Rajat Masiwal, Colin Aitken, Adam Marchakitus
et al.
Artificial intelligence weather prediction (AIWP) models now often outperform traditional physics-based models on common metrics while requiring orders-of-magnitude less computing resources and time. Open-access AIWP models thus hold promise as transformational tools for helping low- and middle-income populations make decisions in the face of high-impact weather shocks. Yet, current approaches to evaluating AIWP models focus mainly on aggregated meteorological metrics without considering local stakeholders' needs in decision-oriented, operational frameworks. Here, we introduce such a framework that connects meteorology, AI, and social sciences. As an example, we apply it to the 150-year-old problem of Indian monsoon forecasting, focusing on benefits to rain-fed agriculture, which is highly susceptible to climate change. AIWP models skillfully predict an agriculturally relevant onset index at regional scales weeks in advance when evaluated out-of-sample using deterministic and probabilistic metrics. This framework informed a government-led effort in 2025 to send 38 million Indian farmers AI-based monsoon onset forecasts, which captured an unusual weeks-long pause in monsoon progression. This decision-oriented benchmarking framework provides a key component of a blueprint for harnessing the power of AIWP models to help large vulnerable populations adapt to weather shocks in the face of climate variability and change.
William E. Chapman, John S. Schreck, Yingkai Sha
et al.
We introduce CAMulator version 1, an auto-regressive machine-learned (ML) emulator of the Community Atmosphere Model version 6 (CAM6) that simulates the next atmospheric state given the prescribed sea surface temperatures and incoming solar radiation. CAMulator explicitly conserves global dry air mass, moisture, and total atmospheric energy while remaining numerically stable over indefinite climate integrations. It successfully reproduces the annual CAM6 climatology and key modes of climate variability, including the El Niño-Southern Oscillation, the North Atlantic Oscillation, and the Pacific-North American pattern, with slightly muted variability. When forced with sea surface temperature (SST) outside the training distribution, CAMulator exhibits a systematic cold bias in high-latitude regions, particularly in boreal winter, likely due to the absence of interactive land and sea ice. Nonetheless, CAMulator achieves these results with a 350 times speedup over CAM6, making it an efficient alternative for generating large ensembles.
Tanyaradzwa Mundoga, Walter Musakwa, Nelson Chanza
Discussions surrounding wildlife governance are lacking in critical engagement concerning the impact of different tiers of governance on elephant conservation and community participation. Although most studies acknowledge that local-level wildlife governance is impacted by higher levels of governance, few have endeavored to analyze the nature and impact of this interface. This study conducted a comprehensive literature review to investigate how studies have addressed this complex subject matter. The study aims to determine how wildlife governance scholarship considers global multilateralism, particularly CITES decisions, and their impact on elephant conservation and community participation. A bibliometric analysis was performed using data from online databases and visualization software. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) were then used as a protocol to select relevant literature from the larger database for content analysis. The findings suggest that, while wildlife governance is a globally documented and acceptable concept, research on the impact of environmental multilateralism, especially CITES, on African elephant conservation and community participation is very limited. Existing studies mainly focus on approaches to elephant and broader wildlife conservation, protected area management, sustainable development, governance approaches, approaches to community participation, CITES, and wildlife trade. There is an identified dearth in the available literature on how the participation of communities living with wildlife, in particular, the African elephant is affected by higher governance structures such as Multilateral Environmental Agreements (MEAs). These findings provide valuable insights for future research trends, highlighting opportunities for building research partnerships and strengthening engagements at all levels of wildlife governance, policy generation, and community participation.
Compound precipitation and wind speed extremes (CPWE) pose significant threats to the sustainable development of urban areas. This study investigated the spatial evolution characteristics, potential population exposure risk, and multidimensional inequality of CPWE within nine urban agglomerations in China, each containing at least one city with a GDP exceeding one trillion yuan, utilizing spatiotemporal statistics and attribution analysis. The results indicated that the intensity of CPWE in these urban agglomerations decreased from southeast to northwest, and the population exposed to mild, moderate, severe, and extreme levels accounted for 58 %, 28.3 %, 11.4 %, and 2.3 % of the total, respectively. Changes in exposure risk were driven by climate effect (58.29 % ± 12.77 %), followed by population (32.15 % ± 6.20 %) and interaction effect (9.55 % ± 5.14 %). Pearl River Delta (PRD) and Yangtze River Delta (YRD), identified as particularly vulnerable, experienced an increase in CPWE intensity exceeding 0.015 /10a. An increase of approximately 0.62 × 104 people per decade was observed for exposure risk, with over 20 % of the population facing severe or extreme levels, mainly due to the climate effect. CPWE exposure risk was significantly unequal across various dimensions (spatial autocorrelation: Moran’s I = 0.3798, P = 0.001; Gini coefficient: 0.08–0.5). Areas characterized by high-risk and balanced development (e.g., PRD, YRD) exhibited lower inequality, whereas regions featuring low-risk and concentrated development (e.g., GPZ) demonstrated higher inequality. The climate effect was the predominant influence in the low-risk areas as well as most high-risk areas. These findings support the targeted implementation of appropriate climate adaptation policies to promote regional sustainable development.
Alexander Grayver, Christopher C. Finlay, Nils Olsen
Tidal flow of seawater across the Earth's magnetic field induces electric currents and magnetic fields within the ocean and solid Earth. The amplitude and phase of the induced fields depends on electrical properties of both the seawater and the solid Earth, thus can be used as a proxy to study seabed properties or potentially for monitoring long-term trends in the global ocean climatology. This paper presents new global oceanic tidal magnetic field models and their uncertainties for four tidal constituents, including $M_2, N_2, O_1$ and $Q_1$, which was not reliably retrieved previously. Models are obtained through a robust least-squares analysis of magnetic field observations from the \textit{Swarm} and CHAMP satellites using a specially designed data selection scheme. We compare the retrieved magnetic signals with several alternative models reported in the literature. Additionally, we validate them using a series of high-resolution global 3-D electromagnetic simulations and place constraints on the conductivity of sub-oceanic mantle for all tidal constituents, revealing an excellent agreement between all tidal constituents and the oceanic upper mantle structure.
The increasing availability of highly resolved spatio-temporal data leads to new opportunities as well as challenges in many scientific disciplines such as climatology, ecology or epidemiology. This allows more detailed insights into the investigated spatially extended systems. However, this development needs advanced techniques of data analysis which go beyond standard linear tools since the more precise consideration often reveals nonlinear phenomena, for example threshold effects. One of these tools is the recurrence plot approach which has been successfully applied to the description of complex systems. Using this technique's power of visualization, we propose the analysis of the local minima of the underlying distance matrix in order to display driving forces of spatially extended systems. The potential of this novel idea is demonstrated by the analysis of the chlorophyll concentration and the sea surface temperature in the Southern California Bight. We are able not only to confirm the influence of El Niño events on the phytoplankton growth in this region but also to confirm two discussed regime shifts in the California current system. This new finding underlines the power of the proposed approach and promises new insights into other complex systems.
Xianggang Cheng, Xiao-Peng Chen, Zhi-Ming Yuan
et al.
Liquid jet formations on water surfaces serve as a cornerstone in diverse scientific disciplines, underpinning processes in climatology, environmental science, and human health issues. Traditional models predominantly focus on pristine conditions, an idealisation that overlooks common environmental irregularities such as the presence of particulate matter on water surfaces. To address this shortfall, our research examines the dynamic interactions between surface particulate matter and cavitation bubbles using floating spheres and spark bubbles. We unveil five novel jet modes, advancing beyond classical models and demonstrating enhanced variability in jet dynamics. We observe that particulates significantly lower the energy threshold for jet formation, showing the enhanced sensitivity of jet dynamics to their presence. The phase diagram and analyses illustrate how the interplay between the dimensionless immersion time of the particulate and the spark bubble's dimensionless depth influences jet mode development, from singular streams to complex cavity forms. These insights not only advance our understanding of jet formation, but also unlock the potential for refined jet manipulation across a broad range of physical, environmental, and medical applications.
As more land is being utilized for large-scale solar energy projects, there are increasing discussions from stakeholders on how to utilize land under solar panels to promote biodiversity. One path is to plant habitat beneficial to pollinators and other insects, but there have been few long-term studies that examine how different vegetation and seed mixes establish underneath solar panels. This study addresses a scientific gap to determine whether native pollinator seed mixes successfully establish over time under solar arrays using a systematic assessment of eight seed mixes planted at three utility-scale solar sites in Minnesota. We assess establishment with a percent native coverage metric, which is an assessment of native species observations compared to total observations during percent cover analyses in our vegetative test plots. The percent native coverage metric allows for a measurement of how the seed mix established and how the seed mix persists over time. The percent native coverage under and in between the solar photovoltaic (PV) arrays rose from 10% after one year of planting to 58% after three years across all sites, while the native coverage of the full sun control area rose from 9.6% to 70% under the same period, showing that native prairie and pollinator plants successfully established under the array, although to a lesser extent than in full sun conditions. Percent native coverage under the PV arrays rose 5- to 8-fold for each of the three sites from over the course of the study, while the coverage of weeds decreased for all three sites over the same period. Percent native coverage varied by seed mix over the project years, but every seed mix experienced a higher percent native coverage year after year under the PV arrays. Our results did not indicate a difference in establishment across placement within the array; the center, west, and east portions of the areas in between panels had similar establishment rates at two out of three sites, indicating that the same seed mix can be applied throughout the array. Out of 101 plant species seeded, we observed the establishment of 68 species in our vegetative test plots, and we detailed the top 20 observed species to inform future seed mix development. Based on these findings, native pollinator vegetation can establish over time at solar arrays, and it can be suitable for creating habitat at utility-scale solar sites.
Exploring the origin and properties of magnetic fields is crucial to the development of many fields such as physics, astronomy and meteorology. We focus on the edge element approximation and theoretical analysis of celestial dynamo system with quasi-vacuum boundary conditions. The system not only ensures that the magnetic field on the spherical shell is generated from the dynamo model, but also provides convenience for the application of the edge element method. We demonstrate the existence, uniqueness and stability of the solution to the system by the fixed point theorem. Then, we approximate the system using the edge element method, which is more efficient in dealing with electromagnetic field problems. Moreover, we also discuss the stability of the corresponding discrete scheme. And the convergence is demonstrated by later numerical tests. Finally, we simulate the three-dimensional time evolution of the spherical interface dynamo model, and the characteristics of the simulated magnetic field are consistent with existing work.
In high-dimensional time-series analysis, it is essential to have a set of key factors (namely, the style factors) that explain the change of the observed variable. For example, volatility modeling in finance relies on a set of risk factors, and climate change studies in climatology rely on a set of causal factors. The ideal low-dimensional style factors should balance significance (with high explanatory power) and stability (consistent, no significant fluctuations). However, previous supervised and unsupervised feature extraction methods can hardly address the tradeoff. In this paper, we propose Style Miner, a reinforcement learning method to generate style factors. We first formulate the problem as a Constrained Markov Decision Process with explanatory power as the return and stability as the constraint. Then, we design fine-grained immediate rewards and costs and use a Lagrangian heuristic to balance them adaptively. Experiments on real-world financial data sets show that Style Miner outperforms existing learning-based methods by a large margin and achieves a relatively 10% gain in R-squared explanatory power compared to the industry-renowned factors proposed by human experts.
We study the existence and multiplicity of solutions of the following free boundary problem $$ (P)\left\{ \begin{array}{rcll} \del u &=& \lam ( \eps +(1-\eps ) H(u-μ))~ \hspace{3mm}&\text{in}~Ω(t)\\ u&=& \overline{u}_{\infty}~\hspace{3mm} &\text{on } ~ \partial Ω(t) \end{array} \right. $$ where $Ω(t) \subset \RR^3$ a regular domain at $t>0$, $\eps,~\overline{u}_{\infty},~λ,~μ$ are a positive parameters and $H$ is the Heaviside step function. \\The problem (P) has two free boundaries: the outer boundary of $Ω(t)$ and the inner boundary whose evolution is implicit generated by the discontinuous nonlinearity $H$. The problem (P) arise in tumor growth models as well as in other contexts such as climatology. First, we show the existence and multiplicity of radial solutions of problem (P) where $Ω(t)$ is a spherical domain. Moreover, the bifurcation diagrams are giving. Secondly, using the perturbation technic combining to local methods, we prove the existence of solutions and characterize the free boundaries of problem (P) near the corresponding radial solutions.
Modeste Kacou, Eric-Pascal Zahiri, Kouakou Christian Yao
et al.
In the District of Abidjan, flooding typically occurs suddenly during intense rainfall events. The individual rainfall event provides the basic element for the study. Its analysis is required to develop solutions for managing the impact of extreme rainfall occurrences. Our study proposes to identify individual rainfall events that occurred in the District of Abidjan from a densified network and analyze some of their characteristics related to the amount of rainfall they provided, their duration, and their level of intensity. A total of 1240 individual rainfall events were identified between 2018 and 2021 using a network of 21 rain gauges. Rainfall events were identified based on criteria such as a minimum inter-event time without rainfall of 30 min, a detection threshold of 0.02 mm/5 min, a minimum duration of 30 min applicable to the average hyetograph, and a minimum of 1 mm of rainfall in at least one rain gauge. The analysis of characteristics related to accumulation, intensity, and duration showed that the rainfall events were essentially convective, with an average duration of more than 2 h and a rainfall of 11.30 mm/event. For 70% of the rainfall events of a mixed nature, the convective episodes last up to 33.33% of the total duration of the event and produce an average of 80% of the cumulative rainfall. The 30-min peak intensities generally occur in the first half of the event. Less than 13.5% of the events have peaks greater than 50 mm/h. The probability of observing more than two, three, or four events per day is high in June and October, the core of the two rainy seasons.
Basel and Geneva have two of the longest meteorological records in Switzerland, covering more than two and a half centuries. The respective monthly temperature series were published over 60 years ago and are part of todays main global temperature data sets. After digitizing the raw sub-daily measurements, we rebuilt the early instrumental part (i.e., before 1864) of the two series at daily resolution using modern methods and additional data sources that were not considered in previous efforts. A comparison with the old series and with other existing recontructions show a generally good agreement only for the last 30 years. Before the 1830s a few systematic differences appear, particularly in summer, suggesting that both new and old versions contain residual inhomogeneities. We use the new series together with other reconstructions to analyze the periods 1791–1807 and 1808–1824, which have been described, respectively, as a warm and cold period in summer in previous studies. Our results suggest that most existing instrumental data sets tend to overestimate summer temperature in Switzerland during the former period, confirming previous results based on proxy records. The overestimation is particularly large (almost 1 °C) in the old Geneva series. On the other hand, we find a probable systematic underestimation of summer temperature in our Basel series. Before the 1780s the agreement between existing reconstructions is poor, so that it is hardly possible to make confident statements about climate variability for the first few decades covered by the series. Nevertheless, the daily resolution of the data allows an insight into individual meteorological events such as cold spells and heat waves.
Weather Forecasting is an attractive challengeable task due to its influence on human life and complexity in atmospheric motion. Supported by massive historical observed time series data, the task is suitable for data-driven approaches, especially deep neural networks. Recently, the Graph Neural Networks (GNNs) based methods have achieved excellent performance for spatio-temporal forecasting. However, the canonical GNNs-based methods only individually model the local graph of meteorological variables per station or the global graph of whole stations, lacking information interaction between meteorological variables in different stations. In this paper, we propose a novel Hierarchical Spatio-Temporal Graph Neural Network (HiSTGNN) to model cross-regional spatio-temporal correlations among meteorological variables in multiple stations. An adaptive graph learning layer and spatial graph convolution are employed to construct self-learning graph and study hidden dependency among nodes of variable-level and station-level graph. For capturing temporal pattern, the dilated inception as the backbone of gate temporal convolution is designed to model long and various meteorological trends. Moreover, a dynamic interaction learning is proposed to build bidirectional information passing in hierarchical graph. Experimental results on three real-world meteorological datasets demonstrate the superior performance of HiSTGNN beyond 7 baselines and it reduces the errors by 4.2% to 11.6% especially compared to state-of-the-art weather forecasting method.
<p>Changes in stratospheric ozone concentrations and increasing concentrations of greenhouse gases (GHGs) alter the temperature structure of the atmosphere and drive changes in the atmospheric and oceanic circulation. We systematically investigate the impacts of ozone recovery and increasing GHGs on the atmospheric and oceanic circulation in the Southern Hemisphere during the twenty-first century using a unique coupled ocean–atmosphere climate model with interactive ozone chemistry and enhanced oceanic resolution. We use the high-emission scenario SSP5-8.5 for GHGs under which the springtime Antarctic total column ozone returns to 1980s levels by 2048 in our model, warming the lower stratosphere and strengthening the stratospheric westerly winds. We perform a spatial analysis and show for the first time that the austral spring stratospheric response to GHGs exhibits a marked planetary wavenumber 1 (PW1) pattern, which reinforces the response to ozone recovery over the Western Hemisphere and weakens it over the Eastern Hemisphere. These changes, which imply an eastward phase shift in the PW1, largely cancel out in the zonal mean. The Southern Hemisphere residual circulation strengthens during most of the year due to the increase in GHGs and weakens in spring due to ozone recovery. However, we find that in November the GHGs also drive a weakening of the residual circulation, reinforcing the effect of ozone recovery, which represents another novel result. At the surface, the westerly winds weaken and shift equatorward due to ozone recovery, driving a weak decrease in the transport of the Antarctic Circumpolar Current and in the Agulhas leakage and a cooling of the upper ocean, which is most pronounced in the latitudinal band 35–45<span class="inline-formula"><sup>∘</sup></span> S. The increasing GHGs drive changes in the opposite direction that overwhelm the ozone effect. The total changes at the surface and in the oceanic circulation are nevertheless weaker in the presence of ozone recovery than those induced by GHGs alone, highlighting the importance of the Montreal Protocol in mitigating some of the impacts of climate change. We additionally compare the combined effect of interactively calculated ozone recovery and increasing GHGs with their combined effect in an ensemble in which we prescribe the CMIP6 ozone field. This second ensemble simulates a weaker ozone effect in all the examined fields, consistent with its weaker increase in ozone. The magnitude of the difference between the simulated changes at the surface and in the oceanic circulation in the two ensembles is as large as the ozone effect itself. This shows the large uncertainty that is associated with the choice of the ozone field and how the ozone is treated.</p>
Shushik Avagyan, Vladimir Katkovnik, Karen Egiazarian
A fast and shallow convolutional neural network is proposed for hyperspectral image super-resolution inspired by Spatial-Spectral Reconstruction Network (SSR-NET). The feature extraction ability is improved compared to SSR-NET and other state-of-the-art methods, while the proposed network is also shallow. Numerical experiments show both the visual and quantitative superiority of our method. Specifically, for the fusion setup with two inputs, obtained by 32× spatial downsampling for the low-resolution hyperspectral (LR HSI) input and 25× spectral downsampling for high-resolution multispectral (HR MSI) input, a significant improvement of the quality of super-resolved HR HSI over 4 dB is demonstrated as compared with SSR-NET. It is also shown that, in some cases, our method with a single input, HR MSI, can provide a comparable result with that achieved with two inputs, HR MSI and LR HSI.
Hanging glaciers hold the absolute dominant number in West China and their changes had important influences on local hydrology, sea-level rise and natural hazards (snow/ice avalanches). However, logistic and operational difficulties have resulted in the lack of in-situ-measured data, leaving us with poor knowledge of the changing behaviors of this type of glacier. Here, we presented the spatiotemporal pattern of seasonal and annual mass changes of a mid-latitude hanging glacier in the Tien Shan based on repeated terrestrial laser scanning (TLS) surveys during the period 2016–2018. The distributed glacier surface elevation changes exhibited highly spatiotemporal variability, and the winter elevation changes showed slight surface lowering at the upper elevations and weak thickening at the glacier terminus, which was contrary to altitudinal elevation changing patterns at the summer and annual scales. Mass balance processes of the hanging glacier mainly occurred during summer and the winter mass balance was nearly balanced (−0.10 ± 0.15 m w.e.). The glacier exhibited more rapid mass loss than adjacent other morphological glacier and the estimated response time of the glacier to climate change was very short (6–9 years), indicating hanging glaciers will experience rapid wastage and disappearance in the future even with climate change mitigation.
Meteorology. Climatology, Social sciences (General)
Surface albedo is an essential parameter in many solar radiation applications. Although several models are available, it remains debatable whether they are applicable to other locations. Using long-term daily measurements of radiation acquired by observational networks in China, this study examined the applicability of six existing albedo models: Ineichen model (IeM), Gueymard model (GM), Dong model (DeM), Iziomon-Mayer model (IMM), Morton model (MM), and Zhou model (ZeM). The evaluation results of model performance through statistical analysis showed that among the available ground albedo models, ZeM had the best overall performance at 12 selected stations, IeM was shown to provide acceptable estimations for locations where albedo records are readily available. The statistical results of individual station have shownthat the number of input parameters is not the only key factor for determining the predictive performance of ground albedo models. In other words, a simple model has potential for accurate estimation of ground albedo with appropriate model parameters. Therefore, the simple two-parameter DeM was selected to re-calibrate with in-situ radiometric measurements, which can be adopted as a surrogate for ZeM to predict surface albedo in China.
AbstractThe surface roughness aerodynamic parameters z0 (roughness length) and d (zero-plane displacement height) are vital to the accuracy of the Monin–Obukhov similarity theory. Deriving improved urban canopy parameterization (UCP) schemes within the conventional framework remains mathematically challenging. The current study explores the potential of a machine-learning (ML) algorithm, a random forest (RF), as a complement to the traditional UCP schemes. Using large-eddy simulation and ensemble sampling, in combination with nonlinear least squares regression of the logarithmic-layer wind profiles, a dataset of approximately 4.5 × 103 samples is established for the aerodynamic parameters and the morphometric statistics, enabling the training of the ML model. While the prediction for d is not as good as the UCP after Kanda et al., the performance for z0 is notable. The RF algorithm also categorizes z0 and d with an exceptional performance score: the overall bell-shaped distributions are well predicted, and the ±0.5σ category (i.e., the 38% percentile) is competently captured (37.8% for z0 and 36.5% for d). Among the morphometric features, the mean and maximum building heights (Have and Hmax, respectively) are found to be of predominant influence on the prediction of z0 and d. A perhaps counterintuitive result is the considerably less striking importance of the building-height variability. Possible reasons are discussed. The feature importance scores could be useful for identifying the contributing factors to the surface aerodynamic characteristics. The results may shed some light on the development of ML-based UCP for mesoscale modeling.