Dynamics of Atmospheres and Oceans最新文献

{"title":"Sensitivity of WRF tropical cyclone simulations in the Philippines to different SST data","authors":"Juan Paolo P. Pamintuan ,&nbsp;Gerry Bagtasa","doi":"10.1016/j.dynatmoce.2025.101578","DOIUrl":"10.1016/j.dynatmoce.2025.101578","url":null,"abstract":"<div><div>Improving the accuracy of tropical cyclone (TC) simulations in numerical weather prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, has been an increasingly important endeavor. Sea-air moisture and energy fluxes are mainly driven by sea surface temperature (SST) and the mixed layer underneath, which have significant effects on the formation and intensification of TCs. We investigated the sensitivity of three Philippine TCs - Typhoons Mangkhut, Goni, and Rai - to different SST datasets and a 1-D ocean mixed layer (OML) model in WRF. We found that the WRF runs with the high-resolution SST data update showed improvements in modeled maximum wind speed and consequently improved the simulated tracks over the archipelagic and/or coastal waters of the Philippines, as it gave better confidence in the (intensity-based) tracking algorithm after TCs made landfall in the country. TC-associated rainfall was also found to be sensitive to SST-updated model runs. Our results show that the use of SST significantly reduces the dry bias of WRF-simulated TC rainfall. The use of the high-resolution GHRSST dataset yielded the best TC simulation results over other SST data by simulating the sensible and latent heat or moisture fluxes over land and sea along coastlines better across the inland archipelagic waters of the Philippines. Disasters due to TCs are often brought about by strong winds and heavy rains over land. Considering that virtually no added computational cost is incurred in including SST update in the WRF model, the use of SST in TC modeling is an efficient method to improve TC hazard simulations.</div></div>","PeriodicalId":50563,"journal":{"name":"Dynamics of Atmospheres and Oceans","volume":"111 ","pages":"Article 101578"},"PeriodicalIF":1.9,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of climate characteristics on the Tibetan Plateau based on mathematical statistics and vegetation response research","authors":"Yida Liu ,&nbsp;Xiaoyan Zhao","doi":"10.1016/j.dynatmoce.2025.101576","DOIUrl":"10.1016/j.dynatmoce.2025.101576","url":null,"abstract":"<div><div>The Tibetan Plateau(TP)’s profound influence on East Asian atmospheric circulation and its indicative role in shaping global climate patterns have garnered widespread scientific interest. This study employs the statistical method of Rotated Empirical Orthogonal Function (REOF) analysis to conduct an objective climate regionalization of the TP. The plateau is subdivided into five regions, and a detailed analysis is conducted on the 73-year trends of precipitation, temperature, ground temperature, evaporation, and wind speed in each of the five subregions. The study reveals that, since the start of observations, precipitation in the northern TP has undergone a transition from high to low, followed by a gradual increase. In contrast, precipitation in the southern TP has shown a slight decrease in recent years. Both air temperature and ground temperature across the TP exhibit an overall oscillatory warming trend. Meanwhile, evaporation has decreased, and wind speed has declined across the entire plateau. The combined effects of precipitation, temperature, evaporation, and wind speed have led to a pronounced warming and wetting trend in the northern TP, while the southern region exhibits a warming and drying trend. This is closely related to the complex mechanisms driven by the interplay of westerlies and monsoons over the TP. Since 2003, changes in the vegetation index on the plateau have shown a strong positive correlation with precipitation, indicating that precipitation is one of the key factors influencing vegetation on the TP.</div></div>","PeriodicalId":50563,"journal":{"name":"Dynamics of Atmospheres and Oceans","volume":"111 ","pages":"Article 101576"},"PeriodicalIF":1.9,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Heatwave-induced thermoregulatory stress in Odisha's coastal and north-eastern districts: Examining the April 2024 event using advanced statistical and geospatial techniques","authors":"Roshan Beuria ,&nbsp;Dipak Sahu ,&nbsp;Sarat Chandra Sahu ,&nbsp;Debabrata Nandi ,&nbsp;Susmita Behera ,&nbsp;Kamal Lochan Mohanta ,&nbsp;Rakesh Ranjan Thakur","doi":"10.1016/j.dynatmoce.2025.101577","DOIUrl":"10.1016/j.dynatmoce.2025.101577","url":null,"abstract":"<div><div>This work investigates the remarkable heatwave that struck Odisha in April 2024, with special emphasis on intensity and duration in the coastal and interior regions. It is of special concern if such patterns arise in coastal districts where comfortable weather is due to the proximity of the sea. There's been an uninterrupted recording of temperatures crossing 40°C for 16 consecutive days i.e., from the 15th to the 30th of April which includes some regions usually considered less vulnerable to extreme heat. The aim of this particular research is focused on synoptic features and focuses on drivers for the specific anomaly in northeastern and coastal districts. The study utilises the historical temperature dataset ranging from 1969 to 2024, applying the heat wave trends according to the IMD classification and 90th percentile threshold. The examination of synoptic features of atmospheric circulation along with vertical thermal structures is the main scope of this research. From data collection, it is observed that there is an exacerbating increase in the frequency and intensity of heat waves alongside UHI effects, and they are linked with transitions of El Niño–La Niña. Along with this, stepping over vegetation and the increase in impervious surfaces also led to delayed sea breeze onset and higher heat retention in coastal zones. With the quantitative analysis that was supplemented by the UHI phenomenon, it was revealed that Thom's discomfort index also displayed significant discomfort to the inhabitants of the area during the heatwave, with values exceeding 28 in most locations, indicating severe discomfort levels. Unlike prior research in Odisha that primarily focused on long-term trends or socio-economic vulnerability, this study uniquely applies high-resolution WRF simulation, satellite reanalysis, and thermoregulatory indices to capture the April 2024 heatwave’s spatial variability, urban–coastal thermal interactions, and atmospheric drivers in an integrated and operationally relevant framework. These findings underscore the urgency of developing adaptive strategies such as urban planning reforms (e.g., greening and zoning), early warning systems tailored to heat thresholds, and region-specific public health interventions to protect vulnerable populations in Odisha and similar coastal environments.</div></div>","PeriodicalId":50563,"journal":{"name":"Dynamics of Atmospheres and Oceans","volume":"111 ","pages":"Article 101577"},"PeriodicalIF":1.9,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing the complexity of tropical cyclone landfall dynamics through the integration of radar data","authors":"Sankhasubhra Chakraborty ,&nbsp;Nikita Goswami ,&nbsp;Sandeep Pattnaik ,&nbsp;B.A.M. Kannan","doi":"10.1016/j.dynatmoce.2025.101575","DOIUrl":"10.1016/j.dynatmoce.2025.101575","url":null,"abstract":"<div><div>Tropical cyclones (TCs) over the Indian subcontinent often led to devastating impacts, especially during landfall. This study exclusively investigates the dynamical changes of the TCs during their landfall phases, covering pre-landfall (PrL), during-landfall (DL), and post-landfall (PoL) periods. Three landfalling TCs—Hudhud (2014), Titli (2018), and Fani (2019)—are simulated using the Weather Research and Forecasting model, incorporating Doppler Weather Radar reflectivity (Rf) data through two sets of experiments: CNTL (without Rf assimilation) and Rf_DA (with Rf assimilation). Rf_DA has minimal impact on the TC track; however, it significantly improves the intensity in terms of minimum central pressure (MCP, 43 % &amp; 8 % reduced error) and maximum sustained surface wind (MSSW, 53 % &amp; 15 % reduced error) for Hudhud and Fani, respectively, during the landfall process. The weakening phase is accurately captured, and structural changes in the DL phase are closely aligned with observations for Rf_DA. Realistic rainfall distribution and associated thermodynamic processes during DL and PoL are better replicated in Rf_DA compared to CNTL. The water budget analysis shows that lower-level moisture convergence (1000–700hPa) and upper-level (400–100hPa) advection are the dominant factors regulating DL and PoL rainfall characteristics of TCs. Furthermore, TC-associated rainfall is strongly influenced by frozen hydrometeors at the mid to upper-level (600–200hPa) and liquid hydrometeors at the lower-level (1000–700hPa) in the DL and PoL phases. In summary, incorporating Rf data considerably improved the key features of TCs—the structure, intensity, and rainfall patterns during the landfall phase. These findings have significant implications for improving early warning systems for TC landfall, especially in coastal areas with high population densities.</div></div>","PeriodicalId":50563,"journal":{"name":"Dynamics of Atmospheres and Oceans","volume":"111 ","pages":"Article 101575"},"PeriodicalIF":1.9,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of thunderstorm evolution using deep learning models with doppler weather radar observations over southern part of India","authors":"Dharmadas Jash ,&nbsp;Sumit Kumar ,&nbsp;Arka Roy ,&nbsp;E.A. Resmi ,&nbsp;R.K. Sumesh ,&nbsp;C.K. Unnikrishnan ,&nbsp;Nita Sukumar","doi":"10.1016/j.dynatmoce.2025.101565","DOIUrl":"10.1016/j.dynatmoce.2025.101565","url":null,"abstract":"<div><div>Thunderstorms are severe weather phenomena causing heavy rainfall and lightning that poses serious threat to agriculture, infrastructure and lives in general. Short scale nature of these events makes it difficult to predict them. In this study we have made an attempt at thunderstorm nowcasting using Deep Learning (DL) models with Doppler weather radar (DWR) data, for the first time over the Indian region. This study utilizes data from a C-band DWR installed at Space Physics Laboratory (8.52 N, 76.89E), Thiruvananthapuram (southern tip of India). DL models incorporating Generative Adversarial Network (GAN) architecture have been developed to predict evolution of pre-monsoon (Mar-May) thunderstorms over southern peninsular India. MAXZ (maximum reflectivity along the vertical) reflectivity data of thunderstorm events during pre-monsoons of 2018–2024 have been used for training and testing of the DL models. Total 4 models have been trained. For 15 &amp; 30 minutes ahead predictions, the Mean Absolute Error (MAE) for the test samples are about 0.8 dB and 1.2 dB respectively. Our DL models are capable of predicting the main convective areas (Z &gt; 40 dBZ) for both 15 and 30 minutes ahead predictions better than some earlier studies. Evolution of the thunderstorm on 13-May-2018 has been studied in detail. Movement of the system has been tracked by following the center of the largest cluster of high reflectivity (Z &gt; 40 dBZ) values. All the four models were able to capture the overall spatial patterns of the reflectivity field well. For 15 minutes ahead predictions, the models predict the movement of the center reasonably well. The scatter plot between the direction of true movement &amp; predicted movement of the center are well correlated. The study demonstrates the ability of the deep learning models in predicting the evolution of thunderstorms over Indian region.</div></div>","PeriodicalId":50563,"journal":{"name":"Dynamics of Atmospheres and Oceans","volume":"111 ","pages":"Article 101565"},"PeriodicalIF":1.9,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic and thermodynamic characteristics of the Bay of Bengal cyclones during 2001–20 and impact of scatterometer winds through composite analysis","authors":"Ipshita Bhasi ,&nbsp;Jagabandhu Panda ,&nbsp;Subodh Kumar ,&nbsp;Debashis Paul ,&nbsp;Ashish Routray","doi":"10.1016/j.dynatmoce.2025.101564","DOIUrl":"10.1016/j.dynatmoce.2025.101564","url":null,"abstract":"<div><div>This study comprehensively investigates the dynamic, and thermodynamic characteristics associated with pre-monsoon and post-monsoon tropical cyclones (TCs) over the Bay of Bengal during 2001–20. For this purpose, numerical simulations using Weather Research and Forecasting model (called CTRL) and the outputs with assimilation through three-dimensional variational data assimilation techniques (called DA) are used. The DA experiments considered modified initial conditions that are generated by employing assimilated scatterometer winds. Accordingly, a total of 74 model simulations are carried out for 37 TCs categorized as Cyclonic Storm (CS), Severe Cyclonic Storm (SCS), and Highly Intensified Cyclonic Storm (HICS), for preparing the composites. Composite analysis involving different category TCs is performed, where the simulated results are compared against India Meteorological Department observations and the Indian Monsoon Data Assimilation and Analysis (IMDAA). The comparison provides an insight regarding the model performance, where DA demonstrates improved estimation of maximum sustained wind, minimum sea level pressure and cyclone track. The seasonal variations of the dynamic characteristics consisting of vertical wind shear, vorticity, and tangential and radial winds are found to strengthen along with TC intensity. Also, an increase in the rate of convergence supported by well-defined wind fields is realized at the TC center. In most instances, both experiments demonstrate similar trends, but DA exhibits improvement in the estimations, specifically for SCS and HICS categories. However, a limited impact of scatterometer wind data assimilation is realized on the dynamic behavior of CS category TCs. The impact is also found to be limited on the thermodynamic properties of all three categories, although the seasonal variation reveals a consistent increasing trend of temperature anomalies with TC intensity, indicating an association with the intensification process.</div></div>","PeriodicalId":50563,"journal":{"name":"Dynamics of Atmospheres and Oceans","volume":"111 ","pages":"Article 101564"},"PeriodicalIF":1.9,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating weather trends and forecasting with machine learning: Insights from maximum temperature, minimum temperature, and rainfall data in India","authors":"Guhan V ,&nbsp;A. Dharma Raju ,&nbsp;Rama Krishna ,&nbsp;K. Nagaratna","doi":"10.1016/j.dynatmoce.2025.101562","DOIUrl":"10.1016/j.dynatmoce.2025.101562","url":null,"abstract":"<div><div>This research presents a comprehensive evaluation of meteorological trends using a combination of statistical and machine learning approaches, focusing on rainfall, minimum temperature (MinT), and maximum temperature (MaxT). The Mann-Kendall trend test and Sen’s slope estimator identified statistically significant upward trends in both MaxT (slope = 0.0154, p = 9.42E-06) and MinT (slope = 0.0190, p = 4.73E-07), indicating a consistent warming climate. Rainfall displayed a positive trend but was not statistically significant (p = 0.9516, slope = 4.07E-05), suggesting random variability rather than a sustained increase.Machine learning models were leveraged to enhance forecasting accuracy for these meteorological parameters. ARIMA exhibited the highest precision for MaxT and Rainfall (MAE = 3.0080, 0.1728; RMSE = 3.4967, 0.2916), while XGBoost demonstrated superior performance for MinT (MAE = 2.7726, RMSE = 3.8555). These findings highlight the critical need for climate adaptation measures, as rising temperatures could intensify heatwaves, escalate energy demands, and affect agricultural productivity.The study underscores the importance of integrating advanced forecasting techniques to support proactive climate resilience planning. By incorporating machine learning models with traditional statistical analyses, this research provides valuable insights into climate trends, aiding policymakers and researchers in formulating effective climate adaptation strategies.</div></div>","PeriodicalId":50563,"journal":{"name":"Dynamics of Atmospheres and Oceans","volume":"110 ","pages":"Article 101562"},"PeriodicalIF":1.9,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144146714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How does HAIKUI remnant produce heavy precipitation in 2023","authors":"Ping Ye ,&nbsp;Yuan Zhu ,&nbsp;Haoya Liu","doi":"10.1016/j.dynatmoce.2025.101563","DOIUrl":"10.1016/j.dynatmoce.2025.101563","url":null,"abstract":"<div><div>In 2023, HAIKUI's prolonged remnant caused exceptional destruction, exposing critical gaps in understanding tropical cyclone (TC) remnant dynamics. While existing studies have documented decay processes of TC remnant, systematic analyses of stage-dependent moisture transport remain lacking. This study combines multi-source data to reveal HAIKUI remnant's structural evolution, moisture trajectories, and precipitation drivers. During the persistence of the HAIKUI remnant, the mountain ranges together with environmental airflow played a pivotal role in isolating the low-pressure system, maintaining its coverage largely unchanged. Based on temporal variations, the lifecycle of HAIKUI remnant can be segmented into three distinct stages: the moving stage, the charging stage, and the separating stage. In the moving stage, the center of HAIKUI remnant and the corresponding rain band gradually shifted westward, exhibiting both barotropic and baroclinic characteristics. The charging stage marked a period where HAIKUI remnant's center hovered over Guangxi Province. The dominance of barotropic structure enhanced moisture convergence, generating an arc-shaped heavy rainfall belt. In the separating stage, the mid- and low-level centers of HAIKUI remnant moved toward opposing directions, resulting in a primarily baroclinic circulation structure. The convergence of cold and warm air led to precipitation in the Pearl River Delta region. The findings highlighted the impact of topography and moisture transport on the evolution and precipitation of TC remnant, offering valuable insights for future predictions of precipitation- and flood-related disasters caused by such remnants.</div></div>","PeriodicalId":50563,"journal":{"name":"Dynamics of Atmospheres and Oceans","volume":"111 ","pages":"Article 101563"},"PeriodicalIF":1.9,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New formulas for estimating initial dilution of buoyancy-dominated jets in a current","authors":"Galip Seckin ,&nbsp;Cagatayhan Bekir Ersu ,&nbsp;Irfan Macit","doi":"10.1016/j.dynatmoce.2025.101561","DOIUrl":"10.1016/j.dynatmoce.2025.101561","url":null,"abstract":"<div><div>For buoyancy-dominated effluents discharged into moving water, three distinct regions are recognized: the buoyancy-dominated near field (BDNF), the buoyancy-dominated far field (BDFF), and an intermediate transition region. Most existing initial dilution formulas—derived from empirical and field studies—focus on the BDNF and BDFF regions while neglecting the transition zone. In this study, two new semi-empirical equations were developed using field and experimental data: one for submerged discharges and another for minimum surface dilution. These equations were calibrated via nonlinear regression, offering a unified approach to effectively calculating initial dilution across both the BDNF and BDFF regions while also addressing the transition region’s dilution in a single step. The proposed formulas were further validated through comparison with an earlier semi-empirical model using the same dataset.</div></div>","PeriodicalId":50563,"journal":{"name":"Dynamics of Atmospheres and Oceans","volume":"110 ","pages":"Article 101561"},"PeriodicalIF":1.9,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144146715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effects of climate change on the thermal stratification of the Gulf of Oman","authors":"Shirin Farkhani,&nbsp;Nasser Hadjizadeh Zaker","doi":"10.1016/j.dynatmoce.2025.101560","DOIUrl":"10.1016/j.dynatmoce.2025.101560","url":null,"abstract":"<div><div>Water temperature and thermal stratification have fundamental effects on marine environments, ecosystems, and water circulation patterns. Marine ecosystems are generally highly sensitive to thermal changes. Global warming can fundamentally alter oceanic temperature fields and thermal stratification(Cheng, 2019)(Cheng, 2019). Therefore, studying the effects of climate change on the thermal characteristics of the oceans and seas is vital. Using numerical modeling, and three Representative Concentration Pathway (RCP) scenarios, we studied the effects of global warming on the sea surface temperature and vertical thermal structure of the Gulf of Oman. Atmospheric data from the ERA5 and CORDEX models were used for recent past (1980–2000) and future (2080–2100) modeling, respectively. Results indicated that, in the future climate, the temperature across the upper 1000 m of the Gulf of Oman will increase. In summer, temperature increments in the surface mixed layer were estimated at + 1.9, + 2.5, and + 3.4°C for RCP 2.6, 4.5, and 8.5, respectively. Below the thermocline, the temperature increments were less than the ones in the surface mixed layer. In winter, future temperature increments in the surface mixed layer were + 1.2, + 1.6, and + 2°C for RCP 2.6, 4.5, and 8.5, respectively. The results indicated a stronger summer thermocline in the future with temperature gradients of 0.055, 0.057, and 0.06 °C/m in the RCP 2.6, 4.5, and 8.5, respectively, which could significantly reduce dissolved oxygen concentration in the lower layers. This study provides insights that can help develop adaptable strategies to manage and mitigate the harmful impacts of global warming on the Gulf of Oman.</div></div>","PeriodicalId":50563,"journal":{"name":"Dynamics of Atmospheres and Oceans","volume":"110 ","pages":"Article 101560"},"PeriodicalIF":1.9,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143948917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}