Monthly Mean Sea Surface Temperature Research Articles

An Assessment of Changes in the Thermal Environment during the COVID-19 Lockdown: Case Studies from the Greenland and Norwegian Seas

The COVID-19 lockdown had a significant impact on human activities, reducing anthropogenic heat and CO2 emissions. To effectively assess the impact of the lockdown on the thermal environment, we used the sliding paired t-test, which we improved from the traditional sliding t-test by introducing the paired t-test for sliding statistical tests, to test the abrupt change in the thermal environment. Furthermore, an additive decomposition model and wavelet analysis method were used to analyze the characteristics of trend and irregular change, coherence, and phase difference of the time series data with respect to the thermal environment. We chose the Greenland Sea and the Norwegian Sea, regions highly sensitive to changes in climate and ocean circulation, as case studies and used remote sensing data of the sea surface temperature (SST) and the atmospheric CO2 concentration data obtained from the Goddard Earth Sciences Data and Information Services Center from January 2015 to December 2021 for the analysis. The results show that although the annual spatial mean SST in 2020 is lower than the mean of all 7 years in most areas of the two seas, there is no evidence of a significant mutation in the decrease in the SST during the lockdown in 2020 compared with the temperatures before, according to the sliding paired t-test. The analysis of the irregular components of the monthly mean SST decomposed by an additive decomposition model also does not show the anomalously low SST during the lockdown in 2020. In addition, the lockdown had almost no impact on the increasing trend of CO2 concentration. The wavelet analysis also shows that there is no obvious anomaly in coherence or phase difference between the periodic variation of the SST and the CO2 concentrations in 2020 compared with other years. These results suggest that the direct effect of the COVID-19 lockdown on the thermal environment of the study area could be negligible.

Harnessing the Power of Graph Representation in Climate Forecasting: Predicting Global Monthly Mean Sea Surface Temperatures and Anomalies

AbstractThe variability of sea surface temperatures (SSTs) is crucial in climate dynamics, influencing marine ecosystems and human activities. This study leverages graph neural networks (GNNs), specifically a GraphSAGE model, to forecast SSTs and their anomalies (SSTAs), focusing on the global scale structure of climatological data. We introduce an improved graph construction technique for SST teleconnection representation. Our results highlight the GraphSAGE model's capability in 1‐month‐ahead global SST and SSTA forecasting, with SST predictions spanning up to 2 years with a recursive approach. Notably, regions with persistent currents exhibited enhanced SSTA predictability, contrasting with equatorial and Antarctic areas. Our GNN outperformed both the persistence model and traditional methods. Additionally, we offer an SST and SSTA graph data set based on ERA5 and a graph generation tool. This GNN case study has shown how the GraphSAGE can be used in SST and SSTA forecasting, and will provide a foundation for further refinements such as adjusting graph construction, optimizing imbalanced regression techniques for extreme SSTAs, and integrating GNNs with other temporal pattern learning methods to improve long‐term predictions.

Spatial and Temporal Variability of Upwelling in the West-Central South China Sea and Its Relationship with the Wind Field

The west-central South China Sea upwelling event is a critical process that regulates the climate and marine ecosystem in the region. In this study, we used sea surface temperature (SST) satellite data from 2000 to 2018 to analyze the spatial and temporal characteristics of upwelling in the west-central South China Sea and combined the wind field data to investigate the effects of wind direction and speed on upwelling. We divided the upwelling sea area into three regions based on the different shoreline angles along the eastern coast of the South China Peninsula: OU_1, OU_2, and OU_3. Our results showed that the upwelling events occurred mainly from May to September in the OU_1 and OU_2 waters. The empirical orthogonal function (EOF) decomposition of the monthly mean SST moment level field indicated a cyclical interannual variation of upwelling in the west-central South China Sea. The correlation analysis showed that wind direction changes have a significant impact on the upwelling intensity center, with the upwelling intensity center moving towards high latitudes and away from the coast when the wind direction changes from north to east. When the wind direction changes from east to south, the upwelling intensity center moves towards low latitudes and near the coast. The average lag time of upwelling events to the wind field in the central and western South China Sea was 38.9 h, with OU_2 showing a longer response time than the other seas. Our study provides important insights into the mechanisms governing upwelling in the west-central South China Sea, which can effectively promote the rational use of ecological resources and provide a scientific basis for marine ecological protection in the region.

Modelling and forecasting the effects of increasing sea surface temperature on coral bleaching in the Indo-Pacific region

ABSTRACT The Coral Triangle (CT) and the South China Sea (SCS) are the world’s great tropical seas, located in the Indo-Pacific (IP) region. It is home to the richest marine ecosystem on Earth, with a total of 76% reef-building coral species as well as 37% coral reef fish species. Unfortunately, this sensitive area is now vulnerable to Sea Surface Temperature (SST) warming. This research explored the possible consequences of SST warming on the rich ecosystems of the IP region, specifically on bleaching of its coral reefs. Reefbase provided coral bleaching records together with the daily NOAA AVHRR Optimum Interpolation (OI) SST V2 dataset (OISSTv2) were used to explore the relationship between coral bleaching and SST in the IP region. Three different categories of monthly mean SST were tested as potential covariates: minimum SST, mean SST and maximum SST, obtained from the OISSTv2. The fitted logistic regression (LR) model revealed a significant and large correlation between coral bleaching and annual maximum monthly mean SST in the study area using the bleaching data from an online database and the time-series of AVHRR images. Predicted maps of coral bleaching based on the LR model were highly consistent with NOAA Coral Reef Watch (CRW) Degree heating Weeks (DHW) maps. However, some important discrepancies resulted from the more specific local fitting used in the LR model. The maximum SST was forecasted from 2020 to 2100 based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) dataset under the Representative Concentration Pathways (RCP2.6) scenario. The fitted logistic regression model was employed to transform the forecasted maximum SST values into maps of the probability of coral bleaching from 2020 to 2100. The results provide considerable cause for concern, including the likelihood of widespread coral bleaching in many places in the IP region over the next 30 years.

Seasonal Prediction of Tropical Cyclones and Storms over the Southwestern Indian Ocean Region Using the Generalized Linear Models

Tropical cyclones (TCs) and storms (TSs) are among the devastating events in the world and southwestern Indian Ocean (SWIO) in particular. The seasonal forecasting TCs and TSs for December to March (DJFM) and November to May (NM) over SWIO were conducted. Dynamic parameters including vertical wind shear, mean zonal steering wind and vorticity at 850 mb were derived from NOAA (NCEP-NCAR) reanalysis 1 wind fields. Thermodynamic parameters including monthly and daily mean Sea Surface Temperature (SST), Outgoing Longwave Radiation (OLR) and equatorial Standard Oscillation Index (SOI) were used. Three types of Poison regression models (i.e. dynamic, thermodynamic and combined models) were developed and validated using the Leave One Out Cross Validation (LOOCV). Moreover, 2 × 2 square matrix contingency tables for model verification were used. The results revealed that, the observed and cross validated DJFM and NM TCs and TSs strongly correlated with each other (p ≤ 0.02) for all model types, with correlations (r) ranging from 0.62 - 0.86 for TCs and 0.52 - 0.87 for TSs, indicating great association between these variables. Assessment of the model skill for all model types of DJFM and NM TCs and TSs frequency revealed high skill scores ranging from 38% - 70% for TCs and 26% - 72% for TSs frequency, respectively. Moreover, results indicated that the dynamic and combined models had higher skill scores than the thermodynamic models. The DJFM and NM selected predictors explained the TCs and TSs variability by the range of 0.45 - 0.65 and 0.37 - 0.66, respectively. However, verification analysis revealed that all models were adequate for predicting the seasonal TCs and TSs, with high bias values ranging from 0.85 - 0.94. Conclusively, the study calls for more studies in TCs and TSs frequency and strengths for enhancing the performance of the March to May (MAM) and December to October (OND) seasonal rainfalls in the East African (EA) and Tanzania in particular.

Küresel iklim değişikliğiyle mücadelede deniz yüzeyi sıcaklığının ileri beslemeli yapay sinir ağları ile tahminlenmesi: Rize örneği, Türkiye

The increase of the world population, especially in the global competition, together with the increasing use of fossil fuel resources to meet energy needs, leads to more greenhouse gases (more than one CO2, methane etc.) emissions and the global climate crisis. In this process, changes in meteorological events such as temperature, precipitation, and wind, attract attention moreover but when considered as a whole, we know that these negative changes in the ecosystem negatively affect many living groups. Sea Surface Temperature (SST) as measured meteorologically is the most important environmental parameter where these changes are monitored and observed. It draws attention to the fact that changes in SST are not limited to living organisms as habitats, but also catalyze many chain reactions, especially socio-economic impacts. Therefore, much of the work is devoted to forecasting studies to adapt to changing habitats and take the necessary precautions against potential risks. Feed-forward artificial neural networks have been commonly used to address these emerging needs. Artificial neural networks, which are a simple imitation of the human neurological system, have been used as an artificial intelligence method in forecasting problems due to their superior performance and not having the limitations of classical time series. In this study, the forecasting of the time series of monthly mean SST temperature obtained from Rize station between the years 2010 and 2020 is performed by using feed-forward artificial neural networks, and the forecasting performance of the corresponding time series is compared with many forecasting methods with different characteristics. The comparison of the methods used the mean square error and mean absolute percentage error criteria, which are commonly used in the forecasting literature. The analysis results showed that the analysis results obtained with the feed-forward artificial neural networks have the best prediction performance. As a result, it can be stated that the sea surface temperature can be forecasted with a very high accuracy using the feed-forward artificial neural networks.

A deep learning model for forecasting global monthly mean sea surface temperature anomalies

Sea surface temperature (SST) variability plays a key role in the global weather and climate system, with phenomena such as El Niño-Southern Oscillation (ENSO) regarded as a major source of interannual climate variability at the global scale. The ability to make long-range forecasts of SST variations and extreme marine heatwave events have potentially significant economic and societal benefits, especially in a warming climate. We have developed a deep learning time series prediction model (Unet-LSTM), based on more than 70 years (1950–2021) of ECMWF ERA5 monthly mean SST and 2-m air temperature data, to predict global 2-dimensional SSTs up to a 24-month lead. Model prediction skills are high in the equatorial and subtropical Pacific. We have assessed the ability of the model to predict SST anomalies in the Niño3.4 region, an ENSO index in the equatorial Pacific, and the Blob marine heatwave events in the northeast Pacific in detail. An assessment of the predictions of the 2019–2020 El Niño and the 2016–2017 and 2017–2018 La Niña show that the model has skill up to 18 months in advance. The prediction of the 2015–2016 extreme El Niño is less satisfactory, which suggests that subsurface ocean information may be crucial for the evolution of this event. Note that the model makes predictions of the 2-d monthly SST field and Nino 3.4 is just one region embedded in the global field. The model also shows long lead prediction skills for the northeast Pacific marine heatwave, the Blob. However, the prediction of the marine heatwaves in the southeast Indian Ocean, the Ningaloo Niño, shows a short lead prediction. These results indicate the significant potential of data-driven methods to yield long-range predictions of SST anomalies.

Prediction of Sea Surface Temperature by Combining Interdimensional and Self-Attention with Neural Networks

Sea surface temperature (SST) is one of the most important and widely used physical parameters for oceanography and meteorology. To obtain SST, in addition to direct measurement, remote sensing, and numerical models, a variety of data-driven models have been developed with a wealth of SST data being accumulated. As oceans are comprehensive and complex dynamic systems, the distribution and variation of SST are affected by various factors. To overcome this challenge and improve the prediction accuracy, a multi-variable long short-term memory (LSTM) model is proposed which takes wind speed and air pressure at sea level together with SST as inputs. Furthermore, two attention mechanisms are introduced to optimize the model. An interdimensional attention strategy, which is similar to the positional encoding matrix, is utilized to focus on important historical moments of multi-dimensional input; a self-attention strategy is adopted to smooth the data during the training process. Forty-three-year monthly mean SST and meteorological data from the fifth-generation ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis (ERA5) are collected to train and test the model for the sea areas around China. The performance of the model is evaluated in terms of different statistical parameters, namely the coefficient of determination, root mean squared error, mean absolute error and mean average percentage error, with a range of 0.9138–0.991, 0.3928–0.8789, 0.3213–0.6803, and 0.1067–0.2336, respectively. The prediction results indicate that it is superior to the LSTM-only model and models taking SST only as input, and confirm that our model is promising for oceanography and meteorology investigation.

Опыт разработки методики долгосрочного прогноза сроков вскрытия ото льда рек бассейна р. Вятки

An attempt is made to develop a method for long-term forecasting of the ice breakup time for the Vyatka River basin, to identify the impact of the distribution of sea surface temperature and geopotential height in the informative regions at the levels H100 and H500 over the Northern Hemisphere on the river ice breakup. The location and boundaries of the informative regions in the fields of H100 and H500 were revealed by the discriminant analysis, the EOF expansion coefficients of the fields of anomalies of monthly mean values of H100 and H500 for January and February and the anomalies of monthly mean sea surface temperature in the North Atlantic and Northwest Pacific were used as potential predictors. The stepwise regression analysis allowed deriving good and satisfactory (S/σ = 0.45–0.73) complex prognostic equations for forecasting the ice breakup time for the Vyatka River basin. The essential influence of H100 and H500 geopotential height fields and the spatial distribution of sea surface temperature anomalies in the North Atlantic and Northwest Pacific in January and February on the river ice breakup time is revealed. It is proposed to improve the method by considering the impact of air temperature, maximum ice thickness per winter, and other indirect characteristics on the processes of river ice breakup in the Vyatka River basin. Keywords: ice regime, long-range forecast, river ice breakup, expansion coefficients, geopotential height fields, spring ice phenomena, energy-active zones of the oceans, complex prognostic equation

Опыт разработки методики долгосрочного прогноза сроков вскрытия ото льда рек бассейна р. Вятки

An attempt is made to develop a method for long-term forecasting of the ice breakup time for the Vyatka River basin, to identify the impact of the distribution of sea surface temperature and geopotential height in the informative regions at the levels H100 and H500 over the Northern Hemisphere on the river ice breakup. The location and boundaries of the informative regions in the fields of H100 and H500 were revealed by the discriminant analysis, the EOF expansion coefficients of the fields of anomalies of monthly mean values of H100 and H500 for January and February and the anomalies of monthly mean sea surface temperature in the North Atlantic and Northwest Pacific were used as potential predictors. The stepwise regression analysis allowed deriving good and satisfactory (S/σ = 0.45–0.73) complex prognostic equations for forecasting the ice breakup time for the Vyatka River basin. The essential influence of H100 and H500 geopotential height fields and the spatial distribution of sea surface temperature anomalies in the North Atlantic and Northwest Pacific in January and February on the river ice breakup time is revealed. It is proposed to improve the method by considering the impact of air temperature, maximum ice thickness per winter, and other indirect characteristics on the processes of river ice breakup in the Vyatka River basin. Keywords: ice regime, long-range forecast, river ice breakup, expansion coefficients, geopotential height fields, spring ice phenomena, energy-active zones of the oceans, complex prognostic equation

Sea Surface Temperature Variability over the Tropical Indian Ocean during the ENSO and IOD Events in 2016 and 2017

2016 and 2017 were marked by strong El Niño and weak La Niña events, respectively, in the tropical East Pacific Ocean. The strong El Niño and weak La Niña events in the Pacific significantly impacted the sea surface temperature (SST) in the tropical Indian Ocean (TIO) and were followed by extreme negative and weak positive Indian Ocean Dipole (IOD) phases in 2016 and 2017, which triggered floods in the Indian subcontinent and drought conditions in East Africa. The IOD is an irregular and periodic oscillation in the Indian Ocean, which has attracted much attention in the last two decades due to its impact on the climate in surrounding landmasses. Much work has been done in the past to investigate global climate change and its impact on the evolution of IOD. The dynamic behind it, however, is still not well understood. The present study, using various satellite datasets, examined and analyzed the dynamics behind these events and their impacts on SST variability in the TIO. For this study, the monthly mean SST data was provided by NOAA Optimum Interpolation Sea Surface Temperature (OISST). SST anomalies were measured on the basis of 30-year mean daily climatology (1981–2010). It was determined that the eastern and western poles of the TIO play quite different roles during the sequence of negative and positive IOD phases. The analysis of air-sea interactions and the relationship between wind and SST suggested that SST is primarily controlled by wind force in the West pole. On the other hand, the high SST that occurred during the negative IOD phase induced local convection and westerly wind anomalies via the Bjerknes feedback mechanism. The strong convection, which was confined to the (warm) eastern equatorial Indian Ocean was accompanied by east–west SST anomalies that drove a series of downwelling Kelvin waves that deepened the thermocline in the east. Another notable feature of this study was its observation of weak upwelling along the Omani–Arabian coast, which warmed the SST by 1 °C in the summer of 2017 (as compared to 2016). This warming led to increased precipitation in the Bay of Bengal (BoB) region during the summer of 2017. The results of the present work will be important for the study of monsoons and may be useful in predicting both droughts and floods in landmasses in the vicinity of the Indian Ocean, especially in the Indian subcontinent and East African regions.

CoralTemp and the Coral Reef Watch Coral Bleaching Heat Stress Product Suite Version 3.1

The National Oceanic and Atmospheric Administration (NOAA) Coral Reef Watch (CRW) program has been providing resource managers, scientific researchers, and other coral reef ecosystem stakeholders with coral bleaching heat stress products for more than 20 years. The development of the CoralTemp sea surface temperature (SST) dataset has allowed CRW to produce the Coral Bleaching Heat Stress product suite with climatologies and daily SST measurements from within the same SST dataset, significantly improving data quality. Previously, the Monthly Mean (MM) SST and Maximum Monthly Mean (MMM) SST climatologies were derived using a different dataset from the near real-time SST. Here we provide an up-to-date description of how each product within the Coral Reef Watch Coral Bleaching Heat Stress product suite version 3.1 is derived, including descriptions of the MM, MMM, SST Anomaly, Coral Bleaching HotSpot and Degree Heating Week (DHW) products.

Hybrid improved empirical mode decomposition and BP neural network model for the prediction of sea surface temperature

Abstract. Sea surface temperature (SST) is the major factor that affects the ocean–atmosphere interaction, and in turn the accurate prediction of SST is the key to ocean dynamic prediction. In this paper, an SST-predicting method based on empirical mode decomposition (EMD) algorithms and back-propagation neural network (BPNN) is proposed. Two different EMD algorithms have been applied extensively for analyzing time-series SST data and some nonlinear stochastic signals. The ensemble empirical mode decomposition (EEMD) algorithm and complementary ensemble empirical mode decomposition (CEEMD) algorithm are two improved algorithms of EMD, which can effectively handle the mode-mixing problem and decompose the original data into more stationary signals with different frequencies. Each intrinsic mode function (IMF) has been taken as input data to the back-propagation neural network model. The final predicted SST data are obtained by aggregating the predicted data of individual series of IMFs (IMFi). A case study of the monthly mean SST anomaly (SSTA) in the northeastern region of the North Pacific shows that the proposed hybrid CEEMD-BPNN model is much more accurate than the hybrid EEMD-BPNN model, and the prediction accuracy based on a BP neural network is improved by the CEEMD method. Statistical analysis of the case study demonstrates that applying the proposed hybrid CEEMD-BPNN model is effective for the SST prediction. Highlights include the following: Highlights. An SST-predicting method based on the hybrid EMD algorithms and BP neural network method is proposed in this paper. SST prediction results based on the hybrid EEMD-BPNN and CEEMD-BPNN models are compared and discussed. A case study of SST in the North Pacific shows that the proposed hybrid CEEMD-BPNN model can effectively predict the time-series SST.

The Remote Responses of Early Summer Cold Vortex Precipitation in Northeastern China to the precedent Sea Surface Temperatures

Based on the daily precipitation data of Northeastern China (NEC), this research study utilized the monthly mean geopotential height field and vertical velocity field data obtained by NCEP/NCAR reanalysis. In addition, the monthly mean SST (sea surface temperature) data obtained by NOAA reconstruction, along with the calculations of NEC precipitation generated on the occurrence date of the annual early summer (June) northeast cold vortex (NECV) (hereinafter referred to as the cold vortex precipitation) were used to analyze the variation characteristics of cold vortex precipitation. The aim of this study was to explain the physical mechanism of the previous SST factors which had influenced the cold vortex precipitation through the force of atmospheric circulations, from the perspective of a statistical analysis. Finally, sensitivity tests of the AM2.1 atmospheric circulation model were conducted to verify the statistical analysis results. The results revealed that since 1961 there had been a significant increase trend in the early summer cold vortex precipitation. The relationships between the NEC early summer cold vortex precipitation and the previous SST were divided into three periods. During the period ranging from 1961 to 1979 (hereinafter referred to as Phase 1), a NAT anomaly positive phase during March to May resulted in the blocking highs of the Ural Mountains and the Sea of Okhotsk becoming strengthened (“dual blocking pattern”) in the early summer, which in turn led to increased early summer cold vortex precipitation. During the period ranging from 1980 to 2000 (hereinafter referred to as Phase 2), a higher Kuroshio SST in April to May stimulated an EU-like teleconnection pattern in the early summer over Eastern Asia. As a result, the cold vortex precipitation in early summer was reduced. After 2001 (hereinafter referred to as Phase 3), the NAT anomaly negative phase during March to May induced strengthened blocking patterns on the northwestern side of Lake Baikal in the early summer, which was accompanied by a strengthened northeastern cold vortex (a pattern of “higher in the north, lower in the south”). Also, an upward vertical velocity anomaly appeared over the NEC, which resulted in increased early summer cold vortex precipitation. The sensitivity tests of this study's numerical model reproduced the force effects of the Kuroshio SST in Phase 2, and the NAT in Phase 3 on the EU-like teleconnection pattern, and the pattern of “higher in the north, lower in the south”. These findings further corroborated the aforementioned results.

Examination of interannual variability of sea surface temperature in the Indian Ocean using the physical decomposition method

The physical decomposition method suggested by Qian (2012) is used to examine the interannual variability of sea surface temperature (SST) and anomaly (SSTA) in the Indian Ocean (IO) for the period 1945−2003. The monthly mean SSTs taken from the global ocean reanalysis produced by the Simple Ocean Data Assimilation (SODA) are decomposed into four terms. The first term is the zonally averaged monthly climatological SST ([ ( )]) Tt ϕ , which features relatively warm surface waters in the tropical IO and relatively colder surface waters over the southern IO. This term also has a relatively low SST pool between the Equator and 20°N. The SST at the center of the pool in summer is about 1−2°C lower than in spring and autumn. The second term is the spatially-varying monthly climatological SSTA * ( ( , )) Tt λ ϕ , due mainly to the topographic effect and seasonal variation in wind forcing. The values of * (,) Tt λ ϕ are negative over the western coastal waters and positive over the eastern coastal and shelf waters in the tropical and northern IO. The third term is the zonally-averaged transient SSTA ([ ( , ) ] ) T t ϕ Y ′ . The largest values of [ ( , )] T t ϕ Y ′ occur over the subtropical and mid-latitudes of the IO, which differs from the SSTA in the tropical waters of the Pacific Ocean. Time series of zonally and meridionally averaged ( ,) T t ϕ Y ′ in the tropical-subtropical IO is strongly correlated with the Indian Ocean basin-wide (IOBW) mode. The fourth term is the spatially-varying transient SSTA * ( ( , ,) ) T t λ ϕ Y′ . The REOF analysis of the fourth term demonstrates that the first REOF is correlated strongly with the South Indian Ocean Dipole (SIOD) mode. The second REOF is correlated strongly with the equatorial Indian Ocean dipole (IOD) mode. The third REOF is highly correlated with the tropical IOBW mode.

Examination of interannual variability of sea surface temperature in the Indian Ocean using the physical decomposition dethod

The physical decomposition method suggested by Qian (2012) is used to examine the interannual variability of sea surface temperature (SST) and anomaly (SSTA) in the Indian Ocean (IO) for the period 1945.2003. The monthly mean SSTs taken from the global ocean reanalysis produced by the Simple Ocean Data Assimilation (SODA) are decomposed into four terms. The first term is the zonally averaged monthly climatological SST ([Tt(ϕ)]), which features relatively warm surface waters in the tropical IO and relatively colder surface waters over the southern IO. This term also has a relatively low SST pool between the Equator and 20°N. The SST at the center of the pool in summer is about 1-2°C lower than in spring and autumn. The second term is the spatially-varying monthly climatological SSTA (Tt*(λ,ϕ)), due mainly to the topographic effect and seasonal variation in wind forcing. The values of Tt*(λ,ϕ) are negative over the western coastal waters and positive over the eastern coastal and shelf waters in the tropical and northern IO. The third term is the zonally-averaged transient SSTA([T(ϕ,t)']Y). The largest values of [T(ϕ,t)']Y occur over the subtropical and mid-latitudes of the IO, which differs from the SSTA in the tropical waters of the Pacific Ocean. Time series of zonally and meridionally averaged T(ϕ,t)'Y in the tropical-subtropical IO is strongly correlated with the Indian Ocean basin-wide (IOBW) mode. The fourth term is the spatially-varying transient SSTA (T(λ,ϕ,t)*Y']. The REOF analysis of the fourth term demonstrates that the first REOF is correlated strongly with the South Indian Ocean Dipole (SIOD) mode. The second REOF is correlated strongly with the equatorial Indian Ocean dipole (IOD) mode. The third REOF is highly correlated with the tropical IOBW mode.

Homogenization of Sea Surface Temperature at Xiao Changshan marine station in the east of the Bohai Sea using the PMT method

Using the PMTred algorithm (penalized maximum T test) and the detailed metadata archive, the monthly mean sea surface temperature (SST) datasets from 1960 to 2011 have been detected and adjusted. Results show that the SST time series has serious problems of inhomogeneity. The changes in observation instruments and observation system are the main causes of the discontinuity. For the monthly SST time series, the negative adjustments have the high proportion, which may be greatly due to the SST decreasing after automation. It is found that the annual mean SST trends have changed obviously before and after adjusted. The increasing trend of annual mean SST after adjustment is higher than before, by up to 0.252 °C/10 yr.

Validation of degree heating weeks as a coral bleaching index in the northwestern Pacific

Mass bleaching is the most significant threat to coral reefs. The United States National Oceanic and Atmospheric Administration monitors world sea surface temperature (SST) and releases warnings for bleaching based on degree heating weeks (DHW), which is the accumulation of temperature anomalies exceeding the monthly maximum mean SST for a given region. DHW values >4.0 °C-weeks are thought to induce bleaching, and those >8.0 °C-weeks are thought to result in widespread bleaching and some mortality. This study validates the effectiveness of DHW as a mass bleaching index by on-site historical observation at eight sites in the northwestern Pacific Ocean. The mass bleaching events occurred during different years at different sites. The recorded years of the bleaching events matched well with DHW values >8 °C-weeks, and the logistically projected probability of bleaching against DHW showed a positive relationship. DHW provides a reasonable threshold for bleaching.

SEA SURFACE TEMPERATURE DAN CHLOROPHYLL-A CONCENTRATION VARIABILITIES OF JAKARTA BAY AND ITS SURROUNDING WATERS

<p>Variability of sea surface temperature (SST) and the concentration of chlorophyll-a (Chla) in Jakarta Bay and its surrounding waters were analyzed based on the time series data of monthly mean SST and Chla estimated by NOAA AVHRR, SeaWiFS, and MODIS satellites for 17 years (1997 to 2014). Generally, the monthly mean SST values fluctuated seasonally with the semi-annual pattern with the maximum value occurred in the second transitional season (Oct-Nov) and relatively high in the first transitional season (Mar). In contrast, the minimum of monthly mean SST values was found during the east season (Jul) and relatively low during the west season (Des-Jan). SST variabilities were generally associated with the sun position, sea water circulation, and rainfall. Based on MODIS data, the minimum-maximum of monthly mean SST ranged of 28.39°C-32.58°C. Seasonal fluctuations with the semi-annual pattern also occurred in the monthly mean Chla values with the maximum value occurred during the west season (Des-Jan) and relatively high values occurred during the east season (Jun). In contrast, the minimum of the monthly mean Chla was found during the second transition season (Sep-Oct) and relatively low during the first transitional season (March-Apr). The minimum-maximum of monthly mean Chla ranged of 0.09 mg/m<sup>3</sup>-2.50 mg/m<sup>3</sup>. Chla fluctuations were generally associated with the variabilities of rainfall, river water discharge, and sea water circulation of the Java Sea. Anomalous climatic factors such as El Nino Southern Oscillation (ENSO) and La Nina produced a significant impact on the SST and Chla interannual variability. Generally, the ENSO phenomenon lo-wers the value SST and Chla, while La Nina events tended to increase Chla values.</p><p><strong><em>Keywords: </em></strong><em>sea surface temperature, chlorophyll-a, fluctuation, NOAA AVHRR, SeaWiFS, MODIS</em></p>

SEA SURFACE TEMPERATURE DAN CHLOROPHYLL-A CONCENTRATION VARIABILITIES OF JAKARTA BAY AND ITS SURROUNDING WATERS

Variability of sea surface temperature (SST) and the concentration of chlorophyll-a (Chla) in Jakarta Bay and its surrounding waters were analyzed based on the time series data of monthly mean SST and Chla estimated by NOAA AVHRR, SeaWiFS, and MODIS satellites for 17 years (1997 to 2014). Generally, the monthly mean SST values fluctuated seasonally with the semi-annual pattern with the maximum value occurred in the second transitional season (Oct-Nov) and relatively high in the first transitional season (Mar). In contrast, the minimum of monthly mean SST values was found during the east season (Jul) and relatively low during the west season (Des-Jan). SST variabilities were generally associated with the sun position, sea water circulation, and rainfall. Based on MODIS data, the minimum-maximum of monthly mean SST ranged of 28.39°C-32.58°C. Seasonal fluctuations with the semi-annual pattern also occurred in the monthly mean Chla values with the maximum value occurred during the west season (Des-Jan) and relatively high values occurred during the east season (Jun). In contrast, the minimum of the monthly mean Chla was found during the second transition season (Sep-Oct) and relatively low during the first transitional season (March-Apr). The minimum-maximum of monthly mean Chla ranged of 0.09 mg/m3-2.50 mg/m3. Chla fluctuations were generally associated with the variabilities of rainfall, river water discharge, and sea water circulation of the Java Sea. Anomalous climatic factors such as El Nino Southern Oscillation (ENSO) and La Nina produced a significant impact on the SST and Chla interannual variability. Generally, the ENSO phenomenon lo-wers the value SST and Chla, while La Nina events tended to increase Chla values.Keywords: sea surface temperature, chlorophyll-a, fluctuation, NOAA AVHRR, SeaWiFS, MODIS