High-resolution Sea Surface Temperature Research Articles

On Predicting Offshore Hub Height Wind Speed and Wind Power Density in the Northeast US Coast Using High-Resolution WRF Model Configurations during Anticyclones Coinciding with Wind Drought

We investigated the predictive capability of various configurations of the Weather Research and Forecasting (WRF) model version 4.4, to predict hub height offshore wind speed and wind power density in the Northeast US wind farm lease areas. The selected atmospheric conditions were high-pressure systems (anticyclones) coinciding with wind speed below the cut-in wind turbine threshold. There are many factors affecting the potential of offshore wind power generation, one of them being low winds, namely wind droughts, that have been present in future climate change scenarios. The efficiency of high-resolution hub height wind prediction for such events has not been extensively investigated, even though the anticipation of such events will be important in our increased reliance on wind and solar power resources in the near future. We used offshore wind observations from the Woods Hole Oceanographic Institution’s (WHOI) Air–Sea Interaction Tower (ASIT) located south of Martha’s Vineyard to assess the impact of the initial and boundary conditions, number of model vertical levels, and inclusion of high-resolution sea surface temperature (SST) fields. Our focus has been on the influence of the initial and boundary conditions (ICBCs), SST, and model vertical layers. Our findings showed that the ICBCs exhibited the strongest influence on hub height wind predictions above all other factors. The NAM/WRF and HRRR/WRF were able to capture the decreased wind speed, and there was no single configuration that systematically produced better results. However, when using the predicted wind speed to estimate the wind power density, the HRRR/WRF had statistically improved results, with lower errors than the NAM/WRF. Our work underscored that for predicting offshore wind resources, it is important to evaluate not only the WRF predictive wind speed, but also the connection of wind speed to wind power.

A Daily High-Resolution Sea Surface Temperature Reconstruction Using an I-DINCAE and DNN Model Based on FY-3C Thermal Infrared Data

The sea surface temperature (SST) is one of the most important parameters that characterize the thermal state of the ocean surface, directly affecting the heat exchange between the ocean and the atmosphere, climate change, and weather generation. Generally, due to factors such as the weather, satellite scanning orbit range, and satellite sensor malfunction, there are large areas of missing satellite remote sensing SST data, greatly reducing data utilization. In this situation, how to use effective data or avenues to rebuild missing SST data has become a research hotspot in the field of ocean remote sensing. Based on the SST data from an FY-3C visible and infrared radiometer with a spatial resolution of 5 km (FY-3C VIRR), an improved data interpolation convolutional autoencoder (I-DINCAE) was used to reconstruct the missing SST data. Through cross-validation, the accuracy of the reconstruction results was quantitatively evaluated with an RMSE of 0.36 °C and an MAE of 0.24 °C. The results showed that the I-DINCAE algorithm outperformed the original DINCAE algorithm greatly. For further optimization, a deep neural network (DNN) was chosen to adjust the error between the reconstructed SST and the in situ data. The RMSE of the final adjusted SST and in situ data is 0.466 °C, and the MAE is 0.296 °C. Compared to the in situ data, the accuracy of the adjusted data has shown a significant improvement over the reconstructed data. This method successfully applies deep-learning technology to the reconstruction of SST data, achieving the full coverage and high accuracy of SST products, which can provide more reliable and complete SST data for marine scientific research.

Ocean Satellite Data Fusion for High-Resolution Surface Current Maps

Real-time reconstruction of ocean surface currents is a challenge due to the complex, non-linear dynamics of the ocean, the small number of in situ measurements, and the spatio-temporal heterogeneity of satellite altimetry observations. To address this challenge, we introduce HIRES-CURRENTS-Net, an operational real-time convolutional neural network (CNN) model for daily ocean current reconstruction. This study focuses on the Mediterranean Sea, a region where operational models have great difficulty predicting surface currents. Notably, our model showcases higher accuracy compared to commonly used alternative methods. HIRES-CURRENTS-Net integrates high-resolution measurements from the infrared or visible spectrum—high resolution Sea Surface Temperature (SST) or chlorophyll (CHL) images—in addition to the low-resolution Sea Surface Height (SSH) maps derived from satellite altimeters. In the first stage, we apply a transfer learning method which uses a high-resolution numerical model to pre-train our CNN model on simulated SSH and SST data with synthetic clouds. The observation of System Simulation Experiments (OSSEs) offers us a sufficient training dataset with reference surface currents at very high resolution, and a model trained on this data can then be applied to real data. In the second stage, to enhance the real-time operational performance of our model over previous methods, we fine-tune the CNN model on real satellite data using a novel pseudo-labeling strategy. We validate HIRES-CURRENTS-Net on real data from drifters and demonstrate that our data-driven approach proves effective for real-time sea surface current reconstruction with potential operational applications such as ship routing.

Intra-tidal upwelling variability off Zhoushan Islands, East China Sea

The coastal upwelling that takes place off the Zhoushan Islands is of great importance in the transport of nutrients, nourishing local fisheries and exerting a substantial environmental influence. While it is known that tides play a central role in triggering and controlling the Zhoushan upwelling, its short-term variability within the tidal cycle remains uncertain, thereby constituting the primary focus of this study, which relies on the analysis of high-resolution Sea Surface Temperature (SST) satellite images and in-situ data. In a tidal cycle, cold upwelling patches exhibit a westward shift during the flood tide and an eastward shift during the ebb tide. The extent of cold upwelling patches is greatest during maximum flood and ebb periods. At maximum ebb, the core temperature within the cold patches notably decreases, which indicates intensified upwelling. At the same time, a similar pattern of sudden increases is also observed throughout the entire water column in the upwelling region. The strong ebb current swiftly transports the upper water mass during maximum ebb, facilitating the compensation of lower water and augmenting the intensity of the upwelling. The calculated vertical velocity within the study area ranged from −9.04 × 10−4 m/s to 12.66 × 10−4 m/s during the observational period, when upwelling showed a significant congruence with low-temperature phenomena. Alternating upwelling and downwelling phases were discernible within a tidal cycle, with upwelling being prevalent. The influence of surface heat flux on seawater below the thermocline and night-time SST was negligible. Furthermore, compared to the vertical velocity, the calculated wind-induced upwelling velocity was much lower, reaching just 3.38 × 10−6 m/s during the observed tidal cycle, which suggests that wind does not exert a primary influence on upwelling within the tidal cycle. This study contributes to an enhanced understanding of upwelling variability under the influence of tides, a topic of paramount importance in the marine environment.

Reconstruction of Continuous High-Resolution Sea Surface Temperature Data Using Time-Aware Implicit Neural Representation

Accurate climate data at fine spatial resolution are essential for scientific research and the development and planning of crucial social systems, such as energy and agriculture. Among them, sea surface temperature plays a critical role as the associated El Niño–Southern Oscillation (ENSO) is considered a significant signal of the global interannual climate system. In this paper, we propose an implicit neural representation-based interpolation method with temporal information (T_INRI) to reconstruct climate data of high spatial resolution, with sea surface temperature as the research object. Traditional deep learning models for generating high-resolution climate data are only applicable to fixed-resolution enhancement scales. In contrast, the proposed T_INRI method is not limited to the enhancement scale provided during the training process and its results indicate that it can enhance low-resolution input by arbitrary scale. Additionally, we discuss the impact of temporal information on the generation of high-resolution climate data, specifically, the influence of the month from which the low-resolution sea surface temperature data are obtained. Our experimental results indicate that T_INRI is advantageous over traditional interpolation methods under different enhancement scales, and the temporal information can improve T_INRI performance for a different calendar month. We also examined the potential capability of T_INRI in recovering missing grid value. These results demonstrate that the proposed T_INRI is a promising method for generating high-resolution climate data and has significant implications for climate research and related applications.

Super Resolution of Satellite-Derived Sea Surface Temperature Using a Transformer-Based Model

Sea surface temperature (SST) is one of the most important factors related to the ocean and the climate. In studying the domains of eddies, fronts, and current systems, high-resolution SST data are required. However, the passive microwave radiometer achieves a higher spatial coverage but lower resolution, while the thermal infrared radiometer has a lower spatial coverage but higher resolution. In this paper, in order to improve the performance of the super-resolution SST images derived from microwave SST data, we propose a transformer-based SST reconstruction model comprising the transformer block and the residual block, rather than purely convolutional approaches. The outputs of the transformer model are then compared with those of the other three deep learning super-resolution models, and the transformer model obtains lower root-mean-squared error (RMSE), mean bias (Bias), and robust standard deviation (RSD) values than the other three models, as well as higher entropy and definition, making it the better performing model of all those compared.

Examining the Consistency of Sea Surface Temperature and Sea Ice Concentration in Arctic Satellite Products

Available observations and a theoretical simulation are used to explore the consistency and relationship between sea surface temperature (SST) and sea ice concentration (SIC) within open-ocean-sea ice mixed satellite pixels as a function of grid resolution. The maximum limiting SST value for a specified SIC and spatial resolution is first examined within collocated satellite-derived products contained within existing Level 4 SST analyses distributed using the data specification from the Group for High Resolution Sea Surface Temperature. The shape of the interdependence is further validated with manually quality-controlled buoy SST and SIC collocations. A parametric equation for the limiting SST value is derived from simulations of a mixed ocean/ice pixel with specified ice fraction and a linear SST gradient extending away from the ice edge. The exponential curve matching the observed interdependence suggests a maximum 5 km pixel-averaged SST at SIC values approaching zero between 6 and 8 °C. This maximum value is significantly greater than the previously assumed limiting values of ~3 °C and the corresponding SST gradient is larger than those typically observed with satellite SST products, but agrees well with recent Saildrone SST observations near ice. The curve provides a conservative limit with which inconsistent SST/SIC pairings can be identified, not only near the ice edge but at intermediate ice concentrations. Application of the filter improves the agreement between the SST/SIC relationship in satellite products and available Saildrone observations as well as the internal consistency of the different satellite products.

Three decades of variability and warming of nearshore waters around Tasmania

The ocean region around Tasmania forms an interface between major ocean basins and represents a significant global ocean hotspot due to a rate of warming higher than the global average. Nearshore Tasmanian waters are of central importance for shallow-water ecosystems and a range of human activities, including recreation, aquaculture and fisheries which have become increasingly impacted by warming in recent decades. However, the fine-scale spatial and temporal variability of sea surface temperature (SST) in these waters is not well understood, limiting opportunities for effective local-scale mitigation and adaptation measures in the face of ongoing warming. We use a high-resolution (0.02 °x 0.02°, 1992–2020) satellite SST received at local stations (validated against in situ temperature) to explore variability over seasonal, interannual, decadal and multi-decadal timescales at spatial scales from the nearshore up to the entire study domain. The SST is used as a proxy for seasonal circulation by removal of the broad-scale radiative forcing (estimated by a region-wide latitudinal gradient of SST). The main circulation drivers of nearshore SST are the East Australian Current Extension (EACx) and Zeehan Current (ZC) acting on the east and west coasts with respective peaks in summer and winter. New results include the seasonality of three summer upwelling systems, the first direct evidence of EACx inflow into eastern Bass Strait, a time series of the endpoint of the ZC/EACx intersection, and a full year description of the ZC, from warm winter inflow transitioning into a summer cooler than ambient intrusion. We find that local SST variations are controlled by coastal geometry with intensified atmospheric forcing in enclosed waters. We have identified two modes of low frequency SST variability (including 1, 2 and 10-year periods) associated with the EACx (63% of the variance) and the ZC (12%). The EACx mode induces a rapid adjustment of the entire ocean region around Tasmania. Multi-decadal warming trends occur in all regions, modulated by coastal boundary flows, changes in upwelling mechanisms and local topography. Trends in the eastern shelf arise from intrusions of EACx waters, with local processes driving trends on the western shelf. Both east and west nearshore SST trends are around + 0.4°C decade−1 with the highest value of + 0.55°C decade−1 on the central west coast, possibly associated with outflow from Macquarie Harbour.

Understanding Differences in Sea Surface Temperature Intercomparisons

Abstract 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.

Co-Occurrence of Atmospheric and Oceanic Heatwaves in the Eastern Mediterranean over the Last Four Decades

Heatwaves are now considered one of the main stressors of global warming. As a result of anthropogenic warming, atmospheric and oceanic heatwaves have increased in frequency, intensity and duration in recent decades. These extreme events have recently become a major concern in climate research due to their economic and environmental impacts on ecosystems. In this study, we investigated the co-occurrence and relationship between atmospheric and marine heatwaves (AHW/MHW) in the Eastern Mediterranean (EMED) over the last four decades (1982–2021). Furthermore, the spatio-temporal variability and trends of sea surface temperature (SST), near-surface air temperature (SAT), AHW and MHW characteristics (frequency and duration) were examined. For these objectives, we used daily gridded high-resolution satellite SST data (0.05° × 0.05°) and the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF-ERA5) atmospheric reanalysis SAT and wind components (0.25° × 0.25°). The results showed an average warming trend of about 0.38 ± 0.08 °C/decade and 0.43 ± 0.05 °C/decade for SAT and SST, respectively. A high statistically significant (p < 0.05) correlation (R = 0.90) was found between AHW and MHW frequency. Our results showed that more than half of the MHWs in the EMED co-occurred with AHWs throughout the study period. The most intense summer MHW in 2021, which co-occurred with AHW, was associated with higher positive anomalies of SAT and SST, and a decrease in the wind speed anomaly.

Comparing eight remotely sensed sea surface temperature products and Bayesian maximum entropy-based data fusion products

Sea surface temperature is an important oceanographic physical attribute. In this work, 5 kinds of infrared SST data sets (including FY3C-VIRR, MetopB-AVHRR3, MODIS-Aqua, MODIS-Terra, SNPP-VIIRS) and 3 kinds of microwave SST data sets (including AMSR-2, GMI, WindSat) were considered for fusion purposes. The area with geographical coordinates ranging from 100° W to 180° W and from 10° N to 10° S was chosen as the study area. Using the Bayesian maximum entropy (BME) method, four SST fusion products were generated from January 1 to January 31, 2020, with temporal resolution of 1 day, spatial resolution of 0.25 decimal degrees, and coverage rate of 100%. The Holdout cross-validation method was employed to evaluate the performance of BME for SST interpolation purposes by using two data sets, i.e., WinSat SST and MODIS-Terra SST data; and the validation results showed that the mean absolute error (MAE) values of the WindSat SST data and the MODIS-Terra SST data were 0.117 mg/m3 and 0.191 mg/m3, respectively, and the root mean square error (RMSE) values were 0.158 mg/m3 and 0.321 mg/m3, respectively. In addition, four SST interpolation products were compared with the Optimum Interpolation Sea Surface Temperature (OISST) and the Group for High-Resolution Sea Surface Temperature (GHRSST) products by combining Argo data with Buoy data treated as in-situ measured data sets. Three (MW-WindSat SST product, ALL-Terra SST product and ALL-WindSat SST product) of the four interpolation products performed better than the OISST and GHRSST data sets. Among them, the ALL-WindSat SST product considering WindSat SST data and other 7 SST data as hard and soft data exhibited the best performance in terms of all three indicators: the coefficient of determination value (0.744), the MAE value (0.82 mg/m3) and the RMSE value (1.082 mg/m3). In sum, the present work made full use of the inclusiveness and integration abilities of the BME method for various types of data, and demonstrated its high accuracy in a number of real world data, thus demonstrating its considerable potential in the context of marine data interpolation and fusion.

On the importance of the atmospheric coupling to the small-scale ocean in the modulation of latent heat flux

In this study, ocean and atmosphere satellite observations, an atmospheric reanalysis and a set of regional numerical simulations of the lower atmosphere are used to assess the coupling between the sea-surface temperature (SST) and the marine atmospheric boundary layer (MABL) as well as the latent heat flux (LHF) sensitivity to SST in the north-west tropical Atlantic Ocean. The results suggest that the SST-MABL coupling depends on the spatial scale of interest. At scales larger than the ocean mesoscale (larger than 150 km), negative correlations are observed between near-surface wind speed (U10m) and SST and positive correlations between near-surface specific humidity (q2m) and SST. However, when smaller scales (1 – 150 km, i.e., encompassing the ocean mesoscale and a portion of the submesoscale) are considered, U10m-SST correlate inversely and the q2m-SST relation significantly differs from what is expected using the Clausius-Clapeyron equation. This is interpreted in terms of an active ocean modifying the near-surface atmospheric state, driving convection, mixing and entrainment of air from the free troposphere into the MABL. The estimated values of the ocean-atmosphere coupling at the ocean small-scale are then used to develop a linear and SST-based downscaling method aiming to include and further investigate the impact of these fine-scale SST features into an available low-resolution latent heat flux (LHF) data set. The results show that they induce a significant increase of LHF (30% to 40% per °C of SST). We identify two mechanisms causing such a large increase of LHF: (1) the thermodynamic contribution that only includes the increase in LHF with larger SSTs associated with the Clausius-Clapeyron dependence of saturating water vapor pressure on SST and (2) the dynamical contribution related to the change in vertical stratification of the MABL as a consequence of SST anomalies. Using different downscaling setups, we conclude that largest contribution comes from the dynamic mode (28% against 5% for the thermodynamic mode). To validate our approach and results, we have implemented a set of high-resolution WRF numerical simulations forced by high-resolution satellite SST that we have analyzed in terms of LHF using the same algorithm. The LHF estimate biases are reduced by a factor of 2 when the downscaling is applied, providing confidence in our results.

Seasonal variability in spatial patterns of sea surface cold- and warm fronts over the continental shelf of the northern South China Sea

Ubiquitous fronts are a key part of energy transfer from large scales to small scales and exert a great impact on material exchange and biogeochemical processes. The spatial pattern and seasonal variability of cold- and warm fronts over the wide shelf of the northern South China Sea (SCS) are investigated using a 20-year time series (2002−2021) of 1-km spatial resolution Group for High Resolution Sea Surface Temperature (GHRSST) images. Our analysis shows distinct spatial and temporal variability in the occurrence of the cold- and warm fronts. Over the inner shelf (depth <50 m), the band-shaped cold fronts are predominately observed during spring through autumn from east of Hainan Island to Taiwan Shoal, with the presence of the maximum intensity and probability in winter. The frontal formations are possibly associated with the joint effect of the Guangdong Coastal Current (GCC) and the South China Sea Warm Current. During summer, the inshore fronts have relatively low probability and gradient magnitude. The warm fronts mainly occur off the western Guangdong coast possibly due to the southwestward-flowing GCC, whereas the cold fronts dominate off the eastern Guangdong coast and the eastern Hainan Island largely because of the coastal upwelling. Over the outer shelf (depth >50 m), the finer-scale cold- and warm fronts are discretely observed, with relatively weaker intensity and lower probability. The frontal activities are very vigorous in winter but slightly quiescent in summer, apparently resulting from the influence of the rich submesoscale processes in the SCS. This study could help improve our understanding of the SCS oceanic multiscale dynamics.

Study of the impact of high resolution ROMS-SST on the simulation of two intense tropical cyclones over Bay of Bengal using ARW modeling system

Impact of high-resolution Sea Surface Temperature (SST) derived from the Regional Ocean Modeling System (ROMS) on two intense vortices (Phailin and Hudhud) developed over the Bay of Bengal (BoB) are investigated. The validation of ROMS-SST with various observational datasets available over BoB showed a reasonably good correlation of greater than 0.90. The root mean square difference is around 0.40 °C. Instigating ROMS-SST as a lower boundary condition to the Advanced weather research (ARW) model improved the TC intensity and rainfall location for TC Phailin. A minor improvement is observed in the intensity of TC Hudhud. Still, with better replication of wind structure and rainfall location than the control experiment, which uses low-resolution Reynolds-SST from the global model output. This right sector peak of latent heat pattern matches better with the observed structure of deep convection observed from infrared satellite imagery for both TCs in the ROMS experiment as compared with the control experiment. However, the model simulated track for the ROMS-SST experiment did not improve the TC track for all the initial conditions for both the TCs. The present modeling study compliments the use of higher resolutions SST downscaled by a mesoscale regional ocean model for the TC simulations

Decadal intensified and slantwise Subpolar Front in the Japan/East Sea

The Subpolar Front in the Japan/East Sea (JES) could far-reaching influence the atmospheric processes over the downstream regions. However its variability on decadal timescale remains less understood. In this study, the decadal trends in the intensity and position of the SPF in the JES during the time period 1985−2020 are analyzed by using four categories of satellite observed high-resolution sea surface temperature products. The results show that there is a significant intensification trend of the SPF at a rate of 0.37°C/100km/decade. The SPF is further divided into three regions based on the meridional sea surface temperature gradient (MSSTG): the eastern (135−138°E), central (130−135°E) and western (128−130°E) regions, respectively. These three regions showed different meridional movements with the eastern SPF moving poleward by 0.08°/decade, the central SPF moving equatorward by −0.11°/decade and the western SPF showing no significant displacements. The reverse meridional movements between the central and eastern SPF increased its skewness. The frontogenesis rate equation is employed to identify the mechanisms of these decadal trends. Results show that the geostrophic advection term, especially its zonal component, had a crucial role in the decadal trends of the intensity and position of the central and eastern SPF. The decadal trend of the central SPF was mainly attributed to the zonal geostrophic advection of the MSSTG associated with the enhancement of the Subpolar Front Current (SFC) in the upstream region, whereas the decadal trend in the eastern SPF was mainly driven by the zonal geostrophic shear advection controlled by the shear of the SFC in the downstream region. Before 2002, the eastern SPF moved poleward at a rate of 0.27°/decade, whereas there was no obvious trend after 2002. Further decomposition showed that this shift was caused by meridional Ekman advection of the MSSTG.

Sea surface temperature retrieval based on simulated space-borne one-dimensional multifrequency synthetic aperture microwave radiometry

The space-borne one-dimensional multifrequency synthetic aperture microwave radiometer (1D-MSAMR) offers new possibilities for detecting high spatial resolution sea surface temperature (SST). To achieve higher SST retrieval accuracy, an SST retrieval algorithm, the two-step retrieval algorithm (TSSR), is proposed based on the multiple linear regression (MLR) algorithm. In this study, we investigated the SST retrieval accuracy of 1D-MSAMR based on simulation experiments. For the study, we assumed that the frequencies of the 1D-MSAMR were 6.9, 10.65, 18.7, 23.8, and 36.5 GHz, and that all frequencies worked in a dual polarization (vertical and horizontal) manner. We used an ocean-atmosphere microwave radiation brightness temperature model and the 1D-MSAMR simulator to simulate the measured brightness temperature based on WindSat data provided by the Remote Sensing Systems (RSS). An MLR algorithm and the TSSR were then developed to retrieve the SST within the incidence angle range of 0°–65°. The results show that the SST retrieval errors of the two SST retrieval algorithms decreased with the increase of incidence angle. The TSSR had higher retrieval accuracy, especially at low incidence angle. The average retrieval accuracy of the TSSR was about 0.3 K higher than that of the MLR algorithm. The retrieval error of the TSSR was also less sensitive to the measurement error of the 6.9 GHz frequency than the MLR algorithm.

Marine Heat Waves over Natural and Urban Coastal Environments of South Florida

Marine Heat Wave (MHW) events are increasingly recognized as an important factor in the sustainability of coastal environments (both natural and urban), in the context of climate change. They are related to increasing trends of Sea Surface Temperature (SST) at the adjacent ocean waters. SST is an important parameter of the earth’s climate and increasing SST trends have been associated with adverse effects on coastal ecosystems, with important environmental and socioeconomic implications. This study focuses on the SST interannual variability over the coastal marine environment of South Florida, which contains several fragile ecosystems, and draws associate effects with adjacent large urban coastal settlements. The methodology is based on high-resolution satellite-derived SST data during a 40-year period (1982–2021), augmented by recent high-resolution model simulations (2012–2020). A generally increasing trend has been detected in the observations over the entire region (0.19 °C/decade). The unusual temperature levels have been associated with the formation of extensive MHW events, which showed interannual positive trends (0.75 events/decade) during the 40-year study period. Specifically, the six most recent years (2015–2021) were characterized by the strongest formation of MHWs with a peak in 2015, 2019 and 2020, with more than 8 events/year and approximately 70 to 110 days/year duration in total. The Florida Keys, especially along the Straits of Florida (southern island coasts), revealed very strong increasing trends. Miami Beach is also characterized by strong interannual trends (1.1 events/decade and 10 days/decade) compared to the enclosed basin of Biscayne Bay. In addition to the influence of atmospheric conditions over all regions around South Florida, the formation of MHWs near the eastern Florida coasts was also controlled by ocean dynamics, related to the warm Florida Current (FC). The evolution of the FC close to the eastern coasts (e.g., Miami Beach) was found to be a pre-condition of MHW formation. Several disastrous events on the biotic environment of South Florida near large urban settlements have been related to the formation of MHWs. The detected positive trends, and especially the recent high peaks of MHW events, may enhance the loss of specific heat-sensitive species, damaging the biodiversity of this tropical coastal environment and weakening the natural coastal protection against tropical storms. Urban planning for sustainable development in South Florida’s coastal cities must take into account MHW trends.

Assessing Sea Surface Temperatures Estimated from Fused Infrared and Microwave Data

Sea surface temperature (SST), a critical parameter of the global ocean–atmosphere system, is an essential element in the study and in the application of marine science. Satellite–infrared observations currently represent the only available method for continuous, large-scale observation of SST. Although passive microwave observations are not blocked by clouds, allowing for data collection in all weather conditions, this technological tool is characterized by low spatial resolution. Conversely, infrared observations offer high resolution but are susceptible to cloud obscuration. Accordingly, a technique that effectively fuses microwave and infrared satellite observations into a high-resolution SST field with global coverage close to the actual distribution is of practical significance. This paper describes fusing MODIS infrared remote sensing and AMSR-2 microwave remote sensing SST data with an optimal interpolation (OI) approach to produce a high-resolution SST data. The study chose the coastal Kuroshio region of China to establish an appropriate scale for examining the spatial structure of SST and attaining a more realistic picture of SST observations and impacts. The included discussion of the sources of error in the fusion process provides a reference for improving the accuracy of fused marine remote sensing data. The study also compared the fused SST results and the current international mainstream multi-temporal resolution of the three using the OI algorithm. We compared the fusion product with ARGO data with and without typhoon impact to explore and practice the OI in SST fusion when evaluating the accuracy of different data in the case of external disturbance being present. The research results have great significance for improving regional SST forecast accuracy while ensuring the applicability of various approaches to fusing SST data by incorporating the influence of typhoons in the offshore region of the East China Sea (ECS). Implications for the future development of SST fusion data are also included in the discussion.

Holocene centennial variability in sea surface temperature and linkage with solar irradiance

The climate periodically fluctuates on various time scales, however, there remains a lack of consensus on the centennial-scale variabilities and associated driving force. A continuous high-resolution sea surface temperature (SST) record allows for the detection of centennial-scale fluctuations. This study presents a high-resolution SST record covering the last 10,000 years based on the analysis of the alkenone unsaturation index in marine sediment cores off the southwest coast of the Korean Peninsula. Alkenone SST's spectral and wavelet analysis revealed significant periodicities of 414, 190, 135, 102, and 89 years at a > 90% confidence level. These cycles exhibit extreme proximity to the solar activity cycles of 353, 206 (Suess/de Vries cycles), 130, and 104–87 years (Gleissberg cycles), suggesting that the multidecadal to centennial variations in SST are linked to solar forcing. To the best of our knowledge, this is the first high-resolution Holocene SST record that all solar activity cycles on centennial scale match, suggesting centennial-scale variability in the climate system and illustrating the role of solar activity on SST change in the mid-latitude region of the Northern Hemisphere.

A zonal and segmentation analysis of the relationships between variations in sea-surface temperatures of the western Pacific Ocean and Southeast Asian rainfall

Abstract With the objective of understanding how terrestrial precipitation in Southeast Asia (SEA) responds to both temporal and spatial variations in sea-surface temperature (SST) of the western Pacific Ocean, we first used the North–South and zonal SST variation analyses for the data set named A Group for High-Resolution Sea-Surface Temperature (GHRSST) Level 4 Multiscale Ultrahigh Resolution (MUR). Second, we applied a localized correlation analysis to the SST data set and rainfall data (Integrated Multi-satellitE Retrievals for Global precipitation measurement (IMERG)) in order to determine the boundaries and characteristics of the complex interactions between SST and rainfall variations. The zonal SST variation analysis result suggested that the warm pool in the North of western Pacific zones close to SEA (120 °E–150 °E) was more sustained and less varied than that in the South zones. In terms of the western Pacific SST impact on rainfall in SEA, the correlation analysis confirmed the well-established concept that the SST of the ocean part close to the SEA land, in general, has more influence on SEA rainfall than does the SST of the ocean part far away from SEA. Still, there were some parts of SEA whose rainfall was not governed by the sea–land juxtaposition.