Spatial and Temporal Variations of Aerosol Optical Thickness over the China Seas from Himawari-8

Six years of hourly aerosol optical thickness (AOT) data retrieved from Himawari-8 were used to investigate the spatial and temporal variations, especially diurnal variations, of aerosols over the China Seas. First, the Himawari-8 AOT data were consistent with the AERONET measurements over most of the China Seas, except for some coastal regions. The spatial feature showed that AOT over high latitude seas was generally larger than over low latitude seas, and it is distributed in strips along the coastline and decreases gradually with increasing distance from the coastline. AOT undergoes diurnal variation as it decreases from 9:00 a.m. local time, reaching a minimum at noon, and then begins to increase in the afternoon. The percentage daily departure of AOT over the East China Seas generally ranged ±20%, increasing sharply in the afternoon; however, over the northern part of the South China Sea, daily departure reached a maximum of >40% at 4:00 p.m. The monthly variation in AOT showed a pronounced annual cycle. Seasonal variations of the spatial pattern showed that the largest AOT was usually observed in spring and varies in other seasons for different seas.

Long-Term Variability of Piscivorous Fish in China Seas Under Climate Change With Implication for Fisheries Management

Due to persistent fishing expansion in the China Seas over the past six decades, fisheries resources have been over-exploited; as a result, exploited fish have become smaller in size and younger in age. Marine piscivorous fish constituted a large portion of Chinese fisheries catch, long-term variability of which has rarely been investigated despite intense fishing pressure and climate change. In this study, we attempt to identify their responses to climate change and fishing activities and to provide scientific basis for sustainable exploitation of these resources. Seven taxa from pelagic to demersal species inhabiting either cold-water or warm-water were selected to represent the piscivorous fish assemblage in the China Seas. Total catch of these piscivorous fish in the China Seas increased during the early 1990s, stabilizing around 1.2 million tons after 1997. Principal component analysis (PCA) showed evident interannual-decadal variabilities in the catch of these fish with step changes around 1985/86 and 1997/98. Individual taxa, however, showed different trends in catches with sharks, rays, and lizardfishes manifesting downward trends while Pacific cod, eels, and hairtail increasing. Common dolphinfish and Japanese-Spanish mackerel increased largely in the 1990s but declined slightly during the 2000s. Although there were temporal overlaps between climate change and fishing variabilities, results of gradient forest analyses indicated that fishing effort imposed the most important influence on piscivorous fish. And among all climate variables explored in this study, sea surface temperature (SST) especially that of the East China Sea, had greatest impacts on variations in piscivorous fish catch, which may have been gradually exacerbated by the continued high fishing intensity. In addition, significant changes were identified in the life history traits in the species we evaluated, such as reduced average body sizes and truncated age compositions, strongly indicating the effect of fishing. We therefore advocate precautionary fishery practices under climate change.

Metasychis varicollaris sp. nov. and report of Metasychis gotoi (Maldanidae, Annelida) from the China Seas

Polychaete species are widely distributed throughout Indo-Pacific and European waters. We collected Metasychis specimens from the China Seas to report on Metasychis varicollaris sp. n. and Metasychis gotoi (Izuka, 1902) in greater detail. Geographic analysis of the potential distribution areas of M. gotoi indicates that it may be found in most coastal areas of China. The newly discovered species, M. varicollaris and M. gotoi, have an overlapping distribution in the northern South China Sea. Metasychis varicollaris sp. n. is characterized by a crenulated cephalic rim, complete collar on chaetiger 1, a packet-shaped anal funnel, and a spirally-fringed notochaetae with spiral pectinate bands imbricated over the main shaft. Our study provides a taxonomic key to all species of Metasychis.

An asymmetric variation of hot and cold SST extremes in the China Seas during the recent warming hiatus period

Extreme sea surface temperatures (SSTs) attract much attention in recent years. However, the detailed spatial and temporal pattern of the extreme SSTs in China Seas has not been well understood. Using the daily SST data set of OISST v2 from January 1, 1982 to December 31, 2013, and based on four extreme SST indices, the frequency and intensity of SST extremes in the China Seas were examined. The analysis showed that the annual mean SST exhibited cooling trend, on pace with a trend of − 0.34 °C/decade during 1998–2013, confirming the previous studies that China Seas also experienced the recent global warming hiatus. But during this recent global warming hiatus, there was a notable asymmetric pattern of greater cooling trends in cold SSTs as compared to the hot SSTs in this region. During 1998–2013, the cold days (CDs) frequency increased significantly by 13 days per decade and cold SST extremes which were below the 10th percentile of each year (SST10p) notably decreased by 0.4 °C per decade. Hot days (HD) and hot SST extremes which were above the 90th percentile of each year (SST90p) slowed down, but without any distinct tendency. Meanwhile, the rates of SST10p and CDs were highly heterogeneous in space. Cold extremes in the near-shore areas are much more sensitive to the global warming hiatus than these in the eastern of the Kuroshio Current. Importantly, hot extremes do not reveal any distinct cooling tendency during 1998–2013, there were more frequent hot days and more intense hot SSTs in this region comparing with 1982–1997. These hot extremes could push some marine organisms, fisheries and ecosystems beyond the limits of their resilience, with cascading impacts on economies and societies.

Prediction of Sea Surface Temperature in the China Seas Based on Long Short-Term Memory Neural Networks

Sea surface temperature (SST) in the China Seas has shown an enhanced response in the accelerated global warming period and the hiatus period, causing local climate changes and affecting the health of coastal marine ecological systems. Therefore, SST distribution prediction in this area, especially seasonal and yearly predictions, could provide information to help understand and assess the future consequences of SST changes. The past few years have witnessed the applications and achievements of neural network technology in SST prediction. Due to the diversity of SST features in the China Seas, long-term and high-spatial-resolution prediction remains a crucial challenge. In this study, we adopted long short-term memory (LSTM)-based deep neural networks for 12-month lead time SST prediction from 2015 to 2018 at a 0.05° spatial resolution. Considering the sub-regional differences in the SST features of the study area, we applied self-organizing feature maps (SOM) to classify the SST data first, and then used the classification results as additional inputs for model training and validation. We selected nine models differing in structure and initial parameters for ensemble to overcome the high variance in the output. The statistics of four years’ SST difference between the predicted SST and Operational SST and Ice Analysis (OSTIA) data shows the average root mean square error (RMSE) is 0.5 °C for a one-month lead time and is 0.66 °C for a 12-month lead time. The southeast of the study area shows the highest predictable accuracy, with an RMSE less than 0.4 °C for a 12-month prediction lead time. The results indicate that our model is feasible and provides accurate long-term and high-spatial-resolution SST prediction. The experiments prove that introducing appropriate class labels as auxiliary information can improve the prediction accuracy, and integrating models with different structures and parameters can increase the stability of the prediction results.