The seasonal variation of the upper layers of the South China Sea (SCS) circulation and the Indonesian through flow (ITF): An ocean model study

The upper layer, wind-driven circulation of the South China Sea (SCS), its through-flow (SCSTF) and the Indonesian through flow (ITF) are simulated using a high resolution model, FVCOM (finite volume coastal ocean model) in a regional domain comprising the Maritime Continent. The regional model is embedded in the MIT global ocean general circulation model (ogcm) which provides surface forcing and boundary conditions of all the oceanographic variables at the lateral open boundaries in the Pacific and Indian oceans. A five decade long simulation is available from the MITgcm and we choose to investigate and compare the climatologies of two decades, 1960–1969 and 1990–1999. The seasonal variability of the wind-driven circulation produced by the monsoon system is realistically simulated. In the SCS the dominant driving force is the monsoon wind and the surface circulation reverses accordingly, with a net cyclonic tendency in winter and anticyclonic in summer. The SCS circulation in the 90s is weaker than in the 60s because of the weaker monsoon system in the 90s. In the upper 50 m the interaction between the SCSTF and ITF is very important. The southward ITF can be blocked by the SCSTF at the Makassar Strait during winter. In summer, part of the ITF feeds the SCSTF flowing into the SCS through the Karimata Strait. Differently from the SCS, the ITF is primarily controlled by the sea level difference between the western Pacific and eastern Indian Ocean. The ITF flow, consistently southwestward below the surface layer, is stronger in the 90s. The volume transports for winter, summer and yearly are estimated from the simulation through all the interocean straits. On the annual average, there is a ∼5.6 Sv of western Pacific water entering the SCS through the Luzon Strait and ∼1.4 Sv exiting through the Karimata Strait into the Java Sea. Also, ∼2 Sv of SCS water enters the Sulu Sea through the Mindoro Strait, while ∼2.9 Sv flow southwards through the Sibutu Strait merging into the ITF. The ITF inflow occurs through the Makassar Strait (up to ∼62%) and the Lifamatola Strait (∼38%). The annual average volume transport of the ITF inflow from the simulation is ∼15 Sv in the 60s and ∼16.6 Sv in the 90s, very close to the long term observations. The ITF outflow through the Lombok, Ombai and Timor straits is ∼16.8 Sv in the 60s and 18.9 Sv in the 90s, with the outflow greater by 1.7 Sv and 2.3 Sv respectively. The transport estimates of the simulation at all the straits are in rather good agreement with the observational estimates. We analyze the thermal structure of the domain in the 60s and 90s and assess the simulated temperature patterns against the SODA reanalysis product, with special focus on the shallow region of the SCS. The SODA dataset clearly shows that the yearly averaged temperatures of the 90s are overall warmer than those of the 60s in the surface, intermediate and some of the deep layers and the decadal differences (90s − 60s) indicate that the overall warming of the SCS interior is a local effect. In the simulation the warm trend from the 60s to the 90s in well reproduced in the surface layer. In particular, the simulated temperature profiles at two shallow sites at midway in the SCSTF agree rather well with the SODA profiles. However, the warming trend in the intermediate (deep) layers is not reproduced in the simulation. We find that this deficiency is mostly due to a deficiency in the initial temperature fields provide by the MITgcm.Singapore. National Research Foundation (Singapore-MIT Alliance for Research and Technology Center. Center of Environment Sensing and Modeling Program

Coupling of a regional atmospheric model (RegCM3) and a regional oceanic model (FVCOM) over the maritime continent

Climatological high resolution coupled climate model simulations for the maritime continent have been carried out using the regional climate model (RegCM) version 3 and the finite volume coastal ocean model (FVCOM) specifically designed to resolve regions characterized by complex geometry and bathymetry. The RegCM3 boundary forcing is provided by the EMCWF-ERA40 re-analysis. FVCOM is embedded in the Global MITgcm which provides boundary forcing. The domain of the coupled regional model covers the entire South China Sea with its through-flow, the entire Indonesian archipelago with the Indonesian through-flow (ITF) and includes a large region in the western Pacific and eastern Indian oceans. The coupled model is able to provide stable and realistic climatological simulations for a specific decade of atmospheric–oceanic variables without flux correction. The major focus of this work is on oceanic properties. First, the coupled simulation is assessed against ocean-only simulations carried out under two different sets of air–sea heat fluxes. The first set, provided by the MITgcm, is proved to be grossly deficient as the heat fluxes are evaluated by a two-dimensional, zonally averaged atmosphere and the simulated SST have anomalous cold biases. Hence the MITgcm fluxes are discarded. The second set, the NCEP re-analysis heat fluxes, produces a climatological evolution of the SST with an average cold bias of ~−0.8 °C. The coupling eliminates the cold bias and the coupled SST evolution is in excellent agreement with the analogous evolution in the SODA re-analysis data. The detailed comparison of oceanic circulation properties with the International Nusantara Stratification and Transport observations shows that the coupled simulation produces the best estimate of the total ITF transport through the Makassar strait while the transports of three ocean-only simulations are all underestimated. The annual cycle of the transport is also very well reproduced. The coupling also considerably improves the vertical thermal structure of the Makassar cross section in the upper layer affected by the heat fluxes. On the other hand, the coupling is relatively ineffective in improving the precipitation fields even though the coupled simulation captures reasonably well the precipitation annual cycle at three land stations in different latitudes.Singapore. National Research Foundation (Center for Environmental Sensing and Monitoring (CENSAM))Singapore-MIT Alliance for Research and Technology (SMART) programNational Natural Science Foundation (China) (NSFC, No. 41106003

Observational and modeling studies of oceanic responses and feedbacks to typhoons Hato and Mangkhut over the northern shelf of the South China Sea

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Dong, W., Feng, Y., Chen, C., Wu, Z., Xu, D., Li, S., Xu, Q., Wang, L., Beardsley, R. C., Lin, H., Li, R., Chen, J., & Li, J. Observational and modeling studies of oceanic responses and feedbacks to typhoons Hato and Mangkhut over the northern shelf of the South China Sea. Progress in Oceanography, 191, (2021): 102507, https://doi.org/10.1016/j.pocean.2020.102507.Meteorological and oceanic responses to Typhoons Hato and Mangkhut were captured by storm-monitoring network buoys over the northern shelf of the South China Sea. With similar shelf-traversing trajectories, these two typhoons exhibited distinctly different features in storm-induced oceanic mixing and oceanic heat transfer through the air-sea interface. A well-defined cold wake was detected underneath the storm due to a rapid drop in sea surface temperature during the Hato crossing, but not during the Mangkhut crossing. Impacts of oceanic mixing on forming a storm-produced cold wake were associated with the pre-storm condition of water stratification. In addition to oceanic mixing produced through the diffusion process by shear and buoyancy turbulence productions, the short-time scale of mixing suggested convection/overturning may play a critical role in the rapid cooling at the sea surface. The importance of convection/overturning to mixing depended on the duration of atmospheric cooling above the sea surface-the longer the atmospheric cooling, the more significant effect on mixing. Including the oceanic mixed layer (OML) in the WRF model was capable of reproducing the observed storm-induced variations of wind and air pressure, but not the air and sea surface temperatures. Process-oriented numerical experiments with the OML models supported both observational and modeling findings. To simulate the storm-induced mixing in a coupled atmospheric and oceanic model, we need to improve the physics of vertical mixing with non-hydrostatic convection/overturning. Warming over the shelf is projected to have a more energetic influence on future typhoon intensities and trajectories.This work was supported by the National Key Research and Development Programs of China with grant numbers 2018YFC-1406201; 2016YFA-0602700; 2018YFC-1506903; 2018YFC-1406205, and the National Sciences Foundation of China with grant number U1811464. S. Li was supported by the oversea Ph.D. fellowship from the China Scholarship Council (No. 1409010025) and Dr. Chen’s Montgomery Charter Chair graduate education funds at the University of Massachusetts-Dartmouth

Lagrangian Study of Particle Transport Processes in the Coastal Gulf of Maine

Turbulent mixing and chaotic advection are two distinct mixing mechanisms in fluid dynamics. These two mechanisms can be used to explain mixing and dispersion in different marine environments. This thesis studied dispersion properties by tracking Lagrangian particle in coastal Gulf of Maine (GoM) and a macro tidal basin- Cobscook Bay, Maine using the free surface, three-dimensional, primitive equation Ocean General Circulation Model (OGCM). A simple Individual Based Model (IBM) is embedded into a Lagrangian tracking program to simulate transport and development of lobster larvae in coastal GoM. Different algorithms of random walk model are tested and compared to mimic sub-grid turbulent diffusion. In an advection dominated marine environment, particles are mainly transported by the major current system and sub-grid mixing tends to enhance the spreading of particles. The effect of second order correction in random walk model in advection dominated current system is less obvious than pure diffusion environment. Connectivity matrices analysis shows that there is annual variability of the retention and transport of lobster larvae in coastal GoM. A finite element OGCM is employed to simulate tidal current and Lagrangian drifter trajectories in Cobscook Bay. Multi small eddy structure has been revealed in tidal circulation and residual current field. Modeled current, water level and drifter trajectories are consistent with observations. The sensitivity of particle spreading and transport path in Cobscook Bay to initial release location and time suggests that chaotic advection may play an important role in horizontal dispersion and water exchanges in an energetic tidal system. In theoretical aspect, three idealized tidal model are reviewed and the critical conditions for chaotic advection occur are summarized. According these criteria, chaotic advection in Cobscook Bay is investigated. Statistic measures show that chaotic advection plays an important role in horizontal dispersion and spatially varied residual current interact with large tidal currents may cause chaotic advection. This mechanism can be representative of many tidal basins and estuaries

A Short-Term Tropical Cyclone Intensity Forecasting Method Based on High-Order Tensor (Student Abstract)

Tropical cyclones (TC) bring enormous harm to human beings, and it is crucial to accurately forecast the intensity of TCs, but the progress of intensity forecasting has been slow in recent years, and tropical cyclones are an extreme weather phenomenon with short duration, and the sample size of TC intensity series is small and short in length. In this paper, we devolop a tensor ARIMA model based on feature reconstruction to solve the problem, which represents multiple time series as low-rank Block Hankel Tensor(BHT), and combine the tensor decomposition technique with ARIMA for time series prediction. The method predicts the sustained maximum wind speed and central minimum pressure of TC 6-24 hours in advance, and the results show that the method exceeds the global numerical model GSM operated by the Japan Meteorological Agency (JMA) in the short term. We further checked the prediction results for a TC, and the results show the validity of the method

Probabilistic forecasting of tropical cyclones intensity using machine learning model

This study proposes a machine learning approach to probabilistic forecasting of tropical cyclone (TC) intensity. The earth system is complex and nonlinear, leading to inherent uncertainty in TC forecasting at all times, and therefore a representation of this uncertainty should be provided. Previous studies construct this uncertainty through ensemble or statistical methods, neither of which can directly characterize this uncertainty and suffer from problems such as excessive computational effort. And for this reason, we propose to assess the forecast without this uncertainty through the forecast distribution. Meanwhile, none of the previous studies on TC intensity forecasting by artificial intelligence methods characterize the uncertainty, so this study is a new supplement to data-driven TC forecasting. During the 2010–2020 evaluation period, the model’s point forecast can outperform the current state-of-the-art operational statistic-dynamical model results, and can obtain forecast intervals to provide reliable probabilistic forecasts, which are critical for disaster warnings

Assessment of Antarctic Sea Ice Cover in CMIP6 Prediction with Comparison to AMSR2 during 2015–2021

A comprehensive assessment of Antarctic sea ice cover prediction is conducted for twelve CMIP6 models under the scenario of SSP2-4.5, with a comparison to the observed data from the Advanced Microwave Scanning Radiometer 2 (AMSR2) during 2015–2021. In the quantitative evaluation of sea ice extent (SIE) and sea ice area (SIA), most CMIP6 models show reasonable variation and relatively small differences compared to AMSR2. CMCC-CM4-SR5 shows the highest correlation coefficient (0.98 and 0.98) and the lowest RMSD (0.98 × 106 km2 and 1.07 × 106 km2) for SIE and SIA, respectively. In the subregions, the models with the highest correlation coefficient and the lowest RMSD for SIE and SIA are inconsistent. Most models tend to predict smaller SIE and SIA compared to the observational data. GFDL-CM4 and FGOALS-g3 show the smallest mean bias (−4.50 and −1.21 × 105 km2) and the most reasonable interannual agreement of SIE and SIA with AMSR2, respectively. In the assessment of sea ice concentration (SIC), while most models can accurately predict the distribution of large SIC surrounding the Antarctic coastal regions, they tend to underestimate SIC and are unable to replicate the major patterns in the sea ice edge region. GFDL-CM4 and FIO-ESM-2-0 exhibit superior performance, with less bias (less than −5%) and RMSD (less than 23%) for SIC in the Antarctic. GFDL-CM4, FIO-ESM-2-0, and CESM2 exhibit relatively high positive correlation coefficients exceeding 0.60 with the observational data, while few models achieve satisfactory linear trend prediction of SIC. Through the comparison with RMSD, Taylor score (TS) consistently evaluates the Antarctic sea ice cover and proves to be a representative statistical indicator and applicable for its assessment. Based on comprehensive assessments of sea ice cover, CESM2, CMCC-CM4-SR5, FGOALS-g3, FIO-ESM-2-0, and GFDL-CM4 demonstrate more reasonable prediction performance. The assessment findings enhance the understanding of the uncertainties associated with sea ice in the CMIP6 models and highlighting the need for a meticulous selection of the multimodel ensemble

Inversion of Ocean Subsurface Temperature and Salinity Fields Based on Spatio-Temporal Correlation

Ocean observation is essential for studying ocean dynamics, climate change, and carbon cycles. Due to the difficulty and high cost of in situ observations, existing ocean observations are inadequate, and satellite observations are mostly surface observations. Previous work has not adequately considered the spatio-temporal correlation within the ocean itself. This paper proposes a new method—convolutional long short-term memory network (ConvLSTM)—for the inversion of the ocean subsurface temperature and salinity fields with the sea surface satellite observations (sea surface temperature, sea surface salinity, sea surface height, and sea surface wind) and subsurface Argo reanalyze data. Given the time dependence and spatial correlation of the ocean dynamic parameters, the ConvLSTM model can improve inversion models’ robustness and generalizability by considering ocean variability’s significant spatial and temporal correlation characteristics. Taking the 2018 results as an example, our average inversion results in an overall normalized root mean square error (NRMSE) of 0.0568 °C/0.0027 PSS and a correlation coefficient (R) of 0.9819/0.9997 for subsurface temperature (ST)/subsurface salinity (SS). The results show that SSTA, SSSA SSHA, and SSWA together are valuable parameters for obtaining accurate ST/SS estimates, and the use of multiple channels in shallow seas is effective. This study demonstrates that ConvLSTM is superior in modeling the subsurface temperature and salinity fields, fully taking global ocean data’s spatial and temporal correlation into account, and outperforms the classic random forest and LSTM approaches in predicting subsurface temperature and salinity fields

Unsupervised Machine Learning for Improved Delaunay Triangulation

Physical oceanography models rely heavily on grid discretization. It is known that unstructured grids perform well in dealing with boundary fitting problems in complex nearshore regions. However, it is time-consuming to find a set of unstructured grids in specific ocean areas, particularly in the case of land areas that are frequently changed by human construction. In this work, an attempt was made to use machine learning for the optimization of the unstructured triangular meshes formed with Delaunay triangulation in the global ocean field, so that the triangles in the triangular mesh were closer to equilateral triangles, the long, narrow triangles in the triangular mesh were reduced, and the mesh quality was improved. Specifically, we used Delaunay triangulation to generate the unstructured grid, and then developed a K-means clustering-based algorithm to optimize the unstructured grid. With the proposed method, unstructured meshes were generated and optimized for global oceans, small sea areas, and the South China Sea estuary to carry out data experiments. The results suggested that the proportion of triangles with a triangle shape factor greater than 0.7 amounted to 77.80%, 79.78%, and 79.78%, respectively, in the unstructured mesh. Meanwhile, the proportion of long, narrow triangles in the unstructured mesh was decreased to 8.99%, 3.46%, and 4.12%, respectively