Browsing by Author "Meng, Lingsheng"
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Item Field data reconstruction and upper ocean circulation variability in tropical Indo-Pacific(University of Delaware, 2022) Meng, LingshengObservations are fundamental for studying and understanding the oceans. While in-situ measurements are limited, satellites can remotely monitor the ocean continuously for extended periods with broad spatial coverages. Thus, they have been providing large and ever-growing data volumes. However, most remote-sensed products are the surface ocean. Despite the shortage of observations, many important processes and features at subsurface and interior oceans need to be detected and studied (e.g., subsurface temperature, density, internal waves and tides, subsurface flows). Consequently, the techniques of constructing subsurface and deep oceans from surface ocean have been developed, i.e., using ocean surface data to reconstruct ocean interior data based on their relationships. Since the data are mostly from remote sensing, they are also known as deep ocean remote sensing (DORS) techniques. Here I developed some data-driven models to estimate ocean subsurface and interior variables from satellite-observed sea surface data by using deep learning methods. A higher resolution (1/4 degree) temperature and salinity (T and S) field data is constructed based on the Argo gridded data (1 degree); while, A super resolution (1/12 degree) T and S field data is constructed based on the reanalysis data. Dynamic height fields and the associated ocean geostrophic flows are also calculated and analyzed. This work enriches oceanic observations with respect to vertical dimension and horizontal resolution, which can largely make up for the paucity of the subsurface and deep ocean observation, and further facilitate the studies of ocean circulation, ocean dynamics, and climate changes. ☐ Ocean circulation transports water mass and redistributes heat, saline, and nutrients in the oceans. Therefore, determining the changes in ocean circulation is essential to understand ocean states and processes and to predict climate change. Upper ocean holds the major and most ocean circulation changes compared with deeper oceans, because wind and heat and water fluxes that mainly driven ocean circulation occur at ocean surface. Pacific Ocean and Indian Ocean are closely connected and they together are called Ind-Pacific oceans. Considering the importance and complexity, upper ocean circulation variability in tropical Indo-Pacific needs to be explored in detail, from different scales and from different perspectives. ☐ Sea level changes within wide temporal-spatial scales have great influence on oceanic and atmospheric circulations. Many previous works have identified long-term sea level change, while regional sea level variations on different time scales. Sea level change on interannual and decadal time-scales needs more studies. Here, sea level anomaly (SLA) was decomposed into interannual and decadal time-scales. The temporal-spatial features of the SLA variability in the Pacific have been examined and associated with climate variability modes. Moreover, decadal SLA oscillations in the Pacific Ocean were identified during 1993-2016, with the phase reversals around 2000, 2004, and 2012. In the tropical Pacific, large sea level variations in the western and central basin were a result of changes in the equatorial wind stress. Furthermore, coherent decadal changes could also be seen in wind stress, sea surface temperature (SST), subtropical cells (STCs) and thermocline depth. ☐ The shallow overturning circulation is a wind-driven circulation in the tropical region, adjusting water mass and heat in 3-dimension. This study investigates the Indian Ocean (IO) shallow overturning circulation during 1958-2017. Their structure consists of a cross-equator cell (CEC) and a southern subtropical cell (SSTC). Both the CEC and the SSTC exhibited significant variability on interannual to decadal timescales during 1958-2017, and this variability mainly resulted from changes in both meridional Ekman transports and meridional geostrophic flows in the upper layer; each component could dominate the shallow cells’ variations in certain years. This work presents a comprehensive study of the interannual to decadal variability of the IO shallow cells and the corresponding reasons and influences and tried to link these variations with other variations in the IO.Item Quasi-Decadal Temperature Variability in the Intermediate Layer of Subtropical South Indian Ocean During the Argo Period(Journal of Geophysical Research: Oceans, 2023-07-28) Huang, Lei; Zhuang, Wei; Wu, Zelun; Zhang,Yang; Meng, Lingsheng; Edwing, Deanna; Yan, Xiao-HaiIt has been reported that the subtropical South Indian Ocean (SIO) has been rapidly warming over the past two decades and can therefore be characterized as one of the major heat accumulators among the oceanic basins. However, this strong warming is not uniformly distributed in the vertical direction. In comparison to the decade-long warming in the upper layer (0–300 m) in 2004–2013, the intermediate layer (300–1,000 m) displays a shorter warming during 2004–2009 and an intense cooling during 2010–2016. By decomposing temperature variations into heaving and spice components, and performing a heat budget analysis, we show that temperature variations in the intermediate layer during these two periods are primarily contributed by isopycnal migrations driven by local wind forcing. Local wind change in the subtropical SIO can be explained by the Indian Ocean Dipole and El Niño–Southern Oscillation during 2004–2016, while Southern Annular Mode (SAM) favors anomalous wind change in mid-latitudes and the formation of basin-wide wind change in the SIO. Additionally, wind forcing in the Subantarctic Mode Water (SAMW) formation region, which is closely linked to the SAM, modulates the anomalous spreading of SAMW into the interior of the subtropical SIO. This, therefore, leads to the SAMW intrusion being of secondary importance to the quasi-decadal temperature variability. Our findings demonstrate the independence of wind-driven temperature changes on the quasi-decadal scale in the intermediate layer of the subtropical SIO under the overall warming background of SIO waters. Key Points - Quasi-decadal temperature variations occur in the intermediate layer (300–1,000 m) of subtropical South Indian Ocean (SIO) - Local wind-driven heaving process is the major driver, spice component arising from the Subantarctic Mode Water intrusion is of secondary importance - The local wind change in the subtropical SIO can be well explained by the combined effects of El Niño–Southern Oscillation, Indian Ocean Dipole and Southern Annular Mode Plain Language Summary Compared to the decade-long warming in the upper layer of the South Indian Ocean (SIO), which has been studied extensively, our understanding of temperature change in the intermediate layer is relatively limited. This study reveals a quasi-decadal temperature cycle in the intermediate layer of the subtropical SIO during the Argo period, which is characterized by a shorter warming period during 2004–2009 and subsequent cooling during 2010–2016. Decomposition of temperature changes suggests that this quasi-decadal temperature variability is primarily driven by the heaving component, which is tightly associated with local wind variability driven by local and remote forcings, whereas the spice change largely contributed by the SAM-related water mass transmission from higher latitudes, is of secondary importance. Thus, this study expands our knowledge of temperature variability in the SIO and demonstrates that the quasi-decadal variability of intermediate layer temperatures in the subtropical SIO serves as a crucial archive for both global and local climate change.Item Reconstructing High-Resolution Ocean Subsurface and Interior Temperature and Salinity Anomalies From Satellite Observations(IEEE Transactions on Geoscience and Remote Sensing, 2021-09-21) Meng, Lingsheng; Yan, Chi; Zhuang, Wei; Geng, Xupu; Yan, Xiao-HaiAccurately retrieving ocean interior parameters from remote sensing observations is essential for ocean and climate studies because direct observations are sparse and costly. Furthermore, high-resolution structure of seawater properties is critical for understanding the oceanic processes and changes on multiple scales. Here, we designed a new method based on a deep neural network to retrieve subsurface temperature anomaly (STA) and subsurface salinity anomaly (SSA) in the Pacific Ocean at high (1/4°) and super (1/12°) horizontal resolution. We utilized multisource satellite-observed sea surface data (e.g., sea level, temperature, salinity, and wind vector) as inputs. The results revealed that our model retrieved the high- and super-resolution STA/SSA with high accuracy, and the model was reliable in a wide range of depths (near surface to 4000 m) and times (all months in 2014). Regarding the high-resolution STA (SSA) estimation, the average coefficient of determination ( R2 ) was 0.984 (0.966), and the average root-mean-squared error (RMSE) was 0.068 °C (0.016 psu). For the super-resolution STA, the average R2 was 0.988 and RMSE was 0.093 °C. Here, we established an effective technique that improved the resolution and accuracy of estimating the ocean interior parameters from satellite observation. The new technique provides some new insights into oceanic observation and dynamics.Item Reconstruction of Three-Dimensional Temperature and Salinity Fields From Satellite Observations(Journal of Geophysical Research: Oceans, 2021-11-07) Meng, Lingsheng; Yan, Chi; Zhuang, Wei; Zhang, Weiwei; Yan, Xiao-HaiObservation of the ocean is crucial to the studies of ocean dynamics, climate change, and biogeochemical cycle. However, current oceanic observations are patently insufficient, because the in situ observations are of difficulty and high cost while the satellite remote-sensed measurements are mainly the sea surface data. To make up for the shortage of ocean interior data and make full use of the abundant satellite data, here we develop a data-driven deep learning model to estimate ocean subsurface and interior variables from satellite-observed sea surface data. Exclusively and simply using satellite data, three-dimensional ocean temperature and salinity fields are successfully reconstructed, which are at 26 level depths from 0 to 2,000 m. We further design a scheme to increase the horizontal resolution from 1° to 1/4°, which is higher than the Argo gridded data. Estimations from our model are accurate, reliable, and stable for a wide range of research areas and periods. Dynamic height fields that are derived from the estimated temperature and salinity, as well as the associated ocean geostrophic flows, are also calculated and analyzed, which indicates the potentials of our model for reconstructing the ocean circulation fields as well. This study enriches oceanic observations with respect to vertical dimension and horizontal resolution, which can largely make up for the paucity of the subsurface and deep ocean observation, both before and during Argo era. This work also provides some new foundations for and insights into geoscience and climate change fields. Plain Language Summary: Ocean data are important for ocean science and climate change studies; however, oceanic observations are difficult and costly and thus are still very sparse in space and time. Satellites have been providing plentiful oceanic observations, but these data are only at the sea surface. To fully utilize the copious satellite data and to make up for the shortage of ocean interior data. Here, we establish a deep learning model to connect the surface ocean with the subsurface and deep oceans, through which, subsurface and deep ocean temperature and salinity data are estimated from the surface data observed by satellites. We also design a new scheme to improve the horizontal resolution of the obtained data. The results show that our model successfully reconstructed the three-dimensional field data of temperature and salinity. Our model could facilitate ocean science studies by largely enriching the ocean data availability.Item Retrieving Ocean Surface Winds and Waves from Augmented Dual-Polarization Sentinel-1 SAR Data Using Deep Convolutional Residual Networks(Atmosphere, 2023-08-11) Xue, Sihan; Meng, Lingsheng; Geng, Xupu; Sun, Haiyang; Edwing, Deanna; Yan, Xiao-HaiSea surface winds and waves are very important phenomena that exist in the air–sea boundary layer. With the advent of climate change, cascade effects are bringing more attention to these phenomena as warmer sea surface temperatures bring about stronger winds, thereby altering global wave conditions. Synthetic aperture radar (SAR) is a powerful sensor for high-resolution surface wind and wave observations and has accumulated large quantities of data. Furthermore, deep learning methods have been increasingly utilized in geoscience, especially the inversion of ocean information from SAR imagery. Here, we propose a method to invert various parameters of ocean surface winds and waves using Sentinel-1 SAR IW mode data. To ensure this method is more robust and scalable, we augmented the input data with dual-polarized SAR imagery, an incident angle, and a more constrained homogeneity test. This method adopts a deeper structure in order to retrieve more wind and wave parameters, and the use of residual networks can accelerate training convergence and improve regression accuracy. Using 1600 training samples filtered by a novel homogeneity test and with significant wave heights between 0 and 10 m, results from error parameters including the root mean square error (RMSE), scatter index (SI), and correlation coefficient (COR) show the great performance of this proposed method. The RMSE is 0.45 m, 0.76 s, and 1.90 m/s for the significant wave height, mean wave period, and wind speed, respectively. Furthermore, the temporal variation and spatial distribution of the estimates are consistent with China–France Oceanography Satellite (CFOSAT) observations, buoy measurements, WaveWatch3 regional model data, and ERA5 reanalysis data.