Variability of chlorophyll concentration in the Arabian Sea and Bay of Bengal as observed from SeaWiFS data from 1997–2000 and its interrelationship with Sea Surface Temperature (SST) derived from NOAA AVHRR

Variability of chlorophyll concentration in the Arabian Sea and Bay of Bengal has been studied using SeaWiFS eight‐day average, 9 km processed data for the period 1997–2000. The interrelationship with sea surface temperature (SST) was studied with the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) derived, best SST product. The chlorophyll pattern shows in general high concentrations during February to March in the Arabian Sea and November to December in the Bay of Bengal. Year‐to‐year variations in temperature show an inverse relation with chlorophyll, at different locations, even on a monthly basis. However, the intraannual variability in chlorophyll at different locations shows differences in the relationship with SST. The Arabian Sea showed an inverse relationship at most of the locations, while a positive relationship was observed in the northwest region during October to December and an inverse relationship during January to April. The Bay of Bengal showed positive relationships at northeast locations, whereas no definite assessment could be made for other locations due to the narrow range of chlorophyll concentration.A longer time series (～10 years) will be required to establish a more concrete relationship but definitely consistent patterns are emerging from this study. The results form an additional dimension to the criteria for partitioning the ocean, required for global productivity or biophysical coupled modelling.     

Bimodal variation of SST and related physical processes over the North Indian Ocean: special emphasis on satellite observations

Using sea surface temperature (SST) and wind speed retrieved by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), for the period of 1998–2003, we have studied the annual cycle of SST and confirmed the bimodal distribution of SST over the north Indian Ocean. Detailed analysis of SST revealed that the summer monsoon cooling (winter cooling) over the eastern Arabian Sea (Bay of Bengal) is more prominent than winter cooling (summer monsoon cooling). A sudden drop in surface short wave radiation by 57 W m^?2 (74 W m^?2) and rise in kinetic energy per unit mass by 24 J kg^?1 (26 J kg^?1) over the eastern Arabian Sea (Bay of Bengal) is observed in summer monsoon cooling period. The subsurface profiles of temperature and density for the spring warming and summer monsoon cooling phases are studied using the Arabian Sea Monsoon Experiment (ARMEX) data. These data indicate a shallow mixed layer during the spring warming and a deeper mixed layer during the summer monsoon cooling. Deepening of the mixed layer by 30 to 40 m with corresponding cooling of 2°C is found from warming to summer monsoon cooling in the eastern Arabian Sea. The depth of the 28°C isotherm in the eastern Arabian Sea during the spring warming is 80 m and during summer monsoon cooling it is about 60 m, while over the Bay of Bengal the 28°C isotherm is very shallow (35 m), even during the summer monsoon cooling. The time series of the isothermal layer depth and mixed layer depth during the warming phase revealed that the formation of the barrier layer in the spring warming phase and the absence of such layers during the summer cooling over the Arabian Sea. However, the barrier layer does exist over the Bay of Bengal with significant magnitude (20–25 m). The drop in the heat content with in first 50 m of the ocean from warming to the cooling phase is about 2.15 × 10⁸ J m^?2 over the Arabian Sea.     

AVHRR satellite remote sensing and shipboard measurements of the thermal plume from the Daya Bay, nuclear power station, China

The 1800 MW Daya Bay Nuclear Power Station (DNPS), China's first nuclear power station, is located on the coast of the South China Sea. DNPS discharges 29 10×10⁵ m³ year⁻¹ of warm water from its cooling system into Daya Bay, which could have ecological consequences. This study examines satellite sea surface temperature data and shipboard water column measurements from Daya Bay. Field observations of water temperature, salinity, and chlorophyll a data were conducted four times per year at 12 sampling stations in Daya Bay during January 1997 to January 1999. Sea surface temperatures were derived from the Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration (NOAA) polar orbiting satellites during November 1997 to February 1999. A total of 2905 images with 1.1×1.1 km resolution were examined; among those images, 342 have sufficient quality for quantitative analysis. The results show a seasonal pattern of thermal plumes in Daya Bay. During the winter months (December to March), the thermal plume is localized to an area within a few km of the power plant, and the temperature difference between the plume and non-plume areas is about 1.5 °C. During the summer and fall months (May to November), there is a larger thermal plume extending 8-10 km south along the coast from DNPS, and the temperature change is about 1.0 °C. Monthly variation of SST in the thermal plume is analyzed. AVHRR SST is higher in daytime than in nighttime in the bay during the whole year. The strong seasonal difference in the thermal plume is related to vertical mixing of the water column in winter and to stratification in summer. Further investigations are needed to determine any other ecological effects of the Daya Bay thermal plume.     

Temporal and spatial variability of satellite sea surface temperature and ocean colour in the Japan/East Sea

Temporal and spatial variability of Sea Surface Temperature (SST) and ocean colour in the Japan/East Sea (JES) are examined during winter and spring using satellite data from Advanced Very High Resolution Radiometer (AVHRR) and Sea-viewing Wide Field of view Sensor (SeaWiFS). The timing of the spring phytoplankton bloom and the locations of the chlorophyll fronts are related to changes in the thermal fields and the locations of the temperature fronts. Daily images of SST and chlorophyll concentration show both differences and similarities of bio-optical and thermal front location, depending on region and season. Four sub-regions in the JES were defined and SST and chlorophyll values were extracted from weekly and monthly composite images to derive summary statistics. SST at the Subpolar Front increased about 7°C over a 1.5-month period from late April to early June in 1999. During this same period, elevated chlorophyll values near the Korean coast and in the southern basin decreased sharply as the phytoplankton bloom that first developed in the southern basin progressed to the front and northward. The SST/chlorophyll relationship is complex and seasonal. Near the Subpolar Front, SST and chlorophyll were positively related in April. In May, highest chlorophyll values corresponded to mixing regimes (such as areas of convergence and divergence at the edges of meanders) and, by June, SST and chlorophyll near the front were inversely related.     

The Adriatic Sea surface temperature historical record from Advanced Very High Resolution Radiometer data (1981–1999)

A long-term time series of Advanced Very High Resolution Radiometer (AVHRR) (1981–1999) data has been used to assess the main physical features in the Adriatic Sea. Individual images were processed to estimate Sea Surface Temperature (SST) values, to create long-term composite fields (weekly, monthly, seasonal scales), and to derive basic statistics for the Northern, Central and Southern regions, each split again into an Eastern and a Western section. At the basin scale, an apparent general warming trend can be recognized in the time series. The linear fit to the seasonal cycles suggests an increase of about 2°C in 20 years, essentially due to a steady rise of summer values. A general north–south gradient can be found during winter, the Northern sections being colder than the Southern ones. An east–west gradient appears during summer, the Western sections being warmer then their Eastern ones. The Northern Adriatic exhibits substantial fluctuations, possibly linked to the cycle of winter cooling and summer warming in the relatively shallow sub-basin. The North Western section shows larger fluctuations than the North Eastern one, with lower winter SST, probably due to the freshwater inflow from the Po River delta. The Southern Adriatic exhibits less variability, possibly influenced by the periodic water exchanges with the Ionian Sea. The South Eastern section shows somewhat larger fluctuations than the South Western one, with higher winter SST, probably due to the inflow of warmer waters from the south. The two Central sections reveal patterns similar to the ones of the whole basin. The observed temperature patterns appear to follow the classical Adriatic cyclonic circulation scheme.     

TRMM derived sea surface temperature in the wake of a cyclonic storm over the central Bay of Bengal

Sea surface cooling associated with a cyclone in the Bay of Bengal was investigated using the data derived from TRMM Microwave Imager (TMI) onboard Tropical Rainfall Measuring Mission (TRMM) spacecraft. Though the TRMM/TMI sensor has all weather capabilities, sea surface temperature (SSTs) can not be obtained during heavy rain conditions. Hence, to overcome the problem of having no observations during the cyclone day, weekly analysis was carried out during the cyclone week (27 March–2 April 2000) and pre‐cyclone week (20–26 March 2000). To compute the magnitude of SST cooling in the cyclone track, weekly SSTs of the cyclone period were subtracted from the pre‐cyclone period. Similar analysis was carried out during non‐cyclonic periods of 20–26 March and 27 March–2 April of 2001, 2002. The analysis indicated that the TMI SST was reduced by maximum of 1.57°C along the cyclone track during the passage of cyclonic storm. Such an activity was not observed during 27 March–2 April 2001 and 2002, indicating that the cooling observed in 27 March–2 April 2000 was due to the cyclonic storm. On the other hand, SST anomalies are positive during 27 March–2 April of 2001, 2002 over these regions. TRMM observations shows higher wind speed and precipitation rate associated with the storm and are responsible for decrease in SST. Analysis of Pathfinder Advanced Very High Resolution Radiometer (AVHRR) SST showed the cyclone induced cooling but the SSTs measurement was blocked by clouds during the cyclone period (27 March–2 April 2000). In the same time, Reynolds SSTs was unable to detect the cooled sea surface. In these circumstances, the cyclone induced sea surface cooling was well captured by TRMM/TMI and had distinct advantage of providing SSTs in presence of cloud as compared to infrared SSTs measurement like those from pathfinder SSTs.     

Interannual variability of convective activity over the tropical Indian Ocean during the El Niño/La Niña events

Convection over the tropical Indian Ocean is important to the global and regional climate. This study presents the monthly climatology of convection, inferred from the outgoing longwave radiation (OLR), over the tropical Indian Ocean. We also examine the impact of El Niño/La Niña events on the convection pattern and how variations in convection over the domain influence the spatial rainfall distribution over India. We used 35 recent years (1974–2008) of satellite-derived OLR over the area, the occurrence of El Niño/La Niña events and high resolution grid point rainfall data over India. The most prominent feature of the annual cycle of OLR over the domain is the movements of convection from south-east to north and north-west during the winter to the summer monsoon season. This feature represents the movement of the inter-tropical convergence zone (ITCZ). The climatology of OLR during the winter months (December–February) over the domain is characterized by high subsidence over central India with a decrease of OLR values towards the north and south. Moderate convection is also seen over the Himalayan Range and the south-east Indian Ocean. In contrast, during the summer (June–September) the OLR pattern indicates deep convection along the monsoon trough and over central India, with subsidence over the extreme north-west desert region. The annual march of convection over the Arabian Sea and Bay of Bengal sector shows that the Arabian Sea has a limited role, compared to the Bay of Bengal, in the annual cycle of the convection over the tropical Indian Ocean. The composite OLR anomalies for the El Niño cases during the summer monsoon season show suppressed convection over all of India and moderate convection over the central equatorial Indian Ocean and over the northern part of the Bay of Bengal. Meanwhile in La Niña events the OLR pattern is nearly opposite to the El Niño case, with deep convection over entire Indian region and adjoining seas and subsidence over the northern Bay of Bengal and extreme north-west region. The spatial variability of the 1°?×?1° summer monsoon rainfall data over India is also examined during El Niño/La Niña events. The results show that rainfall of the summer monsoon season over the southern peninsular of India and some parts of central India are badly affected during El Niño cases, while the region lying along the monsoon trough and the west coast of India have received good amounts of rainfall. This spatial seasonal summer monsoon rainfall distribution pattern seems to average out the influence of El Niño events on total summer monsoon rainfall over India. It seems that, in El Niño events, the convection pattern over the Bay of Bengal remains unaffected during summer monsoon months and thus this region plays an important role in giving good summer monsoon rainfall over the northern part of India, which dilutes the influence of El Niño on seasonal scale summer monsoon rainfall over India. These results are also confirmed by using a monthly bias-corrected OLR dataset.     

Interannual waves in the sea surface temperatures of the Pacific Ocean

Various oscillatory modes of sea surface temperatures (SSTs) observed over a period of 8.8 years with the NASA Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) and for 13 years with the NOAA Advanced Very High Resolution Radiometer (AVHRR), the latter sensing in the thermal infrared band, are described for the Pacific Ocean. The various modes are isolated by a combination of techniques designed also to accommodate non-stationary phenomena. After detrending and removing the seasonal cycle from each grid map element of the data, singular value decomposition (SVD) is used to separate the data into spatial and temporal parts to facilitate the modal analysis. Empirical Mode Decomposition is then used to separate the temporal parts of the data into approximately seven intrinsic modal functions (IMFs) for the temporal parts of the first five principal components (PCs) resulting from the SVD. A filtered time sequence of SST grids is then obtained by selecting IMFs with periods longer than 1.5 years and then reconstructing the SST grid maps from the filtered PCs. The time sequence of SMMR SSTs in the Pacific Ocean shows El Niño Southern Oscillation (ENSO) oscillations not only along the Equator, but also in both the North and South Pacific, with, in fact, even larger amplitudes than along the Equator. A similar analysis was applied to the SST record from the AVHRR instrument. During the period of overlap with the SMMR record, similarities occur in the equatorial region, but the records are by no means identical. The AVHRR SSTs do not show any strong oscillations in the South Pacific.     

Deriving the operational nonlinear multichannel sea surface temperature algorithm coefficients for NOAA-15 AVHRR/3

The National Oceanic and Atmospheric Administration (NOAA) currently uses Nonlinear Sea Surface Temperature (NLSST) algorithms to estimate sea surface temperature (SST) from NOAA satellite Advanced Very High Resolution Radiometer (AVHRR) data. In this study, we created a three-month dataset of global sea surface temperature derived from NOAA-15 AVHRR data paired with coincident SST measurements from buoys (i.e. called the SST matchup dataset) between October and December 1998. The satellite sensor SST and buoy SST pairs were included in the dataset if they were coincident within 25 km and 4 hours. A regression analysis of the data in this matchup dataset was used to derive the coefficients for the operational NLSST equations applicable to NOAA-15 AVHRR sensor data. An independent matchup dataset (between January and March 1999) was also used to assess the accuracy of these day and night operational NLSST algorithms. The bias was found to be 0.14°C and 0.08°C for the day and night algorithms, respectively. The standard deviation was 0.5°C or less.     

Provincial nature of chlorophyll and sea surface temperature observed by satellite

Monthly composite SeaWiFS derived chlorophyll, National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) derived sea surface temperature (SST) and QuikScat derived wind data of 2003–2005 (January–December) were analysed to study the provincial nature of chlorophyll‐a (chl‐a), SST and wind speed in the Arabian sea and Bay of Bengal. The study was confined to five provinces, three in the Arabian Sea and two in the Bay of Bengal. Results indicate provincial variability in the SST‐chl‐a relation. It suggests that the correlation between chlorophyll and SST is not always negative. A negative correlation was observed in provinces 1, 2 and 3 for all the seasons, whereas, except for the month of January–February, it was positively correlated for province 4. Analysis shows the provincial specific nature of chlorophyll variability to physical forcing and suggests that treatment of such a problem should not be undertaken on the basin scale.     

Thermal fronts of the southern South China Sea from satellite and in situ data

The major goal of this study was to find match-ups between thermal fronts mapped from satellite sea surface temperature (SST) imagery and from in situ data in the southern South China Sea (SSCS), using 11 ship surveys conducted by the South China Sea Institute of Oceanology (SCSIO) between 1987 and 1999. Fronts were automatically detected by the Cayula–Cornillon multi-image edge detection algorithm (CCA) applied to satellite-derived maps of the Advanced Very High Resolution Radiometer (AVHRR) SST obtained from the Pathfinder project (8364 twice-daily global fields with 9 km resolution between 1985 and 1996). Twice-daily near-instant frontal maps were composited without any averaging or smoothing to produce individual monthly frontal maps covering the period from January 1985 through December 1996 (144 maps in total). Although the SSCS is a tropical sea with little SST difference between water masses, the CCA turned out to be an effective tool for front mapping in the SSCS. Out of the 11 ship surveys analysed in this study, four surveys produced satisfactory match-ups. The percentage of match-ups is considered reasonably high given that (1) ship surveys were not optimized to cross fronts, therefore most in situ sections missed fronts; (2) satellite measurements of SST with AVHRR are hampered by cloudiness, therefore satellite-derived frontal maps might miss some fronts masked by persistent cloudiness. Fronts are more distinct in winter, when cross-frontal SST gradients are enhanced. From oceanographic vertical sections and horizontal maps, fronts are much sharper in the subsurface layer (represented here by 50 m level). Nonetheless, the CCA successfully detected SST fronts with a cross-frontal step as small as 1°C.     

Intraseasonal signals in the daily high resolution blended Reynolds sea surface temperature product over the tropical Indian Ocean and their validation

National Oceanic and Atmospheric Administration daily sea surface temperature (SST) products based on Advanced Microwave Scanning Radiometer (AMSR) and Advanced Very High Resolution Radiometer (AVHRR) have been used to understand the variability in the tropical Indian Ocean SST. These products are comparable with the deep sea moored buoy observations and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) SST in the tropical Indian Ocean. However considerable difference is noticed between these satellite SST products and deep sea buoys, especially at the intraseasonal time scale. Further the first Complex Empirical Orthogonal Function (CEOF) mode of TMI and AVHRR SST explains respectively 46.49% and 46.19% of the total variance. The second CEOF mode of TMI and AVHRR SST explains respectively 23.19% and 18.94% of the total SST variance in the tropical Indian Ocean. The AVHRR SST product is important because this daily product has been available since 1985. The analysis shows that AMSR measurements are contributing considerably to the understanding of the tropical Indian Ocean SST variability. Though satellite SST products are able to capture the observed intraseasonal variability reasonably well, more accurate satellite SST products are therefore necessary to understand the climatologically important Indian Ocean region and its air–sea interaction processes.     

Seasonal SST patterns along the West India shelf inferred from AVHRR

Sea surface temperature (SST) patterns along the west India shelf, extending from 8° to 24°N, are analyzed during 1993-1996 to characterize seasonal variability using the advanced very high-resolution radiometer (AVHRR) SST, momentum and heat fluxes derived from ERS-1 winds and NCEP/NCAR reanalysis data. During winter monsoon (December-March), a 4-year mean SST spatial pattern shows a strong cooling north of 15°N due to surface heat depletion, while warm SSTs evolve in the south due to the intrusion of warm equatorial water. Cold water occupies the entire shelf during summer monsoon, with high degree of SST cooling dominating the Kerala coast, where Ekman pumping and upwelling promoted by the dominant alongshore wind stress component overwhelms the surface heat loss. The spectral analysis reveals semiannual and annual peaks in SST and forcing functions, which highlight the influence of monsoon forcing on the SST variability along the west India shelf.     

On the consistency in variations of the South China Sea Warm Pool as revealed by three sea surface temperature datasets

The areal and intensity indices of the South China Sea Warm Pool (SCSWP) derived from three datasets, the Advanced Very High Resolution Radiometer (AVHRR), Tropical Rainfall Measuring Mission's Microwave Imager (TMI) and Optimum Interpolation Version 2 (OI.v2) sea surface temperature (SST), are generally consistent with each other at monthly, seasonal and interannual scales. However, the three records are different in some cases. First, minor differences among the monthly records of intensity index are observed in the period July to September. Secondly, the interannual records of SCSWP intensity derived from AVHRR and OI.v2 are different in autumn during the period 1990-1996. The reason is not yet clear and nor is it clear which record best represents fluctuations in SCSWP intensity. These suggest that various drawbacks of the three datasets, such as low resolution of OI.v2, and cloud and rain contamination on AVHRR and TMI data, would be serious enough to allow deviation from each other to appear. Merging AVHRR and TMI SST data might be the way leading to a more convincing time series of SCSWP. In addition, changes of areal and intensity indices are not always consistent with each other, for example, they have different monthly patterns. Although the three interannual records of intensity index in three seasons all capture the main Multivariate ENSO Index (MEI) signals at a half-year lag, only those which are in the summer significantly correlated with MEI.     

Comparisons of regional satellite sea surface temperature gradients derived from MODIS and AVHRR sensors

Regional sea surface temperature (SST) gradients were examined for a 6-year (2003–2008) period using data from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and the Advanced Very High Resolution Radiometer (AVHRR) on two NOAA satellite platforms. Two regions, one in the California Current System and the other in the Gulf Stream, representing an eastern boundary upwelling region and strong western boundary current, respectively, were chosen to investigate the seasonal variability, statistical differences and similarities, and correlations with respect to the two sets of SST gradients. Results indicated higher gradient magnitudes using MODIS SST in relative comparison to those derived from AVHRR that are attributed to instrument and algorithm differences. These observed differences are important for any studies that employ SST gradients, such as fisheries investigations that have traditionally relied on AVHRR SST gradients only.     

Analysis of sea surface temperature time series of the south-eastern North Atlantic

The dominant periods in time series of sea surface temperature (SST) of the south-eastern North Atlantic are determined and related to atmospheric forcing and ocean dynamics. We analyse five-day composite images of a 10.5-year-long (from 10 July 1981 to 31 December 1991) time series of Advanced Very High Resolution Radiometer (AVHRR) onboard NOAA satellites. The dominant signal present in the whole region is the annual cycle. It explains 70% of the SST variance in the northern region and 40% in the southern. The pattern of the annual amplitudes is related to the seasonal cooling and warming cycle in the region. The second dominant period is a semi-annual frequency, estimated by means of periodograms of the residual time series with the annual cycle subtracted. This semi-annual frequency is responsible of making short springs and long autumns. The semi-annual frequency is present in 44% of the time series in the region, contrary to the generalized idea that a time series must always contain it. The geographical distribution of the semi-annual component of SST suggests that it is associated with the curl of the wind stress. The third dominant period is four years, found in three different areas: south of the Canary islands, off the Cape Verde islands and towards the northwest of Lanzarote Island. The main effect of this signal is to increase the maximum temperature every four years and to decrease the minimum temperature two years later. The 4-year signal does not seem to be associated with any atmospheric forcing field. The presence of a signal in the curl of the wind stress with periodicities of 25–30 days located south of the Canary Islands led us to conclude that the curl of the wind stress is important for the generation and shedding of eddies downstream these islands.     

Comparison of sea-surface temperature observations from TIROS-N and ships in the North Indian Ocean during MONEX (May–July 1979)

Extensive meteorological observations from a variety of platforms were made during MONEX (May–July 1979). Utilizing this unique opportunity, a comparison has been carried out between the in situ ship observations of sea-surface-temperature (SST) in the North Indian Ocean region (Arabian Sea and Bay of Bengal) and the remotely sensed SST observations by the polar orbiting satellite TIROS-N. The results indicate that the TIROS-N derived SST measurements are generally lower than the in situ ship measurements. Further, the difference between in situ and satellite SST measurements is found to be positively correlated with the water-vapor content of the atmosphere. The implications of these results are discussed.     

High spatial resolution sea surface climatology from Landsat thermal infrared data

High spatial and temporal resolution maps of sea surface temperature (SST) have numerous applications in coastal and estuarine systems. A climatology map, tracking SST as a function of year-day, was produced at Southern New England using 53 Landsat TM and ETM+ thermal infrared data. A recursive curve-fitting algorithm was used to fit these data and eliminate cloud contamination, resulting in an average daily temperature at every 60-m pixel. The climatology was validated against long-term in situ records that were analyzed with the same techniques. The results show, as expected, that isolated and shallow water bodies undergo more extreme temperature variation (−2 to 25 °C) than deeper, well-connected embayments (1 to 21 °C) or the coastal ocean (4 to 18 °C). The coastal ocean is shown to lag insolation and shallow lakes by up to 44 days, with embayments showing a gradation between these extremes. Despite the subtle temperature range variation, there is rich detail in the spatial patterns which are relevant to the applied sciences of coastal and estuarine systems. The spatial pattern of the climatology reveals anomalous patterns, such as occur where anthropogenic forcing alters climatological patterns. The heat budget of Mount Hope Bay in northeast Narragansett Bay has anthropogenic thermal input from a large power plant, and this input is reflected in the climatology. From the results, it is seen that Narragansett Bay has, on average, a mean annual temperature of 11.86±0.41 °C, while the Mount Hope Bay system is consistently warmer at 12.30±0.21 °C and shows a delayed response to autumn cooling. The long history of Landsat data acquisition can be used to create a climatology of coastal and estuarine scale dynamics at an order of magnitude finer scale resolution than AVHRR climatologies.     

Total precipitable water over the Arabian Ocean and the Bay of Bengal using SSM/I data

Using Special Sensor Microwave/Imager (SSM/I) data, the total precipitable water (TPW) over the Arabian Ocean and the Bay of Bengal has been estimated. The monthly and seasonal variations of TPW show a very systematic pattern that correlates closely with the monsoon.     

The satellite-measured sea surface temperature change in the Gulf of Finland

Climate change in Baltic region and in the Gulf of Finland is an accomplished fact in human brains and in science. The purpose of this research is to retrieve quantitative level of changes for sea surface temperature (SST) of the Gulf of Finland. Two space systems National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) provided satellite data about temperature of the sea surface. SST data covers period 1981–2014 and includes 444 monthly data scenes with spatial resolution about 10 km. Data quality analysis displays high reliability of NOAA/AVHRR and Aqua/MODIS satellite information. The Gulf of Finland’s average annual SST has changed from 6.8°C in 1982 up to 8.2°C in 2014. Its mean speed of warming is about 0.04°C year^–1. The growth of the temperature was irregular, in the middle of 80th year, the temperature dropped down to 5.0°C, and then sharply increased up to 7.3°C in 1989. SST growth in the Gulf of Finland coincides with air temperature and sea temperature growth. The climate change in the Gulf of Finland has special significance due to the fragility of the northern ecosystems and high anthropogenic load.