Sea Surface Temperature Analysis Research Articles

Interannual Variability in Summertime Sea Fog Over the Northern Yellow Sea and Its Association With the Local Sea Surface Temperature

AbstractThe Yellow Sea (YS) experiences heavy advection fog during boreal summer, which seriously affects marine transport and harbor activities. Using a set of long‐term observations, the present study reveals the interannual variability in the YS fog during its core season (May‐June‐July, MJJ). The spaceborne lidar supports that a station‐based index represents the fog over the northern YS in a seasonal mean sense. The composite soundings according to the YS fog index show that the temperature difference is evident within the boundary layer and becomes indistinguishable in the free atmosphere. This implies that the driver of the MJJ YS fog originates from the bottom boundary of the fog layer. Regression analyses of sea surface temperature (SST) and air‐sea fluxes show that the local SST strongly modulates the northern YS fog at interannual scale. The fog during foggier years features a significant decrease in specific humidity, suggesting that the colder sea surface facilitates fog occurrence through strong turbulent cooling. This study identifies the local SST as a key driver of the interannual variation in the northern YS fog during the core season and provides predictive values by linking the YS fog to the preceding SST.

The estimation of mackerel tuna (Euthynnus affinis) fishing ground in Malacca Strait

In fishing activity, the success is indicated by the number of catches that extremely influenced by the conditions of fishing ground. Information of the potential fishing ground is needed for fishermen to increase the effectiveness and efficiency in fishing activity. Sea Surface Temperature (SST) and chlorophyll-a in waters are two things that can affect the potential of fishing ground. The research aims to create distribution map of SST and chlorophyll-a and to presume the potentials of fishing ground in Malacca Strait based on distribution of SST and chlorophyll-a in Malacca Strait (2015-2019). The data of SST and chlorophyll-a distribution in Malacca Strait were using Aqua MODIS satellite imagery. Based on the result of SST and chlorophyll-a analysis, potential fishing ground in Malacca Strait is expected located around East Aceh in Western season, Malaysian waters in the Transition I season, between Asahan and Malaysian waters in Eastern season and between Deli Serdang and East Aceh in the Transition II season.

Sea surface temperature in continental and insular coastal Colombian waters: observations of the recent past and near-term numerical projections

Sea surface temperature (SST) simulation for the 1970-2007 period and its projection up to 2035 was carried out with the Regional Ocean Modeling System (ROMS) for the Colombian Caribbean Sea (CCS) and the Colombian Pacific Basin (CPB). Spatial bias using the International Comprehensive Ocean-Atmosphere Data Sets (ICOADS) and Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) for 1982-2007 shows an adequate representation of surface characteristics in both marine study regions, especially on the CPB. The 1970-2007 period's deviation measures reveal a good fit between ICOADS data and simulated SST. The spatial distribution of linear multi-annual regression (ICOADS 1970-2007) evidences a similar positive trend in both regions (0.01ºC yr -1 ). Differences in the spatial distribution of trends between the two regions (ROMS 2008-2035) prescribe that general warming of CCS will be less (0.01ºC yr -1 ) than that of CPB (0.02ºC yr -1 ). The average SST trend in waters close to continental and insular coastal stations of CCS and CPB obtained from ICOADS (1970-2015) are 0.016 and 0.013°C yr -1 , respectively. The average trend in the same stations obtained from the projection using ROMS (2008-2035) is 0.025°C yr -1 for CCS and 0.015°C yr -1 for CPB. The SST projection for 2008-2035 in the CCS shows the highest SST increase slope in waters near Punta Gallinas and in the CPB it is evidenced in waters near Malpelo Island.

IASI-Derived Sea Surface Temperature Data Set for Climate Studies.

Sea surface temperature (SST) is an essential climate variable, that is directly used in climate monitoring. Although satellite measurements can offer continuous global coverage, obtaining a long‐term homogeneous satellite‐derived SST data set suitable for climate studies based on a single instrument is still a challenge. In this work, we assess a homogeneous SST data set derived from reprocessed Infrared Atmospheric Sounding Interferometer (IASI) level‐1 (L1C) radiance data. The SST is computed using Planck's Law and simple atmospheric corrections. We assess the data set using the ERA5 reanalysis and the EUMETSAT‐released IASI level‐2 SST product. Over the entire period, the reprocessed IASI SST shows a mean global difference with ERA5 close to zero, a mean absolute bias under 0.5°C, with a SD of difference around 0.3°C and a correlation coefficient over 0.99. In addition, the reprocessed data set shows a stable bias and SD, which is an advantage for climate studies. The interannual variability and trends were compared with other SST data sets: ERA5, Hadley Centre's SST (HadISST), and NOAA's Optimal Interpolation SST Analysis (OISSTv2). We found that the reprocessed SST data set is able to capture the patterns of interannual variability well, showing the same areas of high interannual variability (>1.5°C), including over the tropical Pacific in January corresponding to the El Niño Southern Oscillation. Although the period studied is relatively short, we demonstrate that the IASI data set reproduces the same trend patterns found in the other data sets (i.e., cooling trend in the North Atlantic, warming trend over the Mediterranean).

An Innovative Method for the Spatial Sampling Analysis of Sea Surface Temperature in the Pacific-Indian Oceans

Zhu, Y.; Zhang, J., and Tang, Q., 2021. An innovative method for the spatial sampling analysis of sea surface temperature in the Pacific-Indian Oceans. Journal of Coastal Research, 37(5), 1053–1062. Coconut Creek (Florida), ISSN 0749-0208. In this study, a pragmatic approach is presented for evaluating the spatial representativeness of the point-scale drifting buoy sea-surface temperature (SST) over the joined area of Asia and the Pacific-Indian Ocean with that help of the high-resolution satellite-derived SST at multipixel scales. The relative spatial sampling error (RSSE) and coefficient of sill (CS) are selected to investigate consistency between the drifting buoy SST and pixel mean value of satellite-derived SST and representativeness of drifting buoy SST at pixel scales of 25 km, 50 km, and 100 km, respectively. The results show that at the 25-km scale, the consistency between the drifting buoy SST and the satellite-derived SST is high, and the spatial heterogeneity within the pixel scale is not obvious, i.e. only two measurements with RSSE are larger than the critical value of all 43,748 measurements. But with the increase in the scale, the number of the drifting buoy SST, which is inconsistent with the satellite-derived SST, is obviously increased (the number of the inconsistent point measurement at the scale of 50 km increased to 42, and at the scale of 100 km it is 59), and the spatial heterogeneity is enhanced. This spatial variation along the direction of latitude change is more obvious. Combining spatial consistency and spatial heterogeneity, no point-scale measurements of the worst spatial representativeness occur at the 25-km pixel scale, in which RSSE and CS are above the critical value. Although drifting buoy SST keeps a high consistency with satellite-derived SST, there are measurements of obvious local variation within pixel scales, and this variation is more obvious along the direction of latitude change. The spatial representativeness of the drifting buoy SST is unstable. This method is helpful to remove the poor-quality reference points during the validation of satellite-derived products, taking point-scale measurements as reference.

Mediterranean faunal evolution and biochronological events during the last 24 kyr

We generated high-resolution biochronological records in the Mediterranean Sea covering the period extending to the Late-glacial. The present study is based on micropaleontological, sea-surface temperature (SST) and oxygen isotopic analyses performed along three well-dated deep-sea cores: REC1353, KET80–19 and MD84–641 recovered in the Siculo-Tunisian Strait, Tyrrhenian Sea and Levantine Basin, respectively. The quantitative distributional patterns of planktic foraminifera permitted to identify seven biozones based on the apparition and/or disappearance of the main specific taxa or by their noticeable abundance peaks. Abundance records display that major changes in planktic foraminiferal assemblages have a similar pattern mainly in central and western basins. In particular, four recognized bio-events can be used to establish or to improve the chronology of Mediterranean deep-sea cores. The comparison of the SST estimates and foraminiferal records with those of NGRIP ice-core shows a similarity between the Greenland climate and the Mediterranean hydrology. This indicates that the main climate changes recorded in the North Atlantic are globally in phase with those observed in the Mediterranean region.

Towards using climate to increase lead time of a malaria early warning system in Mozambique

BackgroundMalaria is one of the greatest recurring threats to public health in Mozambique with approximately 10 million cases and thousands of deaths reported annually. Although a malaria early warning system is being established in the country, it is focused on short-term (4–8 week) prediction windows. Increased understanding of the links between quasi-predictable interannual climate variability and malaria could lengthen the lead time for the malaria early warning system and enhance planning and actionable mitigation efforts by public health officials in the country. MethodsTo identify patterns of interannual variability, we did two principal component analyses of processed weekly district-level malaria incidences for the period 2010–17 in Mozambique and southern Africa regional precipitation for the period 1981–2019. We also did linear regression analyses of sea surface temperatures onto a precipitation index, and composites of various climate variables to establish links between precipitation and modes of climate variability. FindingsTwo dominant spatiotemporal patterns collectively account for 81% of the interannual variability of malaria in Mozambique. These patterns consisted of a primary hotspot over the central and southern part of the country and a secondary hotspot over the northern third of the country. We found these patterns to be closely related to precipitation variability driven by climate phenomena: the El Niño-Southern Oscillation and the Subtropical Indian Ocean Dipole. InterpretationQuasi-predictable global and regional climate phenomena influence regional precipitation over southern Africa. Knowledge of these associations can inform an enhancement of the malaria early warning system in Mozambique by lengthening early warning lead times. Similar analyses are possible for other countries whose climate is dominated by tropical modes of climate variability. FundingNone.

Sea Surface Temperature (SST) analysis during ElNiño-LaNiña in the Java Sea

El Niño-La Niña effected climate conditions in Indonesia. The purpose of this study was to analyze the SST during El Niño-La Niña. The data used is the ENSO index SST Niño 3.4 anomaly in 1986-2017 and also SST data in the year of the El Niño-La Niña event with weak, moderate, and strong. It found that SST tended to be higher during El Niño than its normal average in the Java Sea by 29°C, while the normal SST is 28.94°C. La Niña SST in the Java Sea has an average of 29.06°C which is 0,6°C warmer than its normal average temperature. It can be concluded that El Niño-La Niña conditions do not greatly affect SST in the Java Sea. SST tends to be warmer during El Niño-La Niña than its normal average in the Java Sea. The average of SST is lower/cooler at 109.5-110.5° East Longitude than that of at the other longitudes. This is due to the mixing current of the cold water mass flow from the South China Sea with the mass of water from the Makassar Strait, each of which carries nutrients.

Development of a MetUM (v 11.1) and NEMO (v 3.6) coupled operational forecast model for the Maritime Continent – Part 1: Evaluation of ocean forecasts

Abstract. This article describes the development and ocean forecast evaluation of an atmosphere–ocean coupled prediction system for the Maritime Continent (MC) domain, which includes the eastern Indian and western Pacific oceans. The coupled system comprises regional configurations of the atmospheric model MetUM and ocean model NEMO at a uniform horizontal resolution of 4.5 km × 4.5 km, coupled using the OASIS3-MCT libraries. The coupled model is run as a pre-operational forecast system from 1 to 31 October 2019. Hindcast simulations performed for the period 1 January 2014 to 30 September 2019, using the stand-alone ocean configuration, provided the initial condition to the coupled ocean model. This paper details the evaluations of ocean-only model hindcast and 6 d coupled ocean forecast simulations. Direct comparison of sea surface temperature (SST) and sea surface height (SSH) with analysis, as well as in situ observations, is performed for the ocean-only hindcast evaluation. For the evaluation of coupled ocean model, comparisons of ocean forecast for different forecast lead times with SST analysis and in situ observations of SSH, temperature, and salinity have been performed. Overall, the model forecast deviation of SST, SSH, and subsurface temperature and salinity fields relative to observation is within acceptable error limits of operational forecast models. Typical runtimes of the daily forecast simulations are found to be suitable for the operational forecast applications.

Seasonal Prediction of Arctic Summer Sea Ice Concentration from a Partial Least Squares Regression Model

The past decade has witnessed a rapid decline in the Arctic sea ice and therefore has raised a rising demand for sea ice forecasts. In this study, based on an analysis of long-term Arctic summer sea ice concentration (SIC) and global sea surface temperature (SST) datasets, a physical–empirical (PE) partial least squares regression (PLSR) model is presented in order to predict the summer SIC variability around the key areas of the Arctic shipping route. First, the main SST modes closely associated with sea ice anomalies are found by the PLSR method. Then, a prediction model is reasonably established on the basis of these PLSR modes. We investigate the performance of the PE PLSR model by examining its reproducibility of the seasonal SIC variability. Results show that the proposed model turns out promising prediction reliability and accuracy for Arctic summer SIC change, thus providing a reference for the further study of Arctic SIC variability and global climate change.

Detection of SST Fronts from a High-Resolution Model and Its Preliminary Results in the South China Sea

AbstractA frontal detection algorithm is developed with the capability of detecting significant frontal segments of sea surface temperature (SST) in the high-resolution South China Sea Operational Forecasting System (SCSOFS). To effectively obtain frontal information, a gradient-based Canny edge detection algorithm is improved with postprocessing designed for high-resolution numerical models, aiming at extracting primary ocean fronts while ensuring the balance of frontal continuity and positioning accuracy. Metrics of frontal probability and strength are used to measure the robustness of the results in terms of mean state and seasonal variability of frontal activities in the South China Sea (SCS). Most fronts are found in the nearshore and form a strip shape extending from the Taiwan Strait to the coast of Vietnam. The SCSOFS is found to reproduce strong seasonal signals dominating the variability of the frontal strength and occurrence probability in the SCS. We implement the algorithm on the daily averaged SST derived from two other SST analyses for intercomparison in the SCS.

Two-Decade Variability of Sea Surface Temperature and Chlorophyll-a in the Northern South China Sea as Revealed by Reconstructed Cloud-Free Satellite Data

Monthly sea surface temperature (SST) and chlorophyll-a (Chl-a) concentration during 1997–2018 in the Northern South China Sea (NSCS) were gap-filled using the Data INterpolating Empirical Orthogonal Functions (DINEOFs) approach. Good accuracy of the reconstructed data was verified based on an independent cross-validation data set. Significant spatial heterogeneities and seasonal variations were observed in the cloud-free SST and Chl-a data. An apparent decreasing trend in SST was found along the coast of Guangdong, east of Hainan Island, and in the Taiwan Strait with an average decreasing rate amounting to 0.01 °C per year. Increase in SST was observed in Beibu Gulf and the deep offshore region in the NSCS at an average rate as high as 0.009 °C per year. An increasing trend in Chl-a was detected in the northern Beibu Gulf, near the mouth of the Pearl River estuary, and around Shantou with an average rate of 0.005 mg <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\cdot \text{m}^{-3}$ </tex-math></inline-formula> per year. EOF analysis of SST and Chl-a time series showed that the first three modes of both SST and Chl-a can explain most of their variances with accumulative contributions of 99.1% and 82.3%, respectively. The effects of surface net heat flux (NHF), Oceanic Niño Index (ONI), wind speed, and river flux on the first three modes of SST and Chl-a were quantitatively elucidated. Factors that affected the temporal and spatial variations of Chl-a and SST in the NSCS were qualitatively discussed, including El Niño-Southern Oscillation (ENSO), monsoon, upwelling, typhoon, internal waves, and Kuroshio intrusion.

La relación entre la precipitación en Ecuador y la temperatura superficial del mar en el Océano Pacífico

In this study we analyze the temporal and spatial relationship of precipitation in Ecuador and the sea surface temperature (SST) in the Pacific Ocean adjacent to the coast of Ecuador. This relation is most evident during the warm phase of El Niño – Southern Oscillation (ENSO) when warming in the eastern tropical Pacific is associated with increased rainfall in the Pacific coastal regions of South America. This region incorporates the entire coastal region of Ecuador and the northern coastal region of Peru. Although all warm events are associated with increased precipitation in the region, the magnitude of this increase varies greatly. The decreased precipitation associated with the cold events is also variable. In both cases this variability cannot be easily explainedusing the indices typically used to measure the strength of ENSO. We make a detailed analysis of SST in a global ocean model incorporating data assimilation and precipitation observations for the periods from 1950-2010 and1981-2010. This study helps to&nbsp; determine the spatial regions in the Pacific where warming has the greatest impact on precipitation and the coastalregions of Ecuador that are most affected by this variability in precipitation

SAM-driven variability of the southwestern Atlantic shelf sea circulation

The objective of the present study is to analyze the interannual variability of the Southwestern Atlantic Continental Shelf (SWACS) circulation and its thermohaline structure. To accomplish it we employed the global ocean reanalysis ORAP5.0, which spans the 35-years period 1979–2013. ORAP5.0 satisfactorily reproduces the most distinctive features of the SWACS circulation and its variability on different timescales, including the interannual scale. The first mode of a joint empirical orthogonal function (EOF) analysis of sea surface temperature (SST), sea surface salinity (SSS), and vertically averaged horizontal velocities is significantly correlated with the first mode of interannual variability of the atmosphere in the Southern Hemisphere, the Southern Annular Mode (SAM). Remarkably, the shelf sea response has been found to be statistically significant not only in the Patagonian Shelf (south of 40 °S), as discussed in the literature, but also in the Northern Shelf (north of 40°S). During the active phases of the leading mode, current anomalies develop as a result of the sea surface height adjustment to Ekman transport at the coastline induced by alongshore wind anomalies. Modification of the advection terms in the salinity and heat budgets due to changes in the circulation and heat surface fluxes generate SST and SSS anomalies along the SWACS. It is suggested that these anomalies drive changes in the mixed layer depth and that these changes can modulate the interannual variability of the chlorophyll-a concentration during austral spring in the frontal zones.

Tidally-induced submesoscale features in the atlantic jet and Western Alboran Gyre. A study based on HF radar and satellite images

An analysis is presented of the submesoscale tidally-induced anticyclonic eddies formed at the eastern mouth of the Strait of Gibraltar and their role in explaining mesoscale features of the Atlantic Jet – Western Alboran Gyre (AJ-WAG) system. These eddies have been identified for the first time using an observational approach based on the analysis of High Frequency Radar (HFR) derived surface currents on the eastern mouth of the Strait which has been complemented with the analysis of Sea Surface Temperature (SST) satellite images. It is found that the major source of positive vorticity to the AJ is provided by its interaction with these anticyclonic eddies which in turn help maintain the water feeding of the upper branch of the WAG. This new role in maintaining the WAG is added to others previously recognized by other authors based on the supply of negative vorticity that these eddies provide to the whole WAG. In addition, these tidally-induced eddies may contribute to the destabilization of the AJ-WAG system when strong and persistent easterly winds coincide with periods of high tidal current amplitude. In these cases, meteorological forcing produces a weak and southward deflected AJ almost disconnected from a displaced eastward WAG. The WAG in turn is being flattened by an overdeveloped North Western Cyclonic Gyre (NWCG) that may be favored by the action of the mentioned eddies.

Chlorophyll-a Variability during Upwelling Events in the South-Eastern Baltic Sea and in the Curonian Lagoon from Satellite Observations

Based on the analysis of multispectral satellite data, this work demonstrates the influence of coastal upwelling on the variability of chlorophyll-a (Chl-a) concentration in the south-eastern Baltic (SEB) Sea and in the Curonian Lagoon. The analysis of sea surface temperature (SST) data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua/Terra satellites, together with Chl-a maps from Medium Resolution Imaging Spectrometer (MERIS) onboard Envisat, shows a significant decrease of up to 40–50% in Chl-a concentration in the upwelling zone. This results from the offshore Ekman transport of more productive surface waters, which are replaced by cold and less-productive waters from deeper layers. Due to an active interaction between the Baltic Sea and the Curonian Lagoon which are connected through the Klaipeda Strait, coastal upwelling in the SEB also influences the hydrobiological conditions of the adjacent lagoon. During upwelling inflows, SST drops by approximately 2–8 °C, while Chl-a concentration becomes 2–4 times lower than in pre-upwelling conditions. The joint analysis of remotely sensed Chl-a and SST data reveals that the upwelling-driven reduction in Chl-a concentration leads to the temporary improvement of water quality in terms of Chl-a in the coastal zone and in the hyper-eutrophic Curonian Lagoon. This study demonstrates the benefits of multi-spectral satellite data for upscaling coastal processes and monitoring the environmental status of the Baltic Sea and its largest estuarine lagoon.

Prediction of Sea Surface Temperature in the China Seas Based on Long Short-Term Memory Neural Networks

Sea surface temperature (SST) in the China Seas has shown an enhanced response in the accelerated global warming period and the hiatus period, causing local climate changes and affecting the health of coastal marine ecological systems. Therefore, SST distribution prediction in this area, especially seasonal and yearly predictions, could provide information to help understand and assess the future consequences of SST changes. The past few years have witnessed the applications and achievements of neural network technology in SST prediction. Due to the diversity of SST features in the China Seas, long-term and high-spatial-resolution prediction remains a crucial challenge. In this study, we adopted long short-term memory (LSTM)-based deep neural networks for 12-month lead time SST prediction from 2015 to 2018 at a 0.05° spatial resolution. Considering the sub-regional differences in the SST features of the study area, we applied self-organizing feature maps (SOM) to classify the SST data first, and then used the classification results as additional inputs for model training and validation. We selected nine models differing in structure and initial parameters for ensemble to overcome the high variance in the output. The statistics of four years’ SST difference between the predicted SST and Operational SST and Ice Analysis (OSTIA) data shows the average root mean square error (RMSE) is 0.5 °C for a one-month lead time and is 0.66 °C for a 12-month lead time. The southeast of the study area shows the highest predictable accuracy, with an RMSE less than 0.4 °C for a 12-month prediction lead time. The results indicate that our model is feasible and provides accurate long-term and high-spatial-resolution SST prediction. The experiments prove that introducing appropriate class labels as auxiliary information can improve the prediction accuracy, and integrating models with different structures and parameters can increase the stability of the prediction results.

Adjusting for Desert-Dust-Related Biases in a Climate Data Record of Sea Surface Temperature

Atmospheric desert-dust aerosol, primarily from north Africa, causes negative biases in remotely sensed climate data records of sea surface temperature (SST). Here, large-scale bias adjustments are deduced and applied to the v2 climate data record of SST from the European Space Agency Climate Change Initiative (CCI). Unlike SST from infrared sensors, SST measured in situ is not prone to desert-dust bias. An in-situ-based SST analysis is combined with column dust mass from the Modern-Era Retrospective analysis for Research and Applications, Version 2 to deduce a monthly, large-scale adjustment to CCI analysis SSTs. Having reduced the dust-related biases, a further correction for some periods of anomalous satellite calibration is also derived. The corrections will increase the usability of the v2 CCI SST record for oceanographic and climate applications, such as understanding the role of Arabian Sea SSTs in the Indian monsoon. The corrections will also pave the way for a v3 climate data record with improved error characteristics with respect to atmospheric dust aerosol.

Background nutrient concentration determines phytoplankton bloom response to marine heatwaves.

Ocean temperature extreme events such as marine heatwaves are expected to intensify in coming decades due to anthropogenic global warming. Reported ecological and economic impacts of marine heatwaves include coral bleaching, local extinction of mangrove and kelp forests and elevated mortalities of invertebrates, fishes, seabirds and marine mammals. In contrast, little is known about the impacts of marine heatwaves on microbes that regulate biogeochemical processes in the ocean. Here we analyse the daily output of a near-global ocean physical-biogeochemical model simulation to characterize the impacts of marine heatwaves on phytoplankton blooms in 23 tropical and temperate oceanographic regions from 1992 to 2014. The results reveal regionally coherent anomalies of shallower surface mixing layers and lower surface nitrate concentrations during marine heatwaves. These anomalies exert counteracting effects on phytoplankton growth through light and nutrient limitation. Consequently, the responses of phytoplankton blooms are mixed, but can be related to the background nutrient conditions of the study regions. The blooms are weaker during marine heatwaves in nutrient-poor waters, whereas in nutrient-rich waters, the heatwave blooms are stronger. The corresponding analyses of sea-surface temperature, chlorophyll a and nitrate based on satellite observations and in situ climatology support this relationship between phytoplankton bloom anomalies and background nitrate concentration. Given that nutrient-poor waters are projected to expand globally in the 21st century, this study suggests increased occurrence of weaker blooms during marine heatwaves in coming decades, with implications for higher trophic levels and biogeochemical cycling of key elements.

Trend analysis and variations of sea surface temperature and chlorophyll-a in the Persian Gulf

Spatial and temporal variations and trends of SST and Chl-a in the Persian Gulf were evaluated using MODIS data from 2002 to 2018. Trend indicator and key features were built up based on Mann-Kendall test, Hurst exponents and Wavelet Transform (WT) techniques to investigated the volatilities, trend estimation and persistence of original and WT signals. The key features revealed that the Chl-a pattern is heterogeneous in both time and spatial scale, whereas SST pattern is more homogenous. Trend analysis of the study area showed identifiable and sustainable trend with maximum tendency to decrease of Chl-a, and increase of SST. More than 48% of the study area is under slight decrease, and about 20% area mostly located at the northern parts show slight increase of Chl-a values. The presented methodology is applicable to the data signals that keep the range of inter-annual approximation components of DWT not less than scale level-4.