Correlation Of Sea Surface Temperature Research Articles

Spatiotemporal Fusion Prediction of Sea Surface Temperatures Based on the Graph Convolutional Neural and Long Short-Term Memory Networks

Sea surface temperature (SST) prediction plays an important role in scientific research, environmental protection, and other marine-related fields. However, most of the current prediction methods are not effective enough to utilize the spatial correlation of SSTs, which limits the improvement of SST prediction accuracy. Therefore, this paper first explores spatial correlation mining methods, including regular boundary division, convolutional sliding translation, and clustering neural networks. Then, spatial correlation mining through a graph convolutional neural network (GCN) is proposed, which solves the problem of the dependency on regular Euclidian space and the lack of spatial correlation around the boundary of groups for the above three methods. Based on that, this paper combines the spatial advantages of the GCN and the temporal advantages of the long short-term memory network (LSTM) and proposes a spatiotemporal fusion model (GCN-LSTM) for SST prediction. The proposed model can capture SST features in both the spatial and temporal dimensions more effectively and complete the SST prediction by spatiotemporal fusion. The experiments prove that the proposed model greatly improves the prediction accuracy and is an effective model for SST prediction.

Multi‐Decadal Skill Variability in Predicting the Spatial Patterns of ENSO Events

AbstractSeasonal hindcasts have previously been demonstrated to show multi‐decadal variability in skill across the twentieth century in indices describing El‐Niño Southern Oscillation (ENSO), which drives global seasonal predictability. Here, we analyze the skill of predicting ENSO events' magnitude and spatial pattern, in the CSF‐20C coupled seasonal hindcasts in 1901–2010. We find minima in the skill of predicting the first (in 1930–1950) and second (in 1940–1960) principal components of sea‐surface temperature (SST) in the tropical Pacific. This minimum is also present in the spatial correlation of SSTs, in 1930–1960. The skill reduction is explained by lower ENSO magnitude and variance in 1930–1960, as well as decreased SST persistence. The SST skill minima project onto surface winds, leading to worse predictions in coupled hindcasts compared to hindcasts using prescribed SSTs. Questions remain about the offset between the first and second principal components' skill minima, and how the skill minima impact the extra‐tropics.

HiGRN: A Hierarchical Graph Recurrent Network for Global Sea Surface Temperature Prediction

Sea surface temperature (SST) is one critical parameter of global climate change, and accurate SST prediction is important to various applications, e.g., weather forecasting, fishing directions, and disaster warnings. The global ocean system is unified and complex, and the SST patterns in different oceanic regions are highly diverse and correlated. However, existing data-driven SST prediction methods mainly consider the local patterns within a certain oceanic region, e.g., El Nino region and the Black sea. It is challenging but necessary to model the global SST correlations rather than that in a specific region to enhance the prediction accuracy of SST. In this work, we proposed a new method called Hierarchical Graph Recurrent Network (HiGRN) to address the issue. First, to learn the dynamic and diverse local SST patterns of specific locations, we design an adaptive node embedding with self-learned parameters to learn various SST patterns. Then we develop a hierarchical cluster generator to aggregate the locations with similar patterns into regional clusters and utilize a graph convolution network to learn the spatial correlations among these clusters. Finally, we introduce a multi-level attention mechanism to fuse the local patterns and regional correlations, and the output is fed into a recurrent network to achieve SST predictions. Extensive experiments on two real-world datasets show that our method largely outperforms the state-of-the-art SST prediction methods. The source code is available at https://github.com/Neoyanghc/HiGRN .

Relationships between the Hadley circulation and tropical sea surface temperature with different meridional structures simulated in CMIP6 models

The variability of the Hadley Circulation (HC) is greatly impacted by the meridional structure of sea surface temperature (SST), which has varied effects depending on its symmetrical or asymmetrical pattern relative to the equator. By using the Coupled Comparison Program International Project Phase 6 (CMIP6) model outputs and reanalysis datasets, this study assesses the capacity of CMIP6 models to simulate the relationship between the HC and tropical SST under different meridional structures, as well as investigates the possible causes for simulation biases. It is shown that the CMIP6 models can successfully reproduce climatological HC, tropical SST, and their spatial patterns of first leading modes under different meridional structures, where the correlation coefficient between simulations and observations reaches 0.8 or above. By comparison, the CMIP6 model outputs exhibit substantial differences in simulating the HC to SST response over the different meridional structures, with obvious inter-model differences. Considering the capability in simulating the HC to tropical SST response, the CMIP6 models are divided into two types, Type I model and Type II model. Models of Type I are those whose simulation results are basically close to the reanalysis data, with the biases being less than 20%. The models of Type II are those whose simulated response ratios are much stronger than those of the reanalysis. It is found that the models of Type II overestimate the intensity of El Niño-Southern Oscillation (ENSO) events, and remarkably underestimate the HC and SST correlation in the equatorial symmetric part, resulting in the inability of the models of Type II to capture the connection of the HC and tropical SST. The results indicate that, the component of the CMIP6 models in reproducing the ENSO events has a considerable impact on the simulation of the HC and tropical SST relationship, which offers recommendations for enhancing the capability of models to simulate large-scale tropical air-sea interactions.

SST Correlation Between Chlorophyll and Turbidity by Landsat MS Image Analysis for the Coast of Izmir Province

Temporal Sea Surface Temperature (SST) analyses by satellite images are quite vital in terms of understanding the sea water quality. Specific water quality criteria include dissolved oxygen, chlorophyll, temperature, depth, pH, salinity, and turbidity, and these criteria are used to determine water quality in seas. In the current study, three criteria which are chlorophyll, temperature (SST) and turbidity were examined through their correlation with SST derived from Landsat sensors. This let to know about the examined criteria at minimum and maximum temperature dates, the relation with respect to temperature change rates, and to understand the events that occur in certain dates. The regular or irregular increases of the detected SST are evidence of sea water quality or pollution resulted from the criteria in the study area. Therefore, first turbid water which contains a high amount of suspended sediment was studied. After the turbidity index was completed, the Chlorophyll study was carried out to detect the algae like substances. The aims of the study are to evaluate the temporal change of water quality in coastal region of Izmir province, using spectral indices, and to contribute to the development of more sensitive qualitative index algorithms in the future. At the end of the study, high correlation coefficients revealed the relationship between SST and indexes.

VARIABILITAS MUSIMAN SUHU PERMUKAAN LAUT DAN ANGIN DI LAUT ARAFURA

Research on sea surface temperature (SST) and wind in the Arafura Sea is very important to answer various processes and phenomena that occur in the water column, for example upwelling and downwelling phenomena, the influence of monsoon winds, ENSO and LaNina phenomena, mixing of water masses, forecasting fishing areas, determination of marine cultivation areas, and so on. This study aims to examine the spatial distribution and variability of sea surface temperature (SST) and wind and to examine the correlation of SST with wind speed and direction in the Arafura Sea. SST data is obtained from the AQUA MODIS satellite which is downloaded on the website http://oceancolor.gsfc.nasa.gov. Wind data obtained from the site: ftp://ftp.ifremer.fr/ifremer/cersat. The results of the analysis show that the SST in the Arafura Sea varies seasonally with a range of 23–34oC and a variation of 3.01-4.60%. The wind speed ranges from 0.02-10 m/s with a variation of 40.07-47.76%. There is a strong correlation between SST and wind speed and direction based on latitude and longitude. The correlation between wind speed and SST ranges from -0.01 to -0.87, while the correlation between wind direction and SST ranges from 0.52 and -0.71. In certain waters there is a positive linear correlation of SST with wind direction of 0.18-0.78

The Relationship between Northeast Pacific Sea Surface Temperatures, Synoptic Evolution, and Surface Air Temperatures over the Pacific Northwest

Abstract The development of sea surface temperature (SST) anomalies over the northeast Pacific and their impacts on lower-tropospheric air temperatures over the Pacific Northwest are examined. Northeast Pacific SST anomalies are influenced by the synoptic-scale flow, with high pressure and weak surface winds associated with developing warm SST anomalies, while large pressure gradients and strong surface winds result in SST declines. SST over the northeast Pacific correlates significantly with surface air temperatures over the Pacific Northwest, with correlations increasing when high-frequency variability is filtered out. The correlations between unfiltered time series of SST and surface air temperature are largest for a zero-day lag and are strongest near the coast, contrasting with weaker correlations over the Columbia basin east of the Cascade Mountains. SST correlations with minimum surface air temperature are largest during the warm season, and maximum temperature correlations are highest in March; both have low correlations during autumn. Model simulations of periods with warm and cold northeast Pacific SST anomalies possess lower-tropospheric air temperature warming or cooling over the coastal zone, with SST influence weakening east of the Cascade crest. Eastern Pacific SST anomalies influence sea level pressure and lower-tropospheric heights, with warm SST anomalies resulting in simulated lowered pressure near the surface and increased heights aloft. The relationship between northeast Pacific SST and surface air temperature over land evince complex feedbacks: SST temperature anomalies can be advected inland from the Pacific, the SST anomalies can influence the synoptic-scale flow that affects the SST anomalies, and the synoptic-scale anomalies that produce the SST anomalies can directly influence temperatures over land. Significance Statement Understanding the connection between northeast Pacific sea surface temperatures and low-level air temperatures over land is valuable for both subseasonal prediction and for examining the fidelity of model physics.

The Characteristics of Fishing Ground Using Purse Seine in Makassar Strait and Flores Sea

In order to optimally utilize marine fishery resources, the Government of the Republic of Indonesia divides Indonesian waters into 11 Fisheries Management Areas (IFMA), one of which is IFMA 713 which includes the waters of Bone Gulf, Bali Sea, Flores Sea and Makassar Strait. This study aims to provide information on the utilization of pelagic fish resources with a focus on the characteristics of fishing areas using purse seine fishing gear in the Makassar Strait and Flores Sea. In this study, 62 fishing trips using purse seines were carried out, each has 31 trips fishing operation in the Makassar Strait and in the waters of Flores Sea, respectively. The main catch and number for each water area was skipjack tuna (Katsuwonus pelamis) as many as 2,367 fish (Makassar Strait). While in the Flores Sea area, it is dominated by little tuna (Euthynnus sp) with a total of 11,039 fish. The results of this study indicate that the correlation of sea surface temperature with chlorophyll-a in the waters of the Makassar Strait and Flores Sea has a negative relationship with an optimum sea surface temperature of 30.5 °C for skipjack tuna and 28.0 °C for little tuna which corresponds to the chlorophyll-a concentration of 0.3 mg m-3 (skipjack tuna) and 0.2 mg m-3 for little tuna. The vertical distribution of skipjack tuna was found at the optimum depth of around 50 m, while most little tuna was caught in deeper waters around 250 m.

The Interdecadal Change of the Relationship Between North Indian Ocean SST and Tropical North Atlantic SST

AbstractThe summertime North Indian Ocean (NIO) and tropical North Atlantic (TNA) sea surface temperature (SST) anomalies exert important impacts on atmospheric circulation and climate variation. Little attention is paid on the interdecadal change of NIO–TNA SST connection. This study reveals that the NIO–TNA SST relationship experiences an obvious interdecadal change around the early 2000s. The NIO and TNA SST correlation is positive and significant before the early 2000s, while this connection becomes weak and insignificant after that. This interdecadal change is closely associated with the changes in the El Niño‐Southern Oscillation (ENSO) intensity. During 1980–2001, strong ENSO forces a sustained SST warming in the southwest Indian Ocean, which further warms the summertime NIO via inducing antisymmetric circulation and reducing the upward surface latent heat flux. Meanwhile, strong ENSO forces a summertime TNA warming via arousing the Pacific North American (PNA) teleconnection. The PNA weakens the TNA trade winds, then reduces the upward surface heat flux and consequently warms the TNA. Thus, NIO and TNA SST connection is established via ENSO events during 1980–2001. In contrast, during 2002–2020, the summertime NIO SST anomaly is still related to ENSO. However, the connection between the TNA SST anomaly and ENSO is interrupted due to the weak ENSO intensity. Such weak ENSO yields a much weaker PNA teleconnection, which is inefficient to generate the TNA warming. Further analysis shows that the TNA warming during 2002–2020 is generated mainly by the negative North Atlantic Oscillation. Therefore, the connection between NIO and TNA SST anomalies collapses under the weak ENSO condition.

STUDY OF SNPP-VIIRS SATELLITE IMAGE FOR ESTIMATION SURFACE GILL NETS FISHING GROUND IN SEMARANG WATERS, CENTRAL JAVA, INDONESIA

Gillnet fishermen in Semarang often have difficulty determining the fishing grounds. The aims of the study were to analysed the spatial and temporal distribution of sea surface temperature (SST) and chlorophyll-a, to analysed the relationship between variables and to study the estimation of fishing grounds. The data collection method used purposive sampling. Map processing using the Inverse Distance Weighted (IDW) method. The data processing method uses normality test, classical assumption test and linear regression test. Analysis of image data spatially (4 miles from the shoreline) and temporally (west, east and transition monsoons). SST and chlorophyll-a tend to be high near the shore. The lowest SST (28.82OC) and the highest chlorophyll-a (5.11 mg/L) occurred in the east monsoon. The highest SST (31.74OC) and the lowest chlorophyll-a occurred in the west monsoon (1.31 mg/L). SST and chlorophyll-a have an erratic distribution pattern during the transitional monsoon. The value of SST and chlorophyll-a correlation partially to catch was 14.3% (weak) and 74% (very strong). The correlation value of SST to chlorophyll-a is 30.2% (medium). The correlation value of SST and chlorophyll-a simultaneously with catch is 75.3% (very strong). Determination of fishing areas based on high chlorophyll-a1 values and low SST. The coordinates for determining the fishing ground include 6° 53' 24,858" South Latitude 110° 26' 0.071" east west monsoon, 6° 55' 43,941" LS 110° 24' 47,903" east longitude transitional monsoon I, 6° 55' 29,439" latitude 110° 25' 54,790" east longitude, and 6° 55' 11,394" latitude 110° 26' 15,904" east longitude of transitional monsoon II.

Hubungan Klorofil-a dan Suhu Permukaan Laut (SPL) Terhadap Hasil Tangkapan Ikan Lemuru Sardinella lemuru di Selat Bali Menggunakan Citra Aqua MODIS Tahun 2009-2018

Oceanographic parameters such as sea surface temperature (SST) and chlorophyll-a in the water can influence the productivity of pelagic fish such as Lemuru. Many studies have been conducted on the relationship between SST and chlorophyll-a and the fluctuations in the catch of Lemuru fish, especially using satellite databased sensing technology. Most of the research using satellite data is done in a short-term period (not climatology), so there are still a lot of unanswered questions. One of them is: how the correlation between the fluctuation in the number of Lemuru fish catches is related to SST and chlorophyll-a in the ENSO and IOD periods. The purpose of this study is to determine the distribution of SST and chlorophyll-a concentrations and their relationship with the yield of Lemuru fish catches in the Bali Strait from 2009 to 2018. Aqua Moderate Imaging Spectroradiometer (MODIS) satellite data level 2 from Oceancolor and data on the number of fish caught in the Bali Strait from the National Fisheries Port (PPN) Pengambengan and UPT PPP Muncar. The method is used with correlation analysis between SST and chlorophyll-a to the number of Lemuru fish caught. The monthly average SST in Bali Strait waters from 2009 to 2018 was approximately 24,76 0C-30, 11 0C, with an estimated monthly chlorophyll-a concentration of 0,10 mg/m3-3,68 mg/m3. The spatial distribution of SST tends to be low in the southern Bali Strait and tends to be high in the northern Bali Strait. The spatial distribution of chlorophyll-a appears to have a high value in the near land area. The cross-correlation test between chlorophyll-a and the number of Lemuru catches showed a positive correlation yield at the 4th month lag. While the correlation of SST to the number of Lemuru fish catches shows a positive correlation at lag (-1).

Multiple Granularity Spatiotemporal Network for Sea Surface Temperature Prediction

Sea surface temperature (SST) prediction has an important practical value in marine disaster prevention and mitigation. Most current methods only use the temporal correlation of SST during prediction, but the spatial correlation is not considered, resulting in low prediction accuracies. In addition, the changing trend of SST as reflected by the single granularity feature is unreliable, and the degrees of dependence between historical SST and future SST tend to vary. In order to overcome these issues, the multiple granularity spatiotemporal network (MGSN) is proposed for SST prediction. The proposed method consists of three parts. First, a multibranch network structure is constructed to extract different temporal features of different granularities. Second, a temporal dependence representation module is developed to represent the different degrees of dependence between historical SST and predicted SST in the temporal dimension. Third, the spatiotemporal fusion prediction module is used to achieve a spatiotemporal prediction of the SST and fuse the prediction results of different granular features. Comparative experiments have been conducted. The experimental results show that the root-mean-square error (RMSE) of the proposed method is reduced by 0.1360, 0.1608, and 0.1448 compared with the RMSE of convolutional LSTM (ConvLSTM), when predicting SST for the next one day, three days, and seven days, respectively. Our method has strong spatiotemporal feature modeling capabilities and is suitable for regional SST prediction.

The effect of Ekman mass transport and Ekman pumping velocity on the variability of sea surface temperature in the Arafura Sea

Arafura sea is located between the southern part of Papua Island and Aru Island. Previous studies on Sea Surface Temperature (SST) have described that the SST is strongly affected by the upwelling, but the effect of Ekman Mass Transport (EMT) and Ekman Pumping Velocity (EPV) has not yet been studied. In other areas, it has been shown that EMT and EPV generated by the winds could affect the SST. Thus, further research is needed to better understand the role of the winds on the variability of SST through the mechanism of EMT and EPV in the Arafura Sea. This study used SST data from a high-resolution satellite image (GHRSST) and wind data from a scatterometer satellite image (MetOp A ASCAT). The data were processed using the composite and time-series correlation. This study shows that the higher the wind speed, causes the colder the SST in the Arafura Sea. In contrast, when the wind speed is lower, the SST tends to be warmer. The variabilities of the SST are mostly related to the mixing process associated with the magnitude of EMT. In the shallow water where the calculated Ekman depth is deeper than the actual depth, EMT is more influencing than EPV. On the deeper water at the northeast of the Island of Aru, the negative EPV induces upwelling, bringing the colder water to the sea surface. Statistically, the correlation between EMT (EPV) and the SST in the shallow water of the Arafura Sea is considered strong (weak). On the other hand, at the deep water of the Arafura Sea (northeast of the Island of Aru) offers a strong correlation between the EPV and the SST, whereas the EMT and the SST correlation is considered weak.

An early-warning indicator for Amazon droughts exclusively based on tropical Atlantic sea surface temperatures

Droughts in tropical South America have an imminent and severe impact on the Amazon rainforest and affect the livelihoods of millions of people. Extremely dry conditions in Amazonia have been previously linked to sea surface temperature (SST) anomalies in the adjacent tropical oceans. Although the sources and impacts of such droughts have been widely studied, establishing reliable multi-year lead statistical forecasts of their occurrence is still an ongoing challenge. Here, we further investigate the relationship between SST and rainfall anomalies using a complex network approach. We identify four ocean regions which exhibit the strongest overall SST correlations with central Amazon rainfall, including two particularly prominent regions in the northern and southern tropical Atlantic. Based on the time-dependent correlation between SST anomalies in these two regions alone, we establish a new early-warning method for droughts in the central Amazon basin and demonstrate its robustness in hindcasting past major drought events with lead-times up to 18 months.

Analyzing the Effects of Sea Surface Temperature (SST) on Soil Moisture (SM) in Coastal Areas of Eastern China

In this paper, we applied the re-analysis data cobe-SST (cobe-sea surface temperature) and Global Land Data Assimilation System (GLDAS) surface soil moisture (SM) data from 1961 to 2011 by using regional correlation analysis and time series causality analysis to trace annual variations in and identify the abnormal relationship of sea surface temperature (SST) in the eastern China Sea and SM in eastern China (EC). We also used satellite Moderate Resolution Imaging Spectroradiometer (MODIS) SST and AMSR-E SM data to examine the correlation of SST and SM in EC from 2004–2009. The results show that the SST in the eastern China Sea has experienced a warming trend since 1987, whereas the SM in EC has shown a drying trend since 1978. Before 1967 and after 1997, SST and SM changed during opposite phases, whereas from 1967 to 1997 they changed during the same phase. The differences between them may result from the abnormal summer precipitation causing abnormal SM. According to the regional correlation analysis, SST of the East China Sea is significantly related to SM in the southeast coastal area, and temporal sequence causality analysis shows that SST is correlated with and has higher influence on SM than vice versa. SM during spring and autumn shows a similar correlation with SST during the four seasons, so that SM in spring and autumn is positively correlated with SST in autumn and negatively correlated with SST in other seasons. SM in summer and winter correlated with SST in the four seasons, contradicting the foregoing conclusions. All these findings indicate that the thermodynamic state of the eastern China Sea has affected SM in EC.

Physical ocean-atmosphere variability over the shelf of South Africa from reanalysis products

Inter-relationships between physical ocean-atmosphere processes over the eastern Agulhas Bank are explored using data assimilation by the Hybrid Coordinate Ocean Model (HYCOM), which provides daily fields and time series off the south coast of South Africa (33.75–34.5S, 24.0–26.5E) in the period 2009–2017. Our objectives are: i) to describe and understand the sharp gradients over the shelf, ii) to quantify the temporal controls and lagged uptake of weather events and, iii) to analyze the spatial effects of capes on mesoscale oceanographic structure. Sea surface temperature (SST) and zonal winds (U-wind) were cross-correlated with salinity, currents, waves and atmospheric variables such as heat budget components, air pressure (SLP), air temperature. Correlations were made over daily and annual time scales to identify the leading ocean-atmosphere interactions and the strength of processes.At daily time scales weak correlations were found between SST and a variety of ocean-atmosphere parameters, suggesting that multiple processes affect the thermodynamic condition. SST showed most correlation with radiation and heat budget components (r = −0.35, 0.39), SLP (r = −0.30) and meridional currents (r = −0.26). In contrast, the simultaneous correlation of SST with U-wind was weak (r = −0.08). We attribute this to: i) delayed response, ii) opposing effects of coastal upwelling and summer heating, and iii) competing shelf-edge processes.Over the mean annual cycle, correlations between SST, U-winds, salinity, radiative fluxes and V-currents were strong, indicating how kinematic and thermodynamic controls conspire to induce seasonality. SST was well correlated (r < −0.82) with SLP, long-wave radiation, salinity and wave height, but weakly correlated with currents. U-wind was correlated significantly with evaporation, radiative fluxes, curl (wind shear), meridional currents and wave period.Case studies of transient cold and warm events south of South Africa are described to reveal the large-scale atmospheric forcing of local SST. In addition, comparisons between the HYCOM reanalysis time series and independent insitu data are provided as a measure of confidence in global data assimilation systems, which open new opportunities for mesoscale oceanographic research in coastal zones.

Time‐varying relationships between ocean conditions and sockeye salmon productivity

AbstractEnvironmental change is occurring at unprecedented rates in many marine ecosystems. Yet, environmental effects on fish populations are commonly assumed to be constant across time. In this study, I tested whether relationships between ocean conditions and productivity of North American sockeye salmon (Oncorhynchus nerka) stocks have changed over the past six decades. Specifically, I evaluated the evidence for non‐stationary relationships between three widely used ocean indices and productivity of 45 sockeye salmon stocks using hierarchical Bayesian models. The ocean indices investigated were the Pacific Decadal Oscillation (PDO), North Pacific Gyre Oscillation (NPGO), and sea surface temperature (SST). I found partial support for time‐varying salmon–ocean relationships. Non‐stationary relationships were strongest for the NPGO and weaker for the SST and PDO indices. Productivity–NPGO correlations tended to shift gradually over time with opposite trends for stocks in British Columbia (B.C.) and western Alaska; for B.C. stocks, the NPGO correlations shifted from significantly negative prior to 1980 to significantly positive after 1990, whereas for western Alaska stocks, the correlations shifted from positive to negative. Productivity–SST correlations showed declining trends for B.C. and Gulf of Alaska stocks, that is, correlations became more negative (B.C.) or less positive (Gulf of Alaska) over time. For the PDO, correlations weakened during the 1980s for western Alaska and B.C. stocks. Overall, these results provide evidence for time‐varying relationships between salmon productivity and environmental conditions over six decades, highlighting the need to recognize that historical responses of salmon populations to environmental change may not be indicative of future responses.

Interdecadal Change in the Relationship of the Western North Pacific Tropical Cyclogenesis Frequency to Tropical Indian and North Atlantic Ocean SST in Early 1990s

AbstractThe present study investigates reasons of interdecadal changes in the relationship between the western North Pacific (WNP) tropical cyclone (TC) genesis and tropical Indian Ocean (TIO) and tropical North Atlantic Ocean (TNA) sea surface temperature (SST) in early‐1990s. It is found that the above interdecadal changes are related to changes in the coherence of TIO and TNA SST variations with equatorial central‐eastern Pacific Ocean (EPO) SST variations. During 1970s through mid‐1980s, TIO and TNA SSTs tend to vary in phase with EPO SST on both interannual and interdecadal time scales. The combined effects of TIO and EPO SST anomalies induce opposite environment and TC changes in southeastern and northwestern WNP, leading to a weak correlation of basin‐wide WNP TC and TIO SST. Similarly, TNA and EPO SST anomalies induce opposite environment and TC changes in northeastern and southeastern WNP and thus basin‐wide WNP TC‐TNA SST correlation is weak. During late 1990s through early 2000s, TIO and EPO SST variations have a weak coherence due to their opposite relations on interannual and interdecadal time scales. TIO SST anomalies alone induce anomalous circulation over most WNP so that basin‐wide WNP TC‐TIO SST relationship is strong. The interannual and interdecadal SST variations remain coherent in TNA and EPO but with a switch of sign in their relationship in early 1990s. TNA and EPO SST anomalies induce same environment and TC changes in northeastern and southeastern WNP, and thus, basin‐wide WNP TC‐TNA SST relationship is strong. Numerical model experiments validate the importance of coherent regional SST anomalies.

Correlation and coherence analysis between sea surface temperature (SST) and surface wind in the Equatorial Western Sumatra Waters

Equatorial Western Sumatra Waters have a tremendous dynamic in ocean characteristics. It directly faces the Indian Ocean exactly located below the equator. Consequently, Equatorial Western Sumatra waters are influenced by the tropical climatic factors such as monsoons and climate variabilities, such as Indian Ocean Dipole (IOD) and Madden Julian Oscillation (MJO) that control sea surface temperature (SST) fluctuation in the Indian Ocean regions. This study aimed to review the correlation and coherence of SST distributed by surface wind in the Equatorial Western Sumatra waters. Wavelet method (cross wavelet transforms and wavelet coherence) was used to analyze the correlation and coherency between SST and surface wind. The annual variation of SST for 365 days period is the strongest event throughout the year caused by either monsoon or the changes of wind speed in the surface. Otherwise, the strongest intra-seasonal SST variation of 35 - 60 days observed from December 2012 to March 2013. The highest surface wind speed occurs in the southern and western waters. The surface wind plays a role in evoking SST distribution of 35 - 60 days period (intra-seasonal variability). Besides, the surface wind with 6 months period (semi-annual variability) influences the SST distribution which is identified only in the southern waters and the Indian Ocean regions. These conditions are possibly predicted as the influence of monsoons.

Remote Chlorophyll-a and SST to Determination of Fish Potential Area in Makassar Strait Waters Using MODIS Satellite Data

The objective of this research was to analyze integrated geospatial data using MODIS Satellite data and field data observation to the determination of potential fish area. This study applied in Makassar Strait 118° 0’ 0” E-119° 37’ 0” E, 3°0’0” S – 4°45’0” S. The methods of this research was conducted using MODIS combine the depth of map waters survey direct interviews and people who participate in fishing activities monthly in January, February, March, April, May, and June. Data were analyzed by using software: Statistics, SeaDas, ENVI, and GIS. The result showed that the horizontal distribution of sea surface temperature (SST) spread evenly or divergent. The distribution of sea temperature in the coastal area was relatively warm in the sea around Barru and Pangkep was ranged 31 to 32 °C. Sea surface temperature dominates in the range of 30 - 31°C. SST correlation coefficient was 0.79 between MODIS and value of observation field data.