Sea Surface Temperature Values Research Articles

Improving the Statistical Representation of Tropical Cyclone In-Storm Sea Surface Temperature Cooling

Abstract This study uses fixed buoy time series to create an algorithm for sea surface temperature (SST) cooling underneath a tropical cyclone (TC) inner core. To build predictive equations, SST cooling is first related to single variable predictors such as the SST before storm arrival, ocean heat content (OHC), mixed layer depth, sea surface salinity and stratification, storm intensity, storm translation speed, and latitude. Of all the single variable predictors, initial SST before storm arrival explains the greatest amount of variance for the change in SST during storm passage. Using a combination of predictors, we created nonlinear predictive equations for SST cooling. In general, the best predictive equations have four predictors and are built with knowledge about the prestorm ocean structure (e.g., OHC), storm intensity (e.g., minimum sea level pressure), initial SST values before storm arrival, and latitude. The best-performing SST cooling equations are broken up into two ocean regimes: when the ocean heat content is less than 60 kJ cm−2 (greater spread in SST cooling values) and when the ocean heat content is greater than 60 kJ cm−2 (SST cooling is always less than 2°C), which demonstrates the importance of the prestorm oceanic thermal structure on the in-storm SST value. The new equations are compared to what is currently used in a statistical–dynamical model. Overall, since the ocean providing the latent heat and sensible heat fluxes necessary for TC intensification, the results highlight the importance for consistently obtaining accurate in-storm upper-oceanic thermal structure for accurate TC intensity forecasts. Significance Statement The ocean provides the heat and moisture necessary for tropical cyclone (TC) intensification. Since the heat and moisture transfer depend on the sea surface temperature (SST), we create statistical equations for the prediction of SST underneath the storm. The variables we use combine the initial SST before the storm arrives, the upper-ocean thermal structure, and the strength and translation speed of the storm. The predictive equations for SST are evaluated for how well they improve TC intensity forecasts. The best-performing equations can be used for prediction in operational statistical models, which can aid intensity forecasts.

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 to 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.

Distribution Patterns of Grey Mullet in the Taiwan Strait in Relation to Oceanographic Conditions

The depletion of the grey mullet population in the Taiwan Strait has generated interest in conservation initiatives and to enhance the effectiveness of restoration initiatives, it is essential to comprehend the specific habitat preferences of the species in question, particularly in relation to environmental changes. In this study, habitat suitability modelling was used to investigate the distribution patterns of grey mullet. Sea surface height (SSH) was the most significant predictor of the grey mullet standardized catch per unit effort (S.CPUE). Furthermore, sea surface temperature (SST) was the second most important oceanographic parameter, followed by mixed-layer depth (MLD). The grey mullet S.CPUE peaked in regions with the summed SST, Chla, salinity, SSH, MLD and current values being 20 °C, 0.9 mg/m3, 34.5 psu, 0.45 m, 18–38 m, and 0.25 m2/s2, respectively. From late October, HSI levels in the northern Taiwan ocean increased, with a consistent upward trend. The distribution expanded in November and December, focusing on the northern and mid-ocean regions west of Taiwan. HSI levels peak in January and February, especially in Taiwan’s coastal region. High HSI distribution shifts away from coastlines and diminishes in April. The findings of this study will contribute to the understanding of this specific species and the approach used in this study may be applicable to other fisheries stocks also.

Seasonal variability of coastal pH and CO2 using an oceanographic buoy in the Canary Islands

Ocean acidification, caused by the absorption of carbon dioxide (CO2) from the atmosphere into the ocean, ranks among the most critical consequences of climate change for marine ecosystems. Most studies have examined pH and CO2 trends in the open ocean through oceanic time-series research. The analysis in coastal waters, particularly in island environments, remains relatively underexplored. This gap in our understanding is particularly important given the profound implications of these changes for coastal ecosystems and the blue economy. The present study focuses on the ongoing monitoring effort that started in March 2020 along the east coast of Gran Canaria, within the Gando Bay, by the CanOA-1 buoy. This monitoring initiative focuses on the systematic collection of multiple variables within the CO2 system, such as CO2 fugacity (fCO2), pH (in total scale, pHT), total inorganic carbon (CT), and other hydrographic variables including sea surface salinity (SSS), sea surface temperature (SST) and wind intensity and direction. Accordingly, the study allows the computation of the CO2 flux (FCO2) between the surface waters and the atmosphere. During the study period, stational (warm and cold periods) behavior was found for all the variables. The lowest SST values were recorded in March, with a range of 18.8-19.3°C, while the highest SST were observed in September and October, ranging from 24.5-24.8°C. SST exhibited an annual increase with a rate of 0.007°C yr-1. Warmer months increased SSS, while colder periods, influenced by extreme events like tropical storms, led to lower salinity (SSS=34.02). The predominant Trade Winds facilitated the arrival of deeper water, replenishing seawater. The study provided insights into atmospheric CO2. Atmospheric fCO2 averaged 415 ± 4 µatm (2020-2023). Surface water fCO2sw presented variability, with the highest values recorded in September and October, peaking at 437 µatm in September 2021. The lowest values for fCO2sw were found in February 2021 (368 µatm). From 2020 to 2023, surface water fCO2sw values displayed an increasing rate of 1.9 µatm yr-1 in the study area. The assessment of fCO2sw decomposition into thermal and non-thermal processes revealed the importance of SST on the fCO2sw. Nevertheless, in the present study, it is crucial to remark the impact of non-thermal factors on near-shallow coastal regions. Our findings highlight the influence of physical factors such as tides, and wind effect to horizontal mixing in these areas. The CT showed a mean concentration of 2113 ± 8 μmol kg-1 and pH at in-situ temperature (pHT,IS) has a mean value of 8.05 ± 0.02. The mean FCO2 from 2020 to 2023 was 0.34 ± 0.04 mmol m-2 d-1 (126 ± 13 mmol m-2 yr-1) acting as a slight CO2 source. In general, between May and December were the months when the area was a source of CO2. Extrapolating to the entire 6 km2 of Gando Bay, the region sourced 33 ± 4 Tons of CO2 yr-1.

On the Estimation of the Interannual Variability of the Ocean Surface Temperature in the Area of the Peruvian Upwelling

The interannual variability of the ocean surface temperature in the area of the Peruvian upwelling for the period 1980–2022 is considered according to the satellite archive GODAS (Global Ocean Data Assimilation System) using the methods of multivariate statistical analysis. Local foci of significant trends, for average annual Sea Surface Temperature (SST) values, were identified near the Peruvian offshore. Four regions (clusters) were obtained, which describe the variability of SST in front off Peru, which could be used to pretend to develop a prognostic oceanographic model. Furthermore, coincidences of temperature fluctuations were found between the first cluster and the region N3+4

Comparative Analysis of the Latest Global Oceanic Precipitation Estimates from GPM V07 and GPCP V3.2 Products

Abstract Satellites bring opportunities to quantify precipitation amount and distribution over the globe, critical to understanding how the Earth system works. The amount and spatial distribution of oceanic precipitation from the latest versions (V07 and the previous version) of the Global Precipitation Measurement (GPM) Core Observatory instruments and selected members of the constellation of passive microwave sensors are quantified and compared with other products such as the Global Precipitation Climatology Project (GPCP V3.2); the Merged CloudSat, TRMM, and GPM (MCTG) climatology; and ERA5. Results show that GPM V07 products have a higher precipitation rate than the previous version, except for the radar-only product. Within ∼65°S–65°N, covered by all of the instruments, this increase ranges from about 9% for the combined radar–radiometer product to about 16% for radiometer-only products. While GPM precipitation products still show lower mean precipitation rate than MCTG (except over the tropics and Arctic Ocean), the V07 products (except radar-only) are generally more consistent with MCTG and GPCP V3.2 than V05. Over the tropics (25°S–25°N), passive microwave sounders show the highest precipitation rate among all of the precipitation products studied and the highest increase (∼19%) compared to their previous version. Precipitation products are least consistent in midlatitude oceans in the Southern Hemisphere, displaying the largest spread in mean precipitation rate and location of latitudinal peak precipitation. Precipitation products tend to show larger spread over regions with low and high values of sea surface temperature and total precipitable water. The analysis highlights major discrepancies among the products and areas for future research.

Analysis of Chlorophyll-A Distribution And Sea Surface Temperature for Estimation of Skipjack Fishing Grounds (Katsuwonus Pelamis) Based on Different Seasons in South Buton Waters

The success of fishing activities is certainly greatly influenced by the conditions of the Fishing Ground (FG). Therefore, information about potential fishing areas is needed by fishermen in fishing activities. Chlorophyll-a is the primary productivity in water. The development of aquatic chlorophyll is influenced by Sea Surface Temperature (SST). The purpose of this study was to analyze the distribution of chlorophyll-a and sea surface temperature (SST) for estimating fish fishing areas based on different seasons in South Buton Waters. This type of research is exploratory research. This study used remote sensing method. The direct interview method with respondents aims to validate data (fishermen) from remote sensing processing. The study used Aqua Modis satellite imagery data to derive parameter values of chlorophyll-a and sea surface temperature. The results of the analysis of the distribution of chlorophyll-a in South Buton waters are the highest with chlorophyll-a concentrations ranging from 0.3-3.0 mg / m³ spread in the waters of Sampolawa and Batauga Districts with surface temperatures ranging from 26 °C and 33 °C. Eastern and Transitional Season II have high levels of chlorophyll-a concentration. This season will be a potential skipjack fishing area in South Buton Waters.

Long term of sea surface temperature prediction for Indonesia seas using multi time-series satellite data for upwelling dynamics projection

Global warming, which impacts global temperatures, has led to an increase in sea surface temperature (SST). This rise significantly affects marine ecological systems, especially in Indonesia. As a result, long-term forecasting of SST dynamics is essential for shaping various policies. However, most studies on SST forecasting have focused on short-term predictions in local or medium-sized areas and often overlook the dynamic upwelling that influences SST. In this paper, we propose a five-year SST prediction for the Indonesian seas, incorporating multi-time-series satellite data to project upwelling dynamics. We utilized two deep learning time series models to construct a predictive model that estimates monthly SST values. This model employs extensive multiple satellite data, encompassing 14,934 points (including SST with a resolution of 0.090, wind, ENSO, heat flux, and solar radiation) from 2003 to 2021. To enrich the training data and mitigate overfitting, we applied data augmentation for time series. Experimental results reveal that all satellite datasets correlate with SST over five years. The 1D-Convolution Neural Network outperformed the Long-Short Term Memory model, exhibiting the lowest mean absolute error of 0.39 °C compared to 0.45 °C. Our model detected a consistent upwelling dynamic over a five-year pattern in the Indonesian seas. These findings suggest that our proposed model offers accurate and efficient long-term monthly SST predictions, crucial for upwelling projections.

Mass mortality of the keratose sponge Sarcotragus foetidus in the Aegean Sea (Eastern Mediterranean) correlates with proliferation of Vibrio bacteria in the tissues.

In the last two decades, episodes of mass mortality in benthic communities have often been associated with climatic anomalies, but the ultimate mechanisms through which they lead to death have rarely been identified. This study reports a mass mortality of wild sponges in the Aegean Sea (Turkey, Eastern Mediterranean), which affected the keratose demosponge Sarcotragus foetidus in September 2021. We examined the occurrence of thermo-dependent bacteria of the genus Vibrio in the sponges, identified through 16S rRNA of colonies isolated from sponge tissue in specific culturing media. Six Vibrio sequences were identified from the sponges, three of them being putatively pathogenic (V. fortis, V. owensii, V. gigantis). Importantly, those Vibrios were isolated from only tissues of diseased sponges. In contrast, healthy individuals sampled in both summer and winter led to no Vibrio growth in laboratory cultures. A 50 years record of sea surface temperature (SST) data for the study area reveals a progressive increase in temperature from 1970 to 2021, with values above 24°C from May to September 2021, reaching an absolute historical maximum of 28.9°C in August 2021. We hypothesize that such elevated SST values maintained for several months in 2021 promoted proliferation of pathogenic Vibrio species (thermo-dependent bacteria) in S. foetidus, triggering or aggravating the course of sponge disease. Thus, vibrioisis emerges as one of the putative mechanisms through which global water warming in the Mediterranean Sea translates into sponge mortality. The historical time course of temperature data for the studied area in the Aegean Sea predicts that recurrent waves of elevated SST are likely to occur in the coming summers. If so, recurrent disease may eventually eliminate this abundant sponge from the sublittoral in the midterm, altering the original bathymetric distribution of the species and compromising its ecological role.

Analysis of Fringescale Sardine Fishing Grounds (Sardinella fimbriata) Based on Sea Surface Temperature and Chlorophyll-A in Tegal Waters, Indonesia

An alternative that can be done in determining the fishing area is to use remote sensing technology through the approach of sea surface temperature distribution and chlorophyll-a. The purpose of this study was to determine the value of the distribution of sea surface temperature and chlorophyll-a, as well as analyze the relationship between sea surface temperature and chlorophyll-a on Fringescale sardine (Sardinella fimbriata). The research method used is the descriptive method. The research data taken are in-situ data and SNPP Viirs satellite imagery data, as well as capture data from 2018 – 2022. Data analysis used multiple linear regression methods and Pearson correlation analysis. The results showed that the sea surface temperature distribution value ranged from 29.5°C – 29.9°C and chlorophyll-a ranged from 0.0545 mg m-3 – 0.4022 mg m-3. The value of sea surface temperature and chlorophyll-a in Tegal waters on land tends to have a higher value compared to high seas waters. Analysis conducted between sea surface temperature and chlorophyll-a on catches found a coefficient of determination of 0.74 or 74% affecting the total catch. The partial correlation between sea surface temperature and catch is 0.81 or 81%. The correlation between chlorophyll-a and catch is 0.86 or 86% where this value is included in the very strong category in influencing the catch of Sardinella fimbriata. Fishermen no longer rely on instinct to predict potential fishing zone each season, but it has taken advantage of variables that affect the presence of S. fimbriata using potential fishing zone map.

Unravelling mechanisms behind population dynamics, biological traits and latitudinal distribution in two benthic ecosystem engineers: A modelling approach

The mechanistic approach consisting of coupling Dynamic Energy Budget (DEB) models to Individual-Based Models (IBMs) allows simulating individual and population biological traits and their dynamics. This approach was developed here to study population dynamics of two sympatric intertidal ecosystem engineers, Arenicola marina and Arenicola defodiens (Annelida Polychaeta) occurring in the North-East Atlantic from Portugal to Sweden. Latitudinal heterogeneity of the two species’ performances were investigated in terms of population dynamics and biological traits using latitudinal differences in environmental forcing variables. The impact of the forcing variables on population dynamics processes (shore colonisation and migration, spawning and recruitment, etc.) within a specific foreshore (mean values and seasonal patterns) was also assessed. Published DEB parameters were used for A. marina and a specific calibration was undertaken for A. defodiens, combining literature data and new laboratory experiments and field data. Our DEB-IBM simulated super-individuals’ growth and reproduction while lugworms were colonising, migrating and dying over a simulated foreshore. Density rules affected population dynamics. Environmental forcings consisted in monthly values of chlorophyll-a (chl-a) concentrations and daily values of SST. Scenarios focusing on the two most contrasted of these forcing variables time series were used to explore their relative effects over populations’ dynamics and on-shore processes were investigated at two sites displaying highly different simulated population abundances. Overall, northern sites with higher chl-a levels performed better displaying higher biomass, maximum length and reproductive outputs for both species. As expected, Sea Surface Temperature (SST) changes between sites did not impact greatly populations dynamics. Under favourable environmental conditions, intra- and inter-specific competitions emerged from the model. Under non-favourable environmental conditions, A. defodiens’ populations crashed and A. marina displayed atypical population processes, with rare spawning events barely allowing the population’s renewal, and lower size at maturity. Further use and development of this model will lead to better insights on the lugworm populations’ evolution over the next decades.

Analysis of major cyclone storms ‘mocha 2023’, ‘Mandous 2022’ of recent years over India

The current study outlines how ocean conditions contributed to the development, intensification and dissipation of the extremely severe cyclonic storms “Mocha” (ESCS) (2023), severe cyclonic storm (SCS) “Mandous” (2022), very severe cyclonic storm (VSCS) “Asani” (2022), and (ESCS) “Tauktae” (2021). ‘Mocha’ tropical cyclones (TC) formed over warm waters over the southeast Bay of Bengal and the adjacent northern Andaman Sea on May 11–12, 2023, ‘Mandous’ over the southeast Bay of Bengal on December 6-7, 2022. The European Centre for Medium-Range Weather Forecasts (ECMWF) generated ECMWF reanalysis V5 (ERA5), which is used in the present study to analyse the sea surface temperature (SST), latent heat flux, relative vorticity (RV), specific humidity, and relative humidity during the lifetime of the storm. The analysis of the TC’s sea surface temperature data from satellites suggests that a much warmer SST was present throughout the cyclone’s existence, which may have been the primary reason for the TC’s fast intensification. Latent Heat flow (LHF) was found to be high along with SST values. As the TC approached the coast, an RV value that was both positive and significant was discovered. During intensification, Specific and Relative Humidity also had high levels.

Monitoring System for Sea Surface Temperature and Wind Speed in FADs Based on the Internet of Things

Sea Surface Temperature (SST) is the temperature of the water measured at sea level. SST is a crucial marine parameter that influences the weather and climate in Indonesia. The value of sea surface temperature will be very closely related to factors such as the depth of measurement from sea level, the intensity of sunlight received, season, weather, water depth, air circulation, and cloud cover. This study aims to design a prototype tool to measure wind speed and sea surface temperature parameters. The components used are the DS18B20 sensor, JL-FS2 sensor, Arduino Mega 2560, and the SIM900a GSM Module. The system is displayed in real-time via the Thingspeak website, accessed via a PC or smartphone. The DS18B20 sensor, as a sea surface temperature sensor, can measure these parameters by looking at the trend of changing day and night temperatures. The JL-FS2, as a wind speed sensor, has been able to measure wind speed parameters during field tests at FADs.

ENSO effects in the southern Gulf of California estimated from satellite data

This study assessed the impact of ENSO events in the seasonal and interannual variability of the sea surface temperature (SST) and phytoplankton biomass (expressed as chlorophyll-a, Chl-a levels) obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite products from June 2002 to December 2018, in two contrasting regions of the southern Gulf of California, the connection between the gulf and the Pacific Ocean, and the coastal region off Mazatlán. The generated dataset was compared with the El Niño–Southern Oscillation (ENSO) indices and a local wind-derived coastal upwelling index (CUI) computed in the coastal region off Mazatlán from the ERA5 monthly average wind. In this region, the results showed that the prevailing northwesterly winds reached 4 m s−1; however, southwesterly winds were observed during summer, mainly from June to August. The CUI showed values of up to 35 m3 s−1 per 100 m of coastline. The prevailing winds induce upwelling most of the year, with peaks in winter more intense during La Niña than during the El Niño event. Regarding SST and Chl-a levels, changes were observed with clear seasonal, semiannual, annual, and interannual variability, with 2.8 and 3.7 years of periodicities. In the coastal zone, seasonal values were maximum SST values (30–31 °C) during the summer and minimum values (20–22 °C) during the winter. The Chl-a levels showed an inverse trend, with minimum values (0.1–1.00 mg m−3) during summer and maximum values (>2 mg m−3) during winter. In the P1 site, in the connection between the gulf and the Pacific Ocean, the results showed during summer maximum SST values (29.5–31 °C) and minimum Chl-a (<0.2 mg m−3), while during winter minimum SST values (21–23 °C) and maximum Chl-a (0.3–2.0 mg m−3). Changes were also observed in both parameters owing to El Niño (high SST, low Chl-a levels) and La Niña (low SST, high Chl-a levels) conditions, which were confirmed by spectral and wavelet analyses. During the study period, La Niña conditions occurred during 2005/2006, 2007/2008, a multiyear event in 2010/2012, and 2017/2018, while El Niño conditions occurred during 2002/2003, 2006/2007, 2009/2010, and a multiyear event 2014/2016. Regarding their frequency, La Niña event occurred twice with a frequency corresponding to two years, and El Niño had a recurrence interval of between three and four years.

Interannual variability of sea surface temperature at the polar latitudes of the Atlantic Ocean

The general regularities and regional differences in the interannual variability of the sea surface temperature in the polar latitudes of the Atlantic Ocean are revealed according to the OI SST reanalysis data for period 1982—2017. The reanalysis contains daily average sea surface temperature values at the nodes of a regular grid 0,25°×0,25°. In this work, areas where the ice concentration did not exceed&#x0D; 90 % were analyzed. In both polar basins, the position of areas with a high level of sea surface temperature&#x0D; interannual variability qualitatively coincides with the position of intense currents. The maximum values of&#x0D; linear sea surface temperature trends are observed in areas of the maximum level of interannual variability,&#x0D; while the trends in long-term sea surface temperature variability in the Arctic and Antarctic are noticeably&#x0D; different. Significant positive sea surface temperature trends prevail in the Atlantic sector of the Arctic.&#x0D; Significant negative trends have been identified only off the northeastern coast of Greenland and north of the Arctic Current. The sea surface temperature decrease on a long-term scale in this area is associated with an increase of cold water transport by the East Greenland and Arctic currents, which are formed as a result&#x0D; of the intensive melting of Arctic ice observed in recent decades. In the Atlantic sector of the Antarctic, on the contrary, significant negative sea surface temperature trends prevail. In the Arctic, the maximum values of positive sea surface temperature trends are observed in August—September during the period of maximum warming of surface waters and minimum concentration of drifting ice. In the Antarctic, the maximum negative sea surface temperature trends in absolute value are observed from August to October, during the period of surface water cooling and maximum ice concentration.

Examining the Consistency of Sea Surface Temperature and Sea Ice Concentration in Arctic Satellite Products

Available observations and a theoretical simulation are used to explore the consistency and relationship between sea surface temperature (SST) and sea ice concentration (SIC) within open-ocean-sea ice mixed satellite pixels as a function of grid resolution. The maximum limiting SST value for a specified SIC and spatial resolution is first examined within collocated satellite-derived products contained within existing Level 4 SST analyses distributed using the data specification from the Group for High Resolution Sea Surface Temperature. The shape of the interdependence is further validated with manually quality-controlled buoy SST and SIC collocations. A parametric equation for the limiting SST value is derived from simulations of a mixed ocean/ice pixel with specified ice fraction and a linear SST gradient extending away from the ice edge. The exponential curve matching the observed interdependence suggests a maximum 5 km pixel-averaged SST at SIC values approaching zero between 6 and 8 °C. This maximum value is significantly greater than the previously assumed limiting values of ~3 °C and the corresponding SST gradient is larger than those typically observed with satellite SST products, but agrees well with recent Saildrone SST observations near ice. The curve provides a conservative limit with which inconsistent SST/SIC pairings can be identified, not only near the ice edge but at intermediate ice concentrations. Application of the filter improves the agreement between the SST/SIC relationship in satellite products and available Saildrone observations as well as the internal consistency of the different satellite products.

Thermal power plants pollution assessment based on deep neural networks, remote sensing, and GIS: A real case study in Iran

To investigate the impact of the Bandar Abbas thermal power plant on the waters of the Persian Gulf coast, a combination of satellite images and ground data was utilized to determine the Sea Surface Temperature (SST) as a thermal index, Total Organic Carbon (TOC) and Chemical Oxygen Demand (COD) as biological indices. Additionally, measurements of SO2, O3, NO2, CO2, CO, and CH4 values in the atmosphere were taken to determine the plant's impact on air pollution. Temperature values of the water for different months were predicted using Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and Cascade neural networks. The results indicate that the waters near thermal power plants exhibit the highest temperatures in July and September, with temperatures reaching approximately 50 °C. Furthermore, the SST values were found to be strongly correlated with ecological indices. The Multiple Linear Regression (MLR) analysis revealed a strong correlation between the temperature and TOC, COD, and O2 in water (RTOC2=0.98), RO22=−0.89, RCOD2=0.87 and O3, NO3, CO2, and CO in the air (RO32=0.99,RNO32=0.97,RCO22=0.95,RCO2=0.96). Finally, the results demonstrate that the LSTM method exhibited high accuracy in predicting the water temperature (R2 = 0.98).

Validation of Himawari-8 Sea Surface Temperature Retrievals Using Infrared SST Autonomous Radiometer Measurements

This study has evaluated five years (2016–2020) of Himawari-8 (H8) Sea Surface Temperature (SST) Level 2 Pre-processed (L2P) data produced by the Australian Bureau of Meteorology (Bureau) against shipborne radiometer SST measurements obtained from the Infrared SST Autonomous Radiometer (ISAR) onboard research vessel RV Investigator. Before being used, all data sets employed in this study have gone through careful quality control, and only the most trustworthy measurements are retained. With a large matchup database (31,871 collocations in total, including 16,418 during daytime and 15,453 during night-time), it is found that the Bureau H8 SST product is of good quality, with a mean bias ± standard deviation (SD) of −0.12 °C ± 0.47 °C for the daytime and −0.04 °C ± 0.37 °C for the night-time. The performance of the H8 data under different environmental conditions, determined by the observations obtained concurrently from RV Investigator, is examined. Daytime and night-time satellite data behave slightly differently. During the daytime, a cold bias can be seen under almost all environmental conditions, including for most values of wind speed, SST, and relative humidity. On the other hand, the performance of the night-time H8 SST product is consistently more stable under most meteorological conditions with the mean bias usually close to zero.

Bergmann's Rule under Rocks: Testing the Influence of Latitude and Temperature on a Chiton from Mexican Marine Ecoregions.

Bergmann's rule relates the trend of increasing body size with higher latitudes, where colder climates are found. In the Mexican Pacific, three marine ecoregions are distinguishable across a latitudinal gradient. Stenoplax limaciformis is an abundant chiton species that is distributed on rocky shores in these ecoregions. Geometric morphometric analyses were performed to describe the shape and size variation of S. limaciformis between marine ecoregions that vary in sea surface temperature with latitude, thus testing Bergmann's rule. Individuals' body shape ranged from elongated to wide bodies. Although there was variation in chitons' body shape and size, the was no evidence of allometry among localities. The Gulf of California is the northernmost ecoregion evaluated in this work, where larger chitons were observed and lower sea surface temperature values were registered. The results suggest that S. limaciformis follows a trend to Bergmann's rule, such as endotherms. These mollusks do not need heat dissipation, but they do need to retain moisture. In addition, larger chitons were observed in zones with high primary productivity, suggesting that chitons do not delay their maturation due to food shortage.

Evaluation of SST Data Products from Multi-Source Satellite Infrared Sensors in the Bohai-Yellow-East China Sea

The measurement of sea surface temperature (SST) is of utmost importance in the realm of oceanography. The increasing utilization of satellite data in SST research has highlighted the crucial need to compare and evaluate various satellite data sources. Using iQuam2 in situ SST data, this study aims to assess the accuracy of SST datasets obtained from three polar-orbiting satellites (AVHRR, Modis-Aqua, and Modis-Terra) and one geostationary satellite (Himawari-8) in the Bohai-Yellow-East China Sea (BYECS) throughout 2019. The results showed a strong correlation between satellite and in situ data, with R correlation coefficients exceeding 0.99. However, the accuracy of the satellite datasets exhibited some variability, with Himawari-8 showing the highest deviation error and MODIS-Aqua showing the least. Subsequently, the Modis-Aqua data were used as a benchmark to evaluate the SST data of the other three satellites over the previous six years (July 2015–June 2021). The results indicate that, in addition to intricate temporal variations, the deviations of the three satellites from Modis-Aqua also show significant spatial disparities due to the effect of seawater temperature. Compared to Modis-Aqua, the deviation of Himawari-8 generally displayed a negative trend in BYECS and showed pronounced seasonal variation. The deviation of AVHRR showed a negative trend across all regions except for a substantial positive value in the coastal region, with the time variation exhibiting intricate features. The SST values obtained from MODIS-Terra exhibited only marginal disparities from MODIS-Aqua, with positive values during the day and negative values at night. All three satellites showed significantly abnormal bias values after December 2020, indicating that the MODIS-Aqua-derived SST reference dataset may contain outliers beyond this period. In conclusion, the accuracy of the four satellite datasets varies across different regions and time periods. However, they could be effectively utilized and integrated with relevant fusion algorithms to synthesize high-precision datasets in the future.