Global Weather Research Articles

Improving global weather and ocean wave forecast with large artificial intelligence models

Improving global weather and ocean wave forecast with large artificial intelligence models

Geospatial Analysis of Climate Change Impacts on Vegetation Dynamics in Owerri West, Imo State

Climate change is a global phenomenon with profound effects on the environment, economies, and societies. It refers to significant changes in global temperatures and weather patterns over time. This study explores the impact of climate change on vegetation in Owerri West, Imo State, Nigeria, using geospatial techniques over a 20-year period (2003–2023). By analyzing satellite imagery and Normalized Difference Vegetation Index (NDVI) values alongside key climatic variables such as precipitation and land surface temperature (LST), the research seeks to quantify the extent of vegetation changes due to climate variability. The study uses Landsat 8 satellite data to evaluate spatiotemporal trends in vegetation health. NDVI values in 2003 ranged from a high of 0.45, indicating healthy and dense vegetation, to a low of -0.04, representing barren areas. By 2013, NDVI values had drastically declined, with a maximum of 0.12 and a minimum of -0.33, despite an increase in precipitation from 3300 mm (2003) to 3900 mm (2013). This decline suggests that other factors, such as extreme weather events or urbanization, contributed to vegetation stress. In 2023, NDVI values showed partial recovery, with a maximum of 0.28 and a minimum of 0.05, although precipitation levels dropped to a range of 600 mm to 540 mm. The broader temperature range in 2023, with a maximum of 32°C and a minimum of 17°C, likely reduced heat stress, allowing for vegetation recovery, though still below the 2003 levels, The findings highlight the complex interplay between climatic variables and vegetation health, where precipitation and temperature changes significantly influence vegetation dynamics. However, the NDVI decline in 2013, despite high precipitation, suggests that anthropogenic factors like urbanization and land use changes also play a critical role. This research emphasizes the importance of integrating remote sensing and GIS for monitoring vegetation responses to climate change. The study calls for sustainable land management practices and climate adaptation strategies to enhance vegetation resilience in the region.

Assessing the impact of ecological, climatic, and socioeconomic factors on age-specific malaria incidence in India: a mixed-model approach using the Global Burden of Disease Study (2010–2019)

BackgroundMalaria continues to be a critical public health concern in India, predominantly driven by complex interplays of ecological, climatic, and socioeconomic factors.MethodsThis study aimed to assess the association between climatic variables (temperature and precipitation) and malaria incidence across India from 2010 to 2019, utilizing data from the Global Historical Weather and Climate Data for climate metrics and the Global Burden of Disease Study for malaria incidence rates. Generalized Linear Mixed Models (GLMMs) with a Poisson distribution were employed to analyze the data, adjusting for socio-economic status, as indexed by the Human Development Index (HDI).ResultsThe results indicated a declining trend in both the number of malaria cases and age-specific incidence rates (ASIR) over the study period. In 2010, India reported approximately 20.7 million cases with an ASIR of 1688.86 per 100,000 population, which significantly reduced to 9.8 million cases and an ASIR of 700.80 by 2019. High malaria incidence was consistently observed in the states of Jharkhand and Odisha, whereas Sikkim reported the lowest numbers. Statistical analysis identified significant associations between malaria incidence and both temperature deviations and precipitation levels, with variations also linked to HDI, suggesting better detection and reporting capabilities in more developed areas.ConclusionThe study underscores the critical interactions between climatic variables and socio-economic factors in shaping the trends of malaria incidence across India. These findings highlight the necessity for adaptive, localized public health strategies that integrate environmental monitoring with socio-economic data to efficiently predict and manage malaria outbreaks.

A probabilistic forecast for multi-year ENSO using Bayesian convolutional neural network

Abstract A robust El Niño Southern Oscillation (ENSO) prediction is essential for monitoring the global climate, regional monsoons, and weather extremes. Despite dedicated efforts spanning decades, the precise prediction of ENSO events through numerical modeling beyond a couple of seasonal lead times remains a daunting challenge. The advent of deep learning-based approaches marks a transformative era in climate and weather prediction. However, many machine learning-based studies attempting ENSO prediction are confined to singular estimates, lacking adequate quantification of uncertainty in learned parameters and overlooking the crucial need for a nuanced understanding of ENSO prediction confidence. Here, we introduce a deep learning-based Bayesian convolutional neural network model that provides robust probabilistic predictions for ENSO with a lead time of up to 9–10 months across all seasons. The Bayesian layers within the convolutional neural network maintain the capability to predict a distribution of learned parameters. Augmented with bias correction, our model reproduces the amplitude of the Niño 3.4 index with fidelity for lead up to 9–10 months. The inherent capacity for uncertainty modeling enhances the reliability of bayesian neural networks (BNNs), making them particularly valuable in operational services. This research holds substantial socio-economic implications as it enhances our forecasting capabilities and rigorously quantifies forecast uncertainties, providing valuable insights for planning and policymaking.

The Global Forest Fire Emissions Prediction System version 1.0

Abstract. The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that estimates biomass burning in near-real time for global air quality forecasting. The model uses a bottom-up approach, based on remotely sensed hotspot locations, and global databases linking burned area per hotspot to ecosystem-type classification at a 1 km resolution. Unlike other global fire emissions models, GFFEPS provides dynamic estimates of fuel consumption, fire behaviour and fire growth based on the Canadian Forest Fire Danger Rating System, plant phenology as calculated from daily global weather and burned-area estimates using near-real-time Visible Infrared Imaging Radiometer Suite (VIIRS) satellite-detected hotspots and historical burned-area statistics. Combining forecasts of daily fire weather and hourly meteorological conditions with a global land classification, GFFEPS produces fuel consumption and emission predictions in 3 h time steps (in contrast to non-dynamic models that use fixed consumption rates and require a collection of burned area to make post-burn estimates of emissions). GFFEPS has been designed for use in operational forecasting applications as well as historical simulations for which data are available. A study was conducted showing GFFEPS predictions through a 6-year period (2015–2020). Regional annual total smoke emissions, burned area and total fuel consumption per unit area as predicted by GFFEPS were generated to assess model performance over multiple years and regions. The model's fuel consumption per unit area results clearly distinguished regions dominated by grassland (Africa) from those dominated by forests (boreal regions) and showed high variability in regions affected by El Niño and deforestation. GFFEPS carbon emissions and burned area were then compared to other global wildfire emissions models, including the Global Fire Assimilation System (GFAS), the Global Fire Emissions Database (GFED4.1s) and the Fire INventory from NCAR (FINN 1.5 and 2.5). GFFEPS estimated values lower than GFAS and GFED (80 % and 74 %) and had values similar to FINN 1.5 (97 %). This was largely due to the impact of fuel moisture on consumption rates as captured by the dynamic weather modelling. Model evaluation efforts to date are described – an ongoing effort is underway to further validate the model, with further developments and improvements expected in the future.

AI-Based Controls for Thermal Comfort in Adaptable Buildings: A Review

Due to global weather changes and pandemics, people are more likely to spend most of their time in indoor environments. In this regard, indoor environment quality is a very important aspect of occupant well-being, which is often ignored in modern building designs. Based on our research, thermal comfort is one of the essential items in building environments that can improve the mental stability and productivity of the occupants if the building’s indoor environment is created in a way that meets the occupants’ comfort requirements. Buildings nowadays operate on adaptive or stationary models to attain thermal comfort, which is based on Fanger’s model of the Predicted Mean Vote (PMV). Based on the literature review, limited work has been carried out to enhance the quality of the inside environment, and most research work has been devoted to building energy management. Moreover, there have been no definite solutions so far that have the capability to detect the thermal comfort requirements of multiple occupants in real time. Modern buildings tend to operate on predefined set point parameters to control the indoor environment based on the measured room temperature, which can be different from the thermal comfort requirements of the occupants. This paper discusses the limitations and assumptions that are associated with the existing thermal comfort solutions and emphasises the importance of having a real-time solution to address the thermal requirements of occupants.

An Impact-Based Flood Forecasting System for Citizen Empowerment

This work addresses the critical issue of flooding, a significant natural hazard, consistently ranked highest in the 2023 World Risk Index. The annual onslaught of tropical cyclones and the associated abnormal rainfall threaten lives, and destroy crops and property, thereby causing great economic damage, demanding urgent and science-based decision-making. We introduce an impact-based flood forecasting system as a proactive anticipatory action (i.e., AA) measure, linking climate services and disaster risk management to mitigate extreme weather impacts. Developed by the University of the Philippines Resilience Institute, the National Operational Assessment of Hazards (NOAH) Center, and Gerry Bagtasa of the Institute of Environmental Science and Meteorology, this system advances disaster resilience efforts, leveraging science-based forecasts to prioritize vulnerable barangays (villages). Unlike traditional early warning systems, the proposed automated system predicts flooding one day in advance and assesses exposure levels based on population distribution. By utilizing global weather forecast models locally calibrated for Philippine conditions and 100-year rain return flood hazard maps for the Philippines, the system forecasts river inundated areas, enabling local government units (LGUs) and humanitarian organizations to prioritize preparations for communities and allocate resources during flooding events effectively. This system enhances the efficiency of disaster response planning, fostering a proactive and impactful strategy to address the recurring threat of flooding in the country. The system also underscores the role of open data in disaster resilience, exemplified by NOAH’s system to disseminating big data, which aligns with open governance principles. By encouraging transparency and stakeholder collaboration, data exchange is promoted, providing actionable information that fosters collaboration between the LGUs, humanitarian organizations, and other stakeholders. Cognizant of the importance of community engagement in disaster resilience, the newly developed impact-based flood forecasting system encourages community involvement by providing easily accessible information, which can be used to validate the forecasts based on local knowledge and experience. This research contributes to the urgent call for anticipatory action in the face of escalating extreme weather events globally.

Physiological and Genetic Aspects of Resistance to Abiotic Stresses in Capsicum Species.

Abiotic stress is one of the key factors harming global agriculture today, seriously affecting the growth and yield of vegetables. Pepper is the most widely grown vegetable in the world, with both high nutritional and economic values. Currently, the increase in global extreme weather events has heightened the frequency of abiotic stresses, such as drought, high and low temperatures, waterlogging, and high salt levels, which impairs pepper growth and development, leading to its reduced yield and quality. In this review, we summarize the research progress on the responses of pepper to abiotic stress in recent years in terms of physiology, biochemistry, molecular level, and mitigation measures. We then explore the existing problems and propose future research directions. This work provides a reference for the cultivation and development of new pepper varieties resistant to abiotic stress.

The first kind of predictability problem of El Niño predictions in a multivariate coupled data‐driven model

AbstractEl Niño‐Southern Oscillation (ENSO) is the dominant atmosphere–ocean coupled mode of year‐to‐year variations in the tropical Pacific. It shows diverse spatiotemporal characteristics and casts major influences on seasonal predictions of global weather–climate extrema. Despite numerous dynamical and statistical models for ENSO prediction and predictability studies, they are commonly subjected to one‐to‐three issues among less skillful simulation of El Niño diversity, huge requirements of computational resources and a low robustness in statistics. Here, an efficient deep‐learning model involving nonlinear coupling of multiple variables is independently developed to study the predictability of two types of El Niño events related to initial uncertainty, which is the first kind of predictability problem. The model can skillfully simulate statistically robust features of observed El Niño diversity in terms of periodicity, amplitude, and seasonal phase‐locking. Using this model, we have revealed mathematically several new types of fastest‐growing initial errors in two types of El Niño predictions based on a novel concept of conditional nonlinear optimal perturbation (CNOP), especially including one that can strengthen central Pacific types of events, which is rarely investigated before. Moreover, CNOPs are superimposed into a numerical model, GFDL CM2p1, for comprehensive validation and growth mechanism mining, which demonstrates the consistent dynamical evolution of initial errors in both numerical and AI models. Our study represents the first attempt to explore the first kind of ENSO predictability problem from perspectives of nonlinear error‐evolving dynamics using a data‐driven model. This is of great importance as it offers us sufficient confidence to perform ENSO‐related (such as the Madden–Julian Oscillation, etc.) mechanisms and predictability studies in the future without strongly relying on dynamical numerical models.

Synthesis of highly transparent and fast-responding photochromic coating by template method with space-limited domains

The persistent global high-temperature weather warns us that improving energy efficiency and reducing CO2 emissions are urgent. Controlling the solar energy input in buildings using photochromic smart coating can effectively reduce the energy consumption required for cooling. However, it remains a challenge to achieve high transparency and fast photoresponse in organic–inorganic hybrid photochromic smart coating. Herein, we utilize space-limited domains to design a template polymer that facilitates the formation of amorphous WO3 nanodots, thereby enhancing the polymer network structure through hydrogen bonding and dipole–dipole interactions, which endow the smart coating with excellent adhesion strength up to 13 MPa. Notably, the photochromic smart coating with amorphous WO3 nanodots exhibits higher transparency (96.8 %) than conventional glass (91 %), and a wide visible light modulation range of 96.8 %-4.8 %. Based on this, the cooling efficacy of the smart photochromic coating was assessed within a model residence under actual environmental conditions. The inner temperature of the model house was reduced by up to 13 °C, which decreased indoor heat absorption by 25.8 %, significantly improving energy efficiency. This work provides an important and attractive strategy for achieving energy-efficient building materials and addressing global climate warming.

Madden Julian Oscillation Moves Faster as the Meridional Moisture Gradient Intensifies in a Warming World

AbstractThe eastward‐moving large‐scale convective system associated with the Madden‐Julian Oscillation (MJO) significantly impact global weather and climate. Recent decades have seen notable changes in the MJO's lifecycle due to non‐uniform tropical ocean warming, with the roles of natural climate variability and anthropogenic influence still requiring quantification. This study examines observed and projected long‐term changes in the MJO phase speed using four twentieth‐century reanalyses and CMIP6 simulations. We find a substantial increase in MJO phase speed in three reanalyses during the twentieth century (0.6–1.2 m s⁻1 century⁻1) and further increase in MJO phase speed during the twenty‐first century (0.3–1.5 m s⁻1 century⁻1), with notable multidecadal fluctuations. We attribute the overall acceleration of the MJO to the global warming‐driven increase in the meridional moisture gradient around the warm pool while attributing the multidecadal variability in the MJO phase speed to changes in the zonal moisture gradient associated with the Pacific Decadal Oscillation.

Rapid Development of A Spatial Digital Twins Using Open-source Solutions

Abstract. City-model spatial Digital Twins are a three-dimensional virtual self-updating twin of a city which is updated by real-time data. This paper explores the effectiveness of open-source data and tools that can generate a Digital Twin to be hosted on a distributed system. A spatial Digital Twin was successfully generated from a city-model made from open-source spatial data with access to real-time data. The Digital Twin application was hosted on a three-tier systems architecture. The exception to the open-source data was a Digital Surface Model (DSM) obtained from a private source. However, the DSM was an optional component. The output only met the minimum requirements of a Digital Twin while the visualization was basic where the live data was limited to global weather data rather than city-specific data. The investigation indicated that even with little data, generating a 3D city scene with an automated live data flow meets the spatial Digital Twin’s minimum requirements according to Piroumian (2023). The main contribution of the study is highlighting methods for generating and hosting a spatial Digital Twin and its associated data onto the web through using a three-tier architecture to store, host and display the Twin onto the web. The areas identified for further research include investigating variations in the distributed systems for hosting and publishing a spatial Digital Twin onto the web. A secondary subject of investigation is exploring methodologies for improving the detail of the spatial Digital Twin’s data for areas that do not receive much live data.

Processes controlling extratropical near‐tropopause humidity and temperature in the ECMWF global weather forecast model

AbstractAccurate representation of near‐tropopause fields is important for the forecast skill of numerical weather prediction models, yet there remain significant systematic forecast errors in the region of the tropopause. Although extratropical near‐tropopause humidity, temperature, and wind model biases have been documented from several models, more knowledge of their causes is required as a step towards reducing these biases. Typically, a moist bias is present in the lowermost stratosphere in the analyses used to initialise forecasts, which leads to a growing cold bias in the lowermost stratosphere over the course of the forecasts due to long‐wave radiative cooling. Experiments are conducted with the European Centre of Medium‐Range Weather Forecasts global forecast system where the humidity in a layer 0–4 km above the tropopause in the extratropics is reduced to correct the moist bias in the initial conditions. In these experiments, the lowermost stratosphere cold bias growth is halved compared with the control and gradually remoistens, returning to typical analysis values with a half‐life of around 8–9 days. The reduction in cooling in the lowermost stratosphere is due to a reduction in long‐wave radiative emission from the water vapour above the tropopause. The main contributors to the remoistening are resolved advective transport and parametrised turbulent mixing in the model, the cloud microphysical process rates being similar in the modified and control experiments. The biases in near‐tropopause moisture transport are almost independent of horizontal resolution. These results show that the temperature bias in the extratropical lower stratosphere can be reduced by correcting the collocated moist bias, but the moist bias cannot be fixed by solely correcting the initial conditions and further model improvements are also required to reduce cross‐tropopause moisture transport.

Uncovering the interannual predictability of the 2003 European summer heatwave linked to the Tibetan Plateau

Known as the Third Pole, the Tibetan Plateau (TP) significantly influences global weather and climate, but its potential for improving subseasonal-to-interannual predictions remains underexplored. Through coupled climate simulations and hindcast experiments, we uncovered interannual predictability of the 2003 European summer heatwave that persisted from June to August with devastating impacts. Hindcasts initialized from the atmosphere, land, and ocean states of a coupled simulation that assimilates soil moisture and soil temperature data over the TP show substantial skill in predicting this heatwave two years in advance. Hindcast sensitivity experiments isolated the indispensable role of the spring TP snow cover anomalies and their impact on the Atlantic and Pacific Oceans in exciting the Rossby waves that contributed to the anomalous European summer temperature. These findings highlight the dominant and remote influence of the TP and motivate research on its role in enhancing the predictability of extreme events worldwide.

Enhanced mass scattering efficiencies of background dust aerosols over East Asia following the passage of dust plumes

The transported dust particles significantly impact global weather and climate. Previous studies focused primarily on the physical and chemical properties of dust plumes, but the interaction mechanism between these dust plumes and background dust aerosols remains unclear. We explored this issue using in-situ observation data of the East Asia dust from the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) during January 2013. We identified a dust plume originating from the Gurbantunggut Desert, which triggered a four-day dust event with an average dust concentration of 67.2 μg m−3. Notably, this dust event led to a significant increase in mass scattering efficiencies of dust, shifting from an inverted U-shape to a continuously increasing pattern with wavelength. The enhanced dust mass scattering efficiency inhibited the development of the planetary boundary layer. These changes persisted for at least two weeks after the event, primarily due to the resuspension of deposited dust particles altering the size of background dust particle. Our findings highlight the ability of dust plumes to enhance the scattering efficiency of background dust aerosols and providing new insights into the complex interactions between dust and the atmosphere.

Microwave absorption properties of ytterbium substituted X-type ferrite in P-, L-, S-, and C-band

Ba1.5Sr0.5Zn2Fe28-xYbxO46 (x = 0.00, 0.07, 0.14, 0.21, and 0.28) hexaferrite were synthesized via sol-gel auto-combustion route to investigate the microwave absorption performance in different frequency bands. The structure has a single phase, and the variation in lattice parameter was observed due to Yb-content as host iron Fe3+ and substituted Yb3+ having different ionic radii. Microwave absorbing materials (MAMs) need to be lightweight, and the maximum crystallite size of prepared material is 41 nm. The physical properties vary with substitution, and the X-ray density value is higher than the bulk density, and the porosity of the prepared sample increases when the bulk density decreases. Micro-strain values are inverse to the crystallite size because Yb3+ has larger ionic radii than Fe3+. Rietveld refinement of the XRD patterns was conducted with a lower significance value. FTIR bands observed between 414 and 500 cm−1 are present due to iron-oxygen bond stretching and bending vibrations at octahedral and tetrahedral lattice sites. The cation distribution is highly responsible for the peak position of octahedral and tetrahedral sites—the dielectric constant increases with frequency because of dipole, interfacial, and electronic/atomic polarization. AC conductivity is very low, reflecting that the material can be used as a dielectric medium in the microwave frequency range's L, S, and C bands. The best MAM among all prepared materials is Ba1.5Sr0.5Zn2Fe27.72Yb0.28O46 which can be used for global positioning systems, weather radar, and satellite feeds as it has an excellent dielectric loss, remarkable tangential loss, and the reflection loss in P-, L-, S-, and C-bands. Other real-life application of all prepared materials are multi-layer chip inductor and longitudinal media recording.

Graph-GIC: A smart and parallelized geomagnetically induced current modelling algorithm based on graph theory for space weather applications

Geomagnetically Induced Current (GIC) refers to the electromagnetic response of the Earth and its conductive modern infrastructures to space weather and would pose a significant threat to high-voltage power grids designed for the alternative current operation. To assess the impact of space weather on the power grid, one needs to calculate the GIC on a national or continental scale. In this study, we developed a smart and parallelized GIC modelling algorithm, Graph GIC. This algorithm deploys a graph representing a power grid in a single-line diagram, in which substations/transformers act as nodes and transmission lines as edges. With these denotations, a power grid and its electric parameters are mathematically represented with an adjacency matrix and an admittance matrix. We used sparse matrix and parallelisation techniques to expedite the intensive computation in cases of large-scale power grids. The Graph GIC was validated with a benchmark grid, applied to the GIC calculation of the 500 kV power grid of Guangdong, China, and conducted preliminary analysis on the grid’s susceptibility to geomagnetic storms. The Graph GIC algorithm has the advantage of an intuitive and highly scalable graph representation of a power grid at any scale. It achieves high-accuracy calculation and a speedup of about 18 times after parallelisation. This algorithm could be applied to assess the impact of space weather on a power grid up to continental scales and could be incorporated into global space weather modelling frameworks.

TC‐GEN: Data‐Driven Tropical Cyclone Downscaling Using Machine Learning‐Based High‐Resolution Weather Model

AbstractSynthetic downscaling of tropical cyclones (TCs) is critically important to estimate the long‐term hazard of rare high‐impact storm events. Existing downscaling approaches rely on statistical or statistical‐deterministic models that are capable of generating large samples of synthetic storms with characteristics similar to observed storms. However, these models do not capture the complex two‐way interactions between a storm and its environment. In addition, these approaches either necessitate a separate TC size model to simulate storm size or involve post‐processing to capture the asymmetries in the simulated surface wind. In this study, we present an innovative data‐driven approach for TC synthetic downscaling. Using a machine learning‐based high‐resolution global weather model (ML‐GWM), our approach can simulate the full life cycle of a storm with asymmetric surface wind that accounts for the two‐way interactions between the storm and its environment. This approach consists of multiple components: a data‐driven model for generating synthetic TC seeds, a blending method that seamlessly integrates storm seeds into the surrounding while maintaining the seed structure, and a model based on a recurrent neural network to correct for biases in storm intensity. Compared to observations and synthetic storms simulated using existing statistical‐deterministic and statistical downscaling approaches, our method shows the ability to effectively capture many aspects of TC statistics, including track density, landfall frequency, landfall intensity, and outermost wind extent. Leveraging the computational efficiency of ML‐GWM, our approach shows substantial potential for TC regional hazard and risk assessment.

Estimation of IFOV Inter-Channel Deviation for Microwave Radiation Imager Onboard FY-3G Satellite

The Microwave Radiation Imager (MWRI) onboard the FengYun satellite plays a crucial role in global change monitoring and numerical weather prediction. Estimating and correcting geolocation errors are important to retrieving accurate geophysical variables. However, the instantaneous field of view (IFOV) inter-channel deviation, which is mainly caused by the structure mounting error and measurement error of feedhorns, is less studied. In this present study, we constructed a general theoretical model to automatically estimate the IFOV inter-channel deviations suitable for conical-scanning instruments. The model can automatically detect the along-track and across-track vectors that pass through the land–sea boundary points and are perpendicular to the actual coastlines. Regarding the midpoints of the vectors as the brightness temperature (Tb) inflection points, the IFOV inter-channel deviation is the pixel offset or distance of the maximum gradients of the Tb near the inflection points for each channel relative to the 89-GHz V-pol channel. We tested the model’s operational performance using the FY-3G/MWRI-Rainfall Mission (MWRI-RM) observations. Considering that parameter uploading adjusted the IFOV inter-channel deviations, the model’s validity was verified by comparing the adjustments calculated by the model with the theoretical changes caused by parameter uploading. The result shows that the differences between them for all window channels are less than 100 m, indicating the model’s effectiveness in evaluating the IFOV inter-channel deviation for the MWRI-RM. Furthermore, the estimated on-orbit IFOV inter-channel deviations for the MWRI-RM show that all channel deviations are less than 1 km, meeting the instrument’s design requirement of 2 km. We believe this study will provide a foundation for IFOV inter-channel registration of passive microwave payloads and spatial matching of multiple payloads.

Synergistic effects of the winter North Atlantic Oscillation (NAO) and El Niño–Southern Oscillation (ENSO) on dust activities in North China during the following spring

Abstract. Dust significantly influences global weather and climate by impacting the Earth's radiative balance. Based on reanalysis datasets, this study explores how the North Atlantic Oscillation (NAO) and El Niño–Southern Oscillation (ENSO) during winter impact dust activities in North China in the following spring. It is found that both the NAO and the ENSO significantly affect dust activities in North China, especially during their negative phases. When both are in their negative phases, their combined impact on dust activities exceeds that of each factor individually. The previous winter's NAO notably affects sea surface temperatures (SSTs) in the North Atlantic, associated with an anomalous tripole SST pattern. These SST anomalies persist into the following spring due to their inherent persistence, inducing an anomalous atmospheric teleconnection wave train that influences dust activities in North China. The ENSO, on the one hand, directly impacts dust activities in North China by modulating circulation over the western North Pacific. Moreover, the ENSO enhances the NAO's effect on North Atlantic SST, which explains the synergistic effects of the ENSO and NAO on dust activities in North China. This study elucidates the combined role of the NAO and ENSO in influencing dust activities in North China, providing one-season-ahead signals for predicting spring dust activities in North China.