface height (SSH) distributions. However, a detailed investigation of the long-term meridional KE movement has not been presented. Investigation of KE path changes helps provide insights into long-term changes in the physical fields in the western North Pacific. In this study, we identified the KE path from satellite-derived SSH and surface current velocity data using a front identification method and showed that the KE migrated northward by approximately 210 km during 1993-2021. We further explored the cause of the northward KE shift based on atmospheric reanalysis data and numerical experiments using a high-resolution ocean general circulation model. It was revealed that the northward KE shift is mostly caused by the trend of wind stress curl in the North Pacific during 1993-2021.

Cold- versus warm-season-forced variability of the Kuroshio and North Pacific subtropical mode water.

The ocean responds to atmospheric variations. Changes in sea surface winds, surface air temperature, and surface air humidity cause upper ocean variability by modulating air-sea momentum and heat exchanges. Upper ocean variability in the mid-latitudes on inter-annual and longer timescales has previously been considered to be attributable to atmospheric variations in the cold season, because atmospheric forcing is stronger in the cold season than in the warm season. However, this idea has not been sufficiently confirmed yet. Although the ocean model is a useful tool to evaluate the impact of the atmospheric forcing in each season, there are no past studies having examined ocean model responses respectively to the cold- and warm-season atmospheric forcing. In this study, we performed numerical experiments with an eddy-resolving ocean general circulation model and investigated oceanic responses to cold- and warm-season atmospheric forcing, focusing on the Kuroshio and North Pacific subtropical mode water (STMW) in the western mid-latitude North Pacific. We found that temporal variations of net Kuroshio transport and STMW distribution/temperature are dominantly controlled by atmospheric forcing in the cold season. These results suggest that cold-season atmospheric variations are key to obtaining insights into large-scale upper ocean variability in the North Pacific subtropical gyre.

Identification of skipjack tuna (Katsuwonus pelamis) pelagic hotspots applying a satellite remote sensing-driven analysis of ecological niche factors: A short-term run.

Skipjack tuna (SJT) pelagic hotspots in the western North Pacific (WNP) were modelled using fishery and satellite remotely sensed data with Ecological Niche Factor Analysis (ENFA) models. Our objectives were to model and predict habitat hotspots for SJT and assess the monthly changes in sub-surface temperatures and mixed layer depths at fishing locations. SJT presence-only monthly resolved data, sea surface temperature, chlorophyll-a, diffuse attenuation coefficient, sea surface heights and surface wind speed were used to construct ENFA models and generate habitat suitability indices using a short-term dataset from March-November 2004. The suitability indices were then predicted for July-October (2007 and 2008). Monthly aggregated polygons of areas fished by skipjack tuna pole and line vessels were also overlaid on the predicted habitat suitability maps. Distributions of sub-surface temperatures and mixed layer depths (MLD) at fishing locations were also examined. Our results showed good fit for ENFA models, as indicated by the absolute validation index, the contrast validation index and the continuous Boyce index. The predicted hotspots showed varying concurrences when compared with 25-degree polygons derived from fished areas. Northward shifts in SJT hotspots corresponded with declining MLDs from March to September. The MLDs were shallower in summer and deeper in autumn and winter months. The habitat hotspots modeled using ENFA were consistent with the known ecology and seasonal migration pattern of SJT. The findings of this work, derived from a short-term dataset, enable identification of SJT hotspots in the WNP, thus contributing valuable information for future research on SJT habitat prediction models.

DNA recovery from a single bacterial cell using charge-reversible magnetic nanoparticles.

Highly efficient DNA recovery from a single bacterial cell was performed by means of imidazole-modified magnetic nanoparticles (Imi-MNPs). The modification by imidazole was confirmed by Fourier transform infrared spectroscopy. The Imi-MNPs were highly efficient at DNA extraction owing to the charge-reversible properties of Imi-MNPs, whereby DNA is attached to the particles at low pH and eluted at high pH because of electrostatic interactions. The DNA recovery ratio was determined by real-time PCR, and it revealed that complete recovery was guaranteed at ≥10(3) genome copies of Bacillus subtilis. Extraction of DNA from single bacterial cells was followed by PCR amplification of 16S rDNA and capillary electrophoresis. We achieved detection of single bacterial cells with a detection rate of 80%. We believe that our DNA recovery strategy may serve as a powerful tool for efficient DNA extraction and should be useful for quality control of cosmetics, foods, and pharmaceutical products.

Simulated rapid warming of abyssal North Pacific waters.

Recent observational surveys have shown significant oceanic bottom-water warming. However, the mechanisms causing such warming remain poorly understood, and their time scales are uncertain. Here, we report computer simulations that reveal a fast teleconnection between changes in the surface air-sea heat flux off the Adélie Coast of Antarctica and the bottom-water warming in the North Pacific. In contrast to conventional estimates of a multicentennial time scale, this link is established over only four decades through the action of internal waves. Changes in the heat content of the deep ocean are thus far more sensitive to the air-sea thermal interchanges than previously considered. Our findings require a reassessment of the role of the Southern Ocean in determining the impact of atmospheric warming on deep oceanic waters.

Pacific decadal oscillation hindcasts relevant to near-term climate prediction.

Decadal-scale climate variations over the Pacific Ocean and its surroundings are strongly related to the so-called Pacific decadal oscillation (PDO) which is coherent with wintertime climate over North America and Asian monsoon, and have important impacts on marine ecosystems and fisheries. In a near-term climate prediction covering the period up to 2030, we require knowledge of the future state of internal variations in the climate system such as the PDO as well as the global warming signal. We perform sets of ensemble hindcast and forecast experiments using a coupled atmosphere-ocean climate model to examine the predictability of internal variations on decadal timescales, in addition to the response to external forcing due to changes in concentrations of greenhouse gases and aerosols, volcanic activity, and solar cycle variations. Our results highlight that an initialization of the upper-ocean state using historical observations is effective for successful hindcasts of the PDO and has a great impact on future predictions. Ensemble hindcasts for the 20th century demonstrate a predictive skill in the upper-ocean temperature over almost a decade, particularly around the Kuroshio-Oyashio extension (KOE) and subtropical oceanic frontal regions where the PDO signals are observed strongest. A negative tendency of the predicted PDO phase in the coming decade will enhance the rising trend in surface air-temperature (SAT) over east Asia and over the KOE region, and suppress it along the west coasts of North and South America and over the equatorial Pacific. This suppression will contribute to a slowing down of the global-mean SAT rise.

Random field model for integration of local information and global information.

This paper presents a proposal of a general framework that explicitly models local information and global information in a conditional random field. The proposed method extracts global image features as well as local ones and uses them to predict the scene of the input image. Scene-based top-down information is generated based on the predicted scene. It represents a global spatial configuration of labels and category compatibility over an image. Incorporation of the global information helps to resolve local ambiguities and achieves locally and globally consistent image recognition. In spite of the model's simplicity, the proposed method demonstrates good performance in image labeling of two datasets.