Abstract: Atmospheric nitrogen dioxide (NO₂) is an important component of reactive nitrogen and plays a key role in the atmospheric nitrogen cycle outside major emission regions. However, its variability under remote background conditions remains poorly characterized, as most observational studies focus on urban or continental environments. This study investigates the background variability of in situ NO₂ measurements at a remote North Atlantic island (Azores) over the period 2015–2024 and examines its association with large-scale atmospheric transport regimes. Monthly NO₂ concentrations were classified into background Atlantic conditions and months influenced by continental air masses using an objective PM₁₀ percentile-based criterion. Differences between regimes were assessed using non-parametric statistics. Although NO₂ concentrations were systematically higher during months associated with continental transport, the differences did not reach statistical significance. Wind speed analysis for the overlapping period 2018–2024 showed consistently higher values during continental transport months, supporting enhanced large-scale advection during these periods. Overall, the results indicate that background NO₂ levels in this remote insular environment exhibit modest but coherent modulation associated with atmospheric transport regimes. These findings contribute to improving the interpretation of reactive nitrogen variability in remote marine settings and highlight the value of island observatories for studying the atmospheric nitrogen cycle.

Abstract: The present study investigates the statistical distribution of slopes in radar reflectivity [S-Ze] in the lower troposphere at the west coast of India using the C-band radar during pre- monsoon months to monsoon months, which spans the different meteorological conditions, including from a drier atmosphere to moist atmosphere. To investigate the S-Ze, we calculated the difference in Ze between 4 to 2 km altitudes in the lower troposphere. For positive [negative] S-Ze, the Ze decreases [increases] towards the surface. The differences in S-Ze in the lower troposphere during pre-monsoon, monsoon onset and monsoon months reveals the precipitation variability. Among all the months, a higher fraction of +ve S-Ze are observed during March and April months compared to other months, and showed that in drier atmosphere the for most of the time Ze tends to decrease towards the surface. However, the average S-Ze shows the highest -ve average -ve S-Ze, during March and April months near the coastal boundaries and associates with the lesser number of profiles. May and June months have a higher fraction of -ve S-Ze [>60%] is observed over the northern latitudes of the study periods, whereas southern AS has a higher fraction of +ve S-Ze. August has the highest fraction of -ve S-Ze, over land and topographic features. September has the highest fraction of +ve S-Ze at the southern latitudes, and at the same time, the study regions are characterized by the drier atmosphere with less updraft. During the pre-monsoon months thermodynamic conditions are more important, where in the drier atmosphere Ze tends to decrease towards the surface. During the monsoon months the dynamics of convective and stratiform precipitation, and either evaporation during the stratiform precipitation along with the convective outburst may increase the lower level RH. Monsoonal months have the less increase or decrease in the hydrometeors size compared to pre-monsoon months, whereas precipitation is more of a convective nature. The results presented here would be an extension of the study from the satellite based observations, and reveals the extension climatology of inclusion of stratiform precipitation.

Abstract: The 27 day and the 11 year solar cycles in extreme ultraviolet radiation (EUV) of the Sun are influencing the Earth’s middle atmosphere. For the first time, the solar cycle influences on geopotential height (or pressure) are analysed by using the Aura Microwave Limb Sounder (Aura/MLS) observations from 2004 to 2021. Composite analysis shows that the mesospheric 27 day variation of the global mean geopotential height is correlated with the 27 day variation of solar radio flux (F10.7cm index) which is a proxy of solar EUV. The maximum of the geopotential height has a phase lag of 4 days with respect to the maximum of EUV. The 11 year solar cycle has a sensitivity of 492m/100sfu in global mean geopotential height at about 94km height. Similarly, the solar cycle influences of the global means of middle atmospheric temperature, ozone, and water vapour are derived and discussed.

Abstract: Accurate liquid water path estimates derived from backscattered solar radiation require knowledge of the vertical structure of cloud droplet effective radius, yet standard bispectral retrievals assume a vertically homogeneous cloud and overestimate liquid water path by up to 45% compared with in situ measurements. We developed a Gauss-Newton optimal estimation retrieval that simultaneously estimates vertical profiles of cloud droplet effective radius and above-cloud integrated water vapor from hyper-spectral solar backscatter measurements in the visible and shortwave infrared. The retrieval solves for effective radius at cloud top and base, cloud optical thickness, and above-cloud integrated water vapor in logarithmic space, using an a priori covariance matrix with off-diagonal elements derived from VOCALS-REx in-situ measurements. Tested on 69 simulated HySICS reflectance spectra constructed from in situ cloud microphysics, the hyperspectral retrieval reduces the average liquid water path error to 17.7%, compared to 45.2% for the standard bispectral method. Applied to 603 EMIT hyperspectral measurements over the southeast Pacific, MODIS-retrieved liquid water path exceeds the hyperspectral estimate by 25.6% on average. These results demonstrate that simultaneous retrieval of above-cloud water vapor is necessary for accurate droplet profile retrievals, and that the upcoming CLARREO Pathfinder instrument, with its 0.3% radiometric uncertainty, should enable routine vertical profiling of cloud droplet size.

Abstract: Reference evapotranspiration (ET0) is most commonly estimated using the FAO-56 Penman-Monteith (PM) equation. However, its application is often limited by the lack of required meteorological parameters. Due to their flexibility, ability to operate with limited input, and high accuracy in estimating ET0, machine learning models have become increasingly relevant in scientific research, offering a practical alternative under limited data conditions. In this study, artificial neural networks (ANNs) were applied to estimate daily ET0 using meteorological data from the Novi Sad station in Vojvodina (Serbia). The dataset consisted of eight meteorological variables relevant to evapotranspiration processes. Analysis showed that some variables had a stronger influence on ET0 prediction than others. To evaluate their combined effect, a series of ANN models with different input combinations was developed and tested. The FAO-56 PM method was used as a benchmark, and model performance was evaluated using R2, NSE, RMSE, and MAE. The highest accuracy was achieved when all variables were included, providing the model with maximum information. The best performance was obtained using a two-hidden-layer architecture with 32 and 16 neurons, resulting in R2 = 0.98, NSE = 97.86%, RMSE = 0.25 mm day-1, and MAE = 0.17 mm day-1.

Abstract: Lightning activity reflects the occurrence of severe convective events and represents the most prominent physical feature of thunderstorm weather. This study utilizes FY-4B/AGRI multi-channel infrared brightness temperature data (with a temporal resolution of 15 minutes and spatial resolution of 4 km) combined with ground-based lightning observation data to construct the AGToLightM model for predicting the probability of thunderstorm occurrence within the next 60 minutes. Based on lightning event characteristics, the model incorporates a Convolutional Block Attention Mechanism (CBAM) to enhance its ability to extract key spatial and spectral features at cloud tops. An adaptive weighted loss function is employed to address the class imbalance issue caused by sparse positive lightning samples. Three study regions—North China, East China, and South China—were selected, utilizing summer 2025 data for model training and validation. Results demonstrate that AGToLightM effectively captures the spatial distribution and evolution trends of thunderstorms, achieving a best Critical Success Index (CSI) of 0.327. In case studies, areas with forecast probabilities exceeding 60% showed spatial consistency with regions exhibiting radar echoes above 50 dBZ. This study validates the effectiveness of the AGToLightM model in integrating multi-source meteorological data for severe convective forecasting, providing technical guidance for enhancing the reliability of short-term thunderstorm forecasts.

Abstract: Using the new theory of "cosmic expansion - atomic expansion" as a tool, this paper summarizes the evolution law of planetary atmospheres and draws the following main conclusions: All the planets in the solar system were born at the same time and had a primordial atmosphere of hydrogen and helium when they were born. The subsequent atmospheres and oceans of all planets are formed by the mixing of the primordial atmosphere with the gases continuously released by the material inside the planets due to the expansion of the universe. The planet's atmosphere and oceans have been in dynamic equilibrium, on the one hand, the atmospheric molecules continue to escape into space, on the other hand, the internal materials of the planet continue to release various gases into the atmosphere. The composition of the planet's atmosphere has been developing in the direction of increasing molecular weight, first dominated by small molecular weight molecules such as hydrogen and helium, and then dominated by medium molecular weight molecules such as nitrogen and water, and then dominated by large molecular weight molecules such as carbon dioxide. Once the planet is completely solidified, the planet's atmosphere will quickly disappear.

Abstract: Climate-related extremes such as floods and droughts have been the main causes of natural disasters in southern Africa in recent years, with noticeable trends in climate extremes being observed. The Limpopo Province in South Africa has been especially prone to these extremes. The extreme weather in Limpopo is mainly caused by a mix of intense tropical weather systems, La Niña conditions, and exacerbated by climate change. Climate change exacerbates current water challenges across the province by affecting rainfall precipitation patterns, distribution, timing and intensity, leading to extreme climate events such as floods and drought. Historical and future trends of precipitation and relevant extreme indices using observed data from the South African Weather Service and CORDEX ensemble model simulations under the RCP4.5 and RCP8.5 scenarios were examined. An analysis of all precipitation data suitable for the study of long-term variability and trend, indicates that most areas underwent drying to various degrees over the last century, especially the central and western parts. Drier conditions over the eastern parts have be-come more prevalent over the last 50 years. Also, more extremes on a sub-seasonal basis were experienced. Regarding future scenarios, three projected time periods were examined: Current climatology (2006 – 2035), near-future (2036 – 2065), and far-future (2066 – 2095), compared to the baseline period (1976-2005). Most areas will experience a further decrease in precipitation under both emission scenarios, especially in the south-east, central and extreme northern parts. In addition, these areas are expected to experience a decrease in the frequency of heavy precipitation days for all periods under both RCP scenarios, mainly due to drying. Consecutive dry days are expected to increase significantly. Transitioning to renewable energy and enhancing natural carbon sinks can reduce emissions, while prioritizing resilience through renewable energy, water management, and climate-smart agriculture will help address climate change challenges in the province.

Abstract: Based on the Multi-Sensor Reanalysis Version 2 dataset, this study analyzes variations in monthly mean total ozone column (TOC) over Northeast China (40–53°N, 115–135°E) during 2015–2024. The study area in winter lies in the transition zone between high polar and low subtropical TOC in East Asian mid-latitudes. Key results indicate that the TOC over Northeast China is consistently higher than the zonal mean TOC of the same latitude band and seasonal cycle demonstrates TOC maximum (minimum) in February (August), one month (two months) earlier than for the Northern Hemisphere midlatitudes. The important role of Brewer–Dobson circulation and quasi-stationary wave (QSW) structure in the TOC distribution over Northeast China is confirmed by the 10-year climatology for January–March. The QSW pattern is characterized by the TOC decrease from the northeastern (~415 DU) to southwestern (~330 DU) parts of the region. The strongest positive (negative) correlations approaching r = 0.9 (r = –0.8) exist between TOC and ozone concentration (temperature) at 50 hPa and 100 hPa, as well as at the surface. These findings can be applied to analyze the ozone observations and stratosphere–surface couplings in the Northeast China region.

Abstract:

Nova Scotia, a province on Canada’s Atlantic coast, has proposed Wind West, a plan to initiate the province’s offshore wind industry. A regional offshore wind report identified eight potential development areas (PDAs), of which four were chosen. The areas were selected to avoid ecologically significant and conflict-of-use areas; however, no consideration was given to tropical cyclones (TCs) and hurricanes (intense tropical cyclones). This paper evaluates the effects of climate change and TCs on offshore wind turbines sighted on Nova Scotia’s continental shelf by analysing historical TC track data to assess the intensity and frequency of extreme wind and wave events on the continental shelf. Correlations between SSTs and extreme weather events were also examined. The findings show no clear long-term trends in TC intensity or frequency in the selected areas, although there is a clear upward trend in sea-surface temperatures (SSTs) since 1950. No strong correlation between rising SSTs and increased storm intensity or frequency within the available datasets were found, though similar studies suggest that these variables have some correlation on aggregate. While climate change is causing conditions for hurricanes to become favorable along the Scotian Shelf, current TC data shows no clear correlation with increasing intensity and frequency over time. The results are affected by the quality of the data. High uncertainty, spatial resolution, and temporal resolution leave large portions of TC tracks unmeasured. Uncertainty associated with pre- and post-1950 data makes conclusions from the results difficult. We propose a measuring buoy in each of the four selected potential development areas cost C$200,000 to develop and C$35,000 to maintain. Each buoy would have a representative radius of 50km, slightly larger than that of each of the four wind energy zones. The additional data collected would allow developers to pick appropriate design standards based on available environmental data and could additionally be used for climate change research. Currently, Nova Scotia faces many limitations developing its offshore; supplying accurate data to assess the risk from extreme weather events to offshore wind turbines is one of the first steps to ensuring success.

Abstract: Warm rain events occur when moist air masses containing elevated precipitable water produce high rainfall rates capable of generating local flash floods. Catalonia, located on the northeastern Mediterranean coast of the Iberian Peninsula, is regularly affected by such episodes: approximately 70% of daily precipitation events exceeding 10 mm with fewer than ten cloud-to-ground lightning flashes can be classified as warm rain. The current research aimed to identify the meteorological conditions most conducive to heavy warm rain episodes in Catalonia. These cases are commonly associated with flash flood episodes in the study region. We have utilized rain gauges, lightning data, radar, and model fields, combined with radio sounding profiles. First, we have identified and characterized warm rain cases, and secondly, we have selected some relevant cases to characterize the phenomenon. These events occur predominantly along the Catalan coast during the warm season, typically following the passage of a cold front, and are associated with shallow convective clouds producing little or no lightning. However, the key determining factor is a characteristic vertical thermodynamic profile: a moist and saturated lower troposphere with high precipitable water beneath a low- to mid-level thermal inversion, weak instability concentrated near the surface. Furthermore, local wind convergence plays a principal role in the rainfall pattern.

Abstract: Hail events commonly affect the Western part of Catalonia, producing important damages mainly in Agriculture sector. The comparison of the weather radar data with the hail-pad registers at ground level allows to improve the diagnosis of hail in thunderstorms and to estimate the maximum hail size. However, there are some limitations using individual radar fields (such as the maximum reflectivity, the echo top or the density of the vertically integrated liquid). The current research has been conducted using quantiles of the vertical profiles of reflectivity for different times before, during and after the hailfall. First, it has been shown that these profiles relate to all the radar parameters. Second, it has been demonstrated that are less sensitive to anomalies of the radar functioning. The final purpose of the project is to develop a real-time tool that improves the surveillance task to discriminate between non-hail and hail thunderstorms and severe and non-severe hail occurrence.

Abstract: Climate extremes are critical constraints on agricultural productivity, particularly in tropical regions experiencing rapid agricultural expansion. This study examines spati-otemporal changes in soybean yields in response to droughts and heatwaves across highly productive municipalities in Brazil from 1989 to 2020. By integrating high-resolution meteorological data, satellite-derived evapotranspiration estimates, and municipal-level crop yield data, we apply standardized drought indices (Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, and Warm Spell Duration Index) to identify climate-yield relationships across Brazil’s heterogeneous agroclimatic zones. Results reveal a marked increase in the frequency and intensity of compound drought–heat events, particularly in the Matopiba frontier, where yield sen-sitivity to hydroclimatic stress is highest. Spatial models confirm that short-term dry events, rather than long-term mean climate shifts, are the dominant drivers of recent yield variability, with significant spatial spillover effects observed across municipalities. The findings underscore the growing vulnerability of rainfed agriculture in Brazil and highlight the critical role of seasonal timing, crop phenology, and regional climate re-gimes in mediating climate risk. This study provides empirical evidence linking com-pound extremes to agricultural performance and offers a scalable framework for early warning systems and climate-resilient policy design.

Abstract: Tropical forests are predicted to become carbon sources by mid-century under climate change. However, this trajectory may not be inevitable for forests under long-term protection. Using 12 years of eddy covariance flux data from a strictly protected tropical forest in Xishuangbanna, China, we develop an explainable machine learning framework (SHAP + Structural Equation Modeling) to disentangle the environmental drivers of net ecosystem exchange (NEE) and evapotranspiration (ET), and project their future trajectories under four CMIP6 climate scenarios. We find a fundamental divergence: while conventional climate models predict a sink-to-source transition by 2050–2066, our data-driven model—trained on conservation-era observations—projects a persistent carbon sink through 2100 across all scenarios. This divergence suggests that long-term protection may buffer tropical forests against climate-driven decline, challenging the prevailing narrative of inevitable carbon loss. We further identify critical environmental thresholds—solar radiation (~200 W m⁻²) and air temperature (~25°C)—beyond which carbon uptake efficiency declines. Our findings provide empirical support for nature-based climate solutions and highlight the need to integrate conservation legacies into Earth system models.

Abstract:

Methane (CH₄) is a major component of natural gas and a potent greenhouse gas. Increasing atmospheric methane concentrations are attributed to emissive anthropogenic activities by an average of 13 ppb per yr since 2020 and are linked to a changing global climate. Mitigating CH₄ emissions from oil and gas production sites has recently become a target to reduce overall greenhouse gas emissions, however, monitoring the efficacy of mitigation strategies depends on accurate quantification of CH₄ emissions at the facility-level. Near-field quantification of methane (CH₄) emissions from oil and gas (O&G) facilities remains challenging due to the effects of atmospheric variability and sensor configuration on atmospheric dispersion models. This study evaluates the performance of two atmospheric dispersion models, the Gaussian Plume (GP) and backward Lagrangian Stochastic (bLS), by comparing calculated CH₄ emissions to controlled single-point emissions of between 0.4 and 5.2 kg CH₄ h^-1. Emissions were calculated by both models using 121 individual sets of measurements comprising five-minute averaged downwind methane mixing ratios and matching meteorological data. Comparison shows the bLS approach showed better predictive performance with twice as many emission estimates were within a factor of two (FAC2) of the known emission rates compared to those calculated using the GP approach. The emissions calculated by the bLS model also had a lower multiplicative error and reduced bias relative to GP. Other error-based metrics further confirmed the bLS model performed better, as it yielded lower RMSE and MAE than GP. Statistical analysis of the emission data shows the lateral and vertical alignment of source and sensor plays a critical role in emission estimations as measurements made closer to the plume centerline and at a distance between 40 to 80 m downwind yielded the best FAC2 agreement. High wind meander degraded ability of both approaches to generate representative emissions particularly with the GP approach as it violates the modelling approach’s assumption of steady-state emissions. Data suggest emissions calculated by the bLS model are comprehensively in better agreement but the computational demands of the modeling approach and integration into fenceline systems limit real-time applicability. While it is likely that the results presented here are suitable for informing leak detection technology in relatively flat unvegetated environments, it is currently unknown if these findings will be applicable in more vertiginous or heavily vegetated oil and gas producing regions of the Marcellus or Uinta Basins.

Abstract: The El Niño–Southern Oscillation (ENSO) is the main driver of interannual climate variability, strongly influencing precipitation, temperature, and extreme events worldwide. In South America, its impacts are well documented. However, studies examining different ENSO types—Eastern Pacific (EP), Central Pacific (CP), and Mixed (MX), defined according to the location of sea surface temperature (SST) anomalies in the tropical Pacific—remain limited, particularly for the Brazilian subtropical climate. This study investigates rainfall variability in the Brazilian subtropical region associated with different ENSO types. Composite analyses of precipitation, wind, and SST anomalies were performed, and monthly rainfall data from 703 stations were used to identify homogeneous regions. The results show the intensity and spatial coherence of rainfall anomalies vary according to El Niño type, with EP events favoring widespread wet conditions and CP events producing more heterogeneous or locally negative anomalies. For La Niña, the intensity and seasonal distribution of negative rainfall anomalies vary by ENSO type: stronger impacts occur in summer (EP), spring (MX), and autumn (CP). These findings improve the understanding of ENSO-related rainfall variability in the Brazilian subtropical region and provide valuable insights for the management of climate-related risks in a region frequently affected by rainfall extremes.

Abstract: A single channel Rayleigh Density Temperature Lidar (RDTL) with a receiver telescope of 85 cm diameter was installed at Poker Flat Research Range (PFRR), Chatanika, Alaska (65°N, 213°E) in November 1997. To increase the incoming signal count the receiver diameter was increased to 1 m in 2016. In order to prevent damage of the photomultiplier tube due to the high incoming signal counts, the RDTL receiver system was modified to a three-channel system. However, temperature calculations from the individual channel retrieval showed a mismatch between them and this created a problem in combining the signal counts from the three channels into one to achieve higher confidence in the data. In this study, a correction procedure has been developed and deployed to the signal counting statistics of the RDTL to eliminate instrumental biases and get 100% agreement in temperatures between the three channels.

Abstract: This study presents a comprehensive assessment of the longwave radiative effects of Arctic tropospheric aerosols based on unique measurements collected at the North Pole drifting station SP 28 in 1987. The primary objective is to compare these historical observations with modern datasets from the Surface Heat Budget of the Arctic Ocean (SHEBA, 1997–1998) and the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC, 2019–2020) to evaluate long term changes in the Arctic radiation regime. Continuous longwave radiation measurements were obtained using high precision spectral pyrgeometers, and a new diagnostic approach was introduced that employs the standard deviation of longwave radiation together with the normalized longwave aerosol effect (NLAE) to identify haze inversion layers. The results show that in 1987, sub inversion haze layers enhanced the downward longwave flux by 15–20 W/m² and increased atmospheric emissivity. In contrast, MOSAiC observations reveal emissivity values that closely match aerosol free model calculations, indicating a substantial decline in Arctic haze and the disappearance of radiatively significant aerosol layers. This shift is consistent with the long term reduction of sulfur dioxide emissions across the Northern Hemisphere and suggests that gaseous components now dominate the formation of inversion radiative properties in the modern Arctic.

Abstract: Urban air quality is a key component of environmental sustainability and public health in large metropolitan areas. Following the substantial but temporary improvements in air quality observed during the COVID-19 lockdowns, it remains unclear whether structural changes in urban air pollution have persisted in the post-pandemic period. This study analyzes the temporal dynamics of major atmospheric pollutants in Mexico City between 2021 and 2024, including CO, NO₂, NOₓ, O₃, PM₁₀, PM₂.₅, and SO₂, using hourly data collected by the official air quality monitoring network SIMAT. Annual and monthly median concentrations were computed to reduce the influence of extreme values and short term pollution episodes. Station level monotonic trends were evaluated using the non-parametric Mann–Kendall test, complemented by Sen’s slope estimator to quantify the magnitude and direction of change. Absolute and relative changes between 2021 and 2024 were also analyzed to capture incremental variations not reflected by trend significance tests, together with hourly monthly analyses to characterize diurnal and seasonal patterns. Results indicate that no statistically significant monotonic trends were detected for any pollutant across the analyzed stations (p > 0.05), suggesting an overall stabilization of air quality levels during the post-pandemic period. Nevertheless, mod-erate increases in annual median concentrations were observed at specific locations, particularly for PM₁₀, PM₂.₅, NO₂, and NOₓ, with relative changes ranging from ap-proximately 5% to 35%. Persistent diurnal and seasonal patterns were identified, closely associated with traffic activity, photochemical processes, and meteorological conditions. These findings suggest that, although no robust long-term trends are evident, incre-mental increases and stable temporal structures remain relevant from a sustainability perspective. Continued monitoring and targeted air quality management strategies are therefore necessary to support long-term urban environmental sustainability.

Abstract: Flow channels on the snow surface are a common phenomenon frequently reported by field observers. The interpretation of those field observations and an understanding of the underlying physical processes are important for forecasting routines and models of avalanche warning, hydrological, or meteorological services. Flow channels on the snow surface are typically associated with rain-on-snow (ROS) events and are often interpreted as an indicator of the approximate snowfall level. However, recent field observations of flow channels on the snow surface without significant liquid precipitation in the Austrian Alps challenge the assumption that ROS events are the sole cause of flow channel formation. In this study, we quantitatively compare liquid water input into the snowpack from melt processes to the amount of rain during a documented flow channel formation event. Using a combination of field observations, energy balance calculations and model simulations, we demonstrate that, in our case study, meltwater was the predominant driver of flow channel formation. Our results indicate that more than 97 % of the total liquid water input originated from melt, while rain contributed only roughly 2 %. These findings highlight the need for a revised interpretation of flow channel formation, suggesting that meltwater-driven flow channels may be more significant than previously assumed.