Search Results (719)

The marine environment in the South Pacific Island Countries (SPICs) is sensitive and vulnerable to climate change. While large-scale changes in this region are well-documented, national-scale analyses that address management needs remain limited. This study evaluated the performance of satellite-derived datasets—including sea surface temperature (SST), sea surface salinity (SSS), Secchi disk depth (SDD), chlorophyll-a (Chl-a), net primary production (NPP), and sea level anomaly (SLA)—against in situ observations, and analyzed their spatial and temporal variability across 12 national Exclusive Economic Zones (EEZs) during 1998–2023. Validation results presented that current satellite datasets could provide applicable information for EEZ-scale analyses. In the past decades, the SPICs experienced a general increase in SST and SLA, accompanied by marked within-EEZ heterogeneity in Chl-a and NPP variations, with Papua New Guinea exhibiting the largest within-EEZ inter-annual variability. In addition to monitoring, satellite data would help to constrain the uncertainty of CMIP6 results in the SPICs, subject to the accuracy of specific products. By 2100, Nauru might experience the most vulnerable EEZ, while the marine environment in the French Polynesian EEZ can keep relatively stable among all 12 EEZs. Meanwhile, CMIP6 projections in the Southeastern EEZs are more sensitive to satellite-based constraints, showing pronounced adjustments. Our results demonstrate the potential of combining validated satellite data with CMIP6 models to provide national-scale decision support for climate adaptation and marine resource management in the SPICs. Full article

Using MODIS-Aqua satellite observations, this study analyzes the spatiotemporal distribution characteristics of particulate organic carbon (POC) in China’s marginal seas from 2003 to 2024. The statistical relationships between various marine environmental variables, including sea surface temperature (SST), nutrients, and primary production (PP), and POC concentrations are explored using partial least squares path modeling (PLS-PM). Finally, a box model approach is conducted to assess the POC budget in the study area. The results indicate that the POC concentration in the marginal seas of China generally exhibits a characteristic of being high in spring and low in summer. The highest concentration of POC is observed in the Bohai Sea, followed by the Yellow Sea, and the lowest in the East China Sea, with coastal waters exhibiting higher POC concentrations compared to the central areas. The spatial distribution and seasonal changes in POC are jointly influenced by PP, water mass exchange, resuspended sediments, and terrestrial inputs. Large-scale climate modes show statistical associations with POC concentration in the open waters of China’s marginal seas. PP and respiratory consumption are identified as the predominant input and output fluxes, respectively, in China’s marginal seas. This study enriches the understanding of carbon cycling processes and carbon sink mechanisms in marginal seas. Full article

The effect of desert dust intrusion on the detection of marine heatwaves (MHWs) has not been discussed in previous publications. In this study we investigated this effect in the Eastern Mediterranean (EM) by separate use of microwave (MW) and infrared (IR) satellite radiometry of nighttime sea surface temperature (SST); they are represented by the SST-MW and SST-IR datasets. For the first time, our analysis provides observational evidence that there was no effect of dust intrusion on the detection of MHWs by SST-MW, when aerosol optical depth (AOD) ranged within an extremely wide interval of 0.3 to 5. In contrast to SST-MW, in the presence of strong dust intrusion (AOD of up to 5), SST-IR was incapable of detecting MHWs. We found an inverse correspondence between daily variations in both SST-IR and AOD. The inverse correspondence indicates that SST-IR was profoundly influenced by desert dust, causing erroneous daily variations in SST-IR. This prevented the detection of MHWs. An essential point of our study is that even in the presence of weak dust intrusion (AOD ranged from 0.3 to 0.4) SST-IR was incapable of detecting MHWs due to the occurrence of erroneous short-term sharp drops in SST-IR. This was because of dust appearance at high altitudes. Our findings highlight that the SST-IR’s incapability to detect MHWs (in the presence of dust intrusion) led to an underestimation of the presence of MHWs by the SST datasets which integrate MW and IR radiometry, i.e. the Multiscale Ultrahigh Resolution (MUR) Global Foundation SST analysis. Full article

Sea Surface Skin Temperature (SSTskin) derived from satellites and its diurnal variation are crucial for climate research, yet conventional ocean models, which primarily solve for the foundation or bulk SST, are not designed to simulate the very thin skin layer temperature (SSTskin). Consequently, specialized parameterizations or coupled model components are often required to obtain SSTskin. This study aimed to capture SSTskin diurnal warming events and evaluate the performance of the improved one-dimensional mixed-layer model (PWP: Price-Weller-Pinkel) in simulating SSTskin. Using high-frequency Himawari-8 satellite observations, a typical diurnal warming event was detected in the coastal waters off northwestern Australia, with the maximum SSTskin diurnal variation reaching 3 °C. The reliability of Himawari-8 data was validated using iQuam in situ observations, showing a mean bias of −0.28 °C. The improved PWP model (incorporating an SSTskin parameterization scheme), forced by ERA5 datasets, was used to simulate SSTskin and its diurnal variation at 90 (0.25° × 0.25°) grid points. Results indicated that the PWP model reproduced the diurnal variation cycle consistently with observations, accurately matched regions with significant warming, and achieved a mean bias of −0.37 °C. However, in low-wind-speed areas (<1 m/s), abnormal SSTskin overestimation (>3 °C) occurred due to rapid thinning of the mixed layer and the absence of horizontal diffusion in this one-dimensional model. The improved PWP model, with its relatively stable SSTskin parameterization scheme, provides a computationally efficient tool for studying vertical processes in the upper ocean. Future work should evaluate vertical mixing schemes under low wind speed conditions to enhance the capability of numerical models to simulate SSTskin. Full article

Validating satellite-derived sea surface temperature (SST) requires resolving spatial and vertical mismatches between remotely sensed measurements and traditional in situ observations. This study evaluates the bias between infrared-based satellite SST and high-resolution in situ measurements collected in the North Sea using the autonomous surface vehicle (ASV) HALOBATES. The ASV enables the direct sampling of the ocean skin layer via a rotating glass disc system, alongside near-surface layer (NSL, 1 m depth) measurements using a flow-through system. Across 37 missions conducted between 2022 and 2023, we quantified biases in our approach and performed match-ups with a level-4 SST product for the North and Baltic Seas. Satellite SST showed strong correlations with in situ observations (r > 0.98), with Deming regression slopes approaching unity for all platforms. Despite this agreement, satellite SST exhibited a consistent cold bias. The mean differences were −0.44 ± 0.60 °C for the skin layer and −0.40 ± 0.52 °C for the NSL. The RMSE values were 0.75 °C for the skin layer and 0.66 °C for the NSL, indicating that satellite SST more closely reflects temperatures at 1 m than those at the skin layer. These findings highlight the importance of depth-resolved in situ measurements for improving remote SST validation. Full article

Tropical cyclones (TCs) induce pronounced sea surface temperature (SST) cooling, which strongly influences their intensity. Accurate prediction of TC intensity is particularly important in coastal regions where landfall occurs. While SST cooling has been extensively studied in the open ocean, its characteristics in coastal seas remain less understood. Using satellite and reanalysis data from 2004 to 2021, this study systematically characterizes SST cooling in China’s coastal seas—the Yellow Sea, East China Sea, Taiwan Strait, and northern South China Sea—and compares the cooling with adjacent offshore regions. Composite analyses of about 6300 TC track points reveal that coastal SST cooling shows significant differences relative to their offshore cooling. Regionally, the Yellow Sea exhibits significantly stronger coastal cooling (−2.5 °C vs. −1.8 °C), whereas the Taiwan Strait shows weaker coastal cooling. Further analyses using a statistical subsampling method reveal that coastal–offshore cooling differences result from the combined effects of TC attributes and pre-TC oceanic conditions, with temperature stratification exerting the dominant control. Furthermore, an increasing trend in coastal cooling is linked to enhanced temperature stratification. These findings highlight the critical role of pre-TC temperature structure in modulating coastal SST responses, with implications for improving intensity forecasts and risk assessments. Full article

The Mauritanian–Senegalese Coastal Upwelling exhibits strong interannual variability, which has been found to be driven by El Niño-Southern Oscillation (ENSO). In addition, ENSO has been shown to be triggered by the Indian Ocean and Atlantic Sea Surface Temperature (SST) variability. Nevertheless, how all these basins impact on the upwelling predictability has not been analyzed so far. Using a satellite product of surface chlorophyll-a as a proxy of marine productivity, this work makes an assessment of the predictability of the Mauritanian–Senegalese Coastal Upwelling marine ecosystem. Different statistical approaches are used to evaluate the relative contribution of the tropical basins, including the Pacific, Indian, equatorial and Tropical North Atlantic SSTs. The results indicate that although most of the upwelling variability stands for ENSO, the Atlantic contributions play an important role in shaping the seasonal prediction skill. These results may have strong implications for fisheries and marine ecosystem management in the region. Full article

Ocean fronts are central to upper-ocean dynamics and ecosystem processes, yet recognizing their evolutionary trends from satellite data remains challenging. We present a 3D U-Net-based multi-task framework that jointly performs ocean-front detection (OFD) and ocean-front evolutionary trend recognition (OFETR) from sea surface temperature gradient heatmaps. Instead of cascading OFD and OFETR in separate stages that pass OFD outputs downstream and can amplify upstream errors, the proposed model shares 3D spatiotemporal features and is trained end-to-end. We construct the Zhejiang–Fujian Coastal Front Mask (ZFCFM) and Evolutionary Trend (ZFCFET) datasets from ESA SST CCI L4 products for 2002–2021 and use them to evaluate the framework against 2D CNN baselines and traditional methods. Multi-task learning improves OFETR compared with single-task training while keeping OFD performance comparable, and the unified design reduces parameter count and daily computational cost. The model outputs daily point-level trend labels aligned with the dataset’s temporal resolution, indicating that end-to-end multi-task learning can mitigate error propagation and provide temporally resolved estimates. Full article

This study investigates the impact of high-resolution Sea Surface Temperature (SST) boundary conditions on atmospheric simulations over the southwestern Atlantic Ocean (12–27° S, 32–48° W). Numerical experiments were conducted using the WRF model with two distinct SST configurations: standard resolution GFS SST data (0.5°) and high-resolution RTG-SST-HR satellite-derived data (0.083°). Simulations covered contrasting seasonal periods (January and July 2016) to capture varying upwelling intensities and atmospheric circulation patterns. Model performance was evaluated against observational data from the Brazilian National Buoy Program (PNBOIA) using statistical metrics including RMSE and Pearson correlation coefficients for wind components. The high-resolution SST experiment demonstrated significant improvements in wind field representation, with RMSE reductions of up to 0.5 m/s for zonal wind components and correlation improvements of approximately 0.1 across multiple validation sites. Most notably, the enhanced SST resolution enabled better representation of mesoscale atmospheric systems, including improved organization and intensification of cyclonic systems in areas near the cyclogenesis regions. The RTG-SST data captured sharp thermal gradients and coastal upwelling signatures that were spatially smoothed in the GFS fields, leading to more realistic surface heat flux patterns and atmospheric boundary layer dynamics. These improvements were particularly pronounced during summer months when thermal gradients were strongest, highlighting the critical importance of accurate SST representation for capturing high-intensity atmospheric phenomena in regions of strong air-sea interaction. Full article

The Antarctic ice sheet, Earth’s largest ice mass, is vital to the global climate system. Analyzing its thermal behavior is crucial for sea-level projections and ice shelf assessments; however, internal temperature studies remain challenging due to the harsh environment and limited access to the site. Using ten years of Soil Moisture Active Passive (SMAP) satellite passive microwave brightness temperature (T_B) data (2015–2025), we examined changes in T_B across Antarctica. Results show a stronger warming trend in West Antarctica, with T_B increasing by over 1.5 K over a decade, while East Antarctica remains relatively stable, showing only seasonal summer warming and winter cooling. Furthermore, T_B in the Antarctic region correlates best with internal temperatures at depths of 500–2000 m, as indicated by the effective soil temperature, as demonstrated by the modeling data and the τ-z model’s inference. However, the total enthalpy is inconsistent with the T_B trend and exhibits the opposite effect when combined with the sensing depth. By comparing the weak trend in surface ice temperature changes, we conclude that the T_B warming trend observed on the western side of the Antarctic over the past decade does not originate from the increasing temperatures within the internal ice shelves, which differs from the increase in temperatures at the Antarctic margins. Full article

Microwave Radiation Imagers (MWRIs) onboard the FY-3B, FY-3C, and FY-3D satellites are the primary sensors for sea surface temperature (SST) observation. Benefiting from the resolution of several key calibration issues in brightness temperature products, MWRI SST records spanning more than a decade have been reprocessed. In this study, these reprocessed SST products are evaluated using direct comparison and the extended triple collocation (ETC) method, along with additional error analyses. Compared with iQuam SST, the reprocessed MWRI SST products from the three satellites show total root mean square errors (RMSEs) of 0.80–0.82 °C and total biases of −0.12 °C to 0.00 °C. ETC analyses based on MWRI, ERA5, and Argo SSTs indicate random errors of 0.76–0.78 °C. Furthermore, the reprocessed MWRI SST products demonstrate temporal stability and exhibit minimal crosstalk effects from sea surface wind speed, columnar water vapor, and columnar cloud liquid water in SST retrievals. Compared with previous versions, the reprocessed products show significant improvements, with consistent performance across FY-3B, FY-3C, and FY-3D. However, differences in SST observations due to the varying local times of the ascending nodes among the three satellites should be corrected in practical applications. Full article

The Advanced Very High-Resolution Radiometer (AVHRR) onboard NOAA-7 through NOAA-19 satellites has been the primary data source for two Climate Data Records (CDRs) that were developed specifically for Arctic and Antarctic studies: the AVHRR Polar Pathfinder (APP) and Extended AVHRR Polar Pathfinder (APP-x). With the decommissioning of these satellites and the loss of the AVHRR, a method for extending the CDRs with the Visible Infrared Imaging Radiometer Suite (VIIRS) on NOAA’s recent satellites is presented. The goal is to produce long-term, continuous, consistent, and traceable CDRs for polar climate research. As a result, APP and APP-x can now be continued as the VIIRS Polar Pathfinder (VPP) and Extended VIIRS Polar Pathfinder (VPP-x) CDRs. To ensure consistency, a VIIRS Global Area Coverage (VGAC) dataset that is comparable to AVHRR GAC data was used to develop an analogous VIIRS Polar Pathfinder suite. Five VIIRS bands (I1, I2, M12, M15, and M16) were selected to correspond to AVHRR Channels 1, 2, 3b, 4, and 5, respectively. A multivariate regression approach was used to intercalibrate these VIIRS bands to AVHRR channels based on data from overlapping AVHRR and VIIRS observations from 2013 to 2018. The data from 2012 and 2019 were reserved for independent validation. For the Arctic region north of 60°N at 14:00/04:00 Local Solar Time (LST) during 2012–2019, mean biases between APP and VPP composites at a spatial resolution of 5 km are −0.85%/3.03% (Channel 1), −1.22%/3.65% (Channel 2), −0.18 K/0.81 K (Channel 3b), 0.01 K/0.24 K (Channel 4), and 0.07 K/0.19 K (Channel 5). Mean biases between APP-x and VPP-x at a spatial resolution of 25 km for the same region and period are −1.52%/−1.48% for surface broadband albedo, 0.69 K/0.61 K for surface skin temperature, and −0.011 m/−0.017 m for sea ice thickness. Similar results were observed for the Antarctic region south of 60°S at 14:00/02:00 LST, indicating strong agreement between APP and VPP, and between APP-x and VPP-x. Full article

This study analyzes winds over the Red Sea (17 N, 39.5 E) and consequences for the northeast African climate in early summer (May–July). As the Indian SW monsoon commences, NNW winds > 6 m/s are channeled over the Red Sea between 2000 m highlands, forming a low-level jet. Although sea surface temperatures of 30C instill evaporation of 8 mm/day and surface humidity of 20 g/kg, the air mass above the marine layer is dry and dusty (6 g/kg, 100 µg/m³). Land–sea temperature gradients drive afternoon sea breezes and orographic rainfall (~4 mm/day) that accumulate soil moisture in support of short-cycle crops such as teff. Statistical analyses of satellite and reanalysis datasets are employed to reveal the mesoscale structure and temporal response of NE African climate to marine winds via air chemistry data alongside the meteorological elements. The annual cycle of dewpoint temperature often declines from 12C to 4C during the Indian SW monsoon onset, followed by dusty NNW winds over the Red Sea. Consequences of a 14 m/s wind surge in June 2015 are documented via analysis of satellite and meteorological products. Moist convection was stunted, according to Cloudsat reflectivity, creating a dry-east/moist-west gradient over NE Africa (13–14.5 N, 38.5–40 E). Diurnal cycles are studied via hourly data and reveal little change for advected dust and moisture but large amplitude for local heat fluxes. Inter-annual fluctuations of early summer rainfall depend on airflows from the Red Sea in response to regional gradients in air pressure and temperature and the SW monsoon over the Arabian Sea. Lag correlation suggests that stronger NNW winds herald the onset of Pacific El Nino. Full article

Etesian winds, the characteristic summer winds over large parts of Greece and the eastern Mediterranean, can cause coastal upwelling, especially over the eastern Aegean. The question that many studies address is whether these northern winds can cause upwelling processes that alter not only sea surface temperature but also chlorophyll concentrations, which are indicative of phytoplankton growth and overall ocean health. The present study is an effort to investigate the above matter over the eastern Aegean, from Lesvos to Ikaria and Samos islands, on a monthly basis, based on all the available satellite chlorophyll data up to now. Full article

Three-dimensional (3D) ocean physical fields are essential for understanding ocean dynamics, but reconstructing them solely from sea-surface remote sensing remains challenging. We present 3DV-Unet, an end-to-end deep learning framework that reconstructs eddy-resolving three-dimensional essential ocean variables (temperature, salinity, and currents) from multi-source satellite data. The model employs a 3D Vision Transformer bottleneck to capture cross-depth and cross-variable dependencies, ensuring physically consistent reconstruction. Trained on 2011–2019 reanalysis and satellite data, 3DV-Unet achieves RMSEs of ~0.30 °C for temperature, 0.11 psu for salinity, and 0.05 m/s for currents, with all R² values above 0.93. Error analyses further indicate higher reconstruction errors in dynamically complex regions such as the Kuroshio Extension, while spectral analysis indicates good agreement at 100 km+ but systematic deviation in the 20–100 km band. Independent validation against 6113 Argo profiles confirms its ability to reproduce realistic vertical thermohaline structures. Moreover, the reconstructed 3D fields capture mesoscale eddy structures and their life cycle, offering a valuable basis for investigating ocean circulation, energy transport, and regional variability. These results demonstrate the potential of end-to-end volumetric deep learning for advancing high-resolution 3D ocean reconstruction and supporting physical oceanography and climate studies. Full article