• STGAT model is developed to reconstruct high-resolution subsurface Temperature from satellites. • Multi-source satellite data and DSTAG enable high-accuracy reconstruction of ocean subsurface temperatures. • Joint training on reanalysis and EN4 data enhances subsurface temperature fidelity. The distribution and variation of ocean temperature are closely associated with ocean dynamic processes. High-precision ocean subsurface temperature fields are critical for studies on ocean dynamics and climate change. While satellite remote sensing provides extensive data on sea surface temperature, it lacks direct observations of ocean subsurface temperatures. In-situ measurements typically result in sparse and uneven spatiotemporal data coverage. Consequently, obtaining accurate high-resolution ocean subsurface temperature data remains a significant challenge in marine science. This study introduces a novel ocean subsurface temperature reconstruction model based on Spatiotemporal Graph Attention Networks (STGAT). STGAT is capable of inferring subsurface temperature fields from multiple satellite-derived sea surface observations, including Sea Level Anomaly (SLA), Sea Surface Temperature (SST), Sea Surface Salinity (SSS) and Sea Surface Wind (SSW), as well as the derived Depth-Specific Temperature Anomaly Gradient (DSTAG). The Kuroshio Extension region in the Northwest Pacific, characterized by intense ocean dynamic activity, was selected as the experimental area. Ocean temperature fields at 21 depth levels (20–1941 m) for 2022 were reconstructed using satellite sea surface observations from 2017 to 2022. The accuracy of the reconstructed subsurface temperature fields was evaluated using the GLORYS12V1 reanalysis datasets and the EN4 in-situ observation datasets. Comparative analysis with reanalysis data demonstrates that the proposed model effectively captures the spatiotemporal characteristics of ocean temperature across all depth levels, accurately reflecting realistic spatial distributions throughout the water column. The reconstructed subsurface temperature fields achieved mean RMSE and R 2 values of 0.916 °C and 0.866, respectively. Validation against EN4 data further confirms that the model’s ability to reproduce vertical thermal variations, with mean RMSE and R 2 values of 0.898 °C and 0.976, respectively. When compared with other machine learning-derived models, the STGAT model demonstrates superior performance in terms of reconstruction accuracy. In conclusion, the STGAT model effectively addresses the challenges posed by complex and highly turbulent oceanic processes, offering a promising new approach for retrieving high-resolution and high-accuracy ocean subsurface temperature data.

• The interplay of physical, chemical, and biological factors control the OMZ formation. • The role of ocean circulation patterns, nutrient enrichment and climate change influences OMZ expansion. • Biogeochemical impact of OMZs on marine biodiversity and their contribution to greenhouse gas emissions. • The complex feedback loops between OMZ expansion and climate change. Oxygen minimum zones (OMZ) are represented by sharply depleted oxygen concentrations in the modern ocean basins. The expansion of these zones is documented since 1960. They have been expanding globally in the world's oceans with profound implications for marine ecosystems and biogeochemical cycles. Under this review, we synthesize and integrate the current knowledge on the factors, dynamics and consequences of OMZ expansion in the modern ocean basins. We have explored the interplay of physical, chemical and biological factors conducive to OMZ formation and intensification, highlighting the role of ocean circulation patterns, nutrient enrichment from anthropogenic activities and augmenting influence of climate change. The impact of OMZs on marine ecology are explored with the focus on physiological stress on marine organisms, habitat compression, shifts in community structure and potential loss of biodiversity. We have also investigated their contribution to greenhouse gas emissions and the biogeochemical significance of OMZs, particularly in the context of nitrogen and other nutrient cycles. Further, this work emphasizes on the complex feedback loops between OMZ expansion and climate change underscoring the urgent need for mitigation and adaptation strategies. At the outset, the study discusses the future research scopes and management approaches crucial for addressing the challenges posed by expanding OMZs thereby ensuring the health and sustainability of modern ocean basins.

Abstract Rapid changes and increasing climatic variability across the widely varied Köppen-Geiger regions of northern Europe generate significant needs for adaptation. Regional planning needs high-resolution projected temperatures. This work presents an integrative statistical bias correction framework that incorporates Vision Transformer (ViT), Convolutional Long Short-Term Memory (ConvLSTM), and Geospatial Spatiotemporal Transformer with Attention and Imbalance-Aware Network (GeoStaNet) models. The framework is evaluated with a multicriteria decision system, Deep Learning-TOPSIS (DL-TOPSIS), for ten strategically chosen meteorological stations encompassing the temperate oceanic (Cfb), subpolar oceanic (Cfc), warm-summer continental (Dfb), and subarctic (Dfc) climate regions. Norwegian Earth System Model (NorESM2-LM) Coupled Model Intercomparison Project Phase 6 (CMIP6) outputs were bias-corrected during the 1951–2014 period and subsequently validated against independent historical observations (1951–2014) of day-to-day temperature metrics, extreme value distributions (99th percentile), and thermodynamic coupling (Diurnal Temperature Range). The ViT showed improved performance (Root Mean Squared Error (RMSE): 1.01 °C; R 2 : 0.92), allowing for production of credible bias-corrected projections. Under the SSP5-8.5 scenario, the Dfc and Dfb climate zones are projected to warm by 4. 8 °C and 3. 9 °C (Summer T m a x ), respectively, by 2100, with expansion in the diurnal temperature range by more than 1. 5 °C. The Time of Emergence signal first appears in subarctic winter seasons (Dfc: ~ 2032), signifying an urgent need for adaptation measures. The presented framework offers station-based, high-resolution estimates of uncertainties and extremes, with direct uses for adaptation policy over high-latitude regions with fast environmental change.

The low-latitude flow of water masses from the Pacific to the Indian Ocean, the Indonesian Throughflow (ITF), is a choke point of the surface ocean return flow of the ocean conveyor belt. Even though the significance of the ITF for the modern global ocean circulation and climate has long been established, little is known about the hemispheric origin of the water masses contributing to its overall transport in the past. Here, we take advantage of the distinctly different isotopic composition of subsurface nitrate in the Northern and Southern Hemisphere source waters to document the admixture of these waters in the ITF through time. Our record of bulk sedimentary δ15N from the Banda Sea, at the heart of the ITF, shows that Southern Hemisphere-sourced subsurface waters contributed significantly to the total ITF transport during the last 800,000 years. Because Southern Ocean processes ultimately set the biogeochemical source signature of the Southern Hemisphere endmember, the Banda Sea record implies an important conduit by which high southern latitude climate and ocean variability is transmitted into the global ocean.

Abstract Forecasting heavy precipitation events (HPEs) in the Mediterranean is crucial but challenging due to the complexity of the processes involved. In this context, artificial intelligence methods have recently proven to be competitive with state-of-the-art numerical weather prediction (NWP). This work focuses on improving the prediction of the occurrence of HPEs over periods from 1 to 24 h using neural network (NN) models. The proposed method uses both ground station observations and data from Météo France’s AROME and ARPEGE NWP models, on two regions with oceanic and Mediterranean climates for the period 2016–18. The verification metric is the Peirce skill score. Results show that the NN model using only observations or NWP data performs better for shorter and longer rainfall accumulation periods, respectively. In contrast, a hybrid method combining both observations and NWP data offers the best performance and remains stable with the rainfall accumulation period. The hybrid method also improves the performance in predicting increasingly intense rainfall, from the 5% to the 0.1% rarest events. The choice of the loss function is found to be an important aspect of this work, where only balanced loss functions provide results insensitive to rare event frequency. Finally, the hybrid method is particularly well suited for the prediction of HPEs in the Mediterranean climate, especially during the fall season, the period during which most HPEs occur. Significance Statement Heavy precipitation events can be deadly, particularly in the Mediterranean, and are difficult to predict. Traditionally, numerical weather prediction models have been used for forecasting such events but have shown limitations. This study explores the use of artificial intelligence methods to forecast the occurrence of heavy precipitation, using numerical weather prediction and observational data. The combination of both types of data, the so-called hybrid method, offers the best results and is stable with the time period and intensity of precipitation events. The hybrid method is found to be particularly well suited in the Mediterranean climate, especially during the fall season, the period during which most heavy precipitation events occur.

The long-term durability of thick multilayer 3D-printed walls is limited by poorly understood drying behaviour, which affects moisture management and performance. This study investigates the drying dynamics of a multilayer 3D-printed wall composed of two concrete layers enclosing a lightweight thermal mortar core, using simultaneous extrusion of structural and insulating mortars to streamline construction, reduce labour, and ensure continuous insulation. Gravimetric monitoring of small- and medium-scale specimens was combined with a validated numerical model (maximum deviation 3%) to analyse long-term moisture evolution. Sensitivity analyses evaluated the influence of temperature, relative humidity (RH), material properties, geometry, and configuration. Under constant reference conditions (20°C, 50% RH), the concrete layers acted as diffusion barriers, delaying core drying to nearly 20 years to reach 80% RH. Increasing temperature from 10°C to 50°C reduced drying time from 45 to 5 years, while decreasing RH from 90% to 10% shortened drying from over 50 years to approximately 12 years. Increasing capillary absorption and reducing vapour diffusion resistance accelerated drying by up to 70–74%. Exterior insulation configurations reduced drying time by 65% compared to the reference geometry. Unsteady-state simulations for Lisbon, Brussels, and Warsaw revealed significant climatic influence. In Mediterranean conditions, south-facing walls reached equilibrium in under four years, while north- and west-facing walls in oceanic climates retained the highest moisture levels. Seasonal moisture peaks occurred during winter. These findings highlight the critical role of material compatibility, façade orientation, and climate-responsive design in ensuring moisture control and durability of multilayer 3D-printed wall systems. • Studied drying in 3D-printed multilayer walls with thermal mortar cores. • Validated a numerical model with < 3% deviation from experimental data. • Drying time is influenced by temperature, humidity, and wall configuration. • Cracking linked to moisture gradients and constrained shrinkage in structural layers. • Concrete vapour resistance and absorption strongly impact moisture retention.

Delivering the ocean climate actions: Building a robust information base to facilitate and enhance the incorporation of blue carbon solutions into Kenya’s climate commitments

The Bandung metropolitan region has faced escalating flood threats (2014–2024); however, oceanic climate–rainfall relationships remain uninvestigated. This study investigates the interannual climate variability influencing extreme precipitation and flooding using historical records and the GPM-IMERG satellite measurements, which had a 90.4% correlation with BMKG. Bojongsoang (117 events), Lembang (49 events), and Braga (42 events) emerge as highest-risk areas. The peak flooding in January 2020 (15 events) coincided with the La Niña and negative Indian Ocean Dipole (IOD) phases. The average daily maximum rainfall during the wet season was 62 mm, compared with 41 mm in the dry season, with 36 heavy rain days versus 8 days. La Niña increased heavy rain days to 62.5 days compared with El Niño (38.6 days) and extreme rainfall to 399.6 mm versus 244.2 mm. Negative IOD enhanced the daily maximum to 76.8 mm versus 56.8 mm during the positive phases. Flood months showed 81.3 heavy rain days versus 14.4 in normal months. Early warning thresholds were established at &gt;70 mm daily maximum, &gt;60 heavy rain days, and &gt;400 mm extreme precipitation.

Accurate wind energy forecasting is critical for integrating wind power into electrical grids due to its inherent variability and uncertainty. This study introduces a systematic framework that integrates large-scale oceanic climate indices and time-lagged features with advanced machine-learning models to enhance short-term wind power prediction. We evaluate four experimental configurations: (A) a baseline using only wind speed; (B) wind plus contemporaneous indices; (C) the addition of 1-12 month lags for both wind and index variables; and (D) MRMR-based feature selection applied to the full lagged set. A comprehensive benchmark using 25 state-of-the-art models is conducted on monthly data from the Pawan Danavi wind farm in Sri Lanka (2015-2019). Results reveal that raw indices alone can degrade forecast accuracy, while incorporating lagged features significantly reduces RMSE and enhances [Formula: see text]. MRMR pruning of the 156 lagged predictors distills the set to three key variables: current wind speed, a nine-month lag of the Atlantic Meridional Mode, and a six-month lagged wind speed. This yields a minimum RMSE of [Formula: see text] and [Formula: see text]. The proposed approach delivers robust, computationally efficient forecasts, supporting more reliable grid operations and informing future integration of climate teleconnections in renewable energy forecasting.

Abstract Investigating the activities of extratropical cyclones and their relationships with atmospheric and oceanic climate indices is essential for understanding the broader dynamics of the atmosphere-ocean system over Antarctica. Based on an extratropical cyclone track dataset, we examine the joint modulation of Antarctic Oscillation (AAO) and El Niño-Southern Oscillation (Oceanic Niño Index, ONI) on extratropical cyclone activity in Antarctica. Extratropical cyclone activity is generally enhanced during both AAO+ &amp; ONI- and AAO- &amp; ONI- phases.We compare sea surface temperature, sea level pressure, geopotential height and wind fields based on atmospheric reanalysis datasets. The AAO phases modulate sea level pressure, westerly winds, and geopotential height, thereby strengthening or weakening atmospheric circulation. Different ONI phases shift the centers of these anomalies. Moreover, the AAO and ONI jointly influence wind shear, with positive Eady growth rate anomalies roughly associated with negative wind shear anomalies. These results provide insights into the dynamics of extratropical cyclone activities in Antarctica.

Abstract. As part of the new Fisheries Act, Fisheries and Oceans Canada (DFO) has made it a priority to disseminate its data publicly. The project proposed here is to create an open-access data product that includes most of the historical temperature and salinity profiles collected in Northwest Atlantic Ocean and its Arctic gateways. This project does not aim to replace a potential database, but rather provides an easily accessible and quality-controlled product that can inform fisheries management and support DFO priorities such as the Ecosystem Approach to Fisheries Management, Marine Spatial Planning and the Blue Economy. The Canadian Atlantic Shelf Temperature-Salinity (CASTS) data product consists of 853 748 individual casts (as of 22 August 2025) collected in a geographical zone corresponding to [35–80° N] and [42–100° W] since 1873. The data sources used to make this product were gathered from multiple sources, including DFO regional archives at the Maurice-Lamontagne Institute (MLI), the Bedford Institute of Oceanography (BIO), and the Northwest Atlantic Fisheries Center (NAFC). Other sources of data include the Fisheries and Marine Institute of Memorial University, data from international ships of opportunity archived by the Marine Environmental Data Services (MEDS), and the Polar Data Catalog. This data product also offers new opportunities to review the changes in the ocean climate of Atlantic Canada, another priority of the Government of Canada. The analysis of these data collected over more than a century also reveals the profound changes undergone by the Northwest (NW) Atlantic Ocean during that period. Climate highlights include large decadal fluctuations of temperature and salinity throughout the entire zone, as well as sustained warming trends on the Scotian Shelf and the Bay of Fundy since the early 1990s, coinciding with an important freshening on the Newfoundland and Labrador Shelf during the same period. The CASTS data product is available at https://doi.org/10.20383/103.01462 (Coyne et al., 2023).

Accurate time series forecasting of sea surface temperature (SST) is essential for understanding the ocean climate system and large-scale ocean circulation, yet it remains challenging due to regime-dependent variability and correlated errors across heterogeneous prediction models. This study addresses these challenges by formulating SST ensemble time series forecasting aggregation as a stochastic, sample-adaptive weighting problem. We propose a diffusion-conditioned ensemble framework in which heterogeneous base forecasters generate out-of-sample SST predictions that are combined through a noise-conditioned weighting network. The proposed framework produces convex, sample-specific mixture weights without requiring iterative reverse-time sampling. The approach is evaluated on short-horizon global SST forecasting using the Global Ocean Data Assimilation System (GODAS) reanalysis as a representative multivariate dataset. Under a controlled experimental protocol with fixed input windows and one-step-ahead prediction, the proposed method is compared against individual deep learning forecasters and conventional global pooling strategies, including uniform averaging and validation-optimized convex weighting. The results show that adaptive, diffusion-weighted aggregation yields consistent improvements in error metrics over the best single-model baseline and static pooling rules, with more pronounced gains in several mid- to high-latitude regimes. These findings indicate that stochastic, condition-dependent weighting provides an effective and computationally practical framework for enhancing the robustness of multivariate time series forecasting, with direct applicability to global SST prediction from large-scale geophysical reanalysis data.

Marine heatwaves have become more frequent and intense under anthropogenic warming, posing increasing threats to marine ecosystems and coastal societies, necessitating a better understanding of their mechanism and predictability. Here we show how ocean dynamics modulate marine heatwaves globally by comparing dynamic and slab ocean climate model simulations. We discover that ocean dynamics significantly promote marine heatwave intensity and duration in mid-to-high latitude oceans, as well as the eastern tropical Pacific where marine heatwaves are inherently linked to extreme El Niño events. Our mixed-layer heat budget analysis unravels that heat accumulation during marine heatwave episodes is strongly influenced by vertical mixing and horizontal transport processes, so that warm sea surface temperature extremes in dynamic ocean differ in magnitude and evolution rhythm from those in slab ocean. We further find robust multi-year potential predictability of marine heatwave in the North Atlantic with a dynamic ocean, owing primarily to the predictability of the Atlantic Meridional Overturning Circulation. Our findings emphasize the irreplaceable role of oceanic dynamics in marine heatwave evolution and predictability, with important implications for future climate extreme prediction and adaptation strategies.

Purpose The study aims to contribute to a nuanced understanding of Living Wall Systems (LWSs) seasonal performance by addressing the following question: To what extent do Living Wall Systems (LWSs) influence microclimatic conditions in a temperate oceanic climate during summer and winter? To address these questions, this study addresses the microclimatic performance of Plant Pixel, a novel living wall system developed within the SOLOCLIM project. Plant Pixel is a prototype featuring 500 modules and plants constructed in Germany. Design/methodology/approach This study assesses the microclimate performance of the Plant Pixel, an innovative LWSs. Plant Pixel is an optimized low-embodied carbon, cost-effective, simple-to-assemble and disassemble prototype. The research uses a Plant Pixel mock-up with 500 modules and plants. A long-term monitoring study was conducted from June 20, 2022, to January 1, 2023, in a temperate oceanic climate, comparing the Plant Pixel with a bare wall. Findings Results indicate that the Plant Pixel achieved maximum temperature reductions of 5.5°C at 10 cm and 3.3°C at 40 cm from the LWSs, exceeding reported cooling values for conventional LWSs in temperate climates. While the LWSs exhibited limited impact on outdoor air temperature during winter, it significantly reduced wind speed, highlighting its potential for thermal moderation year-round. These findings highlight the role of modular LWSs in urban climate adaptation strategies, providing practical insights for policymakers, urban planners and architects. Originality/value This study addresses the question: To what extent do LWSs influence microclimatic conditions in a temperate oceanic climate during summer and winter? This paper quantifies the Plant Pixel's (an innovative LWSs) microclimatic performance in a temperate oceanic climate by analyzing outdoor temperature, humidity, solar radiation and wind velocity parameters. These results highlight the influence of vegetated surface treatments on local microclimates and support the integration of LWSs into urban heat mitigation efforts. Furthermore, this research helps urban planners, landscape architects and architects seeking to integrate LWSs to enhance the livability of urban spaces.

Exposomic determinants substantially influence the variability of molecular IgE-mediated sensitization in allergic rhinitis and conjunctivitis across bioclimatic regions, underscoring their relevance in precision allergology. This study aimed to characterize molecular sensitization profiles in Spanish patients with respiratory allergy from distinct geographic and climatic areas. A cross-sectional, multicenter study was performed in 12 Spanish cities. The study population comprised 291 patients diagnosed with allergic rhinitis and/or conjunctivitis according to the modified ARIA/DECA criteria. Participants underwent skin prick testing with standardized allergen extracts and multiplex molecular IgE analysis. Clinical, demographic, and regional bioclimatic variables were integrated to define exposomic sensitization patterns. Patients previously treated with allergen immunotherapy or biologics were excluded. Regional pooled sera were analyzed by ELISA and IgE immunoblotting to validate molecular data and identify IgE binding to nonrecombinant or poorly characterized allergenic components. Distinct regional sensitization profiles were identified. Grass pollen allergens predominated in oceanic and continental climates, while olive and cypress pollens were more frequent in Mediterranean areas. Sensitization to house dust mite, particularly Dermatophagoides pteronyssinus and Blomia tropicalis, was highly prevalent in subtropical and humid zones. Molecular assays confirmed skin test findings and identified major allergenic molecules, including Phl p 1, Ole e 1, Der p 1, and Der p 2, along with region-specific components such as Der p 23, Cup a 1, Alt a 1, and Pla a 2. This multicenter exposomic study demonstrated that climatic diversity modulates allergen sensitization in Spain, supporting region-tailored precision diagnostic and therapeutic strategies in respiratory allergy.

Recently, international judicial forums have issued landmark advisory opinions on the subject of the ocean–climate nexus. The opinions are based on the recognition of the interconnection between the United Nations Framework Convention on Climate Change (UNFCCC) and the United Nations Convention on the Law of the Sea (UNCLOS). All judicial forums stated that Small Island Developing States (SIDS) are a distinct focus due to their disproportionate vulnerability to climate change, as reported by the Intergovernmental Panel on Climate Change (IPCC). According to the opinions, SIDS could become uninhabitable in the coming years, necessitating urgent global climate action. The United Nations (UN) has acknowledged the unique challenges of SIDS through various resolutions, which emphasise the need for climate justice and adherence to the 1.5 C climate target. Sustainable Development Goal 14 (SDG 14) brought attention to the direct impacts of climate change on oceans and the issues faced by SIDS. This paper reviews the historical and legal developments necessary for the sustainable development of SIDS, emphasising the nexus between climate change, ocean governance, and human rights. It highlights the potential for further advocacy and the interconnected nature of SDG 14 with judicial opinions.

ABSTRACT Although we live in the Anthropocene—the geological age of humankind, wherein humans have measurably impacted the biosphere—we struggle to narrate the Anthropocene. In particular, we struggle to give narrative shape to its foremost feature: anthropogenic climate change. Describing the limits of storytelling in the era of human‐driven climate change, both David Wallace‐Wells and Amitav Ghosh identify a “failure of imagination” in popular media. Ghosh defines our age as one of “derangement”—i.e., a distorted, anthropocentric worldview characterized by “modes of concealment” at odds with our supposed ecological self‐awareness. Literature often enables such derangement, and many critics have taken fiction to task over its inability to narrate the climate crisis. In my article, I conversely suggest the generative possibilities of derangement in climate‐minded fiction. I analyze how certain texts make narrative fodder out of our “failure of imagination,” telling human‐centered stories about our inability to tell nonhuman stories—a process of representation and reception I call deliberate derangement . I focus on a pair of climate fictions set around bodies of water: A. S. Byatt's “Sea Story” (2013) and Vajra Chandrasekera's “Half‐Eaten Cities” (2018). I argue that these two cli‐fi short stories explicitly embrace and enact “modes of concealment” in order to call attention to the barriers and boundaries that impede humanity's ecological imagination. This deliberate derangement occurs via human encounters with the material and symbolic waters of the ocean, a planetary‐scale nonhuman entity whose presence and agency highlights the limits of a human‐centered worldview. By actively foregrounding literal (and littoral), cognitive, and narrative horizons, this pair of oceanic cli‐fi texts seek to show us the Anthropocene by not showing us the Anthropocene. What results is an uncanny presence‐in‐absence—a haunting reminder of our deranged, limited awareness of the more‐than‐human world.

Abstract Prydz Bay is a major Antarctic Bottom Water production region adjacent to a major cold cavity ice shelf. Its underlying mixing processes are little known, although they determine the intensity of water mass transformation. Using microstructure measurements, we reveal detailed regional variations of dissipation and mixing in Prydz Bay during summer. In the upper layer, turbulence dominates the continental shelf, presenting weak dissipation rate of thermal variance χ θ [ O (10 −10 )°C 2 s −1 ], but showing the elevated dissipation rate of turbulent kinetic energy ε [ O (10 −8 ) W kg −1 ] and thermal eddy diffusivity K θ [ O (10 −4 ) m 2 s −1 ]. On the continental slope, diffusive convection prevails, with one-order greater χ θ and one-order smaller ε and K θ . On the shelf break, turbulence and diffusive convection coexist, with elevated ε , χ θ , and K θ reaching the orders of 10 −8 W kg −1 , 10 −8 °C 2 s −1 , and 10 −4 m 2 s −1 , respectively. Tidal current’s encountering with the ice shelf, background current’s impinging on the rough topography, and the intrusion of modified Circumpolar Deep Water all contribute to these spatial variations. The dissipation ratios Γ for both turbulence (0.05) and diffusive convection (0.14) are statistically smaller than those in waters of mid- and low latitudes because water in Prydz Bay is primarily stratified by salinity; hence, the strong temperature gradient is associated with a weak stratification. Based on these features, we propose a new indicator to differentiate turbulence and double diffusion. This study is helpful for better understanding Antarctic mixing processes, and their contributions to local water mass transformation and to global ocean circulation and climate. Significance Statement Prydz Bay is important in producing Antarctic Bottom Water and hence shaping global ocean circulation and climate. This bottom water production and spreading are catalyzed by microscale mixing, yet the local mixing processes remain unclear. Based on field measurements, we reveal the regional differences in dissipation/mixing intensities and its drivers in upper Prydz Bay during the austral summer. Turbulence and diffusive convection dominate the water in and away from the continental shelf, respectively, leading to spatially contrasted mixing intensities. Notably, an important parameter, dissipation ratio, in Prydz Bay is clearly smaller than that of mid- and low latitudes. This is directly linked to the vertical transition of water masses. This study improves our understanding of mixing in Prydz Bay and its influences on other multiscale dynamics.

The blue nexus unveiled: Interlinking marine pollution, circular economy, and the blue economy in ocean sustainability.

<sec><p indent="0mm">East Asia faces increasing environmental risks under climate change and intensifying human activities. The region is dominated by the East Asian monsoon, which is driven by land–sea thermal contrast. Regional conditions are further shaped by strong interactions among climate, air pollution, ecosystem dynamics, and human activities. Understanding these cross-sphere processes and their associated risks requires high-resolution models that explicitly represent key mechanisms. Regional Earth system models address this need by providing higher resolution and more detailed process representations than global models. This review summarizes the development of the Regional Integrated Earth System Model (RIEMS), led by the Institute of Atmospheric Physics, Chinese Academy of Sciences, in collaboration with Nanjing University and other institutions. We describe RIEMS’s evolution from a regional climate model to a comprehensive Earth system model, emphasizing key advances and the evaluation of its latest version, RIEMS 3.0. </sec><sec> The development of RIEMS has progressed through three major stages. Early versions (RIEMS 1.0 and 2.0) established the foundation by integrating land surface processes, ocean components, and atmospheric chemistry with regional atmospheric dynamics. RIEMS 2.0 represented a major step forward by adopting a non-hydrostatic framework (Mesoscale Model 5 version 3; MM5v3) and incorporating spectral nudging, which effectively reduced large-scale circulation drift in long-term integrations. It also integrated the Atmosphere–Vegetation Interaction Model (AVIM) and online aerosol chemistry, enabling robust simulations of vegetation–climate feedbacks and aerosol–monsoon interactions. </sec><sec> RIEMS 3.0 marks a shift toward a fully coupled “Atmosphere–Ocean–Land–Human” system. A key advance is the implementation of non-flux-adjusted coupling between the atmospheric component (Weather Research and Forecasting model version 4; WRF v4) and the ocean component (LASG/IAP Climate Ocean Model; LICOM-np) through the Ocean Atmosphere Sea Ice Soil version 3 (OASIS3) coupler. This configuration ensures rigorous conservation of energy and mass across the air–sea interface, substantially improving the representation of critical regional features such as the Western Pacific Subtropical High and tropical cyclone precipitation. To explicitly represent human influences, RIEMS 3.0 incorporates terrestrial carbon and nitrogen (CN) biogeochemical cycles into its land surface schemes (NoahMP-CN and AVIM-CN), enabling dynamic assessment of ecosystem responses to fertilization and nitrogen deposition. In addition, it includes an advanced urban canopy model to represent anthropogenic heat and impervious-surface effects, together with a multi-source satellite data assimilation system for initializing land surface states. </sec><sec> Long-term experiments (1991–2014) demonstrate that RIEMS 3.0 effectively reduces systematic biases. For example, it lowers the root-mean-square error of 2-m air temperature over eastern China to approximately <sc>1.0 K,</sc> outperforming both standalone WRF simulations and the CMIP6 multi-model mean. In the MICS-Asia III intercomparison, RIEMS 3.0 shows leading performance among participating models in simulating PM_2.5 concentrations during severe pollution events in the Beijing–Tianjin–Hebei region. </sec><sec> Looking ahead, development of the next-generation RIEMS 4.0 is underway to support national carbon neutrality strategies. Future work will focus on three strategic directions: (1) extending terrestrial biogeochemistry to include phosphorus (P) cycling, enabling complete C–N–P interactions to refine estimates of ecological carbon sinks; (2) building a seamless “land–river–ocean” continuum to simulate the transport of water, nutrients, and pollutants from terrestrial sources to coastal oceans, thereby clarifying mechanisms underlying coastal hypoxia and acidification; and (3) strengthening bidirectional coupling between atmospheric chemistry and physics to better resolve aerosol–cloud–radiation interactions. RIEMS 4.0 also aims to leverage artificial intelligence—through machine-learning parameterizations and differentiable modeling—to improve computational efficiency and predictive skill, providing a robust scientific basis for climate adaptation and sustainable development in East Asia. </sec>