Lacydonia shohoensis (Annelida, Lacydoniidae) sp. nov. – a new lacydonid species from deep-sea sunken wood discovered at the Nishi-Shichito Ridge, North-western Pacific Ocean

Lacydonia Marion & Bobretsky, 1875 is the sole genus in the family Lacydoniidae Bergström, 1914. We herein describe the new species of Lacydonia shohoensis sp. nov. from 2042-m deep bottoms at Shoho Seamount of the Nishi-Shichito Ridge, the Northwest Pacific Ocean. It is most similar to L. anapaulae Rizzo et al., 2016 in having a depression on the median anterior region and lacking lateral lobes on the posterior margin of prostomium whereas it is distinguished by possessing pygidium dorsally pigmented with three reddish spots and non-pigmented pygidial lateral cirri equally elongated.

Another Record: Ocean Warming Continues through 2021 despite La Niña Conditions

The increased concentration of greenhouse gases in the atmosphere from human activities traps heat within the climate system and increases ocean heat content (OHC). Here, we provide the first analysis of recent OHC changes through 2021 from two international groups. The world ocean, in 2021, was the hottest ever recorded by humans, and the 2021 annual OHC value is even higher than last year’s record value by 14 ± 11 ZJ (1 zetta J = 1021 J) using the IAP/CAS dataset and by 16 ± 10 ZJ using NCEI/NOAA dataset. The long-term ocean warming is larger in the Atlantic and Southern Oceans than in other regions and is mainly attributed, via climate model simulations, to an increase in anthropogenic greenhouse gas concentrations. The year-to-year variation of OHC is primarily tied to the El Niño-Southern Oscillation (ENSO). In the seven maritime domains of the Indian, Tropical Atlantic, North Atlantic, Northwest Pacific, North Pacific, Southern oceans, and the Mediterranean Sea, robust warming is observed but with distinct inter-annual to decadal variability. Four out of seven domains showed record-high heat content in 2021. The anomalous global and regional ocean warming established in this study should be incorporated into climate risk assessments, adaptation, and mitigation.

A new species of the genus Coryphella(Gastropoda: Nudibranchia) from the Kuril Islands

A new species of the family Coryphellidae, Coryphella alexanderi sp. nov. is described based on specimens collected in the Kuril Islands, NorthWest Pacific, from the upper sublittoral to 200 m depth. An integrative analysis was conducted, including a molecular phylogenetic analysis based on four markers (COI, 16S, H3, 28S), an automatic species delimitation method ABGD, and an analysis of the external and internal morphology using light and scanning electron microcopy. The distinctiveness of Coryphella alexanderi sp. nov. is well established both morphologically and genetically, and it differs from externally similar species in radular characters. Phylogenetically Coryphella alexanderi sp. nov. is closely related to Coryphella trophina, which occurs sympatrically in the same geographic and bathymetric ranges. Coryphella alexanderi sp. nov. appears to be restricted to the middle and northern Kuril Islands, which is consistent with the high numbers of endemic taxa in this area.

Nonstationary Bayesian Modeling of Extreme Flood Risk and Return Period Affected by Climate Variables for Xiangjiang River Basin, in South-Central China

The accurate design flood of hydraulic engineering is an important precondition to ensure the safety of residents, and the high precision estimation of flood frequency is a vital perquisite. The Xiangjiang River basin, which is the largest river in Hunan Province of China, is highly inclined to floods. This paper aims to investigate the annual maximum flood peak (AMFP) risk of Xiangjiang River basin under the climate context employing the Bayesian nonstationary time-varying moment models. Two climate covariates, i.e., the average June-July-August Artic Oscillation and sea level pressure in the Northwest Pacific Ocean, are selected and found to exhibit significant positive correlation with AMFP through a rigorous statistical analysis. The proposed models are tested with three cases, namely, stationary, linear-temporal and climate-based conditions. The results both indicate that the climate-informed model demonstrates the best performance as well as sufficiently explain the variability of extreme flood risk. The nonstationary return periods estimated by the expected number of exceedances method are larger than traditional ones built on the stationary assumption. In addition, the design flood could vary with the climate drivers which has great implication when applied in the context of climate change. This study suggests that nonstationary Bayesian modelling with climatic covariates could provide useful information for flood risk management.

Feature Comparison of Two Mesoscale Eddy Datasets Based on Satellite Altimeter Data

Although a variety of ocean mesoscale eddy datasets are available for researchers to study eddy properties throughout the global ocean, subtle differences in how these datasets are produced often lead to large differences between one another. This study compares the Global Ocean Mesoscale Eddy Atmospheric-Oceanic-Biological interaction Observational Dataset (GOMEAD) with the well-recognized Mesoscale Eddy Trajectory Atlas in four regions with strong eddy activity: the Northwest Pacific Subtropical Front (SF), Kuroshio Extension (KE), South China Sea (SCS), and California Coastal Current (CC), and assesses the relative advantages and disadvantages of each. It was identified that while there is a slight difference in the total number of eddies detected in each dataset, the frequency distribution of eddy radii presents a right-skewed normal distribution, tending towards larger radii eddies, and there are more short- than long-lived eddies. Interestingly, the total number of GOMEAD eddies is 8% smaller than in the META dataset and this is most likely caused by the GOMEAD dataset’s underestimation of total eddy numbers and lifespans due to their presence near islands, and the tendency to eliminate eddies from its database if their radii are too small to be adequately detected. By contrast, the META dataset, due to tracking jumps in detecting eddies, may misidentify two eddies as a single eddy, reducing total number of eddies detected. Additionally, because the META dataset is reliant on satellite observations of sea surface level anomalies (SLAs), when SLAs are weak, the META dataset struggles to detect eddies. The GOMEAD dataset, by contrast, is reliant on applying vector geometry to detect and track eddies, and thus, is largely insulated from this problem. Thus, although both datasets are excellent in detecting and characterizing eddies, users should use the GOMEAD dataset when the region of interest is far from islands or when SLAs are weak but use the META dataset if the region of interest is populated by islands, or if SLAs are intense.

Onset and Decline Rates of Marine Heatwaves: Global Trends, Seasonal Forecasts and Marine Management

Changing ocean conditions due to anthropogenic climate change, particularly the increasing severity and frequency of extreme events, are a growing concern for a range of marine sectors. Here we explore the global trends in marine heatwaves (MHWs), specifically onset and decline rates, two metrics which describe how quickly a MHW will emerge or disappear from a location. These rates determine the reaction window—the start of a MHW event to peak MHW temperatures—and the coping window—time from peak temperatures to the end of an event—two important time periods relevant to a marine decision-maker. We show that MHW onset and decline rates are fastest in dynamic ocean regions and that overall, the global trend in onset rate is greater than the global trend in decline rate. We map ocean regions where these rates are changing together with forecast skill from a seasonal dynamical model (ACCESS-S). This analysis highlights areas where the length of the preparation window for impending MHWs is increased by using forecasts, and areas where marine decision-makers should be prepared for rapid responses based on realtime observations as MHWs evolve. In regions such as south Africa and Kerguelen, northwest Atlantic, northwest Pacific, southwest South Atlantic and off Australian east coast where rapid median onset and decline rates are observed, there is also a positive trend in onset and decline rates i.e., MHWs are developing and declining more rapidly. This will be a concern for many decision-makers operating in these regions.

Identification of tropical cyclones via deep convolutional neural network based on satellite cloud images

Tropical Cyclones (TCs) are one of the most destructive natural disasters. For the prevention and mitigation of TC-induced disasters, real-time monitoring and prediction of TCs is essential. At present, satellite cloud images (SCIs) are utilized widely as a basic data source for such studies. Although great achievements have been made in this field, lack of concerns on the identification of TC fingerprint from SCIs have become a potential issue, since it is a prerequisite step for follow-up analyses. This paper presents a methodology which identifies TC fingerprint via Deep Convolutional Neural Network (DCNN) techniques based on SCIs of more than 200 TCs over the Northwest Pacific basin. Two DCNN models have been proposed and validated, which are able to identify the TCs from not only single-TC featured SCIs but also multi-TCs featured SCIs. Results show that both models can reach 96 % of identification accuracy. As the TC intensity strengthens, the accuracy becomes better. To explore how these models work, heat maps are further extracted and analyzed. Results show that all the fingerprint features are focused on clouds during the testing process. For the majority of TC images, the cloud features in TC’s main parts, i.e., eye, eyewall and primary rainbands, are most emphasized, reflecting a consistent pattern with the subjective method.