Ocean Modelling最新文献

{"title":"Spatiotemporal variability and drivers of modeled primary production rates in the Northern Humboldt Current System","authors":"Rodrigo Mogollón ,&nbsp;François Colas ,&nbsp;Vincent Echevin ,&nbsp;Jorge Tam ,&nbsp;Dante Espinoza-Morriberón","doi":"10.1016/j.ocemod.2024.102347","DOIUrl":"10.1016/j.ocemod.2024.102347","url":null,"abstract":"<div><p>A coupled physical-biogeochemical model was employed to explore the spatiotemporal dynamics of primary production (PP) rates within the Northern Humboldt Current System (NHCS). The coastal zone spanning 250 km from the shore, from 3°to 18°S, stands out as a highly productive upwelling region, exhibiting an average surface PP value of 2.5 mol C m⁻³ yr⁻¹. Correspondingly, the average vertically integrated PP within the euphotic layer amounts to 13 mol C m⁻² yr⁻¹. In this context, summer emerges as the peak of productivity, yielding 18 mol C m⁻² yr⁻¹, while winter signifies the period of least productivity, with 9 mol C m⁻² yr⁻¹. Our study revealed that surface PP variability is primarily driven by changes in surface chlorophyll and phytoplanktonic biomass (mainly diatoms), followed by changes in photosynthetically active radiation (PAR) levels. During summertime, these three drivers contribute to substantial positive anomalies in surface PP. However, the reduction in nutrient availability resulting from weakened upwelling-favorable winds has a slight negative impact on surface PP rates. Yet, this decline is offset by a positive thermal effect during the warmer season. In contrast, during the winter season, a significant decrease in surface chlorophyll concentrations due to a vertical redistribution into a deeper mixed layer significantly diminishes surface PP. Furthermore, the reduction in both PAR levels and biomass concentrations has a comparable effect, further contributing to the decrease in surface PP rates during wintertime. At a depth of 20 m, changes in PP are primarily driven by variations between the opposing influences of PAR and chlorophyll concentrations. While PAR adheres to the seasonal cycle of warming and cooling throughout the year, chlorophyll-driven anomalies exhibit an inverse pattern to those at the surface, influenced by the vertical dilution effect within the mixed layer. Overall, this study provides valuable insights into the complex interplay of drivers that govern PP dynamics across various depths within one of the world’s most productive marine regions.</p></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"189 ","pages":"Article 102347"},"PeriodicalIF":3.2,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140167101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seasonal predictability of SST anomalies and marine heatwaves over the Kuroshio extension region in the Copernicus C3S models","authors":"Chenguang Zhou ,&nbsp;Hong-Li Ren ,&nbsp;Yu Geng ,&nbsp;Run Wang ,&nbsp;Lin Wang","doi":"10.1016/j.ocemod.2024.102361","DOIUrl":"https://doi.org/10.1016/j.ocemod.2024.102361","url":null,"abstract":"<div><p>The warm sea surface temperature anomalies (SSTAs) and marine heatwaves (MHWs) in the Kuroshio Extension (KE) region have profound impacts on local and surrounding ecological and climatic systems. This study evaluates the seasonal prediction skills of KE-SSTAs and KE-MHWs based on six dynamical models from the Copernicus Climate Change Service (C3S) using different observational datasets for verification and further investigates the main sources of predictability. The results show that current dynamical models can provide reliable predictions for KE-SSTAs for up to about 4 months, but they are challenging to accurately predict the occurrence of KE-MHWs. Compared with single models, the C3S multi-model ensemble mean is usually more skillful in predicting KE-SSTAs and KE-MHWs at most lead times. With lead time increasing, the dynamical models tend to underestimate the mean intensity and annual frequency of the KE-MHWs and overestimate their mean duration. The performance of models in predicting KE-SSTAs is largely dependent on their ability to predict the Pacific Decadal Oscillation, Interdecadal Pacific Oscillation, and El Niño–Southern Oscillation which all significantly influence the KE-SSTAs. The results indicate that these three climate modes are the main sources of seasonal predictability for KE-SSTAs and KE-MHWs. These results provide a deeper understanding of the dynamical seasonal predictability of SSTAs and MHWs in the KE region.</p></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"189 ","pages":"Article 102361"},"PeriodicalIF":3.2,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140160314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating maximum initial wave amplitude of subaerial landslide tsunamis: A three-dimensional modelling approach","authors":"Ramtin Sabeti ,&nbsp;Mohammad Heidarzadeh","doi":"10.1016/j.ocemod.2024.102360","DOIUrl":"https://doi.org/10.1016/j.ocemod.2024.102360","url":null,"abstract":"<div><p>Landslide tsunamis, responsible for thousands of deaths and significant damage in recent years, necessitate the allocation of sufficient time and resources for studying these extreme natural hazards. This study offers a step change in the field by conducting a large number of three-dimensional numerical experiments, validated by physical tests, to develop a predictive equation for the maximum initial amplitude of tsunamis generated by subaerial landslides. We first conducted a few 3D physical experiments in a wave basin which were then applied for the validation of a 3D numerical model based on the Flow3D-HYDRO package. Consequently, we delivered 100 simulations using the validated model by varying parameters such as landslide volume, water depth, slope angle and travel distance. This large database was subsequently employed to develop a predictive equation for the maximum initial tsunami amplitude. For the first time, we considered travel distance as an independent parameter for developing the predictive equation, which can significantly improve the predication accuracy. The predictive equation was tested for the case of the 2018 Anak Krakatau subaerial landslide tsunami and produced satisfactory results.</p></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"189 ","pages":"Article 102360"},"PeriodicalIF":3.2,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1463500324000477/pdfft?md5=cbce449a7da064a94d9ed64858be8277&pid=1-s2.0-S1463500324000477-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140162875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved efficient physics-based computational modeling of regional wave-driven coastal flooding for reef-lined coastlines","authors":"Camila Gaido-Lasserre ,&nbsp;Kees Nederhoff ,&nbsp;Curt D. Storlazzi ,&nbsp;Borja G. Reguero ,&nbsp;Michael W. Beck","doi":"10.1016/j.ocemod.2024.102358","DOIUrl":"https://doi.org/10.1016/j.ocemod.2024.102358","url":null,"abstract":"<div><p>Coastal flooding affects low-lying communities worldwide and is expected to increase with climate change, especially along reef-lined coasts, where wave-driven flooding is particularly prevalent. However, current regional modeling approaches are either insufficient or too computationally expensive to accurately assess risks in these complex environments. This study introduces and validates an improved computationally efficient and physics-based approach to compute dynamic wave-driven regional flooding on reef-lined coasts. We coupled a simplified-physics flood model (SFINCS) with a one-dimensional wave transformation model (XBeach-1D). To assess the performance of the proposed approach, we compared its results with results from a fully resolving two-dimensional wave transformation model (XBeach-2D). We applied this approach for a range of storms and sea-level rise scenarios for two contrasting reef-lined coastal geomorphologies: one low relief area and one high relief area. Our findings reveal that SFINCS coupled with XBeach-1D generates flood extents comparable to those produced by XBeach-2D, with a hit rate of 92%. However, this method tends to underpredict the flood extent of weaker, high-frequency storms and overpredict stronger, low-frequency storms. Across scenarios, our approach overpredicted the mean flood water depth, with a positive bias of 7 cm and root mean square difference of 15 cm. Offering approximately 100 times greater computational efficiency than its two-dimensional XBeach counterpart, this flood modeling technique is recommended for wave-driven flood modeling in scenarios with high computational demands, such as modeling numerous scenarios or undertaking detailed regional-scale modeling.</p></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"189 ","pages":"Article 102358"},"PeriodicalIF":3.2,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1463500324000453/pdfft?md5=073cd4d1aec1bbca05f5ad012460f98b&pid=1-s2.0-S1463500324000453-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140191986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assimilation of new rocket dropsonde data using WRFDA and its impact on numerical simulations of typhoon NORU","authors":"Yu Wei ,&nbsp;Yonghang Chen ,&nbsp;Bingke Zhao ,&nbsp;Qiong Liu ,&nbsp;Yu Xin ,&nbsp;Lei Zhang ,&nbsp;jingyao Luo ,&nbsp;Tongqiang Liu ,&nbsp;Yi Zheng","doi":"10.1016/j.ocemod.2024.102343","DOIUrl":"10.1016/j.ocemod.2024.102343","url":null,"abstract":"<div><p>On September 26 at 2100 UTC and September 27 at 0900 and 2300 UTC, three rockets platform carrying dropsondes (TFTC-400) devices were launched off the east coast of Hainan Island to conduct a launch experiment aimed at detecting Typhoon NORU (2216). The experiment yielded valuable data that were subsequently analyzed to ascertain temperatures, wind speeds, and relative humidity in the atmosphere. Of the four experiments conducted, employing three distinct control variable configurations (CVs), we utilized the 3DVAR of WRF Data Assimilation (WRFDA) to assimilate rocket sounding data and the NCEP ADP Global Upper Air Observational Weather Data from the research data archive dataset that was jointly produced by the Center for Weather and Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). In one experiment, no data assimilation was performed (CTL). These experiments were designed to assess the impact of these observational datasets on typhoon predictions of the Weather Research &amp; Forecasting Model (WRF) numerical simulations. Utilizing the assimilated background field, a 24-hour forecast was conducted, and the assimilation simulation was analyzed with regard to typhoon path, intensity, precipitation, and improvements in the background field. The results reveal that, on average, the three-assimilation experiment led to a 30 % reduction in track error compared to the CTL. Additionally, the assimilation experiment for CV7 of control variable configurations brought the maximum wind speed closer to observed data than the CTL between 6 and 12 h. The TS (threat score) evaluation of simulated 24-hour precipitation in the model domain indicates that the three assimilation schemes exhibit a degree of improvement in the forecast scores for 24-hour cumulative typhoon precipitation. Nevertheless, the simulation results for minimum sea-level pressure are unsatisfactory. To establish statistical significance, additional cases within the relevant region are necessary for result validation.</p></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"189 ","pages":"Article 102343"},"PeriodicalIF":3.2,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140107705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multivariate Upstream Kuroshio Transport (UKT) Prediction and Targeted Observation Sensitive Area Identification of UKT Seasonal Reduction","authors":"Bin Mu ,&nbsp;Yifan Yang-Hu ,&nbsp;Bo Qin ,&nbsp;Shijin Yuan","doi":"10.1016/j.ocemod.2024.102344","DOIUrl":"10.1016/j.ocemod.2024.102344","url":null,"abstract":"<div><p>Variation and seasonal reduction in the Upstream Kuroshio Transport (UKT) have important impacts on surrounding climate and oceanic circulation systems. Therefore, reliable UKT prediction is crucial. In this paper, we propose an intelligent UKT prediction model, KuroshioNet, which is firstly pre-trained with simulation data generated by the Regional Ocean Modeling System (ROMS) and then fine-tuned with reanalysis data of the Simple Ocean Data Assimilation (SODA). Operating at a five-day time resolution and a 0.5°spatial resolution, KuroshioNet has the capability to predict multivariate fields associated with upstream Kuroshio, including 3D variables like velocity, temperature, as well as salinity and 2D variables like sea surface height. Subsequently, the UKT is computed from the predicted fields. We evaluate and analyze the experimental results, which show that KuroshioNet has a lead time of 55 days for UKT prediction. In order to enhance the physical interpretability of KuroshioNet, we conduct an ablation experiment to evaluate the impact of each predictor on prediction skill. It demonstrates that selecting zonal velocity, meridional velocity, temperature, salinity, and SSH contributes to UKT prediction by KuroshioNet. Besides, by analyzing model performance and visualizing what the convolutional kernels learn, we find that KuroshioNet, which has learned from ROMS data, is capable of obtaining better initial performance and acquiring more active kernels to better learn the features in SODA data. Furthermore, we identify the targeted observation sensitive area of UKT seasonal reduction by KuroshioNet with the saliency map method, which is situated to the east of upstream kuroshio. The sensitive area is consistent with the result identified by numerical models and yields 38.1% improvement on prediction demonstrated by observing system simulation experiments.</p></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"189 ","pages":"Article 102344"},"PeriodicalIF":3.2,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140107739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BinWaves: An additive hybrid method to downscale directional wave spectra to nearshore areas","authors":"Laura Cagigal ,&nbsp;Fernando J. Méndez ,&nbsp;Alba Ricondo ,&nbsp;David Gutiérrez-Barceló ,&nbsp;Cyprien Bosserelle ,&nbsp;Ron Hoeke","doi":"10.1016/j.ocemod.2024.102346","DOIUrl":"10.1016/j.ocemod.2024.102346","url":null,"abstract":"<div><p>Accurate and timely early warning systems are a vital component in mitigating the risks faced by coastal communities worldwide. Unlike aggregated wave parameters, information extracted from the complete directional wave spectra is often indispensable in the development of such systems in multi-modal environments, such as remote islands, where concurrent waves from various directions are common. Dynamically simulating the wave propagation, although accurate, can be computationally demanding and time-consuming, particularly for resource-constrained communities. In this study, we introduce as an alternative, a novel additive hybrid model known as BinWaves. This model relies on the propagation of a reduced number of monochromatic wave systems and linear wave theory, facilitating the efficient reconstruction of the full directional wave spectra in nearshore areas. To showcase the capabilities of BinWaves, we have implemented the system in the Pacific Islands of Samoa and American Samoa and validated it against full spectral numerical simulations and available buoy data. Given its similar accuracy and higher computational efficiency when compared with dynamic wave models, BinWaves has proven to be a great alternative for reconstructing historical time series, or, more importantly analysing climate change scenarios, tasks that go beyond the capacities of small islands developing states.</p></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"189 ","pages":"Article 102346"},"PeriodicalIF":3.2,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1463500324000337/pdfft?md5=ad7cfd776ea472201cef31ecd3cacfbb&pid=1-s2.0-S1463500324000337-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140108262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based forecasting model for chlorophyll-a response to tropical cyclones in the Western North Pacific","authors":"Haobin Cen ,&nbsp;Guoqing Han ,&nbsp;Xiayan Lin ,&nbsp;Yu Liu ,&nbsp;Han Zhang","doi":"10.1016/j.ocemod.2024.102345","DOIUrl":"https://doi.org/10.1016/j.ocemod.2024.102345","url":null,"abstract":"<div><p>Tropical cyclones cause increases in sea surface chlorophyll-a concentration, which is important for studying variations in the regional marine environment. Precisely forecasting the variations of sea surface chlorophyll-a concentration induced by tropical cyclones remains a challenge. In this research, a bidirectional long short-term memory (BiLSTM) neural network deep learning model was applied to predict the variations of sea surface chlorophyll-a concentration induced by typhoons in the Western North Pacific (WNP). Typhoons occurring between 2011 and 2020 were used as training cases and those from 2021 to 2022 as forecasting and testing cases. The input variables of the deep learning model include the sea surface wind at 10 meters (U₁₀ and V₁₀), sea surface temperature anomaly (SSTA), and sea surface chlorophyll-a concentration. The output variable was the chlorophyll-a concentration one day after the passage of the typhoon. Data from the previous 7 days were used to predict the chlorophyll-a concentration one day after the typhoon's passage, and the rolling forecast method was employed to predict chlorophyll-a concentration in the following 7 days. To assess the impact of input variables on the model's forecasting performance, ablation experiments were conducted. The results showed that when using U₁₀, V₁₀, and chlorophyll-a from the previous seven days as inputs, the model exhibited the best overall forecasting performance. Taking Typhoons Chanthu, In-fa, and Malou as examples, the root mean square error (RMSE) for the forecast results are 0.0143 mg · m⁻³, 0.0087 mg · m⁻³, and 0.0030 mg · m⁻³, the mean absolute errors (MAE) are 0.0072 mg · m⁻³, 0.0074 mg · m⁻³, and 0.0025 mg · m⁻³, and the spatial anomaly correlation coefficients (ACC) are 0.9968, 0.9775, and 0.9721, respectively. The results reveal that the most accurate forecasting performance was observed during the mid-phase of the moderate-intensity Typhoon Muifa, with RMSE, MAE, and ACC values of 0.0040 mg · m⁻³, 0.0032 mg · m⁻³, and 0.9894, respectively. The BiLSTM neural network model had the best forecasting performance for typhoons of moderate intensity and during the mid-term phase. This is because moderate-intensity typhoons or the mature phase of any typhoon tend to have relatively stable and more predictable paths, resulting in better predictions of chlorophyll-a concentrations. In future work, we intend to extend our training and forecasting to typhoons of various intensities, aiming to further refine and enhance predictive performance.</p></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"189 ","pages":"Article 102345"},"PeriodicalIF":3.2,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140062728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A numerical study of multiscale current effects on waves in the northern South China Sea","authors":"Liqun Jia ,&nbsp;Renhao Wu ,&nbsp;Fei Shi ,&nbsp;Bo Han ,&nbsp;Qinghua Yang","doi":"10.1016/j.ocemod.2024.102342","DOIUrl":"10.1016/j.ocemod.2024.102342","url":null,"abstract":"<div><p>The current effects on waves (CEW) are of interest owing to their importance for our understanding of wave dynamics. However, there is a lack of research on the effects of multiscale currents on waves in the northern South China Sea. In this study, we conducted a series of process-oriented numerical experiments to quantitatively investigate the characteristics of multiscale currents and their effects on surface waves. The results indicate that the high-resolution simulated currents with tides show more submesoscale processes, where the spatial variability of significant wave height (Hs) at the 10–100 km scale exceeds that in low-resolution simulated currents by a factor of 24 and that in tideless simulated currents by a factor of 39. The divergent component of the surface current dominates the CEW in the northern South China Sea. High-resolution currents induce more refraction of wind waves with shorter wave periods. Furthermore, we investigated the impact of tropical cyclones on the CEW and found that they briefly increase the divergence and relative vorticity of surface currents while temporarily weakening the modulation of submesoscale CEW. This research highlights the importance of submesoscale currents and tidal currents in wave simulations, thus contributing to the improvement of observational and numerical simulation methods.</p></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"188 ","pages":"Article 102342"},"PeriodicalIF":3.2,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139923122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accuracy and stability analysis of horizontal discretizations used in unstructured grid ocean models","authors":"Fabricio Rodrigues Lapolli ,&nbsp;Pedro da Silva Peixoto ,&nbsp;Peter Korn","doi":"10.1016/j.ocemod.2024.102335","DOIUrl":"10.1016/j.ocemod.2024.102335","url":null,"abstract":"<div><p>One important tool at our disposal to evaluate the robustness of Global Circulation Models (GCMs) is to understand the horizontal discretization of the dynamical core under a shallow water approximation. Here, we evaluate the accuracy and stability of different methods used in, or adequate for, unstructured ocean models considering shallow water models. Our results show that the schemes have different accuracy capabilities, with the A- (NICAM) and B-grid (FeSOM 2.0) schemes providing at least 1st order accuracy in most operators and time integrated variables, while the two C-grid (ICON and MPAS) schemes display more difficulty in adequately approximating the horizontal dynamics. Moreover, the theory of the inertia-gravity wave representation on regular grids can be extended for our unstructured based schemes, where from least to most accurate we have: A-, B, and C-grid, respectively. Considering only C-grid schemes, the MPAS scheme has shown a more accurate representation of inertia-gravity waves than ICON. In terms of stability, we see that both A- and C-grid MPAS scheme display the best stability properties, but the A-grid scheme relies on artificial diffusion, while the C-grid scheme does not. Alongside, the B-grid and C-grid ICON schemes are within the least stable. Finally, in an effort to understand the effects of potential instabilities in ICON, we note that the full 3D model without a filtering term does not destabilize as it is integrated in time. However, spurious oscillations are responsible for decreasing the kinetic energy of the oceanic currents. Furthermore, an additional decrease of the currents’ turbulent kinetic energy is also observed, creating a spurious mixing, which also plays a role in the strength decrease of these oceanic currents.</p></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"189 ","pages":"Article 102335"},"PeriodicalIF":3.2,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1463500324000222/pdfft?md5=9caf7e4ad314a4e6136390bd78ddafc7&pid=1-s2.0-S1463500324000222-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139922964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}