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In the past decade (2010–2019), the annual maximum typhoon storm surge (AMTSS) accounted for 46.6% of the total direct economic loss caused by marine disasters in Chinese mainland, but its prediction in advance is challenging. By analyzing records of 23 tide-gauge stations, we found that the AMTSSs in Shanghai, Zhejiang and Fujian show significant positive correlations with the El Niño-Southern Oscillation (ENSO). For the 1987–2016 period, the maximum correlation is achieved at Pingtan station, where correlation coefficient between the AMTSS and Niño-3.4 is 0.55. The AMTSS occurring in El Niño years are stronger than those in non-El Niño years by 9–35 cm in these areas. Further analysis suggests that a developing El Niño can greatly modulate the behaviors of Northwest Pacific typhoons. Strong typhoons tend to make landfall in southeast China with stronger intensities and northward shifted landfall positions. This study indicates that the modulation effect by ENSO may provide potential predictability for the AMTSS, which is useful for the early alert and reduction of storm surge damages.

The global physical and biogeochemical environment has been substantially altered in response to increased atmospheric greenhouse gases from human activities. In 2023, the sea surface temperature (SST) and upper 2000 m ocean heat content (OHC) reached record highs. The 0–2000 m OHC in 2023 exceeded that of 2022 by 15 ± 10 ZJ (1 Zetta Joules = 10 21 Joules) (updated IAP/CAS data); 9 ± 5 ZJ (NCEI/NOAA data). The Tropical Atlantic Ocean, the Mediterranean Sea, and southern oceans recorded their highest OHC observed since the 1950s. Associated with the onset of a strong El Niño, the global SST reached its record high in 2023 with an annual mean of ∼0.23°C higher than 2022 and an astounding > 0.3°C above 2022 values for the second half of 2023. The density stratification and spatial temperature inhomogeneity indexes reached their highest values in 2023.

Changes in ocean heat content (OHC), salinity, and stratification provide critical indicators for changes in Earth’s energy and water cycles. These cycles have been profoundly altered due to the emission of greenhouse gasses and other anthropogenic substances by human activities, driving pervasive changes in Earth’s climate system. In 2022, the world’s oceans, as given by OHC, were again the hottest in the historical record and exceeded the previous 2021 record maximum. According to IAP/CAS data, the 0–2000 m OHC in 2022 exceeded that of 2021 by 10.9 ± 8.3 ZJ (1 Zetta Joules = 10 21 Joules); and according to NCEI/NOAA data, by 9.1 ± 8.7 ZJ. Among seven regions, four basins (the North Pacific, North Atlantic, the Mediterranean Sea, and southern oceans) recorded their highest OHC since the 1950s. The salinity-contrast index, a quantification of the “salty gets saltier—fresh gets fresher” pattern, also reached its highest level on record in 2022, implying continued amplification of the global hydrological cycle. Regional OHC and salinity changes in 2022 were dominated by a strong La Niña event. Global upper-ocean stratification continued its increasing trend and was among the top seven in 2022.

This paper documents development of a multiple‐Graphics Processing Unit (GPU) version of FUNWAVE‐Total Variation Diminishing (TVD), an open‐source model for solving the fully nonlinear Boussinesq wave equations using a high‐order TVD solver. The numerical schemes of FUNWAVE‐TVD, including Cartesian and spherical coordinates, are rewritten using CUDA Fortran, with inter‐GPU communication facilitated by the Message Passing Interface. Since FUNWAVE‐TVD involves the discretization of high‐order dispersive derivatives, the on‐chip shared memory is utilized to reduce global memory access. To further optimize performance, the batched tridiagonal solver is scheduled simultaneously in multiple‐GPU streams, which can reduce the GPU execution time by 20–30%. The GPU version is validated through a benchmark test for wave runup on a complex shoreline geometry, as well as a basin‐scale tsunami simulation of the 2011 Tohoku‐oki event. Efficiency evaluation shows that, in comparison with the CPU version running at a 36‐core HPC node, speedup ratios of 4–7 and above 10 can be observed for single‐ and double‐GPU runs, respectively. The performance metrics of multiple‐GPU implementation needs to be further evaluated when appropriate. Plain Language Summary Numerical modeling of surface wave dynamics is necessary for coastal infrastructure design. FUNWAVE‐Total Variation Diminishing is a widely accepted open‐source wave model for simulating surface wave propagation and wave‐driven processes in the nearshore region, as well as tsunami wave propagation at oceanic scales. Due to the complexity of governing equations and corresponding numerical methods, the modeling of wave dynamics usually depends on the use of High Performance Clusters, which are both expensive and power consuming. To address this problem, Graphics Processing Unit (GPU)‐accelerated computing is introduced in the FUNWAVE‐Total Variation Diminishing for wave dynamics modeling in this study. GPUs were originally used for image processing and visualization purpose in personal computers. Because GPUs have thousands of “Cores” that can implement arithmetic computations simultaneously, they are now widely employed to facilitate computing‐intensive tasks such as deep learning and engineering computations. We find that by porting wave model to GPU devices, the modeling of surface wave dynamics over a large domain can be achieved by an affordable stand‐alone PC with GPU cards installed. Key Points The fully nonlinear Boussinesq wave model FUNWAVE‐TVD is ported to multiple‐GPU for acceleration The GPU version is ideal for solving wave problems over large computational domains in a stand‐alone machine

I/O is often a performance bottleneck in global ocean circulation models with fine spatial resolution. In this paper, we present an asynchronous parallel I/O framework and demonstrate its efficacy in the Mass Conservation Ocean Model (MaCOM) as a case study. By largely reducing I/O operations in computing processes and overlapping output in I/O processes with computation in computing processes, this framework significantly improves the performance of the MaCOM. Through both reordering output data for maintaining data continuity and combining file access for reducing file operations, the I/O optimizing algorithms are provided to improve output bandwidth. In the case study of the MaCOM, the cost of output in I/O processes can be overlapped by up to 99% with computation in computing processes as decreasing output frequency. The 1D data output bandwidth with these I/O optimizing algorithms is 3.1 times faster than before optimization at 16 I/O worker processes. Compared to the synchronous parallel I/O framework, the overall performance of MaCOM is improved by 38.8% at 1024 computing processes for a 7-day global ocean forecast with 1 output every 2 h through the asynchronous parallel I/O framework presented in this paper.

Storm surge is often the marine disaster that poses the greatest threat to life and property in coastal areas. Accurate and timely issuance of storm surge warnings to take appropriate countermeasures is an important means to reduce storm surge-related losses. Storm surge numerical models are important for storm surge forecasting. To further improve the performance of the storm surge forecast models, we developed a numerical storm surge forecast model based on an unstructured spherical centroidal Voronoi tessellation (SCVT) grid. The model is based on shallow water equations in vector-invariant form, and is discretized by Arakawa C grid. The SCVT grid can not only better describe the coastline information but also avoid rigid transitions, and it has a better global consistency by generating high-resolution grids in the key areas through transition refinement. In addition, the simulation speed of the model is accelerated by using the openACC-based GPU acceleration technology to meet the timeliness requirements of operational ensemble forecast. It only takes 37 s to simulate a day in the coastal waters of China. The newly developed storm surge model was applied to simulate typhoon-induced storm surges in the coastal waters of China. The hindcast experiments on the selected representative typhoon-induced storm surge processes indicate that the model can reasonably simulate the distribution characteristics of storm surges. The simulated maximum storm surges and their occurrence times are consistent with the observed data at the representative tide gauge stations, and the mean absolute errors are 3.5 cm and 0.6 h respectively, showing high accuracy and application prospects.

This paper presents an efficient algorithm for generating a spherical multiple-cell (SMC) grid. The algorithm adopts a recursive loop structure and provides two refinement methods: (1) an arbitrary area refinement method and (2) a nearshore refinement method. Numerical experiments are carried out, and the results show that compared with the existing grid generation algorithm, this algorithm is more flexible and operable.

In order to realize the signal detection under the condition of lower SNR, this paper introduced the adaptive phase length based on the Duffing chaotic system and verified the measured signal at the optimal excitation frequency. The existence of the target signal was judged by observing whether there are two consecutive intermittent chaos in the time domain. Then the envelope of the intermittent chaos was obtained by Hilbert transform. Finally, the exact value of envelope spectrum was obtained by using the one-and-half-dimension spectrum, which can calculate the precise value of the frequency of the signal to be measured. The experimental results showed that the proposed algorithm can achieve a lower SNR than the conventional detection. Compared with the general chaotic detection, this algorithm can realize smart self-adaptation. It is unnecessary to specify different excitation frequencies and chaotic thresholds for different frequencies to be measured. In addition to the existence of the target signal judgment, the algorithm can also achieve accurate calculation of the frequency of the signal to be measured.

The systematic discrepancies in both tsunami arrival time and leading negative phase (LNP) were identified for the recent transoceanic tsunami on 16 September 2015 in Illapel, Chile by examining the wave characteristics from the tsunami records at 21 Deep-ocean Assessment and Reporting of Tsunami (DART) sites and 29 coastal tide gauge stations. The results revealed systematic travel time delay of as much as 22 min (approximately 1.7% of the total travel time) relative to the simulated long waves from the 2015 Chilean tsunami. The delay discrepancy was found to increase with travel time. It was difficult to identify the LNP from the near-shore observation system due to the strong background noise, but the initial negative phase feature became more obvious as the tsunami propagated away from the source area in the deep ocean. We determined that the LNP for the Chilean tsunami had an average duration of 33 min, which was close to the dominant period of the tsunami source. Most of the amplitude ratios to the first elevation phase were approximately 40%, with the largest equivalent to the first positive phase amplitude. We performed numerical analyses by applying the corrected long wave model, which accounted for the effects of seawater density stratification due to compressibility, self-attraction and loading (SAL) of the earth, and wave dispersion compared with observed tsunami waveforms. We attempted to accurately calculate the arrival time and LNP, and to understand how much of a role the physical mechanism played in the discrepancies for the moderate transoceanic tsunami event. The mainly focus of the study is to quantitatively evaluate the contribution of each secondary physical effect to the systematic discrepancies using the corrected shallow water model. Taking all of these effects into consideration, our results demonstrated good agreement between the observed and simulated waveforms. We can conclude that the corrected shallow water model can reduce the tsunami propagation speed and reproduce the LNP, which is observed for tsunamis that have propagated over long distances frequently. The travel time delay between the observed and corrected simulated waveforms is reduced to <8 min and the amplitude discrepancy between them was also markedly diminished. The incorporated effects amounted to approximately 78% of the travel time delay correction, with seawater density stratification, SAL, and Boussinesq dispersion contributing approximately 39%, 21%, and 18%, respectively. The simulated results showed that the elastic loading and Boussinesq dispersion not only affected travel time but also changed the simulated waveforms for this event. In contrast, the seawater stratification only reduced the tsunami speed, whereas the earth’s elasticity loading was responsible for LNP due to the depression of the seafloor surrounding additional tsunami loading at far-field stations. This study revealed that the traditional shallow water model has inherent defects in estimating tsunami arrival, and the leading negative phase of a tsunami is a typical recognizable feature of a moderately strong transoceanic tsunami. These results also support previous theory and can help to explain the observed discrepancies.