Daily Sea Surface Temperature Research Articles

A coordination attention residual U-Net model for enhanced short and mid-term sea surface temperature prediction

Sea surface temperature (SST) is crucial for studying global oceans and evaluating ecosystems. Accurately predicting short and mid-term daily SST has been a significant challenge in oceanography. Traditional deep learning methods can handle temporal data and spatial features but often struggle with long-range spatiotemporal dependencies. To address this, we propose a coordination attention residual U-Net(CResU-Net) model designed to better capture the dynamic spatiotemporal correlations of high-resolution SST. The model integrates coordinate attention mechanisms, multiple residual modules, and depthwise separable convolutions to enhance prediction capabilities. The spatiotemporal variations of SST across different areas of the South China Sea are complex, making accurate predictions challenging. Experiments across various regions of the South China Sea show the model’s effectiveness and robust generalization in predicting high-resolution daily SST. For a 10-day forecast period, the model achieves approximately 0.3 °C in RMSE, outperforming several advanced models.

Seasonal Early Warning of Impacts of Harmful Algal Blooms on Farmed Shellfish in Coastal Waters of Scotland

AbstractHarmful Algal Blooms (HABs) can produce phycotoxins that accumulate in shellfish and subsequently poison aquatic predators and human consumers, potentially causing significant economic impacts to the shellfish aquaculture industry. HAB events are challenging to foresee as they are driven by complex inter‐annual and seasonal changes in physical, chemical and biological factors. Accounting for these environmental drivers and their interactions in statistical models allows for the development of HAB early warning systems. Typically, these have a forecasting horizon of 1–2 weeks, allowing shellfish businesses and regulators to increase monitoring intensity and take evasive action, including harvesting suspensions to protect consumer health. However, there is critical need for longer‐term predictions of risk, to enable more proactive mitigation, business planning, harvest scheduling and supply chain management. We present a statistical framework for providing seasonal‐scale early warnings of the occurrence and impacts of Dinophysis spp. HABs on shellfish aquaculture in Scotland, UK. We use penalized smooth functions of winter‐spring daily sea surface temperature to predict the severity and impact of ensuing summer blooms, including the percentage of toxicity measurements exceeding the harvesting closure threshold, as well as the anticipated start, end, and overall duration of closures. We illustrate the application of this framework to two Scottish aquaculture regions: One with a high spatial concentration of harvesting sites (Shetland) and one with more dispersed sites (West Scotland and the Hebrides). Through a comprehensive yearly prediction experiment, we demonstrate considerable skill in predicting the impact of unseen HAB seasons at a regional level.

Hydrological Data Projection Using Empirical Mode Decomposition: Applications in a Changing Climate

This paper demonstrates the effectiveness and superiority of Empirical Mode Decomposition (EMD) in projecting non-stationary hydrological data. The study focuses on daily Sea Surface Temperature (SST) sequences in the Niño 3.4 region and uses EMD to forecast the probability of El Niño events. Applying the Mann–Kendall test at the 5% significance level reveals a significant increasing trend in SST changes in this region, particularly noticeable after 1980. This trend is associated with the occurrence of El Niño and La Niña events, which have a recurrence interval of approximately 8.4 years and persist for over a year. The modified Oceanic Niño Index (ONI) proposed in this study demonstrates high forecast accuracy, with 97.56% accuracy for El Niño and 89.80% for La Niña events. Additionally, the EMD of SST data results in 13 Intrinsic Mode Functions (IMFs) and a residual component. The oscillation period increases with each IMF level, with IMF7 exhibiting the largest amplitude, fluctuating between ±1 °C. The residual component shows a significant upward trend, with an average annual increase of 0.0107 °C. These findings reveal that the EMD-based data generation method overcomes the limitations of traditional hydrological models in managing non-stationary sequences, representing a notable advancement in data-driven hydrological time series modeling. Practically, the EMD-based 5-year moving process can generate daily sea temperature sequences for the coming year in this region, offering valuable insights for assessing El Niño probabilities and facilitating annual updates.

Ocean feedback on 50–80-day boreal winter intraseasonal oscillation in the Indian Ocean: comparison of atmospheric and coupled CESM2 simulations

Abstract The role of sea surface temperature (SST) variability in the boreal winter intraseasonal oscillation (ISO) over the tropical Indian Ocean (TIO) is investigated in this study using the Community Earth System Model Version 2 (CESM2). An atmospheric general coupled model (AGCM) run forced by daily SSTs derived from a parallel coupled circulation model (CGCM) run is compared with reanalysis and the original coupled simulation. The comparison of atmospheric-coupled GCMs reveals the frequency and spatial tendency of the SST effect on the atmospheric ISO. The lower-frequency component of the ISO over the TIO is significantly intensified in the AGCM run. The SST-to-atmosphere effect is most prominent over the southeastern TIO. In the AGCM run, low-level wind anomalies related to SST appear west of the ISO convection center, contrasting with the previous findings that the SST effect appears east of the convection center.

Sea surface temperature prediction by stacked generalization ensemble of deep learning

Accurate sea surface temperature (SST) prediction is of great significance for fishery farming, marine ecological protection, and planning of maritime activities. In this paper, the stacked generalization ensemble is demonstrated for SST prediction to improve every single deep-learning model. Long-term high-resolution satellite-derived SST is used with the sub-regions of the Taiwan Strait and East China Sea taken as the study area. We select the Multilayer Perceptron, Long short-term memory (LSTM), Convolutional Neural Networks (CNN), CNN-LSTM as individual learners, and Convolutional LSTM as the meta-learner. The individual learners are trained and validated on the retained data subset I, while the meta-learner is trained and validated by constructing the samples with the predictions of validated individual learners on the retained data subset II. The two types of models are evaluated on the same test dataset with root-mean-square error and coefficient of efficiency as the scoring criteria. We find that the meta-model outperforms any individual model and other baselines for the one-day-/three-day-ahead forecasts in the Taiwan Strait and one-day-/three-day-/five-day-ahead predictions in the East China Sea. Furthermore, when the lead time is 1 day and 3 days, the meta-model has a better spatial distribution of prediction metrics across all grid points in the Taiwan Strait sub-area. For the East China Sea sub-region, the meta-model advantage is extended to the lead time of 5 days. Probably due to the higher quality of offshore satellite data, the prediction ability enhancement of the stacked ensemble applied in the East China Sea is better than that in the Taiwan Strait. The better-performing meta-model prediction suggests that the stacked generalization ensemble is encouraging and promising for improving the short-term prediction of the daily SST field.

Seasonal dynamics of global marine heatwaves over the last four decades

Marine heatwaves (MHWs), prolonged periods of abnormally high sea temperature, have greater devastating impacts on marine ecosystem services and socioeconomic systems than gradual long-term ocean warming. Despite growing evidence of increases in MHW frequency, duration, and intensity, their interseasonal variations remain unclear. Using satellite-derived daily sea surface temperature (SST) data from 1982 to 2022, this work reveals a strong seasonality in MHWs. Typically, the highest cumulative intensity, characterizing total impacts on ecosystems, occurs during the local warm seasons in most oceans, leading to a significant interseasonal difference between warm and cold seasons. The interseasonal difference is predominantly driven by air-sea heat flux, rather than oceanic horizontal advection and vertical process. An increase in these interseasonal differences is observed in mid and high latitudes, with a significant increase in the warm season and a weaker trend in the cold season. In the Equatorial Pacific and Western Equatorial Indian Ocean, intense MHWs are primarily exacerbated by the El Niño-Southern Oscillation (ENSO), which also determines interseasonal variations in MHWs. Understanding the seasonality of MHWs can help better formulate corresponding policies to reduce economic and ecological losses caused by these events and can improve the accuracy of future predictions.

The Amazon River plume—a Lagrangian view

AbstractHydrographic data, nutrient data and bulk rates of nitrate uptake and primary production were determined in the Amazon River plume (ARP) in the Western Tropical North Atlantic (WTNA) during three cruises in May 2018, June/July 2019, with RV Endeavor and April/May 2021 with RV Meteor. Using daily quasi‐geostrophic surface velocity data from satellite observations, the geographical positions of the stations of observations were transformed onto Lagrangian coordinates to obtain a dynamically coherent and consistent spatial distribution. After the transformation, the observed surface salinity and temperature fields were consistent with the flow fields, the ARP formed a coherent structure and the retroflection of the North Brazil Current became visible. By transforming other surface variables such as nitrate concentration, photosynthetically available radiation, turbidity, bulk rates of nitrate uptake, and primary production onto Lagrangian coordinates, patterns became consistent with the physical variables at the surface. The use of “synchronous” fields as done here by transformation onto Lagrangian coordinates is essential for spatially structured analyses of data collected over tens of days in a highly dynamic region characterized by complex flow fields with low persistence such as the WTNA. Therefore, the use of the Lagrangian method provides a powerful tool for exploring spatial distributions of biologically relevant factors in regions with complex and dynamic flow patterns. These spatial distributions are qualitatively in agreement with satellite images of daily sea surface temperature and composites of monthly mean Chlorophyll a distributions.

Attributing daily ocean temperatures to anthropogenic climate change

Ocean temperatures are rising and hit record levels around the world in 2023. While trends are clear and likely strongly connected to human-caused climate change, the oceans also exhibit variability on the daily level, leading to local extremes such as marine heatwaves. We present an operational system to estimate the impact of human-caused climate change on daily sea surface temperatures anywhere in the ocean. This system uses a multi-method approach combining observed trends and paired control/forced climate model runs from CMIP6. Our approach is novel in its flexibility and ease of application for global, daily use for any day since the beginning of the satellite era (1982–2023). The system allows for rapid evaluation for further study of attributable ocean temperatures and real-time communications of attributable ongoing events. We apply the system to well-documented heatwaves in the Tasman Sea, Gulf of Maine, and Mediterranean Sea over the past decade, as well as global conditions in July 2023, to confirm that the system produces estimates consistent with other attribution methods, and to simulate how our system handles interesting events as they are occurring. Each of these events strongly reflected impacts of climate change: their temperatures were consistently made at least four times as likely to occur in our human-influenced climate than in a world without climate change. Meanwhile, in July 2023, almost all ( >70 %) of the ocean’s temperatures were made at least twice as likely to occur on any given day. Rapid attribution of daily ocean temperatures provides a pathway for quantifying the influence of climate change on ecological impacts like coral bleaching and on ocean-generated/influenced storms like tropical cyclones.

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.

Sea Surface Temperature Prediction in China Sea Based on SAM-LSTM Approach

Sea Surface Temperature (SST), a critical environmental element in the ocean, significantly impacts the global atmosphere-ocean energy balance and holds the potential to trigger severe weather like droughts, floods, and El Niño events. Therefore, the prediction of future SST dynamics is crucial to identifying these extreme events and mitigating the damage they caused. In this study, we introduce a time series prediction method based on the Self-Attention Mechanism-Long Short-Term Memory (SAM-LSTM) model. In addition, the historical time-series satellite data of SST anomaly (SSTA) is used instead of SST itself considering that the fluctuations of SST are very small compared to their absolute magnitudes. The Seasonal-Trend decomposition using Loess (STL) method is adopted to decompose the complex non-linear SSTA time series into trend components, seasonal components, and residual components. Then, the deseasonalized time series data at 6 locations in the Bohai Sea are used to train and valid the developed SAM-LSTM model. After that, the validated models are applied to the Yellow Sea, East China Sea, and South China Sea. The experimental results show that the combination of STL time series decomposition and SAM-LSTM can achieve high-precision prediction of daily SSTA than LSTM. This suggests that the methodology used in this paper has a good application for short-term daily SST prediction.

SST Forecast Skills Based on Hybrid Deep Learning Models: With Applications to the South China Sea

We explore to what extent data-driven prediction models have skills in forecasting daily sea-surface temperature (SST), which are comparable to or perform better than current physics-based operational systems over long-range forecast horizons. Three hybrid deep learning-based models are developed within the South China Sea (SCS) basin by integrating deep neural networks (back propagation, long short-term memory, and gated recurrent unit) with traditional empirical orthogonal function analysis and empirical mode decomposition. Utilizing a 40-year (1982–2021) satellite-based daily SST time series on a 0.25° grid, we train these models on the first 32 years (1982–2013) of detrended SST anomaly (SSTA) data. Their predictive accuracies are then validated using data from 2014 and tested over the subsequent seven years (2015–2021). The models’ forecast skills are assessed using spatial anomaly correlation coefficient (ACC) and root-mean-square error (RMSE), with ACC proving to be a stricter metric. A forecast skill horizon, defined as the lead time before ACC drops below 0.6, is determined to be 50 days. The models are equally capable of achieving a basin-wide average ACC of ~0.62 and an RMSE of ~0.48 °C at this horizon, indicating a 36% improvement in RMSE over climatology. This implies that on average the forecast skill horizon for these models is beyond the available forecast length. Analysis of one model, the BP neural network, reveals a variable forecast skill horizon (5 to 50 days) for each individual day, showing that it can adapt to different time scales. This adaptability seems to be influenced by a number of mechanisms arising from the evident regional and global atmosphere–ocean coupling variations on time scales ranging from intraseasonal to decadal in the SSTA of the SCS basin.

Seasonal variation of Marine Heat Wave in the East China Sea

The satellite-based daily sea surface temperature was used to study the Marine Heat Wave (MHW) in the East China Sea. The seasonal variation of MHW is significant. In winter, the 200 m isobath on the shelf is identified as a section with high-frequency MHW. The duration of MHW is relatively long on the slope and deep basin, and mean intensity of MHW is obviously high in the central Taiwan Strait. In summer, the frequency of MHW is relatively high near the coast of the China Mainland. Meanwhile, the shelf section of the 200 m isobath is also high in frequency. Otherwise, the duration of MHW on the shelf is short. In the northern shelf, the mean intensity of MHW is obviously higher. The total days and maximum intensity of MHW are also discussed, aiming at giving near-fully description of MHW in the East China Sea.

Remote Estimates of Sea Surface Nitrate and Its Trends From Ocean Color in the Northwest Pacific

AbstractSea surface nitrate (SSN) plays an important role in assessing new production and phytoplankton growth in the ocean, yet it has been challenging to estimate SSN from satellites due to its complex and varying relationship with different environmental proxies. The different SSN trends in the northwest Pacific reported in previous studies call for more detailed research to examine the interannual variabilities in SSN. We addressed this problem by developing a stacking‐random‐forest based algorithm for Moderate Resolution Imaging Spectroradiometer (MODIS). It allows estimating SSN from daily sea surface temperature (SST) and Chlorophyll‐a concentration (Chl) at a spatial resolution of 4 km. For SSN ranging between 0.0005 and 25.88 μmol/kg (N = 3,452), the model had a root mean square difference of 1.34 μmol/kg (5.3%) and coefficient of determination of 0.92. Further independent validation and sensitivity tests demonstrated the validity of the algorithm in retrieving SSN. Using this novel data record, for the first time, we investigated the SSN interannual variabilities and trends from MODIS. Overall, the SSN showed a weak decreasing trend of −0.01 ± 0.007 μmol kg−1 yr−1 (p < 0.05) in the northwest Pacific in 2002–2020, associated with an increasing trend in SST (0.03 ± 0.01˚C yr−1 at p < 0.05) and insignificant trend (0.001 ± 0.001 mg m−3 yr−1 at p > 0.05) in Chl. The interannual variabilities of SSN were significantly correlated with the environmental proxies (SST, Chl) and the climate indices (Pacific Decadal Oscillation and North Pacific Gyre Oscillation). The SSN trends can be further restricted with more data available.

The 1991–2020 sea surface temperature normals

AbstractThe 1991–2020 climate normals for sea surface temperature (SST) are computed based on the NOAA Daily Optimum Interpolation SST dataset. This is the first time that high‐resolution SST normals with global coverage can be achieved in the satellite SST era. Normals are one of the fundamental parameters in describing and understanding weather and climate and provide decision‐making information to industry, public, and scientific communities. This product suite includes SST mean, standard deviation, count and extreme parameters at daily, monthly, seasonal and annual time scales on 0.25° spatial grids. The main feature of the SST mean state revealed by the normals is that in the Tropics, the Indo‐Pacific Ocean is dominated by the warm pool (SST ≥ 28°C) while the eastern Pacific is characterized by the cold tongue (SST ≤ 24°C); in the midlatitudes, SSTs are in zonal patterns with high meridional gradients. Daily SST standard deviations are generally small (<1.0°C) except in frontal zones (>1.5°C) mostly associated with ocean currents such as the Gulf Stream, Kuroshio and Equatorial Currents. Compared to the 1982–2011 climatology, the 1991–2020 mean SSTs increased over most global areas but obvious cooling is seen in the Southern Ocean, eastern tropical South Pacific Ocean and North Atlantic warming hole. The Indo‐Pacific warm pool (IPWP) is found to have strengthened in both intensity and coverage since 1982–2011. By a count parameter criterion of ≥300 days annually with SST ≥ 28°C, the IPWP coverage increased 33% from 1982–2011 to 1991–2020. The global mean SST of 1991–2020 is warmer than that of 1982–2011, and the warming rate over 1991–2020 doubles that over 1901–2020.

The effect of diurnal warming of sea‐surface temperatures on the propagation speed of the Madden–Julian oscillation

AbstractThe diurnal warm layer in the upper ocean develops during low surface winds and high incoming solar radiation conditions, often increasing sea‐surface temperatures (SSTs) by up to 1°C. The suppressed phase of the Madden–Julian Oscillation (MJO) favours the formation of such a layer. Here, we analyse the coupled ocean–atmosphere and atmosphere‐only numerical weather prediction systems of the UK Met Office to reveal that important differences arise from the representation of the diurnal warm layer in the coupled model. Though both models are skilful in predicting the MJO to at least a 7‐day lead time, the coupled model predicts approximately 12% faster MJO RMM phase speed propagation than the atmosphere‐only model due to the ability to resolve diurnal warming in the upper ocean that rectifies onto MJO‐associated SST anomalies. The diurnal warming of SST (dSST) in the coupled model leads to an increase in daily mean SST compared with the atmosphere‐only model persisted foundation SST. The strength of the dSST in the coupled model is modulated by MJO conditions. During suppressed MJO conditions on lead day 1, the dSST is enhanced, leading to 0.2°C warmer daily mean MJO‐associated SST anomalies and increased convection in the coupled model by lead day 7. During active MJO convection, the dSST is suppressed, leading to 0.1°C colder MJO‐associated SST anomalies in the coupled model and reduced convection by lead day 7. This variability in dSST further amplifies the MJO propagation speed, underlining the importance of the two‐way feedback between the MJO and the diurnal cycle of SST and the need to accurately represent this process in coupled models.

Analysis of Marine Heatwaves in China’s Coastal Seas and Adjacent Offshore Waters

Marine heatwaves (MHWs) are changing global ecosystems and bearing profound socio-economic impacts, yet our understanding of the spatial features, temporal evolution characteristics, and regional differences in China’s marginal seas remains insufficient. In this study, the spatio-temporal variation characteristics of the frequency, mean intensity, maximum intensity, cumulative intensity, duration and total days of MHWs are systematically analyzed based on daily sea surface temperature data from Operational Sea Surface Temperature and Ice Analysis (OSTIA) for the period of 1983–2020. The results show the following: The annual mean frequency of MHWs in China’s coastal seas is 1.4–4.6 counts per year and increases gradually from north to south. The annual mean of mean intensity, maximum intensity and cumulative intensity are, respectively, in the ranges of 0.3–2.9 °C, 0.4–3.7 °C and 3.9–41.7 °C days, all of which show a significant decreasing trend from north to south. The annual mean of duration and total days of MHWs are in the respective ranges 8.7–19.7 and 18.9–69.1 days. The annual mean of frequency, cumulative intensity, duration and total days all show a clear increasing trend, with respective linear increases of 1.03 counts, 3.57 °C days, and 1.98 and 17.58 days per decade. The annual means of the mean intensity and maximum intensity have no obvious upward trend, with the exceptions of the Bohai and Yellow Seas. Finally, MHWs were divided into the four categories of moderate, strong, severe and extreme, with the results showing that moderate ones account for more than 70% of all heatwave events, while strong, severe and extreme ones, respectively, account for about 25%, 2% and 0.02%.

Prior stress by marine heatwaves and micro-habitat fragmentation drive the colonisation of epifaunal assemblages in marine forests

Macroalgae species of the genus Cystoseira s.l. (Fucales, Phaeophyceae) and genera Cystoseira, Ericaria, and Gongolaria are habitat-forming species from temperate areas characterised by their high productivity and associated biodiversity. These key species are declining worldwide due to multiple anthropogenic and biotic pressures, such as habitat loss or climate change-related effects. These impacts are leading to local/regional extinctions of marine forests and increasing their fragmentation and isolation. Discrete extreme temperature events in the ocean, known as marine heatwaves (MHWs), are emerging as threats to marine systems in many areas of the planet due to climate change. We analysed 74 yr of daily sea surface temperature data on the occurrence of MHWs in the Cantabrian Sea (northern Iberian Peninsula), finding a clear increase in their frequency, duration and intensity in the last 20 yr. We also performed a 10 d mesocosm experiment to assess how MHWs affect fragmented and non-fragmented patches of Ericaria selaginoides. After this experiment, the experimental patches were deployed in a tidal rock pool for 10 d to detect effects on composition and structure of associated epifaunal assemblages. Mesocosm experiment results showed how the biomass, productivity and oxygen consumption of E. selaginoides significantly decreased as MHW intensity increased. Field deployment revealed that abundance, composition and structure of the epifaunal assemblages were significantly affected by the interaction of prior stress by MHWs and patch fragmentation. Overall, our results showed how a canopy-forming macroalga is affected by extreme temperature events and, consequently, having effects on the colonisation of epifauna according to the level of microhabitat fragmentation with potential implications for the conservation of these endangered systems.

Analysis of mesoscale Eddy in the Nordic seas and Barents Sea using multi-satellite data

Under the background of climate change, the characteristics of meso-scale eddies (radius > 20 km) in the Nordic Sea are worthy of further study. In this study, the daily sea level anomaly (SLA) product provided by archiving, validation, and interpretation of satellite oceanographic data (AVISO) and the daily sea surface temperature (SST) data provided by remote sensing systems (REMSSs) were collected from January 2003 to December 2021. The collected data were daily averaged with a spatial grid resolution of 0.25°. The large-scale eddies with a radius of >80 km and activity intensity (EKE) values of >0.02 m2/s were more detectable using SST data Moreover, the spatial distribution between the SLA-based and SST-based eddies had the opposite correlation with respect to the water depth. The SST-based eddies were more sensitive to seasonal variations. The long-term variations in the eddy parameters and the North Atlantic Oscillation Index (NAOI) during 2003–2021 were studied, and it was found that the radius of the SLA-based eddies was likely consistent with the NAOI and the EKE of the SLA-based eddies has the highest correlation (=0.2) with the NAOI, however, the correlation between eddy parameters and the NAOI is not significant. Collectively, SLA data has good performance on eddy identification in most cases, while SST data is suitable for investigating the activities of mesoscale eddies with large radius (> 80 km) and strong EKE (>0.02 m2/s).

Satellite signature of the instantaneous wind response to mesoscale oceanic thermal structures

AbstractThe thermal air–sea interaction mechanism that modulates the atmospheric mixing due to sea‐surface temperature (SST) variability is studied with long‐term consistent satellite records. Statistical analyses of daily and instantaneous wind and SST data are performed over the major western boundary currents (WBCs). This wind–SST coupling, which is mediated by atmospheric mixing, is found to be very relevant on daily, and even shorter, time scales. Co‐located and simultaneous SST and surface wind fields (from Advanced Very High Resolution Radiometer and Advanced Scatterometer data) reveal that the atmosphere responds instantaneously to the presence of SST structures with a larger coupling coefficient with respect to daily and monthly time‐averaged fields. The coupling strength varies seasonally over WBCs in the Northern Hemisphere, with wintertime coupling being the lowest. Reanalysis data show that this behaviour is related to the seasonality of the air–sea temperature difference over the region of interest. Over the Northern Hemisphere WBCs, dry and cold continental air masses drive very unstable conditions, associated with very weak thermal air–sea coupling.

Spatial variation and antecedent sea surface temperature conditions influence Hawaiian intertidal community structure.

Global sea surface temperatures (SSTs) are increasing, and in Hawai'i, rates of ocean warming are projected to double by the end of the 21st century. However, current nearshore warming trends and their possible impacts on intertidal communities are not well understood. This study represents the first investigation into the possible effects of rising SST on intertidal algal and invertebrate communities across the Main Hawaiian Islands (MHI). By utilizing citizen-science data coupled with high-resolution, daily SST satellite measurements from 12 intertidal sites across the MHI from 2004-2019, the response of intertidal algal and invertebrate abundance and community diversity to changes in SST was investigated across multiple spatial scales. Results show high rates of SST warming (0.40°C Decade-1) over this study's timeframe, similar to predicted rates of warming for Hawai'i by the end of the 21st century. Changes in abundance and diversity in response to SST were variable among intertidal sites, but differences in antecedent SST among intertidal sites were significantly associated with community dissimilarity. In addition, a statistically significant positive relationship was found between SST and Simpson's diversity index, and a significant relationship was also found between SST and the abundance of six dominant taxa. For five of these six dominant taxa, antecedent SSTs over the 6-12 months preceding sampling were the most influential for describing changes to abundance. The increase in community diversity in response to higher SSTs was best explained by temperatures in the 10 months preceding sampling, and the resultant decreased abundance of dominant turf algae. These results highlight rapidly warming nearshore SSTs in Hawai'i and the longer-term effects of antecedent SSTs as significant drivers of change within Hawaiian intertidal communities. Therefore, we suggest that future research and management should consider the possibility of lagging effects of antecedent SST on intertidal communities in Hawai'i and elsewhere.