YongChui Zhang’s research while affiliated with National Defense University and other places

Plain Language Summary Mesoscale eddies typically carry water with different characteristics from the surrounding environment, allowing them to transport moisture and energy at the ocean–atmosphere interface, thereby influencing the characteristic properties of evaporation ducts over the sea. To date, researchers have focused on the effects of various oceanic and atmospheric processes on evaporation ducts. However, there has been a persistent lack of research on the properties of evaporation ducts under the impact of mesoscale eddies. In this study, the variability in the evaporation duct environment under the influence of eddies and the underlying mechanisms are statistically analyzed. The results showed that ubiquitous mesoscale eddies could moderately modulate evaporation duct properties, which in turn could influence the effectiveness of shipborne radar technology, such as electromagnetic propagation, communication, and target detection.

Estimating subsurface thermohaline structure from concurrent satellite data is a meaningful way to enrich internal oceanic observations. As a powerful tool for data mining, many studies have used machine learning in subsurface reconstruction, but most conventional applications have been purely black-box in nature without further consideration of oceanic characteristics. Instead, proposed here for the first time is a semi-explicit deep network for reconstructing the oceanic interior from surface data. Named EEFFNN, the method embeds empirical orthogonal functions extracted from reanalysis data (the EE part of the name) into the inner framework of a feed-forward neural network (the FFNN part of the name). Comparison with Argo profiles and reanalysis data shows that EEFFNN can significantly outperform conventional machine-learning algorithms in estimating subsurface thermohaline structures and especially subsurface-intensified eddies. Also, EEFFNN can perform thermohaline reconstruction in one pass, making it more lightweight than "shallow" machine-learning algorithms such as random forest. Overall, EEFFNN shows promise for being applied to operational thermohaline reconstruction in the near future.

Influenced by both the Pacific and Indian Oceans, the temperature field in the Indonesian Sea are complicated. In order to reveal the spatial-temporal variability, the surface, thermocline and intermediate layers of the temperature field are studied based on the global ocean reanalysis data of Copernicus Marine Environment Monitoring Service (CMEMS) from 1993 to 2019 and the Empirical Orthogonal Function (EOF) analysis method. The results show that there is a warming trend of 1.36×10 ⁻² °C /year in the surface layer. The first two modes both show reverse changes close to the Pacific and Indian Oceans. The correlation coefficients between the first and second modal time coefficients and the Niño 3.4 index are 0.62 and 0.48, respectively. And the correlation coefficient between the second modal time coefficient and the ITF inflow is -0.45. In the thermocline, there is a warmer trend, which is 4.78×10 ⁻² °C /year. The correlation index of the first and second modal time coefficient with the Niño 3.4 and DMI indices are 0.87 and 0.43, respectively. The correlation index between the first and second modal time coefficient and the ITF inflow are -0.60 and 0.38, respectively. In the intermediate layer, the warming trend is 2.18×10 ⁻² °C /year. From 1993 to 1999, from 2000 to 2016 and from 2017 to 2019, the Sulu Sea and northern Halmahera Sea experienced three periods of warming, cooling and warming, respectively. The study is helpful for further understanding the variation of the temperature field in the Indonesian Sea.

The evaporation duct over the sea affects the propagation of electromagnetic waves, impacts the performance of electromagnetic systems accordingly, such as the shipborne radars. In order to reveal the spatiotemporal variations of the evaporation duct in the Kuroshio Extension (KE) region, where is one of the most intense air-sea interaction regions, the study utilized a 30-year ERA5 reanalysis dataset (from 1993 to 2022) and conducted statistical analysis on the monthly, seasonal variations and spatial distribution characteristics of the evaporation duct based on the NPS evaporation duct diagnostic model. The results enrich the database of maritime evaporation duct characteristics and improve the resolution and accuracy compared to previous studies.

Introduction The Indonesian Throughflow (ITF) connects the Pacific Ocean and the Indian Ocean. It plays an important role in the global ocean circulation system. The interannual variability of ITF transport is largely modulated by climate modes, such as Central-Pacific (CP) and Eastern-Pacific (EP) El Niño and Indian Ocean Dipole (IOD). However, the relative importance of these climate modes importing on the ITF is not well clarified. Methods Dominant roles of the climate modes on ITF in specific periods are quantified by combining a machine learning algorithm of the random forest (RF) model with a variety of reanalysis datasets. Results The results reveal that during the period from 1993 to 2019, the average ITF transport derived from high-resolution reanalysis datasets is -14.97 Sv with an intensification trend of -0.06 Sv year⁻¹, which mainly occurred in the upper layer. Four periods, which are 1993–2000, 2002–2008, 2009–2012 and 2013–2019, are identified as Niño 3.4, Dipole Mode Index (DMI), no significant dominant index, and DMI dominated, respectively. Discussion The corresponding sea surface height differences between the Northwest Tropical Pacific Ocean (NWP) and Southeast Indian Ocean (SEI) in these three periods when exist dominant index are -0.50 cm, 0.99 cm and -3.22 cm, respectively, which are responsible for the dominance of the climate modes. The study provides a new insight to quantify the response of ITF transport to climate drivers.

Based on the global ocean reanalysis data of COPERNICUS MARINE ENVIRONMENT MONITORING SERVICE (CMEMS) from 1993 to 2019, the long-term thermohaline trends in the Sulawesi Sea are analyzed. The results show that the temperature of the upper middle layer of the Sulawesi Sea has a long-term increasing trend, and the fastest warming is located at 130m, which is 0.05°C/yr, implying a total of 1.35°C in the 27 years. For salinity, there is a whole negative trend, except for the surface layer. Further study shows that the temperature and salinity interannual variations show positive and negative correlation with Niño index, respectively.