Pub Date : 2025-01-28DOI: 10.1016/j.ocemod.2025.102500
Shuangying Du , Rong-Hua Zhang
In traditional ocean-atmosphere coupled modeling for El Niño-Southern Oscillation (ENSO) studies, statistical methods are typically used to represent the instantaneous linear relationship between monthly-averaged anomalies of sea surface temperature (SST) and wind stress (τ). Recently, deep learning (DL) techniques have presented promising prospects for ENSO modeling, and the integration of neural networks (NNs) with dynamical models is an active research area. This study incorporates the Residual convolution blocks and a Convolutional Block Attention Module (the Res-CBAM block) into the original UNet configuration to build a new RCUNet-based model, denoted as , which uses SST anomalies (SSTAs) during multi-day time intervals (TIs) to derive daily responses. Sensitivity tests to TIs are performed to illustrate how daily responses are dependent on the way multi-day SST forcings are used; the comparisons with different TIs show that when taking TI=3 days, the model can more precisely represent the relationship between SSTAs and anomalies. Next, daily anomalies obtained from the model are used to force an intermediate ocean model (IOM) in the ocean-only experiments, displaying coherent phase transitions and spatiotemporal evolutions of oceanic and atmospheric anomalies during typical ENSO events, which highlights the advantages of using the DL-based atmospheric model with multi-day SST time sequence information incorporated for ocean modeling. Furthermore, a new intermediate coupled model (ICM) is formed, named the ICM-RCUNet, in which the original atmospheric component represented by singular value decomposition (SVD) analyses is replaced by the model that is used as an atmospheric component, and a daily coupling is conducted with multi-day SST forcings. The ICM-RCUNet simulations exhibit interannual oscillations of atmospheric and oceanic states in the tropical Pacific, demonstrating the applicability of integrating physics-based dynamical ocean models with atmospheric NNs in ENSO-related studies. Further implications for ocean and coupled modelings using NNs are discussed.
{"title":"An RCUNet-based sea surface wind stress model with multi-day time sequence information incorporated and its applications to ENSO modeling","authors":"Shuangying Du , Rong-Hua Zhang","doi":"10.1016/j.ocemod.2025.102500","DOIUrl":"10.1016/j.ocemod.2025.102500","url":null,"abstract":"<div><div>In traditional ocean-atmosphere coupled modeling for El Niño-Southern Oscillation (ENSO) studies, statistical methods are typically used to represent the instantaneous linear relationship between monthly-averaged anomalies of sea surface temperature (SST) and wind stress (τ). Recently, deep learning (DL) techniques have presented promising prospects for ENSO modeling, and the integration of neural networks (NNs) with dynamical models is an active research area. This study incorporates the Residual convolution blocks and a Convolutional Block Attention Module (the Res-CBAM block) into the original UNet configuration to build a new RCUNet-based <span><math><mi>τ</mi></math></span> model, denoted as <span><math><msub><mi>τ</mi><mtext>RCUNet</mtext></msub></math></span>, which uses SST anomalies (SSTAs) during multi-day time intervals (TIs) to derive daily <span><math><mi>τ</mi></math></span> responses. Sensitivity tests to TIs are performed to illustrate how daily <span><math><mi>τ</mi></math></span> responses are dependent on the way multi-day SST forcings are used; the comparisons with different TIs show that when taking TI=3 days, the <span><math><msub><mi>τ</mi><mtext>RCUNet</mtext></msub></math></span> model can more precisely represent the relationship between SSTAs and <span><math><mi>τ</mi></math></span> anomalies. Next, daily <span><math><mi>τ</mi></math></span> anomalies obtained from the <span><math><msub><mi>τ</mi><mtext>RCUNet</mtext></msub></math></span> model are used to force an intermediate ocean model (IOM) in the ocean-only experiments, displaying coherent phase transitions and spatiotemporal evolutions of oceanic and atmospheric anomalies during typical ENSO events, which highlights the advantages of using the DL-based atmospheric <span><math><mi>τ</mi></math></span> model with multi-day SST time sequence information incorporated for ocean modeling. Furthermore, a new intermediate coupled model (ICM) is formed, named the ICM-RCUNet, in which the original atmospheric component represented by singular value decomposition (SVD) analyses is replaced by the <span><math><msub><mi>τ</mi><mtext>RCUNet</mtext></msub></math></span> model that is used as an atmospheric component, and a daily coupling is conducted with multi-day SST forcings. The ICM-RCUNet simulations exhibit interannual oscillations of atmospheric and oceanic states in the tropical Pacific, demonstrating the applicability of integrating physics-based dynamical ocean models with atmospheric NNs in ENSO-related studies. Further implications for ocean and coupled modelings using NNs are discussed.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"194 ","pages":"Article 102500"},"PeriodicalIF":3.1,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143232643","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}
Pub Date : 2025-01-27DOI: 10.1016/j.ocemod.2025.102503
Oleksandr Nesterov
<div><div>The Arabian / Persian Gulf, hereinafter referred to as the Gulf, is one of the major sources of freshwater through desalination in Kuwait, Bahrain, Qatar, the United Arab Emirates, the eastern Kingdom of Saudi Arabia (KSA), as well as in the coastal regions of the Iranian provinces Bushehr and Hormozgan, and the Oman governorate Musandam. Over the past four decades, the seawater desalination capacities in these states have been steadily growing. By 2023 the total production capacity has reached 25.6 Mm<sup>3</sup>/day, more than double that in 2006 (∼12.1 Mm<sup>3</sup>/day), and it is expected to add at least 6.2 Mm<sup>3</sup>/day by 2030. However, not only the Gulf serves as a source of freshwater for domestic, agricultural and industrial use, but also as a sink for the reject brine. Being a semi-enclosed water body characterized by relatively high evaporation rates of 1.5 – 2 m/year on average, small and reportedly declining river inflows, and negligible precipitations, the Gulf may eventually become impacted by anthropogenic changes in salinities, raising concerns about acceptable freshwater production rates and the overall resilience of its ecosystem. In this study, to estimate a combined impact of the desalination facilities on salinities on global and local scales, a 3D high-resolution free-surface baroclinic model of the Gulf's hydrodynamics was established using the open-source unstructured-grid Semi-implicit Cross-scale Hydroscience Integrated System Model. The horizontal resolution of the numerical mesh varied from approximately 4 km in the Sea of Oman to higher than a hundred meters in some coastal areas of the Gulf, which is notably higher than in previous studies, with the vertical resolution of up to 64 local-sigma-coordinate levels. The desalination capacities of 738 coastal plants were considered based on the 35<sup>th</sup> Worldwide Desalting Inventory of 2023. Two simulations were carried out over 3 years to assess their impact: one without the effect of the plants (baseline), and the other one with the effect of the plants. The baseline model was validated against field measurements. The volume, near-surface and near-bed averaged salinity impacts in the whole Gulf were found converging to 0.03, 0.04, and 0.05 g/l, respectively. Higher salinity increments were modeled closer to the plants, in some cases at considerable distances. For example, the impact on the median salinities at the seabed was estimated to exceed 0.1 g/l as far as 50 km away from Jubail in KSA, and Al Abu Fontas and Umm Al Houl in Qatar. Even higher increments were modeled in poorly flushed zones, such as ∼0.3 g/l in the whole Gulf of Salwa, and in the range of 0.1 – 0.5 g/l in most of the Abu Dhabi canals. Although such salinity impacts are unlikely to pose environmental threats in the present, the effect of desalination may become more profound in the future, especially in view that salinities in the Gulf have remained quasi-equilibrium over the
{"title":"An assessment of seawater desalination impact on salinities in the Arabian / Persian Gulf using a 3D circulation model","authors":"Oleksandr Nesterov","doi":"10.1016/j.ocemod.2025.102503","DOIUrl":"10.1016/j.ocemod.2025.102503","url":null,"abstract":"<div><div>The Arabian / Persian Gulf, hereinafter referred to as the Gulf, is one of the major sources of freshwater through desalination in Kuwait, Bahrain, Qatar, the United Arab Emirates, the eastern Kingdom of Saudi Arabia (KSA), as well as in the coastal regions of the Iranian provinces Bushehr and Hormozgan, and the Oman governorate Musandam. Over the past four decades, the seawater desalination capacities in these states have been steadily growing. By 2023 the total production capacity has reached 25.6 Mm<sup>3</sup>/day, more than double that in 2006 (∼12.1 Mm<sup>3</sup>/day), and it is expected to add at least 6.2 Mm<sup>3</sup>/day by 2030. However, not only the Gulf serves as a source of freshwater for domestic, agricultural and industrial use, but also as a sink for the reject brine. Being a semi-enclosed water body characterized by relatively high evaporation rates of 1.5 – 2 m/year on average, small and reportedly declining river inflows, and negligible precipitations, the Gulf may eventually become impacted by anthropogenic changes in salinities, raising concerns about acceptable freshwater production rates and the overall resilience of its ecosystem. In this study, to estimate a combined impact of the desalination facilities on salinities on global and local scales, a 3D high-resolution free-surface baroclinic model of the Gulf's hydrodynamics was established using the open-source unstructured-grid Semi-implicit Cross-scale Hydroscience Integrated System Model. The horizontal resolution of the numerical mesh varied from approximately 4 km in the Sea of Oman to higher than a hundred meters in some coastal areas of the Gulf, which is notably higher than in previous studies, with the vertical resolution of up to 64 local-sigma-coordinate levels. The desalination capacities of 738 coastal plants were considered based on the 35<sup>th</sup> Worldwide Desalting Inventory of 2023. Two simulations were carried out over 3 years to assess their impact: one without the effect of the plants (baseline), and the other one with the effect of the plants. The baseline model was validated against field measurements. The volume, near-surface and near-bed averaged salinity impacts in the whole Gulf were found converging to 0.03, 0.04, and 0.05 g/l, respectively. Higher salinity increments were modeled closer to the plants, in some cases at considerable distances. For example, the impact on the median salinities at the seabed was estimated to exceed 0.1 g/l as far as 50 km away from Jubail in KSA, and Al Abu Fontas and Umm Al Houl in Qatar. Even higher increments were modeled in poorly flushed zones, such as ∼0.3 g/l in the whole Gulf of Salwa, and in the range of 0.1 – 0.5 g/l in most of the Abu Dhabi canals. Although such salinity impacts are unlikely to pose environmental threats in the present, the effect of desalination may become more profound in the future, especially in view that salinities in the Gulf have remained quasi-equilibrium over the","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"194 ","pages":"Article 102503"},"PeriodicalIF":3.1,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143372939","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}
Pub Date : 2025-01-26DOI: 10.1016/j.ocemod.2025.102504
Jinzhen Yu , Xueqing Zhang , Xiaoxuan Sheng , Fangjing Deng , Yanping Wang , Wensheng Jiang , Jihong Zhang
Mass transport process is vital for the spatial distribution of materials in coastal environments, and comprehending this process is essential for efficient marine pollution control and environmental conservation. As tides is an essential process in coastal environments, the patterns of practical material distribution are decoded in the tide-dominated Bohai Sea with multi-frequency tides by employing the Lagrangian flow network (LFN). Here, the parameter of degree represents the mass transport process, and the concept of the hydrodynamic province is used to depict the spatial distribution of passively transported materials. The emergent pattern of hydrodynamic provinces driven by multiple tidal constituents aligns with the climatological salinity trend, pollutant distribution and phytoplankton community in the springtime Bohai Sea, demonstrating the practical utility of the LFN. The mechanism elucidated by the LFN can be mainly attributed to the intertidal Lagrangian residual velocity induced by tides. Particularly in spring, the tide-induced residual current in the Bohai Sea significantly contributes to basin-scale circulation, while the wind-driven component in winter and the thermohaline part in summer play a more important role than those in spring. In the realm of marine management and conservation strategies, taking into account both terrestrial and maritime perspectives, and incorporation of the marine mass transport process is crucial for the formulation of effective policies.
{"title":"Mechanism and application of the Lagrangian flow network method to indicate practical material distribution patterns in the tide-dominated Bohai Sea with multi-frequency tides","authors":"Jinzhen Yu , Xueqing Zhang , Xiaoxuan Sheng , Fangjing Deng , Yanping Wang , Wensheng Jiang , Jihong Zhang","doi":"10.1016/j.ocemod.2025.102504","DOIUrl":"10.1016/j.ocemod.2025.102504","url":null,"abstract":"<div><div>Mass transport process is vital for the spatial distribution of materials in coastal environments, and comprehending this process is essential for efficient marine pollution control and environmental conservation. As tides is an essential process in coastal environments, the patterns of practical material distribution are decoded in the tide-dominated Bohai Sea with multi-frequency tides by employing the Lagrangian flow network (LFN). Here, the parameter of degree represents the mass transport process, and the concept of the hydrodynamic province is used to depict the spatial distribution of passively transported materials. The emergent pattern of hydrodynamic provinces driven by multiple tidal constituents aligns with the climatological salinity trend, pollutant distribution and phytoplankton community in the springtime Bohai Sea, demonstrating the practical utility of the LFN. The mechanism elucidated by the LFN can be mainly attributed to the intertidal Lagrangian residual velocity induced by tides. Particularly in spring, the tide-induced residual current in the Bohai Sea significantly contributes to basin-scale circulation, while the wind-driven component in winter and the thermohaline part in summer play a more important role than those in spring. In the realm of marine management and conservation strategies, taking into account both terrestrial and maritime perspectives, and incorporation of the marine mass transport process is crucial for the formulation of effective policies.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"194 ","pages":"Article 102504"},"PeriodicalIF":3.1,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147302","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}
Pub Date : 2025-01-25DOI: 10.1016/j.ocemod.2025.102502
Leo Peach , Nick Cartwright , Guilherme Viera da Silva , Darrell Strauss
Machine Learning (ML) is becoming an increasingly popular and important tool for predicting ocean wave conditions. Here it is applied to downscale offshore conditions to a nearshore location utilising more detailed representations of the offshore wave field using 1D wave spectra. Our aim is to identify some of the sensitivities in input data when using machine learning to conduct downscaling (a common application) and present results from different approaches. The results demonstrate that downscaling wave conditions using ML can be enhanced using 1D wave spectra to improve performance. Here, we obtained a 27 % reduction in root mean squared error in significant wave height when compared to integrated parameter only machine learning approach with performance improved when using 1D wave spectra. Though we identified that the Long-Term Short-Term Memory approach applied here improved performance overall, it also appears there is not a one-size fits-all approach for all wave parameters. Careful feature selection (which features to include or exclude when training a model), feature engineering (such as feature encoding and sequence selection) and model configuration continue to be key factors in achieving accurate wave conditions.
{"title":"Enhancing downscaled ocean wave conditions with machine learning and wave spectra","authors":"Leo Peach , Nick Cartwright , Guilherme Viera da Silva , Darrell Strauss","doi":"10.1016/j.ocemod.2025.102502","DOIUrl":"10.1016/j.ocemod.2025.102502","url":null,"abstract":"<div><div>Machine Learning (ML) is becoming an increasingly popular and important tool for predicting ocean wave conditions. Here it is applied to downscale offshore conditions to a nearshore location utilising more detailed representations of the offshore wave field using 1D wave spectra. Our aim is to identify some of the sensitivities in input data when using machine learning to conduct downscaling (a common application) and present results from different approaches. The results demonstrate that downscaling wave conditions using ML can be enhanced using 1D wave spectra to improve performance. Here, we obtained a 27 % reduction in root mean squared error in significant wave height when compared to integrated parameter only machine learning approach with performance improved when using 1D wave spectra. Though we identified that the Long-Term Short-Term Memory approach applied here improved performance overall, it also appears there is not a one-size fits-all approach for all wave parameters. Careful feature selection (which features to include or exclude when training a model), feature engineering (such as feature encoding and sequence selection) and model configuration continue to be key factors in achieving accurate wave conditions.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"194 ","pages":"Article 102502"},"PeriodicalIF":3.1,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148260","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}
Pub Date : 2025-01-22DOI: 10.1016/j.ocemod.2025.102501
Vassilios D. Vervatis , Pierre De Mey-Frémaux , John Karagiorgos , Bénédicte Lemieux-Dudon , Nadia K. Ayoub , Sarantis Sofianos
A Bay of Biscay model configuration is used as a test case to assess the data-based consistency of ensemble-based ocean model uncertainties of several types: [A] built-in stochastic parameterizations at regional ocean scales, [B] ocean model response to a global atmospheric model ensemble and [C] both A and B simultaneously. Ensembles of varying length were generated. In addition to a seasonal-range ensemble, three medium-range ensembles were carried out over successive overlapping segments permitting to compare consistency metrics for different lead times. The largest spread was obtained for the C case, although most of the model uncertainties were attributable to the stochastic ocean parameterizations in A. We addressed the question of which ensemble type and lead time was able to provide the most realistic model uncertainties given observations of SST, sea level, and Chlorophyll a, using a theoretical and diagnostic consistency analysis framework expanded from Vervatis et al. (2021a). In our results, consistency was satisfactory for the stochastic ensembles of types A and C, for the “aged” error cases (but only marginally with respect to the “young” error cases), and whenever physical and biogeochemical uncertainty processes were active in the region and could be detected by the observational networks, such as the onset of the spring shoaling of the thermocline and the phytoplankton abundance primary bloom. Sea level empirical consistency was improved when a wide range of low- to high-frequency errors were included in the signal of dynamic atmospheric process in the data and in the model inverse barometer. These findings provide additional insight that can help configure ensemble-based methods in academic studies and in operational ocean forecasting systems.
{"title":"Regional ocean model uncertainties using stochastic parameterizations and a global atmospheric ensemble","authors":"Vassilios D. Vervatis , Pierre De Mey-Frémaux , John Karagiorgos , Bénédicte Lemieux-Dudon , Nadia K. Ayoub , Sarantis Sofianos","doi":"10.1016/j.ocemod.2025.102501","DOIUrl":"10.1016/j.ocemod.2025.102501","url":null,"abstract":"<div><div>A Bay of Biscay model configuration is used as a test case to assess the data-based consistency of ensemble-based ocean model uncertainties of several types: [A] built-in stochastic parameterizations at regional ocean scales, [B] ocean model response to a global atmospheric model ensemble and [C] both A and B simultaneously. Ensembles of varying length were generated. In addition to a seasonal-range ensemble, three medium-range ensembles were carried out over successive overlapping segments permitting to compare consistency metrics for different lead times. The largest spread was obtained for the C case, although most of the model uncertainties were attributable to the stochastic ocean parameterizations in A. We addressed the question of which ensemble type and lead time was able to provide the most realistic model uncertainties given observations of SST, sea level, and Chlorophyll a, using a theoretical and diagnostic consistency analysis framework expanded from Vervatis et al. (2021a). In our results, consistency was satisfactory for the stochastic ensembles of types A and C, for the “aged” error cases (but only marginally with respect to the “young” error cases), and whenever physical and biogeochemical uncertainty processes were active in the region and could be detected by the observational networks, such as the onset of the spring shoaling of the thermocline and the phytoplankton abundance primary bloom. Sea level empirical consistency was improved when a wide range of low- to high-frequency errors were included in the signal of dynamic atmospheric process in the data and in the model inverse barometer. These findings provide additional insight that can help configure ensemble-based methods in academic studies and in operational ocean forecasting systems.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"194 ","pages":"Article 102501"},"PeriodicalIF":3.1,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147303","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}
Pub Date : 2025-01-09DOI: 10.1016/j.ocemod.2024.102494
Rodrigo Mogollón , Jorge Quispe , François Colas , Jorge Tam
In this study, we investigated the role of atmospheric and oceanographic variability in shaping surface Marine Heatwaves (MHWs) in the Northern Humboldt Current System (NHCS) over the last two decades. Through a series of pluriannual hydrodynamic model simulations, we highlighted the importance of considering at least high-frequency atmospheric variability to accurately reproduce the observed MHW characteristics. On the contrary, when synoptic atmosphere variability is removed, the simulation results in fewer, longer, less frequent, less intense, and less severe MHWs. As a consequence, simulations forced with low-frequency data are only able to partially reproduce persistent MHWs. Additionally, using the heat balance analysis, it is shown that at the north, short-lived events are primarily driven by advective causes, while persistent events show an equal contribution between advection and heat fluxes. In the south, changes in heat fluxes are crucial in forming MHWs. During the dissipation phase of MHWs, cooling is dominated by advective processes, mainly coastal upwelling, in both coastal regions. Overall, these findings indicate a reduced dependence on high-frequency oceanic forcing and highlight the need to consider the atmospheric variability in regional downscaling of global climate model simulations to capture almost the full range of MHW events in the NHCS.
{"title":"Role of atmospheric and oceanographic frequency variability on surface Marine Heatwaves in the Northern Humboldt Current System","authors":"Rodrigo Mogollón , Jorge Quispe , François Colas , Jorge Tam","doi":"10.1016/j.ocemod.2024.102494","DOIUrl":"10.1016/j.ocemod.2024.102494","url":null,"abstract":"<div><div>In this study, we investigated the role of atmospheric and oceanographic variability in shaping surface Marine Heatwaves (MHWs) in the Northern Humboldt Current System (NHCS) over the last two decades. Through a series of pluriannual hydrodynamic model simulations, we highlighted the importance of considering at least high-frequency atmospheric variability to accurately reproduce the observed MHW characteristics. On the contrary, when synoptic atmosphere variability is removed, the simulation results in fewer, longer, less frequent, less intense, and less severe MHWs. As a consequence, simulations forced with low-frequency data are only able to partially reproduce persistent MHWs. Additionally, using the heat balance analysis, it is shown that at the north, short-lived events are primarily driven by advective causes, while persistent events show an equal contribution between advection and heat fluxes. In the south, changes in heat fluxes are crucial in forming MHWs. During the dissipation phase of MHWs, cooling is dominated by advective processes, mainly coastal upwelling, in both coastal regions. Overall, these findings indicate a reduced dependence on high-frequency oceanic forcing and highlight the need to consider the atmospheric variability in regional downscaling of global climate model simulations to capture almost the full range of MHW events in the NHCS.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"194 ","pages":"Article 102494"},"PeriodicalIF":3.1,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147304","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}
Pub Date : 2025-01-08DOI: 10.1016/j.ocemod.2024.102496
Ajin Cho , Hajoon Song , Hyodae Seo , Rui Sun , Matthew R. Mazloff , Aneesh C. Subramanian , Bruce D. Cornuelle , Arthur J. Miller
Relative wind (RW; wind relative to surface currents) has been shown to play a crucial role in air-sea interactions, influencing both atmospheric and oceanic dynamics. While the RW effects through momentum flux are well-documented, those through turbulent heat fluxes remain unknown. In this study, we investigate two distinct surface current feedbacks – those associated with the momentum flux and turbulent heat fluxes – by modifying respective bulk formulations in the regional ocean-atmosphere coupled system, and analyze both immediate and seasonal changes in the boundary layers. Our results show that strong ocean currents in the Kuroshio Current and Extension significantly impact surface coupling, with responses generally contingent on the wind-current angle: an increase (decrease) in air-sea momentum and turbulent heat fluxes occurs when the low-level wind and surface currents are aligned (opposed). The instantaneous responses to surface currents include changes in low-level wind, surface current speed, and humidity, which are consistent with anticipated changes for a given wind-current angle based on the bulk formulations. While the wind-current angle is still an important factor, it does not adequately capture the seasonal responses. On the seasonal timescale, both surface current feedbacks can alter the path of the Kuroshio Extension and mesoscale activities, resulting in different background states that affect air-sea momentum and turbulent heat exchanges. Our results suggest that the energetic current system, such as the Kuroshio Current and Extension, can be significantly influenced by surface current coupling through both momentum and turbulent heat fluxes.
{"title":"Dynamic and thermodynamic coupling between the atmosphere and ocean near the Kuroshio current and extension system","authors":"Ajin Cho , Hajoon Song , Hyodae Seo , Rui Sun , Matthew R. Mazloff , Aneesh C. Subramanian , Bruce D. Cornuelle , Arthur J. Miller","doi":"10.1016/j.ocemod.2024.102496","DOIUrl":"10.1016/j.ocemod.2024.102496","url":null,"abstract":"<div><div>Relative wind (RW; wind relative to surface currents) has been shown to play a crucial role in air-sea interactions, influencing both atmospheric and oceanic dynamics. While the RW effects through momentum flux are well-documented, those through turbulent heat fluxes remain unknown. In this study, we investigate two distinct surface current feedbacks – those associated with the momentum flux and turbulent heat fluxes – by modifying respective bulk formulations in the regional ocean-atmosphere coupled system, and analyze both immediate and seasonal changes in the boundary layers. Our results show that strong ocean currents in the Kuroshio Current and Extension significantly impact surface coupling, with responses generally contingent on the wind-current angle: an increase (decrease) in air-sea momentum and turbulent heat fluxes occurs when the low-level wind and surface currents are aligned (opposed). The instantaneous responses to surface currents include changes in low-level wind, surface current speed, and humidity, which are consistent with anticipated changes for a given wind-current angle based on the bulk formulations. While the wind-current angle is still an important factor, it does not adequately capture the seasonal responses. On the seasonal timescale, both surface current feedbacks can alter the path of the Kuroshio Extension and mesoscale activities, resulting in different background states that affect air-sea momentum and turbulent heat exchanges. Our results suggest that the energetic current system, such as the Kuroshio Current and Extension, can be significantly influenced by surface current coupling through both momentum and turbulent heat fluxes.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"194 ","pages":"Article 102496"},"PeriodicalIF":3.1,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147306","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}
Pub Date : 2025-01-07DOI: 10.1016/j.ocemod.2025.102497
E. Juanara, C.Y. Lam
A tsunami triggered by volcanic collapse is a low-probability but high-impact event. Unlike tsunamis triggered by earthquakes which the mechanism is well understood, volcanic tsunami events have complex trigger mechanisms and occur with little to no warning such as the event in December 2018 of the Anak Krakatau volcano tsunami, making it more difficult to detect and issue a warning. We adopted collapse tsunami machine learning (ML) approach model which does not require source information, to predict maximum tsunami amplitude on four coastal stations. Observations from six synthetic observation stations around Anak Krakatau volcano were used as input for collapse tsunami ML. The 320 collapse scenarios triggering tsunamis with various parameters and directions were generated to train model. To evaluate the accuracy and reliability of the tsunami simulations, we conducted a comparison between the simulated waveforms and those recorded at four coastal stations during the December 2018 event. The RMSE values between predicted and actual (via forward tsunami) of Random Forest model consistently provide the most accurate predictions ranging from 0.0586 to 0.1945 across three out of the four stations. We also applied deep learning algorithms, LSTM, and Complex LSTM to predict tsunami full waveform by using short-duration observation as input. Furthermore, we also pointed out the potential of risk management that can be explored and integrated from results of the maximum tsunami amplitude and arrival time predictions for support decision-making. We suggest that the ML approach could be a good alternative for volcanic tsunamis early warning purposes.
{"title":"Machine Learning Approaches for Early Warning of Tsunami Induced by Volcano Flank Collapse and Implication for Future Risk Management: Case of Anak Krakatau","authors":"E. Juanara, C.Y. Lam","doi":"10.1016/j.ocemod.2025.102497","DOIUrl":"10.1016/j.ocemod.2025.102497","url":null,"abstract":"<div><div>A tsunami triggered by volcanic collapse is a low-probability but high-impact event. Unlike tsunamis triggered by earthquakes which the mechanism is well understood, volcanic tsunami events have complex trigger mechanisms and occur with little to no warning such as the event in December 2018 of the Anak Krakatau volcano tsunami, making it more difficult to detect and issue a warning. We adopted collapse tsunami machine learning (ML) approach model which does not require source information, to predict maximum tsunami amplitude on four coastal stations. Observations from six synthetic observation stations around Anak Krakatau volcano were used as input for collapse tsunami ML. The 320 collapse scenarios triggering tsunamis with various parameters and directions were generated to train model. To evaluate the accuracy and reliability of the tsunami simulations, we conducted a comparison between the simulated waveforms and those recorded at four coastal stations during the December 2018 event. The RMSE values between predicted and actual (via forward tsunami) of Random Forest model consistently provide the most accurate predictions ranging from 0.0586 to 0.1945 across three out of the four stations. We also applied deep learning algorithms, LSTM, and Complex LSTM to predict tsunami full waveform by using short-duration observation as input. Furthermore, we also pointed out the potential of risk management that can be explored and integrated from results of the maximum tsunami amplitude and arrival time predictions for support decision-making. We suggest that the ML approach could be a good alternative for volcanic tsunamis early warning purposes.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"194 ","pages":"Article 102497"},"PeriodicalIF":3.1,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148261","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}
The ocean model is accelerated using the mixed-precision method for the second-order moment (SOM) advection scheme, which is highly accurate but computationally demanding. The execution time of the subroutine of the SOM scheme is successfully reduced by 43%, and since the SOM accounted for about 30% of the total time, the overall reduction in computation time is about 13%. A series of 300-year simulations showed that the mixed-precision method has sufficiently small negative impact on temperature (less than 0.02 °C), confirming that it can be used for climate simulations. When all the calculations are done in a simple single precision, an unacceptable difference in zonal mean temperature as a climate model of up to 0.4 °C in the deep ocean and 1 °C in the thermocline appears after the 300-year integration. We also conduct a sensitivity study using an idealized rectangular model, finding that consistency between the continuity equation and the tracer advection is necessary to guarantee accuracy in long-term integration, and it is shown that this consistency can be checked in a short time in a small rectangular domain as an salinity anomaly. In addition to the mixed-precision method, we have introduced two other methods for calculating the single-precision SOM algorithm. One is the Kahan method to compensate for the loss of information in the addition of some important variables represented in single precision, and the other is the conversion reduction method to compensate for the error due to data conversion between single and double precision at the entry and exit of subroutines. When both the methods are used simultaneously, the accuracy is comparable to mixed precision. We also evaluate them in an eddying OGCM and find that errors are difficult to evaluate because they are hidden by the inherent nonlinearity of the model at the stage when the nonlinearity develops.
{"title":"On the speeding up and accuracy of the Second Order Moment (SOM) advection scheme using a mixed-precision method","authors":"Hideyuki Nakano, L. Shogo Urakawa, Kunihiro Aoki, Yuma Kawakami, Shoji Hirahara","doi":"10.1016/j.ocemod.2024.102495","DOIUrl":"10.1016/j.ocemod.2024.102495","url":null,"abstract":"<div><div>The ocean model is accelerated using the mixed-precision method for the second-order moment (SOM) advection scheme, which is highly accurate but computationally demanding. The execution time of the subroutine of the SOM scheme is successfully reduced by 43%, and since the SOM accounted for about 30% of the total time, the overall reduction in computation time is about 13%. A series of 300-year simulations showed that the mixed-precision method has sufficiently small negative impact on temperature (less than 0.02 °C), confirming that it can be used for climate simulations. When all the calculations are done in a simple single precision, an unacceptable difference in zonal mean temperature as a climate model of up to 0.4 °C in the deep ocean and 1 °C in the thermocline appears after the 300-year integration. We also conduct a sensitivity study using an idealized rectangular model, finding that consistency between the continuity equation and the tracer advection is necessary to guarantee accuracy in long-term integration, and it is shown that this consistency can be checked in a short time in a small rectangular domain as an salinity anomaly. In addition to the mixed-precision method, we have introduced two other methods for calculating the single-precision SOM algorithm. One is the Kahan method to compensate for the loss of information in the addition of some important variables represented in single precision, and the other is the conversion reduction method to compensate for the error due to data conversion between single and double precision at the entry and exit of subroutines. When both the methods are used simultaneously, the accuracy is comparable to mixed precision. We also evaluate them in an eddying OGCM and find that errors are difficult to evaluate because they are hidden by the inherent nonlinearity of the model at the stage when the nonlinearity develops.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"194 ","pages":"Article 102495"},"PeriodicalIF":3.1,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147307","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}
The urgency of climate change calls for the exploration of a variety of multi-forcing scenarios based on Shared Socio-economic Pathways. Ensuring the reliability of the climate projections is therefore an imperative prerequisite. In this paper, we examined the impact of the vertical variability and temporal frequency of the chlorophyll field used to force the NEMOMED12 ocean circulation model in the absence of a biogeochemical model on some key physical characteristics, mainly seawater temperature. Our analysis reveals that forcing by a chlorophyll field that is homogeneous in the vertical direction favours heat accumulation below the Deep Chlorophyll Maximum, leading to a positive temperature bias increasing with time. The extrapolation of the trend determined over the 11-year simulations leads to a bias in temperature as high as +1 °C after 100 years in the intermediate layer. Comparison with in situ data clearly shows that forcing the model with a realistically varying Chl field over the vertical allows the model to better represent temperature and avoid the presence of this bias. Additionally, we find that using the same chlorophyll field saved at different time frequencies, namely daily, monthly and monthly climatology, to force the NEMOMED12 model also creates temperature differences between simulations that increase with time, especially in the intermediate layer. The simulation forced by the daily chlorophyll is warmer in the surface layers than the two others, and we suggest that this is due to the asymmetric role of chlorophyll extremes on heat distribution. Finally, using a monthly chlorophyll climatology to force the NEMOMED12 ocean circulation model seems to be sufficient for physical modelling of the Mediterranean basin if the vertical variability of the Chl field is realistic.
{"title":"Analysis of the impact of vertical variation and temporal frequency of the chlorophyll forcing field on modelled temperature in the Mediterranean Sea and potential implications for regional climate projections","authors":"Yutong Zhang , Florence Sevault , Romain Pennel , Melika Baklouti","doi":"10.1016/j.ocemod.2024.102490","DOIUrl":"10.1016/j.ocemod.2024.102490","url":null,"abstract":"<div><div>The urgency of climate change calls for the exploration of a variety of multi-forcing scenarios based on Shared Socio-economic Pathways. Ensuring the reliability of the climate projections is therefore an imperative prerequisite. In this paper, we examined the impact of the vertical variability and temporal frequency of the chlorophyll field used to force the NEMOMED12 ocean circulation model in the absence of a biogeochemical model on some key physical characteristics, mainly seawater temperature. Our analysis reveals that forcing by a chlorophyll field that is homogeneous in the vertical direction favours heat accumulation below the Deep Chlorophyll Maximum, leading to a positive temperature bias increasing with time. The extrapolation of the trend determined over the 11-year simulations leads to a bias in temperature as high as +1 °C after 100 years in the intermediate layer. Comparison with in situ data clearly shows that forcing the model with a realistically varying Chl field over the vertical allows the model to better represent temperature and avoid the presence of this bias. Additionally, we find that using the same chlorophyll field saved at different time frequencies, namely daily, monthly and monthly climatology, to force the NEMOMED12 model also creates temperature differences between simulations that increase with time, especially in the intermediate layer. The simulation forced by the daily chlorophyll is warmer in the surface layers than the two others, and we suggest that this is due to the asymmetric role of chlorophyll extremes on heat distribution. Finally, using a monthly chlorophyll climatology to force the NEMOMED12 ocean circulation model seems to be sufficient for physical modelling of the Mediterranean basin if the vertical variability of the Chl field is realistic.</div></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":"194 ","pages":"Article 102490"},"PeriodicalIF":3.1,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147305","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}
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