Influence Of Sea Surface Temperature Research Articles

Testing the relationship between bleaching severity and climate change during the fourth large-scale coral bleaching event

The Fourth Global Coral Bleaching Event unfolded amidst the accelerating impacts of climate change, underscoring the urgent need to reassess the relationship between coral bleaching trends and climatic shifts. This study examined the influence of sea surface temperature (SST) anomalies and cumulative thermal stress, quantified by Degree Heating Weeks (DHW), on coral bleaching rates from early 2023 to mid-2024. Satellite-derived coral risk data were analyzed for correlation with field survey data. The results revealed that SST anomalies alone were insufficient to significantly predict coral bleaching events. However, a significant positive correlation was found between DHW and coral bleaching rates, indicating that cumulative thermal stress is a critical predictor of bleaching events. This finding emphasizes the necessity of implementing an effective monitoring and early warning system based on DHW thresholds to mitigate the impacts of coral bleaching, especially given the increasing frequency of such events in the context of climate change.

Seasonal–Diurnal Distribution of Lightning over Bulgaria and the Black Sea and Its Relationship with Sea Surface Temperature

A seasonal–diurnal analysis of land-sea contrast in lightning activity over Bulgaria and the Black Sea over 10 years is presented here. The maximum number of flashes over both surface types is registered during the summer (with a peak over Bulgaria in June and over the Black Sea in July) and a minimum number in winter (December/February, respectively). During spring, the maximum flash density is observed over Bulgaria (in May), while in autumn, it is over the Black Sea (in September). The results show that only in autumn lightning activity dominates over the Black Sea compared to over land (Bulgaria), while in winter, spring, and summer is vice versa. For this reason, an additional investigation was conducted to determine whether there is a relationship between lightning activity and the sea surface temperature (SST) of the Black Sea in autumn. The analysis reveals that the influence of SST on the formation of thunderstorms over the Black Sea varies depending on the diurnal time interval, with the effect being more significant at night. At nighttime intervals, there is a clear trend of increasing mean flash frequency per case with rising SST (linear correlation coefficients range from R = 0.92 to 0.98), while during the daytime, this trend is not as evident. This indicates that, during the day, other favorable atmospheric processes have a greater influence on the formation of thunderstorms than sea-surface temperature, while in the autumn night hours, the higher SST values probably play a more significant role in thunderstorms formation, in combination with the corresponding orographic conditions.

2023 temperatures reflect steady global warming and internal sea surface temperature variability

2023 was the warmest year on record, influenced by multiple warm ocean basins. This has prompted speculation of an acceleration in surface warming, or a stronger than expected influence from loss of aerosol induced cooling. Here we use a recent Green’s function-based method to quantify the influence of sea surface temperature patterns on the 2023 global temperature anomaly, and compare them to previous record warm years. We show that the strong deviation from recent warming trends is consistent with previously observed sea surface temperature influences, and regional forcing. This indicates that internal variability was a strong contributor to the exceptional 2023 temperature evolution, in combination with steady anthropogenic global warming.

Responses of Lower-Stratospheric Water Vapor to Regional Sea Surface Temperature Changes

Abstract The variability of stratospheric water vapor (SWV) plays a crucial role in stratospheric chemistry and Earth’s energy budget, strongly influenced by sea surface temperature (SST). In this study, we systematically investigate the response of lower-SWV (LSWV) to regional sea surface temperature changes using idealized SST patch experiments within a climate model. The results indicate that LSWV is most sensitive to tropical sea surface temperature, with the strongest response occurring in late autumn and early winter. Warming of the tropical Indian Ocean and western Pacific (WP) leads to stratospheric drying, while warming of the tropical Atlantic (TA) and eastern Pacific results in stratospheric moistening. The drying impact on LSWV due to warming in the western Pacific Ocean exceeds the wet effect in the eastern Pacific Ocean by approximately 60%. The variations in tropical SST influence LSWV by modulating the temperature at the tropical tropopause layer, especially over the Indo-Pacific warm pool through Matsuno–Gill responses. Furthermore, the response of LSWV to tropical SST changes exhibits nonnegligible nonlinearity, which indicates the importance of nonlinearity in determining the LSWV response to global surface warming. Significance Statement In this study, we explore how changes in the temperature of the ocean’s surface can affect the amount of water vapor in the stratosphere, a layer of Earth’s atmosphere. Understanding this relationship is important because water vapor in the stratosphere can influence both our climate and the chemistry of the atmosphere. Using a climate model, we found that water vapor in the lower stratosphere is especially responsive to temperature changes in tropical ocean regions. Specifically, when the Indian Ocean and the western Pacific get warmer, the stratosphere tends to get drier. On the other hand, warming in the Atlantic and eastern Pacific leads to more moisture in the stratosphere. The way these changes add up is complex and not simply a sum of individual parts, especially in tropical warm pool regions. Our findings have implications for how we understand and predict the impacts of climate change on stratospheric water vapor.

Using artificial neural networks and citizen science data to assess jellyfish presence along coastal areas

Abstract Jellyfish blooms along coastal areas can pose significant challenges for beach users and local authorities. Understanding the factors influencing jellyfish presence is crucial for effective management and mitigation strategies. In this study, citizen science data from the Andalusian coast (232 beaches, in 40 different localities) and machine learning techniques are used to investigate if the presence and absence of jellyfish along coastal areas can be predicted. A multi‐layer perceptron (MLP) neural network was employed to classify user comments regarding jellyfish presence or absence, achieving an accuracy of approximately 96%. The MLP model demonstrated robustness in handling non‐linear classification problems and noise, although it showed lower precision for predicting jellyfish presence, likely due to an imbalance in the dataset. Environmental data were also incorporated to characterise the influence of sea surface temperature, wind direction and wind speed on jellyfish distribution. The results align with previous studies, suggesting these environmental factors significantly impact jellyfish presence. Synthesis and applications. This research provides actionable recommendations for beach management. The implementation of continuous monitoring of sea surface temperature and wind conditions will enable more accurate predictions of jellyfish distribution. Adaptive management strategies that respond dynamically to environmental data will help mitigate the impact of jellyfish blooms on coastal tourism and public health.

The role of Arctic sea ice loss in the interdecadal trends of the East Asian summer monsoon in a warming climate

The East Asian summer monsoon precipitation has exhibited a well-known “southern China flood and northern China drought” pattern in recent decades. The increase in aerosols and warming oceans are recognized as two important forcings that control of the precipitation trends over East Asian land. However, in this study, by using large ensemble simulations from the CMIP6 Polar Amplification Model Intercomparison Project (PAMIP), the influence of Arctic amplification, serving as the prominent feature of global warming, is very important in modulating the East Asian summer precipitation pattern, which is comparable to the influence of sea surface temperature (SST). Additionally, the observed “southern China flood and northern China drought” pattern only exists in July and August, whereas a triple pattern with the precipitation positive anomaly center over Middle China occurs in June. These patterns are closely connected with the regional differences in Arctic sea ice loss from June to July, affected through both the Rossby waves propagating in a weaker westerly jet and the decrease in the large-scale meridional thermal contrast in a warming climate.

Thermal tolerance as a driver of reef fish community structure at the isolated tropical Mid-Atlantic Ridge Islands

Reef fish communities are shaped by historical and ecological factors, including abiotic and biotic mechanisms at different spatial scales, determining species composition, abundance and biomass. The oceanic islands in the Mid-Atlantic Ridge (St. Peter and St. Paul's Archipelago - SPSPA, Ascension, and St. Helena), exhibiting differences in community structure along a 14-degree latitudinal and a 10 °C thermal gradient. We investigate the influence of sea surface temperature, area, age, isolation and phosphate on reef fish community structures. Reef fish trophic structure varies significantly across the islands, with planktivores and herbivore-detritivores showing the highest abundances in SPSPA and Ascension, while less abundant in St. Helena, aligning with the thermal gradient. Variations in reef fish community structures were predominantly influenced by thermal regimes, corroborating the expansion of species' thermal niche breadth at higher latitudes and lower temperatures. This study highlights that in addition to biogeographic factors, temperature is pivotal on shaping oceanic island reef fish community structure.

Logistic curve modelling of sea surface temperature and latent heat flux variability in the Tropical Indian Ocean

The Tropical Indian Ocean (TIO) is one of the most vulnerable regions to climate change due to its unique ocean-atmosphere interactions. The region is characterized by warm temperatures, high evaporation rates, and strong convection, making it particularly vulnerable to greenhouse gases. This susceptibility leads to an overall trend of warming across the Earth's surface and atmosphere, intensifying sea surface temperatures (SST) and increasing evaporation rates, consequently influencing Surface Latent Heat Flux (SLHF). The strong influence of both SST and SLHF on atmospheric circulation and precipitation patterns makes them critical factors in determining the region's climate, particularly in monsoon-dominated regions. Analysis reveals a consistent upward trend in both SST and SLHF in the central region of TIO. To model this, standard Logistic Curve Model (LCM) has been applied to these parameters averaged over the central region of the TIO. The model is run for a period of 20 years from 2001 to 2020, grouped into four lustrums. The LCM-derived SST and SLHF are in good consistent with observed datasets during the above periods with, correlation coefficients ranging from 0.92 to 0.96 for SST and 0.86–0.90 for SLHF. Extending the model spatially across the entire TIO region explains the ability to project fluctuations in SST and SLHF values across different seasons. These findings highlight the model's relevance for capturing short-term, long-term, and seasonal variability of the parameters, providing important insights into regional climate dynamics.

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

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.

Impacts of Atmospheric Internal Variations on the Variability of Sea Surface Temperature Based on the Hydra‐SINTEX Model

AbstractOcean–atmosphere interactions largely control the variabilities of the climate system on Earth. However, how much atmospheric internal signals contribute to climate variabilities remains uncertain over many parts of the globe. Here, we develop an interactive ensemble coupled model (called Hydra‐SINTEX) to investigate the influences of atmospheric internal variations (AIVs) on the mean‐states and variability of the climate system. The results show that, while climatological mean‐states are little affected, the AIVs can largely influence climate variabilities over the globe. We pay particular attention to two regions, that is, the tropical eastern Indian Ocean, which is the key area of the Indian Ocean Dipole (IOD), and the subtropical North Pacific. We found that sea surface temperature (SST) variabilities in these two regions are much reduced without the AIVs but with distinct mechanisms. Without the AIVs, the intensity of the IOD is largely reduced in association with weakened air–sea coupling in the tropics. This indicates the importance of atmospheric noise forcing on the development of the IOD. In contrast, the reduction of SST variability in the subtropical North Pacific is caused by the absence of the AIVs that are generated by both mid‐latitude atmospheric processes and weakened remote influence of the tropical SST in accordance with the reduced SST signals there.

Climatic resilience: Marine heatwaves do not influence the variations of green crab (Carcinus maenas) megalopae supply patterns to a Western Iberian estuary

Extreme climatic events like marine heatwaves (MHWs) are becoming more frequent, intense, and longer lasting all around the world. The consequences of these anomalously warm periods are devastating for marine ecosystems. Still, little is known about these extreme events off the western Iberia coast. Here we analyzed MHW events occurring from 1982 to 2020 on the Aveiro coast, western Iberia coast of Portugal. A total of 79 events were detected for the region, with an average duration of 15.8 days, and a mean intensity of 1.9 °C ± 0.4 °C above the 90th percentile of sea surface temperatures (SST) for the region. The maximum intensity of the events has increased by 0.5 °C over the last decade. The relation between SST, and therefore, MHW events, the North Atlantic Oscillation index (NAO), and the regional Iberian Upwelling Index (UI) was identified. The intense upwelling of the region seems to mitigate the duration of warming conditions, resulting in shorter MHW events. Furthermore, the impacts of SST and MHW events on the supply patterns of Carcinus maenas megalopae were examined, utilizing daily data from 2002, 2006–2009, 2012, and 2013, collected at the entrance of Ria de Aveiro. Cross-correlations were employed to assess the effect of SST on megalopae supply, while ordinary least square cumulative sums were used to identify variations over time. The influence of SST on supply was noticed with a 5-to-11-day lag, but this relation changed over the years. Contrary to our hypothesis, we found no evidence supporting a diminishment in megalopae supply due to MHW events. These elusive findings, coupled with the apparent lack of influence of these extreme events, highlight the relatively weak intensity and brief duration of the MHW events in the region, coupled with the high thermal tolerance of these species.

On the Role of Indian Ocean SST in Influencing the Differences in Atmospheric Variability Between 2020–2021 and 2021–2022 La Niña Boreal Winters

AbstractThe difference in observed atmospheric anomalies over the Northern Hemisphere winter between 2021–22 and 2020–21 La Niña years indicated a tripole pattern consisting of a Japan cyclone, a Bering Sea anticyclone, and a cyclone over the North American continent. This feature, however, was not replicated in the North American Multi‐Model Ensemble (NMME) forecasts. A set of model sensitivity experiments was performed to better understand the cause of this discrepancy. The results revealed the possible role of the influence of sea surface temperature (SST) anomalies, particularly over the Indian Ocean, on the observed circulation differences that was further modulated by internal atmospheric variability. The failure in predicting circulation changes in NMME was next attributed to the errors in SST predictions over the Indian Ocean and highlights the need for improvements in SST forecasts over this region.

Tropical Cyclogenesis Bias over the Central North Pacific in CMIP6 Simulations

Abstract Current coupled climate models contain large biases in simulating tropical cyclogenesis, reducing the confidence in tropical cyclone (TC) projection. In this study, we investigated the influence of sea surface temperature (SST) biases on TC genesis in the Coupled Model Intercomparison Project phase 6 simulations from 1979 to 2014. Positive TC genesis biases were found over the tropical central North Pacific (CNP) in most of climate models, including the high-resolution models. Compared to coupled models, TC genesis density (TCGD) simulations over CNP in uncoupled models forced by observational SST improved obviously. A warm SST bias over the tropical CNP in the coupled models is the main cause of TC genesis biases. The SST bias–induced diabatic heating leads to an anomalous Gill-type atmospheric circulation response, which contributes to a series of favorable environmental conditions for TC formation over the CNP. Numerical experiments were also performed with HiRAM to demonstrate the influence of SST biases on the TCGD simulation, further confirming our conclusion. The current results highlight the importance of improving TC simulation in state-of-the-art climate models by reducing SST simulation bias.

The Influence of Sea Surface Temperature From ECMWF Reanalysis Data on the Nonuniformity of Evaporation Duct

The Influence of Sea Surface Temperature From ECMWF Reanalysis Data on the Nonuniformity of Evaporation Duct

Plio‐Pleistocene Southwest African Hydroclimate Modulated by Benguela and Indian Ocean Temperatures

AbstractFuture projections of southwestern African hydroclimate are highly uncertain. However, insights from past warm climates, like the Pliocene, can reveal mechanisms of future change and help benchmark models. Using leaf wax hydrogen isotopes to reconstruct precipitation (δDp) from Namibia over the past 5 million years, we find a long‐term depletion trend (−50‰). Empirical mode decomposition indicates this trend is linked to sea surface temperatures (SSTs) within the Benguela Upwelling System, but modulated by Indian Ocean SSTs on shorter timescales. The influence of SSTs on reconstructed regional hydroclimate is similar to that observed during modern Benguela Nio events, which bring extreme flooding to the region. Isotope‐enabled simulations and PlioMIP2 results suggest that capturing a Benguela Nio‐like state is key to accurately simulating Pliocene, and future, regional hydroclimate. This has implications for future regional climate, since an increased frequency of Benguela Nios poses risk to the ecosystems and industries in the region.

Regime Shifts in the Damage Caused by Tropical Cyclones in the Guangdong–Hong Kong–Macao Greater Bay Area of China

Tropical cyclones (TCs) pose a significant threat in terms of wind-induced damage and storm risk to the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) of China. In this research, annual power dissipation index (PDI) and storm surge and wave destructive potential (SDP) index from June to November were used to estimate the damage caused by the TC events in the buffer zone of the GBA. The regime shifts in 1993 and 2013 were identified through the Bayesian changepoint detection in six TC datasets. The TC-induced damage during 1994–2012 (the low-damage period) was weaker than that in 1977–1993 and 2013–2020 (the high-damage periods). The intensity and size of stronger TCs are the dominant factors responsible for the interdecadal changes in the TC damage. This may be explained by the influences of sea surface temperature (SST), surface latent heat flux and mid-level relative humidity. During high-damage periods, TCs can extract more energy from the ocean, leading to increased TC intensity and larger size. Conversely, during low-damage periods, TCs undergo a decrease in energy intake, resulting in reduced TC power and smaller size. The variations in the SST are relative to the Luzon Strait transport. In addition, the reduction in TC translation speed is unfavorable for the development of TCs in low-damage periods. Further research suggested that mid-level steering flow affects the TC movement velocity. The results offer valuable insights into the variations in the TC-induced damage in the GBA.

Relationship between the rainfall index for Southern Brazil and the indexes of the Tropical Pacific and the Tropical Atlantic Oceans

The proposed study verified the possible influences of sea surface temperature (SST) anomalies in the equatorial Pacific and the tropical Atlantic on the rainfall in the southern region of Brazil. The rainfall stations used have monthly data for the period from 1977 to 2015 and are distributed throughout that region. Monthly TSM anomalies in the Niño 3.4 and Niño 1 + 2 areas, the Southern Oscillation Index and the Monthly Tropical South Atlantic Temperature Index were also used, from the database provided by the Climate Prediction Centre. The results show the association of precipitation in the South region with variations in sea surface temperature in the Tropical Pacific Ocean and, to a lesser extent, with sea surface temperatures in the Tropical South Atlantic Ocean.

The environmental niche of the squid-jigging fleet in the North Pacific Ocean based on automatic identification system data

The vast number of fishing fleets and limited regulatory capacity pose challenges to the effective monitoring and management of marine fishery resources. To understand the fishing characteristics of spatial distribution for squid-jigging fleets in the North Pacific Ocean (NPO), satellite-based automatic identification system (AIS) data during 2016 ∼ 2020 and marine environmental data were applied to explore the drivers of fishing activities based on the boosted regression tree (BRT) and generalized additive model (GAM). The results showed that the BRT model has better performance than the GAM. The spatial distribution of squid-jigging fleets has a significant seasonality. Specifically, the spatial distribution of Fishing effort (FE) gradually shifted to the northwest from May to September, while gradually shifted to the southwest from October to November. The favourable area for focusing FE was the 150°E ∼ 165°E, 40°N ∼ 44°N. The latitudinal distribution of FE was not wide and the longitudinal distribution was long. Variations in the marine environment constantly influenced the FE of the squid-jigging fleets. Overall, the influence of sea surface temperature (T0) on FE was most significant. The favourable range of T0 for FE was 12 ∼ 20°C. Water temperature at the 300 m depth (T300) was also an important factor through the fishing season. The favourable range of T300 for FE was 3 ∼ 6°C. There were obvious differences in the influence of other environmental factors on FE in different months. Different environmental variables drive the spatial distribution of fishing effort in different months. This study may help to scientifically and effectively guide fishery management and sustainable development by evaluating the spatial variations in fishing activity.

Exploring biophysical links of catch rate from mid-water trawl fishery in the north-west coast of India, Eastern Arabian Sea: A remote sensing approach

Fish frequently shift their distribution ranges as a result of changes in preferred environmental factors. Knowledge on distribution of fish in relation to their environmental optima is crucial for improving the understanding of fishing grounds and planning sustainable exploitation. This study investigated the monthly variability in environmental factors impacting the catch rate and the spatio-temporal distribution patterns of fish along northwest coast of India (NWCI) from 2017 to 2019. The time series images of sea surface temperature (SST), sea surface height anomaly (SSHa), chlorophyll-a concentration (CHL), and euphotic depth (Zeu) indicate close coupling between bio-physical variables in the region. Generalized Additive Model (GAM) applied to the catch rate of mid-water trawlers found that CHL and Zeu were the most influential variables for ribbonfish distribution, while CHL and SST influence squid distribution and SST and SSHa influence cuttlefish distribution. The total deviation explained 37.70 %, 10.70 %, and 22.28 % in the final model for simulating the spatio-temporal distribution of ribbonfish, squid, and cuttlefish, respectively. Environmental factors were significant in the final GAM model (p < 0.05). Spatio-temporal distribution patterns of fishery resources from mid-water trawlers showed that they were related to biophysical changes in the northwest coast of India. The high catch rate was observed in the northern latitudes during the post-monsoon (October), the major fishing season in the region. However, it shrank and shifted to southern latitudes during the winter (January). This study could be adapted to nearby countries in the Arabian Sea region for an effective and useful tool to monitor and manage the fishery resources. Furthermore, it could aid in decision-making for policymakers and resource managers to formulate strategies for holistic marine fishery management and sustainable resource exploitation.

Reconstructing Long-Term Arctic Sea Ice Freeboard, Thickness, and Volume Changes from Envisat, CryoSat-2, and ICESat-2

Satellite altimeters have been used to monitor Arctic sea ice (ASI) thickness for several decades, but whether the different altimeter missions (such as radar and laser altimeters) are in agreement with each other and suitable for long-term research needs to be investigated. To analyze the spatiotemporal characteristics of ASI, continuous long-term first-year ice, and multi-year ice of ASI freeboard, thickness, and volume from 2002 to 2021 using the gridded nadirization method from Envisat, CryoSat-2, and ICESat-2, altimeter data are comprehensively constructed and assessed. The influences of sea surface temperature (SST) and sea surface wind field (SSW) on ASI are also discussed. The freeboard/thickness and extent/area of ASI all varied seasonally and reached their maximum and minimum in April and October, March and September, respectively. From 2002 to 2021, the freeboard, thickness, extent, and area of ASI all consistently showed downward trends, and sea ice volume decreased by 5437 km3/month. SST in the Arctic rose by 0.003 degrees C/month, and the sea ice changes lagged behind this temperature variation by one month between 2002 and 2021. The meridional winds blowing from the central Arctic region along the eastern coast of Greenland to the North Atlantic each month are consistent with changes in the freeboard and thickness of ASI. SST and SSW are two of the most critical factors driving sea ice changes. This study provides new data and technical support for monitoring ASI and exploring its response mechanisms to climate change.