Sea Surface Research Articles

Performance of CMIP5 and CMIP6 models in reproducing the Interdecadal Pacific Oscillation and its global impacts

AbstractThis study assessed the capability of the historical simulations of phase 5 and 6 of the Coupled Model Intercomparison Project (CMIP5/6) in reproducing the temporal and spatial characteristics of the Interdecadal Pacific Oscillation (IPO) and its impact on global surface air temperature (SAT), surface equivalent potential temperature (Thetae_sfc) and precipitation. The IPO index time series simulated by CMIP5/6 models deviated from observations and struggled to capture the phase evolution characteristics of the IPO. Nevertheless, CMIP5/6 models successfully captured the horseshoe‐shaped sea surface temperature anomaly in the Pacific. Additionally, the CMIP5/6 models were able to simulate the IPO's 10–30‐year period. Notably, the simulated IPO index exhibited a statistically significant upward trend, which was absent in observations. Additionally, the IPO‐related global land SAT, Thetae_sfc and precipitation simulated by CMIP5/6 models performed differently in boreal winter and boreal summer. Furthermore, the IPO‐related global land SAT performed better in CMIP5/6 models during boreal winter than that in boreal summer. In CMIP6 models, it improved during both boreal winter and summer compared to CMIP5 models. In terms of the IPO‐related global land Thetae_sfc, CMIP5/6 models also performed better during boreal winter than in boreal summer. However, CMIP5 models outperformed CMIP6 models during the boreal summer. In terms of the IPO‐related global land precipitation, CMIP5/6 models performed better during boreal summer compared to boreal winter. Moreover, the IPO‐related global land precipitation in CMIP6 models improved significantly in boreal winter, but almost the same in boreal summer, compared to CMIP5 models. Further studies showed that the enhancements in simulating IPO's spatial pattern did not correspond to improvements in the model's ability to simulate IPO's global teleconnections.

A quantitative explanation for the large impacts of El Niño during its decaying stage

Abstract This study examines the comparative atmospheric circulation and tropical sea surface temperature (SST) relationships during the developing and decaying stages of El Niño from a meridional structure standpoint. Results indicate a transition in the variability of the first two modes of the Hadley Circulation (HC) during these stages, with the first mode exhibiting a larger explained variance in the decaying stage. The regime change in HC variability corresponds to underlying anomalous SST distributions, as confirmed by sensitive experiments. Quantitative assessment reveals the HC-SST response amplitudes are approximately two times stronger during the decaying stage compared to the developing stage. Employing the Kuo-Eliassen (KE) equation, diabatic heating anomalies during the decaying stage explain the difference in air-sea response intensity between the two stages. Diabatic heating variations are identified as the primary contributor to amplification or reduction of air-sea response intensity during the respective El Niño stages, providing insights into the different air-sea processes throughout the El Niño lifespan.

Research on the Inversion of Chlorophyll-a Concentration in the Hong Kong Coastal Area Based on Convolutional Neural Networks

Chlorophyll-a (Chl-a) concentration is a key indicator for assessing the eutrophication level in water bodies. However, accurately inverting Chl-a concentrations in optically complex coastal waters presents a significant challenge for traditional models. To address this, we employed Sentinel-2 MSI sensor data and leveraged the power of five machine learning models, including a convolutional neural network (CNN), to enhance the inversion process in the coastal waters near Hong Kong. The CNN model demonstrated superior performance with on-site data validation, outperforming the other four models (R2 = 0.810, RMSE = 1.165 μg/L, MRE = 35.578%). The CNN model was employed to estimate Chl-a concentrations from images captured over the study area in April and October 2022, resulting in the creation of a thematic map illustrating the spatial distribution of Chl-a levels. The map indicated high Chl-a concentrations in the northeast and southwest areas of Hong Kong Island and low Chl-a concentrations in the southeast facing the open sea. Analysis of patch size effects on CNN model accuracy indicated that 7 × 7 and 9 × 9 patches yielded the most optimal results across the tested sizes. Shapley additive explanations were employed to provide post-hoc interpretations for the best-performing CNN model, highlighting that features B6, B12, and B8 were the most important during the inversion process. This study can serve as a reference for developing machine learning models to invert water quality parameters.

Sea Level Variability Assessment along the African Coast

Studying changes in the sea level is essential for the sustainable development of coastal areas. The aim of this study was to analyse time series and investigate the regional variability of the sea surface and the effect of individual factors on its behaviour. In this study, we utilised the most recent satellite altimetry data (over a period of 29 years, i.e., from 1993 to 2022) and tide gauge observations (long-term time series) in order to estimate changes in the sea level. Unlike in previous studies, the rates of the sea level changes around the entire African coast were determined. In addition, time series of the changes in the sea surface temperature and salinity were used to determine the rate of changes arising from the thermal expansion of the ocean. The regional rate of the sea level rise between 1993 and 2022 deviated significantly from the global average, with values ranging from 2.48 to 5.44 mm/year (based on satellite altimetry data, depending on the location of the point).

HEAT BUDGET OF THE BARENTS SEA SURFACE IN WINTER

Variability of the total heat balance (HB) of the Barents Sea during the cold period of the year has been studied. The cold period is that of cooling of the sea surface when the heat flux is permanently oriented towards the atmosphere. The contribution of two major components of the HB, i. e. sensible and latent heat fluxes, to the observed increase of the total winter heat transfer at the sea-atmosphere interface has been estimated. Data on short-wave and long-wave radiation fluxes, and sensible and latent heat values were obtained from the atmos-pheric reanalysis of the European Center for Medium-term Weather Forecasting ERA5. HB of the sea surface was calculated as a sum of short-wave and long-wave radiation fluxes and those of sensible and latent heat. The total HB, as well as the total flux of sensible and latent heat for the cold period were calculated by summing up the corresponding values between the start and end dates of the cooling season. Calculations demonstrated an increase in the sum of HB over the cold season for the northern part of the Barents Sea (up to 2000 MJ/m2 over 40 years), and a decrease in the southern part of the sea (up to 1000 MJ/m2 over 40 years). In the northern part of the sea, the contribution of sensible and latent heat fluxes decreases to 0,3-0,4. The observed trend of sum of HB over the cold season and its turbulent components could with high probability be explained by increasing difference between air temperature and sea surface temperature.

Applications of the Fourier neural operator in a regional ocean modeling and prediction

In this paper, we apply the Fourier neural operator (FNO) paradigm to ocean circulation and prediction problems. We aim to show that the complicated non-linear dynamics of an ocean circulation can be captured by a flexible, efficient, and expressive structure of the FNO networks. The machine learning model (FNO3D and the recurrent FNO2D networks) trained by simulated data as well as real data takes spatiotemporal input and predicts future ocean states (sea surface current and sea surface height). For this, the double gyre ocean circulation model driven by stochastic wind stress is considered to represent an ideal ocean circulation. In order to generate the training and test data that exhibits rich spatiotemporal variability, the initial states are perturbed by Gaussian random fields. Experimental results confirm that the trained models yield satisfactory prediction accuracy for both types of FNO models in this case. Second, as the training set, we used the HYCOM reanalysis data in a regional ocean. FNO2D experiments demonstrated that the 5-day input to 5-day prediction yields the averaged root mean square errors (RMSEs) of 5.0 cm/s, 6.7 cm/s, 7.9 cm/s, 8.9 cm/s, and 9.4 cm/s in surface current, calculated consecutively for each day, in a regional ocean circulation of the East/Japan Sea. Similarly, the RMSEs for sea surface height were 2.3 cm, 3.5 cm, 4.2 cm, 4.6 cm, and 4.9 cm, for each day. We also trained the model with 15-day input and 10-day prediction, resulting in comparable performance. Extensive numerical tests show that, once learned, the resolution-free FNO model instantly forecasts the ocean states and can be used as an alternative fast solver in various inference algorithms.

Deep Learning-Based Detection of Oil Spills in Pakistan’s Exclusive Economic Zone from January 2017 to December 2023

Oil spillages on a sea’s or an ocean’s surface are a threat to marine and coastal ecosystems. They are mainly caused by ship accidents, illegal discharge of oil from ships during cleaning and oil seepage from natural reservoirs. Synthetic-Aperture Radar (SAR) has proved to be a useful tool for analyzing oil spills, because it operates in all-day, all-weather conditions. An oil spill can typically be seen as a dark stretch in SAR images and can often be detected through visual inspection. The major challenge is to differentiate oil spills from look-alikes, i.e., low-wind areas, algae blooms and grease ice, etc., that have a dark signature similar to that of an oil spill. It has been noted over time that oil spill events in Pakistan’s territorial waters often remain undetected until the oil reaches the coastal regions or it is located by concerned authorities during patrolling. A formal remote sensing-based operational framework for oil spills detection in Pakistan’s Exclusive Economic Zone (EEZ) in the Arabian Sea is urgently needed. In this paper, we report the use of an encoder–decoder-based convolutional neural network trained on an annotated dataset comprising selected oil spill events verified by the European Maritime Safety Agency (EMSA). The dataset encompasses multiple classes, viz., sea surface, oil spill, look-alikes, ships and land. We processed Sentinel-1 acquisitions over the EEZ from January 2017 to December 2023, and we thereby prepared a repository of SAR images for the aforementioned duration. This repository contained images that had been vetted by SAR experts, to trace and confirm oil spills. We tested the repository using the trained model, and, to our surprise, we detected 92 previously unreported oil spill events within those seven years. In 2020, our model detected 26 oil spills in the EEZ, which corresponds to the highest number of spills detected in a single year; whereas in 2023, our model detected 10 oil spill events. In terms of the total surface area covered by the spills, the worst year was 2021, with a cumulative 395 sq. km covered in oil or an oil-like substance. On the whole, these are alarming figures.

A Semi-Supervised Model for Fine-Grained Identification of Oil Emulsions on the Sea Surface Using Hyperspectral Imaging

A Semi-Supervised Model for Fine-Grained Identification of Oil Emulsions on the Sea Surface Using Hyperspectral Imaging

Sea ice melt pond bathymetry reconstructed from aerial photographs using photogrammetry: a new method applied to MOSAiC data

Abstract. Melt ponds are a core component of the summer sea ice system in the Arctic, increasing the uptake of solar energy and impacting the ice-associated ecosystem. They were thus one of the key topics during the 1-year drift campaign Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) in the Transpolar Drift 2019/2020. Pond depth is a dominating factor in describing the surface meltwater volume; it is necessary to estimate budgets and used in model parameterization to simulate pond coverage evolution. However, observational data on pond depth are spatially and temporally strongly limited to a few in situ measurements. Pond bathymetry, which is pond depth spatially fully resolved, remains unexplored. Here, we present a newly developed method to derive pond bathymetry from aerial images. We determine it from a photogrammetric multi-view reconstruction of the summer ice surface topography. Based on images recorded on dedicated grid flights and facilitated assumptions, we were able to obtain pond depth with a mean deviation of 3.5 cm compared to manual in situ observations. The method is independent of pond color and sky conditions, which is an advantage over recently developed radiometric airborne retrieval methods. It can furthermore be implemented in any typical photogrammetry workflow. We present the retrieval algorithm, including requirements for the data recording and survey planning, and a correction method for refraction at the air–pond interface. In addition, we show how the retrieved surface topography model synergizes with the initial image data to retrieve the water level of individual ponds from the visually determined pond margins. We use the method to give a profound overview of the pond coverage on the MOSAiC floe, on which we found unexpected steady pond coverage and volume. We were able to derive individual pond properties of more than 1600 ponds on the floe, including their size, bathymetry, volume, surface elevation above sea level, and temporal evolution. We present a scaling factor for single in situ depth measurements, discuss the representativeness of in situ pond measurements and the importance of such high-resolution data for new satellite retrievals, and show indications for non-rigid pond bottoms. The study points out the great potential to derive geometric properties of the summer sea ice surface emerging from the increasingly available visual image data recorded from uncrewed aerial vehicles (UAVs) or aircraft, allowing for an integrated understanding and improved formulation of the thermodynamic and hydrological pond system in models.

Description of regression models for predicting the dynamics of pink salmon returns in the Kamchatka region based on climate-oceanological and population-genetic data

Several regression models for predicting returns of pink salmon in the Kamchatka region are presented. The data for 1990–2023 were analyzed. Among available climatic and oceanological indices, the most suitable for using as predictors for forecasting of pink salmon returns were the Pacific Decadal Oscillation (PDO) index, Western Pacific Cyclonic Index (WP), Arctic Oscillation (AO) index, and the sea surface temperature anomaly in the North Pacific. Multi-dimensional models of the «stock–recruitment» type were built on identified statistical patterns, which allowed to estimate potential abundance of the pink salmon returns to northeastern and western Kamchatka. Besides, methods for predicting the abundance of pink salmon returns on the data of fish counting in the sea are considered, using the materials of TINRO trawl surveys conducted in the Bering and Okhotsk Seas in the fall seasons of 2012–2023. To determine the abundance of pink salmon originated from West Kamchatka, genetic identification of regional composition of juveniles in mixed trawl catches was used. All tested methods have a high level of determination, but simpler regressive models are more prospective for practical forecasting of general trend in dynamics of pink salmon stocks in the Kamchatka region due to very weak generalization ability of more complicated models.

Homogeneity Detection and Adjustment of Sea Surface Salinity along the Coast of the Northern South China Sea

In this study, we applied the penalized maximum F test (PMF) method in the Relative Homogenization test V4 (RHtestV4) package without reference series to improve the reliability of monthly mean long-term sea surface salinity (SSS) data. The data were obtained from six coastal hydrological stations along the coast of the northern SCS, spanning from January 1960 to December 2018. Based on the detailed metadata, taking the influence of regional climate change factors into full account, the inhomogeneity of these SSS data was detected and adjusted. The findings indicate that all six coastal hydrological stations exhibited breakpoints, and among them, 22 breakpoints were identified in total, which were the causes of inhomogeneity in the monthly SSS time series. The primary factors contributing to these breakpoints were human-related and, specifically, related to changes in instruments. The average adjustment of monthly quantile matching (QM) of the salinity series ranged from around −4.25 to 3.33‰. The quality of the adjusted annual mean SSS time series was greatly improved. Notably, the annual mean SSS of the NZU and ZPO coastal hydrological stations in Guangdong Province exhibited a significant downward trend, indicating a trend of seawater freshening. Conversely, the WZU, BHI, HKO and QLN coastal hydrological stations in the Guangxi and Hainan coastal areas displayed an upward trend in SSS. This study fills the gap in current research on inhomogeneity detection and adjustment of SSS along the coast of the northern SCS. It also provides reliable and accurate first-hand information for research on climate change and marine science along the coast of the northern SCS.

Ocean Complexity Shapes Sea Surface Temperature Variability in a CESM2 Coupled Model Hierarchy

Abstract To improve understanding of ocean processes impacting monthly sea surface temperature (SST) variability, we analyze a Community Earth System Model version 2 hierarchy in which models vary only in their degree of ocean complexity. The most realistic ocean is a dynamical ocean model, as part of a fully coupled model (FCM). The next most realistic ocean, from a mechanically decoupled model (MDM), is like the FCM but excludes anomalous wind stress-driven ocean variability. The simplest ocean is a slab ocean model (SOM). Inclusion of a buoyancy coupled dynamic ocean as in the MDM, which includes temperature advection and vertical mixing absent in the SOM, leads to dampening of SST variance everywhere and reduced persistence of SST anomalies in the high latitudes and equatorial Pacific compared to the SOM. Inclusion of anomalous wind stress-driven ocean dynamics as in the FCM leads to higher SST variance and longer persistence timescales in most regions compared to the MDM. The net role of the dynamic ocean, as an overall dampener or amplifier of anomalous SST variance and persistence is regionally dependent. Notably, we find that efforts to reduce the complexity of the ocean models in the SOM and MDM configurations result in changes in the magnitude of the thermodynamic forcing of SST variability compared to the FCM. These changes, in part, stem from differences in the seasonally varying mixed layer depth and should be considered when attempting to quantify the relative contribution of certain ocean mechanisms to differences in SST variability between the models.

Symbiont community dynamics in the turbid reef specialist, Turbinaria reniformis, along a latitudinal and environmental gradient in Western Australia

AbstractThermal stress triggers the breakdown of the obligate symbiosis between the cnidarian coral host and its autotrophic dinoflagellates of the family Symbiodiniaceae. This diverse family exhibits pronounced functional variation that has large implications for the survival of their coral host. In this study, we explored patterns of symbiont community composition and diversity in the coral Turbinaria reniformis, a turbid reef specialist, along a latitudinal and environmental gradient in Western Australia. Using metabarcoding of the internal transcribed spacer region 2, we explored symbiont community patterns, their environmental drivers, and potential associations with host genetic structure. Our findings reveal a predominance of Cladocopium across our study area, with distinct regional composition influenced primarily by sea surface temperature. Geographical distance and host genetic data did not align with symbiont community divergence, suggesting a complex interplay of environmental and genetic factors t shaping the community structure. This study underscores Cladocopium stability in Western Australia across large distances and strong environmental gradients. It also highlights the highly diversified lineage community that may explain T. reniformis ability to thrive in a wide range of environmental conditions.

Impact of Summer North Atlantic Sea Surface Temperature Tripole on Precipitation over Mid–high-latitude Eurasia

Abstract Eurasia is a sensitive and high-risk region for global climate changes, where climate anomalies significantly influence natural ecosystems, human health, and economic development. The North Atlantic tripole (NAT) sea surface temperature anomaly is crucial to interannual precipitation variations in Eurasia. Several studies have focused on the link between the NAT and climate anomalies in winter and spring. However, the mechanism by which the summer NAT impacts climate anomalies in Eurasia remains unclear. This study examines how the NAT impacts interannual variations of summer precipitation in mid-high-latitude Eurasia. Precipitation variations are associated with the atmospheric teleconnection triggered by the NAT. When the NAT is in its positive phase, the anomalous atmospheric diabatic heating over the North Atlantic excites an equivalent-barotropic Rossby wave train response that propagates eastward toward the Eurasia, resulting in atmospheric circulation anomalies over the region. The combined effects of atmospheric circulation, radiative forcing, and water vapor transport anomalies lead to decreased precipitation across Northern Europe and central Eurasia, with higher precipitation anomalies over the Northeast Asia. Finally, numerical experiments verify that the summer NAT excites atmospheric teleconnections that propagate downstream, affecting precipitation anomalies in mid-high-latitude Eurasia. This study provides a scientific basis for predicting Eurasian summer precipitation and strengthening disaster management strategies.

Potential Near‐Term Wetting of the Southwestern United States if the Eastern and Central Pacific Cooling Trend Reverses

AbstractNear‐term projections of drought in the southwestern United States (SWUS) are uncertain. The observed decrease in SWUS precipitation since the 1980s and heightened drought conditions since the 2000s have been linked to a cooling sea surface temperature (SST) trend in the Equatorial Pacific. Notably, climate models fail to reproduce these observed SST trends, and they may continue doing so in the future. Here, we assess the sensitivity of SWUS precipitation projections to future SST trends using a Green's function approach. Our findings reveal that a slight redistribution of SST leads to a wetting or drying of the SWUS. A reversal of the observed cooling trend in the Central and East Pacific over the next few decades would lead to a period of wetting in the SWUS. It is critical to consider the impact of possible SST pattern trends on SWUS precipitation trends until we fully trust SST evolution in climate models.

Investigating the spatial propagation patterns of meteorological drought events and underlying mechanisms using complex network theory: A case study of the Yangtze River Basin, China

AbstractThe spatial propagation patterns of meteorological drought events (MDEs) and underlying mechanisms contribute to elucidating and forecasting drought evolution. In this study, gridded MDEs in the Yangtze River Basin (YRB) throughout the entire year, wet season and dry season were extracted from 3-month Standardized Precipitation Evapotranspiration Index (SPEI-3) series. Event synchronization (ES) and complex networks (CN) were employed to construct the MDE synchronization networks and MDE spatial propagation networks for various periods. The former were utilized to identify MDE synchronized subregions where MDEs co-occur and co-evolve in the YRB, while the latter were used to quantify the MDE spatial propagation patterns over both the basin and its subregions. The driving mechanisms behind MDE spatial propagation were further investigated by diagnosing the concomitant drought-inducing climate systems. The findings reveal the presence of four MDE synchronized subregions during the wet season and five subregions during the entire year and dry season. These subregions exhibited distinct spatial propagation patterns of MDEs, aligning with overall findings across the YRB. Notable differences were observed between wet and dry seasons, with various subregions exhibiting distinctive spatial propagation patterns during each season. These patterns are driven by variations in the controlling atmospheric circulation systems, leading to anomalies of wind patterns and moisture distribution, ultimately resulting in deficient moisture supply. The variations of tropical sea surface thermal conditions, influences of the Tibetan Plateau and MDE self-propagation triggered by land–atmosphere feedback are considered as three primary influencing factors for MDE spatial propagation in the YRB.

Phytoplankton Assemblage over a 14-Year Period in the Adriatic Sea: Patterns and Trends

Considering the role of phytoplankton in the functioning and health of marine systems, it is important to characterize its responses to a changing environment. The central Adriatic Sea, as a generally oligotrophic area, is a suitable environment to distinguish between regular fluctuations in phytoplankton and those caused by anthropogenic or climatic influences. This study provides a long-term perspective of phytoplankton assemblage in the central eastern Adriatic Sea, with 14 years of continuous time series data collected at two coastal and two offshore stations. The predominant phytoplankton groups were diatoms and phytoflagellates, but their proportion varied depending on the vicinity of the coast, as evidenced also by the distribution of chlorophyll a. In the coastal environment, the phytoplankton biomass was substantially higher, with a higher proportion of microphytoplankton, while small phytoplankton accounted for the majority of biomass in the offshore area. In addition, a decreasing trend in diatom abundance was observed in the coastal waters, while such trend was not so evident in the offshore area. Using a neural gas algorithm, five clusters were defined based on the contribution of the major groups. The observed increase in diversity, especially in dinoflagellates, which outnumber diatom taxa, could be a possible adaptation of dinoflagellates to the increased natural solar radiation in summer and the increased sea surface temperature.

Characteristics of Marine Heatwaves in the Southeastern Baltic Sea Based on Long-Term In Situ and Satellite Observations

Marine heatwaves (MHWs) are known to pose a threat to aquatic ecosystems and coastal communities and, as a result, they receive significant attention nowadays, thus motivating our scientific interest in better understanding the regional patterns of these events. In this study, we analyze MHWs in the SE part of the Baltic Sea, defining them as anomalously warm water events, where the water temperature exceeds the 90th percentile threshold of the corresponding calendar day for at least five or more consecutive days. Our study is based on a combination of long-term (1993–2023) in situ data, field measurements, and satellite-derived sea surface temperature data during the warm (May–August) period. Study results suggest that although short-lived (5–9 days) MHW events typically dominate throughout the analyzed period, the occurrences of longer (more than three weeks) and more intense MHWs have increased in our study region in the recent decade. The heatwaves are observed both in coastal and open waters, with SST anomalies up to around 5–6 °C above the 90th percentile threshold during extreme events, extending thermal influence as deep as 20 m during prolonged and more intense events. We believe that the results of this study contribute to a better understanding of MHW patterns in the study region, which is important from an ecological and socio-economic point of view, providing valuable insights for human health aspects as well.

Underwater acoustic propagation and scattering from a dynamic rough sea surface using the Finite-Difference Time-Domain method.

Models for underwater acoustic propagation typically assume that the sea surface is smooth or rough but frozen in time. Long-duration transmissions on the order of tens of seconds are being considered for next-generation SONAR. These types of signals improve target resolution and tracking. However, they can interact with the sea surface at many different wave displacements during transmission. This violates the "frozen" boundary assumption and causes additional transmission losses and Doppler effects on the received signal. Full-wave propagation models can be used to better understand the mechanisms behind these phenomena. This understanding leads to better system design without having to perform expensive at-sea experiments. In this paper, a finite-difference time-domain (FDTD) method is implemented to model the impact of roughness and motion on the sea surface. The FDTD method is a full-wave numeric technique that allows an arbitrary function to define the boundary points. Surface motion is accomplished by modifying these boundary points at each time step. A variable subgrid sea surface boundary technique is developed to improve the accuracy of these FDTD simulations. The rough, time-evolving sea surface is modeled using a Pierson-Moskowitz frequency spectrum, which is simple to implement and fully defined by wind speed and direction.

Intensive Radiosonde Observations of Environmental Conditions on the Development of a Mesoscale Convective System in the Baiu Frontal Zone

AbstractMesoscale convective systems (MCSs) that occur in the Baiu frontal zone (BFZ) can cause devastating flash floods during early summer in Japan; however, the environmental conditions necessary for their development require further investigation. High‐frequency atmospheric soundings, conducted using multiple marine vessels in the East China Sea on 19 June 2022, captured the detailed environmental conditions pertaining to the development of an MCS within the BFZ. The MCS, which developed rapidly without any remarkable preceding synoptic or mesoscale disturbance in the mid‐ or upper troposphere, caused intense precipitation exceeding 80 mm/hr. The MCS persisted for approximately 6 hr, and it intensified when the influx of nearly saturated air near the sea surface toward a weak surface front overlapped with the influx of free‐tropospheric moist air. The influx of nearly saturated air near the sea surface ensured conditional instability within the lower troposphere. The influx of moist air in the free troposphere contributed to the near‐saturation conditions above the boundary layer, a feature inherent to the BFZ, and played an important role in minimizing the reduction in the buoyancy of air parcels. The results of this study indicate that a better forecast of the horizontal distribution of free tropospheric moist air is beneficial for limiting the potential area of genesis of MCS in the BFZ, and a more comprehensive understanding of the vertical variations in moisture transport contributes to an improved forecast skill for MCS in the BFZ.