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Coral reefs are facing severe threats and are at risk of accelerated decline due to climate change‐induced changes in their environment. Ongoing efforts to understand the mechanisms of coral response to warming rely on multiple sources of temperature data. Yet, it remains uncertain whether the Sea Surface Temperature (SST) data used for coral reef studies are consistent among different data products, despite potential implications for conservation. A better understanding of the consistency among the different SST data applied to coral reefs may facilitate the fusion of data into a standard product. This will improve monitoring and understanding of the impact of global warming on coral reefs. Four types of SST data across North‐Western and South‐Western Australia are compared to assess their differences and ability to observe high thermal stress during historical coral bleaching events. The four SST data sources included those derived from Global Circulation Models, NOAA CoralTemp SST product, ESA CCI SST product, and coral core derived SST. Coral bleaching risk indicators, Degree Heating Week (DHW), and Degree Heating Month (DHM) were calculated using these sources and compared for consistency. DHW and DHM were inconsistent among data sets and did not accurately reflect high thermal stress metrics during moderate and severe bleaching events. Some reefs did not experience bleaching in spite of high DHWs and DHMs, suggesting a mismatch in data scales, or perhaps other oceanographic factors and coral adaptation. By exploring the differences and similarities among these four data sources, this study highlights the need to compare existing indicators of thermal stress from different data sets. Plain Language Summary: Climate change and warming have resulted in global coral bleaching events, severely compromising our environment's health. Monitoring the changes in ocean temperatures around them is essential to maximizing our efforts to protect them. Different ocean temperature data products exist and are being used without understanding their differences. To highlight these differences, the present study compares historical warming from climate models and remote and in situ sensors and known bleaching events on five reefs across Western Australia. Key Points: Temperature data sources did not provide consistent risk indicators for coral bleachingAcross five reefs, coral bleaching risk indicators differed in their ability to predict the observed coral bleaching eventsTemperature data in daily and monthly temporal resolutions differed in the accuracy of coral bleaching risk indicators [ABSTRACT FROM AUTHOR]

Abstract: Lack of reliable observational metadata represents a key barrier to understanding sea surface temperature (SST) measurement biases, a large contributor to uncertainty in the global surface record. We present a method to identify SST measurement practice by comparing the observed SST diurnal cycle from individual ships with a reference from drifting buoys under similar conditions of wind and solar radiation. Compared to existing estimates, we found a larger number of engine room‐intake (ERI) reports post–World War II and in the period 1960–1980. Differences in the inferred mixture of observations lead to a systematic warmer shift of the bias adjusted SST anomalies from 1980 compared to previous estimates, while reducing the ensemble spread. Changes in mean field differences between bucket and ERI SST anomalies in the Northern Hemisphere over the period 1955–1995 could be as large as 0.5°C and are not well reproduced by current bias adjustment models. [ABSTRACT FROM AUTHOR]

We analyse the regional variability in observed sea surface height (SSH), sea surface temperature (SST) and ocean colour (OC) from the ESA Climate Change Initiative datasets over the period 1993-2011. The analysis focuses on the signature of the ocean large-scale climate fluctuations driven by the atmospheric forcing and do not address the mesoscale variability. We use the ECCO version 4 ocean reanalysis to unravel the role of ocean transport and surface buoyancy fluxes in the observed SSH, SST and OC variability. We show that the SSH regional variability is dominated by the steric effect (except at high latitude) and is mainly shaped by ocean heat transport divergences with some contributions from the surface heat fluxes forcing that can be significant regionally (confirming earlier results). This is in contrast with the SST regional variability, which is the result of the compensation of surface heat fluxes by ocean heat transport in the mixed layer and arises from small departures around this background balance. Bringing together the results of SSH and SST analyses, we show that SSH and SST bear some common variability. This is because both SSH and SST variability show significant contributions from the surface heat fluxes forcing. It is evidenced by the high correlation between SST and buoyancy-forced SSH almost everywhere in the ocean except at high latitude. OC, which is determined by phytoplankton biomass, is governed by the availability of light and nutrients that essentially depend on climate fluctuations. For this reason, OC shows significant correlation with SST and SSH. We show that the correlation with SST displays the same pattern as the correlation with SSH with a negative correlation in the tropics and subtropics and a positive correlation at high latitude. We discuss the reasons for this pattern. [ABSTRACT FROM AUTHOR]

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Satellite remote sensing is the best way to derive sea surface skin temperature (SSTskin) in the Arctic. However, as surface temperature retrieval algorithms in the infrared (IR) part of the electromagnetic spectrum are designed to compensate for atmospheric effects mainly due to water vapor, MODIS SSTskin retrievals have larger uncertainties at high latitudes where the atmosphere is very dry and cold, which is an extreme in the distribution of global conditions. MODIS R2019 SSTskin fields are currently derived using latitudinally and monthly dependent algorithm coefficients, including an additional band above 60°N to better represent the effects of Arctic atmospheres. However, the R2019 processing of MODIS SSTskin still has some unrevealed error characteristics. This study uses 21 years (2002–2022) of collocated, simultaneous satellite brightness temperature (BT) data from Aqua MODIS and in situ buoy-measured subsurface temperature data from iQuam for validation. Unlike elsewhere over the oceans, the 11 μm and 12 μm BT differences are poorly related to the column water vapor at high latitudes, resulting in poor atmospheric water vapor correction. Anomalous BT difference signals are identified, caused by the temperature and humidity inversions in the lower troposphere, which are especially significant during the summer. Although the existence of negative BT differences is physically reasonable, this makes the retrieval algorithm lose its effectiveness. Moreover, the statistics of the MODIS SSTskin data when compared with the iQuam buoy temperature data show large differences (in terms of mean and standard deviation) for the matchups at the Northern Atlantic and Pacific sides of the Arctic due to the disparity of in situ measurements and distinct surface and vertical atmospheric conditions. Therefore, it is necessary to further improve the retrieval algorithms to obtain more accurate MODIS SSTskin data to study surface ocean processes and climate change in the Arctic. [ABSTRACT FROM AUTHOR]

Leveraging the Copernicus high‐resolution multi‐year Mediterranean Sea Surface Temperature (SST) dataset, 15 selected tropical‐like cyclones (TLCs) are analyzed with the objective of elucidating the anomalies of SST at the time of cyclogenesis and the connection between the change in SST during the cyclone lifetime and its characteristics. The long‐term SST increase associated with climate change is identified by comparing detrended and original anomalies. Detrending removes the effect of the intensification of SST anomaly over time, revealing that no significant anomalies generally emerge in the early stages of the TLC lifetimes. Conversely, winter events exhibit early‐stage positive SST anomalies. Also, high SST values were observed during the intensification of the most intense cyclones. A cold SST anomaly is left after the passage of the cyclones, due to the intense sea surface fluxes extracting heat from the sea. Plain Language Summary: Mediterranean tropical‐like cyclones are severe weather events able to produce large socio‐economic and environmental impact, as well as considerable damage. On average, 1.5 such events affect the Mediterranean each year. In this study we assess the Sea Surface Temperature conditions related to the events reported in the literature of the past four decades. We use a high‐resolution multi‐year SST dataset over the Mediterranean Sea, to reveal the potential relationship between the change in SST before and during the cyclone's lifetime and their features. Our results show that SST plays an important role in the intensification phases of the events, while no significant SST anomaly emerges in the early stages for most of the cyclones. Key Points: No significant SST anomalies are found at the early stage of medicanes, except for the winter cases that exhibit a positive anomalySST value is particularly high for the most intense cyclones (September cases)The passage of the cyclones over the sea induces a cooling mainly during their mature phase [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) data from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the Sentinel-3 satellites have been used in the Met Office's Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) since 2019 (Sentinel-3A SST data since March 2019 and Sentinel-3B data since December 2019). The impacts of using SLSTR SSTs and the SLSTR as the reference sensor for the bias correction of other satellite data have been assessed using independent Argo float data. Combining Sentinel-3A and -3B SLSTRs with two Visible Infrared Imaging Radiometer Suite (VIIRS) sensors (onboard the joint NASA/NOAA Suomi National Polar-orbiting Partnership and National Oceanic and Atmospheric Administration-20 satellites) in the reference dataset has also been investigated. The results indicate that when using the SLSTR as the only reference satellite sensor, the OSTIA system becomes warmer overall, although there are mixed impacts in different parts of the global ocean. Using both the VIIRS and the SLSTR in the reference dataset leads to moderate but more consistent improvements globally. Numerical weather prediction (NWP) results also indicate a better performance when using both the VIIRS and the SLSTR in the reference dataset compared to only using the SLSTR at night. Combining the VIIRS and the SLSTR with latitudinal weighting shows the best validation results against Argo, but further investigation is required to refine this method. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is a crucial physical parameter in meteorology and oceanography. This study demonstrates that the influence of earth incidence angle (EIA) on the SST retrieved from the microwave radiation imager (MWRI) onboard FengYun-3 (FY-3) meteorological satellites should not be ignored. Compared with algorithms that do not consider the influence of EIA in the regression, those that integrate the EIA into the regression can enhance the accuracy of SST retrievals. Subsequently, based on the recalibrated Level 1B data from the FY-3/MWRI, a long-term SST dataset was reprocessed by employing the algorithm that integrates the EIA into the regression. The reprocessed SST data, including FY-3B/MWRI SST during 2010-2019, FY-3C/MWRI SST during 2013-2019, and FY-3D/MWRI SST during 2018-2020, were compared with the in-situ SST and the SST dataset from the Operational Sea Surface Temperature and Ice Analysis (OSTIA). The results show that the FY-3/MWRI SST data were consistent with both the in-situ SST and the OSTIA SST dataset. Compared with the Copernicus Climate Change Service V2.0 SST, the absolute deviation of the reprocessed SST, with a quality flag of 50, was less than 1.5°C. The root mean square errors of the FY-3/MWRI orbital, daily, and monthly SSTs, with a quality flag of 50, were approximately 0.82°C, 0.69°C, and 0.37°C, respectively. The primary discrepancies between the FY-3/MWRI SST and the OSTIA SST were found mainly in the regions of the western boundary current and the Antarctic Circumpolar Current. Overall, this reprocessed SST product is recommended for El Niño and La Niña events monitoring. [ABSTRACT FROM AUTHOR]

Coastal areas are among the most productive areas in the world, ecologically as well as economically. Sea Surface Temperature (SST) has evolved as the major essential climate variable (ECV) and ocean variable (EOV) to monitor land–ocean interactions and oceanic warming trends. SST monitoring can be achieved by means of remote sensing. The current relatively coarse spatial resolution of established SST products limits their potential in small-scale, coastal zones. This study presents the first analysis of the TIMELINE 1 km SST product from AVHRR in four key European regions: The Northern and Baltic Sea, the Adriatic Sea, the Aegean Sea, and the Balearic Sea. The analysis of monthly anomaly trends showed high positive SST trends in all study areas, exceeding the global average SST warming. Seasonal variations reveal peak warming during the spring, early summer, and early autumn, suggesting a potential seasonal shift. The spatial analysis of the monthly anomaly trends revealed significantly higher trends at near-coast areas, which were especially distinct in the Mediterranean study areas. The clearest pattern was visible in the Adriatic Sea in March and May, where the SST trends at the coast were twice as high as that observed at a 40 km distance to the coast. To validate our findings, we compared the TIMELINE monthly anomaly time series with monthly anomalies derived from the Level 4 CCI SST anomaly product. The comparison showed an overall good accordance with correlation coefficients of R > 0.82 for the Mediterranean study areas and R = 0.77 for the North and Baltic Seas. This study highlights the potential of AVHRR Local Area Coverage (LAC) data with 1 km spatial resolution for mapping long-term SST trends in areas with high spatial SST variability, such as coastal regions. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) constitutes a pivotal physical parameter in the investigation of atmospheric, oceanic, and air–sea exchange processes. The retrieval of SST through satellite passive microwave (PMW) technology effectively mitigates the interference posed by cloud cover, addressing a longstanding challenge. Nevertheless, conventional functional representations often fall short in capturing the intricate interplay of factors influencing SST. Leveraging neural networks (NNs), known for their adeptness in tackling nonlinear and intricate problems, holds great promise in SST retrieval. Nonetheless, NNs exhibit a high sensitivity to initial weights and thresholds, rendering them susceptible to local optimization issues. In this study, we present a novel machine learning (ML) approach for SST retrieval using PMW measurements, drawing from the Sparrow Search Algorithm (SSA) and Back-Propagation neural network (BPNN) methodologies. The core premise involves the optimization of the BP neural network's initial weights and thresholds through an enhanced SSA algorithm employing various optimization strategies. This optimization aims to provide superior parameters for the training of the BP neural network. Employing AMSR2 brightness temperature data, sea surface wind speed data, and buoy SST measurements, we construct the ISSA-BP model for sea surface temperature retrieval. The validation of the ISSA-BP model against the test data is conducted and compared against the multiple linear regression (MLR) model, an unoptimized BP model, and an unimproved SSA-BP model. The results manifest an impressive R-squared (R2) value of 0.9918 and a root-mean-square error (RMSE) of 0.8268 °C for the ISSA-BP model, attesting to its superior accuracy. Furthermore, the ISSA-BP model was applied to retrieve global sea surface temperatures on 15 July 2022, yielding an R2 of 0.9926 and an RMSE of 0.7673 °C for the OISST product on the same day, underscoring its excellent concordance. The results indicate that SST can be efficiently and accurately retrieved using the model proposed in this paper, based on satellite PMW measurements. This finding underscores the potential of employing machine learning algorithms for SST retrieval and offers a valuable reference for future studies focusing on the retrieval of other sea surface parameters. [ABSTRACT FROM AUTHOR]

The Fengyun-4A (FY-4A) satellite is a new-generation geostationary meteorological satellite developed by China. The advanced geosynchronous radiation imager (AGRI), one of the key payloads onboard FY-4A, can monitor sea surface temperature (SST). This paper compares FY-4A/AGRI SST with in situ and Himawari-8/advanced Himawari imager (AHI) SST. The study area spans 30°E–180°E, 60°S–60°N, and the study period is from January 2019 to December 2021. The matching time window of the three data is 30 min, and the space window is 0.1°. The quality control criterion is to select all clear sky and well-distributed matchups within the study period, removing the influence of SST fronts. The results of the difference between FY-4A/AGRI and in situ SST show a bias of −0.12 °C, median of −0.05 °C, standard deviation (STD) of 0.76 °C, robust standard deviation (RSD) of 0.68 °C, and root mean square error (RMSE) of 0.77 °C for daytime and a bias of 0.00 °C, median of 0.05 °C, STD of 0.78 °C, RSD of 0.72 °C, and RMSE of 0.78 °C for nighttime. The results of the difference between FY-4A/AGRI SST and Himawari-8/AHI SST show a bias of 0.04 °C, median of 0.10 °C, STD of 0.78 °C, RSD of 0.70 °C, and RMSE of 0.78 °C for daytime and the bias of 0.30 °C, median of 0.34 °C, STD of 0.81 °C, RSD of 0.76 °C, and RMSE of 0.86 °C for nighttime. The three-way error analysis also indicates a relatively larger error of AGRI SST. Regarding timescale, the bias and STD of FY-4A/AGRI SST show no seasonal correlation, but FY-4A/AGRI SST has a noticeable bias jump in the study period. Regarding spatial scale, FY-4A/AGRI SST shows negative bias at the edge of the AGRI SST coverage in the Pacific region near 160°E longitude and positive bias in high latitudes of the southern hemisphere. The accuracy of FY-4A/AGRI SST depends on the satellite zenith angle and water vapor. Further research on the FY-4A/AGRI SST retrieval algorithm accounting for the variability of water vapor will be conducted. [ABSTRACT FROM AUTHOR]

The eruption of Hunga Tonga‐Hunga Ha'apai (HTHH) volcano on 15 January 2022 injected a great amount of H2O and a moderate amount of SO2 into the stratosphere, producing a pronounced and persistent sulfate aerosol layer centered around the mid‐stratosphere, mostly confined to Southern Hemisphere (SH) tropics. These aerosols affect the Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals of sea surface temperature (SST) where negative biases reached −0.3 K and an annual mean of −0.1 K north of 40°S in the SH. The spatial and temporal evolutions of MODIS SST anomalies are presented. Radiative transfer simulations demonstrate the aerosol effect on MODIS SST retrievals by causing an additional brightness temperature (BT) deficit at 11 μm and a reduction in BT differences since the characteristic of spectral attenuation between 11 and 12 μm is opposite to that of H2O. A correction for HTHH aerosol effects in the retrieval algorithm is therefore desirable. Plain Language Summary: The limit on the accuracy of sea surface temperature (SST), an Essential Climate Variable, derived from measurements of infrared radiometers on satellites is determined by the correction for the effects of the intervening atmosphere. Anomalous atmospheric conditions cause larger errors. The eruption of the submarine volcano Hunga Tonga‐Hunga Ha'apai in January 2022 injected much water vapor and other material into the atmosphere. By comparing satellite‐derived SSTs with those measured from drifting buoys in the latitude zone significantly influenced by the volcano's eruption, we quantify the negative SST errors introduced and monitor the evolution of the volcano's influence for the following year. It will be important to generate corrections to satisfy the high accuracy requirements for SST in scientific studies, for example, as input to climate models. Key Points: Stratospheric sulfate aerosols in Tonga eruption plume affect sea‐surface temperature from Moderate Resolution Imaging SpectroradiometerNegative biases of sea‐surface temperature were pronounced in Southern Hemispheric tropics initially and spread to mid‐latitude after MayA negative offset of 11–12 μm brightness temperature difference is due to more absorption by aerosol at 11 μm, opposite to H2O absorption [ABSTRACT FROM AUTHOR]

In the following decade(s), a set of satellite missions carrying thermal infrared (TIR) imagers with a relatively high noise equivalent differential temperature (NEdT) are expected, e.g., the high resolution TIR imagers flying on the future Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA), Land Surface Temperature Monitoring (LSTM) and NASA-JPL/ASI Surface Biology and Geology Thermal (SBG) missions or the secondary payload on board the ESA Earth Explorer 10 Harmony. The instruments on board these missions are expected to be characterized by an NEdT of ⪆0.1 K. In order to reduce the impact of radiometric noise on the retrieved sea surface temperature (SST), this study investigates the possibility of applying a multi-pixel atmospheric correction based on the hypotheses that (i) the spatial variability scales of radiatively active atmospheric variables are, on average, larger than those of the SST and (ii) the effect of atmosphere is accounted for via the split window (SW) difference. Based on 32 Sentinel 3 SLSTR case studies selected in oceanic regions where SST features are mainly driven by meso to sub-mesoscale turbulence (e.g., corresponding to major western boundary currents), this study documents that the local spatial variability of the SW difference term on the scale of ≃3 × 3 km2 is comparable with the noise associated with the SW difference. Similarly, the power spectra of the SW term are shown to have, for small scales, the behavior of white noise spectra. On this basis, we suggest to average the SW term and to use it for the atmospheric correction procedure to reduce the impact of radiometric noise. In principle, this methodology can be applied on proper scales that can be dynamically defined for each pixel. The applicability of our findings to high-resolution TIR missions is discussed and an example of an application to ECOSTRESS data is reported. [ABSTRACT FROM AUTHOR]

The existence of a cool sea surface skin layer in the global ocean during both day and night is generally recognized. However, a warm skin should be present if the total surface net heat flux (Qnet ${Q}_{\mathrm{n}\mathrm{e}\mathrm{t}}$) were to be from the atmosphere into ocean. Saildrone, an advanced uncrewed surface vehicle, has been shown to be able to provide sufficiently accurate sea skin temperature (SSTskin) and subsurface temperature (SSTdepth) data at high latitudes. Using those SST data along with meteorological parameters from a Saildrone deployed in the Arctic in the summer of 2019, some warm skin layers were identified due to the Qnet ${Q}_{\mathrm{n}\mathrm{e}\mathrm{t}}$ gain resulting from the combined effect of positive air‐sea temperature difference, humid surface air and cloudy skies. Furthermore, most warm skins here were found during and shortly after rainfall events. It is essential to incorporate the ability to simulate warm skin layers in the present cool skin models. Plain Language Summary: In most cases, the sea‐surface temperature of the skin of the ocean (SSTskin) is cooler than the temperature at the base of skin layer, which is a layer in which the vertical transport of heat is primarily by molecular conduction rather than turbulent mixing. By analyzing the Saildrone data measured in 2019 summer at the Pacific sector of the Arctic Ocean, some unusual warmer SSTskin signals were revealed and which are shown to be physically reasonable. This finding indicates the near‐surface thermal structure needs to be better understood and further studied. Key Points: Using skin and subsurface ocean temperatures and atmospheric variables from Saildrones to identify warm skin layers at high latitudesWarm skins are mostly present with positive air‐sea temperature difference and humid air under cloudy skiesWarm skin is often associated with the occurrence of rainfall and model schemes for the warm skin necessarily need to be established [ABSTRACT FROM AUTHOR]

Our study shows that the intercomparison among sea surface temperature (SST) products is influenced by the choice of SST reference, and the interpolation of SST products. The influence of reference SST depends on whether the reference SSTs are averaged to a grid or in pointwise in situ locations, including buoy or Argo observations, and filtered by first-guess or climatology quality control (QC) algorithms. The influence of the interpolation depends on whether SST products are in their original grids or preprocessed into common coarse grids. The impacts of these factors are demonstrated in our assessments of eight widely used SST products (DOISST, MUR25, MGDSST, GAMSSA, OSTIA, GPB, CCI, CMC) relative to buoy observations: (i) when the reference SSTs are averaged onto 0.25° × 0.25° grid boxes, the magnitude of biases is lower in DOISST and MGDSST (<0.03°C), and magnitude of root-mean-square differences (RMSDs) is lower in DOISST (0.38°C) and OSTIA (0.43°C); (ii) when the same reference SSTs are evaluated at pointwise in situ locations, the standard deviations (SDs) are smaller in DOISST (0.38°C) and OSTIA (0.39°C) on 0.25° × 0.25° grids; but the SDs become smaller in OSTIA (0.34°C) and CMC (0.37°C) on products' original grids, showing the advantage of those high-resolution analyses for resolving finer-scale SSTs; (iii) when a loose QC algorithm is applied to the reference buoy observations, SDs increase; and vice versa; however, the relative performance of products remains the same; and (iv) when the drifting-buoy or Argo observations are used as the reference, the magnitude of RMSDs and SDs become smaller, potentially due to changes in observing intervals. These results suggest that high-resolution SST analyses may take advantage in intercomparisons. Significance Statement: Intercomparisons of gridded SST products be affected by how the products are compared with in situ observations: whether the products are in coarse (0.25°) or original (0.05°–0.10°) grids, whether the in situ SSTs are in their reported locations or gridded and how they are quality controlled, and whether the biases of satellite SSTs are corrected by localized matchups or large-scale patterns. By taking all these factors into account, our analyses indicate that the NOAA DOISST is among the best SST products for the long period (1981–present) and relatively coarse (0.25°) resolution that it was designed for. [ABSTRACT FROM AUTHOR]

Stripe noise is a common issue in sea surface temperatures (SSTs) retrieved from thermal infrared data obtained by satellite-based multidetector radiometers. We developed a bispectral filter (BSF) to reduce the stripe noise. The BSF is a Gaussian filter and an optimal estimation method for the differences between the data obtained at the split window. A kernel function based on the physical processes of radiative transfer has made it possible to reduce stripe and random noise in retrieved SSTs without degrading the spatial resolution or generating bias. The Second-Generation Global Imager (SGLI) is an optical sensor on board the Global Change Observation Mission–Climate (GCOM-C) satellite. We applied the BSF to SGLI data and validated the retrieved SSTs. The validation results demonstrate the effectiveness of BSF, which reduced stripe noise in the retrieved SGLI SSTs without blurring SST fronts. It also improved the accuracy of the SSTs by about 0.04 K (about 13%) in the robust standard deviation. Significance Statement: This method reduces stripe noise and improves the accuracy of SST data with minimal compromise of spatial resolution. The method assumes the relationship between the brightness temperature and the brightness temperature difference in the split window based on the physical background of atmospheric radiative transfer. The physical background of the data provides an easy solution to a complex problem. Although destriping generally requires a complex algorithm, our approach is based on a simple Gaussian filter and is easy to implement. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is an important indicator of marine system. In this study, the hybrid physically-statistically based air2water model was modified for the forecasting of SST. The hybrid model combines empiricism and theory, and balances the complexity and accuracy between the process-based physical models and statistical models. Daily observed SST data (2009–2019) from six stations in the Baltic Sea were used for the evaluation of model performance. Two metrics including the root mean squared error (RMSE) and the Nash-Sutcliffe efficiency coefficient (NSE) were used for model assessment. With the increase of air temperature, SST presents a clear warming trend (0.133°C/year–0.166°C/year), and air temperature warms faster than SST in the studied stations. The modelling results indicated that the model performs well for SST forecasting (in the validation period, mean value of RMSE is 1.245°C, and mean value of NSE is 0.961). Cross-validation results showed that the model is transferable in unknown stations. However, the model works a little bit worse in the warm period due to the impact of the upwelling phenomenon. Overall, the model is a promising tool for the prediction of SST. [ABSTRACT FROM AUTHOR]

As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed. [ABSTRACT FROM AUTHOR]

The 40+ years-long sea surface temperature (SST) dataset from 4 km Global Area Coverage (GAC) data of the Advanced Very High-Resolution Radiometers (AVHRR/2s and/3s) flown onboard ten NOAA satellites (N07/09/11/12/14/15/16/17/18/19) has been created under the NOAA AVHRR GAC SST Reanalysis 2 (RAN2) Project. The data were reprocessed with the NOAA Advanced Clear Sky Processor for Ocean (ACSPO) enterprise SST system. Two SST products are reported in the full ~3000 km AVHRR swath: 'subskin' (highly sensitive to true skin SST, but debiased with respect to in situ SST) and 'depth' (a closer proxy for in situ data, but with reduced sensitivity). The reprocessing methodology aims at close consistency of satellite SSTs with in situ SSTs, in an optimal retrieval domain. Long-term orbital and calibration trends were compensated by daily recalculation of regression coefficients using matchups with drifters and tropical moored buoys (supplemented by ships for N07/09), collected within limited time windows centered at the processed day. The nighttime Sun impingements on the sensor black body were mitigated by correcting the L1b calibration coefficients. The Earth view pixels contaminated with a stray light were excluded. Massive cold SST outliers caused by volcanic aerosols following three major eruptions were filtered out by a modified, more conservative ACSPO clear-sky mask. The RAN2 SSTs are available in three formats: swath L2P (144 10-min granules per 24 h interval) and two 0.02° gridded (uncollated L3U, also 144 granules/24 h; and collated L3C, two global maps per 24 h, one for day and one for the night). This paper evaluates the RAN2 SST dataset, with a focus on the L3C product and compares it with two other available AVHRR GAC L3C SST datasets, NOAA Pathfinder v5.3 and ESA Climate Change Initiative v2.1. Among the three datasets, the RAN2 covers the global ocean more completely and shows reduced regional and temporal biases, improved stability and consistency between different satellites, and in situ SSTs. [ABSTRACT FROM AUTHOR]

The contribution of ocean fronts to the properties and temporal evolution of Sea Surface Temperature (SST) structure functions have been investigated using a numerical model of the California Current system. First, the intensity of fronts have been quantified by using singularity exponents. Then, leaning on the multifractal theory of turbulence, we show that the departure of the scaling of the structure functions from a straight line, known as anomalous scaling, depends on the intensity of the strongest fronts. These fronts, at their turn, are closely related to the seasonal change of intensity of the coastal upwelling characteristics of this area. Our study points to the need to correctly reproduce the intensity of the strongest fronts and, consequently, properly model processes such as coastal upwelling in order to reproduce SST statistics in ocean models. Plain Language Summary: Forecasting the evolution of the Earth's climate requires to predict the evolution of the statistical characteristics of essential climate variables such as the Sea Surface Temperature. In this study, it has been found that some of such statistical properties depend on the intensity of the strongest fronts in the ocean. This implies that those ocean, or climate, models that fail to correctly predict their intensity will not be able to correctly reproduce the statistical characteristics of key variables such as temperature. The area analyzed in this study is the California Current system, where the strongest fronts are modulated by the seasonal evolution of the upwelling. Therefore, our results imply that such a system has to be correctly modeled, or parametrized, in order to properly reproduce the statistics of ocean temperatures. Key Points: The intensity of Sea Surface Temperature (SST) fronts is quantified using singularity exponents, which measure the continuity of the fieldAnomalous scaling of SST structure functions is correlated to the the intensity of the strongest frontsThe variability of the strongest fronts depends on the seasonal variability of the coastal upwelling in the area of study [ABSTRACT FROM AUTHOR]

Wide bandwidth radiometer systems that make measurements at multiple frequencies in the range from 300 MHz to 2 GHz have been proposed to address parameters important for understanding issues in the cryosphere associated with climate change such as ice sheet thickness and temperature. It is also possible with such a system to retrieve sea surface salinity (SSS), which is important for understanding the impact of climate change on ocean circulation at high latitude. In contemporary sensors for retrieving SSS, such as on Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), sea surface temperature (SST), another parameter important for understanding ocean circulation and necessary in the retrieval of salinity, is treated as an ancillary parameter obtained from an independent source. However, both SSS and SST have peaks in sensitivity below 1 GHz; and it has been shown that measurements at multiple frequencies in this portion of the spectrum can take advantage of this peak in sensitivity to improve the accuracy of the retrieval of SSS. In this manuscript, it will be shown that there is also the potential to retrieve SST and, in cold water, the possibility for improved accuracy over existing retrievals. Plain Language Summary: Analysis is presented showing that it may be possible to retrieve both ocean surface salinity and temperature at low microwave frequencies where brightness temperature has peaks in sensitivity to both. Key Points: Measurement of sea surface temperature (SST) is possible as part of low‐frequency wideband remote sensing of the cryosphereMeasurement of SST to support wideband remote sensing of sea surface salinity is possibleMeasurement of SSS and SST possible together with low‐frequency wideband sensor [ABSTRACT FROM AUTHOR]

Studies have indicated that marine heatwaves (MHWs) have had severe impacts on the marine ecosystem in the Pacific, Atlantic, and Indian Oceans, but there have been few studies focused on MHWs in the Arctic. On the other hand, amplified rapid warming in the Arctic region makes it a hotspot strategically and economically worldwide. In this study, we documented that the average intensity of MHWs in the Arctic was comparable with that in the other regions of the global oceans. The annual intensity, frequency, duration, and areal coverage of MHWs have increased significantly in recent decades. The increase of the annual duration is mainly owing to the postponed end time, thus the prolonged periods, of the MHW seasons. Our analysis indicates that the increasing trends of the annual intensity, frequency, duration, and areal coverage in the Arctic are closely associated with the increasing surface air temperature and decreasing sea‐ice concentration under the global warming environment. These features are robust across three different sea surface temperature (SST) products and using different MHW criteria. Plain Language Summary: Events of extremely warm waters in the oceans are known as marine heatwaves (MHWs). Past MHW research has used sea surface temperature (SST) to diagnose MHWs and has focused on the tropical and subtropical oceans. The questions we address here are: (a) Are there any MHW events in the Arctic region where SSTs are generally low? and (b) Are the MHWs weaker or stronger in the Arctic than in the other oceans? Our study indicates that: (a) MHWs do exist in the Arctic, (b) their strengths increase with time, and (c) they are stronger than those in the other oceans in the most recent decades. These MHWs may have a strong impact on the Arctic bio‐ecosystem due to their low heat tolerance since the seasonal variation of SST in the Arctic is small. Key Points: The average marine heatwaves (MHWs) in the Arctic are as strong as in the other ocean basins in 1982–2020The annual intensity are stronger in 2000–2020 than in 1982–2000, which is consistent with changes in air temperature, sea‐ice, and cloudThe increase of annual duration is largely associated with the postponed end time of the MHW seasons [ABSTRACT FROM AUTHOR]

A new globally reconstructed sea surface temperature (SST) analysis dataset developed by the China Meteorological Administration (CMA-SST), available on 2° × 2° and monthly resolutions since 1900, is described and assessed in this study. The dataset has been constructed from a newly developed integrated dataset with denser and wider sampling of in situ SST observations and follows similar analysis techniques to the Extended Reconstructed SST, version 5 (ERSST.v5). Assessments show that the larger observation quantity of the input data source is beneficial to making the reconstructed SSTs more realistic than those reconstructed with ICOADS3.0 + GTS (International Comprehensive Ocean—Atmosphere Dataset 3.0 and Global Telecommunication System), especially in China's offshore sea area. Besides, a specific parameter for bias correction has been upgraded to be self-adaptive to the input data source, and serves as a mediator to improve the accuracy of the reconstructed SSTs. Generally, the reconstructed CMA-SST dataset is comparable to currently congeneric products. Its biases are similar to those of ERSST.v5, the Centennial Observation-Based Estimates of SST version 2 (COBE-SST2), the Hadley Centre Sea Ice and SST dataset version 2 (HadISST2), and the Hadley Centre SST dataset version 3 (HadSST3); and more specifically, they are closest to ERSST.v5 and lower than HadISST2 and HadSST3 at high latitudes of the Southern Hemisphere where in situ observations are limited. Moreover, its temporal characteristics, such as the year-to-year variations of globally averaged SST anomalies and time series of the Niño-3.4, Atlantic multidecadal oscillation, and Pacific decadal oscillation indices are also a good match to those of congeneric products. Although the warming rates of CMA-SST are a little higher in many regions over the periods 1900–2019 and 1950–2019, they are found to be acceptable and within the quantified uncertainties of ERSST.v5. However, there are noticeable differences in the strength and stability of spatial standard deviations among the various datasets, as well as low correlations between CMA-SST and the other products around 60°S where in situ sampling is very limited. These aspects necessitate further investigation and improvement of CMA-SST. [ABSTRACT FROM AUTHOR]

The NOAA Daily Optimum Interpolation Sea Surface Temperature dataset (DOISST) has recently been updated to v2.1 (January 2016–present). Its accuracy may impact the climate assessment, monitoring and prediction, and environment-related applications. Its performance, together with those of seven other well-known sea surface temperature (SST) products, is assessed by comparison with buoy and Argo observations in the global oceans on daily 0.25° × 0.25° resolution from January 2016 to June 2020. These seven SST products are NASA MUR25, GHRSST GMPE, BoM GAMSSA, UKMO OSTIA, NOAA GPB, ESA CCI, and CMC. Our assessments indicate that biases and root-mean-square difference (RMSDs) in reference to all buoys and all Argo floats are low in DOISST. The bias in reference to the independent 10% of buoy SSTs remains low in DOISST, but the RMSD is slightly higher in DOISST than in OSTIA and CMC. The biases in reference to the independent 10% of Argo observations are low in CMC, DOISST, and GMPE; also, RMSDs are low in GMPE and CMC. The biases are similar in GAMSSA, OSTIA, GPB, and CCI whether they are compared against all buoys, all Argo, or the 10% of buoy or 10% of Argo observations, while the RMSDs against Argo observations are slightly smaller than those against buoy observations. These features indicate a good performance of DOISST v2.1 among the eight products, which may benefit from ingesting the Argo observations by expanding global and regional spatial coverage of in situ observations for effective bias correction of satellite data. [ABSTRACT FROM AUTHOR]

A joint effort between the Copernicus Climate Change Service (C3S) and the Group for High Resolution Sea Surface Temperature (GHRSST) has been dedicated to an intercomparison study of eight global gap-free sea surface temperature (SST) products to assess their accurate representation of the SST relevant to climate analysis. In general, all SST products show consistent spatial patterns and temporal variability during the overlapping time period (2003–18). The main differences between each product are located in the western boundary current and Antarctic Circumpolar Current regions. Linear trends display consistent SST spatial patterns among all products and exhibit a strong warming trend from 2012 to 2018 with the Pacific Ocean basin as the main contributor. The SST discrepancy between all SST products is very small compared to the significant warming trend. Spatial power spectral density shows that the interpolation into 1° spatial resolution has negligible impacts on our results. The global mean SST time series reveals larger differences among all SST products during the early period of the satellite era (1982–2002) when there were fewer observations, indicating that the observation frequency is the main constraint of the SST climatology. The maturity matrix scores, which present the maturity of each product in terms of documentation, storage, and dissemination but not the scientific quality, demonstrate that ESA-CCI and OSTIA SST are well documented for users' convenience. Improvements could be made for MGDSST and BoM SST. Finally, we have recommended that these SST products can be used for fundamental climate applications and climate studies (e.g., El Niño). [ABSTRACT FROM AUTHOR]

The HY-2B satellite, equipped with the scanning microwave radiometer (SMR), was launched on 25 October 2018 and began observation on 30 October 2018. With the SMR data, we retrieved the sea surface temperature (SST), along with the ocean wind speed (OWS), total columnar water vapour (TCWV), and cloud liquid water (TCLW), within an optimal estimation (OE) Framework. Through the comparison with Argo profiling floats data, mean difference of 0.01°C, and standard deviation of 0.47 ∘ C are inferred for SST within the latitude range of 60°S–60°N. The contrast against the WindSat standard products also shows an encouraging result, in which the standard deviation for SST, OWS, TCWV, and TCLW are 0.49 ° C , 0.64 m s−1, 1.23 mm, and 0.029 mm, respectively. To investigate the effect of oceanic and atmospheric state on SST retrieval, the sensitivities of brightness temperature (TB) at considered frequency to SST, OWS, TCWV, and TCLW are carefully analysed, and seven combinations of SMR frequencies are tested to retrieve different parameters. The results emphasize the importance of low-frequency observations in retrieving accurate SST and suggest that the influence of wind speed and atmospheric vapour content is non-negligible. It is also indicated that the role of the higher frequency (23.8 and 37 GHz) is limited for SST retrieval. This study presents an assessment for SST retrieval from SMR measured data and provides a reference about frequency selection for measuring SST from passive microwave radiometer. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is an essential climate variable, that is directly used in climate monitoring. Although satellite measurements can offer continuous global coverage, obtaining a long‐term homogeneous satellite‐derived SST data set suitable for climate studies based on a single instrument is still a challenge. In this work, we assess a homogeneous SST data set derived from reprocessed Infrared Atmospheric Sounding Interferometer (IASI) level‐1 (L1C) radiance data. The SST is computed using Planck's Law and simple atmospheric corrections. We assess the data set using the ERA5 reanalysis and the EUMETSAT‐released IASI level‐2 SST product. Over the entire period, the reprocessed IASI SST shows a mean global difference with ERA5 close to zero, a mean absolute bias under 0.5°C, with a SD of difference around 0.3°C and a correlation coefficient over 0.99. In addition, the reprocessed data set shows a stable bias and SD, which is an advantage for climate studies. The interannual variability and trends were compared with other SST data sets: ERA5, Hadley Centre's SST (HadISST), and NOAA's Optimal Interpolation SST Analysis (OISSTv2). We found that the reprocessed SST data set is able to capture the patterns of interannual variability well, showing the same areas of high interannual variability (>1.5°C), including over the tropical Pacific in January corresponding to the El Niño Southern Oscillation. Although the period studied is relatively short, we demonstrate that the IASI data set reproduces the same trend patterns found in the other data sets (i.e., cooling trend in the North Atlantic, warming trend over the Mediterranean). Plain Language Summary: Sea surface temperature (SST) is an essential variable for monitoring climate, as defined by the Global Climate Observing System (GCOS; https://gcos.wmo.int/en/essential-climate-variables/sst). Satellite measurements can offer global continuous SST measurements, but their stability over the time needs to be assured. In this work, we present a new data set derived from the Infrared Atmospheric Sounding Interferometer, IASI (flying aboard the Metop satellites), and compare it with other available data sets. This comparison shows that our data set produces similar means, variability and trends as other data sets, with the advantage that it is derived with a single algorithm from a single well‐calibrated instrument. This assures there are no substantial changes to the instrument characteristics over time that might result in artificial trends. Key Points: First IASI algorithm focused on sea surface temperature (SST) suitable for climate studiesThe IASI‐derived SST data set is compared with other available data setsClimate variability and trends are shown and compared to other data sets [ABSTRACT FROM AUTHOR]

The NOAA/NESDIS/NCEI Daily Optimum Interpolation Sea Surface Temperature (SST), version 2.0, dataset (DOISST v2.0) is a blend of in situ ship and buoy SSTs with satellite SSTs derived from the Advanced Very High Resolution Radiometer (AVHRR). DOISST v2.0 exhibited a cold bias in the Indian, South Pacific, and South Atlantic Oceans that is due to a lack of ingested drifting-buoy SSTs in the system, which resulted from a gradual data format change from the traditional alphanumeric codes (TAC) to the binary universal form for the representation of meteorological data (BUFR). The cold bias against Argo was about −0.14°C on global average and −0.28°C in the Indian Ocean from January 2016 to August 2019. We explored the reasons for these cold biases through six progressive experiments. These experiments showed that the cold biases can be effectively reduced by adjusting ship SSTs with available buoy SSTs, using the latest available ICOADS R3.0.2 derived from merging BUFR and TAC, as well as by including Argo observations above 5-m depth. The impact of using the satellite MetOp-B instead of NOAA-19 was notable for high-latitude oceans but small on global average, since their biases are adjusted using in situ SSTs. In addition, the warm SSTs in the Arctic were improved by applying a freezing point instead of regressed ice-SST proxy. This paper describes an upgraded version, DOISST v2.1, which addresses biases in v2.0. Overall, by updating v2.0 to v2.1, the biases are reduced to −0.07° and −0.14°C in the global ocean and Indian Ocean, respectively, when compared with independent Argo observations and are reduced to −0.04° and −0.08°C in the global ocean and Indian Ocean, respectively, when compared with dependent Argo observations. The difference against the Group for High Resolution SST (GHRSST) Multiproduct Ensemble (GMPE) product is reduced from −0.09° to −0.01°C in the global oceans and from −0.20° to −0.04°C in the Indian Ocean. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) data obtained from coastal stations in Jiangsu, China during 2010–2014 are quality controlled before analysis of their characteristic semidiurnal and seasonal cycles, including the correlation with the variation of the tide. Quality control of data includes the validation of extreme values and checking of hourly values based on temporally adjacent data points, with 0.15°C/h considered a suitable threshold for detecting abnormal values. The diurnal variation amplitude of the SST data is greater in spring and summer than in autumn and winter. The diurnal variation of SST has bimodal structure on most days, i.e., SST has a significant semidiurnal cycle. Moreover, the semidiurnal cycle of SST is negatively correlated with the tidal data from March to August, but positively correlated with the tidal data from October to January. Little correlation is detected in the remaining months because of the weak coastal-offshore SST gradients. The quality control and understanding of coastal SST data are particularly relevant with regard to the validation of indirect measurements such as satellite-derived data. [ABSTRACT FROM AUTHOR]

The thermodynamic and emissive properties of the ocean thermal skin layer are crucial contributors to air‐sea heat flux. In order to properly observe ocean surface temperature without disturbing any delicate fluid mechanical processes, thermal infrared imaging is often used. However, wind impacting the ocean surface complicates the extraction of meaningful information from thermal imagery; this is especially true for transient forcing phenomena such as wind gusts. Here, we describe wind gust‐water surface interaction through its impact on skin layer thermal and emissive properties. Two key physical processes are identified: (1) the growth of centimeter‐scale wind waves, which increases interfacial emissivity, and (2) microscale wave breaking and shear, which mix the cool skin layer with warmer millimeter‐depth water and increase the skin temperature. As more observations are made of air‐sea interaction under transient forcing, the full consideration of these processes becomes increasingly important. Plain Language Summary: When a wind gust impacts an air‐water interface, two separate processes work to increase the temperature sensed by an infrared camera. The shortwave‐roughened surface becomes more emissive, and the skin layer (upper tens to hundreds of micrometers) becomes warmer as it is mixed by microscale wave breaking. The present paper identifies the effects of both processes in a field observational data set. This work is important to the quantification of air‐sea heat flux from thermal infrared measurements. Key Points: Wind gusts produce transient ocean skin layer thermal fronts that propagate near the observed wind speedWind gust fronts disrupt the ocean thermal skin layer due to microbreaking and increase emissivity due to capillary‐gravity wave growthFollowing wind gust front passage, capillary‐gravity wave relaxation reduced surface emissivity faster than the cool skin was restored [ABSTRACT FROM AUTHOR]

One of the largest sources of uncertainty in estimates of global temperature change is that associated with the correction of systematic errors in sea surface temperature (SST) measurements. Despite recent work to quantify and reduce these errors throughout the historical record, differences between analyses remain larger than can be explained by the estimated uncertainties. We revisited the method used to estimate systematic errors and their uncertainties in version 3 of the Met Office Hadley Centre SST data set, HadSST. Using comparisons with oceanographic temperature profiles, we make estimates of biases associated with engine room measurements and insulated buckets and constrain the ranges of two of the more uncertain parameters in the bias estimation: the timing of the transition from uninsulated to insulated buckets in the middle twentieth century and the estimated fractions of different measurement methods used. Here, we present HadSST.4.0.0.0, based on release 3.0.0 and 3.0.1 of the International Comprehensive Ocean‐Atmosphere Data Set supplemented by drifting buoy measurements from the Copernicus Marine Environmental Monitoring Service. HadSST.4.0.0.0 comprises a 200‐member "ensemble" in which uncertain parameters in the SST bias scheme are varied to generate a range of adjustments. The evolution of global average SST in the new data set is similar to that in other SST data sets, and the difference between data sets is reduced during the middle twentieth century. However, the changes also highlight a discrepancy in the global‐average difference between adjusted SST and marine air temperature in the early 1990s and hence between HadSST.4.0.0.0 and, the National Oceanic and Atmospheric Administration SST data set, ERSSTv5. Key Points: We describe the construction of HadSST.4.0.0.0, a climate data set of sea surface temperature change from 1850 to 2018A range of bias adjustments was generated to create an ensemble of SST data sets with the ensemble spread partly constrained by oceanographic profile measurementsNew estimates reduce discrepancy between data sets during the middle twentieth century and the recent slowdown in warming but highlight a divergence in the early 1990s [ABSTRACT FROM AUTHOR]

The relative roles of buoy and Argo observations in two sea surface temperature (SST) analyses are studied in the global ocean and tropical Pacific Ocean over 2000–16 using monthly Extended Reconstructed SST version 5 (ERSSTv5) and Daily Optimum Interpolation SST version 2 (DOISST). Experiments show an overall higher impact by buoys than Argo floats over the global oceans and an increasing impact by Argo floats. The impact by Argo floats is generally larger in the Southern Hemisphere than in the Northern Hemisphere. The impact on trends and anomalies of globally averaged SST by either one is small when the other is used. The warming trend over 2000–16 remains significant by including either buoys or Argo floats or both. In the tropical Pacific, the impact by buoys was large over 2000–05 when the number of Argo floats was low, and became smaller over 2010–16 when the number and area coverage of Argo floats increased. The magnitude of El Niño and La Niña events decreases when the observations from buoys, Argo floats, or both are excluded. The impact by the Tropical Atmosphere Ocean (TAO) and Triangle Trans-Ocean Buoy Network (TRITON) is small in normal years and during El Niño events. The impact by TAO/TRITON buoys on La Niña events is small when Argo floats are included in the analysis systems, and large when Argo floats are not included. The reason for the different impact on El Niño and La Niña events is that the drifting buoys are more dispersed from the equatorial Pacific region by stronger trade winds during La Niña events. [ABSTRACT FROM AUTHOR]

The oceanic feedback to the atmospheric boreal summer intraseasonal oscillation (BSISO) is examined by diagnosing the sea surface temperature (SST) modification of surface fluxes and moist static energy on intraseasonal scales. SST variability affects intraseasonal surface latent heat (LH) and sensible heat (SH) fluxes, through its influence on air‐sea moisture and temperature gradients (∆q and ∆T, respectively). According to bulk formula decomposition, LH is mainly determined by wind‐driven flux perturbations, while SH is more sensitive to thermodynamic flux perturbations. SST fluctuations tend to increase the thermodynamic flux perturbations over active BSISO regions, but this is largely offset by the wind‐driven flux perturbations. Enhanced surface fluxes induced by intraseasonal SST anomalies are located ahead (north) of the convective center over both the Indian Ocean and the western Pacific, favoring BSISO northward propagation. Analysis of budgets of column‐integrated moist static energy (⟨m⟩) and its time rate of change (∂⟨m⟩/∂t) shows that SST‐modulated surface fluxes can influence the development and propagation of the BSISO, respectively. LH and SH variability induced by intraseasonal SSTs maintain 1–2% of ⟨m⟩ day−1 over the equatorial western Indian Ocean, Arabian Sea, and Bay of Bengal but damp about 1% of ⟨m⟩ day−1 over the western North Pacific. The contribution of intraseasonal SST variability to ∂⟨m⟩/∂t can reach 12–20% over active BSISO regions. These results suggest that SST variability is conducive, but perhaps not essential, for the propagation of convection during the BSISO life cycle. Key Points: LH is mainly determined by wind‐driven flux, while SH is more sensitive to thermodynamic fluxSST variability increases thermodynamic flux over active BSISO regions, but this is largely offset by wind‐driven fluxSST variability supports convection over the western Indian Ocean, Arabian Sea, and Bay of Bengal, and contributes BSISO propagation [ABSTRACT FROM AUTHOR]

The diurnal cycle of sea surface temperature (SST) is an important component of the ocean‐atmosphere system and is necessary for accurately computing air‐sea heat fluxes. Ocean temperatures in the near‐surface are highly sensitive to atmospheric conditions and can vary significantly depending on time of day. Ocean general circulation models are unable to fully capture the near‐surface diurnal SST variability, because they do not possess the necessary vertical structure and resolution. Furthermore, SST observations come from a number of sources that represent the temperature at various near‐surface depths. This presents difficulties when assimilating SST observations as well as constructing robust climate records of SST. In this study we model the fine‐scale near‐surface structure allowing SST comparisons between foundation SST, SST at depth, subskin SST, and skin SST. Hourly model results, forced and initialized using readily available reanalysis data, are from a 2‐year period, 2013–2014, over the Mediterranean Sea. Various solar absorption parameterizations are examined, and the resulting SSTs are compared to Spinning Enhanced Visible and InfraRed Imager‐derived observations of the skin temperature. Plain Language Summary: The sea surface temperature (SST) is an important variable of the climate system. It governs the exchange of energy between the ocean and atmosphere. Accurate knowledge of SST is essential for weather and ocean forecasting. The vast majority of our observations of SST are remotely sensed and derived from satellite instruments that record the temperature within the top 1 mm. These observations are complemented by a mixture of ocean moorings and gliders that measure the temperature at various near‐surface depths (∼20 cm to 1 m). SSTs are highly sensitive to atmospheric conditions and can vary significantly depending on time of day and near‐surface depth. This presents difficulties when combining different observations to construct robust climate records of SST. In addition, large‐scale computational models of the ocean typically resolve temperature in a surface layer ∼1 m thick and are unable to fully capture SST variability. It is therefore challenging to properly compare and/or merge modeled SSTs with observed SSTs. To address these challenges, we present a computational model of the fine‐scale near‐surface ocean structure. The model is tested in the Mediterranean Sea and used to produce a 2‐year data set that makes possible SST comparisons at various near‐surface depths at any hour of the day. Key Points: Simulated near‐surface diurnal SSTs, for the Mediterranean Sea, are compared to SEVIRI observationsA modeled record for contrasting skin SST, subskin SST, SST at depth, and foundation SSTAn examination of various solar absorption parameterizations on near-surface temperatures [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The monthly global 2° × 2° Extended Reconstructed Sea Surface Temperature (ERSST) has been revised and updated from version 4 to version 5. This update incorporates a new release of ICOADS release 3.0 (R3.0), a decade of near-surface data from Argo floats, and a new estimate of centennial sea ice from HadISST2. A number of choices in aspects of quality control, bias adjustment, and interpolation have been substantively revised. The resulting ERSST estimates have more realistic spatiotemporal variations, better representation of high-latitude SSTs, and ship SST biases are now calculated relative to more accurate buoy measurements, while the global long-term trend remains about the same. Progressive experiments have been undertaken to highlight the effects of each change in data source and analysis technique upon the final product. The reconstructed SST is systematically decreased by 0.077°C, as the reference data source is switched from ship SST in ERSSTv4 to modern buoy SST in ERSSTv5. Furthermore, high-latitude SSTs are decreased by 0.1°-0.2°C by using sea ice concentration from HadISST2 over HadISST1. Changes arising from remaining innovations are mostly important at small space and time scales, primarily having an impact where and when input observations are sparse. Cross validations and verifications with independent modern observations show that the updates incorporated in ERSSTv5 have improved the representation of spatial variability over the global oceans, the magnitude of El Niño and La Niña events, and the decadal nature of SST changes over 1930s-40s when observation instruments changed rapidly. Both long- (1900-2015) and short-term (2000-15) SST trends in ERSSTv5 remain significant as in ERSSTv4. [ABSTRACT FROM AUTHOR]

Satellite-derived sea surface temperatures (SSTs) from the tropical rainfall measuring mission (TRMM) microwave imager (TMI) and the advanced microwave scanning radiometer for the earth observing system (AMSR-E) were compared with non-pumped near-surface temperatures (NSTs) obtained from Argo profiling floats over the global oceans. Factors that might cause temperature differences were examined, including wind speed, columnar water vapor, liquid cloud water, and geographic location. The results show that both TMI and AMSR-E SSTs are highly correlated with the Argo NSTs; however, at low wind speeds, they are on average warmer than the Argo NSTs. The TMI performs slightly better than the AMSR-E at low wind speeds, whereas the TMI SST retrievals might be poorly calibrated at high wind speeds. The temperature differences indicate a warm bias of the TMI/AMSR-E when columnar water vapor is low, which can indicate that neither TMI nor AMSR-E SSTs are well calibrated at high latitudes. The SST in the Kuroshio Extension region has higher variability than in the Kuroshio region. The variability of the temperature difference between the satellite-retrieved SSTs and the Argo NSTs is lower in the Kuroshio Extension during spring. At low wind speeds, neither TMI nor AMSR-E SSTs are well calibrated, although the TMI performs better than the AMSR-E. [ABSTRACT FROM AUTHOR]

Hourly sea surface temperature (SST) observations from the geostationary satellite are increasingly used in studies of the diurnal warming of the surface oceans. The aim of this study is to derive the spatial and temporal distribution of diurnal warming in the China seas and northwestern Pacific Ocean from Multi-functional Transport Satellite (MTSAT) SST. The MTSAT SST is validated against drifting buoy measurements firstly. It shows mean biases is about-0.2°C and standard deviation is about 0.6°C comparable to other satellite SST accuracy. The results show that the tropics, mid-latitudes controlled by subtropical high and marginal seas are frequently affected by large diurnal warming. The Kuroshio and its extension regions are smaller compared with the surrounding regions. A clear seasonal signal, peaking at spring and summer can be seen from the long time series of diurnal warming in the domain in average. It may due to large insolation and low wind speed in spring and summer, while the winter being the opposite. Surface wind speed modulates the amplitude of the diurnal cycle by influencing the surface heat flux and by determining the momentum flux. For the shallow marginal seas, such as the East China Sea, turbidity would be another important factor promoting diurnal warming. It suggests the need for the diurnal variation to be considered in SST measurement, air-sea flux estimation and multiple sensors SST blending. [ABSTRACT FROM AUTHOR]

The monthly Extended Reconstructed Sea Surface Temperature (ERSST) dataset, available on global 2° × 2° grids, has been revised herein to version 4 (v4) from v3b. Major revisions include updated and substantially more complete input data from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) release 2.5; revised empirical orthogonal teleconnections (EOTs) and EOT acceptance criterion; updated sea surface temperature (SST) quality control procedures; revised SST anomaly (SSTA) evaluation methods; updated bias adjustments of ship SSTs using the Hadley Centre Nighttime Marine Air Temperature dataset version 2 (HadNMAT2); and buoy SST bias adjustment not previously made in v3b. Tests show that the impacts of the revisions to ship SST bias adjustment in ERSST.v4 are dominant among all revisions and updates. The effect is to make SST 0.1°-0.2°C cooler north of 30°S but 0.1°-0.2°C warmer south of 30°S in ERSST.v4 than in ERSST.v3b before 1940. In comparison with the Met Office SST product [the Hadley Centre Sea Surface Temperature dataset, version 3 (HadSST3)], the ship SST bias adjustment in ERSST.v4 is 0.1°-0.2°C cooler in the tropics but 0.1°-0.2°C warmer in the midlatitude oceans both before 1940 and from 1945 to 1970. Comparisons highlight differences in long-term SST trends and SSTA variations at decadal time scales among ERSST.v4, ERSST.v3b, HadSST3, and Centennial Observation-Based Estimates of SST version 2 (COBE-SST2), which is largely associated with the difference of bias adjustments in these SST products. The tests also show that, when compared with v3b, SSTAs in ERSST.v4 can substantially better represent the El Niño/La Niña behavior when observations are sparse before 1940. Comparisons indicate that SSTs in ERSST.v4 are as close to satellite-based observations as other similar SST analyses. [ABSTRACT FROM AUTHOR]

Sea surface temperature ( SST) datasets have been generated from satellite observations for the period 1991-2010, intended for use in climate science applications. Attributes of the datasets specifically relevant to climate applications are: first, independence from in situ observations; second, effort to ensure homogeneity and stability through the time-series; third, context-specific uncertainty estimates attached to each SST value; and, fourth, provision of estimates of both skin SST (the fundamental measurement, relevant to air-sea fluxes) and SST at standard depth and local time (partly model mediated, enabling comparison with historical in situ datasets). These attributes in part reflect requirements solicited from climate data users prior to and during the project. Datasets consisting of SSTs on satellite swaths are derived from the Along-Track Scanning Radiometers ( ATSRs) and Advanced Very High Resolution Radiometers ( AVHRRs). These are then used as sole SST inputs to a daily, spatially complete, analysis SST product, with a latitude-longitude resolution of 0.05°C and good discrimination of ocean surface thermal features. A product user guide is available, linking to reports describing the datasets' algorithmic basis, validation results, format, uncertainty information and experimental use in trial climate applications. Future versions of the datasets will span at least 1982-2015, better addressing the need in many climate applications for stable records of global SST that are at least 30 years in length. [ABSTRACT FROM AUTHOR]

Quasi-operational shipborne radiometers provide a fiducial reference measurement (FRM) for satellite validation of satellite sea surface skin temperature (SSTskin) retrievals. External reference blackbodies are required to verify the performance and to quantify the accuracy of the radiometer calibration system. They provide a link in an unbroken chain of comparisons between the shipborne radiometer and a traceable reference standard. A second-generation water bath blackbody reference radiance source has been developed for this purpose. The second generation Concerted Action for the Study of the Ocean Thermal Skin (CASOTS-II) blackbody has a 110-mm-diameter aperture cylinder-cone geometry coated with NEXTEL suede 3103 paint. Interchangeable aperture stops reduce the cavity aperture diameter and minimize stray radiation. Monte Carlo modeling techniques show the effective emissivity of the cavity to be >0.9999 (aperture < 30 mm). The cavity is immersed in a water bath that is vigorously stirred using a pump that slowly heats the water bath at a mean rate of ~0.6 K h−1. The temperature of the water bath is measured using a thermometer traceable to the International System of Units (SI) standards. The worst-case radiance temperature of the CASOTS-II blackbody system is traceable to the SI with an uncertainty of 58 mK (millikelvin). When operating under typical laboratory conditions using an aperture of 40 mm, the uncertainty is 16 mK. An intercomparison with the U.K. National Physical Laboratory Absolute Measurements of Blackbody Emitted Radiance (AMBER) reference radiometer found no significant differences within 75 mK (110-mm aperture) or 50 mK (40-mm aperture), which is the combined uncertainty of the comparison and the reference standard for SI traceability of ISAR radiometer SSTskin records used for satellite SST validation. Applications of the CASOTS-II blackbody to monitor the calibration of shipborne radiometers are described and measurement protocols are proposed. [ABSTRACT FROM AUTHOR]

HY-2 satellite is the first satellite for dynamic environmental parameters measurement of China, which was launched on 16th August 2011. A scanning microwave radiometer (RM) is carried for sea surface temperature (SST), sea surface wind speed, columnar water vapor and columnar cloud liquid water detection. In this paper, the initial SST product of RM was validated with in-situ data of National Data of Buoy Center (NDBC) mooring and Argo buoy. The validation results indicate the accuracy of RMSST is better than 1.7°C. The comparison of RM SST and WindSat SST shows the former is warmer than the latter at high sea surface wind speed and the difference between these SSTs is depend on the sea surface wind speed. Then, the relationship between the errors of RM SST and sea surface wind speed was analyzed using NDBC mooring measurements. Based on the results of assessment and errors analysis, the suggestions of taking account of the affection of sea surface wind speed and using sea surface wind speed and direction derived from the microwave scatteromter aboard on HY-2 for SST product calibration were given for retrieval algorithm improvement. [ABSTRACT FROM AUTHOR]

The Suomi National Polar-Orbiting Partnership ( S-NPP) satellite was successfully launched on 28 October 2011. It carries five new-generation instruments, including the Visible Infrared Imaging Radiometer Suite (VIIRS). The VIIRS is a whiskbroom radiometer that scans the surface of the earth using a rotating telescope assembly, a double-sided half-angle mirror, and 16 individual detectors. Substantial efforts are being made to accurately calibrate all detectors in orbit. As of this writing, VIIRS striping is reduced to levels below those seen in corresponding Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) bands and meets the program specifications and requirements. However, the level 2 SST products derived from level 1 sensor data records (SDRs) thermal emissive bands still show residual striping. These artifacts reduce the accuracy of SST measurements and adversely affect cloud masking and the output of downstream applications, such as thermal front detection. To improve the quality of SST imagery derived from the VIIRS sensor, an adaptive algorithm was developed for operational use within the National Environmental Satellite, Data, and Information Service (NESDIS)'s SST system. The methodology uses a unidirectional quadratic variational model to extract stripe noise from the observed image prior to nonlocal filtering. Evaluation of the algorithm performance over an extended dataset demonstrates a significant improvement in the Advanced Clear-Sky Processor for Oceans (ACSPO) VIIRS SST image quality, with normalized improvement factors (NIF) varying between 5% and 25%. [ABSTRACT FROM AUTHOR]

A new method of producing sea surface temperature (SST) data for numerical weather prediction is suggested, which is obtained from the assimilation of satellite-derived SST into an atmosphere-ocean mixed layer coupled model. The Weather Research and Forecasting (WRF) Model and the Noh mixed layer model are used for the atmosphere and ocean mixed layer models, respectively. Data assimilation (DA) is carried out in two steps, based on the estimation from the covariance matching method that the daily mean SST of satellite data is more accurate than the model data, if the number of data in a grid per day is sufficiently large-that is, the daily mean SST bias correction in the first DA and the sequential SST anomaly correction in the second DA. For the second DA, the model restarts from the initial condition corrected by the first DA, and DA is applied every 30 min using the nudging method. The daily mean and the diurnal variation of satellite SST are assimilated to the bulk and skin SST, respectively. The modeled results with the new data assimilation scheme are validated by statistical comparison with independent satellite and buoy data such as correlation coefficient, root-mean-square difference, and bias. Furthermore, the sensitivity and seasonal variation of the weighting factor in the second DA are examined. The new approach illustrates the possibility of applying the atmosphere-ocean mixed layer coupled model for the production of SST data combined with the assimilation of satellite data. [ABSTRACT FROM AUTHOR]

This study describes how the hull temperature (Ttop) measurements from multisensor surface velocity program (SVP) drifters can be combined with other measurements to provide quantitative information on near-surface vertical temperature stratification during large daily cycles. First, Ttop is compared to the temperature measured at 17 -cm depth from a float tethered to the SVP drifter. These 2007-12 SVP drifters present a larger daily cycle by 1%-3% for 1°-2°C daily cycle amplitudes, with a maximum difference close to the local noon. The difference could result from flow around the SVP drifter in the presence of temperature stratification in the top 20 cm of the water column but also from a small influence of internal drifter temperature on Ttop. The largest differences were found for small drifters (Technocean) for very large daily cycles, as expected from their shallower measurements. The vertical stratification is estimated by comparing these hull data with the deeper T or conductivity C measurements from Sea-Bird sensors 25 (Pacific Gyre) to 45 cm (MetOcean) below the top temperature sensor. The largest stratification is usually found near local noon and early afternoon. For a daily cycle amplitude of 1°C, these differences with the upper level are in the range of 3%-5% of the daily cycle for the Pacific Gyre drifters and 6%-10% for MetOcean drifters with the largest values occurring when the midday sun elevation is lowest. The relative differences increase for larger daily cycles, and the vertical profiles become less linear. These estimated stratifications are well above the uncertainty on Ttop. [ABSTRACT FROM AUTHOR]

The Along-Track Scanning Radiometer (ATSR) Reprocessing for Climate (ARC) project aims to create an independent climate data record of sea surface temperatures (SSTs) covering recent decades that can be used for climate change analysis. Here, the ARC SSTs are assessed using comparisons with collocated drifting buoy observations and a three-way error analysis that also includes Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) data. The SSTs using the three-channel nighttime retrievals in the ARC data at 1-m depth are found to have a warm bias of 0.054 K (standard deviation 0.151 K) with respect to the drifting buoy data for the 1995-2009 time period using ATSR-2 and Advanced Along-Track Scanning Radiometer (AATSR) instrument data. However, when studying the two-channel retrievals, the ATSR-1 data are found to be less stable and with more extreme values than in later years. Some dependence on latitude, season, and fields such as total column water vapor is found in the ATSR-2 and AATSR period. An assessment of the ARC SST uncertainty shows a stable bias for low uncertainty values but more deviation above 0.6 and 0.35 K for the two- and three-channel nighttime retrievals, respectively. The three-way error analysis reveals a standard deviation of error of 0.14 K for the ARC 1-m depth SSTs using the three-channel nighttime retrieval. Estimates of the standard deviation of error for the drifting buoys are also produced and show evidence of improvement in the buoy network in the years 2003-09 from 0.19 to 0.15 K. [ABSTRACT FROM AUTHOR]

This study investigates the nonlinear processes by which the ocean diurnal variations can affect the intraseasonal sea surface temperature (SST) variability in the Atlantic Ocean. The Centre National de Recherches Méétééorologiques one-dimensional ocean model (CNRMOM1D) is forced with the 40-yr ECMWF Re-Analysis (ERA-40) surface fluxes with a 1-h frequency in solar heat flux in a first simulation and with a daily forcing frequency in a second simulation. This model has a vertical resolution of 1 m near the surface. The comparison between both experiments shows that the daily mean surface temperature is modified by about 0.3°°--0.5°°C if the ocean diurnal variations are represented, and this correction can persist for 15--40 days in the midlatitudes and more than 60 days in the tropics. The so-called rectification mechanism, by which the ocean diurnal warming enhances the intraseasonal SST variability by 20%%--40%%, is found to be robust in the tropics. In contrast, in the midlatitudes, diurnal variations in wind stress and nonsolar heat flux are shown to affect the daily mean SST. For example, an intense wind stress or nonsolar heat flux toward the atmosphere during the first half of the day followed by weak fluxes during the second half result in a shallow mixed layer. The following day, the preconditioning results in heat being trapped near the surface and the daily mean surface temperature being higher than if these diurnal variations in surface forcings were not resolved. [ABSTRACT FROM AUTHOR]

Analyses based on atmospheric infrared radiative transfer simulations and collocated ship and satellite data are used to investigate whether knowledge of vertical atmospheric water vapor distributions can improve the accuracy of sea surface temperature (SST) estimates from satellite data. Initially, a simulated set of satellite brightness temperatures generated by a radiative transfer model with a large maritime radiosonde database was obtained. Simple linear SST algorithms are derived from this dataset, and these are then reapplied to the data to give simulated SST estimates and errors. The concept of water vapor weights is introduced in which a weight is a measure of the layer contribution to the difference between the surface temperature and that measured by the satellite. The weight of each atmospheric layer is defined as the layer water vapor amount multiplied by the difference between the SST and the midlayer temperature. Satellite-derived SST errors are then plotted against the difference in the sum of weights above an altitude of 2.5 km and that below. For the simple two-channel (with typical wavelengths of 11 and 12 μμm) analysis, a clear correlation between the weights differences and the SST errors is found. A second group of analyses using ship-released radiosondes and satellite data also show a correlation between the SST errors and the weights differences. The analyses suggest that, for an SST derived using a simple two-channel algorithm, the accuracy may be improved if account is taken of the vertical distribution of water vapor above the ocean surface. For SST estimates derived using algorithms that include data from a 3.7- μμm channel, there is no such correlation found. [ABSTRACT FROM AUTHOR]