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.

IVAN MIKHAILOVICH OVCHINNIKOV (1931–2000). TO THE 90TH ANNIVERSARY OF THE BIRTH OF THE OCEAN SCIENTIST

This article is dedicated to the 90th anniversary of Ivan Mikhailovich Ovchinnikov (1931– 2000) – Doctor of Geographical Sciences, former Director of the Black Sea Experimental Research Station (CHENIS) Institute of Oceanology of the USSR Academy of Sciences, a remarkable scientist-oceanographer, researcher of the Equatorial counterflow, mesoscale eddy, hydrological conditions of the Mediterranean Sea. The article reflects the life path of I.M. Ovchinnikov and his main achievements in Russian oceanology.

Similarities and Contrasts in Time-Mean Striated Surface Tracers in Pacific Eastern Boundary Upwelling Systems: The Role of Ocean Currents in Their Generation

Eastern boundary upwelling systems feature strong zonal gradients of physical and biological properties between cool, productive coastal oceans and warm, oligotrophic subtropical gyres. Zonal currents and jets (striations) are therefore likely to contribute to the transport of water properties between coastal and open oceanic regions. For the first time, multi-sensor satellite data are used to characterize the time-mean signatures of striations in sea surface temperature (SST), salinity (SSS), and chlorophyll-a (Chl-a) in subtropical eastern North/South Pacific (ENP/ESP) upwelling systems. In the ENP, tracers exhibit striated patterns extending up to ~2500 km offshore. Striated signals in SST and SSS are highly correlated with quasi-zonal jets, suggesting that these jets contribute to SST/SSS mesoscale patterns via zonal advection. Striated Chl-a anomalies are collocated with sea surface height (SSH) bands, a possible result of mesoscale eddy trains trapping nutrients and forming striated signals. In the ESP, the signature of striations is only found in SST and coincides with the SSH bands, consistently with quasi-zonal jets located outside major zonal tracer gradients. An interplay between large-scale SST/SSS advection by the quasi-zonal jets, mesoscale SST/SSS advection by the large-scale meridional flow, and eddy advection may explain the persistent ENP hydrographic signature of striations. These results underline the importance of quasi-zonal jets for surface tracer structuring at the mesoscale.

Features of Intraseasonal Variability Observed in the Upper-Layer Current in the Northern South China Sea

This study reveals the features of the strong intraseasonal variability (ISV) of the upper-layer current in the northern South China Sea (NSCS) based on four long-time mooring observations and altimeter data. The ISV of the upper-layer current in the NSCS consists of two dominant periods of 10–65 days and 65–110 days. The ISV with period of 10–65 days is much strong in the Luzon Strait and decays rapidly westward along the slope. The ISV with the period of 65–110 days is relatively strong along the slope with two high cores at 115 and 119°E, whereas it is weak in the Luzon Strait. The 10–65-day ISV can propagate directly from the western Pacific into the NSCS for most of the time. However, due to its long wavelength, the 65–110-day ISV propagates into the NSCS indirectly, possibly similar to the wave diffraction phenomenon. The spatial differences between the two main frequency bands are primarily due to the baroclinic and barotropic instabilities. The spatial distribution of the upper-layer ISV is closely associated with the mesoscale eddy radius of the NSCS. The eddy radius is directly proportional to the strength of 65–110-day ISV, but it is inversely proportional to the strength of 10–65-day ISV.

Sinking Rate and Community Structures of Autumn Phytoplankton Responses to Mesoscale Physical Processes in the Western South China Sea

To examine the influence of mesoscale eddy on the natural phytoplankton community and its sinking rate changes, a comprehensive investigation cruise was carried out in the western South China Sea in autumn 2016. A total of 108 phytoplankton species were found, which belong to 54 phytoplankton genera; most of them were dominated by Dinophyta (54 genera), followed by Bacillariophyta (50 genera), Cyanophyta (3 genera), and Chrysophyta (1 genus). Bacillariophyta and Dinophyta were the main phytoplankton communities in the investigated sea area. The sinking rate of phytoplankton ranged from 0.12 to 3.17 m day–1, determined by the SETCOL method. The highest phytoplankton sinking rate was found in the 200-m water layer, followed by the DCM layer. No significant correlation was found between phytoplankton sinking rates and most of the environmental parameters during this cruise. At a similar time, we have carried out the estimation of carbon flux in the investigated sea area by using the sinking rate of phytoplankton, which showed that the carbon flux ranged from 2.41 × 10–6 to 0.006 mg C m–2 day–1; in addition, the maximum was at the 200-m layer. Phytoplankton community and sinking rate were significantly affected by the mesoscale eddy processes. The cold eddy could affect the community distribution of diatom and dinoflagellate, and the upwelling mainly affects the community of dinoflagellate. Both of them could contribute to a higher sedimentation rate of phytoplankton in the surface and DCM layers. Warm eddy could reduce the abundance of phytoplankton in the surface layer; simultaneously, the sinking rate of phytoplankton in the shallow water layer above 100 m is also reduced. These results can fill in the knowledge gap of mesoscale eddy processes in the study of phytoplankton community change and sinking rate; furthermore, it can provide insights into phytoplankton carbon and its implementation in further carbon sink.

The Identification and Prediction of Mesoscale Eddy Variation via Memory in Memory With Scheduled Sampling for Sea Level Anomaly

Ocean mesoscale eddies are ubiquitous in world ocean and account for 90% oceanic kinetic energy, which dominate the upper ocean flow field. Accurately predicting the variation of ocean mesoscale eddies is the key to understand the oceanic flow field and circulation system. In this article, we propose to make an initial attempt to explore spatio-temporal predictability of mesoscale eddies, employing deep learning architecture, which primarily establishes Memory In Memory (MIM) for sea level anomaly (SLA) prediction, combined with the existing mesoscale eddy detection. Oriented to the western Pacific ocean (125°−137.5°E and 15°−27.5°N), we quantitatively investigate the historic daily SLA variability at a 0.25° spatial resolution from 2000 to 2018, derived by satellite altimetry. We develop the enhanced MIM prediction strategies, equipped with Gated Recurrent Unit (GRU) and spatial attention module, in a scheduled sampling manner, which overcomes the gradient vanishing and complements to strengthen spatio-temporal features for long-term dependencies. At the early stage, the real value SLA input guides the model training process for initialization, while the scheduled sampling intentionally feeds the newly predicted value, to resolve the distribution inconsistency of inference. It has been demonstrated in our experiment results that our proposed prediction scheme outperformed the state-of-art approaches for SLA time series, with MAPE, RMSE of the 14-day prediction duration, respectively, 5.1%, 0.023 m on average, even up to 4.6%, 0.018 m for the effective sub-regions, compared to 19.8%, 0.086 m in ConvLSTM and 8.3%, 0.040 m in original MIM, which greatly facilitated the mesoscale eddy prediction. This proposed scheme will be beneficial to understand of the underlying dynamical mechanism behind the predictability of mesoscale eddies in the future, and help the deployment of ARGO, glider, AUV and other observational platforms.

Mesoscale Eddy-Induced Ocean Dynamic and Thermodynamic Anomalies in the North Pacific

Oceanic mesoscale eddies are associated with large thermodynamic anomalies, yet so far they are most commonly studied in terms of surface temperature and in the sense of composite mean. Here we employ an objective eddy identification and tracking algorithm together with a novel matching and filling procedure to more thoroughly examine eddy-induced thermodynamic anomalies in the North Pacific, their relationship with eddy amplitude (SSH), and the percentage of variability they explain on various timescales from submonthly to interannual. The thermodynamic anomalies are investigated in terms of sea surface temperature (SST), isothermal layer depth (ITD), and upper ocean heat content (HCT). Most eddies are weak in amplitude and are associated with small thermodynamic anomalies. In the sense of composite mean, anticyclonic eddies are generally warm eddies with deeper isothermal layer and larger heat content, and the reverse is true for cyclonic eddies. A small fraction of eddies, most probably subsurface eddies, exhibits the opposite polarities. Linear relationships with eddy amplitude are found for each of the thermodynamic parameters but with different level of scatter and seasonality. HCT-amplitude relation scatters the least and has the smallest seasonal difference, ITD-amplitude relation has the largest scatter and seasonality, while SST-amplitude relation is in between. For the Kuroshio and Oyashio Extension region, the most eddy-rich region in the North Pacific, eddies are responsible for over 50% of the total SSH variability up to the intra-seasonal scale, and ITD and HCT variability up to interannual. Eddy-induced SST variability is the highest along the Oyashio Extension Front on the order of 40–60% on submonthly scales. These results highlight the role of mesoscale eddies in ocean thermodynamic variability and in air-sea interaction.