Satellite-based Sea Surface Temperature Research Articles

Satellite-based time-series of sea-surface temperature since 1980 for climate applications

A 42-year climate data record of global sea surface temperature (SST) covering 1980 to 2021 has been produced from satellite observations, with a high degree of independence from in situ measurements. Observations from twenty infrared and two microwave radiometers are used, and are adjusted for their differing times of day of measurement to avoid aliasing and ensure observational stability. A total of 1.5 × 1013 locations are processed, yielding 1.4 × 1012 SST observations deemed to be suitable for climate applications. The corresponding observation density varies from less than 1 km−2 yr−1 in 1980 to over 100 km−2 yr−1 after 2007. Data are provided at their native resolution, averaged on a global 0.05° latitude-longitude grid (single-sensor with gaps), and as a daily, merged, gap-free, SST analysis at 0.05°. The data include the satellite-based SSTs, the corresponding time-and-depth standardised estimates, their standard uncertainty and quality flags. Accuracy, spatial coverage and length of record are all improved relative to a previous version, and the timeseries is routinely extended in time using consistent methods.

Validation and Improvement of COCTS/HY-1C Sea Surface Temperature Products.

In oceanographic study, satellite-based sea surface temperature (SST) retrieval has always been the focus of researchers. This paper investigates several multi-channel SST retrieval algorithms for the thermal infrared band, and evaluates the accuracy of the COCTS/HY-1C SST products. NEAR-GOOS in situ SST data are utilized for validation and improvement, and a three-step matching procedure including geographic location screening, cloud masking, and homogeneity check is conducted to match in situ SST data with satellite SST data. Two improvement schemes, including nonlinear regression and regularization iteration, are proposed to improve the accuracy of the COCTS/HY-1C SST products and the typical application scenarios and the algorithm characteristics of these two schemes are discussed. The standard deviation of residual between retrieved SST and measured SST for these two data improvement algorithms, which are considered as the main indexes for assessment, result in an improvement of 13.245% and 14.096%, respectively. In addition, the generalization ability of the SST models under two data improvement methods is quantitatively compared, and the factors affecting the model accuracy are also carefully evaluated, including the in situ data acquisition method and measurement time (day/night). Finally, future works about SST retrieval with COCTS/HY-1C satellite data are summarized.

Validation of satellite-based sea surface temperature products against in situ observations off the western coast of Sumatra

This study validated the sea surface temperature (SST) datasets from the Group for High-Resolution SST Multi Product Ensemble (GMPE), National Oceanic and Atmospheric Administration (NOAA) Optimal Interpolation (OI) SST version 2 and 2.1 (OIv2 and OIv2.1), and Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2) in the area off the western coast of Sumatra against in situ observations. Furthermore, the root mean square differences (RMSDs) of OIv2, OIv2.1, and ECCO2 were investigated with respect to GMPE, whose small RMSD < 0.2 K against in situ observations confirmed its suitability as a reference. Although OIv2 showed a large RMSD (1–1.5 K) with a significant negative bias, OIv2.1 (RMSD < 0.4 K) improved remarkably. In the average SST distributions for December 2017, the differences among the 4 datasets were significant in the areas off the western coast of Sumatra, along the southern coast of Java, and in the Indonesian inland sea. These results were consistent with the ensemble spread distribution obtained with GMPE. The large RMSDs of OIv2 corresponded to high clouds, and it was suggested that the change in the satellites used for SST estimation contributed to the improvement in OIv2.1.

Diel to Seasonal Variation of Picoplankton in the Tropical South China Sea

Eight diel surveys on picoplankton (Prochlorococcus, Synechococcus, picoeukaryotes, and heterotrophic bacteria) abundance at the South East Asian Time-Series Station (SEATS; 18°N; 116°E) were conducted during the period of 2010 to 2014. The results indicated that Prochlorococcus and picoeukaryotes showed a subsurface maximum in warm seasons (spring, summer, and fall) and were abundant at the surface in the cold season (winter). Synechococcus and heterotrophic bacteria exhibited higher cell numbers at the surface and decreased with depth throughout the year. Although not all, some clear diel patterns for picoplankton were observed. Picophytoplankton usually peaked in the nighttime; picoeukaryotes peaked at ~7 to 8 p.m., followed by Synechococcus (peaking at 1 a.m.) and Prochlorococcus (peaking at 2 a.m.). Unlike these picoautotrophs, heterotrophic bacteria could peak either at dusk (i.e., 7 p.m.) or at noon. Seasonally, Prochlorococcus was more abundant in the warm than the cold seasons, while Synechococcus and picoeukaryotes showed blooms in the winter of 2013 and 2011, respectively. Heterotrophic bacteria showed no significant seasonality. Regression analysis indicated that ~73% of the diel-to-seasonal variation of the euphotic zone depth-integrated picophytoplankton biomass (i.e., PicoBeu) could be explained by the changes of the mixed-layer depth (MLD), and this suggested that inorganic nutrient supply could be the major controlling factor in their growth. The strong linear relationship (coefficient of determination, R2 of 0.83, p &amp;lt; 0.01) between sea surface temperature (SST) and PicoBeu implied, for the first time, a potential of using satellite-based SST to trace the biomass of picophytoplankton in the pelagic areas of the northern South China Sea.

Ocean Response to Super-Typhoon Haiyan

Present paper studies the ocean response to super-typhoon Haiyan based on satellite and Argo float data. First, we show the satellite-based surface wind and sea surface temperature during super-typhoon Haiyan, and evaluate the widely-used atmospheric and oceanic analysis-or-reanalysis datasets. Second, we investigate the signals of Argo float, and find the daily-sampling Argo floats capture the phenomena of both vertical-mixing-induced mixed-layer extension and nonlocal subsurface upwelling. Accordingly, the comparisons between Argo float and ocean reanalysis reveal that, the typhoon-induced upwelling in the ocean reanalysis needs to be further improved, meanwhile, the salinity profiles prior to typhoon arrival are significantly biased.

FogNet: A multiscale 3D CNN with double-branch dense block and attention mechanism for fog prediction

The reduction of visibility adversely affects land, marine, and air transportation. Thus, the ability to skillfully predict fog would provide utility. We predict fog visibility categories below 1600 m, 3200 m and 6400 m by post-processing numerical weather prediction model output and satellite-based sea surface temperature (SST) using a 3D-Convolutional Neural Network (3D-CNN). The target is an airport located on a barrier island adjacent to a major US port; measured visibility from this airport serves as a proxy for fog that develops over the port. The features chosen to calibrate and test the model originate from the North American Mesoscale Forecast System, with values of each feature organized on a 32 × 32 horizontal grid; the SSTs were obtained from the NASA Multiscale Ultra Resolution dataset. The input to the model is organized as a high dimensional cube containing 288 to 384 layers of 2D horizontal fields of meteorological variables (predictor maps). In this 3D-CNN (hereafter, FogNet), two parallel branches of feature extraction have been designed, one for spatially auto-correlated features (spatial-wise dense block and attention module), and the other for correlation between input variables (variable-wise dense block and attention mechanism.) To extract features representing processes occurring at different scales, a 3D multiscale dilated convolution is used. Data from 2009 to 2017 (2018 to 2020) are used to calibrate (test) the model. FogNet performance results for 6, 12− and 24−h lead times are compared to results from the High-Resolution Ensemble Forecast (HREF) system. FogNet outperformed HREF using 8 standard evaluation metrics.

Northwest monsoon upwelling within the Indonesian seas

ABSTRACT The previous studies of upwelling within the Indonesian seas only focused on the Southeast Monsoon (SEM) season. The western coast of Sumatra Island; the southern coast of Java Island, Sunda Islands, and Sulawesi Island; the Banda Sea, the Maluku Sea, and the Arafura Sea are well-known SEM upwelling areas. However, the upwelling events during Northwest monsoon (NWM) have never been investigated. The investigation of NWM upwelling is challenging due to the broad cloud coverage, limiting the observation of infrared and visible sensors of the satellites. This study used the blended products of the satellite-based sea surface temperature (SST), chlorophyll-a (chl-a), sea level anomaly (SLA), surface wind, and rainfall to identify the NWM upwelling occurrences. Remote sensing reflectances 443 nm and 555 nm were also used to examine the influence of suspended sediment and organic matter which may bias the chl-a concentration. Along the northern coast of the Lesser Sunda Islands, the north coast of Papua Island and the Malacca Strait are the areas of NWM upwelling as denoted by the positive anomaly of chl-a, the negative anomaly of SST, and the negative anomaly of SLA. Focusing on the area along the northern coast of the Lesser Sunda Islands, the NWM upwelling is generated by offshore Ekman Mass Transport that reduces SST and increases chl-a. From 2010 onwards, El Niňo Southern Oscillation has a more consistent effect on the NWM upwelling than Indian Ocean Dipole. El Niňo (La Niňa) tends to weaken (strengthen) upwelling. The magnitude of NWM upwelling is weaker than SEM upwelling.

Compound high-temperature and low-chlorophyll extremes in the ocean over the satellite period

Abstract. Extreme events in the ocean severely impact marine organisms and ecosystems. Of particular concern are compound events, i.e., when conditions are extreme for multiple potential ocean ecosystem stressors such as temperature and chlorophyll. Yet, little is known about the occurrence, intensity, and duration of such compound high-temperature (a.k.a. marine heatwaves – MHWs) and low-chlorophyll (LChl) extreme events, whether their distributions have changed in the past decades, and what the potential drivers are. Here we use satellite-based sea surface temperature and chlorophyll concentration estimates to provide a first assessment of such compound extreme events. We reveal hotspots of compound MHW and LChl events in the equatorial Pacific, along the boundaries of the subtropical gyres, in the northern Indian Ocean, and around Antarctica. In these regions, compound events that typically last 1 week occur 3 to 7 times more often than expected under the assumption of independence between MHWs and LChl events. The occurrence of compound MHW and LChl events varies on seasonal to interannual timescales. At the seasonal timescale, most compound events occur in summer in both hemispheres. At the interannual timescale, the frequency of compound MHW and LChl events is strongly modulated by large-scale modes of natural climate variability such as the El Niño–Southern Oscillation, whose positive phase is associated with increased compound event occurrence in the eastern equatorial Pacific and in the Indian Ocean by a factor of up to 4. Our results provide a first understanding of where, when, and why compound MHW and LChl events occur. Further studies are needed to identify the exact physical and biological drivers of these potentially harmful events in the ocean and their evolution under global warming.

Patterns, Drivers, and Ecological Implications of Upwelling in Coral Reef Habitats of the Southern Red Sea

AbstractCoral reef ecosystems are highly sensitive to thermal anomalies, making them vulnerable to ongoing global warming. Yet, a variety of cooling mechanisms, such as upwelling, can offer some respite to certain reefs. The Farasan Banks in the southern Red Sea is home to hundreds of coral reefs covering 16,000 km2 and experiences among the highest water temperatures of any coral‐reef region despite exposure to summertime upwelling. We deployed an array of temperature loggers on coral reefs in the Farasan Banks, enabling us to evaluate the skill of satellite‐based sea surface temperature (SST) products for capturing patterns of upwelling. Additionally, we used remote sensing products to investigate the physical drivers of upwelling, and to better understand how upwelling modulates summertime heat stress on coral communities. Our results show that various satellite SST products underestimate reef‐water temperatures but differ in their ability to capture the spatial and temporal dynamics of upwelling. Monsoon winds from June to September drive the upwelling in the southern Red Sea via Ekman transport of surface waters off the shelf, and this process is ultimately controlled by the southwest Indian monsoon in the Arabian Sea. Further, the timing of the cessation of monsoon winds regulates the maximum water temperatures that are reached in September and October. In addition to describing the patterns and mechanisms of upwelling, we discuss the potential ecological implications of this upwelling system, including modulation of coral bleaching events and effects on biodiversity, sea turtle reproduction, fish pelagic larval duration, and planktivore populations.

Hydrodynamics of the choke point between Cape Town and Antarctica during the austral summer of 2019

This study addresses the hydrodynamics inferred from the expendable conductivity-temperature-depth (XCTD) observations carried out in the southwestern Indian Ocean sector of the Southern Ocean along two transects: the Lazarev Sea to Cape Town (track-1) and Cape Town to Prydz bay (track-2) during the austral summer of 2019. The vertical temperature and salinity structures revealed an eddy extending up to 44°S (45°S) on track-1 (track-2). South of the eddy, we encountered frontal zones extending to 53°S (59°S) on track-1(track-2). The frontal locations identified from XCTD and satellite-based sea surface temperature and absolute dynamic topography coincided, with the latter placed within the latitudinal limits identified from the XCTD data. Meandering of the Polar front (PF), the southern Antarctic Circumpolar Current (ACC) Front, and the Southern Boundary of the ACC was observed from 90 to 550 km southward. The Winter Water which was confined to the south of 50°S was detected at deeper depth (~350 m) on track-1, compared to a depth of 100 m on track-2, and its thickness varied from zero to 1.2 m on track-1 and from 0.5 to 2.5 m on track-2. The vertical thermohaline structure revealed the northward subduction of a mixture of Antarctic Surface Water and Subantarctic Surface Water up to 45.5°S and down to 500 (320) m on track-1 (track-2). The volume transport (relative to 1000 m) accounted for 87% of that estimated in the literature (90 ± 2.4 Sv) and 34.7 Sv across track-1 and -2, respectively. It was found that 70% of the volume transport was confined to the ACC frontal region and 26% of the total transport occurred in the 100–500 m slab. The cumulative heat and salt content was 2% and 1.2% higher between 39° and 66°S, compared to that estimated from 2008 data along track-1. We used a satellite-based absolute dynamic topography field, to trace out Agulhas current (AC) and its retroflection current, and eddies detached from the meanders. A higher dynamic topography gradient across the polar front facilitates enhanced transport. Sea surface temperature fields revealed that the meanders in the AC propagate southwest with an offshore extent of 30–300 km.

Comparison of Satellite-Based Sea Surface Temperature to In Situ Observations Surrounding Coral Reefs in La Parguera, Puerto Rico

Coral reefs are among the most biologically diverse ecosystems on Earth. In the last few decades, a combination of stressors has produced significant declines in reef expanse, with declining reef health attributed largely to thermal stresses. We investigated the correspondence between time-series satellite remote sensing-based sea surface temperature (SST) datasets and ocean temperature monitored in situ at depth in coral reefs near La Parguera, Puerto Rico. In situ temperature data were collected for Cayo Enrique and Cayo Mario, San Cristobal, and Margarita Reef. The three satellite-based SST datasets evaluated were NOAA’s Coral Reef Watch (CoralTemp), the UK Meteorological Office’s Operational SST and Sea Ice Analysis (OSTIA), and NASA’s Jet Propulsion Laboratory (G1SST). All three satellite-based SST datasets assessed displayed a strong positive correlation (&gt;0.91) with the in situ temperature measurements. However, all SST datasets underestimated the temperature, compared with the in situ measurements. A linear regression model using the SST datasets as the predictor for the in situ measurements produced an overall offset of ~1 °C for all three SST datasets. These results support the use of all three SST datasets, after offset correction, to represent the temperature regime at the depth of the corals in La Parguera, Puerto Rico.

Preliminary Study on Detection of Marine Heat Waves using Satellite-based Sea Surface Temperature Anomaly in 2017-2018

Preliminary Study on Detection of Marine Heat Waves using Satellite-based Sea Surface Temperature Anomaly in 2017-2018

On the use of NLSST and MCSST for the study of spatio-temporal trends in SST gradients

ABSTRACTOngoing efforts are dedicated by meteorological and oceanographic agencies to improve the accuracy of Sea Surface Temperature (SST) estimates from satellite observations via improved retrieval algorithms and validation data. An important application of satellite-based SST observations is the analysis of the spatio-temporal characteristics of ocean fronts, which depend on several parameters including the SST retrieval scheme from Top-of-Atmosphere Brightness Temperatures. In this study, we focus on two widely used SST retrieval algorithms, namely the Multichannel SST (MCSST) and the Nonlinear SST (NLSST). Using night-time Level 2 SST derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua from 2005 to 2015 over the Southwestern Atlantic Ocean (SAO), we show that 1) the spatial distribution and temporal variability of SST gradient magnitudes derived from these two SST retrieval schemes are different despite statistical consistency of SST fields 2) the widely used NLSST formulation introduces a correlation between SST gradient magnitudes and SST values. This correlation, likely due to the use of a first guess SST in the NLSST formulation, is not observed in the MCSST data and may affect the study of long-term changes in ocean dynamics.

Thermal fronts and attracting Lagrangian Coherent Structures in the north Bay of Bengal during December 2015–March 2016

We investigate two-dimensional advective stirring by satellite-derived ocean surface currents in the north Bay of Bengal during December 2015–March 2016 using the framework of Lagrangian Coherent Structures (LCS). The forward and backward finite-time Lyapunov exponent (fFTLE and bFTLE, respectively) fields indicate that freshwater parcels from the river run-off are subjected to intense stirring by the ocean surface currents, which contain both geostrophic and wind-forced Ekman currents. Large-scale thermal fronts present in the satellite-based sea surface temperature (SST) fields are then shown to closely resemble attracting LCS (aLCS) that govern horizontal advective stirring on subseasonal time scales. Statistical estimates of the angle between the thermal fronts (of all spatial scales present in the SST data) and the aLCS from the entire north Bay of Bengal show a preference to align with each other. Significantly, in the presence of sufficiently strong aLCS in their neighbourhood, thermal fronts are shown to have a strong preference to align with them. Finite-time advection of fluid particles over around a week to compute the bFTLE fields results in the strongest alignment between the thermal fronts and neighbouring aLCS. The robustness of our results are established by presenting analyses of (i) geostrophic currents from altimeter data, and (ii) a reanalysis-based fine-scale salinity field. Attracting LCS, identified as sufficiently strong ridges in the bFTLE fields, are argued to represent reasonable estimates of the location and orientation of fronts (thermal or salinity) formed by advective stirring over subseasonal time scales.

Ocean warming along temperate western boundaries of the Northern Hemisphere promotes an expansion of Cochlodinium polykrikoides blooms.

Since the early 1990s, ocean temperatures have increased and blooms of the icthyotoxic dinoflagellate Cochlodinium polykrikoides (a.k.a. Margalefidinium polykrikoides) have become more widespread across the Northern Hemisphere. This study used high-resolution (1-30 km), satellite-based sea surface temperature records since 1982 to model trends in growth and bloom season length for strains of C. polykrikoides inhabiting North American and East Asian coastlines to understand how warming has altered blooms in these regions. Methods provided approximately 180× greater spatial resolution than previous studies of the impacts of warming on harmful algae, providing novel insight into near shore, coastal environments. Along the US East Coast, significant increases in potential growth rates and bloom season length for North American ribotypes were observed with bloom-favourable conditions becoming established earlier and persisting longer from Chesapeake Bay through Cape Cod, areas where blooms have become newly established and/or intensified this century. Within the Sea of Japan, modelled mean potential growth rates and bloom season length of East Asian ribotypes displayed a significant positive correlation with rising sea surface temperatures since 1982, a period during which observed maximal cell densities of C. polykrikoides blooms have significantly increased. Results suggest that warming has contributed, in part, to altering the phenology of C. polykrikoides populations, potentially expanding its realized niche in temperate zones of the Northern Hemisphere.

SENSITIVITY OF UPPER OCEAN DYNAMICS IN HIGH-RESOLUTION TROPICAL INDIAN OCEAN MODEL TO DIFFERENT FLUX PARAMETERIZATION: CASE STUDY FOR THE BAY OF BENGAL (BOB)

Abstract. Simulation experiments using a high-resolution ocean general circulation model (OGCM) of the tropical Indian Ocean (TIO) were carried out to assess the model’s sensitivity to different flux parameterization. The flux formulation proposed by Kara et al. (2000) is used in the control run (CR). One more experiment differing in the bulk fluxes formulation for the computation of momentum, freshwater and heat is carried out. In the first experiment (CR), actual wind is used for the computation of the exchange coefficient in air-sea bulk flux formulation. In the second experiment (E1), model surface current is used in the wind stress formulation to compute the turbulent air-sea fluxes for TIO region. The formulation used in E1 is the same as it is used in CR, instead of actual wind, relative wind component is used in flux formulas. Both experiments are carried out for the period 2014–2016. The OGCM is forced using the daily fields of winds, radiation and freshwater fluxes obtained from ERA-Interim Reanalysis. In this study, we examine and quantify the performance of the above-mentioned experiments with respect to observations from ARGO, satellite-based sea surface temperature (SST) and sea surface salinity (SSS) for the year 2015. We observe that the upper ocean dynamics is significantly modulated by different flux algorithms. The errors in simulated SST is reduced by ∼8% to 10% in E1 compared to CR, respectively. The temperature errors in the top 20 m depth are reduced by 8% in E1. It is found that this flux formulation using relative winds is effective in accurately simulating the upper ocean dynamics in strong wind regimes of the Bay of Bengal.

Impact of freshwater plumes on intraseasonal upper ocean variability in the Bay of Bengal

The Monsoon Intraseasonal Oscillations (MISO) is known to be strongly coupled to the upper ocean variability in the Bay of Bengal (BoB). Here, we analyze high-resolution moored observations from the northern BoB (18 °N, in comparison with an array of moorings to the south along 90 °E (8, 12, and 15 °N) to examine the observed temperature and salinity variability during the 2015 summer monsoon season. The heat budget analysis indicates that the mixed-layer (ML) temperature tendency at 18 °N in late summer (August and September) deviated greatly from the tendency that is expected based on surface heat flux alone. Examination of moored ML salinity and satellite sea surface salinity (SSS) fields suggests that southward extension of riverine freshwater plumes to the mooring site in late summer plays an important role in modulating the heat balance. The associated shoaling of the ML results in enhanced penetrative heat loss and warming beneath the surface layer. Price-Weller-Pinkel (PWP) 1-D model simulations, which account for vertical mixing, suggest that the entrainment of this subsurface warm water under monsoon wind improves closure of the temperature balance. Long-term analysis of satellite-based sea surface temperature (SST) and rainfall data indicates that late summer ML shoaling is accompanied by amplified intraseasonal (10–60 day) SST variability and reduced MISO precipitation variability. Overall, these results demonstrate the critical importance of freshwater plumes to improved understanding of the upper-ocean heat budget and air-sea interaction in the BoB.

Subpixel variability and quality assessment of satellite sea surface temperature data using a novel High Resolution Multistage Spectral Interpolation (HRMSI) technique

A novel interpolation technique is applied to assessment of the quality of sea surface temperature (SST) observations and quantitative analysis of the subpixel variability within satellite footprints of different size. Using retrieved satellite data as input, the new, global, multistage interpolation technique generates a trigonometric polynomial, providing a representation of the underlying physical SST field in functional form. The resulting interpolating function can be efficiently and accurately evaluated anywhere within the domain over which it was derived and its moments calculated to estimate the mean and variance of the field over desired sub-regions. Application of the technique is demonstrated for SST retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS), Spinning Enhanced Visible and Infrared Imager (SEVIRI), and Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) sensors. Comparison of the functional form with the data from which it was derived demonstrates how the technique can potentially help to identify small observational artifacts such as MODIS scan striping and residual cloud contamination. Integrals of the interpolating functions over successively larger spatial scales successfully emulate the retrieved SST at the different effective spatial resolutions and the second moments are consistent with the direct sample variances, and hence representative of the spatial SST variability of the available finer-resolution observations over the coarser scales. Using the approach, the variability of 1-km-resolution SST observations on open ocean grids of both 5- and 25-km resolution is found to be ~0.07 K. In regions of sharper gradients such as associated with strong localized diurnal warming, the variability within 25-km-resolution grids increases to as much as 0.4 K for sampling at 1-km resolution. The variability of 1-km observations on a 25-km-resolution grid is about 2.4 times greater than that on a 5-km-resolution grid. Broader application of the technique globally could help better quantify regional variations in the spatial variability, which would subsequently improve uncertainty estimates for existing satellite-based SST products.

Evaluating SST Analyses with Independent Ocean Profile Observations

The difficulty in effectively evaluating sea surface temperature (SST) analyses is finding independent observations, since most available observations have been used in the SST analyses. In this study, the ocean profile measurements [from reverse thermometer, CTD, mechanical bathythermograph (MBT), and XBT] above 5-m depth over 1950–2016 from the World Ocean Database (WOD) are used (data labeled pSSTW). The biases of MBT and XBT are corrected based on currently available algorithms. The bias-corrected pSSTW over 1950–2016 and satellite-based SST from the European Space Agency (ESA) Climate Change Initiative (CCI) over 1992–2010 are used to evaluate commonly available SST analyses. These SST analyses are the Extended Reconstructed SST (ERSST), versions 5, 4, and 3b, the Met Office Hadley Centre Sea Ice and SST dataset (HadISST), and the Japan Meteorological Administration (JMA) Centennial In Situ Observation-Based Estimates of SST version 2.9.2 (COBE-SST2). Our comparisons show that the SST from COBE-SST2 is the closest to pSSTW and CCI in most of the Pacific, Atlantic, and Southern Oceans, which may result from its unique bias correction to ship observations. The SST from ERSST version 5 is more consistent with pSSTW than its previous versions over 1950–2016, and is more consistent with CCI than its previous versions over 1992–2010. The better performance of ERSST version 5 over its previous versions is attributed to its improved bias correction applied to ship observations with a baseline of buoy observations, and is seen in most of the Pacific and Atlantic.

A New High-Resolution Sea Surface Temperature Blended Analysis

AbstractThe National Oceanic and Atmospheric Administration’s (NOAA) office of National Environmental Satellite, Data, and Information Service (NESDIS) now generates a daily 0.05° (∼5 km) global high-resolution satellite-based sea surface temperature (SST) analyses on an operational basis. The new analysis combines SST data from U.S., Japanese, and European geostationary infrared imagers, and low-Earth-orbiting infrared (United States and Europe) SST data, into a single high-resolution 5-km product. An earlier version produced a 0.1° (∼11 km) resolution, a resolution chosen to approximate the Nyquist sampling criterion for the midlatitude Rossby radius (∼20 km), in order to preserve mesoscale oceanographic features such as eddies and frontal meanders. Comparison between the two analyses illustrates that the higher-resolution grid spacing has more success in this regard. The analysis employs a rigorous multiscale optimum interpolation (OI) methodology that approximates the Kalman filter, together with a data-adaptive correlation length scale, to ensure a good balance between detail preservation and noise reduction. The product accuracy verified against globally distributed buoys is ∼0.02 K, with a robust standard deviation of ∼0.25 K. The new analysis has proven a significant success even when compared to other products that purport to have a similar resolution. This analysis forms the basis for other operational environmental products such as coral reef bleaching risk and ocean heat content for tropical cyclone prediction. Forthcoming enhancements include the incorporation of microwave SST products from low-Earth-orbiting platforms [e.g., Global Change Observation Mission for Water-1 (GCOM-W1)] in order to improve the resolution of SST features in areas of persistent cloud and correct for diurnal effects via a turbulence model of upper-ocean heating.