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The offshore China (OC) region is a significant sea area in the Western Pacific and many researchers have been interested in the distribution of sea surface temperature (SST) fronts in this area. In this study, the Cayula and Cornillon single image edge detection algorithm was used to detect SST fronts using the Daily Optimum Interpolation Sea Surface Temperature data from 1982 to 2021. The results revealed that there are eighteen SST fronts in OC— three in the Bohai Sea, seven in the Yellow Sea, two in the East China Sea, five in the South China Sea, one in the Pacific Ocean east of Taiwan province, China—and among them a new front was detected in the Yellow Sea and named the Yellow Sea Ring Front. The frequency of most fronts showed a tendency of initially increasing and then decreasing from January to September, followed by a trend of growing steadily from October to December. The frequency of a few fronts showed a decreasing tendency from January to September and an increasing tendency from October to December. The frequency of most fronts is highest in winter and lowest in summer. In spring and autumn, the frequency of most fronts is lower than that in winter and higher than that in summer. The annual average frontal probability of five-ninths of the fronts showed an upward trend, and the annual average frontal probability value of one-third of the fronts showed a downward trend. The rest of the fronts showed a stable trend. The results of this paper also showed that the Liaodong Bay Front and the Bohai-Laizhou Bay Front did not form a complete front, as previously reported. In addition, the frontal probability of the Bohai Front to the north of 39° N was in the tendency of decreasing. [ABSTRACT FROM AUTHOR]

For the purpose of exploring the long-term variation of regional sea surface temperature (SST), this paper studies the historical SST in regional sea areas and the emission pattern of greenhouse gases, proposing a Grey model of regional SST atmospheric reflection which can be used to predict SST variation in a long time span. By studying the Grey systematic relationship between historical SST data, the model obtains the development law of temperature variation, and further introduces different greenhouse gas emission scenarios in the future as the indexes coefficient to determine the corresponding changing results of seawater temperature in the next 50 years. Taking the North Atlantic Ocean as an example, the cosine similarity test method is used to verify the model proposed in this paper. The accuracy of the model is as high as 0.99984. The model predicts that the regional SST could reach a maximum of 15.3 ∘ C by 2070. This model is easy to calculate, with advantages of the high accuracy and good robustness. [ABSTRACT FROM AUTHOR]

Since 2002, China has launched four Haiyang-1 (HY-1) satellites equipped with the Chinese Ocean Color and Temperature Scanner (COCTS), which can observe the sea surface temperature (SST). The planned new generation ocean color observation satellites also carry a sensor for observing the SST represented by the payload in this paper. We analyze the spectral brightness temperature (BT) difference between the payload and the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra for the thermal infrared channels (11 and 12 µm) based on atmospheric radiative transfer simulation. The bias and standard deviation (SD) of spectral BT difference for the 11 µm channel are −0.12 K and 0.15 K, respectively, and those for the 12 µm channel are −0.10 K and 0.03 K, respectively. When the total column water vapor (TCWV) decreases from the oceans near the equator to high-latitude oceans, the spectral BT difference of the 11 µm channel varies from a positive deviation to a negative deviation, and that of the 12 µm channel basically remains stable. By correcting the MODIS BT observation using the spectral BT differences, we produce the simulated BT data for the thermal infrared channels of the payload, and then validate it using the Infrared Atmospheric Sounding Interferometer (IASI) carried on METOP-B. The validation results show that the bias of BT difference between the payload and IASI is −0.22 K for the 11 µm channel, while it is −0.05 K for the 12 µm channel. The SD of both channels is 0.13 K. In this study, we provide the simulated BT dataset for the 11 and 12 µm channels of a payload for the retrieval of SST. The simulated BT dataset corrected may be used to develop SST-retrieval algorithms. [ABSTRACT FROM AUTHOR]

Recent studies have highlighted the increasingly dominant role of external forcing in driving Atlantic and Pacific Ocean variability during the second half of the 20th century. This paper provides insights into the underlying mechanisms driving interactions between modes of variability over the two basins. We define a set of possible drivers of these interactions and apply causal discovery to reanalysis data, an ensemble of pacemaker simulations where the observed El Niño Southern Oscillation (ENSO) is prescribed, and a pre-industrial control simulation. We also utilize large ensemble means of historical simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to quantify the effect of external forcing and improve the understanding of its impact. By conducting a causal analysis of the historical time series, a regime switch is identified in the interactions between major modes of Atlantic and Pacific climate variability. Causal networks derived from pacemaker simulations support this finding and further demonstrate that the effect of external forcing could favor an Atlantic-driven regime between 1985 and 2014 where warming tropical North Atlantic sea surface temperatures induce a La Niña-like cooling in the equatorial Pacific during the following season through a strengthening of the Pacific Walker Circulation. This negative sign effect was not detected when the historical external forcing signal is removed in the pacemaker ensemble. The analysis of the pre-industrial control run further supports the notion that the Atlantic and Pacific modes of natural climate variability exert contrasting impacts on each other even in the absence of external forcing. We show that causal discovery can quantify previously unknown connections and thus provides important potential to contribute to a deeper understanding of the mechanisms driving changes in regional and global climate variability. [ABSTRACT FROM AUTHOR]

It has been widely recognized that tropical cyclone (TC) genesis requires favorable large‐scale environmental conditions. Based on these linkages, numerous efforts have been made to establish an empirical relationship between seasonal TC activities and large‐scale environmental favorability in a quantitative way, which lead to conceptual functions such as the TC genesis index. However, due to the limited amount of reliable TC observations and complexity of the climate system, a simple analytic function may not be an accurate portrait of the empirical relationship between TCs and their ambiences. In this research, we use convolution neural networks (CNNs) to disentangle this complex relationship. To circumvent the limited amount of seasonal TC observation records, we implement transfer‐learning technique to train ensemble of CNNs first on suites of high‐resolution climate model simulations with realistic seasonal TC activities and large‐scale environmental conditions, and then on a state‐of‐the‐art reanalysis from 1950 to 2019. The trained CNNs can well reproduce the historical TC records and yields significant seasonal prediction skills when the large‐scale environmental inputs are provided by operational climate forecasts. Furthermore, by inputting the ensemble CNNs with 20th century reanalysis products and Phase 6 of the Coupled Model Intercomparison Project (CMIP6) simulations, we investigated TC variability and its changes in the past and future climates. Specifically, our ensemble CNNs project a decreasing trend of global mean TC activity in the future warming scenario, which is consistent with our future projections using high‐resolution climate model. Plain Language Summary: Tropical cyclones (TCs) require favorable large‐scale environmental conditions to form. Pioneer studies show that these conducive conditions include warm sea surface temperatures (SSTs), sufficient low‐level vorticities and mid‐level humilities, as well as weak‐to‐moderate vertical wind shears. Several follow‐up studies have focused on improving the empirical linkage between number of TC and environmental conditions and developed sets of TC genesis index based on conventional statistical methods. Although these indices can capture climatology of TC genesis spatial distributions and seasonal variation reasonably well, their representations of interannual TC variability are degraded. With the aim to better represent TC interannual variability and long‐term trend using large‐scale environmental conditions, we trained ensembles of convolution neural networks (CNNs) based on the combination of observations and large sets of high‐resolution dynamical climate simulations. The trained CNNs perform significantly well in capturing observed TC interannual‐to‐multidecadal variability and are broadly applicable to many areas of seasonal TC activities. Using a deep learning technique, this paper introduces a new potential avenue to improve our understanding of TC variability and future changes. Key Points: Ensemble convolutional neural networks (CNNs) are trained to emulate seasonal tropical cyclone (TC) activity using environmental factorsThe trained CNNs can be utilized to study seasonal TC variability, and their changes in the past, current and future climatesSkillful seasonal TC predictions can be made using CNN‐based statistical‐dynamical hybrid framework [ABSTRACT FROM AUTHOR]

Nepal experienced record-breaking summer monsoon precipitation in 2020, becoming the wettest year of the last four decades (1980–2020). This paper explores the role of large-scale atmospheric circulation and sea surface temperature patterns in tipping an otherwise typical wet summer into an extreme one in 2020. The unusually high precipitation was fuelled by the Western North Pacific Anticyclone (WNPAC), concomitant with the strong presence of moist static energy and enhanced vertical velocity in and near Nepal. Sea surface temperature anomalies showed an intense Indian Ocean warming and features of a La Niña, succeeding a weak El Niño event that had sustained the WNPAC. The southwesterly winds on the northern flank of then WNPAC contributed excess moisture transport and convergence to Nepal, consequently resulting in elevated precipitation. This feature is consistent with previous wet summer years, but the presence of WNPAC in those years followed the El Niño event. Therefore, it appears that the Indo-western Pacific Ocean Capacitor (IPOC) effect exerts the above-normal precipitation for the summer of 2020 without involving the El Niño event, while anthropogenic warming may have further tipped the balance. [ABSTRACT FROM AUTHOR]

Polar and subpolar oceans play a particularly important role in the global climate and its temporal changes, yet these regions are less well sampled than the rest of the global ocean. To better understand the physical or biogeochemical properties and their variabilities in these regions, accurate data mapping is crucial. In this paper, we introduce a mapping methodology that includes a water column shrinking and stretching constraint (SSC) based on the principle of conservation of potential vorticity. To demonstrate the mapping scheme efficiency, we map the ocean temperature in the southern Sea of Okhotsk, where the bottom topography comprises a broad and shallow shelf, a sharp continental slope, and a deep oceanic basin. Such topographic features are typical of polar and subpolar marginal seas. Results reveal that the SSC integrated (SSCI) mapping strongly reduces the mapping error in the broad and shallow shelf compared with a recently introduced topographic constraint integrated (TCI) mapping procedure. We also tested our mapping scheme in the Southern Ocean, which has a comparatively slanted shelf, a wider and gentler slope, and a deep and broad oceanic basin. We found that the SSCI and TCI methods are practically equivalent there. The SSCI mapping is thus an effective method to map the ocean's properties in various topographic environments and should be adequate in all polar and subpolar regions. Importantly, we introduced a standardized procedure for determining the decorrelation length scales—a necessary step prior to implementing any mapping scheme—in any topographic conditions. [ABSTRACT FROM AUTHOR]

The organisation of trade-wind cumulus clouds in the vicinity of Barbados can affect the amount and lifetime of the clouds thereby potentially playing an important role in the top of the atmosphere radiation budget. This paper presents a case study of a flower cloud system that occurred on 2 February 2020 near Barbados during the EUREC4A field campaign. The evolution of a cluster of clouds that developed into a flower system is investigated from 0000 UTC to 2300 on 2 February using GOES-16 satellite IR and visible images, dropsonde data, and ERA5 reanalysis data. The cloud system began as small clouds ~ 400 km to the north-east of Barbados. Aggregation of clouds continued to occur as the system moved towards Barbados. A striking feature was the development of a large area of rain in the central region of the system in the later stages of development when the area plateaued. Several cloud arcs associated with cold pools became visible as they emerged from the cloud shield. The flower system began to decay about 7 hours after the maximum values of derived effective radius. The environmental conditions determined from ERA5 reanalysis along the trajectory of the flower were examined. The sea surface temperatures were 26.9 ± 0.3 °C along the trajectory. The surface wind speed in the proximity of the flower increased during the first six hours in the early development stage from 7 m s−1 to 9 m s−1 and then decreased gradually to about 5 m s−1 during the next 12 hours. The inversion strength measured with dropsonde was greater in the flower than in its surrounding locations due to the mesoscale variation in temperature field and, probably, the interactions between the flower and the atmosphere. The cloud system was surrounded by high total precipitable water in the early stages and along the trajectory as it developed into the flower. The Barbados region was marked by high aerosol optical depth on the day. The life cycle of the flower cloud system characterized in this paper will help evaluate numerical simulations of shallow convective organisation. [ABSTRACT FROM AUTHOR]

The NOAA in situ Sea Surface Temperature (SST) Quality Monitor (iQuam) online system collects in situ SSTs from various sources, performs quality control (QC), and provides QC'ed data to users. Like many other in situ QCs, the iQuam QC employs comparisons with Level 4 SST analysis. However, the current daily L4 analyses do not capture the diurnal cycle, nor do they resolve the fine structure of SST in dynamic areas. As a result, high-quality in situ SSTs significantly deviating from the L4 SST may be rejected. This paper discusses the new Diurnal Reference Check (DRC), which addresses overscreening for buoys whose sampling frequency is sufficient for resolving the diurnal cycle. The DRC separates records from individual buoys into 24-h segments and characterizes each segment with the median nighttime (MNT) SST and the amplitude of the diurnal signal (ADS). The segment is rejected if the ADS is unrealistically large or if the difference between the MNT and L4 SST exceeds a geographically dependent threshold. The outliers are further screened out by comparison of individual in situ SSTs with the MNT. All thresholds are determined from the analysis of matchups with reprocessed NOAA SSTs from multiple low-orbiting satellites. The satellite matchups are also used to validate the QC results. The DRC minimizes the overscreening, increases the number of high-quality in situ data by ~5%, and reduces the QC reliance on the L4 analysis. In addition, a new retrospective satellite-based quality check is introduced to identify matchups, which are most useful for training SST algorithms and validation of reprocessed satellite data. [ABSTRACT FROM AUTHOR]

In a previous paper, we introduced (i) a specific hyperspectral mixing model for the sea bottom, based on a detailed physical analysis that includes the adjacency effect, and (ii) an associated unmixing method that is supervised (i.e., not blind) in the sense that it requires a prior estimation of various parameters of the mixing model, which is constraining. We here proceed much further, by first analytically showing that the above model can be seen as a specific member of the general class of mixing models involving spectral variability. Therefore, we then process such data with the IP-NMF unsupervised (i.e., blind) unmixing method that we proposed in previous works to handle spectral variability. Such variability especially occurs when the sea depth significantly varies over the considered scene. We show that IP-NMF then yields significantly better pure spectra estimates than a classical method from the literature that was not designed to handle such variability. We present test results obtained with realistic synthetic data. These tests address several reference water depths, up to 7.5 m, and clear or standard water. For instance, they show that when the reference depth is set to 7.5 m and the water is clear, the proposed approach is able to distinguish various classes of pure materials when the water depth varies up to ± 0.2 m around this reference depth, over all pixels of the analyzed scene or over a "subscene": the overall scene may first be segmented, to obtain smaller depths variations over each subscene. The proposed approach is therefore effective and can be used as a building block in performing the subpixel classification of the sea bottom for shallow water. [ABSTRACT FROM AUTHOR]

Observations of ocean-driven grounding-line retreat in the Amundsen Sea Embayment in Antarctica raise the question of an imminent collapse of the West Antarctic Ice Sheet. Here we analyse the committed evolution of Antarctic grounding lines under the present-day climate. To this aim, we first calibrate a sub-shelf melt parameterization, which is derived from an ocean box model, with observed and modelled melt sensitivities to ocean temperature changes, making it suitable for present-day simulations and future sea level projections. Using the new calibration, we run an ensemble of historical simulations from 1850 to 2015 with a state-of-the-art ice sheet model to create model instances of possible present-day ice sheet configurations. Then, we extend the simulations for another 10 000 years to investigate their evolution under constant present-day climate forcing and bathymetry. We test for reversibility of grounding-line movement in the case that large-scale retreat occurs. In the Amundsen Sea Embayment we find irreversible retreat of the Thwaites Glacier for all our parameter combinations and irreversible retreat of the Pine Island Glacier for some admissible parameter combinations. Importantly, an irreversible collapse in the Amundsen Sea Embayment sector is initiated at the earliest between 300 and 500 years in our simulations and is not inevitable yet – as also shown in our companion paper Part 1,. In other words, the region has not tipped yet. With the assumption of constant present-day climate, the collapse evolves on millennial timescales, with a maximum rate of 0.9 mma-1 sea-level-equivalent ice volume loss. The contribution to sea level by 2300 is limited to 8 cm with a maximum rate of 0.4 mma-1 sea-level-equivalent ice volume loss. Furthermore, when allowing ice shelves to regrow to their present geometry, we find that large-scale grounding-line retreat into marine basins upstream of the Filchner–Ronne Ice Shelf and the western Siple Coast is reversible. Other grounding lines remain close to their current positions in all configurations under present-day climate. [ABSTRACT FROM AUTHOR]

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The SSS (sea surface salinity) is an important factor affecting global climate changes, sea dynamic environments, global water cycles, marine ecological environments, and ocean carbon cycles. Satellite remote sensing is a practical way to observe SSS from space, and the key to retrieving SSS satellite products is to establish an accurate sea surface brightness temperature forward model. However, the calculation results of different forward models, which are composed of different relative permittivity models and SSR (sea surface roughness) brightness temperature increment models, are different, and the impact of this calculation difference has exceeded the accuracy requirement of the SSS inversion, and the existing SSR brightness temperature increment models, which primarily include empirical models and theoretical models, cannot match all the relative permittivity models. In order to address this problem, this paper proposes a universal DNN (deep neural network) model architecture and corresponding training scheme, and provides different SSR brightness temperature increment models for different relative permittivity models utilizing DNN based on offshore experiment data, and compares them with the existing models. The results show that the DNN models perform significantly better than the existing models, and that their calculation accuracy is close to the detection accuracy of a radiometer. Therefore, this study effectively solves the problem of SSR brightness temperature correction under different relative permittivity models, and provides a theoretical support for high-precision SSS inversion research. [ABSTRACT FROM AUTHOR]

El Niño-Southern Oscillation (ENSO) asymmetry in predictability on springtime initial condition remains unclear. From the perspective of the spring predictability barrier (SPB), this paper investigates the ENSO asymmetry in SPB and explores the potential factors that may lead to this asymmetry. Both the observation and 29 Coupled Model Intercomparison Project Phase 6 (CMIP6) models show that the spring sea surface temperature (SST) persistence is significantly higher in El Niño years than that in La Niña years, and the SPB intensity is stronger in La Niña years than that in El Niño years. Through the recharge oscillator model, observation and CMIP6 models, we demonstrate that the nonlinear wind stress response to SST anomalies in spring is the main cause of the asymmetric SPB intensity. By the mixed-layer heat budget of the tropical Pacific in the spring, we further identify that a stronger response of zonal wind stress in El Niño events can cause a stronger zonal advection feedback, which finally leads to a weaker SPB and enhances the predictability of El Niño. In contrast, the cooling SST in the spring only leads to weak easterly anomalies, the zonal advection feedback is relatively weaker, thus SPB is stronger and the predictability of La Niña is lower. From the perspective of SPB, we suggest that El Niño is more predictable than La Niña. [ABSTRACT FROM AUTHOR]

We introduce ModE-Sim (Modern Era SIMulations), a medium-sized ensemble of simulations with the atmospheric general circulation model ECHAM6 in its LR (low-resolution) version (T63; approx. 1.8 ∘ horizontal grid width with 47 vertical levels). At the lower boundary we use prescribed sea surface temperatures and sea ice that reflect observed values while accounting for uncertainties in these. Furthermore we use radiative forcings that also reflect observed values while accounting for uncertainties in the timing and strength of volcanic eruptions. The simulations cover the period from 1420 to 2009. With 60 ensemble members between 1420 and 1850 and 36 ensemble members from 1850 to 2009, ModE-Sim consists of 31 620 simulated years in total. ModE-Sim is suitable for many applications as its various subsets can be used as initial-condition and boundary-condition ensembles to study climate variability. The main intention of this paper is to give a comprehensive description of the experimental setup of ModE-Sim and to provide an evaluation, mainly focusing on the two key variables, 2 m temperature and precipitation. We demonstrate ModE-Sim's ability to represent their mean state, to produce a reasonable response to external forcings, and to sample internal variability. Through the example of heat waves, we show that the ensemble is even capable of capturing certain types of extreme events. [ABSTRACT FROM AUTHOR]

This paper describes the distribution, abundance and seasonal variation in blue whales (BWs) on the south coast of Sri Lanka, off Mirissa, where they are faced with anthropogenic threats. Data collection encompassed opportunistic sightings by whale-watching (WW) operations. This study revealed that large aggregations of BWs consistently overlapped with busy shipping lanes located between Dondra Head and Galle within important foraging and breeding regions on the south coast. Throughout 2015 (except June and July), 729 BWs were sighted over 177 sighting days. The sighting frequency was higher during the northeast monsoon and the first intermonsoon and lower throughout the second intermonsoon (n = 9) and latter part of the southwest monsoon. The highest frequencies of BW encounters per day occurred in April (n = 15) and December (n = 20), while the mean annual group size per sighting was 3.07 ± 0.24. From the January-to-April season, 13 mother–calf combinations and 1 pregnant cow were sighted off the southern coastline of Mirissa, suggesting the calving season peaks between the months of March and April in Sri Lanka. As this important habitat overlapped with the busiest shipping lanes, fishing and commercial whale-watching activities, the authorities have to take action toward the conservation of this ecosystem and whales as well as their safe navigation. [ABSTRACT FROM AUTHOR]

To evaluate the performance of the eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) algorithm, presented in the Part 1 companion paper to this paper, we apply the XBAER algorithm to the Sea and Land Surface Temperature Radiometer (SLSTR) instrument on board Sentinel-3. Snow properties – snow grain size (SGS), snow particle shape (SPS) and specific surface area (SSA) – are derived under cloud-free conditions. XBAER-derived snow properties are compared to other existing satellite products and validated by ground-based and aircraft measurements. The atmospheric correction is performed on SLSTR for cloud-free scenarios using Modern-Era Retrospective Analysis for Research and Applications (MERRA) aerosol optical thickness (AOT) and the aerosol typing strategy according to the standard XBAER algorithm. The optimal SGS and SPS are estimated iteratively utilizing a look-up-table (LUT) approach, minimizing the difference between SLSTR-observed and SCIATRAN-simulated surface directional reflectances at 0.55 and 1.6 µm. The SSA is derived for a retrieved SGS and SPS pair. XBAER-derived SGS, SPS and SSA have been validated using in situ measurements from the recent campaign SnowEx17 during February 2017. The comparison shows a relative difference between the XBAER-derived SGS and SnowEx17-measured SGS of less than 4 %. The difference between the XBAER-derived SSA and SnowEx17-measured SSA is 2.7 m 2 /kg. XBAER-derived SPS can be reasonably explained by the SnowEx17-observed snow particle shapes. Intensive validation shows that (1) for SGS and SSA, XBAER-derived results show high correlation with field-based measurements, with correlation coefficients higher than 0.85. The root mean square errors (RMSEs) of SGS and SSA are around 12 µm and 6 m 2 /kg. (2) For SPS, aggregate SPS retrieved by XBAER algorithm is likely to be matched with rounded grains while single SPS in XBAER is possibly linked to faceted crystals. The comparison with aircraft measurements, during the Polar Airborne Measurements and Arctic Regional Climate Model Simulation Project (PAMARCMiP) campaign held in March 2018, also shows good agreement (with R=0.82 and R=0.81 for SGS and SSA, respectively). XBAER-derived SGS and SSA reveal the variability in the aircraft track of the PAMARCMiP campaign. The comparison between XBAER-derived SGS results and the Moderate Resolution Imaging Spectroradiometer (MODIS) Snow-Covered Area and Grain size (MODSCAG) product over Greenland shows similar spatial distributions. The geographic distribution of XBAER-derived SPS over Greenland and the whole Arctic can be reasonably explained by campaign-based and laboratory investigations, indicating a reasonable retrieval accuracy of the retrieved SPS. The geographic variabilities in XBAER-derived SGS and SSA both over Greenland and Arctic-wide agree with the snow metamorphism process. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Blooms of various types of seaweeds have been reported worldwide, with recent expansions in surface waters. While most of the expansions have been attributed to eutrophication due mainly to human activities, any potential role of climate change is unclear. Here we show that, in the East China Sea and Yellow Sea, increased biomass of Sargassum horneri (S. horneri, brown seaweed) from 2000 to 2021 appears to be caused primarily by ocean warming, as S. horneri prefers a certain temperature range to grow. In contrast, while increases of Ulva prolifera (U. prolifera, green seaweed) in the same regions might also be related to ocean warming, during the same period, human activities such as coastal aquaculture or seaweed mitigation may muddle such effects. With the projected ocean warming in the next decades, we hypothesize that S. horneri blooms may occur earlier during the year and may continue to expand in the future. Plain Language Summary: Seaweed macroalgae blooms have been reported around the world in the last two decades, yet the exact reasons are often unclear. This paper attempts to solve this puzzle for the Sargassum and Ulva seaweeds in the East China Sea and Yellow Sea using long‐term observations and laboratory‐based seaweed physiology measurements. The paper shows not only how seaweeds have expanded in the two marginal seas in the past four decades, but also how a warming ocean (due to climate variability) and nutrient enrichment (due mainly to human activities) controlled such expansions. Among 63 large marine ecosystems (LMEs) in the world, the East China Sea (where seaweeds expanded) is one of the three LMEs that experienced "super‐fast" warming. Given the IPCC‐projected seawater temperature rise of 1.6°C by 2050, will these seaweeds continue to expand? Key Points: Multi‐sensor satellite data show significant and near‐synchronized expansions of Sargassum horneri and Ulva prolifera in the past 38 yearsOcean warming appears to be a main factor behind the recent expansions of S. horneri because of its physiological requirementOcean warming may also play a role in U. prolifera expansions, but the effect appears confounded by human mitigation efforts [ABSTRACT FROM AUTHOR]

Simple Summary: Red sea bream iridoviral disease (RSIVD) generates serious economic losses by causing mass mortality events of rock bream during the season with high water temperature in the Republic of Korea and other Asian countries. However, very few studies have investigated RSIV kinetics in rock bream under various rearing water temperatures. In this paper, we investigated the viral load shedding of RSIV into seawater after artificially infecting rock bream (Oplegnathus fasciatus) with the virus. Overall, our data suggest that the viral load shedding of RSIV into seawater varies depending on water temperature and virus inoculation concentration. Our results reveal the potential of non-invasive virus detection approaches, such as the utilization of environmental DNA in fish farms. In addition, we showed that the quantitative analysis of seawater viruses can indirectly improve our understanding of disease progression in fish, potentially contributing to enhanced disease control. Red sea bream iridoviral disease (RSIVD) causes serious economic losses in the aquaculture industry. In this paper, we evaluated RSIV kinetics in rock bream under various rearing water temperatures and different RSIV inoculation concentrations. High viral copy numbers (approximately 103.7–106.7 RSIV genome copies/L/g) were observed during the period of active fish mortality after RSIV infection at all concentrations in the tanks (25 °C and 20 °C). In the group injected with 104 RSIV genome copies/fish, RSIV was not detected at 21–30 days post-infection (dpi) in the rearing seawater. In rock bream infected at 15 °C and subjected to increasing water temperature (1 °C/d until 25 °C) 3 days later, the virus replication rate and number of viral copies shed into the rearing seawater increased. With the decrease in temperature (1 °C/d) from 25 to 15 °C after the infection, the virus replicated rapidly and was released at high loads on the initial 3–5 dpi, whereas the number of viral copies in the fish and seawater decreased after 14 dpi. These results indicate that the number of viral copies shed into the rearing seawater varies depending on the RSIV infection level in rock bream. [ABSTRACT FROM AUTHOR]

Based on satellite-observed and reanalysis data, this study investigates a thermal front east of the Gulf of Thailand (TFEGT) during the winter from 1982 to 2021. TFEGT exhibits distinct seasonal and interannual variation, emerging in December, peaking in February, gradually diminishing in March, and completely dissipating in April. Notably, the occurrence probability, area, and intensity of the thermal front are significantly higher in January and February compared with December and March. Through the application of a mixed temperature equation, we identify that geostrophic advection, driven by windinduced western boundary current in the South China Sea (SCS), plays a crucial role in the formation of the TFEGT. In winter, the prevailing northeast monsoon propels the western boundary current through wind stress curl, causing the southward transport of cold water from north to south. This cold water encountered warm water within the Gulf of Thailand (GoT), leading to the formation of TFEGT. Furthermore, the interannual variation of TFEGT is closely associated with the El Niño-Southern Oscillation (ENSO). In El Niño (La Niña) years, the northeast monsoon weakens (enhances), resulting in a weaker (stronger) western boundary current, ultimately influencing the weakening (enhancement) of TFEGT. [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]

Consequences of the mesoscale Thermal FeedBack (TFB) on the ocean dynamics are studied in the South‐East Pacific (SEP) using a high‐resolution regional ocean–atmosphere coupled model. Three simulations are compared: the first one is a fully coupled simulation. In the second one, the TFB has been removed with an online smoothing of the Sea Surface Temperature (SST) conditions used by the atmosphere. In the third one, to disentangle the impact of the nearshore and the offshore TFB, the smoothing is only applied in the offshore region. In the SEP, the coastal upwelling cold tongue constitutes a permanent mesoscale SST pattern. We show that this SST pattern alters the coastal wind structure, reducing the coastal upwelling‐favorable wind intensity. So, the nearshore TFB reduces the coastal surface current and the vertical velocities. As a result, the Eddy Kinetic Energy (EKE) generation by baroclinic conversion is also weakened. In the offshore region, on the contrary, the oceanic mean state is not affected by the TFB and only the EKE is weakened. Composites above the coherent eddies show that the heat flux response to the mesoscale SST anomalies is responsible for the mesoscale activity weakening over the whole studied area. Although the wind response to the SST anomalies has a very weak mean impact on the EKE generation through wind work, we show that it strongly modifies the mean oceanic vertical velocity anomalies over the coherent eddies. Plain Language Summary: The ocean dynamics is usually seen as driven by the atmosphere. However, the influence between the ocean and the atmosphere is mutual and the oceanic surface conditions feedback on the atmosphere. In this paper, we focus on the response of the atmosphere to the sea surface temperature mesoscale structures (structures that ranges from a few tens of km to a few hundred km). This response, called Thermal Feedback (TFB), is modeled with a high‐resolution ocean‐atmosphere coupled model to investigate its consequences on the oceanic dynamics in the South‐East Pacific. This region is characterized by a wind‐driven upwelling of sub‐surface waters near the coast, forming a sea surface coastal cold tongue. We show that this structure alters in return the coastal wind structure, which contributes to reduce the upwelling and the surface current. The generation of mesoscale structures, such as coherent eddies and filaments, is also reduced. The mesoscale structures are also weakened in the offshore region. We analyze the atmospheric response above the coherent eddies and show that the heat flux response to the SST anomalies is responsible for the eddy damping. Vertical velocities are also created in the eddy by the wind response to the SST anomalies. Key Points: Mesoscale thermal feedback decreases the mean coastal upwelling‐favorable wind, surface current and nearshore eddy generationMesoscale thermal feedback on the heat fluxes decreases the coherent eddy intensity through baroclinic conversionCoherent eddy mean vertical velocity is strongly modified by the mesoscale thermal feedback [ABSTRACT FROM AUTHOR]

Studying Arctic ice formation stays in the focus of research groups over the past decades in the context of ice cover changes, thermal budget and climate agenda in general. Nevertheless, the phenomenon's underlying mechanisms are still not completely understood and described. The main reason for the lack in understanding is the limited experimental access to the field data when it comes to the processes that occur below the ice floe. Thus, there is a need to build competent analogies between the natural (ocean water–ice) and laboratory (here: binary alloy) conditions of the experiment as a step of data preparation for the verification of the mathematical model. In the current paper, the existing qualitative models describing the process of melting and crystallization were expanded and the experimental method was developed copying the layering of the natural ocean water–ice mixture. The experimental set‐up for studying the solidification within the intermediate zone was designed for Al–Cu alloys being the system with appropriate solidus line for creating a sufficient concentration gradient and by that temperature dependent phase fraction under isothermal conditions. The gained experimental data were used for validating a binary phase‐field model for solidification considering moving boundaries. The model includes the description of the free energy of both phases and their respective diffusion coefficients. It allows modeling a binary system at a mesoscopic spatial level by including the concentration‐driven phase transition and resolidification in the two‐phase region. The novel results will help the quantitative understanding of solidification phenomena and are highly‐evaluated from interdisciplinary point of view, including glaciology and geosciences, being ultimately significant for the understanding the global climate change landscape. [ABSTRACT FROM AUTHOR]

The detection and the functioning of the mid-summer drought (MSD) represent valuable information due to its socio-economic implications for the Caribbean. Current methods using local-scale rainfall to define the MSD have some limitations. This paper presents a novel approach to detect MSD from regional-scale weather types (WT). Long sequences of a typical summertime anticyclonic WT allow the detection of the onset and demise dates of the MSD for most years in 1950–2021. The MSD defined with WT (MSD-WT) begins rather abruptly on June 13th and concludes on August 21st, on average. While the interannual variations of the MSD-WT onset are relatively weak, long-term trends since 1950 indicate progressive earlier onsets, longer durations, and weakening intensities of the MSD-WT. Our results do not show the anticipated westward shift of the North Atlantic subtropical high pressure typically related to MSD. Instead, they indicate a halt of its eastward shift close to the MSD-WT onset. The absence of significant correlations between MSD-WT onset and either the Tropical North Atlantic or the Eastern Tropical Pacific sea surface temperature (SST) in May–June also suggests a mostly intrinsic atmospheric mechanism locked to the annual cycle, thus limiting the seasonal predictability of the MSD-WT onset. Furthermore, our results indicate a lag between the timing of increased subsidence, acceleration of the Caribbean low-level jet (CLLJ), and intensification of the deep convection over Central America and Mexico. This leads us to develop a novel hypothesis putting forward the monsoon circulation over Meso-America as a potential precursor of the increased subsidence over the Caribbean basin and the synchronous acceleration of CLLJ. The MSD-WT demise is more gradual and corresponds to a decrease in regional-scale mean sea level pressure and a weakening of the CLLJ. MSD-WT demise dates exhibit also relatively larger interannual variations but, unlike its onset, do not show any significant long-term trend. Anomalously warm (cold) Tropical North Atlantic and cold (warm) East Pacific SST from early June are correlated with an earlier (later) MSD-WT demise than usual. MSD-WT demise seems to involve regional-scale air-sea couplings and exhibits thus more seasonal predictability. Finally, despite a consistent response to the CLLJ, distinct processes seem to produce the relatively dry conditions observed over the Pacific coast of Central America and the Caribbean basin during the MSD. [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]

Simple Summary: This paper analyzed the different relationships between earlywood and latewood as well as total tree-ring growth and the climate factors and reconstructed 106 years of May–June mean temperature (TMJ) in the Tongbai Mountains based on the earlywood width chronology of Pinus taiwanensis Hayata. It also analyzed the linkages to the Indian and Pacific Oceans. This paper found that earlywood width chronology has better response to the climate factors than latewood width and total tree-ring width. This study also found that the main limiting factors that restrained radial growth of Pinus taiwanensis Hayata in the Tongbai Mountains were May–June mean temperature and mean maximum temperature. The reconstructed TMJ series have a better reliability and are significantly negatively correlated with sea surface temperature (SST) over the tropical Western Pacific Ocean and Indian Ocean and are significantly positively correlated with SST over the subtropical Pacific Ocean. Finally, the periodic fluctuations of TMJ in the Tongbai Mountains might be related to the quasi-biennial interannual oscillation of SST over the Indo-Pacific equatorial region (QBO). The results of this study are significant for further understanding and exploring forest growth and climate change in the climatic transition zone. The Tongbai Mountains are an ecologically sensitive region to climate change, where there lies a climatic transitional zone from a subtropical to a warm–temperate monsoon climate. The northern boundary of Pinus taiwanensis Hayata is here; thus, climate information is well recorded in its tree rings. Based on developed earlywood width (EWW), latewood width (LWW) and total ring width (RW) chronologies (time period: 1887–2014 year) of Pinus taiwanensis Hayata in the Tongbai Mountains in central China, this paper analyzed characteristics of these chronologies and correlations between these chronologies and climate factors. The correlation results showed that earlywood width chronology contains more climate information than latewood width chronology and total ring width chronology, and mean temperature and mean maximum temperature in May–June were the main limiting factors for radial growth of Pinus taiwanensis Hayata. The highest significant value in all correlation analyses is −0.669 (p < 0.05) between earlywood width chronology and May–June mean temperature (TMJ) in the pre-mutation period (1958–2005) based on mutating in 2006. Thus, this paper reconstructed May–June mean temperature using earlywood width chronology from 1901 to 2005 (reliable period of earlywood width chronology is 1901–2014). The reconstructed May–June mean temperature experienced eight warmer periods and eight colder periods and also showed 2–3a cycle change over the past 105 years. The spatial correlation showed that the reconstructed series was representative of the May–June mean temperature variation in central and eastern China and significant positive/negative correlation with the sea surface temperature (SST) of the subtropical Pacific Ocean and the tropical Western Pacific Ocean and Indian Ocean from the previous October to the current June. This also indicated that May–June mean temperature periodic fluctuations might be related to the quasi-biennial oscillation (QBO) in the tropical Western Pacific Ocean and Indian Ocean. The results of this study have extended and supplemented the meteorological records of the Tongbai Mountains and have a guiding significance for forest tending and management in this area. [ABSTRACT FROM AUTHOR]

Recent research on iceberg subsea risk modelling has produced strong evidence for changes in the iceberg regime in the Grand Banks region that are likely to have significant implications for development activities offshore Newfoundland and Labrador. An analysis of iceberg trajectory data from 2000 onwards [C-CORE, 2020] shows a significant decrease in iceberg grounding rates on the Grand Banks when compared to an analysis of 1980s data [Banke, 1989]. Iceberg profile data collected since 2012 shows distinct changes in the length-draft relationship when compared to data collected in the 1980s [Bruce et al., 2021]. These changes, along with analysis of other relevant parameters such as iceberg frequency, geometry, and drift speed, show that the change in iceberg grounding rates is consistent with observed changes in iceberg characteristics. In this paper, the observed changes in iceberg characteristics between the 1980s and 2000-2020 are linked to changes in the air and sea surface temperature, significant wave height, and sea ice climate regime offshore Newfoundland and Labrador over this period. Projected further changes in these variables to 2050 are explored using output from a climate model. These changes will have significant implications for iceberg risk to both subsea and surface facilities, and are likely part of an ongoing trend that will continue to decrease iceberg presence and risk in the region. [ABSTRACT FROM AUTHOR]

We develop a new data assimilative (DA) approach by combining two parallel frameworks: a parallel DA framework (PDAF) and a flexible model coupling framework (ESMF). The new DA system is built on the ESMF at the top level that drives the PDAF and any combination of Earth system modeling (ESM) components, to allow maximum flexibility and easy implementation of data assimilation for fully coupled ESM applications. We demonstrate the new DA system using a 3D unstructured-grid ocean model as ESM in this paper. The new system is validated using a simple benchmark and applied to a realistic case of Kuroshio simulation around Taiwan. The new system is demonstrated to significantly improve the model skill for temperature, velocity and surface elevation before, during and after typhoon events. The flexibility and ease of implementation make the new system widely applicable for other coupled ESMs. [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]

Surface temperature is one of the first ocean variables investigated. Ocean temperature is a key indicator of global climate change. The anomalies in ocean temperature have caused significant deterioration of marine systems. Existing works on surface temperature anomaly considered a suite of other remote sensing measurements such as wave height, salinity and models such as support vector machine, self-organizing maps and convolutional neural networks. Neural networks are used for predicting the surface temperature. This paper proposes use of long short-term memory, a recurrent neural network method to the estimate sea surface temperature anomalies based on previous year's sea surface temperature anomalies. NOAA OI SST V2 dataset with 40 years of data is used in the experimentation. Auto-regression is used during data preprocessing. The basic LSTM method with 3 blocks of size 2 is enhanced to 50 neurons. This proposed LSTM model has been assessed for performance on time series data, yearly wise and for the entire dataset and found that the model has been able to predict the anomalies with a reasonably good precision. The model produced error of 0.036 indicating that the model is feasible for predicting the temperature anomaly and mean absolute error of 0.14 on the testing data. [ABSTRACT FROM AUTHOR]

The present paper studies the dynamic association among the sea surface temperature (SST) data through directed limited penetrable visibility graph (DLPVG) method. We analyse the fluctuation trend of temperature in different regions by constructing an SST-dynamic association network to explore the topology structure. By analysing the SST data of the East China Sea, we find that there are association patterns among multivariate SST and a few types of patterns play a significant role in the movement of the sea water. Furthermore, the experiments also show that the multivariate SST has a close relationship with El Niño events, which indicates that the network is of great significance to the research and prediction of the El Niño phenomenon. [ABSTRACT FROM AUTHOR]

Remotely sensed sea-surface temperature (SST) retrievals with a significant positive bias during the occurrence of upwelling phenomena along the southeastern coast of Brazil were reported in our companion paper. As a result, this paper proposes an automated bias correction algorithm to improve the MODIS long-wave multichannel SST (MCSST) retrievals during the abovementioned conditions in this region. In this paper, MODIS daytime SST data (SSTMODIS) and differences between brightness temperatures in MODIS channels 31 and 32 (BT31 − BT32) are analyzed simultaneously with hourly wind surface conditions, in situ SST at 0.3 and 10 m in depth (SSTbuoy03 and SSTbuoy10), and sensible and latent heat fluxes from the Cabo Frio buoy data (at 23° S, 42° W) during 2014. The obtained results show that some upwelling events present air temperature ($T_{\mathrm {air}}$) greater than SSTbuoy03 and low-atmospheric water vapor content. A simultaneous occurrence of these factors during upwelling conditions may lead to a warm-skin layer effect and may cause BT31 to be greater than SSTbuoy03 and BT31 − BT32 to be small (−0.18 °C ± 0.22 °C), affecting the MCSST performance. The proposed bias correction algorithm uses a least-squares curve between SSTbuoy03 and SSTMODIS retrievals when BT31 − BT32 ≤ 0.5 °C (i.e., dry atmospheric conditions). The bias correction algorithm has significantly improved the SSTMODIS bias (RMSE) from 1.43 °C to −0.2 °C (1.60 °C to 0.58 °C) when applied to 22 cloud-free pixels of MODIS during January–March of 2015. [ABSTRACT FROM AUTHOR]

Large volumes of cool water are drawn up to the surface in the tropical oceans. A companion paper shows that the cool water reaches the surface in or near the upwelling zones off northern and southern Africa and Peru. The cool water has a subantarctic origin and spreads extensively across the Atlantic and Pacific basins after it reaches the surface. Here, we look at the spreading in two low‐resolution ocean general circulation models and find that the spreading in the models is much less extensive than observed. The problem seems to be the way the upwelling and the spreading are connected (or not connected) to the ocean's large‐scale overturning. As proposed here, the cool upwelling develops when warm buoyant water in the western tropics is drawn away to become deep water in the North Atlantic. The "drawing away" shoals the tropical thermocline in a way that allows cool subantarctic water to be drawn up to the surface along the eastern margins. The amounts of upwelling produced this way exceed the amounts generated by the winds in the upwelling zones by as much as 4 times. Flow restrictions make it difficult for the warm buoyant water in our models to be drawn away. Plain Language Summary: A companion paper uses the radioactive isotope carbon‐14 to map the upwelling in the ocean basins north of the Antarctic Circumpolar Current (ACC). It shows that deep water drawn up to the surface to the south of the ACC is drawn up to the surface a second time in a number of upwelling areas in the tropics. In this paper, we attempt to simulate the tropical upwelling in an ocean circulation model and find that it is largely missing. Other circulation features are missing as well. The other features operate on the opposite sides of the ocean basins from the upwelling areas and help carry warm water out of the tropics ("outflows"). The combination of missing elements leads to a hypothesis about the upwelling mechanism. Our hypothesis is that the tropical upwelling is driven by the outflows and the fact that the warm water being carried away ultimately becomes deep water again in the North Atlantic. Key Points: Cool subantarctic water is drawn up to the surface in the tropical oceansThe upwelling tends to be poorly expressed in ocean general circulation modelsA new mechanism is proposed to explain how the upwelling is tied into the ocean's large‐scale overturning [ABSTRACT FROM AUTHOR]

In this paper, the focus is on precipitation rate retrieval using 118.75 and 183.31-GHz channels from the microwave humidity and temperature sounder (MWHTS) onboard the FY-3C satellite. These channels provide not only atmospheric humidity profile measurements but they also provide temperature profile measurements. These measurements play important roles detecting and retrieving precipitation rates. Data from the MWHTS and the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA) 3B42 are matched in time and space. Matched datasets are used to verify precipitation detection algorithms and precipitation rate retrieval algorithms. To retrieve the precipitation rate, the multiple linear regression algorithm and back propagation (BP) neutral network algorithm are applied in the ten models proposed in this paper. After detailed retrieval analysis between these two algorithms, advanced algorithms are presented this paper to improve the accuracy of the precipitation rate retrieval. In our algorithms, the correlation coefficient of the precipitation rate over the ocean can reach 0.82, the bias is approximately 0.07 mm/hr, and the root mean square error (RMSe) is less than 0.4 mm/hr. Over land, the correlation coefficient is approximately 0.73, the bias is 0.1 mm/hr, and the RMSe is less than 0.5 mm/hr. These results show that our retrieval algorithms improve the retrieval accuracy. Moreover, we study a number of typhoons and find that the FY-3C MWHTS works well to retrieve typhoon precipitation rates. The potential sensitivities of the temperature and humidity channels are analyzed, which can improve the retrieval accuracy and lay the foundation for further research. [ABSTRACT FROM AUTHOR]

The topical issue includes 22 papers on different aspects of meteorology and climatology of the Mediterranean and Black Seas. The papers are grouped into three categories, based on scale analysis of processes, and whether the study is dominantly related to the atmospheric or oceanic phenomenon. The category “Storms, Extremes and Mesoscale Processes” encompasses seven papers, ranging from observation and modelling data analysis of extreme atmospheric (severe winds, hailstorms, waterspout, cyclones) and oceanic (meteotsunamis, surface waves) events, occurring locally, but being connected to wider patterns and processes. The six papers in category “Atmospheric Climate, Variability and Climate Change” contain analyses of long-term observations and climate projection outputs, but also assess atmosphere-ocean-land interactions and cycles, that are important in a climate perspective. The category “Ocean Climate and Variability” includes nine papers, which map ocean processes (sea level variability, circulation, sea surface temperatures, vertical thermal structure, water masses and dense water formation) on a climate timescale, both, in the Mediterranean and Black Sea sub-basins, or taking them as a whole. [ABSTRACT FROM AUTHOR]

This paper discusses the intersatellite radiometric calibration for microwave sounders in support of NASA’s Global Precipitation Measurement (GPM) mission. This paper is an extension of previous GPM Intersatellite Calibration Working Group work, which assesses the robustness of the Central Florida Remote Sensing Laboratory “double difference” technique for sounders intercalibration. In this paper, we will analyze the intercalibration between the Advanced Technology Microwave Sounder on board the National Polar-orbiting Operational Environmental Satellite System Preparatory Project, the Microwave Humidity Sounder on board the MetOp-A and MetOp-B and NOAA-18 and NOAA-19, and the Sondeur Atmosphérique du Profil d’Humidité Intertropicale par Radiométrie sounder onboard the Megha-Tropiques satellite. The sensitivity of the calibration to the geophysical parameters and incidence angles is investigated using two years data. Here, it is observed that a reliable intercalibration can be made only when the path lengths of the two sensors are similar, so when matching the scan angles for the two sensors, the sensitivity of the biases upon the water vapor profiles is significantly reduced. [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 (SST) prediction has attracted increasing attention, due to its crucial role in understanding the Earth's climate and ocean system. Existing SST prediction methods are typically based on either physics-based numerical methods or data-driven methods. Physics-based numerical methods rely on marine physics equations and have stable and explicable outputs, while data-driven methods are flexible in adapting to data and are capable of detecting unexpected patterns. We believe that these two types of method are complementary to each other, and their combination can potentially achieve better performances. In this paper, a space-time partial differential equation (PDE) is employed to form a novel physics-based deep learning framework, named the space-time PDE-guided neural network (STPDE-Net), to predict daily SST. Comprehensive experiments for SST prediction were conducted, and the results proved that our method could outperform the traditional finite-difference forecast method and several state-of-the-art deep learning and physics-guided deep learning methods. [ABSTRACT FROM AUTHOR]

The trend of global Sea Surface Temperature (SST) has attracted widespread attention in several ocean-related fields such as global warming, marine environmental protection and marine biodiversity. Sea surface temperature is influenced by climate change; with the accumulation of data from ocean remote sensing observations year by year, many scholars have started to use deep learning methods for SST prediction. In this paper, we use a dynamic region partitioning approach to process ocean big data and design a framework applied to a global SST short-term prediction system. On the architecture of a Long Short-Term Memory (LSTM) network, two deep learning multi-region SST prediction models are proposed, which extract temporal and spatial information of SST by encoding, using feature transformation and decoding to predict future multi-step states. The models are tested using OISST data and the model performance is evaluated by different metrics. The proposed MR-EDLSTM model and MR-EDConvLSTM model obtained the best results for short-term prediction, with RMSE ranging from 0.2712 °C to 0.6487 °C and prediction accuracies ranging from 97.60% to 98.81% for ten consecutive days of prediction. The results show that the proposed MR-EDLSTM model has better prediction performance in coastal areas, while the MR-EDConvLSTM model performs better in predicting the sea area near the equator. In addition, the proposed deep learning model has a smaller RMSE compared to the forecasting system based on the ocean model, indicating that the deep learning method has certain advantages in predicting global SST. [ABSTRACT FROM AUTHOR]

The observation facts of trend and variability are estimated for a century‐long timescale, as well as some possible attributions affecting temperature extremes over Beijing‐Tianjin‐Hebei region. In this paper, the new constructed daily maximum and minimum temperature series during the last 100 years in situ stations of Beijing, Tianjin and Baoding city are used, and two 30‐year baseline climatology of 1961–1990 and 1980–2010 are both used in calculating extreme temperature events. The results indicate that there are long‐lasting, frequent and serious extreme warming over Beijing‐Tianjin‐Hebei region, especially for extreme low temperature, with larger declining trends (at 5% significance) from 1981 to 2010 baseline than those from 1961 to 1990 baseline. And from a statistical perspective, El Niño decay years and surface sea temperature (SST) anomalies at some typical ocean regions of the Arabian Sea and the Bengal Bay on the northern Indian Ocean, the Western Pacific and the Mid Atlantic, Southern Indian Ocean and around 45°–85°E, 68°–82°N on the southwest Arctic Ocean can be used as the warning signals denoting extreme warming over Beijing‐Tianjin‐Hebei region. Additionally, urbanization has a significant (at 5% significance) effect on extreme warming over Beijing‐Tianjin‐Hebei region since 1979, with 24.3% warming contribution for annual minimum temperature. [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]

Sea surface temperature (SST) is characterized by abundant warm and cold structures that influence the overlying atmospheric boundary layer dynamics through two different mechanisms. First, turbulence and large eddies in the lower troposphere are affected by atmospheric stability, which can be modified by local SST, resulting in enhanced vertical mixing and larger surface winds over warmer waters. Second, the thermodynamic adjustment of air density to the underlying SST structures and the subsequent changes in atmospheric pressure drive secondary circulations. This paper aims to disentangle the effects of these processes and explore the environmental conditions that favor them. Two main environmental variables are considered: the large-scale air–sea temperature difference (proxy for stability) and wind speed. Using 5 years of daily reanalyses data, we investigate the 10-m wind response to SST structures. Based on linear regression between wind divergence and SST derivatives, we show that both mechanisms operate over a large spectrum of conditions. Ten-meter wind divergence is strongly impacted by the local SST via its effect on vertical mixing for midwind regimes in slightly unstable to near-neutral conditions, whereas the secondary circulation is important in two distinct regimes: low wind speed with a slightly unstable air column and high background wind speed with a very unstable air column. The first regime is explained by the prolonged Lagrangian time that the air parcel stays over an SST structure while the second one is related to strong heat fluxes at the air–sea interface, which greatly modify the marine atmospheric boundary layer properties. Location and frequency of the environmentally favorable conditions are discussed, as well as the response in low-cloud cover and rainfall. Significance Statement: The main objective of this study is to explore the wind response to thermal structures at the sea surface under different environmental conditions using the latest atmospheric reanalysis. Recent literature suggests that fine-scale air–sea interactions affect a large spectrum of atmospheric dynamics, from seasonal to weather-type regimes. It is thus important to characterize the atmospheric response to ocean surface variability. Our findings describe the environmental conditions for which the two main physical processes through which the atmosphere responds to sea surface temperature structures are active the most and can guide the development of high-resolution observing missions and campaigns in specific geographical locations and seasons to retrieve data that can be used to improve parameterization in models. [ABSTRACT FROM AUTHOR]

The emergence of 100‐Kyr glacial cycles (The Mid‐Pleistocene Transition [MPT]) is attributed in part to slower global overturning circulation and iron stimulation of biological carbon drawdown in the Southern Ocean. We present foraminifera‐bound nitrogen isotope values and polar planktic foraminifera abundances from the Agulhas Plateau that show that increases in biogenic sediment accumulation coincide with northward migrations of the Subtropical Frontal Zone (STFZ) and elevated foraminifera‐bound nitrogen isotope values during MPT glacial episodes. The nitrogen isotope values of two planktic foraminifera species, Globigerina bulloides and Globorotalia inflata show remarkable coherence amongst the sea surface temperature gradient between the STFZ and SAZ, and polar foraminifera abundances, indicating a strong relationship between nitrogen isotope dynamics above the Agulhas Plateau and migrations of the STFZ. Northward migration of the STFZ may have been essential to prolonging glacial intervals by increasing deep ocean carbon storage via a northward shift of the South Westerly Winds and a reduction in upwelling, delivery of fresher surface waters into the upper limb of global overturning circulation, or inhibiting heat and salt delivery to the Atlantic as Agulhas Leakage. Plain Language Summary: The Mid‐Pleistocene Transition (MPT) is a unique period of time, during which the natural rhythm of Earth's climate shifted, the pace of glacial and interglacial cycles changed from 40,000 years to 100,000 years as ice sheets expanded, carbon accumulated in the deep ocean, and the planet cooled. The exact cause of these changes is unclear, but internal system feedbacks were likely at play. As climate changes today and the oceans increasingly take up carbon, the MPT provides a unique case study to investigate Earth's natural climate system and important carbon cycle feedbacks. In this paper we use the nitrogen isotopic composition of plankton shells and other biogeochemical records to show that leading up to the MPT the Southern Ocean underwent major physical changes and expanded as 100,000 years glacial cycles emerged. Our data indicates that the expansion of the Southern Ocean delivered cold, nutrient rich waters northward where they could mix with warm, salty Indian and Atlantic Ocean waters as hypothesized by other studies. Key Points: Foraminifera‐bound nitrogen isotope values from the Agulhas Plateau record latitudinal migrations of the Subtropical FrontElevated foraminifera‐bound nitrogen isotope values after 900 ka are consistent with Southern sourced nitrateSouthern Ocean contraction during a "super interglacial" brings more subtropical nitrate to the Agulhas Plateau [ABSTRACT FROM AUTHOR]

Existing research has proven the increase in sea surface temperature (SST) due to global warming. However, the sea bottom temperature (SBT) may exhibit different characteristics in various regional seas. The East China Shelf Seas (ECSSs), which are important shelf seas in the Western Pacific, hold ecological significance when analyzing their SBT variations in a warming future. This article investigates both the interannual and interdecadal SBT variations from 2006 to 2100, utilizing the projection results from phase 5 of the Climate Model Intercomparison Project (CMIP5) sponsored by the Intergovernmental Panel on Climate Change (IPCC). We conducted an analysis of the interdecadal variation by comparing the SBTs from the 2030s, 2060s, and 2090s to the SBT observed in the 2010s. Our findings reveal a significant increase in SBT in the ECSSs. By 2100, the region is projected to experience enhanced warming of 1.18 °C. The springtime warming intensity of the Bohai Sea, reaching 1.92 °C, can be twice the rate of global ocean warming. The outer shelf of the ECSSs also exhibits significant increases in SBT. Through an analysis of the correlation between SBT and ocean currents, we investigate the potential mechanisms behind these observations. This paper provides insights into future SBT variations from both an interannual and interdecadal perspective, explaining the causes and the projected increase in environmental stresses on the benthic ecosystem over the next eighty years. [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]

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]

Eddies are structures of recognized physical and biogeochemical relevance. Because of their small space and time scales, submesoscale eddy detection is a challenge, and detection methods for these structures are scarce. In this study, we detect submesoscale eddies (SME) by means of a method implemented on satellite chlorophyll-a (CSAT) and sea surface temperature (SST) data from Visible Infrared Imaging Radiometer Suite (VIIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) images. The performance of this method is shown through the characterization of the shape and size of the detected eddies. The trajectory and characteristics of one particular eddy could be observed for 6 days over the Argentine continental shelf. The eddy reached the highest CSAT concentration (1.52 mg m−3, decreasing to less than 0.8 mg m−3 in about 3.5 km), and the lowest SST on the same day it developed its largest size. All SME detected within the study period are in agreement with cyclonic eddies and have a radius smaller than the baroclinic Rossby radius of deformation. This paper presents a workflow for the automated recognition of SME that could be applied to other regions with similar characteristics, or different ones with previous changes in the parameters. [ABSTRACT FROM AUTHOR]