Ocean Modelling最新文献

{"title":"CanESM5-derived ocean wave projections — Considerations for coarse resolution climate models","authors":"Mercè Casas-Prat ,&nbsp;Leah Cicon ,&nbsp;Benoit Pouliot ,&nbsp;Natacha B. Bernier ,&nbsp;Alex J. Cannon ,&nbsp;Rodney Chan","doi":"10.1016/j.ocemod.2024.102430","DOIUrl":"10.1016/j.ocemod.2024.102430","url":null,"abstract":"<div><div>This study presents the first set of CanESM5-driven wave projections for two emission scenarios (SSP5-8.5 and SSP2-4.5) and two time periods for mid- and end-century. While coarse resolution climate models, like CanESM5, might be less attractive for development of ocean wave projections, their results are needed to explore the full range of inter-model uncertainty in CMIP6 projections. Considering the coarse resolution limitation, wave simulations were obtained with a proposed computationally efficient 2-step bias-correction approach that consists of (i) calibrating the wind-to-wave energy transfer in the ocean wave model to reduce the underestimation of extremes resulting from coarse resolution, and (ii) bias-correcting the surface winds with a multivariate bias-correction to reduce remaining systematic biases. Results showed overall good performance in comparison with state of the art reanalysis and satellite data. Resulting projections provide increased understanding of future changes in wave conditions, confirming previously reported global-scale changes, such as higher waves in the eastern tropical Pacific and lower waves in the North Atlantic. They also provide more detailed information for areas affected by sea ice conditions in comparison to the latest CMIP5-based wave ensembles, which is critical for the Arctic region, a hotspot for ocean wave changes. Moreover, while the largest changes are typically seen by the end-century under SSP5-8.5, this study reveals that for some variables and areas, such as the mean wave period, larger changes occur for lower warming levels as a result of competing driving factors. Finally, the presented projections can contribute to ongoing efforts to generate a large multi-model ensemble of wave projections based on CMIP6.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"192 ","pages":"Article 102430"},"PeriodicalIF":3.1,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314190","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":"Deep learning approaches in predicting tropical cyclone tracks: An analysis focused on the Northwest Pacific Region","authors":"Peng Hao,&nbsp;Yaqi Zhao,&nbsp;Shuang Li,&nbsp;Jinbao Song,&nbsp;Yu Gao","doi":"10.1016/j.ocemod.2024.102444","DOIUrl":"10.1016/j.ocemod.2024.102444","url":null,"abstract":"<div><div>In this study, we conducted a comprehensive and integrated test of tropical cyclone track prediction using deep learning technologies, aiming to enhance the efficiency and accuracy of the prediction methods. We employed the Best Track dataset from the China Meteorological Administration's Tropical Cyclone Data Center, which covers the Northwest Pacific region from 1949 to 2023. This dataset provides comprehensive coverage, encompassing critical tropical cyclone details like time, latitude, longitude, and wind speed. Our focus was on evaluating and comparing different deep learning models, including Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), and Gated Recurrent Units (GRU), for their effectiveness in handling complex time series data. Through detailed analysis of various model configurations, including factors such as input-output lengths, hidden size, the number of layers, the implementation of bi-directional networks, and attention mechanisms, we discovered that LSTM and GRU models significantly outperform traditional RNN models in dealing with long-term dependencies and enhancing prediction accuracy. Moreover, the LSTM model, used to forecast key tropical cyclones during the 2023 Pacific tropical cyclone season, achieved mean errors of 21.84 km, 37.56 km, and 26.12 km for Typhoons Mawar, Doksuri, and Saola, respectively. This method also demonstrated high efficiency in rapid response to extreme weather changes, processing each tropical cyclone's forecast in just about 8 s. The results not only illustrate the practical utility of deep learning in tropical cyclone track prediction but also provide new, effective tools for disaster prevention and mitigation efforts.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"192 ","pages":"Article 102444"},"PeriodicalIF":3.1,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434459","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":"Storm surge modelling along European coastlines: The effect of the spatio-temporal resolution of the atmospheric forcing","authors":"Miguel Agulles ,&nbsp;Marta Marcos ,&nbsp;Angel Amores ,&nbsp;Tim Toomey","doi":"10.1016/j.ocemod.2024.102432","DOIUrl":"10.1016/j.ocemod.2024.102432","url":null,"abstract":"<div><p>The spatio-temporal resolution of atmospheric forcing plays a key role in the accuracy of simulated storm surges with hydrodynamic numerical models. Here, we generate five hydrodynamic hindcasts of coastal storm surges along the European Atlantic and the Mediterranean Sea coasts, forced with atmospheric fields of varying temporal (hourly and daily) and spatial (0.25<span><math><mo>°</mo></math></span> to 2<span><math><mo>°</mo></math></span>) resolution since 1940. Our results, that are validated with insitu tide gauge observations, show that storm surges obtained with daily forcing underestimate the magnitude of coastal extreme sea level events by up to 50% compared to hourly simulations and observations. Nevertheless, low-resolution simulations capture the temporal variability of storm surges, including strong episodes. Furthermore, taking advantage of the consistent set of coastal storm surge hindcasts, we demonstrate that storm surges forced with daily mean atmospheric fields, when bias corrected via quantile mapping, provide accurate values of daily maxima as calculated by a high-resolution hindcast. This transformation paves the way to obtain daily maxima storm surge estimates from low-resolution atmospheric fields, as those typically provided by large-scale and global climate models, at a lower computational cost.</p></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"192 ","pages":"Article 102432"},"PeriodicalIF":3.1,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1463500324001197/pdfft?md5=369ebad2817cdd4ef1ab796bdc13ed68&pid=1-s2.0-S1463500324001197-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142240422","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":"The effect of shallow water bathymetry on swash and surf zone modeled by SWASH","authors":"Juliana Franco Lima ,&nbsp;Leandro Farina ,&nbsp;Pedro Veras Guimarães ,&nbsp;Ana Flávia Caetano Bastos ,&nbsp;Pedro de Souza Pereira ,&nbsp;Mauro Michelena Andrade","doi":"10.1016/j.ocemod.2024.102440","DOIUrl":"10.1016/j.ocemod.2024.102440","url":null,"abstract":"<div><p>Submerged topography in shallow waters is fundamental in the propagation and dissipation of ocean waves in the surf and swash zones. However, obtaining accurate bathymetric data in this region is challenging due to the high temporal and spatial environmental variability. The bottom boundary condition can directly affect the accuracy of numerical models used for shallow water simulations. In this study, the performance of the SWASH numerical model in describing wave runup in the swash zone is assessed using different bathymetric boundary conditions. The first method involves using data measured in the surf zone obtained by a Unmanned Aerial Vehicle (UAV), and analyzing it using the cBathy algorithm. The second method utilizes a regular bathymetric mesh generated from Dean’s equilibrium profile combined with beach topography data. The third method relies exclusively on interpolation methods using data from deep waters and beach profiles. This interpolation approach is the most used among SWASH users when detailed or updated surf zone bathymetry is unavailable. Based on the numerical simulations performed, not incorporating data from the surf zone resulted in a 4% increase in the runup estimated and approximately a 2% difference in identifying the swash zone position. The method to obtain bathymetry through the cBathy algorithm, used in this article, is cost-effective and can be used to reduce uncertainties in surf zone numerical simulations, induced by the lack of knowledge about the bottom conditions.</p></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"192 ","pages":"Article 102440"},"PeriodicalIF":3.1,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142163711","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":"Explainable AI in lengthening ENSO prediction from western north pacific precursor","authors":"Liping Deng ,&nbsp;Krishna Borhara ,&nbsp;Parichart Promchote ,&nbsp;Shih-Yu Wang","doi":"10.1016/j.ocemod.2024.102431","DOIUrl":"10.1016/j.ocemod.2024.102431","url":null,"abstract":"<div><p>In this short communication, we report initial success in utilizing existing Explainable Artificial Intelligence (XAI) methodology to investigate an emerging precursor of the El Niño-Southern Oscillation (ENSO), manifest as sea surface temperature anomalies (SSTA) in the Western North Pacific (WNP), and its impact on enhancing ENSO prediction accuracy. Our analysis reveals that integrating WNP SSTA with established XAI techniques significantly increases the predictability of ENSO states. We found marked improvement in prediction accuracy, from a 60 % baseline to over 85 % for forecasting moderate warm, cold, and neutral ENSO states one year ahead. For higher magnitude events, precision surpasses 90 %. This work, intended as a follow-up to recent studies, underscores the potential of augmenting emerging XAI with additional SST signals to advance long-term climate forecasting capabilities.</p></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"192 ","pages":"Article 102431"},"PeriodicalIF":3.1,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142240421","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":"On warm bias and mesoscale dynamics setting the Southern Ocean large-scale circulation mean state","authors":"Mathias Zeller,&nbsp;Torge Martin","doi":"10.1016/j.ocemod.2024.102426","DOIUrl":"10.1016/j.ocemod.2024.102426","url":null,"abstract":"<div><p>A realistic representation of the Southern Ocean (SO) in climate models is critical for reliable global climate projections. However, many models are still facing severe biases in this region. Using a fully coupled global climate model at non-eddying (1/2<span><math><msup><mrow></mrow><mrow><mo>∘</mo></mrow></msup></math></span>) and strongly eddying (1/10<span><math><msup><mrow></mrow><mrow><mo>∘</mo></mrow></msup></math></span>) grid resolution in the SO, we investigate the effect of a 0.5 °C, 1.0 °C and 1.6 °C warmer than observed SO on i) the spin-up behaviour of the high-resolution simulation, and ii) the representation of main dynamical features, i.e., the Antarctic circumpolar current (ACC), the subpolar gyres, the overturning circulation and the Agulhas regime in a quasi-equilibrium state. The adjustment of SO dynamics and hydrography critically depends on the initial state and grid resolution. When initialised with an observed ocean state, only the non-eddying configuration quickly builds up a strong warm bias in the SO. The high-resolution configuration initialised with the biased non-eddying model state results in immense spurious open ocean deep convection, as the biased ocean state is not stable at eddying resolution, and thus causes an undesirable imprint on global circulation. The SO heat content also affects the large-scale dynamics in both low- and high-resolution configurations. A warmer SO is associated with a stronger Agulhas current and a temperature-driven reduction of the meridional density gradient at 45<span><math><msup><mrow></mrow><mrow><mo>∘</mo></mrow></msup></math></span>S to 65<span><math><msup><mrow></mrow><mrow><mo>∘</mo></mrow></msup></math></span>S and thus a weaker ACC. The eddying simulations have stronger subpolar gyres under warmer conditions while the response in the non-eddying simulations is inconsistent. In general, SO dynamics are more realistically represented in a mesoscale-resolving model at the cost of requiring an own spin-up.</p></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"191 ","pages":"Article 102426"},"PeriodicalIF":3.1,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1463500324001136/pdfft?md5=c824446514c61f0a304b3c0a1852f65a&pid=1-s2.0-S1463500324001136-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142149145","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":"Hierarchical stacked spatiotemporal self-attention network for sea surface temperature forecasting","authors":"Yuxin Zhao,&nbsp;Dequan Yang,&nbsp;Jianxin He,&nbsp;Kexin Zhu,&nbsp;Xiong Deng","doi":"10.1016/j.ocemod.2024.102427","DOIUrl":"10.1016/j.ocemod.2024.102427","url":null,"abstract":"<div><p>Sea surface temperature (SST) is a highly complex spatiotemporal variable, which stems from its susceptibility to non-linear dynamical processes and substantial spatiotemporal variability. In particular, accurately forecasting small-scale SST is a formidable challenge due to the compounded effects of diverse physical processes spanning across various scales. In this study, we employ deep learning methods to mine the ocean’s evolutionary patterns, as the ocean’s dynamic mechanisms are inherently embedded in spatiotemporal data. We propose a hierarchical stacked spatiotemporal self-attention mechanism (HSSSA) network architecture. The hierarchical stacked encoder–decoder architecture provides the capability for feature fusion and extraction at different scales. The spatial self-attention and temporal self-attention modules simultaneously focus on information from different spatial locations and time steps, allowing the exploration of spatiotemporal patterns in the complex dynamics of the ocean. The experiments are conducted on a high-resolution East China Sea dataset (<span><math><mrow><mn>1</mn><mo>/</mo><mn>10</mn><mo>°</mo><mo>×</mo><mn>1</mn><mo>/</mo><mn>10</mn><mo>°</mo></mrow></math></span>) to demonstrate the forecast performance of the proposed model for refined ocean variables. The 15-day forecasts indicate that the HSSSA method outperforms the EOF-ARIMA and CNN-Transformer methods.</p></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"191 ","pages":"Article 102427"},"PeriodicalIF":3.1,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142097210","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":"Dependence of dense filament frontogenesis in a hydrostatic model","authors":"Yalin Fan ,&nbsp;Zhitao Yu ,&nbsp;Peter Sullivan ,&nbsp;Adam Rydbeck","doi":"10.1016/j.ocemod.2024.102429","DOIUrl":"10.1016/j.ocemod.2024.102429","url":null,"abstract":"<div><p>In this study, a hydrostatic model - the Navy Coastal Ocean Model (NCOM) is used to analyze the temporal evolution of a cold filament under moderate wind (along / cross filament) and surface cooling forcing conditions. The experimental framework adhered to the setup used in large eddy simulations by Sulllivan and McWilliams (2018). For each forcing scenario, the impact of horizontal resolutions is systematically explored through varies model resolutions of 100 m, 50 m, and 20 m; and the influence of horizontal mixing is investigated by adjusting the Smagorinsky constant within the Smagorinsky horizontal mixing scheme. The role of surface gravity waves is also assessed by conducting experiments both with and without surface wave forcing.</p><p>The outcomes of our study revealed that while the hydrostatic model is able to predict the correct characteristics/physical appearance of filament frontogenesis, it fails to capture the precise dynamics of the phenomenon. Horizontal mixing parameterization in the model was found to have marginal effect on frontogenesis, and the frontal arrest is controlled by the model's subgrid-scale artificial regularization procedure instead of horizontal shear instability. Consequently, higher resolution is corresponding to stronger frontogenesis in the model. Thus, whether the hydrostatic model can produce realistic magnitude of frontogenesis is purely dependent on the characteristic of the front/filament simulated and model resolution. Moreover, examination of the parameterized effect of surface gravity wave forcing through vertical mixing unveiled a limited impact on frontogenesis, suggesting that the parameterization falls short in representing the real physics of wave-front interaction.</p></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"191 ","pages":"Article 102429"},"PeriodicalIF":3.1,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1463500324001161/pdfft?md5=9775941fed94aa12d3c2c40130b3625f&pid=1-s2.0-S1463500324001161-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137410","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":"A boundary perturbation approach for regional wave ensemble forecast","authors":"Nícolas A. Bose ,&nbsp;Leandro Farina","doi":"10.1016/j.ocemod.2024.102428","DOIUrl":"10.1016/j.ocemod.2024.102428","url":null,"abstract":"<div><p>In this study, we developed and validated two wave ensemble prediction systems (WEPS) to forecast wave conditions along the southeastern coast of Australia. Using the SWAN model (GEN3 ST6), we integrated complex bathymetric features with an unstructured grid and validated model outputs against buoy observations from Sydney, Port Kembla, and Batemans Bay. The two WEPS, SWAN-WW3 and SWAN-Pert, utilize different methodologies: SWAN-WW3 derives boundary conditions from NCEP’s Global Wave Ensemble System, while SWAN-Pert employs Latin Hypercube Sampling for boundary perturbations based on historical data. Our results demonstrate that both systems effectively predict significant wave height (<span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>s</mi></mrow></msub></math></span>), with SWAN-Pert showing improved forecast accuracy in certain metrics compared to SWAN-WW3. Despite underdispersion in spread-skill diagrams, both WEPS exhibited good agreement with observed data. Additionally, rank histograms revealed that SWAN-Pert is more reliable at shorter lead times. This study highlights the potential of integrating statistical sampling methods and ensemble systems for enhancing regional wave forecasting accuracy.</p></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"191 ","pages":"Article 102428"},"PeriodicalIF":3.1,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142087313","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":"Estimation of Antarctic sea ice thickness through observation of wave attenuation","authors":"Francesca De Santi ,&nbsp;Marcello Vichi ,&nbsp;Alberto Alberello","doi":"10.1016/j.ocemod.2024.102421","DOIUrl":"10.1016/j.ocemod.2024.102421","url":null,"abstract":"<div><p>The Close-Packing model – a physically based model for wave attenuation in sea ice – is used to infer sea ice thickness from wave observations collected in the Antarctic marginal ice zone during the PIPERS experiment. The model, calibrated for Arctic conditions, predicts ice thickness in good agreement with independent satellite measurements. The calibrated Close -Packing model, which is expressed in a simple monomial form, appears to have broad validity and, therefore, can be a suitable option for operational purposes.</p></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"191 ","pages":"Article 102421"},"PeriodicalIF":3.1,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1463500324001082/pdfft?md5=2b8eb58fd856e44da2b3b8a8061e889c&pid=1-s2.0-S1463500324001082-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142083993","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}