Climate System Research Articles

Data fusion of ground-based GNSS, radio occultation and empirical model to predict the ionospheric peak electron density via artificial neural networks

In this work, we develop a model to predict the ionospheric peak electron density based on artificial neural network (ANN) utilizing long-term observation data from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) spanning from 2008 to 2018. New constraints such as the International Reference Ionosphere peak electron density results (IRI-NmF2) and vertical total electron content (VTEC) are considered. A preliminary regression analysis is performed via the random forest algorithm to assess the significance of the input parameters including year, month, day, local time, latitude, longitude, F10.7, Kp, IRI-NmF2 and VTEC. Results show that the root-mean-square error (RMSE) of predicted NmF2 validated by testing dataset is reduced from 1.739 × 105 to 1.417 × 105 el/cm3 when applying additional constraints. The aided ANN model performs better in the quiet time (with RMSE 9.433 × 104 el/cm3) than in disturbed time (1.784 × 105 el/cm3). Furthermore, the ANN predictions are compared with the original COSMIC data and ionosonde observation data. Separate discussions are conducted for different latitudes. For the COSMIC data in 2014, the RMSEs for the low-, middle- and high-latitude data are 2.728 × 105 el/cm3, 1.511 × 105 el/cm3, and 1.076 × 105 el/cm3, respectively; for the ionosonde data at different latitudes, the errors for ANN-fitted NmF2 are 3.314 × 105 el/cm3, 1.585 × 105 el/cm3, and 1.403 × 105 el/cm3, respectively. Under different solar activity conditions, the ANN model demonstrates superior prediction performance compared to the IRI model.

Multi-objective optimization of a solar-assisted cogeneration system in hot climate: An exergoeconomic and exergoenvironmental assessment

Multi-objective optimization of a solar-assisted cogeneration system in hot climate: An exergoeconomic and exergoenvironmental assessment

Experimental study on the application of a Passive Displacement Dual Coil Cooling system in a tropical climate

Experimental study on the application of a Passive Displacement Dual Coil Cooling system in a tropical climate

Pathways of south-derived iodine-129 intrusion into Tibet as revealed by its spatial distribution.

Pathways of south-derived iodine-129 intrusion into Tibet as revealed by its spatial distribution.

TOWARDS AN INDEX OF COASTAL RESILIENCE: A METRIC TO SUPPORT ADAPTATION PLANNING IN A CHANGING CLIMATE

Understanding the resilience of coastal systems in a changing climate is key to support the transition from traditional management towards more sustainable planning. This work describes a pilot methodology developed and applied to quantify a Coastal Resilience Index (CRI) for a 2 km sand dune system at Nairn in North-East Scotland. As a proof-of- concept exercise, CRI was kept simple but incorporates inputs that look at the system’s baseline conditions, historic change and future projections. Asset’s locations and dune and beach morphology and dune vegetation are the variables that were used. The site was schematised into cross-shore transects at 10 m spacing as a grid for the CRI calculation. CRI performance and suitability was assessed by calculating it temporally and using different sources of underlying data (Topography, EO, combination of both) to understand sensitivity to varying inputs. Resilience results indicate that temporal changes align with the observed decadal historic change assessment. Assuming topography-sourced CRI provides the most robust results, EO-sourced CRI was shown to under/overestimate resilience by as much as one class interval. The CRI, tested at Nairn as proof of concept, shows promising results as a metric to quantify dune resilience in support of adaptation planning. The use of EO data is encouraging for a potential implementation of resilience calculations strategically, at regional or national scales, at low cost. Further developments are planned to move towards a “near-real-time” monitoring system that continuously acquires and integrates data, providing an up-to-date measure of resilience.

Transforming inland water monitoring: integrating remote sensing and next-generation in situ technologies

Biodiversity loss, pollution, and the impacts of climate change have reached critical levels, creating the need for transformative changes in society and human behavior to reverse these trends and restore nature to a central place in people’s lives. Effective environmental monitoring is essential to this effort, with a particular focus on aquatic environments - especially inland waters - due to their profound connections to society, human health, and climate systems. In recent decades, satellite remote sensing has emerged as a powerful tool for monitoring water quality in inland water systems, driven by advances in orbital sensor technology. Earth observation techniques offer unique perspectives to limnology, enabling comprehensive views of multiple aquatic ecosystems simultaneously, regional to global coverage, long-term data collection through time-series analysis, and valuable inputs to predictive models. Moreover, remote sensing facilitates the retrieval of diverse parameters across increasing numbers of smaller lakes, including surface area, elevation, and biogeochemical data. When applied correctly, remote sensing technologies enable the monitoring of temporal changes across vast numbers of water bodies, helping to identify long-term trends and detect immediate changes in aquatic environments. The growing availability and potential of remote sensing products for aquatic studies have added a critical spatial dimension to traditional methods. Historical Earth observation data can complement existing long-term monitoring datasets, providing robust support for management and conservation strategies. However, many remote sensing methodologies and products used in applied aquatic studies often receive insufficient attention to their specific limitations and requirements. A thorough understanding of remote sensing methods for inland waters is essential for their effective application and the accurate interpretation of results. Freshwater ecosystems present significant challenges due to their optical complexity and biogeochemical variability. Common remote sensing products designed for terrestrial or oceanic applications are often unsuitable for inland waters. For instance, atmospheric correction tailored to the unique conditions of inland waters, including adjacency effects, is critical but frequently overlooked. Similarly, misunderstandings about the assumptions and quality of remote sensing products can undermine the reliability of findings in recent limnological research. This presentation highlights the current and future technical capabilities of remote sensing for inland water quality monitoring, emphasizing the need for stronger connections between the remote sensing, in situ observation, and modeling communities. To address this, harmonized methods and techniques must be developed, optimized, and implemented to monitor the diversity of aquatic habitats while maintaining data integrity amidst evolving methodologies. This raises key considerations for dataset managers: whether to adopt emerging methods or maintain established approaches, and how to ensure data continuity and quality during methodological transitions. The trend toward collaborative, interdisciplinary research in aquatic sciences - leveraging automated data collection and Big Data (from satellite images and imaging flow cytometry) - further underscores the importance of adaptive strategies for ecosystem monitoring. The growing acceptance of remote sensing technology in limnology, combined with the standardization of satellite-based water quality products and the adoption of new in situ technologies for calibration and validation, presents a unique opportunity for inland water monitoring. Achieving this vision will require closer collaboration between aquatic scientists, remote sensing experts, and data scientists. Efforts must focus on calibrating and validating new in situ and remote sensing technologies for water quality products using biogeochemical and radiometric data. This will enable users to contextualize results and understand the trade-offs inherent in using these advanced datasets. Greater synergies between these communities are needed to harmonize products, provide training materials and best-practice guides, and re-evaluate earlier findings using improved methodologies. Such efforts will address current limitations and significantly enhance our capacity to monitor and manage rapidly changing inland waters effectively.

Tropical monsoon rainfall can be predicted with lead times up to 10 months

Tropical monsoons play a critical role in shaping regional and global climate systems, with profound ecological and socio-economic impacts. However, their long-term prediction remains challenging due to the complex interplay of regional dynamics, global climate drivers, large-scale teleconnections, and inherent non-stationarities in the climate system. Here, we introduce a unified network-based framework for predicting monsoon precipitation across diverse tropical regions. By leveraging global 2-meter air temperature fields, this approach captures large-scale climate teleconnections, such as the El Niño-Southern Oscillation and Rossby waves, enabling accurate forecasts for four key monsoon systems: the South American, East Asian, West African, and Indian monsoons. Our framework achieves remarkable forecasting accuracy with lead times of 4-10 months, outperforming traditional systems such as Seasonal Forecast System 5 and Climate Forecast System version 2. Beyond its predictive capabilities, the framework offers flexibility for application to other regions and climate phenomena, advancing our understanding of global climate dynamics. These findings have far-reaching implications for disaster preparedness, resource management, and sustainable development.

Genetic connectivity shapes the population structure of Puccinia polysora in the pathogen's winter-reproductive regions.

Southern corn rust, caused by Puccinia polysora Underw., is one of the worldwide maize disease. With the change of global climate and farming system, southern corn rust has become one of the major diseases that seriously threaten the safety of maize production in China. The disease is air-borne, and presents the regional epidemic characteristics in China; however, its population structure in different regions is still unclear. In this study, we used High-throughput sequencing techniques with a Genotyping-by-Sequencing approach to study the population structure of P. polysora in the pathogen's winter-reproductive regions. Population genetic analysis indicated that the P. polysora isolates from Ledong, Hainan, collected in July, formed a distinct genetic group, indicating seasonal genetic differentiation within this region. However, the remaining isolates from Hainan, Guangdong, and Guangxi were clustered into two main genetic groups, with no significant genetic differentiation detected among the populations from these three provinces. This suggests frequent genetic exchange among P. polysora populations in Hainan, Guangdong, and Guangxi, leading to overall genetic homogeneity. These findings underscore the role of genetic connectivity in shaping the population structure of P. polysora in the pathogen's winter-reproductive regions, offering novel insights into its genetic dynamics. Furthermore, the results provide valuable information to support the development of effective strategies for managing P. polysora in China.

A Deep Learning Inversion Method for 3D Temperature Structures in the South China Sea with Physical Constraints

The South China Sea, a vital marginal sea in tropical–subtropical Southeast Asia, plays a globally significant role in marine biodiversity and climate system dynamics. The accurate monitoring of its thermal structure is essential for ecological and climatic studies, yet retrieving subsurface temperature remains challenging due to complex ocean–atmosphere interactions. This study develops a Convolutional Long Short-Term Memory (ConvLSTM) neural network, integrating multi-source satellite remote sensing data, to reconstruct the Ocean Subsurface Temperature Structure (OSTS). To address the multiparameter complexity of temperature retrieval, physical constraints—particularly the heat budget balance of water bodies—are incorporated into the loss function. Experiments demonstrate that the physics-informed ConvLSTM model significantly improves the temperature estimation accuracy by simultaneously optimizing the physical consistency and predictive performance. The proposed approach advances ocean remote sensing by synergizing data-driven learning with thermodynamic principles, offering a robust framework for understanding the South China Sea’s thermal variability.

Models and scenarios for solar radiation modification need to include human perceptions of risk

Abstract Solar radiation modification (SRM) is a climate intervention method that reflects a portion of incoming solar radiation to cool the Earth and could be used to ameliorate the impacts of climate change, but that provokes strong reactions from experts and the public alike. Research has explored both the biophysical and human behavioral aspects of SRM but have not integrated these processes in a single framework. Our expectations for SRM development and deployment will be biased and inaccurate until we integrate the feedbacks between human behavioral and cognitive processes and the biophysical and climate system. We propose a framework for describing these feedbacks and how they may mediate transitions in the development and operationalization of SRM as a climate intervention. We consider components such as public trust in SRM, moral hazard concerns, climate risk perceptions, and societal disruptions, and illustrate how the driving processes could change across the pre-development, post-development, and post-deployment phases of SRM operationalization to affect outcomes around SRM deployment and climate change. Our framework illustrates the importance of feedbacks between climate change, risk perceptions, and the human response and the necessity to integrate such feedbacks in the development of future scenarios for SRM.

A GPU parallelization of the neXtSIM-DG dynamical core (v0.3.1)

Abstract. The cryosphere plays a crucial role in the Earth's climate system, making accurate sea-ice simulation essential for improving climate projections. To achieve higher-resolution simulations, graphics processing units (GPUs) have become increasingly appealing due to their higher floating-point peak performance compared to central processing units (CPUs). However, harnessing the full theoretical performance of GPUs often requires significant effort in redesigning algorithms and careful implementation. Recently, several frameworks have emerged that aim to simplify general-purpose GPU programming. In this study, we evaluate multiple such frameworks, including CUDA, SYCL, Kokkos, and PyTorch, for the parallelization of neXtSIM-DG, a finite-element-based dynamical core for sea ice. Based on our assessment of usability and performance, CUDA demonstrates the best performance while Kokkos is a suitable option for its robust heterogeneous computing capabilities. Our complete implementation of the momentum equation using Kokkos achieves a 6-fold speedup on the GPU compared to our OpenMP-based CPU code, while maintaining competitiveness when run on the CPU. Additionally, we explore the use of lower-precision floating-point types on the GPU, showing that switching to single precision can further accelerate sea-ice codes.

Atmospheric Electricity Data From Lerwick During 1964 to 1984

ABSTRACTA dataset of the atmospheric Potential Gradient (PG) from Lerwick observatory in Shetland is now available, which provides hourly‐averaged PG for each month, from January 1964 to July 1984. The measurements were made consistently, with calibrated and well‐maintained instrumentation. Co‐located meteorological observations are also available from the same site, where disturbing effects of air pollution are small. Other sources of atmospheric data such as satellite observations became increasingly abundant during the era of the measurements, making broader comparisons possible. On average, the Lerwick PG measurements contain a diurnal cycle characteristic of the global circuit and show relationships with the El Niño‐Southern Oscillation (ENSO), especially in December. The value of the data is in the information it contains about the global atmospheric electric circuit, which is embedded in the climate system.

A MODIFIED OPERATIONAL MATRIX METHOD FOR INVESTIGATION OF THE NONLINEAR DYNAMICS OF FRACTIONAL SYSTEMS

Operational matrix method is a useful tool for solving systems of fractional initial value problems. We approximate the solution using Block Pulse functions. To find the coefficients of this expansion, we have to determine the operational matrices for the integral, derivative, and product operators. However, this approach can result in a large system of nonlinear algebraic equations. In most cases, solving this system requires high computational costs, time, and is not easy to implement, leading to limited accuracy. In this paper, we propose a new modified version of this approach that eliminates the need for a system to find the coefficients of the solution expansion. We can find the coefficients explicitly and iteratively in terms of the previous coefficients. We derive the new approach and prove that finding the coefficients in this iterative way will produce a sequence of functions that converges uniformly to the unique solution of the system under consideration. Additionally, we prove the existence and uniqueness of the solution to our problem. The new approach is numerically tested using several examples, and two applications are investigated: one from the optimal control theory and the other from the ENSO system in the global climate. We use several measures to calculate the error, such as the [Formula: see text]-error and the minimization error. Comparison with several researchers shows that the modified version is more accurate, cheaper, easier to implement, and requires less computational time than the operational matrix method.

Spatiotemporal Analysis and Anomalous Trends of Asia AOD (2001–2024): Insights from a Deep Learning Fusion Model and EOF Decomposition

Long-term investigations of Aerosol Optical Depth (AOD) across Asia are crucial for understanding its regional impacts on the global climate system. However, satellite-derived AOD datasets frequently suffer from missing values due to factors such as cloud cover, algorithmic limitations, and various atmospheric conditions. To overcome these challenges, this study employs the deep learning model TabNet, incorporating Digital Elevation Model (DEM) data and ERA5 meteorological variables, to fuse MERRA-2 AOD with MODIS MAIAC AOD observations. The resulting integration yields a high-resolution, seamless daily AOD dataset for Asia spanning the period from 2001 to 2024. The fused dataset demonstrates significant improvements over the original MERRA-2 AOD, with an increase in the coefficient of determination (R2) by 0.1065 and a reduction in root mean square error (RMSE) by 0.0369. Spatio-temporal analysis, conducted using Empirical Orthogonal Function (EOF) decomposition, reveals that AOD concentrations across Asia are strongly influenced by anthropogenic factors, including industrial activities, transportation emissions, and biomass burning. The results indicate a generally increasing trend in AOD from 2001 to 2014, followed by a declining trend from 2015 to 2024. Notably, EOF results show a marked rise in AOD levels in Mongolia after 2020, likely attributable to an uptick in dust storm activity. This research offers valuable insights into the spatiotemporal trends of aerosols across Asia, underscoring the need for sustained air quality measures to mitigate pollution and protect public health.

Impact of Tropical Cyclones on Summertime Multiscale Perturbations in the Western North Pacific: A Focus on Barotropic and Baroclinic Processes

Abstract This study investigates the impact of tropical cyclones (TCs) on various scales of climate systems in the western North Pacific across 41 summer seasons (1979–2019). Updated TC-removed data are compared with reanalysis data to discern TC contributions to intraseasonal oscillations (ISOs), submonthly wave patterns, and synoptic waves. Upon TC removal, the monsoon trough retreats and the subtropical high extends westward, shifting ISO systems southwestward from east of Taiwan to the South China Sea with their intensity halved. Perturbation kinetic energy (PKE), a key measure of perturbation intensity, decreases considerably—by as much as 80% in the ISO westerly phase and 60% in the ISO easterly phase—at 850 hPa for both the submonthly and synoptic cases. Similar reduction ratios are noted in the barotropic and baroclinic conversion fields, with the former dominating PKE increasing rate in the lower troposphere and the latter in the upper troposphere. The higher frequency of TC occurrences in the westerly phase results in a more pronounced energy reduction following TC removal compared to the easterly phase. Moreover, synoptic PKE exceeds its submonthly counterpart, and the baroclinic conversion exhibits a north–south orientation, both due to the upstream recurving TCs that redirect their paths toward Japan. These findings enhance our understanding of climate variability, thereby advancing climate modeling accuracy.

Implementation of Singular Spectrum Analysis Method for Prediction of Average Sunshine Duration

Solar irradiance is the process by which radiant energy from the sun reaches the earth. BMKG states that solar irradiance reaches 100% when the sun shines for 8 hours a day. Less than 8 hours of solar irradiance a day can affect local and global climate systems. This research aims to analyze and predict of average sunshine duration in Pasuruan with the Singular Spectrum Analysis (SSA) method. Based on the SSA model for optimal solar irradiation with and Grouping Effect , this study analyzes the prediction of average sunshine duration in Pasuruan which produces a Mean Absolute Percentage Error (MAPE) value of 19.53%. The results indicate that the predictions are effectively categorized for estimating the average solar irradiance. The highest average was in July at 60.1% and the lowest average was in November at 12.82%

Transition of El Niño to La Niña can be driven by regional perturbations a year ahead

Abstract Interannual forecasts provide skilful predictions of El Niño-Southern oscillation (ENSO) up to a year in advance, however our understanding of what drives the ensemble skill and diversity of outcomes across members is limited. Using a fully coupled ocean–atmosphere ensemble forecasting system, we investigate the causality of regional perturbations on the evolution of ENSO at interannual timescales. Using forecasts initialised on 1 November 2009, transplanting more realistic cooler conditions in the South Pacific across ensemble members on 1 January 2010 significantly cools the resulting 2010/2011 winter ENSO one year later. The imposed perturbations migrate equatorward via wind–evaporation–sea surface temperature feedback and significantly alter tropical zonal gradients during late spring and summer. This drives the ensemble towards La Niña conditions, in line with observations. Repeating the experiment with warmer South Pacific conditions, results in the reverse signal and warms ENSO one year later. Across the experiments we find an almost four-fold increase in probability of La Niña and a three-fold decrease in probability of El Niño, demonstrating that long lead regional perturbations can systematically tip the climate system between ENSO states. Predicted surface conditions are significantly impacted across many parts of the world and the forecast global annual mean surface temperature for 2010 is significantly cooled, resulting in better agreement with observations. Our results demonstrate sensitivity of ENSO evolution and the global climate system to specific regional perturbations and provide new insights for interannual climate prediction.

Variation of 239,240Pu in Coral and Its Response to the Climate System in South China Sea.

With the acceleration of climate change, understanding the behavior of the anthropogenic radioactive substances─particularly their responses to the climate system─has become critical for assessing their transport, transfer, and impact on the ecosystems. However, this remains underexplored, particularly in the South China Sea (SCS), where radioactivity is derived from both the close-in fallout of the Pacific Proving Ground (PPG) and the global fallout. Additionally, this region is quite sensitive to climate change. A coral core collected from Xisha Island, SCS, was initially analyzed for high-radiotoxicity 239,240Pu. Approximately 72-84% of plutonium in coral originated from the close-in fallout of PPG through ocean current compared to the direct global fallout. However, the 239,240Pu concentration still remains in background levels and does not show a significant radiation risk. After 1980, a distinct pattern emerged characterized by a "higher" concentration but a "lower" 240Pu/239Pu atom ratio compared to the levels in the open west Pacific. This is primarily attributed to the seasonal upwelling of subsurface seawater on the continental shelf of SCS, driven by the prevailing southwest monsoon. Significantly elevated 239,240Pu concentrations were observed during typical ENSO years 1983, 1988, and 1997. This is due to the elevated temperature, coral bleaching, and expulsion of symbiotic zooxanthellae. After expulsion, zooxanthellae containing higher 239,240Pu compared to skeleton rapidly die, and their debris directly deposit onto the coral skeleton, in contrast to the metabolic way of 239,240Pu during normal years. This finding offers critical insights into ecosystem protection in SCS amid global changes and the potential threat of nuclear contamination.

Effect of Climate Change on Vegetable Production and Mitigation Strategies in India

The Earth's climate system is undergoing a rapid transformation due to human activities, leading to climate change at an unprecedented pace. Climate change poses a serious threat to agriculture globally, with vegetable farming in India highly susceptible to climatic fluctuations. Vegetables, which are vital for food and nutritional security, are extremely sensitive to extreme temperatures, changed precipitation regimes, higher atmospheric CO₂ levels, and rising soil salinity. Such environmental stresses damage physiological, reproductive, and developmental processes, leading to reduced yields, lowered quality, and enhanced vulnerability to pests and diseases. While high CO₂ levels can promote growth in the short term, extended exposure tends to decrease the quality of crops. Moreover, catastrophic events like soil erosion, storms, hailstorms, volcanic eruptions, and tsunamis bring about extensive physical damage to crops and degrade soil health, contributing further to food insecurity. India's vegetable sector, which depends largely on smallholder farmers, is especially vulnerable under these conditions. To counteract these effects, several adaptive measures have been suggested, such as taking up resource-saving agronomic options, organic farming promotion, water-use efficiency through drip irrigation and rainwater harvesting, and implementation of vegetable grafting technology. Furthermore, the production of climate-resilient cultivars that are heat, drought, and salt tolerant is crucial for maintaining productivity in susceptible areas. An integrated strategy that merges technological innovation, sustainable agriculture practices, and support from policies is necessary to enhance the resilience of vegetable production systems. Vegetable farming must be tackled in relation to its impact on climate change in order to maintain long-term food and livelihood security in India.

Earth's Energy Imbalance More Than Doubled in Recent Decades

AbstractGlobal warming results from anthropogenic greenhouse gas emissions which upset the delicate balance between the incoming sunlight, and the reflected and emitted radiation from Earth. The imbalance leads to energy accumulation in the atmosphere, oceans and land, and melting of the cryosphere, resulting in increasing temperatures, rising sea levels, and more extreme weather around the globe. Despite the fundamental role of the energy imbalance in regulating the climate system, as known to humanity for more than two centuries, our capacity to observe it is rapidly deteriorating as satellites are being decommissioned.