Explore the vast range of titles available.

The mean vertical structure of mesoscale eddies in the Peru‐Chile Current System is investigated by combining the historical records of Argo float profiles and satellite altimetry data. A composite average of 420 (526) profiles acquired by Argo floats that surfaced into cyclonic (anticyclonic) mesoscale eddies allowed constructing the mean three‐dimensional eddy structure of the eastern South Pacific Ocean. Key differences in their thermohaline vertical structure were revealed. The core of cyclonic eddies (CEs) is centered at ∼150 m depth within the 25.2–26.0 kg m−3 potential density layer corresponding to the thermocline. In contrast, the core of the anticyclonic eddies (AEs) is located below the thermocline at ∼400 m depth impacting the 26.0–26.8 kg m−3 density layer. This difference was attributed to the mechanisms involved in the eddy formation. While intrathermocline CEs would be formed by instabilities of the surface equatorward coastal currents, the subthermocline AEs are likely to be shed by the subsurface poleward Peru‐Chile Undercurrent. In the eddy core, maximum temperature and salinity anomalies are of ±1°C and ±0.1, with positive (negative) values for AEs (CEs). This study also provides new insight into the potential impact of mesoscale eddies for the cross‐shore transport of heat and salt in the eastern South Pacific. Considering only the fraction of the water column associated with the fluid trapped within the eddies, each CE and AE has a typical volume anomaly flux of ∼0.1 Sv and yields to a heat and salt transport anomaly of ±1–3 × 1011 W and ±3–8 × 103 kg s−1, respectively. Key Points A new methodology is proposed to assess the eddy vertical structure from ARGO profiles Cyclonic and anticyclonic eddies show clear distinct vertical structures They impact differently on heat and salt transports of the thermo‐ and subthermocline

Mesoscale eddies are ubiquitous oceanic phenomena and play an important role in ocean circulation, ocean dynamics, and the transport of material energy. Temperature anomalies are a crucial parameter that reflects the state of mesoscale eddies. This study proposes a Gaussian function model to fit the vertical temperature anomaly (TA) profile to facilitate the analysis of variations, and the principle of the model is based on the fact that each TA profile tends to fluctuate around one or more peaks. The model is extracted and validated using Argo profiles within cyclonic and anticyclonic eddies in the Northwest Pacific Ocean spanning over the period from 2002 to 2021. The validation demonstrates that the model can accurately recover the vertical TA profiles with a limited number of parameters. This makes it suitable for analysing the spatial distribution patterns that require a large sample count. The analysis indicates that eddies with different TA profiles have a spatial aggregation effect in geographic distribution. Eddies with lower extreme temperature anomalies, at depths of 200–300 m, are mainly distributed along two bands on the north side of the Kuroshio Extension (KE) and the North Equatorial Current. Eddies with extreme TAs at the deepest depth (500–600 m) are distributed along the KE.

IntroductionIn underwater acoustic applications, the three-dimensional sound speed distribution has a significant impact on signal propagation. However, the traditional sound speed profile (SSP) measurement method requires a lot of manpower and time, and it is difficult to popularize. Satellite remote sensing can collect information on a large ocean surface area, from which the underwater information can be derived.MethodIn this paper, we propose a method for reconstructing the SSP based on an extensible end-to-end tree boosting (XGBoost) model. Combined with satellite remote sensing data and Argo profile data, it extracts the characteristic matrix of the SSP and analyzes the contribution rate of each order matrix to reduce the introduction of noise. The model inverts the SSP above 1000 m in the South China Sea by using the root mean square error (RMSE) as the precision evaluation index.ResultThe results showed that the XGBoost model could better reconstruct the SSP above 1000 m, with a RMSE of 1.75 m/s. Compared with the single empirical orthogonal function regression (sEOF-r) model of the linear regression method, the RMSE of the XGBoost model was reduced by 0.59 m/s.DiscussionFor this model, the RMSE of the inversion results was smaller, the robustness was better, and the regression performance was superior to that of the sEOF-r model at different depths. This study provided an efficient tree boosting model for SSP reconstruction, which could reliably and instantaneously monitor the 3D sound speed distribution.

The Lofoten Vortex (LV) is a quasi-permanent anticyclonic eddy with the characteristic of periodic regeneration in the Lofoten Basin (LB), which is one of the major areas of deep vertical mixing in the Nordic Sea. Our analysis of the LV contributes to our understanding of the variations in convective mixing in the LB. Based on drifter data and satellite altimeter data, the climatological results show that the LV has the sea surface characteristics of relative stability in terms of its spatial position and significant seasonal variations in its physical characteristics. Combined with the temperature and salinity data of Argo profiles, the vertical structures of the LV are presented here in terms of their spatial distribution and monthly variations. The wavelet analysis of the satellite sea surface temperature (SST) data shows that the period of SST anomaly (SSTA) in the LV sea area is 8–16 years. In the stage marked by a decreasing (increasing) trend of SSTA, the vertical mixing is strengthened (weakened). Current vertical mixing is clearly revealed by the Argo profiles, and the SSTA shows a significant impact of cooling. However, against a background of warming and freshening, this vertical mixing will be greatly weakened in the next increasing trending stage of the SSTA.

The current lack of high-precision information on subsurface seawater is a constraint in fishery research. Based on Argo temperature and salinity profiles, this study applied the gradient-dependent optimal interpolation to reconstruct daily subsurface oceanic environmental information according to fishery dates and locations. The relationship between subsurface information and matching yellowfin tuna (YFT) in the western and central Pacific Ocean (WCPO) was examined using catch data from January 1, 2008 to August 31, 2017. The seawater temperature and salinity results showed differences of less than ±0.5°C and ±0.01 compared with the truth observations respectively. Statistical analysis revealed that the most suitable temperature for YFT fishery was 28–29°C at the near-surface. The most suitable salinity range for YFT fishery was 34.5–36.0 at depths shallower than 300 m. The suitable upper and lower bounds on the depths of the thermocline were 90–100 m and 300–350 m, respectively. The thermocline characteristics were prominent, with a mean temperature gradient exceeding 0.08°C/m. These results indicate that the profiles constructed by gradient-dependent optimal interpolation were more accurate than those of the nearest profiles adopted.

The inadequate spatial resolution of altimeter results in low identification efficiency of oceanic eddies, especially for small-scale eddies. It is well known that eddies can not only induce sea surface signal but more importantly have typical vertical structure characteristics. However, although the vertical structure characteristics are usually used for statistical analysis, they are seldom considered in the process of eddy recognition. This study is devoted to identifying eddies from the perspective of their vertical signal derived from the 18-year Argo data. Due to the irregular and noisy profile pattern, the direct identification of eddy core from Argo profile is deemed to be a challenge. With the popularity of artificial intelligence, a new hybrid method that combines the advantages of convolutional neural network (CNN) with extreme gradient boosting (XGBoost) is proposed to extract the representative vertical feature and identify eddy from a profile. First, CNN is employed as a feature extractor to automatically obtain vertical features from the input profile at the bottom of the network. Second, the obtained high-dimensional feature vectors are inputted into the XGBoost model, combined with other profile features for classifying profiles that are outside altimeter-identified eddies (Alt eddy). Finally, extensive experiments are implemented to demonstrate the efficiency of the proposed method. The results show that the classification accuracy of CNN-XGBoost model can reach 98%, and about 36% eddies are recaptured. These eddies, dubbed CNN-XGB eddies, are benchmarked against Alt eddies for the vertical structure and geographical distribution, demonstrating a similar or even stronger vertical signal and a prominent eddy belt in the tropical ocean. Within the proposed theory framework, there are various potentials to obtain a better outlook for eddy identification and in situ float observations.

Surface and subsurface temperature changes in the Kuroshio induced by tropical cyclones (TCs) were investigated using both 10-year observational datasets (SST maps and Argo data) and temperature budget analysis of idealized numerical simulations. Although Argo data are very limited during a TC’s passage, they provided unique in situ measurements at the subsurface of the Kuroshio. Compared to the surface water in the Kuroshio and in the general ocean, the subsurface water of the Kuroshio shows a rapid temperature warming (recovery) after a TC’s passage. Budget analysis on the model simulations suggested that the temperature changes at surface Kuroshio are dominated by the wind-induced vertical mixing, while the subsurface temperature changes are primarily dominated by TC-induced Ekman pumping (downwelling-upwelling-downwelling pattern). The Kuroshio subsurface water is warmed up mainly by the downwelling process, and then transported downstream by strong Kuroshio currents. Sensitivity experiments suggested that the recovery time of the subsurface temperature cooling is more sensitive to TC translation speeds and less sensitive to the Kuroshio current velocities.

The efficacy of several model error schemes in the Ensemble Kalman Filter (EnKF) data assimilation is investigated through a series of sensitivity experiments, in which the Argo and other in situ temperature and salinity profiles are assimilated into an ocean general circulation model (OGCM) for the Pacific Ocean. Different schemes for combining the additive inflation, multiplicative inflation, one‐step bias correction and two‐stage bias correction are evaluated in the framework of the EnKF. Experimental results indicate that the additive inflation is the key technique that can maintain ensemble spread in an adequate range. When sufficient observations are available, the assimilation system with additive inflation scheme can efficiently reduce both model bias and random errors. The combination of additive inflation and multiplicative inflation can further improve the performance of the assimilation system, in particular when the additive inflation underestimates model error. The bias correction schemes, the one‐step method and the persistent bias method are effective in reducing the model bias only within a relatively short initial assimilation period and in some regions. Further improvement from the bias correction schemes is not evident as the assimilation period increases. Key Points Additive inflation is the key for maintaining ensemble spread Bias correction schemes have effect only within short initial assimilation period IAU reduces the impact of bias correction on data assimilation

Annual Rossby wave is a key component of the ENSO phenomenon in the equatorial Pacific Ocean. Due to the paucity and seasonal bias in historical hydrographic data, previous studies on equatorial Rossby waves only gave qualitative description. The accumulation of Argo measurements in recent years has greatly alleviated the data problem. In this study, seasonal variation of the equatorial Pacific Ocean is examined with annual harmonic analysis of Argo gridded data. Results show that strong seasonal signal is present in the western equatorial Pacific and explains more than 50% of the thermal variance below 500 m. Lag-correlation tracing further shows that this sub-thermocline seasonal signal originates from the eastern equatorial Pacific via downward and southwestward propagation of annual Rossby waves. Possible mechanisms for the equatorward shift of Rossby wave path are also discussed.

The Argo temperature and salinity profiles in 2005–2009 are assimilated into a coastal ocean general circulation model of the Northwest Pacific Ocean using the ensemble adjustment Kalman filter (EAKF). Three numerical tests, including the control run (CTL) (without data assimilation, which serves as the reference experiment), ensemble free run (EnFR) (without data assimilation), and EAKF experiment (with Argo data assimilation using EAKF), are carried out to examine the performance of this system. Using the restarts of different years as the initial conditions of the ensemble integrations, the ensemble spreads from EnFR and EAKF are all kept at a finite value after a sharp decreasing in the first few months because of the sensitive of the model to the initial conditions, and the reducing of the ensemble spread due to Argo data assimilation is not much. The ensemble samples obtained in this way can well represent the probabilities of the real ocean states, and no ensemble inflation is necessary for this EAKF experiment. Different experiment results are compared with satellite sea surface temperature (SST) data and the Global Temperature-Salinity Profile Program (GTSPP) data. The comparison of SST shows that modeled SST errors are reduced after data assimilation; the error reduction percentage after assimilating the Argo profiles is about 10 % on average. The comparison against the GTSPP profiles, which are independent of the Argo profiles, shows improvements in both temperature and salinity. The comparison results indicated a great error reduction in all vertical layers relative to CTL and the ensemble mean of EnFR; the maximum value for temperature and salinity reaches to 85 % and 80 %, respectively. The standard deviations of sea surface height are employed to examine the simulation ability, and it is shown that the mesoscale variability is improved after Argo data assimilation, especially in the Kuroshio extension area and along the section of 10°N. All these results suggest that this system is potentially useful for improving the simulation ability of oceanic numerical models.