Environmental and Earth Sciences

Abstract: The present study aimed to evaluate the climatic suitability of Actinidia arguta (mini kiwifruit) across Europe. We first calibrated a species distribution model (SDM) on occurrence records from the native East Asian range and then transferred it to the European domain. Alongside standard bioclimatic variables, we incorporated a custom spring frost indicator into the modeling workflow. The five algorithms (GLM, RF, MAXNET, GAM, GBM) showed moderate-to-good predictive performance, with mean validation TSS values ranging from 0.618 to 0.656 and mean AUC values from 0.884 to 0.912. Temperature seasonality (BIO4) was the most influential predictor, with precipitation of the warmest quarter (BIO18) and spring minimum temperature (spring_min) showing similarly high secondary importance. Spatial projections indicated broad areas of climatic suitability across Central and Southeastern Europe; however, a considerable share of these suitable areas also coincided with zones where temperatures during critical phenological stages of A. arguta regularly fall to potentially damaging levels. Taken together, these results indicate that climatic suitability alone tends to overestimate actual cultivation potential, and that the risk of late-spring frost must be treated as an explicit component of any species distribution modeling framework for perennial crops.

Abstract: Local-scale evaluation of climate models is essential to ensure the reliability of climate projections. This study evaluates the performance of 32 NEX-GDDP-CMIP6 models and constructs a multi-model ensemble for simulating monthly precipitation, maximum temperature (Tmax), and minimum temperature (Tmin) at four stations in northern Togo (Dapaong, Mango, Kara, Niamtougou) over the 1983–2014 period. Model performance was quantified using four metrics (bias, RMSE, Pearson correlation coefficient R, and Willmott’s d index), integrated into a Comprehensive Rating Metric (RM) for the overall ranking. Results showed that most models reproduced temperatures more accurately than precipitation. For Tmax, RM scores ranged from 0.03 (MPI-ESM1-2-HR, NESM3) to 0.97 (INM-CM4-8, the best score in the study), with generally high performance. For Tmin, greater spread among models was observed, with RM scores ranging from 0.00 (CMCC-CM2-SR5) to 0.91 (KACE-1-0-G and UKESM1-0-LL). For precipitation, RM scores ranged from 0.23 (CanESM5) to 0.86 (HadGEM3-GC31-LL), with systematic underestimation across nearly all models. Based on the overall ranking across the three variables, 18 models achieved an RM score above the 0.5 threshold. These models demonstrated reliable performance in reproducing the climate of northern Togo and were retained to form the multi-model ensemble for climate projections. These results provide a robust and reproducible evaluation framework for climate modeling in the Sudano-Sahelian region of West Africa.

Abstract: The Source-Environment-Response (SER) framework introduced a decomposition of radon transport into source-driven and environmental components, but treated the environment as a passive correction rather than an active memory accumulator. We present QSER (Generalized Source-Environment-Response), which extends SER by making the memory kernel explicit through the environmental accumulator equation. The QSER framework separates the governing transport operator into conser-vative (advective) and dissipative (diffusive and decay) parts, revealing the observed concentration as the difference between a conservative ghost field S and an environmental memory field E that accu-mulates the history of dissipative interactions. We derive the full three-dimensional time-dependent solution for atmospheric radon transport, including generalizations for stochastic velocity fields and shape-changing plumes while preserving strict mass conservation. The memory kernel “Q”, which is the Green’s function of the system, captures how past emissions influence present concentrations. The QSER decomposition solution successfully recovers industry-standard atmospheric dispersion models (Gaussian plume, RESRAD-OFFSITE, CAP88-PC, ISCLT3, SCREEN3, ALOHA/CAMEO) as special cases. Validation against the Nazaroff solution for soil radon transport demonstrates exact decomposition and recovery of physical parameters (F, v, a, D). Four internal consistency tests pro-vide built-in verification of the decomposition and Physical consistency of results. The framework transforms ill-posed inverse problems into a manageable sequential optimization over conservative source parameters, enabling robust parameter estimation for radon monitoring applications in earth-quake precursor studies, uranium exploration, and atmospheric transport. All implementation code is available at “https://github.com/1030ahmad1030/QSER”.

Abstract: Global Navigation Satellite System (GNSS) radio occultation (RO) has evolved into a heterogeneous multi-mission observing system that combines national, partner, and commercial constellations. This review and outlook synthesize NOAA/STAR assessments of GNSS RO commercial data, including Spire and PlanetiQ, and compares their quality with those of national missions, COSMIC-2, MetOp, and other missions. We emphasize the results in the neutral atmosphere. To examine heterogeneous multiple RO missions, we organized the comparisons along a pathway from i) orbit and viewing geometry through antenna gain and received signal, ii) examining their signal-to-noise ratio (SNR) and penetration, iii) quantifying the phase quality, retrieval bias and uncertainty, iv) demonstrating their impacts on numerical weather prediction (NWP), and climate and environmental applications. The reviewed comparisons show that higher SNR generally improves tracking and lower-tropospheric penetration but does not by itself remove biases and uncertainty arising from water-vapor irregularity, multipath, super-refraction, departures from spherical symmetry, or processing choices. Spire and PlanetiQ retrievals are broadly compatible with COSMIC-2 and partner missions in the principal atmospheric layers, and their additional profiles improve spatiotemporal sampling and provide incremental value for global and tropical-cyclone prediction. For long-term environmental records, however, operational usefulness is not sufficient: common processing, inter-mission stability assessment, sampling correction, and mission-specific uncertainty characterization are required. Multi-mission RO observations support upper-troposphere and lower-stratosphere temperature records, tropospheric water-vapor variability and trend analyses, and planetary-boundary-layer height estimates when these conditions are met. The synthesis yields an application-conditioned evaluation framework that separates signal quality, retrieval quality, usable observation yield, sampling complementarity, and downstream impact. The principal outlook priorities are uncertainty-aware quality control and error specification, improved treatment of super-refraction and lower-tropospheric retrievals, sustained multi-mission reprocessing and intercomparison, and observing-system design that balances measurement quality with coverage.

Abstract: Atmospheric compound pollution is an important challenge in China, with the nonlinear interaction between PM2.5 and O3 presenting a complex pattern of mutual offset and synergistic pollution. This study focused on Puyang, a typical oil and gas industrial city in North China (one of the “2+26” cities), to explore long-term trends and nonlinear interactions of PM_2.5 and O₃ based on monitoring data from 2015 to 2025. PM_2.5 decreased from 79.6 μg·m⁻³ in 2015 to 39.3 μg·m⁻³ in 2025, while MdA8 O₃ increased from 123.0 μg·m⁻³ to 189.6 μg·m⁻³. A weak positive correlation between PM_2.5 and O₃ has been observed when MdA8 O₃ was greater than 160 μg·m⁻³ and PM2.5 was less than 35 μg·m⁻³. The correlation coefficient was 0.38 in winter, 0.37 in summer, 0.26 in autumn, and 0.21 in spring, respectively. O₃ production efficiency (OPE) showed a declining trend and was positively correlated with PM_2.5 (R = 0.46-0.65), with monthly averages consistently above 7, indicating that O₃ was primarily controlled by NOx in this city. This study proposed a seasonal VOCs-NOx control strategy targeting summer photochemical O₃ and autumn–winter dual-exceedance to mitigate PM_2.5, optimize OPE, and reduce exceedance days.

Abstract: Sudden Stratospheric Warmings (SSWs) are rare polar stratospheric extremes far less documented over Antarctica than the Arctic. A pair of successive midwinter Antarctic SSWs occurred in July–August 2024, featuring severe polar vortex distortion and sharp stratospheric temperature rises. Using MERRA-2/ERA5 reanalysis and Aura MLS satellite ozone data, we diagnose wave 1 and wave 2 planetary wave forcing and associated ozone variations. Abnormally strong upward-propagating Southern Hemisphere tropospheric planetary waves drove the 2024 SSW. Wave 1 triggered the first July warming peak, while amplified wave 2 dominated the second intense vortex disruption in early August. These waves delivered westward momentum that weakened the polar night jet and destabilized the vortex. Accelerated Brewer–Dobson circulation (BDC) enhanced poleward ozone transport, raising polar ozone levels and supplying extra stratospheric heat. Wave–ozone coupling shows clear selectivity: wave 1 induces negative total ozone column anomalies across 15°S–50°S midlatitudes, whereas wave 2 generates broad positive ozone signals over 50°S–65°S and primarily controls polar ozone enrichment. Fading planetary wave activity after the warming peaks gradually restored the polar vortex and stratospheric thermal structure. This study provides key observational evidence for rare Antarctic SSWs and advances understanding of stratosphere–troposphere coupling and polar ozone dynamics under global warming.

Abstract: We derived distributions of inertia gravity waves in the polar middle atmosphere from 2005 to 2021 by using temperature profiles observed by the Microwave Limb Sounder (Aura/MLS) on the Aura satellite. In the southern polar atmosphere, there is enhanced gravity wave activity in the mesosphere above the Weddell sea. The enhanced gravity wave activity occurs with small interannual variations in January and in July from 2005 to 2021. The vertical evolution of gravity wave distributions is analysed by averaging all gravity wave maps in January or July from 2005 to 2021 and showing them separately at altitudes from 44 km to 90 km. In winter season (July), the well-known hotspot of gravity wave activity in the stratosphere over the southern Andes is isolated from the hotspot of gravity wave activity in the mesosphere over the Weddell sea. There is no evolution from the stratospheric gravity wave hotspot to the mesospheric gravity wave hotspot which is more south- and eastward. We also present the gravity wave distributions for the northern polar middle atmosphere which have no clear hotspots and generally smaller amplitudes compared to the southern polar middle atmosphere.

Abstract: The risk arising from the uncontrolled burning of waste is significant given the spatial conditions and the exposure to human health. The environmental risk associated with the emission of harmful gases and particulates into the air as a result of uncontrolled waste burning is the subject of an analysis of the legal consequences of the incident. In order to determine the risks involved, it is necessary to analyse the volume of emissions, the qualitative composition of these emissions, the range of pollutant dispersion during the event, and the level of human exposure in relation to the distribution of the pollutants present. As part of this study, the authors have undertaken work to address a research gap involving the development of a methodology for assessing environmental risk based on an analysis of the degree of exposure of people and the environment to air emissions from a waste storage facility fire. The resulting risk is assessed using analytical methods based on mathematical modelling of pollutant dispersion using the Gaussian “plume” model (Model Pasquilla) using a computer programme called Operat FB and analysing the reference levels set by the NDS (Maximum permissible concentration), EPA (The Environmental Protection Agency), OSHA (Occupational Safety and Health Administration) and under local Polish legislation. This paper presents a case study, namely the uncontrolled incineration of waste resulting from the treatment of municipal waste.

Abstract: Reliable prediction of the El Niño–Southern Oscillation (ENSO) at multi-year lead times remains challenging because forecast skill generally declines beyond 12–18 months. This study presents a streamlined Informer-based framework designed to predict the Niño 3.4 index up to 36 months in advance using one-dimensional climate time series. The proposed model was developed following an analysis of the earlier Multimodal ENSO Forecast framework, which showed that most of the long-lead predictive skill originated from the time-series branch, whereas the spatial branch added substantial computational cost with limited benefit. Historical simulations from CMIP5 and selected CMIP6 models were used for training, followed by calibration with GODAS observations from 1980 to 2000 and independent validation over 2001–2020. The model achieved anomaly correlation coefficient values of 0.92, 0.75, 0.56, 0.46, 0.48, and 0.41 at lead times of 1, 6, 12, 18, 24, and 30 months, respectively. At directly comparable lead times, its performance was consistently higher than that of the CNN benchmark, with the largest difference observed at 18 months. The model also maintained useful predictive skill at extended lead times and showed reduced sensitivity to the spring predictability barrier. In addition, the simplified architecture required substantially less computational effort than the original multimodal framework and supported inference on a standard CPU. These results demonstrate that an efficiently optimized time-series architecture, combined with climate-model-based data augmentation, can provide competitive and computationally practical ENSO forecasts at multi-year horizons.

Abstract: The Northern Tianshan Urban Agglomeration (NTUA) in Xinjiang, China, represents a globally distinctive arid-region air-pollution system where the interaction of mountain–valley meteorology, Central Asian dust transport, and intensive industrial and residential emissions produces a complex multi-pollutant environment. This review synthesizes research progress over the past fifteen years on the atmospheric environment of the NTUA, covering ozone, particulate matter (PM2.5 and PM10), brown carbon, black carbon, dust aerosols, volatile organic compounds (VOCs), nitrogen oxides (NOx), free radicals, and reactive oxygen species. We examine spatiotemporal distribution patterns, chemical speciation, source apportionment, photochemical transformation pathways, and health risk implications across the three NTUA sub-clusters (Urumqi–Changji–Shihezi–Wujiaqu, Kuitun–Dushanzi–Wusu, and Karamay). The evidence reveals a progressive seasonal transition from dust-dominated coarse-mode aerosol loading in spring to secondary inorganic and carbonaceous fine-mode dominance in winter, with ozone formation controlled by a regime shift from VOC-limited winter conditions to NOx-limited summer photochemistry. Carbonaceous aerosols exhibit contrastive source patterns: black carbon is dominated by local fossil-fuel combustion, while brown carbon shifts from primary biomass-burning sources in winter to secondary photochemical formation in summer. The atmospheric oxidation capacity, although poorly constrained due to the absence of direct radical measurements, is shaped by the interplay of photolytic initiation, VOC–NOx chain propagation, and dust-surface heterogeneous termination. Health risk assessments indicate that the combined PM2.5–ozone–dust exposure burden produces a multi-pollutant health risk profile that single-pollutant epidemiological models cannot adequately capture. Critical research gaps include the absence of direct radical and ROS measurements, the lack of emission inventories that resolve dust–anthropogenic pollutant interactions, and the need for long-term cohort studies specific to the arid-region pollution mixture. We propose a prioritized research agenda centered on integrated observational and modeling frameworks that simultaneously constrain the dust–anthropogenic mixing state, the radical-mediated oxidation capacity, and the multi-pollutant health exposure pathway.

Abstract: Atmospheric blocking is a major feature of mid-latitude circulation and is closely associ-ated with the occurrence of extreme weather events over Europe. Among the different blocking regimes, Omega blocks are characterized by their persistent tripolar structure, which favors the development of prolonged heatwaves. In late June 2026, Western Eu-rope experienced one of the earliest and most intense heatwaves of recent decades. This study investigates the synoptic, dynamical, and climatic mechanisms associated with this event using ERA5 reanalysis data. The evolution of the 500 hPa geopotential height, 850 hPa temperature, polar jet stream, Rossby wave breaking, and dynamical tropopause (2 PVU) was analyzed together with anomalies in sea surface temperature (SST), meridional temperature gradient, soil moisture, and sensible heat flux. The results show that the heatwave developed under a persistent Omega blocking pattern, which promoted strong subsidence, enhanced solar heating, and sustained warm air advection over Western Europe. The event was further amplified by land–atmosphere feedbacks associated with anomalously dry soils. In addition, a pronounced cold SST anomaly over the subpolar North Atlantic preceded the onset of the blocking and coincided with a weakened me-ridional temperature gradient and reduced lower-tropospheric baroclinicity along the climatological position of the polar jet stream. These findings are consistent with recent studies suggesting that North Atlantic cooling associated with a weakened Atlantic Me-ridional Overturning Circulation (AMOC) may create favorable conditions for persistent summer Omega blocking and associated European heatwaves.

Abstract: This study investigated the meteorological factors influencing solar irradiance variability across eight sites in Zambia, using a 10-year monthly dataset covering 2014 to 2024. Ground-based solar irradiance and meteorological data from the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) network, complemented by NASA POWER satellite data and Meteonorm sunshine duration data were used. Physical plausibility tests were performed on the SASSCAL records prior to analysis, after which temporal, spatial, worst-month, and correlation-based methods were used to characterise irradiance behaviour and its atmospheric controls. The results show a consistent annual irradiance cycle which reaches its peak in September-October and declines during the rainy season or cool-dry season depending on a specific location. Kalabo and Samfya showed the highest mean irradiance and seasonal stability, whereas Mpulungu and Mwinilunga recorded lower solar irradiance values and deeper wet-season minima. Worst-month analysis identified January as the critical low-resource month for northern and lake-proximate sites, and June-July for plateau, western, and southern locations. Cloud amount, precipitation and relative humidity emerged as the main suppressors of irradiance, while temperature showed a positive relation and wind speed exhibited site-dependent effects. Atmospheric transparency, as reflected in sunshine duration contrasts, rather than minor differences in astronomical day length, explained the observed spatial differences. These results demonstrate that Zambia's solar resource cannot be characterised by a single national baseline, and that site-specific meteorological conditions should be the basis for solar resource assessment and photovoltaic planning across the country.

Abstract: This study investigates the sensitivity of hurricane track and intensity forecasts to various cumulus parameterization schemes with scale-awareness, including the Simplified Arakawa-Schubert (SAS), Relaxed Arakawa-Schubert (RAS), and Chikira-Sugiyama Arakawa-Wu (CSAW), within the NOAA Global Forecast System (GFS) version 17 framework. Using a set of nine initial conditions for Hurricane Ian (2022), we demonstrate that schemes utilizing a spectrum of cloud types (RAS and CSAW) produce westward-shifted trajectories compared to the single-cloud SAS scheme, which exhibits an eastward track bias. For intensity forecasts, SAS was more closely aligned with observations over RAS and CSAW. When testing the sensitivity of the CSAW convective parameterization with Wb (cloud-base vertical velocity) spectrum and entrainment rates, a critical inverse relationship was revealed between parameterized convective strength and resolved storm intensity. Specifically, reducing the Wb range or increasing entrainment attenuates the sub-grid scale convective response, facilitating a compensatory intensification of the resolved-scale vortex and kinetic energy. Spectral analysis indicates that cloud-spectrum schemes exhibit reduced energy variance in low-frequency modes, which directly impacts large-scale steering. Furthermore, potential vorticity (PV) analysis at 500 hPa confirms that track divergence is governed by the spatial orientation of PV anomalies, with the storm propagating toward regions of maximum PV gradient. These findings underscore that the representation of the cloud spectrum and the subsequent energy partitioning between parameterized and resolved scales are fundamental controls on tropical cyclone evolution in high-resolution numerical weather prediction models.

Abstract: Event-based vision sensors (EVS) offer an innovative approach to optical sensing by responding solely to changes in photon flux with asynchronous pixels. This enables high-speed imaging with minimal data throughput and a large dynamical range >120dB. Despite their growing use in edge detection and the observation of Earth science phenomena such as lightning and transient luminous events (TLEs), optimizing EVS performance specifically for high-speed obervations on microsecond timescales, where the photon flux is key, remains challenging. This is due to the unitless, non-intuitive nature of their bias settings, which drives the working principles of the sensor. In this study, we perform a comprehensive calibration of an event-based camera, focusing on three critical parameters: contrast thresholds, refractory period, and corner frequency of the inherent low-pass filter. Using a pre-calibrated LED source and newly developed event generation code, we establish physical relationships between these raw digital bias settings and the camera's response characteristics. Our results show tunable ranges of 5%–190% for contrast thresholds, 12–480 \(\mu\)s for refractory periods, and 1–11 kHz for corner frequencies. These calibrations provide the fundamental camera characteristics for accurate optical signal reconstruction and improved detection of fast transient phenomena. The approach is demonstrated on a Prophesee EVK4; however, the proposed calibration method can be applied to any EVS camera that employs similar hardware components and bias settings, providing a necessary framework for the quantitative use of EVS cameras in atmospheric remote sensing.

Abstract: Because of the large dynamic range of scales between 3-D turbulence and global circulation, the effects of small scale turbulence, and indeed turbulence at many scales, often need to be parameterized in some way for input into large global-scale computer models. Ideally, it would be good to have a computer program large enough, and powerful enough, to solve all motions at all scales simultaneously, but this objective is still far from possible. Yet if implemented poorly, parameterization can lead to errors which can propagate through the model. While turbulence is often considered as a “wastebasket” for larger scale motions, here we look in the other direction, and examine how these smaller scale motions work back to affect the larger-scale flows. The nature of background drag and diffusive forces is reviewed in the context of impact on larger scale motions, and the ways that these forces are implemented in models is discussed. The lack of use of measured (as distinct from hypothetical) small-scale turbulence data is noted. It is also noted that vertical diffusion is conceptually more important for atmospheric coupling than horizontal diffusion, and so-called “two-dimensional (2-D) turbulence”, sometimes discussed in regard to atmospheric mixing, is less capable of vertical mixing because associated organized vertical motions are generally weak. Very strong evidence from the Global Atmospheric Sampling Program (GASP) for a dominant gravity-wave spectral region at horizontal scales of 200-1000 km, as low in altitude as the tropopause, is presented. Errors in interpretation of earlier well-cited analyses of these data (often incorrectly cited as evidence for 2-D turbulence) are presented, which have profound impact on previous beliefs about the relative roles of gravity waves and nominally 2-D turbulence. Non- Kolmogorov diffusive processes which contribute to drag, diffusion and mixing, but that have rarely been practically employed, are considered, including the impact of intermittency, wave saturation, “whitecaps” and Stokes Diffusion. When these processes are included, typical realistic diffusion coefficients seem to be 2-3 × higher than those predicted by the Cospar International Reference Atmosphere. Finally a comparison between diffusivities using the Whole Atmosphere Community Climate Model (WACCM6) at the National Center for Atmospheric Research in the USA, and the Japanese Atmospheric GCM for Upper Atmosphere Research (JAGUAR), using very different strategies, is undertaken.

Abstract:

The ECOSTRESS mission provides high resolution thermal infrared observations that support a wide range of applications ranging from evapotranspiration monitoring and drought assessment to Land Surface Temperature (LST) and emissivity retrieval. Accurate estimation of Precipitable Water Vapour (PWV) is critical for these applications because of strong influence of atmospheric water vapour on thermal infrared radiative transfer that influences emissivity retrieval. The current ECOSTRESS processing chain estimates PWV from GEOS5-FP numerical weather prediction data. In this study, we investigate an alternative approach that retrieves PWV directly from ECOSTRESS thermal brightness temperatures using symbolic Regression (PYSR). Using more than 269,000 spatio-temporally matched ECOSTRESS-GNSS observations, we derived a unified all-season analytical formula capable of estimating PWV without relying on external atmospheric profiles or ancillary emissivity. Instead, the proposed model relies only on readily available and temporally stable ancillary variables, namely digital elevation model (DEM) and Normalized Difference Vegetation Index (NDVI) data. The resulting analytical formulation (All-season PySR) derived using PySR symbolic retrieval achieved a Root Mean Square Error (RMSE) of 7.22 mm and an R² of 0.624 when evaluated against GNSS-derived precipitable water vapor observations. In order to improve the results, season and regime specific PySR formulas were first developed to provide interpretable PWV estimates for various conditions. These formulas form the initial retrieval component of the Climate-Adaptive Ensemble formula. The Climate Adaptive Ensemble (CAE) combines PySR formulas, ECOSTRESS inputs and historical ERA5 water-vapour profiles to perform a global analytical ridge regression. The resulting output achieved an RMSE of 5.35mm and R² of 0.781 on test GNSS observations. The CAE formula was further validated on external radiosonde dataset and independent GNSS observations to evaluate its robustness. The methodology proposed here will be extremely useful for future satellite missions using thermal sensors such as TRISHNA (Thermal Infra-Red Imaging Satellite for High-resolution Natural resource Assessment) and LSTM (Land Surface Temperature Radiometer) as the methodology could help in retrieving PWV instantaneously for atmospheric correction instead of depending on external products.

Abstract: Cold-pool structure controls the confinement and transport environment relevant to surface wintertime ozone in the mountainous Uinta Basin, Utah, USA, but point surface values alone do not establish that structure. We evaluated deterministic responses in Weather Research and Forecasting (WRF) simulations of the 2 February 2013 high-ozone episode to the supplied Global Forecast System (GFS) or North American Model (NAM) driving analyses, nest feedback, boundary-layer and surface-layer treatment, slope-aware radiation, and terrain source/resolution. Across a common five-hour sample, GFS-driven members were warmer at the evaluated surface sites, less stable in the lowest 500 m, and marginally stronger fixed-layer advection than matched NAM members. All GFS-driven runs retained a weaker cold pool, defined as a smaller vertically integrated heat deficit; every simulation gained heat deficit (strengthened the cold pool) over the retained window. Results suggest the driving product (GFS, NAM) in this case study made more difference to cold-pool generation than other changes in WRF by an order of magnitude. Responses to feedback, parameterisation variation were negligible, while the fineness of terrain modulated the speed of cold-pool formation likely due to a lower roughness in coarse orography. The meteorological results, a longtime impediment to local air-chemistry forecasts, advise subsequent air-chemistry modelling on ingesting the necessary cold pool, trapping precursors and pollutants.

Abstract: The northern part of Ethiopia is vulnerable to extreme droughts, which subsequently affect agricultural production and food security. This study therefore, analyzed the spatiotemporal variability of extreme temperature and rainfall across ten selected observation stations in the central and northwest zones of Tigray using a long-term climate data obtained from NASA POWER (1983-2023). The data were subjected to rigorous quality control analysis using the RClimDex v.1 software to detect potential erroneous or missing values. Trend analysis was performed on 21 of the 27 extreme temperature and rainfall indices recommended by the joint CCL/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI). The non-parametric Mann-Kendall trend test and Sen’s slope estimator were employed to determine the trends in temperature and rainfall across the study areas over the past forty years. The trend analysis demonstrated a highly significant (p < 0.01) increase in warming indicators such as warm nights (TN90p), warm days (TX90p), maximum value of the daily maximum (TXx), and minimum (TNx) temperature ranges, while cooling indicator indices like cold spell duration indicator (CSDI), cool nights (TN10p), and cool days (TX10p) exhibited a highly significant negative trend over the study period. Furthermore, TXx and TNx increased at rates ranging from 0.02 to 0.025 °C per year and 0.013 to 0.016 °C per year, respectively. Annual rainfall showed an increasing trend ranging from 0.38 to 3.5 mm per year. Notably, the frequency of heavy (R10mm) and extremely heavy (R25 mm) rainfall events revealed a highly significant (p < 0.01) positive trend, raising risks of flooding, soil erosion, and crop failure for local farmers. These findings highlight the urgent need for adaptive strategies to protect Tigray’s agriculture and livelihoods under changing climatic conditions.

Abstract: The deposition of light-absorbing aerosols like dark brown carbon (d-BrC) accelerates cryospheric melt, yet accurately modeling this radiative forcing is hindered by a lack of empirical data. To address this gap, we developed a novel, low-footprint laboratory snow synthesis and deposition apparatus. This system couples the cryogenic generation of nature-identical snow with controlled aerosol dry deposition, allowing spectral albedo reductions to be quantified via an integrating sphere spectrophotometer. The setup was rigorously validated using Cabojet, a highly absorbing BC proxy, achieving high-fidelity optical closure with the Snow, Ice, and Aerosol Radiative (SNICAR) model (root-mean-square error < 0.022) and establishing a 165 parts per billion (ppb) detection limit of BC in snow. Applying this validated methodology to nebulized d-BrC tarballs revealed that the dry deposition of ∼1000 ppb d-BrC drives a visible broadband albedo decrease of 0.06. This apparatus offers a highly controlled, empirical platform to ground-truth theoretical radiative forcing calculations for diverse, real-world cryospheric contaminants. Further, by introducing a modular and rapid laboratory set up, these experiments overcome the limitations of outdoor field studies and resource-intensive cold rooms.

Abstract: Climate relationships with agricultural insurance claims are unlikely to be spatially uniform because crops, hazards, and insurance experience vary geographically. To identify geographic variation in climate predictors of claim severity, we fit six geographically weighted random forest models to monthly county observations from 1994 through 2022, pairing corn, soybeans, or wheat with drought or excess moisture/precipitation/rain. The outcome was logarithmically transformed positive loss per claim, adjusted to constant 2022 dollars using the Consumer Price Index for All Urban Consumers. Models included six monthly climate predictors, seasonal controls, and year, with each local forest using an adaptive neighborhood of the 10 nearest eligible counties. Total positive climate importance was greatest in the Great Plains and Midwest but varied by commodity and cause. Maximum temperature ranked first in five of the six models, while actual evapotranspiration ranked first for soybean excess moisture claims. Corn and soybean drought models emphasized maximum temperature and vapor pressure deficit, while excess moisture models showed more heterogeneous patterns. These findings demonstrate that a single national profile of climate importance cannot adequately represent local relationships between climate and claim severity. The analysis is conditional on positive payments and does not estimate claim occurrence or risk normalized for exposure.