Browsing by Author "Zhuang, Wei"
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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 Rapid Sea Level Rise in the Tropical Southwest Indian Ocean in the Recent Two Decades(Geophysical Research Letters, 2023-12-27) Huang, Lei; Zhuang, Wei; Lu, Wenfang; Zhang, Yang; Edwing, Deanna; Yan, Xiao-HaiIt has been reported that the sea level falls in the tropical Southwest Indian Ocean (SWIO) from the 1960s to the early 2000s. However, a rising trend of 4.05 ± 0.56 cm/decade has occurred during the recent two decades with our analysis showing that manometric sea level contributes 41% to this sea level rise. 30% of this rise is due to steric sea level (SSL) change in the upper 2,000 m with SSL rise in the upper 300 m of secondary importance. Conversely, thermal expansion below the thermocline (300–2,000 m), likely caused by water mass spread from the Southern Ocean, induces major contribution to SSL changes. Compared to existing studies demonstrating the contribution of thermal variations above the thermocline to sea level variability in the tropical SWIO, this study emphasizes the importance of ocean mass and deeper ocean changes in a warming climate. Key Points - Rapid sea level rise occurs in the tropical Southwest Indian Ocean (SWIO) since the early 2000s - The ocean mass addition and the upper 2,000 m ocean warming contribute significantly to the total sea level rise - The upper 2,000 m ocean warming is primarily attributed to thermal expansion below the thermocline associated with the spread of water masses Plain Language Summary Global ocean sea level change is spatially and temporally nonuniform due to oceanic and atmospheric dynamics. The tropical Southwest Indian Ocean (SWIO) experienced a sea level fall from the 1960s to the early 2000s. However, a rapid sea level rise has occurred over the last two decades in the tropical SWIO that is faster than the global average. The ocean mass increase due to extra water input leads to an essential impact on sea level rise in the tropical SWIO. Compared to previous studies demonstrating the effect of thermal expansion in the upper 300 m, this study shows larger contributions from deeper ocean (300–2,000 m) warming over the past two decades. Overall, this study highlights the importance of ocean mass and deeper water thermal structure in regulating tropical SWIO sea level rise in a changing climate, as well as the need for observations and direct assessment of the abyssal ocean beneath 2,000 m.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.