Long-term Climate Trends Research Articles

The Impact of Climate Variability on the Livelihoods of Smallholder Farmers in an Agricultural Village in the Wider Belfast Area, Mpumalanga Province, South Africa

The purpose of this study was to investigate the impact of climate change on smallholder farmers in South Africa, particularly focusing on the relationship between agriculture and weather patterns. Understanding this connection is crucial for helping farmers adapt to changing climate conditions and improve their resilience and sustainability. This research analyses 33 years of climate data (1990–2023) from the Belfast weather station to identify long-term climate trends, seasonal shifts, and the frequency of extreme weather events. Statistical analysis, including the Mann–Kendall test, revealed significant changes in temperature, rainfall, and the intensity of extreme weather events, indicating that climate change is already affecting the region. Specifically, the research highlighted significant damage to agricultural infrastructure, such as greenhouses, due to climate-related wind events. This study emphasises the importance of using digital technologies to monitor weather patterns in real-time, aiding in decision-making, and enhancing agricultural efficiency. Additionally, it calls for further research into the social impacts of climate variability, including its effects on community cohesion, migration, and access to social services among smallholder farmers. These findings provide a foundation for developing effective interventions to support the resilience of smallholder farming communities in the face of climate change. Future studies need to consider how climate variability affects farmers’ abilities to access markets, both in terms of transport and product quality.

Subsidence and Uplift in Active and Closed Lignite Mines: Impacts of Energy Transition and Climate Change

This study examines the combined effects of decommissioning lignite mining operations and long-term climate trends on groundwater systems and land surface movements in the Konin region of Poland, which is characterised by extensive open-pit lignite extraction. The findings reveal subsidence rates ranging from −26 to 14 mm per year within mining zones, while land uplift of a few millimetres per year occurred in closed mining areas between 2015 and 2022. Groundwater levels in shallow Quaternary and deeper Paleogene–Neogene aquifers have declined significantly, with drops of up to 26 m observed near active mining, particularly between 2009 and 2019. A smaller groundwater decline of around a few metres was observed outside areas influenced by mining. Meteorological data show an average annual temperature of 8.9 °C from 1991 to 2023, with a clear warming trend of 0.0050 °C per year since 2009. Although precipitation patterns show a slight increase from 512 mm to 520 mm, a shift towards drier conditions has emerged since 2009, characterised by more frequent dry spells. These climatic trends, combined with mining activities, highlight the need for adaptive groundwater management strategies. Future research should focus on enhanced monitoring of groundwater recovery and sustainable practices in post-mining landscapes.

Editorial preface to special issue: Response of marine and terrestrial environments to Triassic–Paleogene hyperthermals

The Mesozoic–Paleogene hyperthermals represent critical intervals of rapid global warming associated with abrupt carbon cycle perturbations. These events can provide valuable deep-time insights into how climate might respond to the current rise in temperatures driven by anthropogenic greenhouse gas emissions. This special issue comprises 17 publications that cover a broad spectrum of the research on hyperthermals, and these can be grouped into four main themes: (1) carbon-cycle perturbations, (2) environmental changes and biogeochemical extremes, (3) biotic responses to warming, and (4) long-term climatic and environmental changes in greenhouse climates. By integrating sedimentological, geochemical, and paleontological methods, the studies in this issue explore carbon sources and release mechanisms, provide insights into the initiation, development, and termination of hyperthermal events, and elucidate their longer-term impacts. The studies provide new insights into the interactions between short-term extreme events and long-term climate trends, offering valuable perspectives on the future trajectory of Earth's climate in response to ongoing anthropogenic warming.

How well can satellite altimetry and firn models resolve Antarctic firn thickness variations?

Abstract. Elevation changes of the Antarctic Ice Sheet (AIS) related to surface mass balance and firn processes vary strongly in space and time. Their subdecadal natural variability is large and hampers the detection of long-term climate trends. Firn models or satellite altimetry observations are typically used to investigate such firn thickness changes. However, there is a large spread among firn models. Further, they do not fully explain observed firn thickness changes, especially on smaller spatial scales. Reconciled firn thickness variations will facilitate the detection of long-term trends from satellite altimetry; the resolution of the spatial patterns of such trends; and, hence, their attribution to the underlying mechanisms. This study has two objectives. First, we quantify interannual Antarctic firn thickness variations on a 10 km grid scale. Second, we characterise errors in both the altimetry products and firn models. To achieve this, we jointly analyse satellite altimetry and firn modelling results in time and space. We use the timing of firn thickness variations from firn models and the satellite-observed amplitude of these variations to generate a combined product (“adjusted firn thickness variations”) over the AIS for 1992–2017. The combined product characterises spatially resolved variations better than either firn models alone or altimetry alone. It provides a higher resolution and a more precise spatial distribution of the variations compared to model-only solutions and eliminates most of the altimetry errors compared to altimetry-only solutions. Relative uncertainties in basin-mean time series of the adjusted firn thickness variations range from 20 % to 108 %. At the grid cell level, relative uncertainties are higher, with median values per basin in the range of 54 % to 186 %. This is due to the uncertainties in the large and very dry areas of central East Antarctica, especially over large megadune fields, where the low signal-to-noise ratio poses a challenge for both models and altimetry to resolve firn thickness variations. A large part of the variance in the altimetric time series is not explained by the adjusted firn thickness variations. Analysis of the altimetric residuals indicate that they contain firn model errors, such as firn signals not captured by the models, and altimetry errors, such as time-variable radar penetration effects and errors in intermission calibration. This highlights the need for improvements in firn modelling and altimetry analysis.

Quantifying urban climate response to large-scale forcing modified by local boundary layer effects

Over the past two decades, the joint manifestation of global warming and rapid urbanization has significantly increased the occurrence of heatwaves and the formation of urban heat islands in temperate cities. Consequently, this synergy has amplified the frequency and duration of periods with tropical nights (TNs) in these urban areas. While the occurrences of such extreme events demonstrate irregular and nonlinear annual patterns, they consistently manifest a discernible rising decadal trend in local or regional climatic data. In urban regions situated amidst hilly or mountainous landscapes, changing wind directions—often associated with uphill or downhill thermal flows—profoundly impact the spread and dispersion of heat-related pollution, creating unique natural ventilation patterns. Using the Lausanne/Pully urban area in Switzerland as examples of hilly and lakeshore temperate cities, this study explores the influence of wind patterns and natural urban ventilation on the nonlinearity of recorded climatic data within an urban environment. This study integrates a mesoscale numerical weather prediction model (COSMO-1), a microscale Computational Fluid Dynamics (CFD) model, field observations, variational mode decomposition technique, and statistical analysis to investigate how wind speed and direction critically influence the nonlinearity of recorded long-term trends of extreme events, specifically focusing on the frequency and duration of TNs in lakeshore and hilly cities. The results strongly indicate a direct correlation between the frequency of TNs and the occurrence of specific moderate wind patterns. These wind patterns are exclusively captured by the microscale CFD model, unlike the mesoscale model, which neglects both urban morphology and complex hilly terrains. The impact of temporal and spatial variability of the wind field on long-term observations at fixed measurement stations suggests that caution should be exercised when relying on limited spatial measurement points to monitor and quantify long-term urban climate trends, particularly in cities located in complex terrains.

Between-year weather differences and long-term environmental trends both contribute to observed vegetation changes in a plot resurvey study.

Repeated surveys of vegetation plots offer a viable tool to detect fine-scale responses of vegetation to environmental changes. In this study, our aim was to explore how the species composition and species richness of dry grasslands changed over a period of 17 years, how these changes relate to environmental changes and how the presence of spring ephemerals, which may react to short-term weather fluctuations rather than long-term climatic trends, may influence the results. A total of 95 plots was surveyed in 2005 and resurveyed in 2022 in dry grasslands of the Kiskunság Sand Ridge (Hungary, Eastern Central Europe), where there has been a significant increase in mean annual temperature during the last decades, while no trends in precipitation can be identified. Db-RDA was performed to reveal compositional changes. The changes in environmental conditions and naturalness state were assessed using ecological and naturalness indicator values. We also compared per-plot richness of all species, native species and non-native species of the old and the new relevés. All analyses were repeated after removing all spring ephemerals. We found clear temporal changes in species composition. Mean temperature indicator values increased, while mean soil moisture indicator values decreased during the 17 years. Also, decreasing per-plot richness was detected both for all species and for native species, while mean naturalness increased. After the removal of spring ephemerals, the compositional changes were less obvious although still significant. The increase in the temperature indicator values remained significant even without the spring ephemerals. However, the decrease in the moisture indicator values, the decrease in the number of all species and native species, as well as the increase in naturalness indicator values disappeared when spring ephemerals were excluded from the analyses. Our study demonstrates that between-year weather differences and long-term environmental trends both contribute to observed vegetation changes.

Climate Change Causes Food Insecurity for Developing Countries

At the moment, climate change is one of the most urgent issue worldwide. This is due to long-term climate trends and changes in social lifestyle. Another reason is the greenhouse gases produced by excessive fuel use. The intensity of weather, the decline in human health, and the devastation of natural ecosystems are all contributing factors. Intergovernmental Panel on Climate Change (IPCC) suggests controlling the avaeage temperate rise at 1.5 degrees Celsius to reduce global warming's intensity. The three major green house gasses responsible for global warming are carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) emitted from burning fossil fuels. Climate change is affecting the entire world, but poor and developing countries are suffering the most. The effects of climate change are all-encompassing. It affects agriculture, human health and crop productivity. A decrease have been observed in maize and wheat production in worst cases of climate change. This deficiency can lead to a food crisis. Poor and developing countries do not have the necessary resources to cope with the situation. They can't mould themselves quickly enough. Due to the lack of resources, they cannot discover new seeds to increase food production and are unable to improve their irrigation system. Climate change and rising temperatures contribute to the rapid growth of harmful bacteria and pathogens. These microbes have become a source of food contamination. Changes in rainfall patterns is also an important issue that affects the agricultural sector and the quality of irrigation water used. The risk of water pollution also increases. The gases produced from food waste, like greenhouse gases, are also harmful. Food waste in neighbourhoods releases methane gas, harming the environment. Commodity shortage and rising prices are major problems for people in poor and developing countries. The best strategy to deal with all these problems is to find new methods for agriculture that cope with climate change. To solve all these problems, government and international organizations should come forward and work together to help poor nations.

Determine SST and SSS distribution and tendency of variation in the northern Atlantic Ocean using k-means clustering

Sea surface temperature (SST) and sea surface salinity (SSS) are crucial parameters in oceanographic research and have important implications for understanding the Earths climate system, ecosystems, and global change. This paper will ascertain SSS and SST in the North Atlantic Ocean. Besides, K-means clustering was utilized to separate the data into several clusters once the data were installed in Matlab. According to the research results, the temperature dropped from the equator to the high northern latitudes. In contrast, salinity showed an initial increase and then a following reduction in trend. Despite rising temperatures and salinity, the region distribution of high SST and high SSS throughout the year remained mostly stable, but the area became larger. One of the regions with high salinity in North Africa has a strong connection to that is adjacent to the Mediterranean. The Earths climate system includes essential elements, including SST and SSS, which are essential to the development and control of the climate. Its better to comprehend seasonal changes in climate patterns and long-term climate trends by investigating temperature and salinity.

Implications of urbanization on the seasonal dynamics and long-term trends in the thermal climate of a city in the Himalayan foothills of India

Implications of urbanization on the seasonal dynamics and long-term trends in the thermal climate of a city in the Himalayan foothills of India

Widening geographic range of Rift Valley fever disease clusters associated with climate change in East Africa

BackgroundRecent epidemiology of Rift Valley fever (RVF) disease in Africa suggests growing frequency and expanding geographic range of small disease clusters in regions that previously had not reported the disease....

A Detailed Examination of Türkiye's Projected Precipitation and Growth Season Trends under Climate Change Condition

This study presents a comprehensive analysis of Türkiye's changing precipitation patterns and growing season dynamics in the context of global warming, utilizing the Coupled Model Intercomparison Project Phase 5 (CMIP5) and various Representative Concentration Pathway (RCP) scenarios. In light of global warming, this study provides a thorough analysis of Türkiye's evolving precipitation patterns and growing season dynamics using multiple RCP scenarios and CMIP5. The research aims to fill a crucial gap in climate research by combining historical data and future projections to assess Türkiye's precipitation path under different greenhouse gas emission scenarios. The study employs linear regression for trend analysis and uses data from the Climate Change Knowledge Portal (CCKP), with a focus on precipitation data from 1986 to 2100. According to various RCP scenarios, this study’s findings show a considerable variation in precipitation trends over the 21st century. The RCP 8.5 scenario predicts a significant decrease in precipitation, which would present difficulties for the management of water resources and agricultural productivity. In contrast, the least severe RCP 2.6 pathway shows a fairly stable pattern of precipitation. Complex seasonal hydrological responses to climate change are revealed by monthly precipitation analysis; RCP8.5 predicts an increase in the frequency of periods of drought and heavy precipitation events. The impact of these changes in precipitation on Türkiye's agricultural growing seasons was further investigated in this study. In high-emission scenarios, there was an initial tendency towards longer growing seasons, which were subsequently followed by shorter ones. This suggests that although global warming might initially result in an extended growing season, it might ultimately cause a reduction in it, particularly in situations where mitigation efforts are minimal. The need for adaptable strategies that can respond to long-term climate trends as well as seasonal variability was highlighted by this research. It draws attention to the fact that to mitigate the effects of climate variability, informed policy decisions and integrated resource management are essential. The results highlight the need for quick action to lower the risks associated with water and highlight the potential advantages of intensive mitigation efforts in stabilizing and extending growing seasons.

Hydrogen isotope records of long-chain alkanes from the monsoon boundary zone over the last 2400 years in North China

The East Asian Summer Monsoon (EASM) plays a key role in the climate dynamics of East Asia, affecting the livelihood of millions of people by contributing to agriculture and water resources. The monsoon boundary zone (MBZ) is particularly sensitive to shifts in climate patterns. However, the complexity of water vapor sources in the region makes it difficult to understand precipitation processes. To address this issue, the records of lipid biomarker abundances and leaf wax stable hydrogen isotope composition of C31–alkanes (δDwax-C31) from a 238 cm sediment core extracted in Lake Dali, located at the northern edge of the EASM, were sampled and examined by 2 cm per sample. Based on analyses of the biogenic molecular indicators(biomarkers), precipitation isotopes, and other indicators of the evolution of the climate environment, we have reconstructed precipitation change history over the past 2400 years. The results show the decrease in alkanes concentrations, along with high A.I. values in Lake Dali sediments indicate an arid trend over the past 2400 years, which have led to a decrease in basin productivity and the development of grasslands. The low temperatures greatly limit the growth of C4 vegetation in this cold region, so the long-chain alkanes in the lake sediments is mainly derived from the leaf wax lipids of C3 vegetation in the grassland. The increase of Paq and the reduction of ACL indicate that the decrease of grass vegetation inputs in the basin and the outbreak of aquatic vegetation. Notably, aquatic vegetation outbreaks may be due to reduced precipitation leading to shrinking lakes and increased nutrients in the lakes. By comparing with the biogenic indicators of Lake Dali and other regional records, the δDwax-C31 value can be used as a proxy for the regional precipitation, with a gradual decrease from about −240 ‰ to about −190 ‰, which indicates a general decreasing trend in the precipitation in the last 2400 years. Specifically, in exception of some significant fluctuations on the centennial to millennial scale, δDwax-C31 value decrease by about 40 ‰ between 1800 and 1600 cal.a BP over a period of only 200 years. As we know, the diminishing solar radiation might lead to a weaker EASM and a reduction in precipitation. Thus, the decrease in regional temperature became the main reason for the anomalous negative precipitation isotope phenomenon due to the lower precipitation from 1800 to 1600 cal.a BP in the monsoon boundary zone (MBZ, this represents only the northern boundary). This study contributes to our understanding of δDwax-C31 within the MBZ and its significance as an indicator of rainfall processes. By analyzing this molecular proxy, the researchers gain insights into the past precipitation patterns and their implications for regional climate. Importantly, the study demonstrates the value of combining biogenic molecular indicators with geochemical indicators, as it leads to improved accuracy in reconstructing regional climate conditions. The findings shed light on long-term climate trends and provide a more comprehensive understanding of the historical impacts of climate change on the EASM region. Moreover, the study's contribution to interpreting the paleoclimate record is crucial for validating climate models used to project future climate scenarios. Overall, this research enhances our knowledge of regional climate dynamics and their implications, offering valuable insights for both historical analysis and future climate projections.

Variabilidade de longo prazo, extremos e mudanças de temperatura e hidrometeorologia na Região Amazônica: Uma revisão

ABSTRACT This review discusses observed hydroclimatic trends and future climate projections for the Amazon. Warming over this region is a fact, but the magnitude of the warming trend varies depending on the datasets and length of the analyzed period. The warming trend has been more evident since 1980 and has further enhanced since 2000. Long-term trends in climate and hydrology are assessed. Various studies have reported an intensification of the hydrological cycle and a lengthening of the dry season in the southern Amazon. Changes in floods and droughts, mainly due to natural climate variability and land use change, are also assessed. For instance, in the first half of the 20th century, extreme flood events occurred every 20 years. Since 2000, there has been one severe flood every four years. During the last four decades, the northern Amazon has experienced enhanced convective activity and rainfall, in contrast to decreases in convection and rainfall in the southern Amazon. Climate change in the Amazon will have impacts at regional and global scales. Significant reductions in rainfall are projected for the eastern Amazon.

Assessing Climate Variability and Its Impacts on Selected Forest Ecosystems in Taraba State: Insights from the Mann-Kendall Trend Analysis of Rainfall and Temperature

This study examines climate variability and its effects on selected forest ecosystems in Taraba State, Nigeria, using Mann-Kendall trend analysis of rainfall and temperature data from 1991 to 2021. Climatic variables, including average monthly rainfall and mean minimum and maximum temperatures, were sourced from the Upper Benue River Basin Development Authority. The Mann-Kendall test was applied to assess trends, revealing slight upward trends in annual rainfall and maximum temperatures, and a subtle downward trend in minimum temperatures, although none were statistically significant. Hypothesis testing was conducted, with results suggesting no significant long-term trends in annual rainfall, maximum temperatures, or minimum temperatures over the study period. Specifically, for annual rainfall, the Mann-Kendall slope was 0.03 with a p-value of 0.96, indicating no significant trend. Similarly, for minimum and maximum temperatures, the Mann-Kendall slopes were -0.01 and 0.01, respectively, with p-values of 0.99, indicating no significant trends. It was recommended that strengthening monitoring efforts to track long-term climate trends accurately, utilizing advanced statistical techniques to discern meaningful patterns from natural variability among other recommendations.

Orographic amplification of El Niño teleconnections on winter precipitation across the Intermountain West of North America

A large proportion of western North America experiences regular water stress, compounded by high seasonal and interannual variability. In the Intermountain West region, the El Niño/Southern Oscillation (ENSO) is a critical control on winter precipitation, but the nature of this signal is entangled with a combination of orographic effects and long-term climate trends. This study employs a spatially distributed, nonlinear spline model to isolate ENSO impacts from these other factors using gauge-based observations starting in 1871. In contrast to previous modelling approaches, our approach uses original gauge data, without shortening the record to accommodate a common period. This enables more detailed separation of ENSO effects from the confounding influence of topography and long-term trends, whereas the longer time frame permits more robust correlation with the ENSO signal. Here we show that the complex topography of the Intermountain West exaggerates the underlying ENSO signal, producing a 2.3–5.8 times increase in the range of ENSO-induced precipitation changes along high-elevation western slopes relative to lower elevations. ENSO effects on winter precipitation can be as large as ± 100 mm at high elevations. Further, our approach reveals that the previously recognized dipolar pattern of positive (negative) association of ENSO with precipitation in the south (north) manifests as an incremental relationship in the south but as a near-binary switch in effects between El Niño and La Niña in the north. The location and extent of the strongest precipitation differences vary during the positive and negative ENSO phases within each region. The intricacies of these spatial- and elevation-based modulations of ENSO impacts are especially informative for the northern centre of this dipole, where ENSO-precipitation relationships have previously been difficult to resolve.

Single field-of-view sounder atmospheric product retrieval algorithm: establishing radiometric consistency for hyper-spectral sounder retrievals

Abstract. The single field-of-view (SFOV) sounder atmospheric product (SiFSAP) retrieval algorithm has been developed to address the need to retrieve high-spatial-resolution atmospheric data products from hyper-spectral sounders and ensure the radiometric consistency between the retrieved properties and measured spectral radiances. It is based on an integrated optimal-estimation inversion scheme that processes data from the satellite-based synergistic microwave (MW) and infrared (IR) spectral measurements from advanced sounders. The retrieval system utilizes the principal component radiative transfer model (PCRTM), which performs radiative transfer calculations monochromatically and includes accurate cloud-scattering simulations. SiFSAP includes temperature, water vapor, surface skin temperature and emissivity, cloud height and microphysical properties, and concentrations of essential trace gases for each SFOV at a native instrument spatial resolution. Error estimations are provided based on a rigorous analysis for uncertainty propagation from the top-of-atmosphere (TOA) spectral radiances to the retrieved geophysical properties. As a comparison, the spatial resolution for the traditional hyper-spectral sounder retrieval products is much coarser than the native resolution of the instruments due to the common use of the “cloud-clearing” technique to compensate for the lack of cloud-scattering simulation in the forward model. The degraded spatial resolution in traditional cloud-clearing sounder retrieval products limits their applications for capturing meteorological or climate signals at finer spatial scales. Moreover, a rigorous uncertainty propagation estimation needed for long-term climate trend studies cannot be given due to the lack of direct radiative transfer relationships between the observed TOA radiances and the retrieved geophysical properties. With the advantages of the higher spatial resolution; the simultaneous retrieval of atmospheric, cloud, and surface properties using all available spectral information; and the establishment of “radiance closure” in the sounder spectral measurements, the SiFSAP provides additional information needed for various weather and climate studies and applications using sounding observations. This paper gives an overview of the SiFSAP retrieval algorithm and assessment of SiFSAP atmospheric temperature, water vapor, clouds, and surface products derived from the Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) data.

A roadmap to estimating agricultural ammonia volatilization over Europe using satellite observations and simulation data

Abstract. Ammonia (NH3) is one of the most important gases emitted from agricultural practices. It affects air quality and the overall climate and is in turn influenced by long-term climate trends as well as by short-term fluctuations in local and regional meteorology. Previous studies have established the capability of the Infrared Atmospheric Sounding Interferometer (IASI) series of instruments, aboard the Metop satellites, to measure ammonia from space since 2007. In this study, we explore the interactions between atmospheric ammonia, land and meteorological variability, and long-term climate trends in Europe. We investigate the emission potential (Γsoil) of ammonia from the soil, which describes the soil–atmosphere ammonia exchange. Γsoil is generally calculated in-field or in laboratory experiments; here, and for the first time, we investigate a method which assesses it remotely using satellite data, reanalysis data products, and model simulations. We focus on ammonia emission potential in March 2011, which marks the start of growing season in Europe. Our results show that Γsoil ranges from 2 × 103 to 9.5 × 104 (dimensionless) in fertilized cropland, such as in the North European Plain, and is of the order of 10–102 in a non-fertilized soil (e.g., forest and grassland). These results agree with in-field measurements from the literature, suggesting that our method can be used in other seasons and regions in the world. However, some improvements are needed in the determination of mass transfer coefficient k (m s−1), which is a crucial parameter to derive Γsoil. Using a climate model, we estimate the expected increase in ammonia columns by the end of the century based on the increase in skin temperature (Tskin), under two different climate scenarios. Ammonia columns are projected to increase by up to 50 %, particularly in eastern Europe, under the SSP2-4.5 scenario and might even double (increase of 100 %) under the SSP5-8.5 scenario. The increase in skin temperature is responsible for a formation of new hotspots of ammonia in Belarus, Ukraine, Hungary, Moldova, parts of Romania, and Switzerland.

EXTREME WAVE CLIMATE OF THE NEW SOUTH WALES COAST

The NSW coast is subject to a relatively moderate wave climate that is periodically affected by large coastal storm events arising from a range of synoptic weather systems. Extreme wave events may cause coastal inundation, beach erosion, damage to property and marine structures, and risks to public safety. This paper describes the statistical analysis of the extreme directional wave climate along the NSW coast based on historical wave buoy data extending back to 1974. From the analysis, long-term trends in the extreme wave climate (storm peaks, storm duration and peak directions) are also identified. The work is largely an extension of the analysis completed by Shand et al (2011), that incorporates an additional 13 years of wave data up to 2022.

Introduction

Introduction

Multi-decadal analysis of past winter temperature, precipitation and snow cover data in the European Alps from reanalyses, climate models and observational datasets

Abstract. Assessing past distributions, variability and trends in the mountain snow cover and its first-order drivers, temperature and precipitation, is key for a wide range of studies and applications. In this study, we compare the results of various modeling systems (global and regional reanalyses ERA5, ERA5-Land, ERA5-Crocus, CERRA-Land, UERRA MESCAN-SURFEX and MTMSI and regional climate model simulations CNRM-ALADIN and CNRM-AROME driven by the global reanalysis ERA-Interim) against observational references (in situ, gridded observational datasets and satellite observations) across the European Alps from 1950 to 2020. The comparisons are performed in terms of monthly and seasonal snow cover variables (snow depth and snow cover duration) and their main atmospherical drivers (near-surface temperature and precipitation). We assess multi-annual averages of regional and subregional mean values, their interannual variations, and trends over various timescales, mainly for the winter period (from November through April). ERA5, ERA5-Crocus, MESCAN-SURFEX, CERRA-Land and MTMSI offer a satisfying description of the monthly snow evolution. However, a spatial comparison against satellite observation indicates that all datasets overestimate the snow cover duration, especially the melt-out date. CNRM-AROME and CNRM-ALADIN simulations and ERA5-Land exhibit an overestimation of the snow accumulation during winter, increasing with elevations. The analysis of the interannual variability and trends indicates that modeling snow cover dynamics remains complex across multiple scales and that none of the models evaluated here fully succeed to reproduce this compared to observational reference datasets. Indeed, while most of the evaluated model outputs perform well at representing the interannual to multi-decadal winter temperature and precipitation variability, they often fail to address the variability in the snow depth and snow cover duration. We discuss several artifacts potentially responsible for incorrect long-term climate trends in several reanalysis products (ERA5 and MESCAN-SURFEX), which we attribute primarily to the heterogeneities of the observation datasets assimilated. Nevertheless, many of the considered datasets in this study exhibit past trends in line with the current state of knowledge. Based on these datasets, over the last 50 years (1968–2017) at a regional scale, the European Alps have experienced a winter warming of 0.3 to 0.4 ∘C per decade, stronger at lower elevations, and a small reduction in winter precipitation, homogeneous with elevation. The decline in the winter snow depth and snow cover duration ranges from −7 % to −15 % per decade and from −5 to −7 d per decade, respectively, both showing a larger decrease at low and intermediate elevations. Overall, we show that no modeling strategy outperforms all others within our sample and that upstream choices (horizontal resolution, heterogeneity of the observations used for data assimilation in reanalyses, coupling between surface and atmosphere, level of complexity, configuration of the snow scheme, etc.) have great consequences on the quality of the datasets and their potential use. Despite their limitations, in many cases they can be used to characterize the main features of the mountain snow cover for a range of applications.