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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]

The Northeastern China cold vortex (NCCV) is one type of strong cyclonic vortex that occurs near Northeastern China (NEC), and NCCV activities are typically accompanied by a series of hazardous weather. This paper employed an automatic algorithm to identify the NCCVs from 1979 to 2018 and analyzed their circulation patterns and climatic impacts by using the defined NCCV intensity index (NCCVI). The analysis revealed that the NCCV activities in summer exhibited a strong inter-annual variability, with an obvious periodicity of 3-4 years and 6-7 years, but without significant trends. In years when the NCCVI was high, NEC experienced negative geopotential height anomalies, cyclonic circulation, and cooler temperature anomalies, which were conducive to the maintenance and development of NCCV activities. Furthermore, large amounts of water vapor converged in NEC through two transportation routes as the NCCVs intensified, leading to a significant positive (negative) correlation with the summer precipitation (surface temperature) in NEC. The Atlantic sea surface temperature (SST) anomalies were closely related to summer NCCV activities. As the Atlantic SST rose, large amounts of surface sensible and latent heat flux were transported into the lower troposphere, inducing a positive geopotential height anomaly that occurred on the east side of the heat source. As a result, an eastward diverging flow was formed in the upper troposphere and propagated downstream, i.e., the eastward propagating Rossby wave train, which eventually led to a coupled circulation in the Ural Mountains and NEC, as well as more intensive NCCV activities in summer. [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]

Simple Summary: Ten years of Mediterranean monk seal stranding data along the Greek coastal areas were analyzed. The spatial distribution of stranding events along with the associated causes of death were modeled and mapped, while the relationship between the possibility of a stranding event and environmental and anthropogenic factors was thoroughly explored. The number of stranding events was found to be higher during the winter period. Moreover, the values of two major teleconnection oscillation indices during the first half of the year as well as the sea surface temperature in the Mediterranean from October to December were positively correlated with the number of stranding events of "unknown" cause. The size of fishing grounds and the number of sightings were associated with a higher probability of "human-related" stranding events. Finally, the central Aegean Sea was found to be a hotspot for "human-related" strandings, while, the eastern Ionian Sea, the southern coasts of the island of Cephalonia, and the gulf between the island of Lefkada and mainland Greece were found to present higher probabilities of monk seal subadult stranding events. This paper presents the results of an analysis of stranding events of the Mediterranean monk seal Monachus monachus over a decade. The analysis involved categorization according to the cause of stranding and seasonality, the identification of hotspot stranding areas and an assessment of possible correlations between stranding events and environmental/climatic patterns using time series analysis. Moreover, Generalized Additive Models (GAMs) were applied to explore the effects of the size of small-scale fishing grounds, the number of species sightings, and the occurrence of reproduction sites on "human-related" strandings. Finally, special focus was put on the central part of the eastern Ionian Sea for the assessment of stranding hotspot areas by means of the Multi-Criteria Decision Analysis (MCDA) approach, based on different kinds of spatial information such as anthropogenic pressures and the location of breeding sites and feeding grounds. Time series analysis results revealed that oscillation indices, during the first half of the year, and sea surface temperature (SST) in the Mediterranean from October to December were positively correlated with monk seal stranding events. GAMs underlined that areas combining extended small-scale fishery grounds and a higher number of sightings were more likely to cause more strandings. Regarding spatial analyses, the central Aegean Sea was highlighted as a hotspot for "human-related strandings", while the MCDA approach emphasized that the southern coasts of Cephalonia and the gulf between Lefkada and mainland Greece were susceptible to subadult strandings. [ABSTRACT FROM AUTHOR]

A multi-season convection-permitting regional climate simulation of the Maritime Continent (MC) using the Met Office Unified Model (MetUM) with 2.2 km grid spacing is presented and evaluated. The simulations pioneer the use of atmosphere–ocean coupling with the multi-column K profile parametrisation (KPP) mixed-layer ocean model in atmospheric convection-permitting climate simulations. Comparisons are made against a convection-parametrised simulation in which it is nested and which in turn derives boundary conditions from the ERA5 reanalysis. This paper describes the configuration, performance of the mean state and variability in the two simulations compared against observational datasets. The models have both minor sea surface temperature (SST) and wet precipitation biases. The diurnal cycle, representation of equatorial waves, and relationship between SST and precipitation are all improved in the convection-permitting model compared to the convection-parametrised model. The Madden–Julian oscillation (MJO) is present in both models with a faster-than-observed propagation speed. However, it is unclear whether fidelity of the MJO simulation is inherent to the model or whether it predominantly arises from the forcing at the boundaries. [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]

Sea ice, as an important component of the Earth's ecosystem, has a profound impact on global climate and human activities due to its thickness. Therefore, the inversion of sea ice thickness has important research significance. Due to environmental and equipment-related limitations, the number of samples available for remote sensing inversion is currently insufficient. At high spatial resolutions, remote sensing data contain limited information and noise interference, which seriously affect the accuracy of sea ice thickness inversion. In response to the above issues, we conducted experiments using ice draft data from the Beaufort Sea and designed an improved GBDT method that integrates feature-enhancement and active-learning strategies (IFEAL-GBDT). In this method, the incident angle and time series are used to perform spatiotemporal correction of the data, reducing both temporal and spatial impacts. Meanwhile, based on the original polarization information, effective multi-attribute features are generated to expand the information content and improve the separability of sea ice with different thicknesses. Taking into account the growth cycle and age of sea ice, attributes were added for month and seawater temperature. In addition, we studied an active learning strategy based on the maximum standard deviation to select more informative and representative samples and improve the model's generalization ability. The improved GBDT model was used for training and prediction, offering advantages in dealing with nonlinear, high-dimensional data, and data noise problems, further expanding the effectiveness of feature-enhancement and active-learning strategies. Compared with other methods, the method proposed in this paper achieves the best inversion accuracy, with an average absolute error of 8 cm and a root mean square error of 13.7 cm for IFEAL-GBDT and a correlation coefficient of 0.912. This research proves the effectiveness of our method, which is suitable for the high-precision inversion of sea ice thickness determined using Sentinel-1 data. [ABSTRACT FROM AUTHOR]

This study addresses a critical gap in understanding anthropogenic influences on tropical climate dynamics by investigating the impact of aerosol‐cloud interactions on large‐scale circulation. Despite extensive research on greenhouse gas‐induced warming and its effects on tropical circulation, the impact of aerosols, particularly their interactions with clouds, on large‐scale circulation remains understudied. Utilizing large‐domain radiative convective equilibrium cloud‐resolving simulations, this research demonstrates that increasing aerosol concentration intensifies tropical overturning circulation, evaluated at the mid‐troposphere (I) $(\mathcal{I})$, strongly correlating with domain mean cloud and radiative properties. Employing a weak temperature gradient approximation, I attribute variations in I $\mathcal{I}$ to changes in clear‐sky radiative cooling rather than stability. These radiative cooling changes are linked to humidity changes driven by warm rain suppression by aerosols. This study's findings underscore the need to take into account microphysical changes, particularly aerosol concentrations, when studying anthropogenic effects on tropical circulation. Plain Language Summary: Clouds intricately couple with the large‐scale overturning circulation in the tropics. Aerosols, small particles suspended in the atmosphere, interact with clouds. Thus, changes in aerosols concentration (Na) have the potential to affect cloud properties and by that the large‐scale circulation, with important implications for tropical climate and climate change. Despite this crucial relationship, the subject has been insufficiently explored. In this paper, I use a simplified, yet realistic, representation of the tropical atmosphere—radiative convective equilibrium large‐domain cloud‐resolving simulations—to investigate the effect of aerosol‐cloud interaction on overturning tropical circulation. By varying Na under diverse sea surface temperatures, I demonstrate that the strength of the overturning circulation intensifies with increasing Na. Utilizing a semi‐analytical expression for subsidence strength in clear‐sky tropical regions, I show that the intensified overturning circulation with increased Na can be attributed to heightened humidity in the mid‐troposphere, leading to intense radiative cooling. This mid‐tropospheric humidification can be explained by warm rain suppression by aerosols, which fosters stronger humidity mixing between clouds and their surroundings. These findings underscore the need to consider aerosol concentration changes when examining anthropogenic impacts on tropical circulation. Key Points: The overturning tropical circulation intensifies with aerosol concentration in a radiative convective equilibrium modelChanges in circulation strength can be explained by changes in diabatic subsidence driven by radiative changesRadiative changes can be explained by increased humidity at the mid‐troposphere due to warm rain suppression [ABSTRACT FROM AUTHOR]

Development of ocean measurement technologies can improve monitoring of the global Ocean Heat Content (OHC) and Heat Storage Rate (HSR) that serve as early-warning indices for climate-critical circulation processes such as the Atlantic Meridional Overturning Circulation and provide real-time OHC assessments for tropical cyclone forecast models. This paper examines the potential of remotely measuring ocean temperature profiles using a simulated Brillouin lidar for calculating ocean HSR. A series of data analysis ('Nature') and Observational Systems Simulation Experiments (OSSEs) were carried out using 26 years (1992–2017) of daily mean temperature and salinity outputs from the ECCOv4r4 ocean circulation model. The focus of this study is to compare various OSSEs carried out to measure the HSR using a simulated Brillouin lidar against the HSR calculated from the ECCOv4r4 model results. Brillouin lidar simulations are used to predict the probability of detecting a return lidar signal under varying sampling strategies. Correlations were calculated for the difference between sampling strategies. These comparisons ignore the measurement errors inherent in a Brillouin lidar. Brillouin lidar technology and instruments are known to contain numerous, instrument-dependent errors and remain an engineering challenge. A significant decrease in the ability to measuring global ocean HSRs is a consequence of measuring ocean temperature from nadir-pointing instruments that can only take measurements along-track. Other sources of errors include the inability to fully profile ocean regions with deep mixed layers, such as the Southern Ocean and North Atlantic, and ocean regions with high light attenuation levels. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is a key parameter in ocean hydrology. Currently, existing SST prediction methods fail to fully utilize the potential spatial correlation between variables. To address this challenge, we propose a spatiotenporal UNet (ST-UNet) model based on the UNet model. In particular, in the encoding phase of ST-UNet, we use parallel convolution with different kernel sizes to efficiently extract spatial features, and use ConvLSTM to capture temporal features based on the utilization of spatial features. Atrous Spatial Pyramid Pooling (ASPP) module is placed at the bottleneck of the network to further incorporate the multi-scale features, allowing the spatial features to be fully utilized. The final prediction is then generated in the decoding stage using parallel convolution with different kernel sizes similar to the encoding stage. We conducted a series of experiments on the Bohai Sea and Yellow Sea SST data set, as well as the South China Sea SST data set, using SST data from the past 35 days to predict SST data for 1, 3, and 7 days in the future. The model was trained using data spanning from 2010 to 2021, with data from 2022 being utilized to assess the model's predictive performance. The experimental results show that the model proposed in this research paper achieves excellent results at different prediction scales in both sea areas, and the model consistently outperforms other methods. Specifically, in the Bohai Sea and Yellow Sea sea areas, when the prediction scales are 1, 3, and 7 days, the MAE of ST-UNet outperforms the best results of the other three compared models by 17%, 12%, and 2%, and the MSE by 16%, 18%, and 9%, respectively. In the South China Sea, when the prediction ranges are 1, 3, and 7 days, the MAE of ST-UNet is 27%, 18%, and 3% higher than the best of the other three compared models, and the MSE is 46%, 39%, and 16% higher, respectively. Our results highlight the effectiveness of the ST-UNet model in capturing spatial correlations and accurately predicting SST. The proposed model is expected to improve marine hydrographic studies. [ABSTRACT FROM AUTHOR]

Basalt fiber–reinforced polymer (BFRP), a composite material known for its cost-effectiveness and excellent resistance to chloride ion corrosion, combined with environmentally friendly geopolymer is expected to contribute to the development of sustainable marine engineering structures. However, concerns still remain regarding the durability of this structure in marine environments. This paper presents an experimental investigation of the durability of basalt fiber–reinforced polymer bars immersed in seawater under the combined effects of the seawater sea-sand geopolymer mortar (SSGM) covering and prestressing. The prestress level was maintained at 20% of the ultimate tensile strength of the BFRP at room temperature in the first 7 days; the alkaline content of the SSGM, represented by the mass ratio of Na2O in the alkaline activator to the precursor, was either 4% or 6%; and the seawater temperatures were room temperature, 40°C, or 60°C. The prestress losses of the BFRP bars were monitored over the entire period, and tensile tests were conducted at 30, 60, 120, and 240 days. The results show that the prestress losses of the BFRP were less than 40% after 240 days of immersion. Prestress accelerated the degradation of the BFRP performance due to fiber damage and microcracking. The alkalinity of the activator in the SSGM did not have a significant effect on the tensile strengths of the BFRP bars immersed in seawater. However, the higher the alkalinity, the higher was the compressive strength of the SSGM and the lower were the prestress losses of the BFRP, which was attributed to the increased compactness friction caused by shrinkage of the SSGM. Carbonates combined with hydrogen ions in the water to form carbonic acid, and hydroxyl groups combined with the cations in the sea sand and the SSGM to form a strongly alkaline solution; these solutions were harmful to the long-term performance of the BFRP bars. [ABSTRACT FROM AUTHOR]

The prediction skill for precipitation anomalies in late spring and summer months—a significant component of extreme climate events—has remained stubbornly low for years. This paper presents a new idea that utilizes information on boreal spring land surface temperature/subsurface temperature (LST/SUBT) anomalies over the Tibetan Plateau (TP) to improve prediction of subsequent summer droughts/floods over several regions over the world, East Asia and North America in particular. The work was performed in the framework of the GEWEX/LS4P Phase I (LS4P-I) experiment, which focused on whether the TP LST/SUBT provides an additional source for subseasonal-to-seasonal (S2S) predictability. The summer 2003, when there were severe drought/flood over the southern/northern part of the Yangtze River basin, respectively, has been selected as the focus case. With the newly developed LST/SUBT initialization method, the observed surface temperature anomaly over the TP has been partially produced by the LS4P-I model ensemble mean, and 8 hotspot regions in the world were identified where June precipitation is significantly associated with anomalies of May TP land temperature. Consideration of the TP LST/SUBT effect has produced about 25–50% of observed precipitation anomalies in most hotspot regions. The multiple models have shown more consistency in the hotspot regions along the Tibetan Plateau-Rocky Mountain Circumglobal (TRC) wave train. The mechanisms for the LST/SUBT effect on the 2003 drought over the southern part of the Yangtze River Basin are discussed. For comparison, the global SST effect has also been tested and 6 regions with significant SST effects were identified in the 2003 case, explaining about 25–50% of precipitation anomalies over most of these regions. This study suggests that the TP LST/SUBT effect is a first-order source of S2S precipitation predictability, and hence it is comparable to that of the SST effect. With the completion of the LS4P-I, the LS4P-II has been launched and the LS4P-II protocol is briefly presented. [ABSTRACT FROM AUTHOR]

A prototype of an autonomous system for the retrieval of oceanographic, wave, and meteorologic data was installed and tested in May 2021 on a Portuguese research vessel navigating on the Atlantic Ocean. The system was designed to be installed in fishing vessels that could operate as a distributed network of ocean data collection. It consists of an automatic weather station, a ferrybox with a water pumping system, an inertial measurement unit, a GNSS unit, an onboard desktop computer, and a wave estimator algorithm for wave spectra estimation. Among several parameters collected by this system's sensors are the air temperature, barometric pressure, humidity, wind speed and direction, sea water temperature, pH, dissolved oxygen, salinity, chlorophyll-a, roll, pitch, heave, true heading, and geolocation of the ship. This paper's objectives are the following: (1) describe the autonomous prototype; and (2) present the data obtained during a full-scale trial; (3) discuss the results, advantages, and limitations of the system and future developments. Meteorologic measurements were validated by a second weather station onboard. The estimated wave parameters and wave spectra showed good agreement with forecasted data from the Copernicus database. The results are promising, and the system can be a cost-effective solution for voluntary observing ships. [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]

Ocean observational gridded products are vital for climate monitoring, ocean and climate research, model evaluation, and supporting climate mitigation and adaptation measures. This paper describes the 4th version of the Institute of Atmospheric Physics (IAPv4) ocean temperature and ocean heat content (OHC) objective analysis product. It accounts for recent developments in quality control (QC) procedures, climatology, bias correction, vertical and horizontal interpolation, and mapping and is available for the upper 6000 m (119 levels) since 1940 (more reliable after ~1957) for monthly and 1° × 1° temporal and spatial resolutions. The IAPv4 is compared with the previous version, IAPv3, and to the other data products, sea surface temperatures (SSTs), and satellite observations. It has a slightly stronger long-term upper 2000 m OHC increase than IAPv3 for 1955-2023, mainly because of newly developed bias corrections. IAPv4 OHC 0-2000 m trend is also higher during 2005-2023 than IAPv3. The first level of IAPv4 is consistent with independent SST datasets. The month-to-month OHC variability for IAPv4 is desirably less than IAPv3 and other OHC products investigated in this study; annual variations are consistent with the net energy imbalance at the top of the atmosphere, and the sea level budget can be closed within uncertainty. [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]

We present a feasibility study for passive sounding of Uranian icy moons using Uranian Kilometric Radio (UKR) emissions in the 100–900 kHz band. We provide a summary description of the observation geometry, the UKR characteristics, and estimate the sensitivity for an instrument analogous to the Cassini Radio Plasma Wave Science (RPWS) but with a modified receiver digitizer and signal processing chain. We show that the concept has the potential to directly and unambiguously detect cold oceans within Uranian satellites and provide strong constraints on the interior structure in the presence of warm or no oceans. As part of a geophysical payload, the concept could therefore have a key role in the detection of oceans within the Uranian satellites. The main limitation of the concept is coherence losses attributed to the extended source size of the UKR and dependence on the illumination geometry. These factors represent constraints on the tour design of a future Uranus mission in terms of flyby altitudes and encounter timing. Plain Language Summary: The large moons of Uranus are hypothesized to have subsurface oceans beneath their icy crusts. This paper analyzes the possibility to use natural radio emissions originating from Uranian auroras to probe for these oceans. Cold ice is transparent to radio waves allowing reflections from liquid water to be readily observed. Monitoring the radio noise patterns from Uranus and searching for the reflections could constitute a direct way to detect the subsurface oceans. Key Points: Passive Sounding of the Uranian moons is a promising concept and no insurmountable obstacles have been identifiedIn the presence of NH3 rich oceans, the direct detection of the ice‐ocean interface is feasibleThe method works best for the outer moons and low ocean temperatures, making it complementary to magnetic induction measurements [ABSTRACT FROM AUTHOR]

During July and August of 2022, the Yangtze River Basin (YRB) experienced its most extreme high temperature (EHT) event since 1979, resulting in large numbers of human casualties and severe economic losses. This paper reveals the spatial and temporal features of the EHT over the YRB (YRB-EHT) in 2022 and disentangles its extreme nature from a historical perspective. Results showed the following: 1) The record-breaking YRB-EHT was directly caused by the adiabatic heating associated with an anomalous barotropic high pressure (or heat dome) and descending motion in the region. The intensified and westward-shifted western North Pacific subtropical high and eastward-extended South Asian high played critical roles in the formation of the heat dome and descending motion anomaly. 2) Convection anomalies over the tropical Atlantic and Pacific induced by the reintensified La Niña–like Pacific sea surface temperature anomaly pattern, along with the strong positive North Atlantic Oscillation (NAO), were the key contributing factors to the formation of the barotropic high pressure anomaly and YRB-EHT. 3) A physics-based empirical simulation model constructed using the factors of the NAO and tropical convection successfully reproduced the historical year-to-year variation of YRB temperatures, as well as the extreme in 2022, implying that the unprecedented 2022 YRB-EHT had universal dynamic origins. This study highlights the importance of the combined impacts of tropical and extratropical forcings in the record-breaking YRB-EHT in 2022, and thus may provide useful clues for seasonal predictions of summer mean or extreme temperatures in the YRB. Significance Statement: Extreme high temperatures hit the Yangtze River Basin (YRB) in summer 2022, causing large numbers of human casualties and severe economic losses. Here, from a historical perspective, we disentangle the dynamic origins of these high temperatures over the YRB. Based on evidence from both observations and model simulations, the present study highlights the importance of the combined impacts of tropical and extratropical routes of influence on these extremely high temperatures in the YRB, and thus may provide useful clues for seasonal predictions of YRB summer mean or extreme temperatures. [ABSTRACT FROM AUTHOR]

Previous studies suggested that Atlantic Multidecadal Variability (AMV) modulations on pan‐Pacific sea surface temperature (SST) variability and prediction are model‐dependent. These results were mainly based on SST forcing experiments in which AMV‐related Atlantic SST anomalies were prescribed. However, the AMV itself is also model‐dependent, but its influences on the Pacific remain unclear. Here, we use multi‐model fully coupled experiments from the Coupled Model Intercomparison Project Phase 6 (CMIP6), along with observations, to study the model‐dependent AMV trans‐basin effects. We found that AMV strength is a key factor: Stronger (Weaker) model AMV than observations overestimates (underestimates) SST response and decadal prediction skills, mainly in the North Pacific. The reason is that stronger positive phased AMV, for example, leads to higher sea level pressure anomalies over the North Pacific, which lifts sea surface height and deepens thermocline to warm SST. Our study highlights the necessity to improve simulations of AMV strength. Plain Language Summary: Pacific sea surface temperature (SST) decadal variability and prediction are important for economy and environment. The Atlantic Multidecadal Variability (AMV) was thought to significantly influence Pacific SST variability and prediction. However, the results of previous studies were model‐dependent. In this paper, we use fully coupled experiments of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models to study what contributes to the model dependency. We found that the strength of the AMV is a key factor influencing AMV trans‐basin modulations on SST variability and decadal prediction, mainly in the North Pacific Ocean. The related mechanisms are discussed. Our paper sheds light on the way to improve North Pacific SST decadal prediction. Key Points: Climate models show a strong inter‐model spread of Atlantic Multidecadal Variability (AMV) strengthModels with stronger (weaker) AMV than observations overestimate (underestimate) North Pacific sea surface temperature (SST) response and decadal predictionThe North Pacific SST response is primarily forced by AMV‐induced wind stress curl and ocean dynamics [ABSTRACT FROM AUTHOR]

Sea surface temperatures (SSTs) vary not only due to heat exchange across the air‐sea interface but also due to changes in effective heat capacity as primarily determined by mixed layer depth (MLD). Here, we investigate seasonal and regional characteristics of the contribution of MLD anomalies to the month‐to‐month variability of SST using observational datasets. First, we propose a metric called Flux Divergence Angle, which can quantify the relative contributions of surface heat fluxes and MLD anomalies to SST variability. Using this metric, we find that MLD anomalies tend to amplify SST anomalies in the extra‐tropics, especially in the eastern ocean basins, during spring and summer. In contrast, MLD anomalies tend to suppress SST anomalies in the eastern tropical Pacific during December‐January‐February. This paper provides the first global picture of the observed importance of MLD anomalies to the local SST variability. Plain Language Summary: Sea surface temperature (SST) is one of the important indicators as well as drivers of climate variability over the globe. SST varies not only due to changes in surface heat fluxes but also due to changes in effective heat capacity as mainly determined by mixed layer depth (MLD). However, the observed characteristics of the latter process associated with the MLD anomalies are limited. In this study, we propose a new metric called "Flux Divergence Angle (FDA)," which can quantify the relative importance of MLD and surface heat flux to the SST variability. Using this metric, we find that the MLD anomalies tend to amplify the local SST variability in the extra‐tropics during spring and summer. On the other hand, MLD anomalies tend to suppress the SST variability in the eastern tropical Pacific during December‐January‐February. This paper provides, for the first time, the global picture of relative importance of MLD anomalies to the SST variability based on observations. Key Points: Relative contributions of mixed layer depth (MLD) and surface heat flux anomalies to sea surface temperature (SST) variability are investigated using the Flux Divergence Angle metricMLD anomalies tend to amplify SST anomalies in the extra‐tropics, especially in eastern part of ocean basin, during the spring and summerMLD anomalies tend to suppress SST anomalies in the eastern tropical Pacific during December‐January‐February [ABSTRACT FROM AUTHOR]

It is known that special combinations of variables can have more predictability than any variable in the combination. While such combinations can be obtained numerically in specific cases, this leaves broader questions unanswered. For example, given the dynamics of a linear stochastic model, what is the maximum predictability? What is its structure? What stochastic forcing maximizes predictability? This paper answers these questions. Specifically, this paper derives an upper bound on the predictability of linear combinations of variables. This bound is achieved when stochastic forcing correlates perfectly between eigenmodes. In a certain limit, the structure of the most predictable combination can be derived analytically. This structure is called the Pascal Mode due to its relation to Pascal's Triangle in a special case. These results provide a new perspective on observation-based climate predictability estimates. For instance, the most predictable component of monthly North Atlantic sea surface temperature is only modestly more predictable than the least-damped eigenmode. This aligns with the theoretical findings, as neither the stochastic forcing nor the dynamical eigenvalues are tailored to enhance predictability. The Pacific shows more predictability relative to the least-damped mode, but this increase is an order of magnitude smaller than the theoretical limit given the dynamics. [ABSTRACT FROM AUTHOR]

In the process of ocean exploration, highly accurate and sensitive measurements of seawater temperature and pressure significantly impact the study of seawater's physical, chemical, and biological processes. In this paper, three different package structures, V-shape, square-shape, and semicircle-shape, are designed and fabricated, and an optical microfiber coupler combined Sagnac loop (OMCSL) is encapsulated in these structures with polydimethylsiloxane (PDMS). Then, the temperature and pressure response characteristics of the OMCSL, under different package structures, are analyzed by simulation and experiment. The experimental results show that structural change hardly affects temperature sensitivity, and square-shape has the highest pressure sensitivity. In addition, with an input error of 1% F.S., temperature and pressure errors were calculated, which shows that a semicircle-shape structure can increase the angle between lines in the sensitivity matrix method (SMM), and reduce the effect of the input error, thus optimizing the ill-conditioned matrix. Finally, this paper shows that using the machine learning method (MLM) effectively improves demodulation accuracy. In conclusion, this paper proposes to optimize the ill-conditioned matrix problem in SMM demodulation by improving sensitivity with structural optimization, which essentially explains the cause of the large errors for multiparameter cross-sensitivity. In addition, this paper proposes to use the MLM to solve the problem of large errors in the SMM, which provides a new method to solve the problem of the ill-conditioned matrix in SMM demodulation. These have practical implications for engineering an all-optical sensor that can be used for detection in the ocean environment. [ABSTRACT FROM AUTHOR]

Changes in ocean temperature over time have important implications for marine ecosystems and global climate change. With the development of artificial intelligence, research on ocean temperature prediction has made some progress. However, existing methods are mostly limited to temperature prediction of isolated points on ocean surface, with less vertical studies. And existing graph neural network-based methods typically use predefined graphs, which cannot adaptively capture unknown associations between data. In this paper, we propose a new adaptive spatiotemporal dynamic graph convolution network to predict three-dimensional sea water temperature. Combined with adaptive graph learning and k nearest neighbor clustering methods, the network can automatically mine unknown dependencies between sequences based on raw data without any prior knowledge. Temporal and spatial dependencies in time series are further captured using temporal convolution and graph convolution. The graph learning, graph convolution, and temporal convolution modules are jointly learned in an end-to-end framework for the three-dimensional seawater temperature prediction task. In this paper, the prediction experiment is carried out using the high-resolution three-dimensional temperature and salt datasets from the Copernicus Global Ocean Physics Reanalysis. The results showed that our method achieved the best predictive performance on all prediction scales compared to current mainstream methods, with MAE increasing by an average of 21.7% and RMSE of 23.3%. [ABSTRACT FROM AUTHOR]

In this paper, we explore different prognostic methods to account for skin sea surface temperature diurnal variations in a coupled ocean-atmosphere regional model of the Mediterranean Sea. Our aim is to characterize the sensitivity of the considered methods with respect to the underlying assumption of how the solar radiation shapes the warm layer of the ocean. All existing methods truncate solar transmission coefficient at a constant warm layer reference depth; instead, we develop a new scheme where this latter is estimated from a chlorophyll dataset as the e-folding depth of solar transmission. This allows spatial and temporal variations of the warm layer extent to depend on seawater transparency. Comparison against satellite data shows that our new scheme improves the diurnal signal especially during winter, spring, and autumn, with an averaged bias on monthly scales year-round smaller than 0.1 K. In April, when most of the drifters measurements are available, the new scheme mitigates the bias during nighttime, keeping it positive but smaller than 0.12 K during the rest of the monthly-averaged day. The new scheme implemented within the ocean model improves the old one by about 0.1 K, particularly during June. All the methods considered here showed differences with respect to objectively analyzed profiles confined between 0.5 K during winter and 1 K in summer for both the eastern and the western Mediterranean regions, especially over the uppermost 60 m. Overall, the surface net total heat flux shows that the use of a skin SST parametrization brings the budget about 1.5 W/m2 closer to zero on an annual basis, despite all simulations showing an annual net heat loss from the ocean to the atmosphere. Our “chlorophyll-interactive” method proved to be an effective enhancement of existing methods, its strength relying on an improved physical consistence with the solar extinction implemented in the ocean component. [ABSTRACT FROM AUTHOR]

Haiyang-1D (HY-1D) is the second operational satellite in China's Haiyang-1 series of satellites, carrying the Chinese Ocean Color and Temperature Scanner (COCTS) to provide ocean color and temperature observations. The radiometric calibration is a prerequisite to guarantee the quality of the satellite observations and the derived products, and the radiometric calibration of the thermal emissive bands of HY-1D/COCTS can effectively improve the accuracy of sea surface temperature (SST) derived from the thermal infrared data. In this paper, a study on the regional cross-calibration of the COCTS thermal emissive bands is conducted for high-accuracy SST observations in the South China Sea. The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the NOAA-20 satellite launched by the National Oceanic and Atmospheric Administration (NOAA) is selected as the calibration reference sensor, and a double-difference cross-calibration method is used for HY-1D/COCTS thermal infrared brightness temperature (BT) evaluation. The results show that the bias of the 11 µm and 12 µm thermal emissive bands of COCTS and VIIRS in the South China Sea are 0.101 K and 0.892 K, respectively, and the differences in BTs between the two sensors show temperature dependence. The cross-calibration coefficients are obtained and used to correct the BT of the COCTS thermal emissive bands. The bias of the BT of the 11 µm and 12 µm bands of COCTS are about 0.01 K after cross-calibration. To further validate the results, COCTS post-calibration data were examined using the NOAA-20 Cross-track Infrared Sounder (CrIS) data as a third-party source. The BT is calculated with the spectral response functions of the COCTS thermal emissive bands using the convolution calculation of the CrIS hyperspectral region observations. The comparison shows a small bias between the post-calibration COCTS thermal emissive band observations and CrIS, which is consistent with the comparison between VIIRS and CrIS. The accuracy of the post-calibration COCTS thermal emissive band BT data in the South China Sea has been significantly improved. [ABSTRACT FROM AUTHOR]

The mobile ocean weather observation system, named Marine Weather Observer (MWO), developed by the Institute of Atmospheric Physics (IAP), consists of a fully solar-powered, unoccupied vehicle and meteorological and hydrological instruments. One of the MWOs completed a long-term continuous observation, actively approaching the center of Typhoon Sinlaku from 24 July to 2 August 2020, over the South China Sea. The in situ and high-temporal-resolution (1 min) observations obtained from MWO were analyzed and evaluated through comparison with the observations made by two types of buoys during the evolution of Typhoon Sinlaku. First, the air pressure and wind speed measured by MWO are in good agreement with those measured by the buoys before the typhoon, reflecting the equivalent measurement capabilities of the two methods under normal sea conditions. The sea surface temperature (SST) between MWO and the mooring buoys is highly consistent throughout the observation period, indicating the high stability and accuracy of SST measurements from MWO during the typhoon evolution. The air temperature and relative humidity measured by MWO have significant diurnal variations, generally lower than those measured by the buoys, which may be related to the mounting height and sensitivity of sensors. When actively approaching the typhoon center, the air pressure from MWO can reflect some drastic and subtle changes, such as a sudden drop to 980 hPa, which is difficult to obtain by other observation methods. As a mobile meteorological and oceanographic observation station, MWO has shown its unique advantages over traditional observation methods, and the results preliminarily demonstrate the reliable observation capability of MWO in this paper. [ABSTRACT FROM AUTHOR]

Large amounts of cooling water are required for the cooling process in coal-fired or nuclear power plants. Because the primary use of water in a power plant is to condense steam and remove waste heat as part of a Rankine cycle, seawater temperature (SWT) is of critical importance for electrical output in power plant applications installed at the seaside. Moreover, the analysis of SWT is an important criterion for researching sea life and global climate, and it also serves as an important indicator of climate change. In this paper, multilayer perceptron, which is in the class of a feed-forward artificial neural network, deep learning approach based on long short-term memory and bidirectional long short-term memory neural networks and data-driven methods, such as adaptive neuro-fuzzy inference system (ANFIS) accompanied by fuzzy c-means, ANFIS with grid partition and ANFIS with subtractive clustering methods were applied to make 1-day ahead SWT predictions. Analyses were conducted using 5-year daily mean SWTs measured by the Turkish State Meteorological Service for Antalya province between 2014 and 2018. The models were evaluated using mean absolute error (MAE), root-mean-square error (RMSE), and correlation coefficient (R). According to the daily SWT prediction, the best MAE, RMSE, and R values were obtained with the ANFIS-SC model, which were 0.1877 °C, 0.2683 °C, and 0.99814, respectively. [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]

Previous studies have reported significant uncertainty in the relationship between El Niño-Southern Oscillation (ENSO) and the winter surface air temperature (SAT) over central North America (NA). This paper uses reanalysis data and statistical methods to investigate the possible modulation effect of the Victoria mode (VM) during the preceding spring on the winter ENSO–SAT relationship. The correlation between ENSO and the winter SAT over central NA was found to be significantly positive during the positive VM phase. However, this significant correlation is almost absent during the negative VM phase. Further analysis revealed that the zonal location of the sea surface temperature anomalies (SSTA) over the tropical Pacific is an essential aspect of this VM modulation. During the positive VM phase, the dominant positive SSTA in the central-eastern tropical Pacific contribute to the formation of the negative phase of the North Pacific Oscillation (NPO)-like teleconnection, which further enhances the anomalous southerly winds and SAT over central NA. In contrast, during the negative VM phase, the Aleutian low-like atmospheric response induced by the central tropical Pacific SSTA is limited to the central North Pacific, which has a limited effect on the variation of the central NA SAT. The VM modulation mechanism is shown to be well reproduced by historical simulations of the Coupled Model Intercomparison Project Phase 6. Our results suggest that, despite the uncertainty associated with the ENSO–SAT connection, ENSO may provide prediction skills for the winter SAT over central NA during the positive VM phase. [ABSTRACT FROM AUTHOR]

Previous studies suggested that tropical sea surface salinity (SSS) can influence tropical Pacific sea surface temperature (SST) through mixing and entrainment and thus it may be a signal for El Niño‐Southern Oscillation (ENSO) prediction. This paper explores the influence of SSS on ENSO spring predictability barrier (SPB) using an empirical dynamic model ‐ Linear Inverse Model (LIM). By coupling and decoupling SSS in the LIM, we find that tropical Pacific SSS plays a significant role in weakening both Central‐Pacific and Eastern‐Pacific ENSO SPB. The evolution of optimal initial structure also shows the importance of SSS dynamics in ENSO. We found an SSS mode that plays the dominant role in SSS impacting ENSO prediction. By the analysis of lead‐lag correlation, we find that this mode can induce easterlies during the spring, which finally leads to a La Niña‐like SST pattern in the winter through zonal advective and thermocline feedbacks. Plain Language Summary: The spring predictability barrier (SPB) is a phenomenon of forecast skill reduction of the El Niño‐Southern Oscillation (ENSO) when it comes to the boreal spring, regardless of the initial month. Sea surface salinity (SSS) can influence sea surface temperature by altering sea surface density and thus it may be a signal for ENSO prediction. Using a linear dynamical model, we find that SSS plays an important role in improving the forecast skill of ENSO and weakening SPB. We further find that SSS can induce the easterlies during the spring, which finally leads to a La Niña‐like SST pattern. Our study suggests that SSS can be used to predict ENSO about 1 year later. Key Points: A linear dynamical model suggests that taking sea surface salinity (SSS) into consideration can strongly weaken Central‐Pacific and Eastern‐Pacific El Niño‐Southern Oscillation (ENSO) spring predictability barrier (SPB)A new SSS mode is found to be important for weakening ENSO SPBThe SSS mode can predict ENSO events about 1 year earlier by inducing sea surface temperature and wind anomalies in the early spring [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]

The long‐term absorption by the oceans of atmospheric carbon dioxide is leading to the slow decline of ocean pH, a process termed ocean acidification (OA). The Arctic is a challenging region to gather enough data to examine the changes in carbonate chemistry over sufficient scales. However, algorithms that calculate carbonate chemistry parameters from more frequently measured parameters, such as temperature and salinity, can be used to fill in data gaps. Here, these published algorithms were evaluated against in situ measurements using different data input types (data from satellites or in situ re‐analysis climatologies) across the Arctic Ocean. With the lowest uncertainties in the Atlantic influenced Seas (AiS), where re‐analysis inputs achieved total alkalinity estimates with Root Mean Squared Deviation (RMSD) of 21 μmol kg−1 and a bias of 2 μmol kg−1 (n = 162) and dissolved inorganic carbon RMSD of 24 μmol kg−1 and bias of −14 μmol kg−1 (n = 262). AiS results using satellite observation inputs show similar bias but larger RMSD, although due to the shorter time span of available satellite observations, more contemporary in situ data would provide further assessment and improvement. Synoptic‐scale observations of surface water carbonate conditions in the Arctic are now possible to monitor OA, but targeted in situ data collection is needed to enable the full exploitation of satellite observation‐based approaches. Plain Language Summary: The long‐term absorption by the oceans of atmospheric carbon dioxide is leading to the slow decline of ocean pH, a process termed ocean acidification (OA). Sea surface salinity and temperature measurements from satellites or in situ re‐analysis products can be used as input to empirical algorithms to calculate OA parameters. This paper provides a first analysis of published Arctic Ocean empirical algorithms to estimate surface water OA parameters using observation‐based data sets. Results show promise in the Atlantic influenced Seas using both in situ re‐analysis and satellite products, but satellite salinity is relatively recent, and a paucity of in situ data in the satellite salinity era precludes a robust assessment. To fully exploit satellite‐based approaches, efforts need to focus on collecting in situ data while these satellites are overhead and operating in orbit. Key Points: Observation‐based data sets enable synoptic spatio‐temporal assessments of Arctic Ocean carbonate system parametersRe‐analysis and satellite products predict total alkalinity with accuracies of ∼21 μmol kg−1Targeted in situ data collection is needed to fully exploit satellite observations in the Arctic to enable carbonate system monitoring [ABSTRACT FROM AUTHOR]

Previous studies have demonstrated that the influence of the El Niño–Southern Oscillation (ENSO) on the Euro-Atlantic atmospheric circulation varies considerably during the boreal winter. Compared to the late winter (January–March) relationship, the early winter (November–December) teleconnection is more uncertain and less understood. In this paper, we revisited this early winter regional ENSO teleconnection using the Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST) and the European Centre for Medium-Range Weather Forecasting (ECMWF) fifth generation reanalysis (ERA5) datasets for the period 1979–2022. It was found that the signal projected well onto the second dominant mode of Euro-Atlantic atmospheric variability, the East Atlantic Pattern (EAP), rather than the previously mentioned North Atlantic Oscillation (NAO). This influence is associated with ENSO-induced dipolar convection anomalies in the Gulf of Mexico and Caribbean Sea (GMCA), which leads to an EAP via exciting Rossby waves propagating northward into the North Atlantic. We further revealed that this ENSO–EAP teleconnection underwent a pronounced interdecadal strengthening around the late 1990s. Prior to the late 1990s, the convective response to ENSO in the GMCA was weak. The atmospheric responses over the Euro-Atlantic were mainly driven by the ENSO-induced convective forcing in the tropical Indian Ocean, which favors an NAO-like pattern. In contrast, since the late 1990s, ENSO has induced stronger precipitation anomalies in the GMCA, which exert a dominant influence on the Euro-Atlantic atmospheric circulation and produce an EAP. These results have useful implications for the further understanding of ENSO-related early winter atmospheric and climate variability in the Euro-Atlantic region. [ABSTRACT FROM AUTHOR]

Deep learning has been acknowledged as an increasingly important technology for ENSO forecasts. The most cutting-edge deep learning algorithm is developed based on Convolutional Neural Network (CNN), which can achieve a multi-year (about 17-month-lead) forecast and has conquered the 'spring forecast barrier' problem. However, this group of methods are still challenged by several critical issues. First, they usually utilize the global sea surface temperature (SST) fields as inputs without considering the specific contributions of variant oceanic regions in ENSO forecasts. Consequently, they cannot effectively investigate the role of the 'teleconnection' mechanism among different oceans (Indian, Pacific, and Atlantic Oceans) and different ocean parts (the tropic and non-tropic regions) especially in the forecast of extreme ENSO events. Second, existing methods mainly utilize the discrete monthly SST fields for ENSO forecasts without investigating the rate-of-changes between adjacent months, which also provides important information to the prediction of variation tendency. To solve these problems, this paper develops a Tendency-and-Attention-Informed Deep Residual Network (TA-DRN) for multi-year ENSO forecasts. The contributions of different oceanic regions can be learned by a spatial attention module while the variation tendency of adjacent previous and current months can be interpreted by the first-and-second order of differences of SST fields. Through informed by these two modules, the performance of TA-DRN can be improved significantly, especially in predicting extreme El Niño and La Niña events. [ABSTRACT FROM AUTHOR]

The annular structure of Antarctic oscillation (AAO) is a research hotpot, but its asymmetry receives less attention. In this paper, the self‐organizing map method is employed to cluster the AAO patterns into symmetric and asymmetric modes in austral autumn. The asymmetry is mainly reflected in the Pacific‐Atlantic sector, and the AAO evolves toward asymmetric positive polarity, with the most pronounced asymmetry in May. Originating from near Australia, the asymmetry indicates a zonal wave train in the Pacific‐Atlantic sector. Both modeled and observed results demonstrate that the sea surface temperature anomaly in the equatorial western Pacific stimulates a local meridional circulation anomaly and induces anomalous Rossby wave sources near Australia subsequently. An anomalous wave train propagating toward the Antarctic Peninsula is formed, and the associated geopotential anomaly enhances the asymmetry of AAO. Asymmetric AAO is conducive to the Antarctic dipole, which modulates the air temperature and sea ice anomalies around Antarctica. Plain Language Summary: The zonal symmetry of the Antarctic oscillation (AAO; also named southern annular mode) is well known to researchers, while the zonal asymmetry has received less attention. We use a clustering method to cluster the AAO modes in austral autumn into symmetric modes and asymmetric modes. The asymmetry of AAO is mainly reflected in the Pacific‐Atlantic sector. The results show that the AAO mode evolves to a positive asymmetric mode, and the asymmetry is most obvious in May. Both simulations and observations suggest that this asymmetry stems from sea surface temperature anomaly in the equatorial western Pacific that stimulates an anomalous local meridional circulation near Australia, and in turn triggers an anomalous wave train propagating toward the Antarctic Peninsula. The associated geopotential anomalies enhance the asymmetry of AAO. The asymmetric AAO favors the Antarctic dipole, regulating the air temperature and sea ice anomalies around Antarctica. Key Points: Via a cluster method, we obtained the zonal asymmetric Antarctic oscillation (AAO) mode, and analyzed its trend and preferred monthThe source of its asymmetry is from the sea surface temperature anomaly in the tropical western PacificThe asymmetric AAO mode has a vital influence on the Antarctic dipole [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 describes the global eddying ocean–sea ice simulation produced at the Euro-Mediterranean Center on Climate Change (CMCC) obtained following the experimental design of the Ocean Model Intercomparison Project phase 2 (OMIP2). The eddy-rich model (GLOB16) is based on the NEMOv3.6 framework, with a global horizontal resolution of 1/16 ∘ and 98 vertical levels and was originally designed for an operational short-term ocean forecasting system. Here, it is driven by one multi-decadal cycle of the prescribed JRA55-do atmospheric reanalysis and runoff dataset in order to perform a long-term benchmarking experiment. To assess the accuracy of simulated 3D ocean fields and highlight the relative benefits of resolving mesoscale processes, the GLOB16 performances are evaluated via a selection of key climate metrics against observational datasets and two other NEMO configurations at lower resolutions: an eddy-permitting resolution (ORCA025) and a non-eddying resolution (ORCA1) designed to form the ocean–sea ice component of the fully coupled CMCC climate model. The well-known biases in the low-resolution simulations are significantly improved in the high-resolution model. The evolution and spatial pattern of large-scale features (such as sea surface temperature biases and winter mixed-layer structure) in GLOB16 are generally better reproduced, and the large-scale circulation is remarkably improved compared to the low-resolution oceans. We find that eddying resolution is an advantage in resolving the structure of western boundary currents, the overturning cells, and flow through key passages. GLOB16 might be an appropriate tool for ocean climate modeling efforts, even though the benefit of eddying resolution does not provide unambiguous advances for all ocean variables in all regions. [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]

This paper expands on work showing that the winter North Atlantic Oscillation (NAO) is predictable on decadal timescales to quantify the skill in capturing the North Atlantic eddy‐driven jet's location and speed. By focusing on decadal predictions made for years 2–9 from the sixth Coupled Model Intercomparison Project over 1960–2005 we find that there is significant skill in jet latitude and, especially, jet speed associated with the skill in the NAO. However, the skill in the NAO, jet latitude and speed indices appears to be sensitive to the period over which it is assessed. In particular, skill drops considerably when evaluating hindcasts up to the present day as models fail to capture the recent observed northern shift and strengthening of the winter eddy‐driven jet, and more thus positive NAO. We suggest that the drop in atmospheric circulation skill is related to reduced skill in North Atlantic sea surface temperatures. Plain Language Summary: Climate models have the capability to predict the evolution of mean atmospheric circulation over long timescales, from annual to decadal and longer. However, models are overestimating the chaotic, unpredictable component of the climate's variability and, although model predictions follow the observed oscillations of the climate, the strength of these oscillations is critically underestimated. While climate models have demonstrated skill in predicting the North Atlantic Oscillation (NAO), this paper assesses model skill in predicting the evolution of the wintertime North Atlantic eddy‐driven jet, with the aim to highlight how much of the predictive skill of the NAO is derived from accurate predictions of the jet's state. We find levels of skill similar to that for the NAO, with slightly higher skill for the jet's strength (speed) over its location (latitude of maximum speed). We also notice a drop in skill over the last decade, as models fail to capture the latest trends in the NAO and jet's evolution, and suggest that it might be related to degradation in skill at predicting surface temperature variability. Key Points: The winter North Atlantic eddy‐driven jet is predictable on decadal time‐scales with skill in jet speed comparable to that for the North Atlantic Oscillation (NAO)Anomalies in the jet are substantially smaller than expected from skill alone and so suffer from noise overestimationSkill drops significantly in the last decade, as hindcasts do not capture the return to positive NAO conditions post‐2010 [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]

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]

Satellite data have revealed that the Greenland and Antarctic Ice Sheets are changing rapidly due to warming air and ocean temperatures. Crucially, Earth Observations can now be used to measure ice sheet mass balance at the continental scale, which can help reduce uncertainties in the ice sheets' past, present, and future contributions to global mean sea level. The launch of satellite missions dedicated to the polar regions led to great progress towards a better assessment of the state of the ice sheets, which, in combination with ice sheet models, have furthered our understanding of the physical processes leading to changes in the ice sheets' properties. There is now a three-decade-long satellite record of Antarctica and Greenland mass changes, and new satellite missions are planned to both continue this record and further develop our observational capabilities, which is critical as the ice sheets remain the most uncertain component of future sea-level rise. In this paper, we review the mechanisms leading to ice sheets' mass changes and describe the state of the art of the satellite techniques used to monitor Greenland's and Antarctica's mass balance, providing an overview of the contributions of Earth Observations to our knowledge of these vast and remote regions. [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]

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 El Niño Southern Oscillation (ENSO) is a major influence on interannual variability of rainfall in stations in the tropical southwest Pacific. Predictions of seasonal rainfall, especially a season or two ahead, are of great value to these countries. This paper therefore examines the correlations over ~ 60 years between seasonal rainfall and 8 ENSO indicators at 16 island stations, allowing for lead times. The results show the influence on rainfall of the position and movement of the South Pacific Convergence Zone (SPCZ) during ENSO events, and that the Southern Oscillation Index (SOI), the sea surface temperature anomaly in the central Pacific (Niño 3.4), and the warm water volume in the eastern Pacific (WWV1) have longer lead times compared to most other ENSO indicators. These indicators can therefore be used with confidence in SCOPIC, a widely used statistical tool for prediction of seasonal rainfall. (As global climate models generally have systematic errors in their depiction of the SPCZ, they cannot yet be used directly to reliably predict seasonal rainfall in this region.) For several sites near the SPCZ, we find that a moderately good forecast of rainfall in both spring and summer can be made from indicators measured in June–July (i.e., 3–6 months in advance.) [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]

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]

A multi-season convection permitting regional climate simulation of the Maritime Continent using the Met Office Unified Model with 2.2-km grid spacing is presented and evaluated. The simulations pioneer the use of atmosphere-ocean coupling with the multi-column K profile parametrisation (KPP) mixed layer ocean model in atmospheric convection permitting climate simulations. Comparisons are made against a convection parametrised simulation in which it is nested, and which in turn derives boundary conditions from ERA5 reanalysis. This paper describes the configuration, performance of the mean state and variability of the two simulations compared against observational datasets. The models both have minor sea surface temperature (SST) and wet precipitation biases. The diurnal cycle, representation of equatorial waves and relationship between SST and precipitation are all improved in the convection permitting model compared to the convection parametrised model. The MJO is present in both models with a faster than observed propagation speed. However, it is unclear whether fidelity of the MJO simulation is inherent to the model or whether it predominantly arises from the forcing at the boundaries. [ABSTRACT FROM AUTHOR]