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Abstract The relatively short duration of available tide gauge records poses challenges for conducting comprehensive statistical analyses of storm surges in the Western North Pacific. To address this issue, we employ a convolutional neural network model to reconstruct the maximum daily storm surge at 160 tide gauges from 1900 to 2010 in the Western North Pacific. The reconstructed dataset serves multiple purposes. Firstly, it facilitates the identification of regions where notable changes in the storm surges have occurred in the past. Additionally, the dataset enables long-term analyses of the storm surge climate, offering insights into historical patterns and variations. Furthermore, it provides a solid foundation for conducting robust extreme value analyses. To ensure accessibility, the data are publicly available through a repository, allowing for easy access and utilization by the broader scientific community and the general public. Overall, our research contributes to the field of oceanography by providing a dataset that aids in understanding the historical storm surge dynamics in the Western North Pacific region.

Abstract Compound wind and precipitation extremes (CWPEs) can severely impact natural and socioeconomic systems. However, our understanding of CWPE future changes, drivers, and uncertainties under a warmer climate is limited. Here, by analyzing the event both on oceans and landmasses via state‐of‐the‐art climate model simulations, we reveal a poleward shift of CWPE occurrences by the late 21st century, with notable increases at latitudes exceeding 50° in both hemispheres and decreases in the subtropics around 25°. CWPE intensification occurs across approximately 90% of global landmasses, and is most pronounced under a high‐emission scenario. Most changes in CWPE frequency and intensity (about 70% and 80%, respectively) stem from changes in precipitation extremes. We further identify large uncertainties in CWPE changes, which can be understood at the regional level by considering climate model differences in trends of CWPE drivers. These results provide insights into understanding CWPE changes under a warmer climate, aiding robust regional adaptation strategy development.

The western Pacific Ocean is the global center for marine biodiversity, with high vulnerability to climate change. A better understanding of the spatiotemporal characteristics and potential drivers of compound marine heatwaves (MHWs) and low-chlorophyll (LChl) extreme events is essential for the conservation and management of local marine organisms and ecosystems. Here, using daily satellite sea surface temperature and model-based chlorophyll concentration, we find that the climatological spatial distribution of MHW-LChl events in total days, duration, and intensity exhibits heterogeneous distributions. The southwest sections of the South China Sea (WSCS) and Indonesian Seas are the hotspots for compound events, with total MHW-LChl days that are more than 2.5 times higher than in the other sub-regions. Notably, there is a trend toward more frequent (> 4.2 d/decade), stronger (> 0.5), and longer-lasting (> 1.4 d/decade) MHW-LChl occurrences in the WSCS. The occurrence of compound MHW-LChl extremes exhibits remarkable seasonal differences, with the majority of these events transpiring during winter. Moreover, there are generally statistically significant increasing trends in MHW-LChl events for all properties on both seasonal and inter-annual timescales. Furthermore, we reveal that the total days of compound MHW-LChl extremes are strongly modulated by large-scale climate modes such as the El Niño-Southern Oscillation and Dipole Mode Index. Overall, pinpointing MHW-LChl hotspots and understanding their changes and drivers help vulnerable communities in better preparing for heightened and compounded risks to marine organism and ecosystems under climate change.

In boreal summer (July–August) 2022, an unprecedented marine heatwave (MHW) occurred in the northwest Pacific Ocean (NWP), while a record-breaking terrestrial heatwave (THW) hit the Yangtze River Basin (YRB). The temperature anomalies caused by this compound MHW-THW event exceeded climatology by 2.5 standard deviations (SDs), breaking the historical record for nearly 100 years, with severe impacts on the ecosystems and social economy. To investigate the underlying causes, we explored the potential roles of anthropogenic forcing, atmospheric circulation, and ‘triple-dip’ La Niña on this compound event using the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model simulations. Results indicate that the 2022-like compound MHW-THW event was extremely unlikely to happen without anthropogenic warming, and that such extreme heatwaves were governed by the climatic mean temperature rather than changes in temperature variability. Notably, the anticyclone circulation patterns and associated high-pressure systems (i.e. western North Pacific subtropical high (WNPSH) and South Asian high (SAH)) increase the probability of a 2022-like MHW-THW event by 3.7 times. However, the La Niña phase has no significant effect on the occurrence probability of such events. We further estimate that the 2022-like MHW-THW event will become 7.5 and 11.4 times more likely under the SSP3-7.0 scenario by the middle and end of the 21st century, respectively. This study demonstrates the contribution of anthropogenic climate change and natural variability to the occurrence of compound MHW-THW events and highlights the urgent need to build mitigation strategies for compound MHW-THW events.

Accurately estimating the ocean’s subsurface thermohaline structure is essential for advancing our understanding of regional and global ocean dynamics. In this study, we propose a novel neural network model based on Convolutional Block Attention Module-Convolutional Neural Network (CBAM-CNN) to simultaneously estimate the ocean subsurface thermal structure (OSTS) and ocean subsurface salinity structure (OSSS) in the tropical Indian Ocean using satellite observations. The input variables include sea surface temperature (SST), sea surface salinity (SSS), sea surface height anomaly (SSHA), eastward component of sea surface wind (ESSW), northward component of sea surface wind (NSSW), longitude (LON), and latitude (LAT). We train and validate the model using Argo data, and compare its accuracy with that of the original Convolutional Neural Network (CNN) model using root mean square error (RMSE), normalized root mean square error (NRMSE), and determination coefficient (R²). Our results show that the CBAM-CNN model outperforms the CNN model, exhibiting superior performance in estimating thermohaline structures in the tropical Indian Ocean. Furthermore, we evaluate the model’s accuracy by comparing its estimated OSTS and OSSS at different depths with Argo-derived data, demonstrating that the model effectively captures most observed features using sea surface data. Additionally, the CBAM-CNN model demonstrates good seasonal applicability for OSTS and OSSS estimation. Our study highlights the benefits of using CBAM-CNN for estimating thermohaline structure and offers an efficient and effective method for estimating thermohaline structure in the tropical Indian Ocean.

The Amundsen Sea is one of the regions with the highest primary productivity in the Antarctic. To better understand the role of the Southern Ocean in the global carbon cycle and in climate regulation, a better understanding of the variations and environmental controls of primary productivity is needed. Using cluster analysis, the Amundsen Sea was divided into nine bioregions. The biophysical differences among bioregions enhanced confidence to identify priorities and regions to study the temporal and spatial variations in primary production. Four nearshore bioregions with high net primary productivity or rapidly increasing rates were selected to analyze temporal and spatial variations in primary productivity in the Amundsen Sea. Due to changes in net solar radiation and sea ice, primary production had significant seasonal variation in these four bioregions. The phenology had changed at two bioregions (6 and 7), which has the third and fourth highest primary production, due to changes in the dissolved iron. Annual primary production showed increasing trends in these four bioregions, and it was significant at three bioregions. The variation in primary production in the bioregion (9), which has the highest primary production, was mainly affected by variations in sea surface temperatures. In the bioregion (8), which has the second-highest primary production, the primary production was significantly positively correlated with sea surface temperature and significantly negatively correlated with sea ice thickness. The long-term changes of primary productivity in bioregions 6 and 7 were thought to be related to changes in the dissolved iron, and dissolved iron was the limiting factor in these two bioregions. Bioregionalization not only disentangles multiple factors that control the spatial differences, but also disentangles limiting factors that affect the phenology, decadal and long-term changes in primary productivity.

Few studies have focused on the projected future changes in wave climate in the Chinese marginal seas. For the first time, we investigate the projected changes of the mean and extreme wave climate over the Bohai Sea, Yellow Sea, and East China Sea (BYE) during two future periods (2021–2050 and 2071–2100) under the RCP2.6 and RCP8.5 scenarios from the WAM wave model simulations with a resolution of 0.1°. This is currently the highest-resolution wave projection dataset available for the study domain. The wind forcings for WAM are from high-resolution (0.22°) regional climate model (RCM) CCLM-MPIESM simulations. The multivariate bias-adjustment method based on the N-dimensional probability density function transform is used to correct the raw simulated significant wave height (SWH), mean wave period (MWP), and mean wave direction (MWD). The annual and seasonal mean SWH are generally projected to decrease (-0.15 to -0.01 m) for 2021–2050 and 2071–2100 under the RCP2.6 and RCP8.5 scenarios, with statistical significance at a 0.1 level for most BYE in spring and for most of the Bohai Sea and Yellow Sea in annual and winter/autumn mean. There is a significant decrease in the spring MWP for two future periods under both the RCP2.6 and RCP8.5 scenarios. In contrast, the annual and summer/winter 99th percentile SWH are generally projected to increase for large parts of the study domain. Results imply that the projected changes in the mean and 99th percentile extreme waves are very likely related to projected changes in local mean and extreme surface wind speeds, respectively.

Sea surface salinity (SSS) extremes, characterized as events surpassing a certain threshold percentile, pose a threat to stenohaline organisms worldwide. This study presents the first global mapping of SSS extreme metrics and investigates their underlying drivers using daily reanalysis data. Our key findings have revealed that mesoscale eddies drive SSS extremes over most of the global ocean with peaks in duration ranging from 5 to 10 d and peaks in intensity ranging from 0.2 to 0.3 g kg ^−1 . Freshwater fluxes (FWFs) and mean currents are generally associated with the occurrence of prolonged and intense SSS extremes in tropical and extratropical oceans, respectively. FWFs related to interannual climate modes contribute to the asymmetric distribution of high and low SSS extremes in the central Pacific Ocean and the western Indian Ocean. These results highlight the distinct impacts of different local drivers on the mean states of SSS extremes.

Abstract Using high‐frequency tide gauge observations from 1980 to 2017, we analyzed changes in extreme sea levels (ESLs) along the Chinese coast to examine whether extreme sea‐level rise acceleration could be observed. The results indicated that the ESL has risen with fluctuations along the Chinese coast. The ESL also featured significant decadal variabilities. The ESL rise was accelerated at most tide gauges throughout the whole study period, especially after the 1980s, while the acceleration patterns varied with station location. The mean sea level (MSL) change was confirmed as the major driver of the ESL change according to the analysis. However, the skew surge may also have affected the extremes at many of the tide gauges, which was observed after detrending. The extreme/MSL had a significant positive correlation with the Atlantic Multidecadal Oscillation, which is supposed to affect the MSL through a combination of variation in sea surface temperature, pressure system, and wind. That sea level oscillation is dominated by Atlantic Multidecadal Oscillation should be taken into consideration when studying accelerations in the rate of ESLs and of MSLs along the Chinese coast.

Abstract Background Deafness, autosomal recessive 77 (DFNB77) is a rare non-syndromic hearing loss (NSHL) worldwide, which is caused by deleterious variants within lipoxygenase homology domains 1 (LOXHD1). Here we identified that a novel missense variant of LOXHD1 was associated with NSHL in a Chinese family under consanguineous marriage. Case presentation A 28-year-old woman suffered a bilateral profound NSHL. Impedance audiometry, temporal bone computerized tomography (TBCT) scans and magnetic resonance imaging-inner ear hydrography (MRI-IEH) did not find any obvious abnormality of middle or inner ear. Routine genetic detection did not find pathogenic variants in common HL-associated genes. Therefore, we performed a whole-exome sequencing (WES) in this family. By trio-WES, co-segregation validation and bioinformatics analysis, we revealed that a novel homozygous variant in this patient, LOXHD1: c.5948C > T (p.S1983F), might be the pathogenic factor. Her parents (heterozygotes) and brother (wild-type) were asymptomatic. Conclusions We successfully identified a novel variant of LOXHD1 associated with a rare NSHL from a Chinese family. Our finds highlight the effectiveness of trio-WES for molecular diagnosis of rare NHSL, and expand the genotypic spectrum of DFNB77.

The ability of forecasting systems to simulate tropical cyclones is still insufficient, and currently, there is an increased interest in improving model performance for intense tropical cyclones. In this study, the impact of reducing surface drag at high wind speeds on modeling wind and wave conditions during the super Typhoon Lingling event over the northwest Pacific Ocean in 2019 is investigated. The model response with respect to the parameterization for momentum exchange at the ocean surface is demonstrated using a fully coupled regional atmosphere model (the Consortium for Small-Scale Modeling-Climate Limited-area Modeling, CCLM) and a wind wave model (WAM). The active two-way coupling between the atmosphere and ocean waves model is enabled through the introduction of sea state-dependent surface drag into the CCLM and updated winds into the WAM. The momentum exchange with the sea surface is modeled via the dependency of the roughness length (Z0) on the surface stress itself and, when applicable, on the wind speed. Several high-resolution runs are performed using one-way or two-way fully coupled regional atmosphere-wave (CCLM-WAM) models. The model simulations are assessed against the best track data as well as against buoy and satellite observations. The results show that the spectral nudging technique can improve the model’s ability to capture the large-scale circulation, track and intensity of Typhoon Lingling at regional scales. Under the precondition of large-scale constraining, the two-way coupling simulation with the proposed new roughness parameterization performs much better than the simulations used in older studies in capturing the maximum wind speed of Typhoon Lingling due to the reduced drag at extreme wind conditions for the new Z0.

Accurately estimating the ocean’s interior structures using sea surface data is of vital importance for understanding the complexities of dynamic ocean processes. In this study, we proposed an advanced machine-learning method, the Light Gradient Boosting Machine (LightGBM)-based Deep Forest (LGB-DF) method, to estimate the ocean subsurface salinity structure (OSSS) in the South China Sea (SCS) by using sea surface data from multiple satellite observations. We selected sea surface salinity (SSS), sea surface temperature (SST), sea surface height (SSH), sea surface wind (SSW, decomposed into eastward wind speed (USSW) and northward wind speed (VSSW) components), and the geographical information (including longitude and latitude) as input data to estimate OSSS in the SCS. Argo data were used to train and validate the LGB-DF model. The model performance was evaluated using root mean square error (RMSE), normalized root mean square error (NRMSE), and determination coefficient (R2). The results showed that the LGB-DF model had a good performance and outperformed the traditional LightGBM model in the estimation of OSSS. The proposed LGB-DF model using sea surface data by SSS/SST/SSH and SSS/SST/SSH/SSW performed less satisfactorily than when considering the contribution of the wind speed and geographical information, indicating that these are important parameters for accurately estimating OSSS. The performance of the LGB-DF model was found to vary with season and water depth. Better estimation accuracy was obtained in winter and autumn, which was due to weaker stratification. This method provided important technical support for estimating the OSSS from satellite-derived sea surface data, which offers a novel insight into oceanic observations.

Reconstructing the vertical structures of the ocean from sea surface information is of great importance for ocean and climate studies. In this study, an ensemble machine learning (Ens-ML) model is proposed to retrieve ocean subsurface thermal structure (OSTS) by using satellite-derived sea surface data and Argo data in the South China Sea (SCS). The input data include sea surface height (SSH), sea surface temperature (SST), sea surface salinity (SSS), sea surface wind (SSW), and geographic information (including longitude and latitude). We select three stable machine learning models, namely, extreme gradient boosting (XGBoost), RandomForest and light gradient boosting machine (LightGBM) as our benchmark models, and then use an artificial neural network (ANN) technique to combine outputs from the three individual models. The proposed Ens-ML model using sea surface data only by SSH, SST, SSS, and SSW performs less satisfactorily than that considering the contribution of geographical information, indicating that the geographical information is essential to estimate the OSTS accurately. The estimated OSTS from the Ens-ML model are compared with Argo data. The results show that the proposed Ens-ML model can accurately estimate the OSTS (upper 1000 m) in the SCS, which is relatively more accurate and precise than the individual models. The performance of the Ens-ML model also varies with season, and better estimation is obtained in winter, which is probably due to stronger mixing and weaker stratification. This study shows the great potential and advantage of the multi-model ensemble of machine learning algorithm for the ocean’s interior information retrieving, showing great potential in expanding the scope of ocean observations.

Understanding the driving factors for precipitation extremes matters for adaptation and mitigation measures against the changing hydrometeorological hazards in Yangtze River basin, a habitable area that provides water resources for domestic, farming, and industrial needs. However, the region is naturally subject to major floods linked to monsoonal heavy precipitation during May–September. This study aims to quantify anthropogenic influences on the changing risk of 2-week-long precipitation extremes such as the July 2019 extreme cases, as well as events of shorter durations, over the middle and lower reaches of Yangtze River basin (MLYRB). Precipitation extremes with different durations ranging from 1-day to 14-days maximum precipitation accumulations are investigated. Gridded daily precipitations based on nearly 2,400 meteorological stations across China are used to define maximum accumulated precipitation extremes over the MLYRB in July during 1961–2019. Attribution analysis is conducted by using the Met Office HadGEM3-GA6 modeling system, which comprises two sets of 525-member ensembles for 2019. One is forced with observed sea-surface temperatures (SSTs), sea-ice and all forcings, and the other is forced with preindustrialized SSTs and natural forcings only. The risk ratio between the exceedance probabilities estimated from all-forcing and natural-forcing simulations is calculated to quantify the anthropogenic contribution to the changing risks of the July 2019–like precipitation extremes. The results reveal that anthropogenic warming has reduced the likelihood of 2019-like 14-days heavy precipitation over the mid–lower reaches of the Yangtze River by 20%, but increased that of 2-days extremes by 30%.

The impacts of El Niño on sea surface salinity (SSS) in the South China Sea (SCS) are investigated using satellite observations, in-situ data, and reanalysis products. Here, we show that positive SSS anomalies cover most of the SCS during the mature phase of El Niño. The physical processes controlling these positive SSS anomalies are different from region to region, and the differences are especially obvious between the northern and southern SCS. In the northern SCS, the positive SSS anomalies are primarily caused by horizontal advection in response to an enhanced Kuroshio intrusion through the Luzon Strait, while changes in surface freshwater fluxes act to reduce SSS. In the southern SCS, the positive SSS anomalies are largely due to reduced surface freshwater fluxes, with ocean dynamics playing a secondary role. An anomalous anticyclone associated with El Niño is mainly responsible for the reduction of surface freshwater fluxes in the southern SCS.

Background: Aberrant hypermethylation of the Septin 9 (SEPT9) is an early event in several human cancers, and increasing studies have reported good performance of methylated SEPT9 (mSEPT9) in cancer diagnosis. Recent studies further focused on its value in cancer prognosis, but results are not clearly elucidated.Methods: A comprehensive search to identify relevant studies about the association between mSEPT9 and cancer prognosis was conducted through the EMBASE, PubMed, and Web of Science databases (up to January 2019). The main outcomes were overall survival (OS) and disease-free survival (DFS). The hazard ratio (HR) and 95% confidence interval (CI) for OS and DFS were extracted from each included study and pooled using a random-effects model.Results: Ten eligible studies comprising 1,266 cancer patients were included. Results demonstrated that mSEPT9 was associated with poor OS (HR = 2.07, 95% CI = 1.40–3.06). Specially, mSEPT9 detected in preoperative plasma predicted worse OS in cancer patients (HR = 3.25, 95% CI = 1.93–5.48). In addition, we also identified a significant association of mSEPT9 with decreased DFS of cancer (HR = 3.24, 95% CI = 1.81–5.79).Conclusion: Our meta-analysis supports that mSEPT9 is associated with reduced OS and DFS in cancer patients. Moreover, detection of mSEPT9 using plasma appears to be a convenient and promising way to predict long-term survival of cancer patients.

This study performed several sensitivity experiments to investigate the impact of atmosphere−wave coupling on the simulated wind and waves over the East China Sea (ECS) with a focus on typhoon events. These experiments include stand-alone regional atmosphere model (CCLM) simulations, stand-alone spectral wave model (WAM) simulations driven by the regional atmospheric model CCLM or ERA5 reanalysis, and two-way (CCLM-WAM) coupled simulations. We assessed the simulated wind speed and significant wave height against in situ observations and remote sensing data and focused on typhoon events in 2010. We analyzed the differences between the experiments in capturing the surface pressure, wind speed, and roughness length. Both ERA5 reanalysis data and our regional model simulations demonstrate high quality in capturing wind and wave conditions over the ECS. The results show that downscaled simulations tend to be closer to in situ observations than ERA5 reanalysis data in capturing wind variability and probability distribution, dominant wind and wave directions, strong typhoon intensity and related extreme significant wave height. In comparison with satellite observations, the CCLM-WAM simulation outperforms the CCLM in reducing wind bias. The coupled and uncoupled simulations are very similar in terms of other wind and wave statistics. Though there is much improvement in capturing typhoon intensity to ERA5, regional downscaled simulations still underestimate the wind intensity of tropical cyclones.

Using hourly sea level data from 15 tide gauges along the Chinese coast and sea level data of three simulations of the Coupled Model Intercomparison Project Phase 5 (CMIP5), we assessed the changes and benefits of the extreme sea level of limiting warming to 1.5°C instead of 2.0°C. Observations show that the extreme sea level has risen with high confidence during the past decades along the coast of China, while the mean sea level change, especially the long-term change plays important roles in the changing process of extreme sea levels. Under the 1.5 and 2.0°C warming scenarios, the sea level will rise with fluctuations in the future, so will the return levels of the extreme sea levels. Compared with the 1.5°C warming condition, the return levels under the 2.0°C warming condition will rise significantly at all tide gauges along the Chinese coast. The results indicate that a 0.5°C warming will bring much difference to the extreme sea levels along the coast of China. It is of great necessity to limit anthropogenic warming to 1.5°C rather than 2.0°C, as proposed by the Paris Climate Agreement, which will greatly reduce the potential risks of future flood disasters along the coast of China and is beneficial for risk response management.

Using hourly sea level data from four tide gauges, the changes of the extreme sea level in the Bohai Sea were analyzed in this work. Three components (i.e., mean sea level, tide and surge) as well as the tide–surge interaction were studied to find which component was important in the changes of extreme sea levels. Significant increasing trends exist in the mean sea level at four tide gauges from 1980 to 2016, and the increase rate ranges from 0.2 to 0.5 cm/year. The mean high tide levels show positive trends at four tide gauges, and the increasing rate (0.1 to 0.3 cm/year) is not small compared with the long-term trends of the mean sea levels. However, the mean tidal ranges show negative trends at Longkou, Qinhuangdao and Tanggu, with the rate from about −0.7 to −0.2 cm/year. At Qinhuangdao and Tanggu, the annual surge intensity shows explicit long-term decreasing trend. At all four tide gauges, the storm surge intensity shows distinct inter-annual variability and decadal variability. All four tide gauges show significant tide–surge interaction, the characteristics of the tide–surge interaction differ due to their locations, and no clear long-term change was found. Convincing evidence implies that the extreme sea levels increase during the past decades from 1980 to 2016 at all tide gauges, with the increasing rate differing at different percentile levels. The extreme sea level changes in the Bohai Sea are highly affected by the changes of mean sea level and high tide level, especially the latter. The surge variation contributes to the changes of extreme sea level at locations where the tide–surge interaction is relatively weak.

We review the state of dynamical downscaling with scale-constrained regional and global models. The methodology, in particular spectral nudging, has become a routine and well-researched tool for hindcasting climatologies of sub-synoptic atmospheric disturbances in coastal regions. At present, the spectrum of applications is expanding to other phenomena, but also to ocean dynamics and to extended forecasting. Additionally, new diagnostic challenges are appearing such as spatial characteristics of small-scale phenomena such as Low Level Jets.

The western Pacific Ocean is the global center for marine biodiversity, with high vulnerability to climate change. A better understanding of the spatiotemporal characteristics and potential drivers of compound marine heatwaves (MHWs) and low-chlorophyll (LChl) extreme events is essential for the conservation and management of local marine organisms and ecosystems. Here, using daily satellite sea surface temperature and model-based chlorophyll concentration, we find that the climatological spatial distribution of MHW-LChl events in total days, duration, and intensity exhibits heterogeneous distributions. The southwest sections of the South China Sea (WSCS) and Indonesian Seas are the hotspots for compound events, with total MHW-LChl days that are more than 2.5 times higher than in the other sub-regions. Notably, there is a trend toward more frequent (> 4.2 d/decade), stronger (> 0.5), and longer-lasting (> 1.4 d/decade) MHW-LChl occurrences in the WSCS. The occurrence of compound MHW-LChl extremes exhibits remarkable seasonal differences, with the majority of these events transpiring during winter. Moreover, there are generally statistically significant increasing trends in MHW-LChl events for all properties on both seasonal and inter-annual timescales. Furthermore, we reveal that the total days of compound MHW-LChl extremes are strongly modulated by large-scale climate modes such as the El Niño-Southern Oscillation and Dipole Mode Index. Overall, pinpointing MHW-LChl hotspots and understanding their changes and drivers help vulnerable communities in better preparing for heightened and compounded risks to marine organism and ecosystems under climate change. [ABSTRACT FROM AUTHOR]