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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. [ABSTRACT FROM AUTHOR]

The study of upper ocean mixing processes, including their dynamics and thermodynamics, has been a primary focus for oceanographers and meteorologists. Wave breaking in deep water is believed to play a significant role in these processes, affecting air–sea interactions and contributing to the energy dissipation of surface waves. This, in turn, enhances the transfer of gas, heat, and mass at the ocean surface. In this paper, we use the FVCOM-SWAVE coupled wave and current model, which is based on the MY-2.5 turbulent closure model, to examine the response of upper ocean turbulent kinetic energy (TKE) and temperature to various wave breaking parametric schemes. We propose a new parametric scheme for wave breaking energy at the sea surface, which is based on the correlation between breaking wave parameter R B and whitecap coverage. The impact of this new wave breaking parametric scheme on the upper ocean under typhoon conditions is analyzed by comparing it with the original parametric scheme that is primarily influenced by wave age. The wave field simulated by SWAVE was verified using Jason-3 satellite altimeter data, confirming the effectiveness of the simulation. The simulation results for upper ocean temperature were also validated using OISST data and Argo float observational data. Our findings indicate that, under the influence of Typhoon Nanmadol, both parametric schemes can transfer the energy of sea surface wave breaking into the seawater. The new wave breaking parameter R B scheme effectively enhances turbulent mixing at the ocean surface, leading to a decrease in sea surface temperature (SST) and an increase in mixed layer depth (MLD). This further improves upon the issue of uneven mixing of seawater at the air–sea interface in the MY-2.5 turbulent closure model. However, it is important to note that wave breaking under typhoon conditions is only one aspect of wave impact on ocean disturbances. Therefore, further research is needed to fully understand the impact of waves on upper ocean mixing, including the consideration of other wave mechanisms. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is a key variable in the study of the global climate system and one of the important parameters in the process of air–sea interaction. Therefore, the demand for SST is developing towards high quality and high precision. In this paper, the ability of the multi-layer perceptron (MLP) optimized by the Aquila optimizer (AO) to solve nonlinear problems and the advantages of the deep belief network (DBN) to effectively process complex data are used to construct the IMLP-DBN inversion algorithm. The algorithm takes into account the influences of atmospheric conditions and satellite zenith angles. The data set is the infrared remote sensing data of the moderate resolution imaging spectroradiometer (MODIS) and the actual measurement data of buoys on sunny days and few clouds. Analysis of the inversion results shows that the root mean square error (RMSE) of the inversion value and the measured value is 0.14, and the sum of square errors (SSE) is 0.78. Compared with the MOD28 product data, the RMSE and SSE of the inversion are reduced by 66% and 24%, respectively. [ABSTRACT FROM AUTHOR]

This paper assessed the phytoplankton structure and its relationship with the physical environment in the coastal region off Mazatlán, Mexico, in two seasons of 2022, a year in which a strong La Niña event took place: (1) the warmer (August) and (2) the transitional period to the cold phase (November), based on hydrographic data and samples collected in two systematic scientific expeditions. The results showed clear differences between both seasons. Regarding total abundance, August reached 125,200 cells L−1, while November amounted to 219,900 cells L−1. Regarding species composition, the diatoms Cylindrotheca closterium and Planktoniella sol were dominant in August, while Thalassionema nitzschioides and Tetramphora decussata dominated the assemblages in November. The dinoflagellate Protoperidinium punctulatum was dominant in both seasons. However, very marked differences in its abundance are reported. The differences observed in the species richness and abundance could be attributed to the physical configuration of the water column, particularly the surface temperature, which showed clear changes between both seasons. The results presented here confirmed the high phytoplankton richness (some of them with the potential to generate harmful algal blooms), abundance, and diversity values of the region, suggesting a strong relationship with the physical environment. [ABSTRACT FROM AUTHOR]

Europe and the Mediterranean are considered climate change hot spots. This is the reason why this paper focuses on the analysis of the trends of essential climate variables in a Mediterranean country, Greece. The analyzed period is 1991–2020, and the dataset used is ERA5-Land (produced by the European Center for Medium-Range Weather Forecasts), which has global coverage and an improved resolution of ~9 × 9 km compared to other datasets. Significant climatic changes across Greece have been put in evidence during the analyzed period. More specifically, the country averaged a 30-year trend of temperature of +1.5 °C, locally exceeding +2 °C, and this increasing trend is positively correlated with the distance of the areas from the coasts. Accordingly, the number of frost days has decreased throughout the country. In terms of rainfall, a major part of Greece has experienced increasing annual rainfall amounts, while 86% of the Greek area has experienced a positive trend of days with heavy rainfall (>20 mm). Finally, a multiple signal of the trend of consecutive dry days was found (statistically non-significant in the major part of Greece). [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) significantly influences the dynamics of the global climate system, impacting climate change, marine ecosystems, and marine engineering. Traditional SST prediction methods, such as time series and machine learning models, often focus solely on temporal features and neglect spatial distribution patterns. In contrast, current deep learning techniques typically limit predictions to short-term periods. This paper introduces a novel SST prediction model that integrates both temporal and spatial dimensions, employing parallel prediction and a spatio-temporal attention mechanism to enhance accuracy. The model achieves long-term SST forecasting, significantly reduces the parameter count and computational effort, and maintains high prediction precision. Experiments in the El Niño 3.4 region and the East China Sea show that this method outperforms existing deep learning approaches, accurately predicting SST over periods ranging from 7 to 60 days with superior efficiency and accuracy. Overall, this work presents an effective new approach for SST prediction with crucial implications for climate change research, marine ecosystems, and marine engineering. [ABSTRACT FROM AUTHOR]

A strong cooling event refers to a sharp change in the average temperature over a short period. The rapid change of temperature has important effect on human health and is highly concerned recent years. Based on the observed temperature data set from stations in Southwest China (SWC) from 1979 to 2017, this paper analyses the characteristics of the strong cooling event (SCE). The result shows that SCE occurs with the highest frequency during the time from February to May. Among them, the frequency of SCE in February exhibits an abrupt change before and after 2005 with a significant increase. Further study reveals that the change of SCE frequency in February is associated with the large‐scale background circulation patterns. After 2005, there is a cyclonic circulation anomaly in Northeast Asia and an anticyclonic circulation anomaly in the Tibet Plateau (TP). This pattern provides a favourable condition for the southward movement of cold air mass, thereby increasing the frequency of SCE in SWC. Furthermore, it is revealed that there is a strong correlation between the variation of SCE frequency in February and sea surface temperatures (SST) in the Northwest Pacific in January before 2005. The cold SST anomaly could favour the occurrence of extreme TD events in SWC through vertical circulation. After 2005, the correlation between SCE and Northwest Pacific SST is not significant. The sea ice in the northern Barents Sea and Kara Sea becomes the dominant impact factor. The abnormally low sea ice concentration is conducive to strengthen the meridional circulation over East Asia, leading to an increasing frequency of SCE in SWC. [ABSTRACT FROM AUTHOR]

From the beginning of the 21st century, the heat waves in Europe have become more frequent, severe and longer lasting, especially in the southern region. The longer duration of heat waves may be partly attributed to wave‐amplification, high‐amplitude waves in the mid‐latitudes, favouring the longer‐lasting blocking systems. The primary objective of the paper was to examine the long‐term changes of hot days and heat wave events and to identify the atmospheric circulation condition that prevails its occurrence. The other object was to investigate the correlations between hot days' occurrence in Poland and sea‐ice cover and sea surface temperature changes in the Arctic and its surroundings. Over the past few decades, Central and Southern Poland have experienced a considerable rise in maximum temperatures, which has led to an increase in the number of hot days. This increase is particularly prominent during the summer months (June, July and August) but also in May and September, which extends the hot season. Although heat waves have become more frequent and persistent since 2006, their mean intensity has remained unchanged. During all heat waves in Poland, the high‐pressure centre was located over the country or its close neighbourhood, mainly in the eastern sector but also in the south, southwest or over Scandinavia. The blocking systems were mainly related to the extended ridge of the Azores High, but also to strong blocking over the North Pole and Greenland reaching Scandinavia or anomalous high pressure in the East Arctic. The longest‐lasting heat waves were usually preceded by air masses advection from the Atlantic, but the highest potential temperature along the trajectories was observed for the southern direction of inflow. The growing number of hot days is statistically significantly related to the decreasing sea‐ice cover and increasing sea surface temperature in Arctic and Subarctic regions. [ABSTRACT FROM AUTHOR]

Global warming is causing poleward expansion of species ranges. Temperate seas, in particular, are undergoing a process known as 'tropicalisation', i.e., the combination of sea-water warming and establishment of southern species. The Ligurian Sea is one of the coldest sectors of the Mediterranean and has thus been characterized by a dearth of warm-temperate species and a comparative abundance of cold-temperate species. This paper uses a time series of sea surface temperature (SST) and new records of thermophilic fish species to reconsider the biogeography of the Ligurian Sea. SST has risen by about 0.7 °C on average between 1948 and 2023, but two phases may be distinguished: a cool one (ended in the mid-1980s) and a warm one (still ongoing); the latter phase shows alternating periods of rapid warming and comparatively stationary temperature. The arrival of thermophilic species coincided with the periods of rapid warming; some of these species were established in the subsequent stationary periods. Heatwaves and climate-related diseases associated with the periods of rapid warming have caused mass mortalities of autochthonous species. Our knowledge on the biogeography of the Ligurian Sea was established during the cool phase; the present situation, however, calls for re-defining the chorological spectrum of the Ligurian Sea biota. [ABSTRACT FROM AUTHOR]

Air temperature variability and prediction are important considering the potential implications on human health, water resource management, agriculture and land-atmosphere interaction. In this paper, we investigate the delayed influence of El Niño on spring Surface Air Temperature (SAT) variability over India using observations and assess its predictability in coupled climate models. Analysis suggests that El Niño, at its decaying phase, exerts a strong influence on the spring SAT over Southern Peninsular India (SPI). This warming is mainly induced by an anomalous anticyclone, which extends from the Western North Pacific (WNP) region to the SPI. The anomalous anticyclone-induced downdraft reduces the cloud cover and increases incoming shortwave radiation at surface, which in turn causes a dry atmospheric column and dry soil moisture over this region. As a result of this decreased soil moisture, latent heat flux into atmosphere reduces and enhances sensible heat transfer, further contributing to dry soil moisture. Both dry soil moisture and anomalous anticyclone contributes to enhanced SAT over SPI region. It is found that 6 years out of the top 10 warmest years over the SPI region correspond to the decaying phase of El Niño, suggesting the importance of the present study. It is also found that the intense SAT warming over SPI aids in increasing frequencies of Discomfort Index hours (exceeding 28 and 30 °C in a day) and the Universal Thermal Climate Index during El Niño decaying spring years. Further, we have examined the predictability of the spring SAT during decaying El Niño years based on the Asia-Pacific Economic Cooperation Climate Center (APCC) models. It is noted that most of the APCC models reasonably predicted the spatial distribution of warm SAT over India during the El Niño decaying spring season at 1 and 3 month lead. This study highlights the predictability of spring SAT a season in advance over India, which would help in planning better adaptation and mitigation strategies. [ABSTRACT FROM AUTHOR]

The South China Sea summer monsoon (SCSSM) onset and withdrawal have critical impacts on the climate of East Asia. However, using different reanalysis data may lead to the uncertainty of the SCSSM. This study uses reanalysis datasets including JRA‐55, ERA5, NCEP‐NCAR NCEP‐1 and NCEP‐DOE NCEP‐2 to calculate onset and withdrawal dates and index of SCSSM from 1991 to 2020, analyses the uncertainty and figures out the possible reasons. Results reveal that the four reanalysis datasets have obvious uncertainty in calculating the withdrawal date of SCSSM, with the deviation being up to 3.4 pentads. Further analysis shows that large differences in thermal conditions and lead to the uncertainties. From the perspective of precipitation, Greater uncertainty exists in the relationship between monsoon retreat and September–October precipitation, NCEP1 and NCEP2 datasets show weak correlation in southern China and Indonesia. Moreover, the uncertainty can be also found in the correlations of SCSSM with global sea surface temperature (SST) anomalies. JRA‐55 and ERA5 show a strong correlation of with SST in several areas, and exhibit a distinct Pacific decadal oscillation (PDO) mode with monsoon onset, while NCEP‐1 and NCEP‐2 have a weaker correlation with SST anomalies than JRA‐55 or ERA5. Comparing multiple factors comprehensively, this paper concludes that JRA‐55 may be more representative in the monsoon study, followed by ERA data. In short, depending on the specific research questions, researchers can choose appropriate data sources to study the SCSSM based on the different aspects and the uncertainty also deserve our special attention when selecting the reanalysis datasets. [ABSTRACT FROM AUTHOR]

The melting of the Greenland ice sheet (GrIS) in summer has rapidly and significantly increased in recent decades, especially for the northern GrIS. Circulation related to GrIS melting is important for understanding the contribution of the GrIS to the global sea level. In this paper, we used the SOM method to obtain three spatial patterns of GrIS melting based on model output data: overall melting, northern melting, and southern melting patterns. We also examined their linkages to the observed atmospheric circulation. GrIS melting is primarily related to Greenland blocking (GB), while different types of GB are responsible for different melting patterns. The overall and northern melting patterns are both associated with high-latitude GB, which is associated with the decay and westward movement of mid-latitude and high-latitude European blocking (EB), respectively. It is found that the generation of mid-latitude and high-latitude EBs are related to mid-latitude eastern North Atlantic warming and Greenland–Norskehavet–North Sea warming, respectively, while the movement of EB is related t upstream weakened zonal wind anomalies. Furthermore, the southern melting pattern is linked to mid-latitude GB, which is associated with the wave train from the East Pacific to Southern Greenland through North America and further related to the mid-latitude of East Pacific warming. [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]

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 _ I N R I ) 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 _ I N R I 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 _ I N R I is advantageous over traditional interpolation methods under different enhancement scales, and the temporal information can improve T _ I N R I performance for a different calendar month. We also examined the potential capability of T _ I N R I in recovering missing grid value. These results demonstrate that the proposed T _ I N R I is a promising method for generating high-resolution climate data and has significant implications for climate research and related applications. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper investigates the process behind climate model biases, especially in rainfall, during the southwest (June to August; JJA) and northeast monsoon (December to February; DJF) seasons over the Philippines using the downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment-Southeast Asia (SEACLID/CORDEX-SEA). Biases in the surface and near-surface climate are assessed over land in eastern (EastPH) and western (WestPH) Philippines using observation and reanalysis datasets. Results show that models with well-represented sea surface temperature (SST) provided better added values after downscaling. Although the CMIP5-driven models tend to overestimate both surface energy and water partitioning, simulations with well-represented SST have better surface energy partitions. In contrast to the reference simulation driven by ERA-Interim reanalysis, the CMIP5-driven simulations tend to underestimate (overestimate) moisture flux convergence during the DJF (JJA) season, which might have contributed to the overall underestimation (overestimation) of rainfall in this season. Further investigation along a vertical transect shows the differences in governing mechanisms for each season. During DJF (JJA) season, the simulation with underestimated (overestimated) SST, near-surface air temperature, and mixing ratio results in underestimated (overestimated) rainfall over EastPH (WestPH). In addition, the CMIP5-driven models were found to overestimate low-level winds, which may have induced the deep convection regardless of season, suggesting the role of low-level winds in the convection scheme used in RegCM4 aside from topography and land cover. [ABSTRACT FROM AUTHOR]

Globally, observations on marine species during marine heatwaves (MHWs) help outline the scope of the MHW's possible biological effects. In line with this effort, this paper presents a 2020 MHW that coincided with a reported "tomato jellyfish" (Crambione mastigophora Maas, 1903) bloom on 23 March 2020 in the Corong-Corong Bay of Palawan, Philippines. Detecting a moderate MHW from 21 March to 04 April 2020, the analysis of sea surface temperatures revealed that most areas surrounding the bloom site attained their peak positive anomalies on the same day as the reported bloom. Certain physical mechanisms present in the first quarter of 2020 may have played a role in the occurrence of both events: the presence of cyclonic eddies and parallel monsoonal winds alongshore can induce upwelling which promotes biological productivity in surface waters, while the observed weakening of winds have been associated with anomalous warming of the sea surface. Further studies are still highly recommended to determine the exact causes of the jellyfish bloom and what conditions make it more likely to happen during MHWs. However, if the C. mastigophora is hypothetically able to continually bloom amidst warming temperatures, the increasing trend of MHW frequency and intensity in the West Philippine Sea (where the reported bloom site is situated) may consequently yield more future co-occurrences. This paper aims to hopefully contribute to the existing knowledge of possible biological impacts associated with extreme marine events, especially in the Philippine context where both jellyfish blooms and MHWs are understudied. [ABSTRACT FROM AUTHOR]

The increase in sea surface temperature (SST), a consequence of climate change, may lead to large-scale redistributions of global fish catch, including tuna in the Eastern Pacific Ocean (EPO). This paper applies the production function approach for modeling catch and SST using high-resolution gridded data for yellowfin and skipjack tuna catch in the EPO. We find a positive but nonlinear (i.e., logarithmic and quadratic) relationship between SST and the carrying capacity of tuna fisheries. The marginal product varies across species, distance from the equator, and the fishing method. For yellowfin tuna, the largest increase in catch occurs for unassociated and dolphin sets in the Northern EPO, while for skipjack tuna the largest increases in catch occur for unassociated and floating object sets in the Southern EPO. These thermal responses to ocean warming may lead to more segregated distribution and increased specialization in effort across the Eastern Pacific. [ABSTRACT FROM AUTHOR]

In terms of SST prediction tasks by machine learning approaches, high-resolution and accurate prediction can hardly be achieved with easily accessible SST data. This is because most of the SST data is spatiotemporally discrete and sparse, meaning a small sample size since it is hard to learn the spatiotemporal correlation given the data of limited density. This paper presents a numerical computation-based few-shot learning method for intelligent SST prediction. The proposed method generates synthetic SST sequences via ROMS and merges them with SST data captured by satellites to deal with small sample size. Convolutional LSTM (Conv-LSTM) network is trained end-to-end in order to learn spatiotemporal correlation of time-varying SST and eventually obtain high-resolution and accurate SST predictions. SST data from August to December in 2011 are employed for model training, and the prediction results are compared to reliable SST reanalysis datasets in the experiment, which shows fine performance of the proposed method. [ABSTRACT FROM AUTHOR]

Globe's primary issue is global warming, water temperatures have accompanied it as the sea surface temperature, and it is the primary attribute to balance the energy on the earth's surface. Sea surface temperature prediction is vital to climate forecast. Downwelling currents carry some of this heat to the ocean's bottom layers, which are also heating, covering far behind the increase in sea surface temperature. In deep learning models, the correct loss function will try to reduce the error and converge fast. The proposed improved loss function correctly estimates how close the predictions made by the long short-term memory match the observed values in the training data. This research considers location-specific sea surface temperature predictions using the improved loss function in the long short-term memory neural network at six different locations around India for daily, weekly, and monthly time horizons. Most existing research concentrated on periodic forecasts, but this paper focused on daily, weekly, and monthly predictions. The improved loss function—long short-term memory, achieved 98.7% accuracy, and this improved loss function overcomes the limitations of the existing techniques and reduces the processing time to ~ 0.35 s. In this research, the sea surface temperature prediction using the improved loss function in the long short-term memory neural network gives better results than the standard prediction models and other existing techniques by considering the long-time dependencies and obtaining features from the spatial data. [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]

In this study, we obtained the first leading mode and principal component 1 (PC1) of North Pacific sea surface temperature (SST). The PC1‐related SST anomalies, located relatively north/south, are referred to as North/South PC1 events. Model outputs, observations, reanalysis datasets and sensitivity experiments show that during the North PC1 events, an enhanced Aleutian low occurs and is located relatively north, favoring anomalous southerlies over western North America. The anomalous southerlies induce a strong warming anomaly over North America via warm advection of the anomalous southerlies and temperature advection of the climatological westerlies over North America. However, the Aleutian low anomaly and corresponding southerly anomaly associated with South PC1 events shift southward, favoring weakened effects of South PC1 events on atmospheric circulations and surface temperatures over North America. The meridional position of PC1 events deserves to be considered in the studies of the PC1 and its related climate changes. Plain Language Summary: Understanding of surface temperature variations is important since it largely affects human and society. This paper categorizes the North Pacific sea surface temperature anomaly events, which are associated with the first leading mode of North Pacific sea surface temperature, into North and South events according to their meridional positions. Using model outputs, observations, reanalysis datasets and sensitivity experiments, we found that the surface temperature anomalies over North America during the North events are quite different from those during the South events. During the North events, an enhanced Aleutian low occurs over high‐latitude North Pacific. The enhanced Aleutian low tends to induce anomalous southerlies over western North America. The anomalous southerlies favor warming anomalies over North America via the warm advection of anomalous southerlies and temperature advection of climatological westerly. However, the Aleutian low anomaly and meridional wind anomaly during the South events shift southward, leading to weakened effects of the South events on atmospheric circulations and surface temperatures over North America. Key Points: North American warming anomaly occurs during North Pacific sea surface temperature anomaly events located northThe warming anomaly is related to an enhanced Aleutian low anomaly located north and climatological westerlyDuring the events located south, North American surface temperature responses are weak due to a southward‐shift Aleutian low anomaly [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is a key factor in the marine environment, and its accurate forecasting is important for climatic research, ecological preservation, and economic progression. Existing methods mostly rely on convolutional networks, which encounter difficulties in encoding irregular data. In this paper, allowing for comprehensive encoding of irregular data containing land and islands, we construct a graph structure to represent SST data and propose a graph memory neural network (GMNN). The GMNN includes a graph encoder built upon the iterative graph neural network (GNN) idea to extract spatial relationships within SST data. It not only considers node but also edge information, thereby adequately characterizing spatial correlations. Then, a long short-term memory (LSTM) network is used to capture temporal dynamics in the SST variation process. We choose the data from the Northwest Pacific Ocean to validate GMNN's effectiveness for SST prediction in different partitions, time scales, and prediction steps. The results show that our model has better performance for both complete and incomplete sea areas compared to other models. [ABSTRACT FROM AUTHOR]

Sea surface temperature is an important parameter of ocean hydrology. Accurate prediction of sea surface temperature is of great significance for ocean economic development and extreme weather prevention. Application of deep learning-based method in sea surface temperature prediction has significantly increased due to its high analytical power. Nevertheless, sea surface temperature time series are so volatile and stochastic, leading to the fact that in-depth analysis and accurate prediction of sea surface temperature are still challenging. Considering non-stationary and nonlinear characteristics in sea surface temperature sequence, variational mode decomposition is adopted as the de-noising module to reduce the influence of noise. Furthermore, the convolutional neural network is combined with the gated recurrent unit network to extract both spatial and temporal features in sea surface temperature sequence, simultaneously. Finally, a sea surface temperature prediction model based on deep learning model with frequency analysis module is proposed in this paper. The sea surface temperature of East China Sea is selected for empirical study. Comparative analysis demonstrated that the proposed model significantly outperforms the existing sea surface temperature prediction models. Especially, even to the mesoscale level, mean absolute error and root mean squared error of the proposed method are still 0.2038 and 0.2481, respectively. [ABSTRACT FROM AUTHOR]

Prediction of summer precipitation in north China (NCP) has long been a challenge partly because its low correlation with previous sea surface temperature (SST) anomalies (SSTA) limits the application of SST in NCP prediction. This study aims to extract optimal predictors of NCP from the SST field using an objective method—empirically optimal screening (EOS). It finds that the optimal precursory signal of NCP lies in the change of SSTA from winter to spring rather than the SSTA itself. This study identifies two optimal precursory signs predicting a positive (negative) NCP anomaly: the anomalous SST cooling (warming) from winter to spring in the coastal area of Somalia and Peru. Interestingly, these two presummer conditions have considerable independence, but they lead to a similar summer development of La Niña (El Niño). In summer, the tropical precipitation anomaly pattern associated with La Niña (El Niño) development excites a meridional wave train over the western Pacific and the circumglobal teleconnection in the Northern Hemisphere. Both of the anomalous wave trains show abnormal high (low) pressure over northeast Asia, which induces the south (north) wind anomalies over north China and produces abundant (deficient) precipitation there. These results highlight the importance of the SST evolution from winter to spring, break through the limitation of SST application in NCP prediction, and thus bring a prospect of improving NCP forecast skills. Significance Statement: Sea surface temperature (SST) anomalies are most used as predictors in climate prediction. However, the forecast of summer precipitation in north China is limited by its low correlation with prior SST anomalies. In this paper, we find that the optimal precursory signal of north China precipitation (NCP) is not the SST anomaly itself, but the changes of SST anomalies from winter to spring in the coastal area of Somalia and Peru. These two precursory signals are almost independent yet indicate similar summer situations leading to NCP anomaly. These results highlight the importance of the dynamic evolution of sea surface temperature in improving the forecast skill of NCP. [ABSTRACT FROM AUTHOR]

One of the main indicators for detecting changes in climate and marine ecosystems around the world is the sea surface temperature (SST). Even with several models presented in the literature, it is still a challenging task when only a single model is considered for SST forecasting. In this context, hybrid approaches that combine statistical models (to estimate linear dependencies) and machine learning models (to estimate nonlinear dependencies from residuals) have attained highlighted accuracy in several time series forecasting problems. In this way, this paper proposes a hybrid system that combines the autoregressive integrated moving average (ARIMA) model with a deep morphological neural network model, which employs dilation-erosion operators to estimate ARIMA's model residuals. In this context, the proposed hybrid system is composed of three stages: (1) time series forecast using the ARIMA model, (2) residuals forecast using the deep morphological neural network and (3) a linear combination of the stages (1) and (2). Three SST time series are used in the experimental analysis. Moreover, the achieved results are evaluated using three relevant statistical measures showing that the proposed hybrid system attains higher accuracy when compared to other hybrid models in the literature. [ABSTRACT FROM AUTHOR]

The coming years will see the launch of several missions (TRISHNA, LSTM, SBG), which will acquire images in four or more spectral bands in thermal infrared (TIR) at high spatial resolution (~50–60 m) and with high temporal revisit (~2–3 days). The derivation of surface temperature and emissivity values from top-of-atmosphere radiances is not straightforward, as it is a non-deterministic process requiring additional information. In this paper, we propose the algorithm DirecTES to efficiently separate surface temperature and emissivity. This algorithm is based on the use of a comprehensive spectral database of emissivity, resulting in a well-posed deterministic problem while not assuming strong hypotheses. The algorithm can also benefit from non-TIR information, such as the acquisitions from the same satellite but in the visible and near-infrared domains, or exogenous data—land/sea mask or soil-occupation map. These would help identify the nature of the surface and therefore improve the temperature and emissivity retrievals. After the complete description of the method, we evaluate the performances of DirecTES on theoretical landscapes in TRISHNA's context under a large range of atmospheric conditions. The retrievals of surface temperature reach RMSEs of 0.8 K over vegetation and 0.5 K over water, including both sensor and atmospheric uncertainties. We then evaluate DirecTES on ECOSTRESS images on sites where the ECOSTRESS Land Surface Temperature (LST) performance has been documented; DirecTES surface temperature retrievals are consistent with the ECOSTRESS LST product and the in-situ data. [ABSTRACT FROM AUTHOR]

Since model bias and associated initialization shock are serious shortcomings that reduce prediction skills in state‐of‐the‐art decadal climate prediction efforts, we pursue a complementary machine‐learning‐based approach to climate prediction. The example problem setting we consider consists of predicting natural variability of the North Atlantic sea surface temperature on the interannual timescale in the pre‐industrial control simulation of the Community Earth System Model. While previous works have considered the use of recurrent networks such as convolutional LSTMs and reservoir computing networks in this and other similar problem settings, we currently focus on the use of feedforward convolutional networks. In particular, we find that a feedforward convolutional network with a Densenet architecture is able to outperform a convolutional LSTM in terms of predictive skill. Next, we go on to consider a probabilistic formulation of the same network based on Stein variational gradient descent and find that in addition to providing useful measures of predictive uncertainty, the probabilistic (Bayesian) version improves on its deterministic counterpart in terms of predictive skill. Finally, we characterize the reliability of the ensemble of machine learning models obtained in the probabilistic setting by using analysis tools developed in the context of ensemble numerical weather prediction. Plain Language Summary: Businesses and government agencies rely heavily on numerical predictions of climate variables such as temperature and precipitation for a wide variety of purposes ranging from integrated assessment to developing mitigation strategies to developing resilience and adaptation strategies. Developing interannual to decadal predictions using comprehensive and complex climate and earth system models, however, are computationally intensive. As such, computationally efficient and accurate surrogates of comprehensive earth system models is highly desired. Data‐driven models using advanced deep learning algorithms are promising for this purpose. This paper first considers a recently proposed convolutional network architecture to develop such a surrogate and then integrates Bayesian inference to this architecture to further assess predictive uncertainty. We show that the resulting Bayesian deep learning model not only improves prediction accuracy but also quantifies the uncertainty arising from the data and model. Key Points: Model bias and associated initialization shock are serious shortcomings that reduce prediction skill in state‐of‐the‐art decadal climate prediction effortsA complementary machine‐learning‐based approach to climate prediction is considered. Both deterministic and probabilistic machine learning approaches are examinedIn addition to providing useful measures of predictive uncertainty, Bayesian versions of deep learning models outperform their deterministic counterparts in terms of predictive skill [ABSTRACT FROM AUTHOR]

Mitigating the impacts of droughts and floods over East Africa (EA) may largely be possible through accurate predictions of the long rains (March–May). Though the close interannual variability between the long rains, Indian and Pacific Oceans has been widely studied, Tanzania's long rain variability remains unclear. This paper examines the Tanzanian long rains dominant modes through an empirical orthogonal function (EOF) analysis, and their relationship with winter sea surface temperature (SST) for the period 1951–2018. Correlation and composite analyses were applied to explore the associated mechanisms. The first EOF (EOF1) mode is characterized by negative anomalies over Tanzania. The second EOF (EOF2) mode depicts positive (negative) anomalies over northern (southern) Tanzania. The third EOF (EOF3) mode reveals positive (negative) anomalies over eastern (western) Tanzania. EOF1 is closely related to the SST anomalies (SSTAs) in the southeastern Indian Ocean (SEIO). When the SSTAs are positive, they subsequently linked to upper (lower) level convergence (southeasterly winds) over Tanzania. Associated with this convergence entails a sinking motion, leading to a negative rainfall anomaly. The southeasterly winds indicate an early onset of the southeast monsoon, leading to normal rainfall been recorded. EOF2 is linked to the SSTAs in the central Pacific (CP). Positive SSTAs are related to reversed Walker circulation (WC) over northern Tanzania, while the upper‐level convergence was observed around southern Tanzania. This meridional dipole vertical motion pattern enhances (suppresses) rainfall anomalies. EOF3 is associated with mega El Niño–Southern Oscillation (ENSO)‐like SSTAs. The SSTAs are related to eastward shift of WC over the Indian Ocean, and zonal cell over the western Indian Ocean with rising limb is located east of Tanzania. The ascending motion enhances rainfall over eastern Tanzania. Predictions based on the SEIO, CP, and mega ENSO index may significantly support long rains prediction efforts and provide early warning. [ABSTRACT FROM AUTHOR]

In this paper, the multi-polar coupling mid-long term prediction method based on Sea Surface Temperature (SST) was introduced into the upper reaches of the Yellow River where China's water resources are relatively scarce. A mid-long term prediction model for the inflow runoff of Longyangxia Reservoir in the upper reaches of the Yellow River was constructed based on SST. The influence factors that dominate the runoff in the upper reaches of the Yellow River were found. The case study shows that the model built in this paper significantly reduces the forecast error (the forecast error decreases from -21% to -2.5% during the period from April to June 2018), which verifies the rationality of the model, the effectiveness of introducing the new technology and the stability of forecasting accuracy. It provides reference for other projects of medium and long-term runoff forecasting. [ABSTRACT FROM AUTHOR]

The 3rd full-mission reanalysis (RAN3) of global sea surface temperature (SST) with a 750 m resolution at nadir is available from VIIRS instruments flown onboard two JPSS satellites: NPP (February 2012–present) and N20 (January 2018–present). Two SSTs, 'subskin' (sensitive to skin SST) and 'depth' (proxy for in situ SST at depth of 20 cm), were produced from brightness temperatures (BTs) in the VIIRS bands centered at 8.6, 11 and 12 µm during the daytime and an additional 3.7 µm band at night, using the NOAA Advanced Clear Sky Processor for Ocean (ACSPO) enterprise SST system. The RAN3 dataset is fully archived at NASA JPL PO.DAAC and NOAA CoastWatch, and routinely supplemented in near real time (NRT) with a latency of a few hours. Delayed mode (DM) processing with a 2 months latency follows NRT, resulting in a more uniform science quality SST record. This paper documents and evaluates the performance of the VIIRS RAN3 dataset. Comparisons with in situ SSTs from drifters and tropical moorings (D+TM) as well as Argo floats (AFs) (both available from the NOAA iQuam system) show good agreement, generally within the NOAA specifications for accuracy (±0.2 K) and precision (0.6 K), in a clear-sky domain covering 18–20% of the global ocean. The nighttime SSTs compare with in situ data more closely, as expected due to the reduced diurnal thermocline. The daytime SSTs are also generally within NOAA specs but show some differences between the (D+TM) and AF validations as well as residual drift on the order of −0.1 K/decade. BT comparisons between two VIIRSs and MODIS-Aqua show good consistency in the 3.7 and 12 µm bands. The 11 µm band, while consistent between NPP and N20, shows residual drift with respect to MODIS-Aqua. Similar analyses of the 8.6 µm band are inconclusive, as the performance of the MODIS band 29 centered at 8.6 µm is degraded and unstable in time and cannot be used for comparisons. [ABSTRACT FROM AUTHOR]

The paper compares air and sea surface temperatures in recent years on two islands in the Adriatic Sea. The data measured at the climatological station Krk on the island of Krk and the main meteorological station Lastovo on the island of Lastovo are used. The island of Krk is located in the north of the Adriatic Sea and Lastovo in the south. Since a significant increase in air and sea surface temperatures has been observed over the last thirty years, the goal is to establish how they reflect at these two stations, 313 km apart. The goal of the analysis is to monitor the changes in these two islands to reduce the negative impacts they may cause. The analysis of sea temperatures showed that global warming has a greater impact in the northern Adriatic than in the southern Adriatic. Air and sea surface temperatures have a faster upward trend on Krk than on Lastovo. Similar to the Mediterranean Sea, a positive trend was observed in the Adriatic Sea for both sea surface temperature and air temperature. [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]

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

In 2014, an unexpected and unusual warm patch of ocean water was discovered in the northeast Pacific Ocean (PO) that moved south, reaching the Mexican coasts; this patch, returning in 2019, was nicknamed "the Blob". This paper aimed to assess the impacts of this phenomenon on the sea surface temperature (SST) and chlorophyll-a (CHLA) levels in the Gulf of California (GC), a high-productivity region. Daily satellite images of SST and CHLA with a spatial resolution of 1 km/pixel were obtained for 2014, 2017, and 2019 from the Moderate Resolution Imaging Spectroradiometer (MODIS). Two disc-shaped areas of around 25 km in diameter were selected in the southern portion of the GC to assess the variability of both parameters quantitatively. An additional site was selected in the PO for comparison and, thus, to better characterize and have a complete vision of these events. The results showed that in the PO, during the periods in which the Blob occurred, the SST values were higher concerning the neutral year (2017), and the levels of CHLA were very low (barely 0.15 mg m-3). Within the GC, the results showed the presence of a strong seasonal variability, with maximum values of SST (>30°C) and the lowest concentrations of CHLA (<2 mg m-3) during the summer, with maximum concentrations of CHLA (~10 mg m-3) observed during the winter months. Contrary to expectations, no dramatic changes in SST and CHLA were observed during the years impacted by the Blob. This apparent absence of negative impacts could be related to different mechanisms in the gulf that "protect" to avoid climate disruptions. The presence of complex geomorphology and hydrodynamic processes at different scales induce mixing and fertilizing of the euphotic layer. Could these factors protect the southern gulf from the negative impacts of the Blob? [ABSTRACT FROM AUTHOR]

Recent rapid melting of the summer Greenland ice sheet (GrIS) and its impact on Earth's climate has attracted much attention. In this paper, we establish a connection between the melting of GrIS and the variability of summer sea surface temperature (SST) anomalies over the North Atlantic on interannual to interdecadal time scales through changes in subseasonal Greenland blocking (GB). It is found that the latitude and width of GB are important for the spatial patterns of the GrIS melting. The melting of the GrIS on interdecadal time scales is most prominent for the positive Atlantic multidecadal oscillation phase (AMO+) because the high-latitude GB and its large width, long lifetime, and slow decay are favored. However, the North Atlantic mid-high latitude warm–cold–warm (cold–warm–cold) tripole, referred to as the NAT+ (NAT−) pattern, on interannual time scales tends to strengthen (weaken) the role of AMO+ in the GrIS melting, especially on the northern or northeastern periphery of Greenland, by promoting (inhibiting) high-latitude GB and increasing (decreasing) its width. It is further revealed that AMO+ (NAT+) favors the persistence and width of GB mainly through producing weak summer zonal winds and a small summer meridional potential vorticity gradient (PVy) in the North Atlantic mid-high latitudes at 55°–70°N (55°–65°N) compared to the role of negative AMO (NAT−). The event frequency and zonal width of GB events and their poleward shift are favored by the combination of NAT+ with AMO+. In contrast, the combination of NAT− and AMO+ tends to suppress reduced summer zonal winds and PVy, thus inhibiting the event frequency of GB events and their poleward shift and zonal width. Significance Statement: Rapid melting of the summer Greenland ice sheet (GrIS) has been observed to frequently occur, especially after the year 2000, leading to a rise in sea level and other effects on Earth's climate. The physical cause of the rapid melting of the GrIS is an important area of research. We establish a connection between the summer melting of the GrIS and different sea surface temperature (SST) modes in the North Atlantic via changes in Greenland blocking. Although the positive Atlantic multidecadal oscillation (AMO+) phase favors the overall melting of GrIS, the phase of the North Atlantic tripole (NAT) SST pattern modulates the strength and location of the GrIS melting. The positive NAT phase (NAT+) with a warm–cold–warm tripole structure in the North Atlantic mid-high latitudes and AMO+ combine to result in a strong warm SST anomaly in the high latitudes of the North Atlantic north of 60°N, which promotes the melting of GrIS on the western, northern, and northeastern peripheries of Greenland via high-latitude Greenland blocking with an increased zonal width. The combination of the negative NAT phase (NAT−) with a cold–warm–cold tripole structure and AMO+ tends to suppress this effect. Thus, our results provide a new understanding of why the melting of GrIS shows a strong variability in strength and region. [ABSTRACT FROM AUTHOR]

Recent rapid melting of the summer Greenland ice sheet (GrIS) and its impact on Earth's climate has attracted much attention. In this paper, we establish a connection between the melting of GrIS and the variability of summer sea surface temperature (SST) anomalies over the North Atlantic on interannual to interdecadal time scales through changes in subseasonal Greenland blocking (GB). It is found that the latitude and width of GB are important for the spatial patterns of the GrIS melting. The melting of the GrIS on interdecadal time scales is most prominent for the positive Atlantic multidecadal oscillation phase (AMO+) because the high-latitude GB and its large width, long lifetime, and slow decay are favored. However, the North Atlantic mid-high latitude warm–cold–warm (cold–warm–cold) tripole, referred to as the NAT+ (NAT−) pattern, on interannual time scales tends to strengthen (weaken) the role of AMO+ in the GrIS melting, especially on the northern or northeastern periphery of Greenland, by promoting (inhibiting) high-latitude GB and increasing (decreasing) its width. It is further revealed that AMO+ (NAT+) favors the persistence and width of GB mainly through producing weak summer zonal winds and a small summer meridional potential vorticity gradient (PVy) in the North Atlantic mid-high latitudes at 55°–70°N (55°–65°N) compared to the role of negative AMO (NAT−). The event frequency and zonal width of GB events and their poleward shift are favored by the combination of NAT+ with AMO+. In contrast, the combination of NAT− and AMO+ tends to suppress reduced summer zonal winds and PVy, thus inhibiting the event frequency of GB events and their poleward shift and zonal width. Significance Statement: Rapid melting of the summer Greenland ice sheet (GrIS) has been observed to frequently occur, especially after the year 2000, leading to a rise in sea level and other effects on Earth's climate. The physical cause of the rapid melting of the GrIS is an important area of research. We establish a connection between the summer melting of the GrIS and different sea surface temperature (SST) modes in the North Atlantic via changes in Greenland blocking. Although the positive Atlantic multidecadal oscillation (AMO+) phase favors the overall melting of GrIS, the phase of the North Atlantic tripole (NAT) SST pattern modulates the strength and location of the GrIS melting. The positive NAT phase (NAT+) with a warm–cold–warm tripole structure in the North Atlantic mid-high latitudes and AMO+ combine to result in a strong warm SST anomaly in the high latitudes of the North Atlantic north of 60°N, which promotes the melting of GrIS on the western, northern, and northeastern peripheries of Greenland via high-latitude Greenland blocking with an increased zonal width. The combination of the negative NAT phase (NAT−) with a cold–warm–cold tripole structure and AMO+ tends to suppress this effect. Thus, our results provide a new understanding of why the melting of GrIS shows a strong variability in strength and region. [ABSTRACT FROM AUTHOR]

Interdecadal variations of the relationship between El Niño–Southern Oscillation (ENSO) and the Indo-China Peninsula (ICP) surface air temperature (SAT) in winter are investigated in the study. Generally, there exists a positive correlation between them during 1958–2015 because the ENSO-induced anomalous western North Pacific anticyclone (WNPAC) is conducive to pronounced temperature advection anomalies over the ICP. However, such correlation is unstable in time, having experienced a high-to-low transition around the mid-1970s and a recovery since the early 1990s. This oscillating relationship is owing to the anomalous WNPAC intensity in different decades. During the epoch of high correlation, the anomalous WNPAC and associated southwesterly winds over the ICP are stronger, which brings amounts of warm temperature advection and markedly heats the ICP. In contrast, a weaker WNPAC anomaly and insignificant ICP SAT anomalies are the circumstances for the epoch of low correlation. It is also found that substantial southwesterly wind anomalies over the ICP related to the anomalous WNPAC occur only when large sea surface temperature (SST) anomalies over the northwest Indian Ocean (NWIO) coincide with ENSO (viz., when the ENSO–NWIO SST connection is strong). The NWIO SST anomalies are capable of driving favorable atmospheric circulation that effectively alters ICP SAT and efficiently modulates the ENSO–ICP SAT correlation, which is further supported by numerical simulations utilizing the Community Atmospheric Model, version 4 (CAM4). This paper emphasizes the non-stationarity of the ENSO–ICP SAT relationship and also uncovers the underlying modulation factors, which has important implications for the seasonal prediction of the ICP temperature. [ABSTRACT FROM AUTHOR]

In this paper, the different effects of the eastern equatorial Pacific (EP) and central equatorial Pacific (CP) Ocean El Niño–Southern Oscillation (ENSO) events on interannual variation in the diurnal sea surface temperature (SST) are explored in both the Niño-3 and Niño-4 regions. In the Niño-3 region, the diurnal SST anomaly (DSSTA) is negative during both EP and CP El Niño events and becomes positive during both EP and CP La Niña events. However, the DSSTA in the Niño-4 region is positive in El Niño years and negative in La Niña years, which is opposite to that in the Niño-3 region. Further analysis indicates that the incident shortwave radiation (SWR), wind stress (WS), and upward latent heat flux (LHF) are the main factors causing the different interannual variations in the DSST. In the Niño-3 region, decreased SWR and increased LHF lead to a negative DSSTA in EP El Niño years, and enhanced WS and increased LHF cause a negative DSSTA in CP El Niño years. Conversely, in that same region, increased SWR and decreased LHF lead to a positive DSSTA in EP La Niña years, and reduced WS and decreased LHF cause a positive DSSTA in CP La Niña years. In the Niño-4 region, the reduced trade wind plays a key role in producing the positive DSSTA, whereas the decreased SWR has an opposite effect that reduces the range of the DSSTA during both EP and CP El Niño events, and conversely the enhanced trade wind plays a key role in producing the negative DSSTA, whereas the increased SWR has an opposite effect that increases the range of the DSSTA during both EP and CP La Niña events. [ABSTRACT FROM AUTHOR]

Comparative estimation of long-term trends in the variability of the largest Atlantic upwellings (Canary and Benguela) has been carried out in this paper. Satellite data on the sea surface temperature and three surface wind datasets prepared by different scientific groups have been used. The difference in the calculated absolute vertical velocities due to alongshore wind drift and horizontal wind-field nonuniformity reach tens of percent. All time series demonstrate the common tendency for the intensification of the upwellings from the 1980s to the early 1990s. This confirms published materials concerning the intensification of Canary and Benguela upwellings under global warming. However, since the late 1990s, the intensification of the upwellings has nearly ceased. This proves the highly important role of the natural interdecadal climate variations in the variability of intensity of generation of upwellings during the period of regular satellite measurements. [ABSTRACT FROM AUTHOR]

Large-scale climatic circulation modulates the weather patterns around the world. Understanding the teleconnections between large-scale circulation and local hydro-climatological variables has been a major thrust area of hydro-climatology research. The large-scale circulation is often quantified in terms of sea surface temperature (SST) and sea-level pressure (SLP). In this paper, we investigate the potential of wavelet neural network (WNN) hybrid model to predict maximum monthly discharge of the Madarsoo watershed, North of Iran considering two large-scale climatic signals like SST and SLP as inputs. Error measures like root-mean-square error (RMSE), and mean absolute error along with the correlation measures like coefficient of correlation (R), and Nash–Sutcliffe coefficient (CNS) were used to quantify the performance of prediction of maximum monthly discharge of three different hydrometry stations of the watershed. In all the cases, the WNN hybrid machine learning model was found to be giving superior performance consistently against the standalone artificial neural network (ANN) model and multiple linear regression model to predict the flood discharges of March and August months. The prediction of flood for August which is more devastating is found to be slightly better than the prediction of floods of March, in the stations served with smaller drainage area. The RMSE, R and CNS of Tamer hydrometry station in August were found to be 0.68, 0.996, and 0.99 m3/s, respectively, for the test period by using WNN model against 1.55, 0.989 and 0.95 by ANN model. Moreover, when evaluated for predicting the maximum monthly discharge in March and August between 2012 and 2013, the wavelet-based neural networks performed remarkably well than the ANN. [ABSTRACT FROM AUTHOR]

• We propose a new sea surface temperature inversion model. • The model is based on deep neural network. • This model is more accurate than traditional method. • The model is applied to different sea areas and we get good results. The traditional sea surface temperature (SST) inversion model has a complicated parameter fitting process and poor adaptability in different sea areas. This paper presents an infrared remote sensing inversion model of SST based on deep neural network to refine the situation. The training data are the moderate-resolution imaging spectroradiometer (MODIS) infrared remote sensing data on sunny days and measured data from buoy in Bohai. The accuracy of inversion results is analyzed, the determination coefficient of inversion and measured values is 0.98, the standard error is 0.71 °C and the mean absolute deviation is 0.85 °C, the results show good accuracy of the model. The accuracy of Bohai SST inversion results is compared with SST products from the MODIS sensors and the inversion model is applied to other sea areas, demonstrating the credibility and portability of the model. The data experiments in this paper prove the feasibility of the model, which provides ideas for global SST inversion. [ABSTRACT FROM AUTHOR]

This paper attempts to enhance our understanding of the causes of Atlantic multidecadal variability (AMV). Following the literature, we define the AMV as the SST averaged over the North Atlantic basin, linearly detrended and low-pass filtered. There is an ongoing debate about the drivers of the AMV, which include internal variability generated from the ocean or atmosphere (or both) and external radiative forcing. We test the role of these factors in explaining the time history, variance, and spatial pattern of the AMV using a 41-member ensemble from a fully coupled version of CESM and a 10-member ensemble of the CESM atmosphere coupled to a slab ocean. The large ensemble allows us to isolate the role of external forcing versus internal variability, and the model differences allow us to isolate the role of coupled ocean circulation. Both with and without coupled ocean circulation, external forcing explains more than half of the variance of the observed AMV time series, indicating its important role in simulating the twentieth-century AMV phases. In this model the net effect of ocean processes is to reduce the variance of the AMV. Dynamical ocean coupling also reduces the ability of the model to simulate the characteristic spatial pattern of the AMV, but forcing has little impact on the pattern. Historical forcing improves the time history and variance of the AMV simulation, while the more realistic ocean representation reduces the variance below that observed and lowers the correlation with observations. [ABSTRACT FROM AUTHOR]

Based on the observational and reanalysis datasets, a dipole pattern of the early summer (June) precipitation over the eastern Tibetan Plateau (TP) is identified through empirical orthogonal function. In contrast to previous researches, this paper introduces the May North Atlantic Oscillation (NAO) as a preceding signal and investigates its connection with subsequent early summer precipitation over the eastern TP during 1980–2019, focusing on predictive aspects. Results reveal that the correlation between NAO and primary eastern TP precipitation pattern (Precip_EOF1) has weakened since 2000. During 1980–2000, an equivalent barotropic Silk Road Pattern (SRP) observed across Eurasia triggers southerly wind anomalies over the TP. This, in turn, leads to the northward shift of the water vapor, which favors the ascending motion over the northern TP (NTP) due to water vapor convergence. Additionally, the SRP-related sensible heating also plays a critical role in vertical motion over the eastern TP by enhancing the TP thermal effect. Consequently, these factors contribute to shaping the dipole pattern of eastern TP precipitation. However, the weakened relationship between NAO and Precip_EOF1 during 2001–2019 is attributed to the absence of NAO-related atmospheric circulation over Eurasia. Further analysis suggests that these circulation anomalies arise from the interdecadal variability of the NAO and its phase shift. Moreover, when the influence of sea surface temperature anomalies over the northern North Atlantic is removed, the correlation of Precip_EOF1 with NAO and Atlantic Multidecadal Oscillation (AMO) both become insignificant. This suggests that the NAO-related dipole pattern of eastern TP precipitation is modulated by preceding northern North Atlantic sea surface temperature rather than AMO on the interdecadal timescale. • The relationship between North Atlantic Oscillation (NAO) and Tibetan Plateau (TP) precipitation has weakened since 2000. • The change in NAO-related Silk Road Pattern (SRP) is the dominant reason for the unstable correlation. • The interdecadal variability in TP precipitation is attributed to the NAO phase shift. • This relationship is modulated by northern North Atlantic sea surface temperature on the interdecadal scale. [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]

The impact of sea surface temperature anomalies (SSTAs) over the North Pacific (32°–45° N, 140° E–150° W) and North Atlantic (north: 52°–68° N, 60°–20° W; south: 0°–30° N, 100°–40° W) on the summer surface air temperature (SAT) over the Mongolian Plateau (MP) is studied using NCEP/NCAR and Climatic Research Unit (CRU) reanalysis data. The results show that the circumglobal teleconnection (CGT) wave train related to the SSTAs in the North Atlantic propagates along with the westerly jet, resulting in a geopotential height anomaly at 200 hPa that favors an SAT anomalies (SATAs) on the MP. In addition, an anticyclonic (cyclonic) circulation anomaly with the characteristics of an atmospheric ultralong wave at 200 hPa over the Eurasia–Pacific region associated with the SSTAs in the North Pacific is also responsible for the positive (negative) SATAs on the MP. The results further reveal that the SSTAs in the North Pacific and the northern North Atlantic act synergistically on the SATAs on the MP. If there are strongly positive (negative) SSTAs in the North Pacific and the northern North Atlantic at the same time, the probability of a strongly positive (negative) SATAs on the MP is 67% (100%) for the research period of this paper. However, if the strongly positive (negative) SSTAs only appear in the North Pacific or the northern North Atlantic, the probability of a strongly positive (negative) SATAs on the MP is less than 34% (31%). The synergism of the SSTAs in the North Pacific and the northern North Atlantic enhances Rossby wave energy in Eurasia, with more Rossby wave energy spreading to the MP that in turn causes the circulation anomaly on the MP. Meanwhile, baroclinic conditions at 850 hPa north of the North Pacific, the northern and southern North Atlantic favor a northward shift of the westerly jet that guides the Rossby wave downstream so that the circulation anomaly that produces the SATAs on the MP is located in the mid–high latitudes. [ABSTRACT FROM AUTHOR]

This paper describes a technique to perform traceable temperature measurements of the seawater column and seawater surface, based on optical fiber Bragg gratings. The paper explains the different phases of the work done: design of the optical fibers, its optical and thermal calibration and onsite measurements of the seawater temperature. In the design of these thermometers, special attention was paid to the involved materials in order to prevent any damage of the fibers due to the exposure to such rugged environment. The fibers were subjected to optical and thermal calibration, with the aim to get traceable measurements and reliable uncertainty calculation of the seawater temperature. The fibers were deployed in the Mediterranean Sea and water temperatures were continuously monitored and compared with the most common used thermometer in this environment, CTD, located in the submarine observatory OBSEA. [ABSTRACT FROM AUTHOR]

In this paper, the combination of the deep evidential regression network (DERN) and empirical orthogonal function (EOF) methods, namely, the DERN-EOF method, was proposed to reconstruct the subsurface temperature profiles. Firstly, the DERN method was compared with the previous machine learning methods used in the subsurface temperature field reconstruction, for example the Self-organizing map method. The new network was shown to have certain advantages of higher accuracy and the capability to provide uncertainty estimation, which enables the reconstruction of the subsurface field with higher belief. Analysis of the method performance suggests that the DERN could improve reconstruction accuracy by almost 20% and that the uncertainty statistics agrees well with the data distribution. Then the DERN method combined with the EOF method was used to reconstruct subsurface temperature profiles at a global scale. Results suggest that the reconstruction error in highly dynamic regions, like the Kuroshio Extension and Gulf Stream regions, is significantly higher than that in other regions, and the uncertainties increase with the reconstruction error, suggesting that the proposed method can provide a relatively believable uncertainty estimation. • A new method was proposed to reconstruct ocean subsurface temperature. • The method was based on the deep evidential regression network and the EOF method. • The method could provide uncertainty estimate and higher regression accuracy. • The method performance was studied both at a regional scale and a global scale. [ABSTRACT FROM AUTHOR]

This paper aims to evaluate spatiotemporal variability of the mean sea level pressure trends over the Black Sea using the gridded 40-year (1979-2018) reanalysis mean sea level pressure data from two different datasets. These datasets are the European Centre for Medium-Range Weather Forecasts ERA-Interim with a spatial resolution of 0.25° and temporal resolution of six hours, and the National Centers for Environmental Prediction/Climate Forecast System Reanalysis with a spatial resolution of 0.5° and temporal resolution of one hour. Data from both databases show that the mean sea level pressure tends to decrease towards the recent years over the entire Black Sea. The long-term averages of mean sea level pressure reflect the spatial variability over the Black Sea with much lower pressures in the eastern part of the Black Sea than that on the western side. The long-term variation is more intense in the eastern part of the Black Sea. Sea Level Anomaly over the Black Sea, spanning 26 years between 1993 and 2018, was analyzed using satellite altimetry data. It was found that there is a high Sea Level Anomaly where the mean sea level pressure is low, described by an inverted barometer response. The 39-year long Sea Surface Temperature Anomaly data (1981-2018) indicate the rising tendency in sea surface temperature towards recent years in the entire Black Sea. An inverse relationship is found between North Atlantic Oscillation index and sea surface temperature anomaly. On a seasonal scale, mean sea level pressure in winter (high-pressure system) is larger than that in summer (low-pressure system). Our analyses show that mean sea level pressure tends to decrease, sea surface temperature anomaly and sea surface temperature anomaly tends to increase in recent years over the Black Sea. [ABSTRACT FROM AUTHOR]