Coupled Ocean Atmosphere Wave Sediment Transport Research Articles

Coastal CUBEnet: an integrated observation and modeling system for sustainable Northern Gulf of Mexico coastal areas

The University of Southern Mississippi has developed the coastal CUBEnet environment. Coastal CUBEnet is a high-resolution, coastal ocean sensor, modeling, and data sharing web-based network that provides the environmental intelligence needed to support the complex modeling of the interlinked processes in the northern Gulf of Mexico. With near-real time data delivery via a common infrastructure, CUBEnet uses state of the art sensors to provide a set of networked measurements systems, visualization tools, and model developments to gain an understanding of the Gulf of Mexico’s marine environments. CUBEnet is also a mechanism for improved human engagement with Gulf of Mexico resources and provides stake holders with the data needed to make informed coastal, environmental, and economic decisions. The Coastal CUBEnet’s data environment utilizes both stationary and uncrewed mobile systems and high-resolution distributed sensors to create a networked platform across the northern coastal Gulf of Mexico. CUBEnet’s modeling environment has developed an implementation of The Coupled Ocean Atmosphere Wave Sediment Transport (COAWST) Model for the Mississippi Bight region that has been applied to investigate shore to shelf advective exchange processes, and their influence on coastal water quality conditions that support the region’s prolific marine ecosystem. CUBEnet’s modeling environment provides prototype modeling applications that are supported by real-time observations of key coastal environmental variables. CUBEnet’s Web accessible visualization tools provide parameter fields and vertical profiles from hydrodynamic models and field observations. Nowcasts and forecast results are available for the Eastern LA coastline, MS coastline, Mobile Bay, and the West coast of Florida.

Effect of wave-dependent mechanisms on storm surge and current simulation during three extreme weather systems

Abstract The wave effect is crucial to coastal ocean dynamics, but the roles of the associated wave-dependent mechanisms, such as the wave-enhanced surface stress, wave-enhanced bottom stress, and three-dimensional wave force, are not yet fully understood. In addition, the parameterizations of each mechanism vary and need to be assessed. In this study, a coupled wave-current model based on the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) model system was established to identify the effect of the wave-dependent mechanism on storm surges and currents during three typical extreme weather systems, i.e., cold wave, extratropical cyclone, and typhoon systems, in a semi-enclosed sea. The effects of the three coupled mechanisms on the surface or bottom stress, in terms of both the magnitude and direction, were investigated and quantified separately based on numerical sensitive analysis. A total of seven parameterizations is used to evaluate these mechanisms, resulting in significant variations in the storm surge and current vectors. The similarities and differences of the wave-induced surge and wave-induced current among the various mechanisms were summarized. The change in the surface stress and bottom stress and the excessive momentum flux due to waves were found to mainly occur in shallow nearshore regions. Optimal choice of the combination of parameterization schemes was obtained through comparison with measured data. The wave-induced current in the open waters with a deep-water depth and complex terrain could generate cyclonic or anticyclonic current vorticities, the number and intensity of which always increased with the enhanced strength and rotation of the wind field increased.

Deep learning of estuary salinity dynamics is physically accurate at a fraction of hydrodynamic model computational cost

AbstractSalinity dynamics in the Delaware Bay estuary are a critical water quality concern as elevated salinity can damage infrastructure and threaten drinking water supplies. Current state‐of‐the‐art modeling approaches use hydrodynamic models, which can produce accurate results but are limited by significant computational costs. We developed a machine learning (ML) model to predict the 250 mg L−1 Cl− isochlor, also known as the “salt front,” using daily river discharge, meteorological drivers, and tidal water level data. We use the ML model to predict the location of the salt front, measured in river miles (RM) along the Delaware River, during the period 2001–2020, and we compare predictions of the ML model to the hydrodynamic Coupled Ocean–Atmosphere‐Wave‐Sediment Transport (COAWST) model. The ML model predicts the location of the salt front with greater accuracy (root mean squared error [RMSE] = 2.52 RM) than the COAWST model does (RMSE = 5.36); however, the ML model struggles to predict extreme events. Furthermore, we use functional performance and expected gradients, tools from information theory and explainable artificial intelligence, to show that the ML model learns physically realistic relationships between the salt front location and drivers (particularly discharge and tidal water level). These results demonstrate how an ML modeling approach can provide predictive and functional accuracy at a significantly reduced computational cost compared to process‐based models. In addition, these results provide support for using ML models in operational forecasting, scenario testing, management decisions, hindcasting, and resulting opportunities to understand past behavior and develop hypotheses.

Development and Evaluation of a Short-Term Ensemble Forecasting Model on Sea Surface Wind and Waves across the Bohai and Yellow Sea

In this study, an ensemble forecasting model for in situ wind speed and wave height was developed using the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model. This model utilized four bias correction algorithms—Model Output Statistics (MOS), Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM) neural network, and Convolutional Neural Network (CNN)—to construct ensemble forecasts. The training data were derived from the COAWST simulations of one year and observations from three buoy stations (Laohutan, Zhifudao, and Lianyungang) in the Yellow Sea and Bohai Sea. After the optimization of the bias correction model training, the subsequent evaluations on the ensemble forecasts showed that the in situ forecasting accuracy of wind speed and wave height was significantly improved. Although there were some uncertainties on bias correction performance levels for individual algorithms, the uncertainties were greatly reduced by the ensemble forecasts. Depending on the dynamic weight assignment, the ensemble forecasts presented a stable performance even when the corrected forecasts by three algorithms had an obvious negative bias. Specifically, the ensemble forecasting bias was found with a mean reduction of about 96%~99% and 91%~95% for wind speed and wave height, and a reduction of about 91%~98% and 16%~54% during the period of Typhoon “Muifa”. For the four correction algorithms, the performance of bias correction was not directly related to the algorithm complexity. However, the strategies with more complex algorithms (i.e., CNN) were more conservative, and simple algorithms (i.e., MOS) might have induced unstable performance levels despite their lower bias in some cases.

Development of a Numerical Prediction Model for Marine Lower Atmospheric Ducts and Its Evaluation across the South China Sea

The Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model serves as the foundation for creating a forecast model to detect lower atmospheric ducts in this study. A set of prediction tests with different forecasting times focusing on the South China Sea domain was conducted to evaluate the short-term forecasting effectiveness of lower atmospheric ducts. The assessment of sounding observation data revealed that the prediction model performed well in predicting the characteristics of all types of ducts. The mean values of the forecasting errors were slightly lower than the reanalysis data but had lower levels of correlation coefficients. At an altitude of about 2000 m, the forecasted error of modified atmospheric refractivity reached peak values and then decreased gradually with increasing altitude. The accuracy of forecasted surface ducts was higher than that of elevated ducts. Noticeable land–sea differences were identified for the spatial distributions of duct characteristics, and the occurrence rates of both the surface and elevated ducts were high at sea. As for the differences among the forecasts of 24, 48, and 72 h ahead, the differences primarily occurred at altitude levels below 20 m and 500 m~1500 m, which are consistent with the differences in the duct height.

Total water levels along the South Atlantic Bight during three along-shelf propagating tropical cyclones: relative contributions of storm surge and wave runup

Abstract. Total water levels (TWLs), including the contribution of wind waves, associated with tropical cyclones (TCs) are among the most damaging hazards faced by coastal communities. TC-induced economic losses are expected to increase because of stronger TC intensity, sea level rise, and increased populations along the coasts. TC intensity, translation speed, and distance to the coast affect the magnitude and duration of increased TWLs and wind waves. Under climate change, the proportion of high-intensity TCs is projected to increase globally, whereas the variation pattern of TC translation speed also depends on the ocean basin and latitude. There is an urgent need to improve our understanding of the linkages between TC characteristics and TWL components. In the past few years, hurricanes Matthew (2016), Dorian (2019), and Isaias (2020) propagated over the South Atlantic Bight (SAB) with similar paths but resulted in different coastal impacts. We combined in situ observations and numerical simulations with the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) modeling system to analyze the extreme TWLs under the three TCs. Model verification showed that the TWL components were well reproduced by the present model setup. Our results showed that the peak storm surge and the peak wave runup depended mainly on the TC intensity, the distance to the TC eye, and the TC heading direction. A decrease in TC translation speed primarily led to longer exceedance durations of TWLs, which may result in more severe economic losses. Wave-dependent water level components (i.e., wave setup and wave swash) were found to dominate the peak TWL within the near-TC field. Our results also showed that in specific conditions, the prestorm wave runup associated with the TC-induced swell may lead to TWLs higher than at the peak of the storm. This was the case along the SAB during Hurricane Isaias. Isaias's fast TC translation speed and the fact that its swell was not blocked by any islands were the main factors contributing to these peak TWLs ahead of the storm peak.

EFFECTS OF WAVE SKEWNESS AND ASYMMETRY ON THE EVOLUTION OF FIRE ISLAND, NEW YORK

Bedload transport of sediment by waves and currents is one of the key physical processes that affect the evolution of coasts, nearshore areas, and the engineering practices there. Wave skewness and asymmetry, both of which increase as waves shoal, result in a net bedload sediment flux over a wave cycle. The impacts of this mechanism on large-scale coastal and shoreline change are investigated in this study, using field observations and Coupled Ocean Atmosphere Wave Sediment Transport (COAWST), a hydrodynamic process-based numerical modeling system (Warner et al., 2010). The study site is Fire Island, New York, located at the Atlantic Coast of the USA, with a focus on the persistent shoreline shape, at the western half of this 50-km-long barrier island, that has been hypothesized to be linked to the sand deposits at the shoreface.

Numerical Simulation of the Flood and Inundation Caused by Typhoon Noru Downstream from the Vu Gia-Thu Bon River Basin

Typhoon Noru (2022) was a historic storm that caused significant damage to the central region of Vietnam. Typhoon Noru has caused strong winds and torrential rainfall in Da Nang, Quang Nam, and Quang Ngai. Quang Nam Province saw many trees and power lines fall, and many areas were flooded. The Da Nang government has reported the typhoon toppled many trees, blew the rooftops of three houses, damaged the walls of several schools, and caused a power outage at some 3200 substations. It resulted in widespread flooding in coastal areas and downstream from the Vu Gia-Thu Bon River river basin. This study evaluates the impact of Typhoon Noru. The results show that: (1) The numerical simulation was applied to re-analyze the offshore meteorological field with the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model as an input for 2D wave propagation and hydraulic models; (2) The study couples the 1D and 2D models in MIKE FLOOD to simulate the flood and inundation caused by Typhoon Noru in the study area. The calibration and validation results of the 1D hydraulic model, the 2D wave propagation model, and the 2D hydrodynamic model were reasonably good, with a Nash coefficient ranging from 0.84 to 0.96 and a percent bias (BIAS) of −0.9% to 7.5%. The results of the simulation showed that the flood and inundation caused by Typhoon Noru resulted in significant damage in two districts: Thang Binh in Quang Nam province and Hoa Vang in Da Nang province. The practical significance of these results is that they provide valuable support for warning systems and troubleshooting efforts related to the impact of typhoons.

The Effect of Surface Oil on Ocean Wind Stress

This study provides, to the best of our knowledge, the first detailed analysis of how surface oil modifies air–sea interactions in a two-way coupled model, i.e., the coupled–ocean–atmosphere–wave–sediment–transport (COAWST) model, modified to account for oil-related changes in air–sea fluxes. This study investigates the effects of oil on surface roughness, surface wind, surface and near-surface temperature differences, and boundary-layer stability and how those conditions ultimately affect surface stress. We first conducted twin-coupled modeling simulations with and without the influence of oil over the Deepwater Horizon (DWH) oil spill period (20 April to 5 May 2010) in the Gulf of Mexico. Then, we compared the results by using a modularized flux model with parameterizations selected to match those selected in the coupled model adapted to either ignore or account for different atmospheric/oceanic processes in calculating surface stress. When non-oil inputs to the bulk formula were treated as being unchanged by oil, the surface stress changes were always negative because of oil-related dampening of the surface roughness alone. However, the oil-related changes to 10 m wind speeds and boundary-layer stability were found to play a dominant role in surface stress changes relative to those due to the oil-related surface roughness changes, highlighting that most of the changes in surface stress were due to oil-related changes in wind speed and boundary-layer stability. Finally, the oil-related changes in surface stress due to the combined oil-related changes in surface roughness, surface wind, and boundary-layer stability were not large enough to have a major impact on the surface current and surface oil transport, indicating that the feedback from the surface oil to the surface oil movement itself is insignificant in forecasting surface oil transport unless the fractional oil coverage is much larger than the value found in this study.

Investigating the precipitation features of monsoon deep depressions over the Bay of Bengal using high‐resolution stand‐alone and coupled simulations

AbstractA comprehensive attempt has been made to simulate the characteristics of monsoon deep depressions (MDD) originating over the Bay of Bengal (BoB) basin, with special emphasis on their rainfall using a coupled ocean–atmospheric model, that is, the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model, and a stand‐alone atmospheric model, that is, the Weather Research and Forecasting (WRF) model, with a lead time of up to 72 hr. It is to be noted that COAWST employs WRF as its atmospheric component, while the Regional Ocean Modeling System (ROMS) is used as the oceanic component. It is found that though the tracks of the four MDDs considered have been reasonably simulated, the intensity was overestimated in both sets of simulations compared to India Meteorological Department (IMD) best estimates. Decomposition of the contributors to rain rate for the composite of the storms in the deep depression (DD) phase revealed that the moisture sources/sinks (Qm) are the major component in modulating the rain rate compared to the cloud sources/sinks. Further analysis of Qm suggested that vertical and horizontal advection of moisture forms the leading contributors to Qm. Validation against Modern Era Retrospective‐Analysis for Research and Analysis (MERRA) reanalysis showed that COAWST captured a more realistic evolution of Qm (specifically vertical advection) compared to its stand‐alone counterpart. Investigation of the composite storm energetics in a vortex‐following control volume showed a scarcity of bulk kinetic energy (volume‐integrated kinetic energy over the control volume) in the later hours of the DD phase in COAWST which led to the dissipation of the storm core, unlike in WRF, where a re‐intensification took place through condensational heating. It is inferred that in spite of the stand‐alone atmospheric model capturing the moisture incursion from the lateral boundaries in the lower levels significantly, the better representation of the vertical structure in COAWST led to more realistic simulations of the storms, increasing the skill in rainfall prediction.

The second-generation real-time ecological environment prediction system for the Guangdong–Hong Kong–Marco Greater Bay Area: Model setup, validation, improvements, and online visualization

With the rapidly growing population and socioeconomic development of the Guangdong–Hong Kong–Marco Greater Bay Area of China, inputs of diverse contaminants have rapidly increased. This poses threats to the water quality of the Pearl River Estuary (PRE) and adjacent seas. To provide valuable information to assist the governors, stakeholders, and decision-makers in tracking changes in environmental conditions, daily nowcasts and two-day forecasts from the ecological prediction system, namely the Coupled Great Bay Ecological Environmental Prediction System (CGEEPS), has been setup. These forecast systems have been built on the Coupled Ocean–Atmosphere–Wave–Sediment Transport modelling system. This comprises an atmospheric Weather Research Forecasting module and an oceanic Regional Ocean Modelling System module. Daily real-time nowcasts and 2-day forecasts of temperature, salinity, NO2 + NO3, chlorophyll, and pH are continuously available. Visualizations of the forecasts are available on a local website (http://www.gbaycarbontest.xyz:8008/). This paper describes the setup of the environmental forecasting system, evaluates model hindcast simulations from 2014 to 2018, and investigates downscaling and two-way coupling with the regional atmospheric model. The results shown that though CGEEPS lacks accuracy in predicting the absolute value for biological and biogeochemical environmental variables. It is quite informative to predict the spatio-temporal variability of ecological environmental changes associated with extreme weather events. Our study will benefit of developing real-time marine biogeochemical and ecosystem forecast tool for oceanic regions heavily impact by extreme weathers.

Modeling the Sea Level Response of the Northern East China Sea to Different Types of Extratropical Cyclones

AbstractSea level response to extratropical cyclones, one of the most disastrous weather systems, varies depending on cyclone types and tracks. This study presents a systematic modeling assessment of storm surge induced by extratropical cyclones in the northern East China Sea using an ocean‐wave coupled model, based on the Coupled Ocean‐Atmosphere‐Wave‐Sediment Transport (COAWST) model system. We identified the type of extratropical cyclone most likely to cause disasters and the areas likely to suffer the worst effects. We conducted sensitivity experiments to analyze the driving mechanisms (such as wind, air pressure, tide, and wave) and influencing mechanisms (such as local and remote meteorological effect and Ekman effect) of storm surge. During strong winds, positive and negative storm surges were consistent with the wind direction, and formed source of coastal trapped waves. When an abnormally high water level occurred in a semi‐enclosed sea area, it propagated mainly along the cyclone direction. The energy of coastal trapped waves was easy to dissipate in case of caps and other locations with large topographic changes. Tide‐surge‐wave interaction and propagation were also greatly affected by coastal trapped waves.

Effects of swell waves on the dynamics of the estuarine turbidity maximum in an idealized convergent partially mixed estuary

The dynamics of estuarine sediments plays a key role in the morphological evolution, water quality, and ecosystem health of estuaries. Estuarine turbidity maxima (ETMs) are ubiquitous in estuaries worldwide and have drawn enormous attention from researchers. Here, we use a coupled ocean–atmosphere–wave–sediment transport modeling system to examine the effects of swell waves on the location, extent, and suspended sediment concentration (SSC) of ETMs in a convergent partially mixed estuary. Our results reveal that swell waves influence the residual circulation, mixing, and bottom stress in the estuary. The wave–current interaction increases the bottom stress in the estuary but reduces mixing in the lower to middle reaches of the estuary, owing to the enhanced landward salt transport, and the increased stratification. The reduced mixing enhances the estuarine circulation in the middle reach of the estuary. These changes cause a landward shift of the primary ETM and an increase in the SSC in the lower estuary. Moreover, the Coriolis force changes the ETM distribution and influences the wave effects. Overall, the findings of this study further elucidate ETM dynamics in an estuary. • Swell wave advects more saline water in the estuary and increases stratification. • Wave-current interaction increases the bottom stress in the lower estuary. • Swell wave causes a landward shift of the primary estuarine turbidity maxima (ETM).

Influence of sea surface wave-dependent roughness on summer precipitation over the Southeastern United States

To study the influence of ocean surface roughness on summer precipitation over the southeastern United States, we perform singular value decomposition (SVD) analysis between the mean summer precipitations and the significant wave height from 1979 to 2017 over the Gulf of Mexico (GOM). The first dominant mode shows that the swell energy in the northern GOM is larger than that in the southern GOM, especially over the region adjacent to the southeastern United States (SE-US). Meanwhile, the precipitation in the SE-US is much heavier than the other regions. Composite analysis shows that when the first pattern takes place, the wind speed is small near the region of the SE-US, swell dominates. There is a cyclonic wind anomaly at 850 hPa over the SE-US, which results in strong upward motion, therefore heavy rainfall in this region. To further study the effect of the swell on the precipitation in the SE-US, we also design two numerical experiments using the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) Modelling System. In the control (CTL) experiment, the popular Charnock aerodynamical roughness length parameterization is adopted. In the other experiment, a wave-dependent roughness scheme is used. The precipitation in the SE-US is better simulated in the wave-dependent roughness experiment than in the CTL experiment. In summer, the 10-m wind speed is weak in the northeastern Gulf of Mexico, and that means the swell is dominant in this region. The wave-dependent surface roughness strengthens the wind, which means that the momentum is transferred from the wave to the atmosphere under swell dominant conditions. The increase of the shear term and the cyclonic wind anomaly in the lower layers of the atmosphere leads to a stronger upward motion which drives heavier rainfall in the SE-US.

An evaluation of vertical mixing parameterization of ocean boundary layer turbulence for cohesive sediments

Accurate parameterization for both diffusivity and turbulent kinetic energy (TKE) dissipation rate is important for the simulation of reactive tracers such as cohesive sediments. We implemented a second-order closure parameterization for mixing in ocean surface boundary layer in the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) model. The parameterization is more suitable than the existing parameterizations in the COAWST model for the modeling of cohesive sediments: It includes the wave-driven Langmuir turbulent effect, a more complete pressure strain covariance parameterization in the eddy viscosity and diffusivity, and also TKE dissipation rate. Solutions using a one-dimensional configuration are compared to solutions using a three-dimensional model that simulates the ocean surface boundary layer turbulence and size distributions of flocs of different sizes. The result shows that the simulation using the newly implemented parameterization reproduces fairly well the profiles of vertical eddy viscosity, TKE dissipation rate, total mass concentration of suspended sediment, and mass averaged settling velocity in wave-driven Langmuir turbulence. The water depth dependence of floc size distribution is also reproduced in the one-dimensional model. In addition, the result based on the standard k − ω model mostly underestimates (up to ∼90%) the averaged settling velocity of suspended sediments in the water column. The result also suggests that misrepresentation of Langmuir turbulence effect in vertical mixing parameterization could cause substantial biases in the forecast/hindcast transport model for cohesive sediment.

Investigation of Ocean Sub-Surface Processes in Tropical Cyclone Phailin Using a Coupled Modeling Framework: Sensitivity to Ocean Conditions

The present study is aimed to investigate sub-surface ocean processes and their contribution to the intensification of a tropical cyclone (TC) from a coupled-modeling perspective. The Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model was employed to simulate TC Phailin, which originated over the Bay of Bengal and made landfall on the eastern coast of India in October 2013. Three sub-surface ocean condition datasets—viz., (a) the European Centre for Medium-Range Weather Forecast (ECMWF) Ocean Reanalysis, (b) the Climate Forecast System Version 2 (CFSV2) Operational Analysis, and (c) the Hybrid Coordinate Ocean Model (HYCOM) Reanalysis datasets—were used for the initial and boundary conditions for the oceanic component of the coupled model in three different simulations of TC Phailin. All the simulations showed a delay in intensification compared to the observation, and ECMWF simulated the most intensified TC. CFSV2 simulated a deeper mixed layer (ML) and higher mixing, which hindered the intensification. Furthermore, higher entrainment of cold water in the ML led to cold water reaching the surface and, consequently, decreased sea surface temperature, which acted as negative feedback in the intensification of the storm in the cases of CFSV2 and HYCOM. ECMWF realistically simulated the interactions of the TC with a cold-core eddy before landfall. A sudden increase in ML heat content, the addition of heat in the ML due to entrainment, and the prevention of cold water reaching the surface were indicative of the breaking of the barrier layer (BL) in ECMWF, which was further corroborated by the spatial distribution of BL thickness in the simulation. This acted as positive feedback in the intensification of the TC. The findings of this study strongly suggest that not only the incorporation of physical oceanic sub-surface processes in the modeling of TCs but also the proper representation of prevailing mesoscale features and ocean sub-surface temperature, salinity, and current profiles in datasets is essential for realistic simulations of TCs.

Multi-instrument observations of microseisms generated by typhoon Kalmaegi (2014) over the Northwestern Pacific

The typhoon-generated microseisms, originating from the complex energy coupling and transferring among the Atmosphere-Ocean-Solid Earth spheres, can be detected remotely by seismometers as the strongest ambient seismic noise. The lack of in situ observations during the passage of typhoons has hampered numerical modeling of wind fields and ocean waves, and limited our understanding of the generation mechanisms of microseisms associated with typhoons. Here we present a comprehensive investigation of microseisms generated by typhoon Kalmaegi (September 2014) based on multiple-instrument constraints from observations including terrestrial and ocean-bottom seismic stations as well as ocean buoys. To understand the generation mechanisms, we apply an improved frequency-domain beamforming method to seismic array data leading to successful location of double-frequency (DF) microseism source regions. For comparison, we calculate the typhoon-induced ocean waves and theoretical source regions of the DF microseisms using the coupled ocean–atmosphere–wave–sediment transport modeling system with validation by ocean buoy observations. Both observations and numerical modeling results reveal two different generation mechanisms for typhoon-induced DF microseisms during the lifespan of Kalmaegi. When over the Philippine Sea, the DF microseisms were generated mainly by opposing ocean waves from two distinct storms. After Kalmaegi entered the South China Sea, the DF microseisms were generated mainly by the fast-moving typhoon with source regions just trailing behind, with the minimum frequencies determined by the typhoon translation speed. DF microseisms generated in coastal source regions were not detected by ocean bottom seismometers, suggesting that DF microseisms might not effectively propagate across the ocean-basin seafloor covered by thick sediments, owing to severe seismic attenuation and spreading losses. This information is crucial for the use of DF microseisms for future tracking and monitoring of typhoons.

Impacts of wave feedbacks and planetary boundary layer parameterization schemes on air-sea coupled simulations: A case study for Typhoon Maria in 2018

Typhoon Maria was a devastating super typhoon hitting China in 2018. We conducted five numerical experiments based on the Coupled Ocean–Atmosphere–Wave–Sediment Transport model system to explore the sensitivity of typhoon track, intensity, structure, and oceanic response to the feedbacks of ocean surface waves (particular attention was paid to wave-induced sea surface roughness [SSR]), and different planetary boundary layer (PBL) parameterization schemes. The model results were in good agreement with observations. It was found that Typhoon Maria's intensity and structure, especially the distribution of air-sea interface heat flux and the shape of the typhoon centre, were highly sensitive to the feedbacks of waves, the choices of PBL and wave-induced SSR parameterization schemes. Previous model results based only on atmospheric models have shown that typhoon simulation accuracy is very sensitive to the parameterization scheme of PBL. In this study, it was revealed that the effect of wave feedbacks on typhoon simulation results could be comparable to that of using different PBL parameterization schemes. The wave feedbacks include the modification of the SSR and the additional cooling induced by wave-enhanced ocean mixing. From the sensitivity experiments, the former was the most dominant, and its main mechanism lies in the correction of the SSR to affect the exchange coefficients of momentum and heat fluxes at the air-sea interface and ultimately the typhoon simulation results. A systematic analysis of sea temperature response during typhoon was conducted, and the response showed small difference in different experiments. This suggests that the difference in typhoon intensity and structure among the experimental simulations is not significantly related to the difference in simulated sea surface temperature, but is most likely related to the difference in momentum and heat fluxes caused by wave feedbacks and the choices of PBL and wave-induced SSR parameterization schemes. The difference of significant wave height (SWH) in experiments with different wave-induced SSR parameterization schemes reached 10% (1.5 m). The typhoon-induced sea surface height (SSH) reached 2.4 m, and the difference of SSH caused by wave feedbacks, different PBL and wave-induced SSR parameterization schemes reached 12.5%, 12.5% and 8.3%, respectively. This study has significant implication for improving the simulation accuracy of typhoon track, intensity, structure, and disasters caused by typhoons.

Impact of the Novaya Zemlya Bora on the Ocean-Atmosphere Heat Exchange and Ocean Circulation: A Case-Study with the Coupled Model

Novaya Zemlya bora is a strong downslope windstorm in the east of the Barents Sea. This paper considers the influence of the Novaya Zemlya bora on the turbulent heat exchange between the atmosphere and the ocean and on processes in the ocean. Another goal of this study is to demonstrate the sensitivity of simulated turbulent fluxes during bora to model coupling between atmosphere, ocean and sea waves. In this regard, a high-resolution numerical simulation of one winter bora episode was carried out using the COAWST (Coupled-Ocean-Atmosphere-Wave-Sediment Transport) modeling system, which includes the atmospheric (WRF-ARW model), oceanic (ROMS model), and sea waves (SWAN model) components. As shown by the simulation results, in the fully coupled experiment, turbulent heat exchange is enhanced in comparison with the uncoupled experiment (by 3% on average over the region). This is due to the atmosphere-sea-waves interaction, and the results are highly sensitive to the choice of roughness parameterization. The influence of the interaction of the atmospheric and oceanic components on turbulent fluxes in this episode is small on average. Bora has a significant impact on the processes in the ocean directly near the coast, forming a strong coastal current and making a decisive contribution to the formation of dense waters. In the open sea, the bora, or rather, the redistribution of the wind and temperature fields caused by the orography of Novaya Zemlya, leads to a decrease in ocean heat content losses due to a decrease in turbulent heat exchange in comparison with the experiment with flat topography.

Influence of oceanic mesoscale eddy on the atmospheric boundary layer based on an idealized model

Often, numerical models rely on parameterization schemes to represent currently unresolved or insufficiently understood phenomena.The influence of ocean mesoscale eddies on the overlying atmosphere is such a phenomenon. Using the Weather Research and Forecasting (WRF) model, the Regional Ocean Modeling System (ROMS) and the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modelling system, this study investigates the influence of idealized oceanic mesoscale eddies on the atmospheric boundary layer (ABL). The idealized model is horizontally homogeneous and set on an f-plane at 35°N. The cyclonic (anticyclonic) eddy causes sea level pressure (SLP) to increase (decrease) and reduces (increases) latent and sensible heat fluxes, surface winds, marine ABL, and atmospheric precipitable water in the coupled and uncoupled cases. Moreover, a momentum budget analysis of the model output shows that the pressure gradient term is the primary contributor to atmospheric momentum balance. Additionally, it should be noted that the SLP adjustment mechanism is important when advection is weak. Comparing the uncoupled and coupled WRF cases, the eddy's effect on atmospheric parameters is more intense in the uncoupled than in the coupled case, which can increase precipitation by 50% and wind speed and heat flux by 20–30%. These results may be potentially important for future parameterizations of mesoscale air-sea interaction processes in numerical model development.