Sea Surface Research Articles

The Impact of Interactive Ocean Dynamics on Atlantic Sea Surface Temperature Variability

Abstract The impact of interactive ocean dynamics on internal variations of Atlantic sea surface temperature (SST) is investigated by comparing preindustrial control simulations of a fully coupled atmosphere–ocean–ice model to the same atmosphere–ice model with the ocean replaced by a motionless slab layer (henceforth slab ocean model). Differences in SST variability between the two models are diagnosed by an optimization technique that finds components whose variance differs as much as possible. This technique reveals that Atlantic SST variability differs significantly between the two models. The two components with the most extreme enhancement of SST variance in the slab ocean model resemble the tripole SST pattern associated with the North Atlantic Oscillation (NAO) and the Atlantic multidecadal variability (AMV) pattern. This result supports previous claims that ocean dynamics are not necessary for the AMV, although ocean dynamics lead to slight increases in the memory of both the AMV and the NAO tripole. The component with the most extreme enhancement of SST variance in the fully coupled model resembles the Atlantic Niño pattern, confirming the ability of our technique to isolate physical modes known to require ocean dynamics. The second component with more variance in the fully coupled model is a mode of subpolar SST variability. Both the reemergence of SST anomalies and changes in ocean heat transport lead to increased SST variance and memory in the subpolar Atlantic. Despite large differences in the mean and variability of SST, atmospheric variability is quite similar between the two models, confirming that most atmospheric variability is generated by internal atmospheric dynamics.

Behavior, site use and demographics of shortfin devil rays, Mobula kuhlii, at a newly-discovered cleaning area in South Africa

Many shark and ray species show affinity to specific sites, making these areas critical for their survival. These include cleaning stations: locations on reefs where cleaner fish remove parasites and clean wounds, which is important for maintaining health. Cleaning stations also function as social gathering sites, or resting points, where courtship and mating can occur. In this study, we identify an aggregation site for the shortfin devil ray, Mobula kuhlii (Family Mobulidae) within the Aliwal Shoal Marine Protected Area in KwaZulu-Natal (KZN), South Africa, and document their behavior. Remote underwater video was used to collect footage of M. kuhlii being cleaned by blue streak cleaner wrasse, Labroides dimidiatus. Generalized additive models (GAMs) were used to assess environmental predictors of M. kuhlii presence on Aliwal Shoal. Mixed models were used to assess the same environmental predictors and their correlation with mean M. kuhlii cleaning duration and number of L. dimidiatus bites per second at the identified cleaning station site. M. kuhlii were present in 56% of observation days, with group numbers up to > 150 individuals. Sea surface temperature was a significant predictor for M. kuhlii presence, while a north to south current was significantly associated with longer mean cleaning durations. These results support findings of mobulid studies in KZN that show increased habitat use during summer temperatures (24–25 °C) and suggest these sites to be important for individual health and social interaction. We hope these findings can be used for development of location-specific management plans to safeguard this Endangered species.

Study on the Typical Environmental Factors in the Middle Part of Zhoushan Fishery Based on HY-1C/D and Other Multi-Source Data

This study utilizes satellite data, including HY-1C/D, along with reanalysis data, to unveil the typical environmental characteristics of the sea surface in the middle of Zhoushan fishery. The article addresses three main issues. The first one is the development of an ocean primary productivity (OPP) inversion algorithm model. The second one is the study of chlorophyll-α (Chl-α) concentration and OPP distribution characteristics in Zhoushan fishery using China’s domestically produced ocean satellite HY-1C/D CZI data. The last one is the revelation of the characteristics of typical environmental factors on the sea surface at Zhoushan fishery by combining HY-1C/D with multi-source data. The results show the following: (1) The middle part of Zhoushan fishery exhibits significant seasonal and regional variations in Chl-α concentration and OPP. Chl-α concentration ranges mainly between 0.2 and 2.9 µg/L, with higher concentrations in spring and summer and lower Chl-α concentrations in autumn and winter. Spatially, Chl-α concentration gradually decreases from west to east. The OPP in the study area ranges from 100 mg·m−2d−1 to 1000 mg·m−2d−1, with high OPP values distributed on the western side, ranging from 400 mg·m−2d−1 to 1000 mg·m−2d−1, and gradually decreasing seaward. The highest OPP occurs in summer and the lowest in winter. (2) The correlation analysis between Chl-α concentration and OPP revealed a strong positive relationship. Consequently, this study developed an empirical model for estimating OPP based on Chl-α concentration and validated its feasibility. The model applies to areas with Chl-α concentrations ranging from 0.2 to 4 µg/L. (3) The convergence of freshwater injection, multiple ocean currents, and seasonal upwelling in the study area brings about a rich supply of nutrients. Additionally, the region is characterized by suitable conditions, including optimal Chl-α concentrations, OPP, SST, salinity, currents, and geological water depths. The synergistic effect of these factors together contributed to the formation of Zhoushan fishery.

Leveraging Transfer Learning and U-Nets Method for Improved Gap Filling in Himawari Sea Surface Temperature Data Adjacent to Taiwan

Satellite sea surface temperature (SST) images are valuable for various oceanic applications, including climate monitoring, ocean modeling, and marine ecology. However, cloud cover often obscures SST signals, creating gaps in the data that reduce resolution and hinder spatiotemporal analysis, particularly in the waters near Taiwan. Thus, gap-filling methods are crucial for reconstructing missing SST values to provide continuous and consistent data. This study introduces a gap-filling approach using the Double U-Net, a deep neural network model, pretrained on a diverse dataset of Level-4 SST images. These gap-free products are generated by blending satellite observations with numerical models and in situ measurements. The Double U-Net model excels in capturing SST dynamics and detailed spatial patterns, offering sharper representations of ocean current-induced SST patterns than the interpolated outputs of Data Interpolating Empirical Orthogonal Functions (DINEOFs). Comparative analysis with buoy observations shows the Double U-Net model’s enhanced accuracy, with better correlation results and lower error values across most study areas. By analyzing SST at five key locations near Taiwan, the research highlights the Double U-Net’s potential for high-resolution SST reconstruction, thus enhancing our understanding of ocean temperature dynamics. Based on this method, we can combine more high-resolution satellite data in the future to improve the data-filling model and apply it to marine geographic information science.

Possibility and quality assessment in seafloor modeling relative to the sea surface using hybrid data

Possibility and quality assessment in seafloor modeling relative to the sea surface using hybrid data

Rainband‐Occurrence Probability in Northern Hemisphere Tropical Cyclones by Synthetic Aperture Radar Imagery

AbstractRainbands are essential to tropical cyclones (TCs), significantly affecting TC structure and intensity change. High‐resolution synthetic aperture radar (SAR) imagery can capture the footprints of rainbands caused by rain‐induced sea surface roughness modification. Using 464 SAR TC images, we investigated the rainband‐occurrence probability of TCs under different hemispheres, local times (LTs), intensities, and ocean basins. Results show that the rainband‐occurrence probability is highest in the downshear‐left quadrant for Northern Hemisphere TCs (downshear‐right quadrant for Southern Hemisphere TCs). For Northern Hemisphere TCs, the rainband‐occurrence probability is overall higher in the early morning (LT), and the peak region of rainband‐occurrence probability appears farther from the TC center in the evening (LT). Compared with weak TCs, the rainband‐occurrence probability becomes higher for strong TCs in the Northern Hemisphere. Furthermore, TCs have a higher rainband‐occurrence probability in the Northwest Pacific than in the North Atlantic and Northeast Pacific.

Emergent constraints on the future East Asian winter surface air temperature changes

Abstract In East Asia, the climate variability in boreal winter is dominated by the East Asian winter monsoon, one of the most energetic monsoon systems that can lead to disasters. The key variable, the East Asian winter surface air temperature (SAT), has significantly changed over the past century and has substantially impacted agriculture, ecosystems, economics, and public health. However, its projections are limited by considerable uncertainties. Here, we identify the first leading mode that explains almost 32.1% of the inter-model spread in future SAT change. Our research delves into the evolution of present-day biases under future scenarios and their consequential impact on the SAT. Models with stronger western currents in the North Pacific exhibit a warmer North Pacific at mid-latitudes during historical periods. Additionally, these models consistently demonstrate stronger western currents in the future, contributing to the amplified warming of the western North Pacific, thereby warming Eurasia via the weakened trough and subtropical jet through barotropic responses to the warm North Pacific. Incorporating observational sea surface temperature (SST) constraints reduces uncertainties by 15.6%, revealing a more reliable SAT change pattern by the end of the 21st century.

Estuary: A Natural Filter Emphasis on A Mathematical Model of Pollution in Mandovi River

An estuary is a partially enclosed coastal body of brackish water with one or more rivers or streams flowing into it and with a free connection to the open sea. These freshwater estuaries also provide many of the ecosystem services and functions that brackish estuaries do, such as serving as natural filters for runoff and providing nursery grounds for many species of birds, fish, and other animals. Estuaries are among the most productive ecosystems in the world. Mandovi River in Goa is one such important Estuary. A mathematical model is presented to protect the Ecosystem

Detectability of Seamount Eruptions Through a Quantum Technology Gravity Mission MOCAST+: Hunga Tonga, Fani Maoré and Other Smaller Eruptions

AbstractSeamount eruptions alter the bathymetry and can occur undetected due to lack of explosive character. We review documented eruptions to define whether they could be detected by a future satellite gravity mission. We adopt the noise level in acquisitions of multi-satellite constellations as in the MOCAST+ study, with a proposed payload of a quantum technology gradiometer and clock. The review of underwater volcanoes includes the Hunga Tonga Hunga Ha’apai (HTHH) islands for which the exposed surface changed during volcanic unrests of 2014/2015 and 2021/2022. The Fani Maoré submarine volcanic eruption of 2018–2021 produced a new seamount 800 m high, emerging from a depth of 3500 m, and therefore not seen above sea surface. We review further documented submarine eruptions and estimate the upper limit of the expected gravity changes. We find that a MOCAST+ type mission should allow us to detect the subsurface mass changes generated by deep ocean submarine volcanic activity for volume changes of 6.5 km3 upwards, with latency of 1 year. This change is met by the HTHH and Fani Maoré volcanoes.

Indian Ocean temperature anomalies predict long-term global dengue trends

Despite identifying El Niño events as a factor in dengue dynamics, predicting the oscillation of global dengue epidemics remains challenging. Here, we investigate climate indicators and worldwide dengue incidence from 1990 to 2019 using climate-driven mechanistic models. We identify a distinct indicator, the Indian Ocean basin-wide (IOBW) index, as representing the regional average of sea surface temperature anomalies in the tropical Indian Ocean. IOBW is closely associated with dengue epidemics for both the Northern and Southern hemispheres. The ability of IOBW to predict dengue incidence likely arises as a result of its effect on local temperature anomalies through teleconnections. These findings indicate that the IOBW index can potentially enhance the lead time for dengue forecasts, leading to better-planned and more impactful outbreak responses.

Climatology and variability of sea surface temperature in the Azores region and its relation to climate indices

Daily sea surface temperature (SST) data from the Advanced Very High Resolution Radiometer from 1982 to 2020 were used to study the spatiotemporal variability and climatology of SST in the Azores. In addition, the relationships between the SST anomaly (SSTA) and three climate index time series for the period 1982–2020 were investigated: the Oceanic Niño Index (ONI), the Southern Oscillation Index (SOI) and the North Atlantic Oscillation (NAO) index. The spatiotemporal variability and climatology of the SST were studied by analyzing annual, seasonal, and monthly mean SST time series and climate fields. Relationships between the SSTA and climate index time series were investigated using Pearson's correlation analysis and wavelet coherence analysis. The annual mean SST time series shows a positive linear trend of about 0.027°C per year over the period 1982–2020. The spatial variability of the linear trend shows that the positive trend is consistent throughout the study area, ranging from 0.024 to 0.034°C per year. The spatial variability is not uniform, and high values of the trend are observed in the upper northwestern part and southwestern half of the basin. Seasonal mean SST fields show a positive North-South latitudinal gradient, with higher temperatures in the south, especially in the southwestern basin. In addition, the seasonal mean SST fields show possible coastal upwelling signatures along the coast of the Azores islands in different seasons. The results of Pearson's correlation analysis between basin-averaged, de-trended monthly mean SSTA and climate indices show that SSTA shows a significant positive correlation with SOI and a negative correlation with ONI from 1982 to 2020. NAO shows positive correlation with SSTA for the December-January-February season. Wavelet coherence analysis confirms significant coherences between the Azores SSTA and the climate-ocean indices at certain time scales and periods.

Human-induced intensified seasonal cycle of sea surface temperature

Changes in the seasonal cycle of sea surface temperature (SST) have far-reaching ecological and societal implications. Previous studies have found an intensified SST seasonal cycle under global warming, but whether such changes have emerged in historical records remains largely unknown. Here, we reveal that the SST seasonal cycle globally has intensified by 3.9 ± 1.6% in recent four decades (1983–2022), with hotspot regions such as the northern subpolar gyres experiencing an intensification of up to 10%. Increased greenhouse gases are the primary driver of this intensification, and decreased anthropogenic aerosols also contribute. These changes in anthropogenic emissions lead to shallower mixed layer depths, reducing the thermal inertia of upper ocean and enhancing the seasonality of SST. In addition, the direct impacts of increased ocean heat uptake and suppressed seasonal amplitude of surface heat flux also contribute in the North Pacific and North Atlantic. The temperature seasonal cycle is intensified not only at the ocean surface, but throughout the mixed layer. The ramifications of this intensified SST seasonal cycle extend to the seasonal variation in upper-ocean oxygenation, a critical factor for most ocean ecosystems.

Escalation of tropical cyclone impacts on the northwestern Bay of Bengal over the past decade

AbstractTropical cyclones have resulted in casualties and economic losses in the areas surrounding the Bay of Bengal (BoB). Thus, a comprehensive investigation of these tropical cyclones holds vital implications for disaster preparedness and mitigation. This paper compares the occurrence of storms in the last two decades, i.e., 2002–2011 and 2012–2021, and results reveal that such storms exhibited predominantly a northwesterly track towards the northwestern BoB, with a severer intensity but equal total storm frequencies. Over the past decade, a southeast-northwest pathway (SNP) was identified, demonstrating a higher incidence of severe tropical cyclones (STC, with lifetime maximum intensity ≥ 64 knots) over the BoB. Further analysis of the changes in the environmental conditions between these two decades indicates that a southeasterly anomaly in the steering flow contributed to the formation of the SNP. During the same period, the more favorable oceanic conditions during the last decade, including higher sea surface temperatures, a greater upper ocean heat content, a thicker warm water layer, and a thicker barrier layer beneath the SNP, favored the development of these storms by providing more heat energy to the storms. The atmospheric conditions, including increased air-sea heat fluxes, moisture, and instability within the lower troposphere, as well as reduced vertical wind shear, facilitated the development of convection within these storm systems. These favorable conditions improved the potential for storm development into STCs and elevated the risk of the northwestern BoB being impacted by more destructive storms.

An adaptive fusion algorithm for coastal sea altimetry based on dual-frequency Beidou-R carrier phase.

This article proposes an integer ambiguity determination method based on Beidou system-reflectometry (Beidou-R) observations of the carrier phase at the B1I and B3I frequencies. To enhance the accuracy of sea surface height (SSH) estimation, this study introduces a parallel filtering algorithm and an adaptive iterative fusion algorithm, enabling data fusion based on the variance at B1I and B3I frequencies. To validate and evaluate the proposed method, a coastal experiment was conducted at the Shenxian River. In this experiment, reflected signals from GEO and IGSO satellites were collected. Data analysis reveals that the method is effective, demonstrating that the root mean square error (RMSE) of SSH achieves 2.85 cm and 2.89 cm for PRN 04 and PRN 33, respectively. Furthermore, the impact of the elevation angle on measurement accuracy is analyzed. This study aims to propose a method to enhance coastal sea surface height estimation, offering potential advancements in sea surface altimetry.

Physics schemes in the first version of NCEP operational hurricane analysis and forecast system (HAFS)

This document summarizes the physics schemes used in two configurations of the first version of the operational Hurricane Analysis and Forecast System (HAFSv1) at NOAA NCEP. The physics package in HAFSv1 is the same as that used in NCEP global forecast system (GFS) version 16 except for an additional microphysics scheme and modifications to sea surface roughness lengths, boundary layer scheme, and the entrainment rate in the deep convection scheme. Those modifications are specifically designed for improving the simulation of tropical cyclones (TCs). The two configurations of HAFSv1 mainly differ in the adopted microphysics schemes and TC-specific modifications in addition to model initialization. Experiments are made to highlight the impacts of TC-specific modifications and different microphysics schemes on HAFSv1 performance. Challenges and developmental plans of physics schemes for future versions of operational HAFS are discussed.

Influence of Natural External Forcings on Interdecadal Variation of Global Land Monsoon over the last millennium in CESM-LME

Abstract Interdecadal variations of the global land monsoon have been previously attributed to internal fluctuations of the climate system, but the role of natural external forcings was under-explored. Here, we investigate this issue by using the Community Earth System Model ensemble simulations over the last millennium (950-1850 A.D.). Our analysis reveals that the surface temperature, with two dominant structures (global cooling/warming and longitudinal sea-surface temperature gradient in the tropical Pacific, which affects the Walker circulation), predominantly shapes the leading forced mode of the global land monsoon. This mode, representing 19% of the total variance, manifests as consistent features across South Asia, the southern part of East Asia, North Australia, South America, and western South Africa, contrasting with other monsoon regions. Under global cooling conditions, the monsoon intensity is enhanced in the northern parts of the East Asian and eastern parts of the North and South African monsoons, but it decreases in the other monsoon regions. Under weak Walker circulation conditions, changes in atmospheric circulation in response to the sea surface temperature gradient in the tropical Pacific are associated with a substantial attenuation of almost all land monsoon regions. It was further shown that the global mean surface temperature and the tropical Pacific temperature gradient jointly account for 75% of the total variance in the leading mode of the global land monsoon, with 29% and 46% as their respective contribution. Furthermore, our results suggest that volcanic eruptions are the dominant external forcing for these variations. These findings provide valuable insights for future research on global monsoon dynamics.

An ensemble-based data assimilation system for forecasting variability of the Northwestern Pacific ocean

AbstractAn adjoint-free four-dimensional variational (a4dVar) data assimilation (DA) is implemented in an operational ocean forecast system based on an eddy-resolving ocean general circulation model for the Northwestern Pacific. Validation of the system against independent observations demonstrates that fitting the model to time-dependent satellite altimetry during a 10-day DA window leads to substantial skill improvements in the succeeding 60-day hindcast. The a4dVar corrects representation of the Kuroshio path variation south of Japan by adjusting the dynamical balance between amplitude/wavelength of the meander and flow advection. A larger ensemble spread tends to reduce the skill in representing the observed sea surface height anomaly, suggesting that it is possible to use the ensemble information for quantifying the forecast error. The ensemble information is also utilized for modification of the background error covariance (BEC), which improves the accuracy of temperature and salinity distributions. The modified BEC yields the skill decline of the Kuroshio path variation during the 60-day hindcast period, and the ensemble sensitivity analysis shows that changes in the dynamical balance caused by the ensemble BEC result in such skill deterioration.

Distinct responses of tropical cyclone activity to spatio-uniform and nonuniform SST warming patterns

Abstract Most previous studies have reported a decrease in global tropical cyclone (TC) genesis frequency (TCGF) under anthropogenic warming. However, little attention has been drawn to the influence of sea surface temperature (SST) warming patterns on TCGF changes. Here, we investigate the impacts of three distinct SST warming patterns on global TCGF: uniform SST warming, nonuniform (El Niño-like) SST warming, and a combination of both. Results show that spatio-uniform SST warming has a limited impact on global TCGF, instead redistributing the TC genesis locations. Conversely, nonuniform SST warming significantly suppresses global TCGF. The combined warming produces a similar decrease in TCGF to nonuniform warming albeit with differences in spatial distribution. This indicates the dominant role of nonuniform SST warming in affecting TCGF and highlights the nonlinearity of the process. Further analysis shows that these differences in TCGF primarily stem from the distinct responses of tropical circulations to the three warming patterns.

Event-based evaluation of operational ENSO forecasting models in 2002–2020: Implications for seasonal water resources management

Given the practical implications of El Niño-Southern Oscillation (ENSO) forecasting for developing decision support systems and its importance in global climate prediction on seasonal to interannual scales, significant efforts have been made to enhance ENSO forecasts. This study presents an event-based evaluation of ENSO forecasts and discusses the implications of such forecasts to water resources management. Using ENSO forecasts for the period 2002–2020, this study evaluates and compares the results of two types of forecasting models, i.e., statistical and dynamical models, in predicting Sea Surface Temperatures (SSTs). Forecasting skills are evaluated for distinct target seasons via two metrics, spearman correlation and mean squared error. In addition, the performance of the two types of models at different lead times are also evaluated. Results reveal that the forecasting skills of these models are comparable, both of which exhibit higher forecasting skills for the boreal fall-winter season and lower skills for the boreal spring season. Event-based analyses show that dynamical and statistical models under-forecast SST anomaly at the onset month for El Niño events, although the forecasting error diminishes with a reduced forecasting lead for most occasions. For La Niña events, SST anomaly forecasting errors could be positive or negative. It is also difficult for the models to accurately predict the quick shift from one ENSO phase to another. Factors that contribute to such challenges are discussed. The implication of ENSO forecasts for water resources management, mainly streamflow forecasts, for a regional water supply utility in the southeastern United States is also discussed. Results from this study provide insights into ENSO forecasting skills and their practical implications.

Advection surface-flux balance controls the seasonal steric sea level amplitude

Along with the mean sea level rise due to climate change, the sea level exhibits natural variations at a large number of different time scales. One of the most important is the one linked with the seasonal cycle. In the Northern Hemisphere winter, the sea level is as much as 20 cm below its summer values in some locations. It is customary to associate these variations with the seasonal cycle of the sea surface net heat flux which drives an upper-ocean thermal expansion creating a positive steric sea level anomaly. Here, using a novel framework based on steric sea level variance budget applied to observations and to the Estimating the Circulation and Climate of the Ocean state estimate, we demonstrate that the steric sea level seasonal cycle amplitude results from a balance between the seasonal sea surface net heat flux and the oceanic advective processes. Moreover, for up to 50% of the ocean surface, surface heat fluxes act to damp the seasonal steric sea level cycle amplitude, which is instead forced by oceanic advection processes. We also show that eddies play an important role in damping the steric sea level seasonal cycle. Our study contributes to a better understanding of the steric sea level mechanisms which is crucial to ensure accurate and reliable climate projections.