Sea Surface Temperature Time Series Research Articles

Time series analysis of sea surface temperature change in the coastal seas of Türkiye

Sea surface temperature (SST) is a crucial geophysical parameter in assessing heat exchange between the air and sea surface. Changes in SST and its accurate prediction play a pivotal role in explaining the global heat balance, determining atmospheric circulations, and constructing global climate models. This work aims to reveal a model for one-month-ahead forecasting of SST time series data along the Türkiye coasts, encompassing the Mediterranean, Aegean, Marmara, and Black Seas, and their long-term future forecast. A long short-term memory (LSTM) neural network and seasonal autoregressive integrated moving average (SARIMA) models are used for this purpose. The ECMWF ERA5 (0.5ox0.5°) monthly SST dataset spanning the years 1970–2023 is used for model development. The results obtained from the LSTM and SARIMA models show that there will be an increasing trend in SSTs along these seacoasts until 2050. The SST measurements of 23.4 °C, 20.2 °C, 17.0 °C, and 16.6 °C recorded along the Mediterranean, Aegean, Marmara, and Black Seas in 2023 are expected to rise to 25.1 °C, 21.9 °C, 18.1 °C, and 18.8 °C, respectively, by 2050. These figures indicate an increase of 7.3%, 8.4%, 6.5%, and 13.3% in the SST values across these coastal seas over the next quarter century.

Evaluating the time to detect biological effects of ocean acidification and warming: an example using simulations of purple sea urchin settlement

Ocean acidification (OA) and ocean warming driven by climate change are important stressors for marine species and systems, but documenting and detecting their long-term impacts on biological responses outside of laboratory settings are challenging due to natural variability caused by complex processes and interactions. We used settlement of purple sea urchins Strongylocentrotus purpuratus in the Southern California Bight (USA) over 6 yr as an example data set to parameterize a simulation model for exploring the time needed to detect environmental effects on a biological response. A generalized linear model was used to describe an index of urchin settlement as functions of pH, sea surface temperature (SST), sea surface salinity (SSS), and spatio-temporal factors, demonstrating that settlement was negatively associated with pH (i.e. lower settlement at higher pH) and positively associated with SST and SSS. Monte Carlo simulations were developed from this base model under a variety of alternative scenarios to estimate the time to detect: (1) annual trends in pH and SST time series, (2) pH and SST effects on urchin settlement, and (3) annual trends in urchin settlement. Time to detect pH and SST effects was predominantly influenced by the underlying strength of the relationships and the model uncertainty. Time to detect annual trends in settlement was more sensitive to the severity of long-term OA and warming trends, which had cumulative (at times opposing) effects. This study highlights the variable time scales (2-60+ yr) that may be necessary to detect biological responses to OA and ocean warming and the sensitivity to different assumptions of the study system.

Temporal variability of sea surface temperature affects marine macrophytes range retractions as well as gradual warming

Record mean sea surface temperatures (SST) during the past decades and marine heatwaves have been identified as responsible for severe impacts on marine ecosystems, but the role of changes in the patterns of temporal variability under global warming has been much less studied. We compare descriptors of two time series of SST, encompassing extirpations (i.e. local extinctions) of six cold-temperate macroalgae species at their trailing range edge. We decompose the effects of gradual warming, extreme events and intrinsic variability (e.g. seasonality). We also relate the main factors determining macroalgae range shifts with their life cycles characteristics and thermal tolerance. We found extirpations of macroalgae were related to stretches of coast where autumn SST underwent warming, increased temperature seasonality, and decreased skewness over time. Regardless of the species, the persisting populations shared a common environmental domain, which was clearly differentiated from those experiencing local extinction. However, macroalgae species responded to temperature components in different ways, showing dissimilar resilience. Consideration of multiple thermal manifestations of climate change is needed to better understand local extinctions of habitat-forming species. Our study provides a framework for the incorporation of unused measures of environmental variability while analyzing the distributions of coastal species.

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.

On the Sea Surface Temperature Forecasting Problem with Deep Dilation-Erosion-Linear Models

The sea surface temperature (SST) is considered an important measure for detecting changes in climate and marine ecosystems. So, its forecasting is essential for supporting governmental strategies to avoid side effects on the global population. In this paper, we analyze the SST time series and suggest that a combination between a linear component and a nonlinear component with long-term dependency can better represent it. Based on this assumption, we propose a deep neural network architecture with dilation-erosion-linear (DEL) processing units to deal with this particular kind of time series. An empirical analysis is performed in this work using three SST time series, where we explore three statistical measures. The experimental results demonstrate that the proposed model outperformed recent and classical literature forecasting techniques according to well-known performance metrics.

Fingerprinting Mediterranean hurricanes using pre-event thermal drops in seawater temperature

Extreme atmospheric-marine events, known as medicanes (short for “Mediterranean hurricanes”), have affected the Mediterranean basin in recent years, resulting in extensive coastal flooding and storm surges, and have occasionally been responsible for several casualties. Considering that the development mechanism of these events is similar to tropical cyclones, it is plausible that these phenomena are strongly affected by sea surface temperatures (SSTs) during their development period (winter and autumn seasons). In this study, we compared satellite data and the numerical reanalysis of SSTs from 1969 to 2023 with in situ data from dataloggers installed at different depths off the coast of southeastern Sicily as well as from data available on Argo floats on the Mediterranean basin. A spectral analysis was performed using a continuous wavelet transform (CWT) for each SST time series to highlight the changes in SSTs prior to the occurrence of Mediterranean Hurricanes as well as the energy content of the various frequencies of the SST signal. The results revealed that decreases in SST occurred prior to the formation of each Mediterranean hurricane, and that this thermal drop phenomenon was not observed in intense extra-tropical systems. The spectral analyses revealed that high CWT coefficients representing high SST energy contents were observed before the occurrence of a Mediterranean hurricane. This information may provide a useful fingerprint for distinguishing Mediterranean hurricanes from common seasonal storms at the onset of these events.

Satellite-based time-series of sea-surface temperature since 1980 for climate applications

A 42-year climate data record of global sea surface temperature (SST) covering 1980 to 2021 has been produced from satellite observations, with a high degree of independence from in situ measurements. Observations from twenty infrared and two microwave radiometers are used, and are adjusted for their differing times of day of measurement to avoid aliasing and ensure observational stability. A total of 1.5 × 1013 locations are processed, yielding 1.4 × 1012 SST observations deemed to be suitable for climate applications. The corresponding observation density varies from less than 1 km−2 yr−1 in 1980 to over 100 km−2 yr−1 after 2007. Data are provided at their native resolution, averaged on a global 0.05° latitude-longitude grid (single-sensor with gaps), and as a daily, merged, gap-free, SST analysis at 0.05°. The data include the satellite-based SSTs, the corresponding time-and-depth standardised estimates, their standard uncertainty and quality flags. Accuracy, spatial coverage and length of record are all improved relative to a previous version, and the timeseries is routinely extended in time using consistent methods.

Spatial pattern of sea surface temperature and chlorophyll-a trends in relation to hydrodynamic processes in the Alborán Sea

Environmental conditions such as temperature, planktonic biomass, and ocean currents play an important role in the development and distribution of marine species. This work aims to estimate, in a high spatial resolution, the actual trends of sea surface temperature (SST) and chlorophyll-a concentration (Chl-a) and to assess the relationship with the local hydrodynamic conditions in the Alborán Sea. To investigate these objectives, time series of SST and Chl-a of satellite sensor data were analyzed during 20 years from January 2001 to December 2020, using the Seasonal-Trend-Loess (STL) decomposition method and the Mann-Kendall seasonality test. The results, obtained with a 95% of confidence, showed that the Alborán Sea basin is subject to sea surface warming evaluated at 0.027 ± 0.008 ° C per year, related to the warming of the Atlantic water mass, which contributes to a decrease of productivity evaluated at -0.0024 ± 0.0003 μg /l per year of Chl-a concentration. These trends are not homogeneous over the entire basin area but show a large regional variation between different parts of the Alborán Sea due to the hydrodynamic process of the Atlantic Jet - Western Alboran Gyre system (AJ-WAG), which is more active especially in summer/autumn seasons and contribute largely to these changes by mixing the waters of the Atlantic Ocean and the Mediterranean Sea.

Machine learning methods to predict sea surface temperature and marine heatwave occurrence: a case study of the Mediterranean Sea

Abstract. Marine heatwaves (MHWs) have significant social and ecological impacts, necessitating the prediction of these extreme events to prevent and mitigate their negative consequences and provide valuable information to decision-makers about MHW-related risks. In this study, machine learning (ML) techniques are applied to predict sea surface temperature (SST) time series and marine heatwaves in 16 regions of the Mediterranean Sea. ML algorithms, including the random forest (RForest), long short-term memory (LSTM), and convolutional neural network (CNN), are used to create competitive predictive tools for SST. The ML models are designed to forecast SST and MHWs up to 7 d ahead. For each region, we performed 15 different experiments for ML techniques, progressively sliding the training and the testing period window of 4 years from 1981 to 2017. Alongside SST, other relevant atmospheric variables are utilized as potential predictors of MHWs. Datasets from the European Space Agency Climate Change Initiative (ESA CCI SST) v2.1 and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis from 1981 to 2021 are used to train and test the ML techniques. For each area, the results show that all the ML methods performed with minimum root mean square errors (RMSEs) of about 0.1 °C at a 1 d lead time and maximum values of about 0.8 °C at a 7 d lead time. In all regions, both the RForest and LSTM consistently outperformed the CNN model across all lead times. LSTM has the highest predictive skill in 11 regions at all lead times. Importantly, the ML techniques show results similar to the dynamical Copernicus Mediterranean Forecasting System (MedFS) for both SST and MHW forecasts, especially in the early forecast days. For MHW forecasting, ML methods compare favorably with MedFS up to 3 d lead time in 14 regions, while MedFS shows superior skill at 5 d lead time in 9 out of 16 regions. All methods predict the occurrence of MHWs with a confidence level greater than 50 % in each region. Additionally, the study highlights the importance of incoming solar radiation as a significant predictor of SST variability along with SST itself.

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.

Influence of Weather and Climate on Multidecadal Trends in Atlantic Hurricane Genesis and Tracks

Abstract This study investigates the relative roles of sea surface temperature–forced climate changes and weather variability in driving the observed eastward shift of Atlantic hurricane tracks over the period from 1970 to 2021. A 10-member initial condition ensemble with a ∼25-km horizontal resolution tropical cyclone permitting atmospheric model (GFDL AM2.5-C360) with identical sea surface temperature and radiative forcing time series was analyzed in conjunction with historical hurricane track observations. While a frequency increase was recovered by all the simulations, the observed multidecadal eastward shift in tracks was not robust across the ensemble members, indicating that it included a substantial contribution from weather-scale variability. A statistical model was developed to simulate expected storm tracks based on genesis location and steering flow, and it was used to conduct experiments testing the roles of changing genesis location and changing steering flow in producing the multidecadal weather-driven shifts in storm tracks. These experiments indicated that shifts in genesis location were a substantially larger driver of these multidecadal track changes than changes in steering flow. The substantial impact of weather on tracks indicates that there may be limited predictability for multidecadal track changes like those observed, although basinwide frequency has greater potential for prediction. Additionally, understanding changes in genesis location appears essential to understanding changes in track location. Significance Statement From the 1970s to the present, there has been an increase in the frequency of North Atlantic hurricanes, but they have also shifted in location to the east, away from land. We explore whether this shift in hurricanes’ locations was caused by climatic factors or randomness to understand if and how these trends will persist. We also consider whether the shift was due to a change in where hurricanes started or how they moved over their lifespan. Analyzing data from observed and simulated hurricanes, we find that the shift was made more likely by climate factors, but ultimately occurred due to random variability in the hurricanes’ starting locations. These results suggest a higher uncertainty in the future location and impact of hurricanes and highlight the importance of studying why hurricanes originate where they do.

Sea Surface Temperature and Marine Heat Wave Predictions in the South China Sea: A 3D U-Net Deep Learning Model Integrating Multi-Source Data

Accurate sea surface temperature (SST) prediction is vital for disaster prevention, ocean circulation, and climate change. Traditional SST prediction methods, predominantly reliant on time-intensive numerical models, face challenges in terms of speed and efficiency. In this study, we developed a novel deep learning approach using a 3D U-Net structure with multi-source data to forecast SST in the South China Sea (SCS). SST, sea surface height anomaly (SSHA), and sea surface wind (SSW) were used as input variables. Compared with the convolutional long short-term memory (ConvLSTM) model, the 3D U-Net model achieved more accurate predictions at all lead times (from 1 to 30 days) and performed better in different seasons. Spatially, the 3D U-Net model’s SST predictions exhibited low errors (RMSE < 0.5 °C) and high correlation (R > 0.9) across most of the SCS. The spatially averaged time series of SST, both predicted by the 3D U-Net and observed in 2021, showed remarkable consistency. A noteworthy application of the 3D U-Net model in this research was the successful detection of marine heat wave (MHW) events in the SCS in 2021. The model accurately captured the occurrence frequency, total duration, average duration, and average cumulative intensity of MHW events, aligning closely with the observed data. Sensitive experiments showed that SSHA and SSW have significant impacts on the prediction of the 3D U-Net model, which can improve the accuracy and play different roles in different forecast periods. The combination of the 3D U-Net model with multi-source sea surface variables, not only rapidly predicted SST in the SCS but also presented a novel method for forecasting MHW events, highlighting its significant potential and advantages.

Impact of the El Niño on Fire Dynamics on the African Continent

Several studies investigated the occurrence of fires in Africa with numerical modeling or applied statistics; however, only a few studies focused on the influence of El Niño on the fire risk using a coupled model. The study aimed to assess the influence of El Niño on wildfire dynamics in Africa using the SPEEDY-HYCOM model. El Niño events in the Eastern Tropical Pacific were classified via sea surface temperature (SST) anomaly based on a predefined climatology between 1961 and 2020 for the entire time series of SST, obtaining linear anomalies. The time series of the SST anomalies was created for the region between 5° N and 5° S and 110° W and 170° W. The events were defined in three consecutive 3-month periods as weak, moderate, and strong El Niño conditions. The Meteorological Fire Danger Index (MFDI) was applied to detect fire hazards. The MFDI simulated by the SPEEDY-HYCOM model for three El Niño categories across different lagged months revealed relevant distinctions among the categories. In the case of ‘Weak’, the maximum variability of fire risk observed at time lags (0, -3, -6, and -9 months) was primarily in Congo, Gabon, and Madagascar. The ‘Moderate’ pattern had similar characteristics to ‘Weak’ except for the lag-6 months and its occurrence in the equatorial zone of Africa. ‘Strong’ showed a remarkable impact in East Africa, resulting in high fire risk, regardless of time lags. Precipitation and evaporation simulations (SPEEDY-HYCOM) indicated that El Niño categories in Africa need particular attention in the central, southern, and southeastern regions emphasizing the significance of lag-0 and lag-6 (evaporation) as well as lag-0, lag-6, and lag-9 (precipitation). The SPEEDY-HYCOM coupled model in conjunction with the MFDI was efficient in assessing climate variabilities in Africa during El Niño events. This model allows the analysis and prediction of wildfire risks based on El Niño events, providing crucial information for wildfire management and prevention. Its simulations uncover significant variations in risks among different El Niño categories and lagged months, contributing to the understanding and mitigation of this environmental challenge.

Climate attribution time series track the evolution of human influence on North Pacific sea surface temperature

We apply climate attribution techniques to sea surface temperature time series from five regional North Pacific ecosystems to track the growth in human influence on ocean temperatures over the past seven decades (1950–2022). Using Bayesian estimates of the Fraction of Attributable Risk (FAR) and Risk Ratio (RR) derived from 23 global climate models, we show that human influence on regional ocean temperatures could first be detected in the 1970s and grew until 2014–2020 temperatures showed overwhelming evidence of human contribution. For the entire North Pacific, FAR and RR values show that temperatures have reached levels that were likely impossible in the preindustrial climate, indicating that the question of attribution is already obsolete at the basin scale. Regional results indicate the strongest evidence for human influence in the northernmost ecosystems (Eastern Bering Sea and Gulf of Alaska), though all regions showed FAR values > 0.98 for at least one year. Extreme regional SST values that were expected every 1000–10 000 years in the preindustrial climate are expected every 5–40 years in the current climate. We use the Gulf of Alaska sockeye salmon fishery to show how attribution time series may be used to contextualize the impacts of human-induced ocean warming on ecosystem services. We link negative warming effects on sockeye fishery catches to increasing human influence on regional temperatures (increasing FAR values), and we find that sockeye salmon migrating to sea in years with the strongest evidence for human effects on temperature (FAR ⩾ 0.98) produce catches 1.4 standard deviations below the long-term log mean. Attribution time series may be helpful indicators for better defining the human role in observed climate change impacts, and may thus help researchers, managers, and stakeholders to better understand and plan for the effects of climate change.

NOAA MODIS SST Reanalysis Version 1

The first NOAA full-mission reanalysis (RAN1) of the sea surface temperature (SST) from the two Moderate Resolution Imaging Spectroradiometers (MODIS) onboard Terra (24 February 2000–present) and Aqua (4 July 2002–present) was performed. The dataset was produced using the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) enterprise SST system from Collection 6.1 brightness temperatures (BTs) in three MODIS thermal emissive bands centered at 3.7, 11, and 12 µm with a spatial resolution of 1 km at nadir. In the initial stages of reprocessing, several instabilities in the MODIS SST time series were observed. In particular, Terra SSTs and corresponding BTs showed three ‘steps’: two on 30 October 2000 and 2 July 2001 (due to changes in the MODIS operating mode) and one on 25 April 2020 (due to a change in its nominal blackbody temperature, BBT, from 290 to 285 K). Additionally, spikes up to several tenths of a kelvin were observed during the quarterly warm-up/cool-down (WUCD) exercises, when the Terra MODIS BBT was varied. Systematic gradual drifts of ~0.025 K/decade were also seen in both Aqua and Terra SSTs over their full missions due to drifting BTs. These calibration instabilities were mitigated by debiasing MODIS BTs using the time series of observed minus modeled (‘O-M’) BTs. The RAN1 dataset was evaluated via comparisons with various in situ SSTs. The data meet the NOAA specifications for accuracy (±0.2 K) and precision (0.6 K), often by a wide margin, in a clear-sky ocean domain of 19–21%. The long-term SST drift is typically less than 0.01 K/decade for all MODIS SSTs, except for the daytime ‘subskin’ SST, for which the drift is ~0.02 K/decade. The MODIS RAN1 dataset is archived at NOAA CoastWatch and updated monthly in a delayed mode with a latency of two months. Additional archival with NASA JPL PO.DAAC is being discussed.

Upwelling characteristics in the Gulf of Riga (Baltic Sea): multiple data source approach

Upwellings are characteristic for the Baltic Sea region including the Gulf of Riga, although the current knowledge is rather limited with only few research conducted in the Gulf itself. Upwelling events in the Gulf of Riga in 2010–2022 were studied by analyzing sea surface temperature time series from coastal stations and SmartBuoy, together with satellite data, model data, and CTD (conductivity, temperature, and depth) surveys. The starting/end point, active, and relaxation phases were defined in each event to describe the characteristics and length of each phase. Upwellings were less frequent (41%) on the eastern coast but lasted longer and had higher temperature drops than on the western coast. On the western coast, a variety of upwelling characteristics between stations only 30 km apart were found with the likely reason being the different orientations of the coastline with respect to the wind direction. Satellite data revealed that on the western coast of the Gulf, rather small upwelling events form along specific sections of the coastline. Of all upwelling events, 30% were characterized by an immediate temperature increase after reaching the minimum temperature, and we suggest that this is related to a distinct change in wind direction. The results from the simulations indicated smaller lateral density and salinity gradients in the sea surface than in larger Baltic Sea gulfs. It signals that conditions for the occurrence of baroclinic instabilities are rather small; thus, we suggest that weaker gradients could explain quite fast upwelling relaxation in the basin if compared to, e.g., the Gulf of Finland.

Granulation-based LSTM-RF combination model for hourly sea surface temperature prediction

ABSTRACT Accurate predictions of sea surface temperature (SST) are crucial due to the significant impact of SST on the global ocean-atmospheric system and its potential to trigger extreme weather events. Many existing machine-learning-based SST predictions adapt the traditional iterative point-wise prediction mechanism, whose predicting horizons and accuracy are limited owing to the high sensitivity to cumulative errors during iterative predictions. Therefore, this paper proposes a novel granulation-based long short-term memory (LSTM)-random forest (RF) combination model that can fully capture the feature dependencies involved in the fluctuation of SST sequences, reduce the cumulative error in the iteration process, and extend the prediction horizons, which includes two sub-models (adaptive granulation model and hybrid prediction model). They can restack the one-dimensional SST time-series into multidimensional feature variables, and achieve a strong forecasting ability. The analysis shows that the proposed model can achieve more accurate prediction-hours in nearly all prediction ranges from 1 to 125 h. The average prediction error of the proposed model in 25–125 h is 0.07 K, similar to that (0.067 K) in the first 24 h, which exhibits a high generalization performance and robustness and isthus a promising platform for the medium- and long-term forecasting of hourly SSTs.

A Stable Isotope Sclerochronology‐Based Forensic Method for Reconstructing Debris Drift Paths With Application to the MH370 Crash

AbstractA flaperon belonging to Malaysian Airlines flight MH370 washed ashore on Réunion Island covered with the barnacle Lepas anatifera in July 2015, more than a year after the plane's disappearance. Here, we report the first high‐precision δ18Ocalcite versus temperature relationship for L. anatifera reared under laboratory conditions to unlock clues to the flaperon's drift path and origin. Using this experimental relationship and known growth rates for L. anatifera, we also demonstrate a new method for (a) converting δ18O data for one of the MH370 barnacles into a dated time series of sea surface temperatures (SSTs) experienced during the last part of the flaperon's drift and (b) identifying best fits between the observed flaperon SST time series and 50,000 SST histories generated from a particle‐tracking simulation. Our new method identifies a flaperon drift path far south of a previous isotope‐based reconstruction. We conclude with specific recommendations for using our method to continue the search for MH370 and other applications.

Lightweight Neural Network for Spatiotemporal Filling of Data Gaps in Sea Surface Temperature Images

Optical remotely sensed data often have data gaps due to cloud coverage, which hinders their full potential in many environmental applications. The question of how to accurately and effectively reconstruct the measurements in the data gaps remains a challenge. In this study, we developed a bidirectional long short-term memory (BiLSTM) model with a novel and adaptable custom temporal penalty layer for spatiotemporal gap-filling. The model was trained and tested in time-series daily night-time sea surface temperature (SST) images acquired by the Himawari-8 satellite. The modelling results showed strong performance, accurately reconstructing spatial and temporal features of cloud affected SST data. Our neural network achieved a per-image MAE of 0.1193°C and per-image RMSE of 0.1293°C. The model was also able to produce realistic SST time-series predictions that were consistent with the expected seasonal variables. Importantly, the BiLSTM model outperformed the previous state-of-the-art Simple Spatial Gapfilling Processor (SSGP) algorithm in terms of error metrics, structural similarity, and computational efficiency. Overall, the results of this study indicates that the BiLSTM neural network model we developed is highly suitable for spatiotemporal gap-filling of SST and other remotely sensed data.

Detection of surface temperature anomaly of the Sea of Marmara

Monitoring sea surface temperature (SST) over a long-term and detecting the anomalies highly contribute to understanding the prevailing water quality of the sea. Earth observation satellite images are the key data sources that offer the long-term SST detection in a cost and time effective way. Since the Sea of Marmara in Türkiye is surrounded by the highly populated provinces, the water quality of the sea has gained importance for scientific and public communities over the years. This article emphasizes on the significance of detecting SST trend and corresponding anomalies of the Sea of Marmara over the past 32 years. To address the SST variations of the Sea of Marmara in time, a comprehensive set of both field and satellite data regarding SSTs were obtained within the context of this study. The SST trend and its anomalies between the years 1990 and 2021 were detected by applying Seasonal-Trend decomposition procedure based on LOESS (STL) method to NOAA OISST V2 data. On the other hand, spatial SST distribution was detected with Landsat-8, Sentinel-3 and NOAA OISST V2 satellite data. SST results were verified with the in-situ data within the scope of accuracy assessment. The results showed that SST time-series data performed an increasing trend and had anomalies mostly during the spring months in the recent years.