Remote Sensing

Research

20 pages, 9833 KiB

Open AccessArticle

Reconstruction of Hourly Gap-Free Sea Surface Skin Temperature from Multi-Sensors

by Qianguang Tu, Zengzhou Hao, Dong Liu, Bangyi Tao, Liangliang Shi and Yunwei Yan

Remote Sens. 2024, 16(22), 4268; https://doi.org/10.3390/rs16224268 - 15 Nov 2024

Viewed by 449

The sea surface skin temperature (SST_skin) is of critical importance with regard to air–sea interactions and marine carbon circulation. At present, no single remote sensor is capable of providing a gap-free SST_skin. The use of data fusion techniques is [...] Read more.

The sea surface skin temperature (SST_skin) is of critical importance with regard to air–sea interactions and marine carbon circulation. At present, no single remote sensor is capable of providing a gap-free SST_skin. The use of data fusion techniques is therefore essential for the purpose of filling these gaps. The extant fusion methodologies frequently fail to account for the influence of depth disparities and the diurnal variability of sea surface temperatures (SSTs) retrieved from multi-sensors. We have developed a novel approach that integrates depth and diurnal corrections and employs advanced data fusion techniques to generate hourly gap-free SST datasets. The General Ocean Turbulence Model (GOTM) is employed to model the diurnal variability of the SST profile, incorporating depth and diurnal corrections. Subsequently, the corrected SSTs at the same observed time and depth are blended using the Markov method and the remaining data gaps are filled with optimal interpolation. The overall precision of the hourly gap-free SST_skin generated demonstrates a mean bias of −0.14 °C and a root mean square error of 0.57 °C, which is comparable to the precision of satellite observations. The hourly gap-free SST_skin is vital for improving our comprehension of air–sea interactions and monitoring critical oceanographic processes with high-frequency variability. Full article

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature III)

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27 pages, 4362 KiB

Open AccessArticle

Himawari-8 Sea Surface Temperature Products from the Australian Bureau of Meteorology

by Pallavi Govekar, Christopher Griffin, Owen Embury, Jonathan Mittaz, Helen Mary Beggs and Christopher J. Merchant

Remote Sens. 2024, 16(18), 3381; https://doi.org/10.3390/rs16183381 - 11 Sep 2024

Viewed by 1026

Abstract

As a contribution to the Integrated Marine Observing System (IMOS), the Bureau of Meteorology introduces new reprocessed Himawari-8 satellite-derived Sea Surface Temperature (SST) products. The Radiative Transfer Model and a Bayesian cloud clearing method is used to retrieve SSTs every 10 min from [...] Read more.

As a contribution to the Integrated Marine Observing System (IMOS), the Bureau of Meteorology introduces new reprocessed Himawari-8 satellite-derived Sea Surface Temperature (SST) products. The Radiative Transfer Model and a Bayesian cloud clearing method is used to retrieve SSTs every 10 min from the geostationary satellite Himawari-8. An empirical Sensor Specific Error Statistics (SSES) model, introduced herein, is applied to calculate bias and standard deviation for the retrieved SSTs. The SST retrieval and compositing method, along with validation results, are discussed. The monthly statistics for comparisons of Himawari-8 Level 2 Product (L2P) skin SST against in situ SST quality monitoring (iQuam) in situ SST datasets, adjusted for thermal stratification, showed a mean bias of −0.2/−0.1 K and a standard deviation of 0.4–0.7 K for daytime/night-time after bias correction, where satellite zenith angles were less than 60° and the quality level was greater than 2. For ease of use, these native resolution SST data have been composited using a method introduced herein that retains retrieved measurements, to hourly, 4-hourly and daily SST products, and projected onto the rectangular IMOS 0.02 degree grid. On average, 4-hourly products cover ≈

10 %

more of the IMOS domain, while one-night composites cover ≈

25 %

more of the IMOS domain than a typical 1 h composite. All available Himawari-8 data have been reprocessed for the September 2015–December 2022 period. The 10 min temporal resolution of the newly developed Himawari-8 SST data enables a daily composite with enhanced spatial coverage, effectively filling in SST gaps caused by transient clouds occlusion. Anticipated benefits of the new Himawari-8 products include enhanced data quality for applications like IMOS OceanCurrent and investigations into marine thermal stress, marine heatwaves, and ocean upwelling in near-coastal regions. Full article

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature III)

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21 pages, 6928 KiB

Open AccessArticle

Quality Assessment of Operational Sea Surface Temperature Product from FY-4B/AGRI with In Situ and OSTIA Data

by Quanjun He, Peng Cui and Yanwei Chen

Remote Sens. 2024, 16(15), 2769; https://doi.org/10.3390/rs16152769 - 29 Jul 2024

Cited by 1 | Viewed by 900

Abstract

The Fengyun-4B (FY-4B) satellite is currently the primary operational geostationary meteorological satellite in China, replacing the previous FY-4A satellite. The advanced geostationary radiation imager (AGRI) aboard the FY-4B satellite provides an operational sea surface temperature (SST) product with a high observation frequency of [...] Read more.

The Fengyun-4B (FY-4B) satellite is currently the primary operational geostationary meteorological satellite in China, replacing the previous FY-4A satellite. The advanced geostationary radiation imager (AGRI) aboard the FY-4B satellite provides an operational sea surface temperature (SST) product with a high observation frequency of 15 min. This paper conducts the first data quality assessment of operational SST products from the FY-4B/AGRI using quality-controlled measured SSTs from the in situ SST quality monitor dataset and foundation SSTs produced by the operational sea surface temperature and sea ice analysis (OSTIA) system from July 2023 to January 2024. The FY-4B/AGRI SST product provides a data quality level flag on a pixel-by-pixel basis. Accuracy evaluations are conducted on the FY-4B/AGRI SST product with different data quality levels. The results indicate that the FY-4B/AGRI operational SST generally has a negative mean bias compared to in situ SST and OSTIA SST, and that the accuracy of the FY-4B/AGRI SST, with an excellent quality level, can meet the needs of practical applications. The FY-4B/AGRI SST with an excellent quality level demonstrates a strong correlation with in situ SST and OSTIA SST, with a correlation coefficient R exceeding 0.99. Compared with in situ SST, the bias, root mean square error (RMSE), and unbiased RMSE (ubRMSE) of the FY-4B/AGRI SST with an excellent quality level are −0.19, 0.66, and 0.63 °C in daytime, and −0.15, 0.70, and 0.68 °C at night, respectively. Compared with OSTIA SST, the bias, RMSE, and ubRMSE of the FY-4B/AGRI SST with an excellent data quality level are −0.10, 0.64, and 0.63 °C in daytime, and −0.13, 0.68, and 0.67 °C at night. The FY-4B/AGRI SST tends to underestimate the sea water temperature in mid–low-latitude regions, while it tends to overestimate sea water temperature in high-latitude regions and near the edges of the full disk. The time-varying validation of FY-4B/AGRI SST accuracy shows weak fluctuations with a period of 3–4 months. Hourly accuracy verification shows that the difference between the FY-4B/AGRI SST and OSTIA SST reflects a diurnal effect. However, FY-4B/AGRI SST products need to be used with caution around midnight to avoid an abnormal accuracy. This paper also discusses the relationships between the FY-4B/AGRI SST and satellite zenith angle, water vapor content, wind speed, and in situ SST, which have an undeniable impact on the underestimation of the FY-4B/AGRI operational SST. The accuracy of the FY-4B/AGRI operational SST retrieval algorithm still needs to be further improved in the future. Full article

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature III)

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22 pages, 2618 KiB

Open AccessArticle

Observation and Projection of Marine Heatwaves in the Caribbean Sea from CMIP6 Models

by David Francisco Bustos Usta, Rafael Ricardo Torres Parra, Lien Rodríguez-López, Maibelin Castillo Alvarez and Luc Bourrel

Remote Sens. 2024, 16(13), 2357; https://doi.org/10.3390/rs16132357 - 27 Jun 2024

Cited by 1 | Viewed by 1259

Abstract

In recent decades, climate change has led to ocean warming, causing more frequent extreme events such as marine heatwaves (MHWs), which have been understudied in the Caribbean Sea. This study addresses this gap using 30 years of daily sea surface temperature (SST) data, [...] Read more.

In recent decades, climate change has led to ocean warming, causing more frequent extreme events such as marine heatwaves (MHWs), which have been understudied in the Caribbean Sea. This study addresses this gap using 30 years of daily sea surface temperature (SST) data, complemented by projections for the 21st century from nineteen Coupled Model Intercomparison Project Phase 6 (CMIP6) models. In the 1983–2012 period, significant trends were observed in the spatially averaged MHWs frequency (1.32 annual events per decade and node) and mean duration (1.47 ± 0.29 days per decade) but not in mean intensity. In addition, MHWs show large monthly variations in these metrics, modulated by interannual and seasonal changes. MHWs seasonality is different in the three used metrics, being more intense and frequent in warm and rainy months (intensity between 1.01 to 1.11 °C, duration 6.79 to 7.13 days) and longer lasting in late boreal winter (intensity between 0.82 to 1.00 °C, duration 7.50 to 8.31 days). The MHWs behavior from two extreme months show that these events can occur in both small and large areas in the Caribbean. Overall, models tend to underestimate the annually averaged MHWs frequency and intensity, while they overestimate duration when compared to observations. MHWs projections are more extreme under SSP585, as they are sensible to the radiational scenario. However, an increase in MHWs intensity and duration (events lasting as much as 154 days by 2100) is expected, driving a decrease in frequency (–37.39 events per decade under SSP585 by 2100). These projections imply that MHWs conditions at the beginning of the century will be nearly permanent in the Caribbean’s future. Nonetheless, caution is advised in interpreting these projections due to differences between models’ simulations and observed data. While advancements in oceanic models within CMIP6 demonstrate progress compared to previous CMIP initiatives, challenges persist in accurately simulating extreme events such as marine heatwaves. Full article

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature III)

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20 pages, 7017 KiB

Open AccessArticle

Inter-Comparison of SST Products from iQuam, AMSR2/GCOM-W1, and MWRI/FY-3D

by Yili Zhao, Ping Liu and Wu Zhou

Remote Sens. 2024, 16(11), 2034; https://doi.org/10.3390/rs16112034 - 6 Jun 2024

Cited by 1 | Viewed by 1227

Abstract

Evaluating sea surface temperature (SST) products is essential before their application in marine environmental monitoring and related studies. SSTs from the in situ SST Quality Monitor (iQuam) system, Advanced Microwave Scanning Radiometer 2 (AMSR2) aboard the Global Change Observation Mission 1st-Water, and the [...] Read more.

Evaluating sea surface temperature (SST) products is essential before their application in marine environmental monitoring and related studies. SSTs from the in situ SST Quality Monitor (iQuam) system, Advanced Microwave Scanning Radiometer 2 (AMSR2) aboard the Global Change Observation Mission 1st-Water, and the Microwave Radiation Imager (MWRI) aboard the Chinese Fengyun-3D satellite are intercompared utilizing extended triple collocation (ETC) and direct comparison methods. Additionally, error characteristic variations with respect to time, latitude, SST, sea surface wind speed, columnar water vapor, and columnar cloud liquid water are analyzed comprehensively. In contrast to the prevailing focus on SST validation accuracy, the random errors and the capability to detect SST variations are also evaluated in this study. The result of ETC analysis indicates that iQuam SST from ships exhibits the highest random error, above 0.83 °C, whereas tropical mooring SST displays the lowest random error, below 0.28 °C. SST measurements from drifters, tropical moorings, Argo floats, and high-resolution drifters, which possess random errors of less than 0.35 °C, are recommended for validating remotely sensed SST. The ability of iQuam, AMSR2, and MWRI to detect SST variations diminishes significantly in ocean areas between 0°N and 20°N latitude and latitudes greater than 50°N and 50°S. AMSR2 and iQuam demonstrate similar random errors and capabilities for detecting SST variations, whereas MWRI shows a high random error and weak capability. In comparison to iQuam SST, AMSR2 exhibits a root-mean-square error (RMSE) of about 0.51 °C with a bias of −0.05 °C, while MWRI shows an RMSE of about 1.26 °C with a bias of −0.14 °C. Full article

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature III)

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18 pages, 4918 KiB

Open AccessArticle

Assessment of Accuracy of Moderate-Resolution Imaging Spectroradiometer Sea Surface Temperature at High Latitudes Using Saildrone Data

by Chong Jia, Peter J. Minnett and Malgorzata Szczodrak

Remote Sens. 2024, 16(11), 2008; https://doi.org/10.3390/rs16112008 - 3 Jun 2024

Cited by 1 | Viewed by 1261

Abstract

The infrared (IR) satellite remote sensing of sea surface skin temperature (SST_skin) is challenging in the northern high-latitude region, especially in the Arctic because of its extreme environmental conditions, and thus the accuracy of SST_skin retrievals is questionable. Several Saildrone [...] Read more.

The infrared (IR) satellite remote sensing of sea surface skin temperature (SST_skin) is challenging in the northern high-latitude region, especially in the Arctic because of its extreme environmental conditions, and thus the accuracy of SST_skin retrievals is questionable. Several Saildrone uncrewed surface vehicles were deployed at the Pacific side of the Arctic in 2019, and two of them, SD-1036 and SD-1037, were equipped with a pair of IR pyrometers on the deck, whose measurements have been shown to be useful in the derivation of SST_skin with sufficient accuracy for scientific applications, providing an opportunity to validate satellite SST_skin retrievals. This study aims to assess the accuracy of MODIS-retrieved SST_skin from both Aqua and Terra satellites by comparisons with collocated Saildrone-derived SST_skin data. The mean difference in SST_skin from the SD-1036 and SD-1037 measurements is ~0.4 K, largely resulting from differences in the atmospheric conditions experienced by the two Saildrones. The performance of MODIS on Aqua and Terra in retrieving SST_skin is comparable. Negative brightness temperature (BT) differences between 11 μm and 12 μm channels are identified as being physically based, but are removed from the analyses as they present anomalous conditions for which the atmospheric correction algorithm is not suited. Overall, the MODIS SST_skin retrievals show negative mean biases, −0.234 K for Aqua and −0.295 K for Terra. The variations in the retrieval inaccuracies show an association with diurnal warming events in the upper ocean from long periods of sunlight in the Arctic. Also contributing to inaccuracies in the retrieval is the surface emissivity effect in BT differences characterized by the Emissivity-introduced BT difference (EΔBT) index. This study demonstrates the characteristics of MODIS-retrieved SST_skin in the Arctic, at least at the Pacific side, and underscores that more in situ SST_skin data at high latitudes are needed for further error identification and algorithm development of IR SST_skin. Full article

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature III)

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25 pages, 12983 KiB

Open AccessArticle

First Analyses of the TIMELINE AVHRR SST Product: Long-Term Trends of Sea Surface Temperature at 1 km Resolution across European Coastal Zones

by Philipp Reiners, Laura Obrecht, Andreas Dietz, Stefanie Holzwarth and Claudia Kuenzer

Remote Sens. 2024, 16(11), 1932; https://doi.org/10.3390/rs16111932 - 27 May 2024

Viewed by 1071

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature III)

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27 pages, 5434 KiB

Open AccessArticle

Characterization and Validation of ECOSTRESS Sea Surface Temperature Measurements at 70 m Spatial Scale

by David S. Wethey, Nicolas Weidberg, Sarah A. Woodin and Jorge Vazquez-Cuervo

Remote Sens. 2024, 16(11), 1876; https://doi.org/10.3390/rs16111876 - 24 May 2024

Viewed by 963

Abstract

The ECOSTRESS push-whisk thermal radiometer on the International Space Station provides the highest spatial resolution temperature retrievals over the ocean that are currently available. It is a precursor to the future TRISHNA (CNES/ISRO), SBG (NASA), and LSTM (ESA) 50 to 70 m scale [...] Read more.

The ECOSTRESS push-whisk thermal radiometer on the International Space Station provides the highest spatial resolution temperature retrievals over the ocean that are currently available. It is a precursor to the future TRISHNA (CNES/ISRO), SBG (NASA), and LSTM (ESA) 50 to 70 m scale missions. Radiance transfer simulations and triple collocations with in situ ocean observations and NOAA L2P geostationary satellite ocean temperature retrievals were used to characterize brightness temperature biases and their sources in ECOSTRESS Collection 1 (software Build 6) data for the period 12 January 2019 to 31 October 2022. Radiometric noise, non-uniformities in the focal plane array, and black body temperature dynamics were characterized in ocean scenes using L1A raw instrument data, L1B calibrated radiances, and L2 skin temperatures. The mean brightness temperature biases were −1.74, −1.45, and −1.77 K relative to radiance transfer simulations in the 8.78, 10.49, and 12.09 µm wavelength bands, respectively, and skin temperatures had a −1.07 K bias relative to in situ observations. Cross-track noise levels range from 60 to 600 mK and vary systematically along the focal plane array and as a function of wavelength band and scene temperature. Overall, radiometric uncertainty is most strongly influenced by cross-track noise levels and focal plane non-uniformity. Production of an ECOSTRESS sea surface temperature product that meets the requirements of the SST community will require calibration methods that reduce the biases, noise levels, and focal plane non-uniformities. Full article

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature III)

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17 pages, 5239 KiB

Open AccessArticle

Characterizing the California Current System through Sea Surface Temperature and Salinity

by Marisol García-Reyes, Gammon Koval and Jorge Vazquez-Cuervo

Remote Sens. 2024, 16(8), 1311; https://doi.org/10.3390/rs16081311 - 9 Apr 2024

Viewed by 1244

Abstract

Characterizing temperature and salinity (T-S) conditions is a standard framework in oceanography to identify and describe deep water masses and their dynamics. At the surface, this practice is hindered by multiple air–sea–land processes impacting T-S properties at shorter time scales than can easily [...] Read more.

Characterizing temperature and salinity (T-S) conditions is a standard framework in oceanography to identify and describe deep water masses and their dynamics. At the surface, this practice is hindered by multiple air–sea–land processes impacting T-S properties at shorter time scales than can easily be monitored. Now, however, the unsurpassed spatial and temporal coverage and resolution achieved with satellite sea surface temperature (SST) and salinity (SSS) allow us to use these variables to investigate the variability of surface processes at climate-relevant scales. In this work, we use SSS and SST data, aggregated into domains using a cluster algorithm over a T-S diagram, to describe the surface characteristics of the California Current System (CCS), validating them with in situ data from uncrewed Saildrone vessels. Despite biases and uncertainties in SSS and SST values in highly dynamic coastal areas, this T-S framework has proven useful in describing CCS regional surface properties and their variability in the past and in real time, at novel scales. This analysis also shows the capacity of remote sensing data for investigating variability in land–air–sea interactions not previously possible due to limited in situ data. Full article

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature III)

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18 pages, 7549 KiB

Open AccessArticle

Historical Marine Cold Spells in the South China Sea: Characteristics and Trends

by Chunhui Li, Wenjin Sun, Jinlin Ji and Yuxin Zhu

Remote Sens. 2024, 16(7), 1171; https://doi.org/10.3390/rs16071171 - 27 Mar 2024

Viewed by 1121

Abstract

Marine cold spells (MCSs) are extreme ocean temperature events impacting marine organisms, yet their characteristics and trends in the South China Sea (SCS) historical period remain unclear. This study systematically analyzes sea surface temperature (SST) and MCSs in the SCS using satellite observation [...] Read more.

Marine cold spells (MCSs) are extreme ocean temperature events impacting marine organisms, yet their characteristics and trends in the South China Sea (SCS) historical period remain unclear. This study systematically analyzes sea surface temperature (SST) and MCSs in the SCS using satellite observation data (OISSTv2.1) from 1982 to 2022. The climatological mean SST ranges from 22 °C near the Taiwan Strait to 29 °C near the Nansha Islands, showing notable variations. Annual SST anomalies demonstrate a heterogeneous spatial trend of approximately 0.21 ± 0.16 °C/decade (p < 0.01) across the SCS, indicating an increase in SST over time. MCS analysis uncovers spatial non-uniformity in frequency, with higher values near the Beibu Gulf and Hainan Island, and longer durations in the northeastern coastal areas. Statistical analysis indicates normal distributions for frequency and duration trends but skewness for intensity and cumulative intensity, reflecting extreme values. Winter months exhibit larger MCS occurrence areas and higher mean intensities, illustrating seasonal variability. Anticipated changes will significantly impact the ecological structure and functioning of the SCS. Full article

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature III)

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17 pages, 4560 KiB

Open AccessArticle

Monitoring Thermal Exchange of Hot Water Mass via Underwater Acoustic Tomography with Inversion and Optimization Method

by Shijie Xu, Fengyuan Yu, Xiaofei Zhang, Yiwen Diao, Guangming Li and Haocai Huang

Remote Sens. 2024, 16(6), 1105; https://doi.org/10.3390/rs16061105 - 21 Mar 2024

Cited by 1 | Viewed by 1031

Abstract

Thermal exchange of underwater water mass caused by marine heat wave is a hot point of research recently. In particular, because the water temperature observation along hot water mass transportation is hard work. Acoustic tomography is an advanced method to measure water temperature [...] Read more.

Thermal exchange of underwater water mass caused by marine heat wave is a hot point of research recently. In particular, because the water temperature observation along hot water mass transportation is hard work. Acoustic tomography is an advanced method to measure water temperature variations via sound signal transmission with multi-station network sensing. The 5 kHz frequency acoustic tomography used for observing water temperature variations caused by ocean heat waves is interesting work. In this paper, the numerical simulation of hot water mass is completed first, then floatation and diffusion of hot water mass in a simulation are monitored by acoustic tomography. A new inversion optimization method is proposed to obtain hot water mass transportation variations at two-dimensional temperature vertical profile. The proposed inversion method adds a regularized mode matrix and the optimization method adds the model correlation matrix to improve the results quality. The accuracy of inversion optimization results is compared and discussed, where the mean temperature error is less than 0.4 °C. Sensing water temperature variation of marine heat waves is verified via acoustic signal transmission and improved inversion optimization method. The water dynamical process observation is an application of acoustic tomography, which can be further used observe underwater environmental characteristics. Full article

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature III)

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14 pages, 7499 KiB

Open AccessArticle

An Extreme Marine Heatwave Event in the Yellow Sea during Winter 2019/20: Causes and Consequences

by Haiqing Yu, Jie Ma, Hui Wang, Qinwang Xing and Lin Jiang

Remote Sens. 2024, 16(1), 33; https://doi.org/10.3390/rs16010033 - 21 Dec 2023

Cited by 1 | Viewed by 1447

Abstract

Increasing evidence has shown that marine heatwaves (MHWs) have destructive impacts on marine ecosystems, and understanding the causes of these events is beneficial for mitigating the associated adverse effects on the provision of ecosystem services. During the 2019/20 boreal winter, a long-lasting extreme [...] Read more.

Increasing evidence has shown that marine heatwaves (MHWs) have destructive impacts on marine ecosystems, and understanding the causes of these events is beneficial for mitigating the associated adverse effects on the provision of ecosystem services. During the 2019/20 boreal winter, a long-lasting extreme MHW event was recorded (over 90 days) in the Yellow Sea (YS). Observations and numerical experiments revealed that the unprecedented winter MHW event was initiated and sustained by a significant decrease in ocean heat loss in the YS, which may be associated with the pronounced weakening of the Siberian high system induced by an extreme positive Arctic Oscillation (AO) event. A weakened northerly wind is the essential prerequisite, while the extreme AO event is only a trigger for the winter MHW in the YS. It was also found that the extreme winter MHW event is likely to substantially affect the seasonal evolution of the vertical thermal structure, which may have a great impact on the whole ecosystem in the YS. This study provides new insights for the prediction of winter MHWs in the YS, as well as their effects on marine ecosystems in a changing climate. Full article

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature III)

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