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The standard way to measure the air temperature (Ta) as the key variable in climate change studies is at 2m height above the surface at a fixed location (weather station). In contrast, the surface temperature (Ts) can be measured by satellites over large areas. Estimation of Ta from Ts is one potential way of overcoming shortages due to uneven or irregular distributions of weather stations. However, whether this is successful has not been assessed in high-elevation regions. This is particularly important in high-elevation regions. In this study, we estimate Ta in the high-elevation desert zone of Kilimanjaro (>4500m) using four models (five models including the benchmark model) with unique sets of inputs using five machine learning (ML) algorithms. Note that different combinations of Ta and Ts were tested as inputs to evaluate the potential of Ts as a proxy for Ta. The Root Mean Square Error (RMSE) for each model was compared with a benchmark model and ranked according to their RMSE. Similarly, models and algorithms were ranked in terms of reliability and consistency. Correspondingly, results were compared with the benchmark model. Three models out of four outperformed the benchmark model in the consistency ranking, while two out of four models outperformed the benchmark model in the reliability ranking. Therefore, ML algorithms are efficient tools for estimating Ta from Ts in this high-elevation desert environment. However, models using Ts only as inputs were not as accurate as models that used Ta from an earlier time period as one of the inputs. This highlights the amount of de-coupling between Ta and TS at high elevations, which provides a challenge for using Ts alone as a proxy for Ta in this zone.

The tracking of thunderstorms provides critical information on their frequency and behaviour for early warning, prediction and preparedness. Thunderstorm tracking has previously been constrained by the boundaries of a particular country or has focused on a particular category such as severe thunderstorms and mesoscale convective systems. However, less severe thunderstorms also pose a risk to life and property and occur more frequently and thus warrant inclusion in investigations of thunderstorm behaviour. In this paper, we present a new thunderstorm event catalogue, including all detected thunderstorms with at least three lightning flashes, derived from a tracking methodology applied to Northwest Europe within the bounds of 48° to 65°N and 15°W to 10°E between 2008 and 2018. The catalogue is based on ATDnet lightning flash data which was clustered into thunderstorms using a spatio-temporal proximity assessment lightning clustering code written in the R coding language. The thunderstorm lightning clusters enabled the production of thunderstorm behaviour statistics such as speed, direction of movement, lightning flashes per minute and duration. This revealed that in winter, thunderstorms are shorter lived and move faster than in summer as well as more typically tracking from the west rather than from the south-west. Thunderstorm behaviour characteristics were attributed to weather pattern types for the first time, providing probabilistic data that can be attributed to synoptic conditions. This can improve preparedness and early warning. Such results demonstrate how this new thunderstorm event catalogue, which includes all types and severities of thunderstorms unconstrained by political boundaries, can provide additional important information to enhance understanding of thunderstorm behaviour in the region. Our lightning clustering method may be useful for similar studies in other locales.

Air Temperature is a fundamental measure of the Earth’s climate but is only measured at fixed locations. Land surface temperature can be measured widely using satellites. To estimate air temperature (Ta) from the surface temperature (Ts) measured on the forested slopes of Kilimanjaro, four models with unique sets of inputs were tested using five machine learning algorithms. The RMSE for each model was compared with a benchmark model. Models and algorithms were ranked according to their RMSE (Root Mean Square Error) The models and algorithms reliability and consistency ranking were calculated. The best model and algorithm were determined. Novel models results were compared with the benchmark model. All models outperformed the benchmark model in the consistency ranking while three out of four models outperformed the benchmark model in the reliability ranking. Thus machine learning improves the estimation of air temperature in this forested environment.

A total lightning (cloud-ground and cloud-cloud) climatology of the UK and Ireland is presented combining three different ground-based lightning location systems over a 12-year period (2008–2019). The study area is divided into seven geographical regions using k-means clustering to identify areas with distinctive seasonal distributions of lightning flashes per km 2/year (referred to as flash density [FD]). Different regions exhibit contrasting summer thunderstorm seasons (e.g., from April to August in the southeast of England and May to July in southern England coastal regions). Summer FD peaks in July in the English Channel and southeast and midland areas of England range from 0.1 to 0.3 FD whilst the southern England coastal region sees FDs in the range 0.03–0.06 FD. Regions more prone to winter thunderstorms are identified as having northwest facing coastlines (

Elevation-dependent warming (EDW), whereby warming rates are stratified by elevation, may increase the threat to the life-supporting solid water reservoir on the Tibetan Plateau. Previous studies have debated whether EDW exists and how it is driven. Using temperatures at 133 weather stations on the Tibetan Plateau during 17 different periods generated using a 30-year sliding window over 1973–2018, this study finds that the existence of EDW varies as the period moves forward, and critically it has become more severe over time. During the early part of the record with weaker regional warming, there were limited changes in snow depth and no EDW, but as time advances and regional warming intensifies, snow depth declines significantly at higher elevations, causing development of EDW. We conclude that enhanced regional warming has caused decreases in snow depth, largely controlling the pattern of EDW on the Tibetan Plateau. This may explain contrasting conclusions on EDW from previous studies which have used data for different periods, and our findings support enhanced EDW and more severe depletion of the Tibetan Plateau solid water reserves in a warmer future.

The information on the projected climate changes over China is of great importance for preparing the nation’s societal adaptiveness to the future natural ecosystem. This study reports the surface mean temperature changes during 2014–2100 over China and its four sub-regions (Northern China, Northwestern China, Southern China, and the Tibetan Plateau) by analyzing 20 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under three Shared Socio-economic Pathway (SSP) scenarios: SSP126, SSP245 and SSP585. The multi-model ensemble mean (MMEM) of 20 CMIP6 models has cold biases over China during 1979–2014, with improved performance compared with the CMIP5 models. In contrast, the CMIP6 models simulate well in the spatial climatology with lower warming rates over China. Relative to 1986–2005, the regionally averaged surface mean temperatures from the MMEM over China under SSP126, SSP245, SSP585 scenarios are projected to increase by 1.31 °C, 1.32 °C, 1.45 °C in the near-term (2021–2040), 1.75 °C, 2.06 °C, 2.66 °C in the mid-term (2041–2060), and 1.08 °C, 2.97 °C, 5.62 °C in the long-term (2081–2100), respectively. The CMIP6 models simulate accelerated warming occurs over the Northwestern China and the Tibetan Plateau, suggesting that the arid and semi-arid regions are particularly sensitive to future climate warming. We quantify uncertainty for future projections of temperature changes over China, and the main sources of uncertainty are model and scenario uncertainty particularly for the regions with the largest cold bias. This suggests that the observational constraints on these regions will lead to significant improvements for climatic projections over China.

The Tibetan Plateau (TP), also called the “Third pole”, is sensitive to climate change due to extensive areas at high elevation presently dominated by snow and ice. In this study, observed surface temperature trends at 150 stations over the TP during 1979–2018 are analyzed and compared with surface temperatures from multiple reanalyses (NCEP1, NCEP2, ERA-Interim, MERRA, JRA55). Observed warming at the stations has a mean annual rate of 0.46 °C/decade during 1979–2018. Although all reanalyses underestimate observed temperatures (cold bias), most reproduce much of the inter-decadal variations of surface temperature shown in the observations. Absolute errors of mean surface temperature (reanalysis minus observation) are closely correlated with elevation errors, suggesting that parts of the cold bias can be interpreted by elevation errors of reanalysis. After elevation-temperature correction, about half of the cold bias is typically eliminated, more for both ERA-Interim and JRA55. Compared with the observations, corrected NCEP2 surface temperatures still have larger cold biases, and fail to capture the overall warming over the TP. Since the elevation-temperature correction fails to improve trend magnitudes even when a significant proportion of the bias has been removed, this suggests that a more sophisticated modeling of the lapse rate in each reanalysis is required to realistically model warming trends across complex topography.

Snow water equivalent (SWE) is a critical parameter for characterizing snowpack, which has a direct influence on the hydrological cycle, especially over high terrain. In this study, SWE from 18 coupled model simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) is validated against the Canadian Sea Ice and Snow Evolution Network (CanSISE) SWE. The model simulations under RCP8.5 and RCP4.5 are employed to investigate projected changes in spring/winter SWE over the Tibetan Plateau (TP) under global warming of 1.5° and 2°C. Most CMIP5 models overestimate the CanSISE SWE. A decrease in mean spring/winter SWE for both RCPs over most regions of the TP is predicted in the future, with most significant reductions over the western TP, consistent with pronounced warming in that region. This is supported by strong positive correlations between SWE and mean temperature in the future in both seasons. Compared with the preindustrial period, spring/winter SWE over the TP under global warming of 1.5° and 2°C will reduce significantly, at faster rates than over China as a whole and the Northern Hemisphere. SWE changes over the TP do not show a simple elevation dependency under global warming of 1.5° and 2°C, with maximum changes in the elevation band of 4000–4500 m. Moreover, there are also strong positive correlations between projected SWE and historical mean SWE, indicating that the initial conditions of SWE are an important parameter of future SWE under specific global warming scenarios.

We present a unique implementation of Python coding in an asynchronous object-oriented programming (OOP) framework to fully automate the process of collecting data with the George Mason University (GMU) Observatory's 0.8-meter telescope. The goal of this project is to perform automated follow-up observations for the Transiting Exoplanet Survey Satellite (TESS) mission, while still allowing for human control, monitoring, and adjustments. Prior to our implementation, the facility was computer-controlled by a human observer through a combination of webcams, TheSkyX, ASCOM Dome, MaxIm DL, and a weather station. We have automated slews and dome movements, CCD exposures, saving FITS images and metadata, initial focusing, guiding on the target, using the ambient temperature to adjust the focus as the telescope cools through the rest of the night, taking calibration images (darks and flats), and monitoring local weather data. The automated weather monitor periodically checks various weather data from multiple sources to automate the decision to close the observatory during adverse conditions. We have organized the OOP code structure in such a way that each hardware device or important higher-level process is categorized as its own object class or "module" with associated attributes and methods, with inherited common methods across modules for code reusability. To allow actions to be performed simultaneously across different modules, we implemented a multithreaded approach where each module is given its own CPU thread on which to operate concurrently with all other threads. After the initial few modules (camera, telescope, dome, data I/O) were developed, further development of the code was carried out in tandem with testing on sky on clear nights. The code, in its current state, has been tested and used for observations on 171 nights, with more planned usage and feature additions., Comment: 69 pages, 17 figures, 3 tables. Accepted for publication in the Journal of Astronomical Telescopes, Instruments, and Systems

Snow plays an essential role in regulating climate change, the hydrological cycle, and various biological processes. Passive microwave snow depth data and gridded data from the Climate Research Unit (CRU_TS4.04) are utilized in this study to investigate spatiotemporal variations of snow depth over the Tibetan Plateau (TP), with special focus on the vertical dimension. The response of snow to elevation‐dependent warming (EDW) is determined accordingly. High mountains experience more rapid warming than lower elevations. During 1980–2014, the total snow depth over the TP decreased; areas with the most significant decreasing trends are mainly concentrated in the northwestern and southwestern parts of the TP. The plateau‐wide decrease in snow depth (−0.24 cm/decade) is mainly affected by increasing temperature (0.30°C/decade). The reduction in snow depth trend intensifies as sub‐regional mean elevation increases from 3,332 m (IID2) to 5,074 m (ID1). A stronger snow depth decrease in high‐elevation sub‐regions generally corresponds to higher warming rates, which demonstrates EDW. The most pronounced correlation between snow depth decrease rate and elevation occurs in the southeastern TP, which covers the largest elevation range on the plateau (from 2,000 to 6,000 m).

Given the threats that climate change poses to solid water reservoirs on the Tibetan Plateau (TP), there is significant interest in understanding spatial patterns of climate change and their causes. Weather station observations have been extensively examined, but are scarce, resulting in an incomplete understanding of climate change across the TP, particularly in the west. Using recent (2001–2015) satellite‐based data sets (2 m air temperature, land surface temperature, albedo and snow cover), this study reveals that mean annual 2 m air temperature in the southwestern TP has decreased by 0.15°C decade−1 in contrast to overall warming (+0.18°C decade−1) on the rest of the TP. Up to 45% (74%) of the variance in the annual (spring) 2 m air temperature can be explained by simultaneous change in snow‐induced albedo in the southwestern TP. The free atmosphere column over this region and Northwest India is cooling, providing a favorable environment for the decrease in 2 m air temperature observed. Moreover, the anomalous water vapor transport into the southwestern TP is advantageous for increased snowfall and the associated decrease in 2 m air temperature. The implications of this anomalous cooling under global warming have yet to be fully considered, in particular for the futures of glaciers and snowpack over the Himalayan Mountains in the southwestern TP.

Surface pressure reflects the deep structure of the overlying atmosphere, and is recognized as an indicator of climate change. In this study, observed surface pressure at 71 stations over the Tibetan Plateau (TP) during 1979–2013 is analyzed and compared with monthly means from multiple reanalyses (NCEP1, NCEP2, ERA-Interim, MERRA and JRA55). During the studied period, surface pressure from both observations and the reanalyses increases slowly up until the mid-2000s but shows a decrease afterwards, leading to a recent fall in pressure. However, the surface pressure over the TP in spring has increased, probably explained by the thermal condition such as diabatic heating change. Observations and the multiple reanalyses are positively correlated at most locations indicating that reanalyses reproduce the interannual variation and long-term trend of observed surface pressure fairly well. Despite high inter-annual correlation, trend magnitudes over 1979–2013 are varied, with observations showing decreased pressure at most stations, but reanalyses showing increases in many cases. Compared with observations however, surface pressures from all reanalyses are underestimated usually by about 3–6%. There are significant positive correlations between surface pressure bias and elevation bias, suggesting that overestimation of elevation partially explains the surface pressure bias. A topographical correction method using the hydrostatic equation is therefore conducted and more than 90% of the biases of the reanalyses can be eliminated. Overall, this study points to the importance of better analyzing the importance of topography in the western TP to enhance understanding of reanalysis uncertainties in this region.

Research has revealed systematic changes in warming rates with elevation (EDW) in mountain regions. However, weather stations on the Tibetan plateau are mostly located at lower elevations (3000‐4000 m) and are non‐existent above 5000 m, leaving critical temperature changes unknown. Satellite LST (Land Surface Temperature) can fill this gap, but needs calibrating against in‐ situ air temperatures (Tair). We develop a novel statistical model to convert LST to Tair, developed at 87 high‐elevation Chinese Meteorological Administration stations. Tair (daily maximum/minimum temperatures) is compared with MODIS Aqua LST (1330 and 0130 local time) for 8 day composites during 2002‐2017. Typically, 80‐95% of the difference between LST and Tair (ΔT) is explained using predictors including LST diurnal range, morning heating/night‐time cooling rates, the number of cloud free days/nights and season (solar angle). LST is corrected to more closely represent Tair by subtracting modelled ΔT. We validate the model using an AWS on Zhadang Glacier (5800 m). Trend analysis at the 87 stations (2002‐2017) shows corrected LST trends to be similar to original Tair trends. To examine regional contrasts in EDW patterns, elevation profiles of corrected LST trends are derived for three ranges (Qilian Mountains, NyenchenTanglha and Himalaya). There is limited EDW in the Qilian mountains. Maximum warming is observed around 4500‐5500 m in NyenchenTanglha, consistent with snowline retreat. In common with other studies, there is stabilisation of warming at very high elevations in the Himalaya, including absolute cooling above 6000 m, but data there is compromised by frequent cloud.

Mountainous regions are globally important, in part because they support large populations and are biodiverse. They are also characterized by enhanced vulnerability to anthropogenic pressures and sensitivity to climate change. This importance necessitates the development of a global reference network of long-term environmental and socioeconomic monitoring— mountain observatories. At present, monitoring is limited and unevenly distributed across mountain regions globally. Existing thematic networks do not fully support the generation of multidisciplinary knowledge required to inform decisions, enact drivers of sustainable development, and safeguard against losses. In this paper, the Mountain Observatories Working Group, established by the Mountain Research Initiative (MRI) Science Leadership Council, identifies geographical and thematic gaps as well as recent advances in monitoring of relevant biophysical and socioeconomic variables in the mountains. We propose principles and ways of connecting existing initiatives, supporting emerging areas, and developing new mountain observatory networks regionally and, eventually, globally. Particularly in the data-poor regions, we aspire to build a community of researchers and practitioners in collaboration with the Global Network on Observations and Information in Mountain Environments, Group on Earth Observations (GEO) Mountains, a GEO Work Programme Initiative.

Numerous applications, including generating future predictions via numerical modelling, establishing appropriate policy instruments, and effectively tracking progress against them, require the multitude of complex processes and interactions operating in rapidly changing mountainous environmental systems to be well monitored and understood. At present, however, not only are environmental available data pertaining to mountains often severely limited, but interdisciplinary consensus regarding which variables should be considered absolute observation priorities remains lacking. In this context, the concept of so-called Essential Mountain Climate Variables (EMCVs) is introduced as a potential means to identify critical observation priorities and thereby ameliorate the situation. Following a brief overview of the most critical aspects of ongoing and expected future climate-driven change in various key mountain system components (i.e. the atmosphere, cryosphere, biosphere and hydrosphere), a preliminary list of corresponding potential EMCVs – ranked according to perceived importance – is proposed. Interestingly, several of these variables do not currently feature amongst the globally relevant Essential Climate Variables (ECVs) curated by GCOS, suggesting this mountain-specific approach is indeed well justified. Thereafter, both established and emerging possibilities to measure, generate, and apply EMCVs are summarised. Finally, future activities that must be undertaken if the concept is eventually to be formalized and widely applied are recommended.

Summary The numerous processes implicated in the rapid and profound climate-driven changes that are underway across the world's mountains must be well monitored, understood, and—as far as possible—accurately projected. However, not only are the available environmental data upon which such activities hinge often severely limited, but interdisciplinary consensus regarding which variables should be considered observation priorities also remains elusive. Here, the concept of Essential Mountain Climate Variables (EMCVs) is introduced as a potential means of ameliorating the situation. After a review of climate-driven environmental change in mountains, a preliminary set of corresponding EMCVs is proposed. Variables pertaining to several disciplines naturally feature prominently. In addition, several are not currently considered to hold broader global relevance, which justifies our mountain-specific approach. Established and emerging possibilities to measure, generate, and apply EMCVs are then summarized. Finally, future activities toward the concept’s formalization are recommended. Ultimately, the approach hopes to increase the utility of mountainous environmental data to both fundamental science and decision making related to environmental management, risk mitigation, and adaptation.

Extreme climate events are often of great interest because of their close relationship with meteorological and hydrological disasters. In this study, four RCM simulations driven by different GCMs (CSIRO‐Mk3.6.0, EC‐EARTH, HadGEM2‐ES, and MPI‐ESM‐MR) are used as an ensemble to project changes in extreme high and low temperatures in China as represented by selected Expert Team on Climate Change Detection indices. Each simulation is performed at 25 km resolution from 1980 to 2099 using RegCM4.4 RCM. Results show the following: (1) All four reasonably reproduce climatological maximum daily maximum temperature (TX), warm spell duration, minimum daily minimum temperature (TN), and cold spell duration, but the majority shows relatively weak ability to capture climatological warm day and cold night frequencies. (2) Although the RCM homogenizes much of the variance originally shown between driving GCM simulations, marked dissimilarities are still shown in western China across the four downscaling simulations, implying a dependency on the individual GCM‐driven RCM simulation for this region. (3) All of China shows an increase in maximum TX, warm days, warm spell duration, minimum TN, and a decrease in cold nights and cold spell duration, during the near‐term, middle‐term, and long‐term. The Tibetan Plateau and its adjacent regions show more significant changes in extreme temperature, indicating stronger sensitivity to changes in extremes. RCM downscaling projects less significant change in extreme temperatures than the driving GCM. (4) The ensemble of RCM simulations projects that area‐averaged maximum TX, warm days, warm spell duration, and minimum TN increase over China as a whole by 2.5 °C, 21.1%, 43.6 days, and 3.3 °C respectively, while cold nights and cold spell duration decrease by 6.8% and 2.0 days respectively during the long‐term under the RCP4.5 scenario relative to 1986–2005. Future episodes of extreme high temperature are likely to require more research as they become more common.

Vegetation cover exerts a strong control on land‐atmosphere interactions. To quantify the relative effects of external forcing (climate change) vs internal forcing (anthropogenic activity) on recent vegetation change over the Tibetan Plateau (TP), we apply an eco‐hydrological diagnostic framework, developed from earlier work. We compare vegetation change during 1986‐2015 based on NDVI (Normalized Difference Vegetation Index) data with changes in environmental conditions (European Centre for Medium‐Range Weather Forecasts Reanalysis 5th‐generation, ERA5). Results show that external forcing is the dominant factor behind significant vegetation change over the southeastern TP during 1986‐2015. In the area with significant vegetation changing, 60.5%/41.5% of pixels have experienced a respective wetting/drying of climate, which in turn has supported greening/browning during 1986‐2005/1996‐2015. However, during the greening/browning transition in the latter period, the proportion of internal forcing on browning increased from 5.62% to 19.4%, indicating that anthropogenic factors are playing an increasingly role on impacting vegetation change in recent decades.

Mountain and high elevation regions often show distinct climate trends in temperature and precipitation, which can contrast those of adjacent lowland regions. In the context of temperature, this phenomenon is known as elevation-dependent warming (EDW). Past temperature trends can increase with elevation, but this is not always so, and they may peak in a critical elevation band, or show more complex elevation profiles. This is controlled by a variety of mechanisms which may be responsible for the observed patterns, including snow albedo feedback, vegetation change, cloud and moisture patterns, aerosol forcing and their interactions.We here present a literature-based meta-analysis of elevation profiles in recent warming rates and, in a more general context, temperature change in mountain regions around the globe. For the recent historical period (~1960-2010) we find that when comparing like with like (i.e. high elevation regions with adjacent low elevation regions) warming rates are mostly stronger at higher elevations. Warming rates have also increased over time, with more recent decades showing stronger warming. On a global scale there is no significant difference between mean warming rates in mountains and in other areas. Thus, elevation-dependency within regions can be masked by differences in geographical location in global meta-analyses. Although there have been far fewer studies on vertical profiles of precipitation changes, we extend our meta-analysis to consider this parameter, where information is available.In addition to the meta-analysis, we compare past temperature and precipitation changes in mountain and lowland regions using global gridded observation-based and reanalysis datasets (e.g. CRU, ERA5, NCEP2) and global climate model simulations (CMIP5). Despite the uncertainties of these datasets (e.g. inhomogeneous underlying station coverage and related interpolation errors, biases, coarse spatial resolution), they allow us to compare different mountain regions globally with the same level of accuracy. There are only a few mountain areas that show distinct differences when their temperature trends are compared with lowland surroundings, but patterns vary by dataset and region. We also explore different extensions of adjacent lowlands, which may influence the quantification of differences in temperature and precipitation trends at high and low elevation.This historical assessment is completed by an analysis of model projections (CMIP5) for studying the expected future evolution of climate change in mountains and contrasts to adjacent lowlands

Surface state data derived from spaceborne microwave sensors with suitable temporal sampling are to date only available in low spatial resolution (25—50 km). Current approaches do not adequately resolve spatial heterogeneity in landscape-scale freeze–thaw processes. We propose to derive a frozen fraction instead of binary freeze–thaw information. This introduces the possibility to monitor the gradual freezing and thawing of complex landscapes. Frozen fractions were retrieved from Advanced Scatterometer (ASCAT, C-band) backscatter on a 12.5-km grid for three sites in noncontinuous permafrost areas in northern Finland and the Austrian Alps. To calibrate the retrieval approach, frozen fractions based on Sentinel-1 synthetic aperture radar (SAR, C-band) were derived for all sites and compared to ASCAT backscatter. We found strong relationships for ASCAT backscatter with Sentinel-1 derived frozen fractions (Pearson correlations of −0.85 to −0.96) for the sites in northern Finland and less strong relationships for the Alpine site (Pearson correlations −0.579 and −0.611, including and excluding forested areas). Applying the derived linear relationships, predicted frozen fractions using ASCAT backscatter values showed root mean square error (RMSE) values between 7.26% and 16.87% when compared with Sentinel-1 frozen fractions. The validation of the Sentinel-1 derived freeze–thaw classifications showed high accuracy when compared to in situ near-surface soil temperature (84.7%–94%). Results are discussed with regard to landscape type, differences between spring and autumn, and gridding. This article serves as a proof of concept, showcasing the possibility to derive frozen fraction from coarse spatial resolution scatterometer time series to improve the representation of spatial heterogeneity in landscape-scale surface state.

Thunderstorm and lightning climatological research is conducted with a view to increasing knowledge about the distribution of thunderstorm-related hazards and to gain an understanding of environmental factors increasing or decreasing their frequency. There are three main methodologies used in the construction of thunderstorm climatologies: thunderstorm frequency, thunderstorm tracking or lightning flash density. These approaches utilise a wide variety of underpinning datasets and employ many different methods ranging from correlations with potential influencing factors and mapping the distribution of thunderstorm day frequencies to tracking individual thunderstorm cell movements. Meanwhile, lightning flash density climatologies are produced using lightning data alone, and these studies therefore follow a more standardised format. Whilst lightning flash density climatologies are primarily concerned with the occurrence of cloud-to-ground lightning, the occurrence of any form of lightning confirms the presence of a thunderstorm and can therefore be used in the compilation of a thunderstorm climatology. Regardless of approach, the choice of analysis method is heavily influenced by the coverage and quality (detection efficiency and location accuracy) of available datasets as well as by the controlling factors which are under investigation. The issues investigated must also reflect the needs of the end-use application to ensure that the results can be used effectively to reduce exposure to hazard, improve forecasting or enhance climatological understanding.

Climate change has been driving terrestrial water storage variations in the high mountains of Asia in the recent decades. This study is based on Gravity Recovery and Climate Experiment (GRACE) data to analyse spatial and temporal variations in terrestrial water storage (TWS) across the Tibetan Plateau (TP) from April 2002 to December 2016. Regional averaged TWS anomaly has increased by 0.20 mm/month (p p

The recession of the current ice fields near the summit of Kilimanjaro has been shown to be controlled largely by climate. Despite detailed research into summit climate, including mass and energy balance modelling, understanding Kilimanjaro as a whole has been limited by lack of observations on the mountain slopes. Analysis of hourly air temperatures, relative humidities and vapour pressures from 22 weather stations installed between September 2012 and 2015 across the mountain from south-west to north-east are presented for the first time. Moisture is shown to move upslope on both sides of the mountain during the afternoon. The north-east slope is less humid and warmer on average than the south-west slope. Temperature differences between slopes reach 4–5 °C during the morning in the rainforest zone (2000–2500 m) and on the crater wall (5000–5550 m). Slope differences are broadly similar in size to local contrasts within the south-west slope caused by the rainforest (at 1890 m) and ice fields (at 5800 m). Although both slopes show similar moisture regimes, there are contrasts in moisture content particularly in the zone just above the current rainforest limit (3000–3200 m). This decoupling extends up to 5000 m in the afternoon because the upslope transport of moisture is both weaker and delayed on the NE slope. At night the upper slopes are highly correlated implying that free-air moisture is the dominant source. Very moist events at crater level tend to be associated with widespread moistening across the whole mountain. These results can be used both to argue for and against the role of deforestation being an important influence on summit climate and therefore ice field recession.

Understanding changes in temperature extremes in a warmer climate is of great importance for society and for ecosystem functioning due to potentially severe impacts of such extreme events. In this study, temperature extremes defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) from CMIP5 models are evaluated by comparison with homogenized gridded observations at 0.5° resolution across the Tibetan Plateau (TP) for 1961–2005. Using statistical metrics, the models have been ranked in terms of their ability to reproduce similar patterns in extreme events to the observations. Four CMIP5 models have good performance (BNU-ESM, HadGEM2-ES, CCSM4, CanESM2) and are used to create an optimal model ensemble (OME). Most temperature extreme indices in the OME are closer to the observations than in an ensemble using all models. Best performance is given for threshold temperature indices and extreme/absolute value indices are slightly less well modelled. Thus the choice of model in the OME seems to have more influences on temperature extreme indices based on thresholds. There is no significant correlation between elevation and modelled bias of the extreme indices for both the optimal/all model ensembles. Furthermore, the minimum temperature (Tmin) is significanlty positive correlations with the longwave radiation and cloud variables, respectively, but the Tmax fails to find the correlation with the shortwave radiation and cloud variables. This suggests that the cloud–radiation differences influence the Tmin in each CMIP5 model to some extent, and result in the temperature extremes based on Tmin.

Located in the northeast of the Iberian Peninsula, Cerdanya experiences intense cold-air pool (CAP) episodes in which a decoupling between the valley atmosphere and the regional circulation develops, especially in winter. A network of 40 temperature sensors was deployed from 2012 to 2015 along seven elevational transects to collect hourly data of temperature and humidity, enabling the measurement of CAP characteristics and behaviour. Empirical orthogonal functions (EOFs) of minimum temperature anomalies were examined to study the spatial distribution of CAP. Days were classified as CAP or no-CAP using EOF analysis. The temporal variation of EOF analysis scores for the first component (PC1) indicates days prone to CAP. A synoptic analysis using principal component analysis (factorization), cluster analysis (classification) and a discriminant analysis (reclassification) was performed successively. Results showed CAP days to be associated with high pressure and light winds, while no-CAP days were associated with surface northerly or north-easterly flow behind frontal passages.

The Cerdanya valley in NE Spain experiences intense cold-air ponding (CAP) which decouples the valley atmosphere from the regional circulation, especially in winter. This makes air temperature prediction a challenge. A network of 40 temperature sensors was installed in 2012 along seven elevational transects to collect hourly temperatures throughout the cold pool, enabling measurement of the detailed cold-pool structure for the first time. Sensors were also installed in upper Conflent valley to the north-east for comparison, where previous research has shown that there is reduced CAP. Sensor data is validated against AWS observations at two locations. Through calculation of hourly lapse rates in various elevation bands for two years we show frequent inversions developing up to 1450 m, and sometimes extending much higher than this, concentrating in winter. Case studies of two intense episodes in December 2012 and January 2013 show that model simulations, despite being able to simulate broad mechanisms of CAP formation, underestimate the amount of cooling. This is due to over-enthusiastic simulation of a low-level jet in the first case, and mountain waves in the second. Solving these model problems has important consequences for future ability to predict episodes of extreme low temperatures and associated hazards (frost, fog) in Cerdanya and mountain valleys elsewhere.

Satellite data, characterized by extensive regional coverage and relatively high spatial resolution, have a distinct advantage for examining elevation‐dependent warming (EDW) across rugged topography in mountain regions where there are sparse in situ observations. Based on recent (2001–2015) comprehensive satellite‐based datasets (surface air temperature, land surface temperature, snow cover, daytime and nighttime cloud), this study finds that annual mean surface air temperature warming rates show rapid decrease above 4500 m despite increasing from 2000 to 4500 m. This indicates a reversal in EDW at the highest elevations on the Tibetan Plateau (TP), which is somehow different from the EDW derived from short‐term land surface temperature presented in an early study. The decrease of warming rate above 4500 m coincides with the elevation at which most of the current solid water resources reside. Thus, their decline may be less rapid than previously thought. Trends in nighttime cloud and snow cover are both correlated with patterns of EDW on the TP, but the leading factor varies on an annual and seasonal basis. Comparing datasets provides important evidence for understanding EDW and its controlling mechanisms in an extreme high‐elevation context.

Cloud plays an important role in regulating radiation and energy exchange, and the hydrological cycle. In this study, the variability of summer vertical integral cloud water content (VCWC) (sum of cloud liquid water content and cloud ice water content) from ERA-Interim reanalysis is investigated over China from 1979 to 2016. We divide the country into regions dominated by monsoonal and non-monsoonal influences, and the Tibetan Plateau. Relationships between summer VCWC and surface mean temperature, precipitation and precipitable water (PW) are investigated by singular value decomposition. Summer VCWC decreases from southeast to northwest with the largest values in the southwestern China. Summer VCWC has increased in the non-monsoon and Tibetan Plateau sub-regions with rates of 1.04 and 3.39 g/m2/decade respectively, which corresponds to an increase of PW, precipitation and surface mean temperature. Summer VCWC has decreased by −2.71 g/m2/decade in the monsoon sub-region, related to decreased precipitation and PW as well as increased surface mean temperature. Temperature rises (decreases) will strengthen (weaken) the atmospheric circulation in favor of increased (decreased) summer VCWC in the non-monsoon/Tibetan Plateau sub-region, but weaken (strengthen) the climate systems in the monsoon sub-region. This explains the contrasting correlations between temperature and summer VCWC in the different regions, suggesting summer VCWC in China is moderated by atmospheric circulation through combined influences of surface mean temperature and PW.

Warming amplification over the Arctic Pole (AP hereafter) and Third Pole (Tibetan Plateau, TP hereafter) can trigger a series of climate responses and have global consequences. Arctic amplification (AA) and Tibetan amplification (TA) are the most significant characteristics of climate change patterns over the two Poles. In this study, trends, mechanisms and consequences of both AA and TA are compared. Based on ERA5 reanalysis during 1979–2020, both AP and TP have undergone significant warming with an annual rate of 0.72 °C/decade and 0.34 °C/decade respectively, which exceeds the rates for the Northern Hemisphere (0.29 °C/decade) and the global means (0.19 °C/decade) over the same period. Based on 22 Coupled Model Intercomparison Project Phase 6 models, AA over the AP is warming at a rate almost four times than the global means and twice as fast over the TP. Although both AA and TA are projected to continue in the future, currently there is no consensus on the dominant mechanisms for AA or TA over the two Poles. Proposed mechanisms of AA can be divided into two types: local climate factors (sea ice-albedo feedback, Planck feedback, temperature gradient feedback, cloud feedback, and water vapor feedback); and poleward heat and moisture transport from lower latitudes (atmospheric circulation effect, ocean circulation effect, and modulation of Pacific and Atlantic SST). Consequences of AA include decline of sea ice cover, retreat of the Greenland ice sheet, permafrost degradation, accelerated disturbances in marine and terrestrial ecosystems, and influences on extreme climate events at lower latitudes. Anthropogenic greenhouse gas emission, snow/ice-albedo feedback, cloud-radiation interactions, water vapor and radiative flux feedbacks, local forcing and feedback processes, land use changes and reduction in total ozone, are generally considered to be the main mechanisms causing TA. TA has caused significant change within the atmosphere and cryosphere over the TP and its surroundings, such as changes in climate extremes, snow cover, the retreat of glaciers, and permafrost degradation. Similarities and differences of warming amplifications over the two Poles are proposed, and the relative contribution of each mechanism to the warming amplifications and how the specific consequences may compare over the two Poles remain unclear and under continuing investigation.

Water vapour in the upper troposphere is the dominant greenhouse gas and provides a large feedback mechanism for amplifying climate change. However, lack of high elevation observations has limited our understating of its long term variability. In this study, the homogenized time series of the upper tropospheric water vapour (UTWV) brightness temperature (BT) over the Tibetan Plateau (TP) during 1979–2012, developed through inter-satellite calibration of high-resolution infrared radiation sounder (HIRS) channel 12 clear-sky measurements, is examined. The HIRS UTWV BT above the TP is increasing (drying), with an annual rate of 0.6 K decade−1 (p < 0.05), with the largest increases above the western TP. On a seasonal basis, HIRS UTWV BT shows only slight changes during 1979–1995, but rapidly increases from 1996 to 2012. Thus, drying is concentrated during recent decades, consistent with reduced water vapour flux and relative humidity. The time series of HIRS UTWV BT is correlated with a Mongolian geopotential height index at 400 hPa and the South Asian summer monsoon index derived from National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis. Thus atmospheric circulation anomalies are a control of changes in UTWV. In summer, increasing horizontal divergence at 400 hPa has suppressed the upward motion of air from the lower to the upper troposphere, reducing conveyance of sensible heat to the upper troposphere and contributing to the drying observed.

The Tibetan Plateau (TP) is also known as the “Third Pole”. Elevation dependent warming (EDW), the phenomenon that warming rate changes systematically with elevation, is of high significance for realistically estimating warming rates and their impacts over the TP. This review summarizes studies of characteristics and mechanisms behind EDW over the TP based on multiple observed datasets and model simulations. Spatial expression of EDW and explanatory mechanisms are still largely unknown because of the lack of suitable data over the TP. The focus is on the roles played by known mechanisms such as snow/ice-albedo feedback, cloud feedback, atmospheric water vapor feedback, aerosol feedback, and changes in land use, ozone and vegetation. At present, there is limited consensus on the main mechanisms controlling EDW. Finally, new perspectives and unresolved issues are outlined, including quantification of EDW in climate model simulations, explanation of the long-term EDW reconstructed from proxies, interaction between the Asian summer monsoon and EDW, importance of EDW for future environmental changes and water resources, and current gaps in understanding EDW over extremely high elevations. Further progress requires a more comprehensive ground observation network, greater use of remote sensing data, and high-resolution climate modeling with better representation of both atmospheric and cryospheric processes.

Global warming may increase the frequency of climate extremes, but systematic examinations at different temperature thresholds are unknown over the Tibetan Plateau (TP). Changes in surface temperature and precipitation extreme indices derived from a multi-model ensemble mean (MMEM) of the Coupled Model Inter-comparison Project Phase 5 (CMIP5) models are examined under global warming of 1.5 °C (RCP2.6), 2 °C (RCP4.5) and 3 °C (RCP8.5) above pre-industrial levels. The TP amplification of future temperature and precipitation changes is evident for all three scenarios, with greater trend magnitudes in extreme indices than those for the whole China, regions between 25°N and 40°N, Northern Hemisphere (land only), Northern Hemisphere and the global mean. The TP amplification is also projected to intensify in each scenario, resulting in faster changes in intensity, duration and frequency of climate extremes. There appears to be greater difference for precipitation-based indices between 2 °C and 3 °C than for temperature, and the differences between 1.5 °C and 2 °C are less dramatic. Overall changes in climate extremes at 2 °C are greater than at 1.5 °C, but differences are less discernible between 3 °C and 2 °C. The Kolmogorov-Smirnov test between simulated and scaled temperature distributions shows that accelerated warming over the TP from 1.5 °C to 2 °C follows a broadly linear response, but the nonlinearity occurs between 2 °C and 3 °C. This suggests that the rate of warming might make a large difference to the future TP amplification at different thresholds.

Variation in snow cover over the Tibetan Plateau (TP) is a key component of climate change and variability, and critical for many hydrological and biological processes. This review first summarizes recent observed changes of snow cover over the TP, including the relationship between the TP snow cover and that over Eurasia as a whole; recent climatology and spatial patterns; inter-annual variability and trends; as well as projected changes in snow cover. Second, we discuss the physical causes and factors contributing to variations in snow cover over the TP, including precipitation, temperature, and synoptic forcing such as the Arctic Oscillation and the westerly jet, and large scale ocean-atmosphere oscillations such as the El Nino–Southern Oscillation (ESNO), the Indian Ocean dipole, and the southern annular mode. Third, linkage between snow cover over the TP and subsequent weather and climate systems are discussed, including the East and South Asian Summer Monsoons, and their subsequent precipitation regimes. Finally, new perspectives and unresolved issues are outlined, including changes in extreme events and related disasters (e.g., avalanches), the use of novel datasets, the possible elevation dependency in snow cover change, expected snow cover changes under 1.5 °C and 2 °C global warming, the physical mechanisms modulating climate extremes in the region, and the linkage between snow cover variation and atmospheric pollution. Despite a large body of work over the TP, we argue that there is a need for more comparative studies using multiple snow datasets, and snow cover information over the western TP and during summer would benefit from more attention in the future.

The Western Mediterranean, in common with other subtropical regions, has certain characteristics that make a purely surface-based synoptic classification inadequate. Sometimes, despite the surface synoptic situation not being conducive to precipitation, a depression aloft can trigger convection and produce strong rains and flash floods. To account for this, we develop a new synoptic classification centred on Catalonia (north-east of the Iberian Peninsula) which combines upper air and surface pressure situations. The existing Jenkinson-Collison classification of the surface pressure field is combined with a new simplified upper air classification based on 500 hPa geopotential height. Despite a reduced number of types at 500 hPa, the combination of both surface and 500 hPa gives many potential combinations. A reduction in the overall number was achieved by adjusting the combined classification to replicate the manual classification of Martin-Vide. The final classification is reduced to 13 synoptic patterns each of which provides a clear and well-defined daily synoptic situation. It is used to characterize days with/without cold air pool (CAP) events, derived from a network of 50 temperature sensors distributed across Cerdanya in Eastern Pyrenees. We show our new classification to be better at discriminating strong CAP days from no-CAP days than classifications based on surface pressure alone. Thus there is potential for the classification to be used by forecasters as a first step for predicting temperature inversions and weather in the Eastern Pyrenees.

The Arctic has experienced the most rapid warming in the world in recent decades. Complex topography combines with low solar elevation to create distinct microclimates in Arctic regions, and for many applications such as ecological response and cryospheric change it is critical to obtain reliable temperature trends at the local scale. Due to lack of weather stations, satellite land surface temperature (LST) is increasingly important as a proxy for air temperature (Tair), but how accurately it can represent microclimates is unknown. For the first time, we compare 10 years (2007–2017) of Tair recorded over a dense network of 65 sites (~25 km2) around Kevo Subarctic Research Station in Finland with equivalent moderate resolution imaging spectroradiometer (MODIS) LST at 1 km resolution from MOD11A2/MYD11A2 8‐day products. We assess whether LST can pick up the extreme local gradients in air temperature (>20 °C/km) caused by cold air drainage. Although there is a high correspondence between LST and Tair anomalies on a synoptic timescale, small‐scale patterns in Tair (lapse rates, aspect contrasts) are not picked up by LST. Temperature gradients in Tair become positive (temperature inversions) in winter, and at night, but LST gradients show almost the reverse. Aspect contrasts in Tair peak in spring and autumn during the day, but LST shows biggest differences in the evening. Land cover has a large influence on LST, tundra heating up/cooling down more than birch or pine forest. The conflation between land cover and elevation means that differential land‐cover response dominates the elevational LST signal. Contrasts between Tair and LST cannot be explained by the number of stations measuring Tair in a pixel, elevation error, timing differences or the frequency of cloud cover within the 8‐day composite. Important features of the Arctic climate such as microscale cold air drainage are thus potentially obscured by land‐cover effects.

Mountain Research and Development, 38 (3), ISSN:0276-4741, ISSN:1994-7151

Monthly surface relative humidity (RH) data for 71 stations in the Tibetan Plateau (TP) provided by the National Meteorological Information Center/China Meteorological Administration are compared with corresponding grid points from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR hereafter) reanalysis. Mean climatologies, interannual variabilities, and trends calculated by the Mann-Kendal method are analyzed during 1961–2013. The annual regional long-term mean surface RH is 55.3%, with a clear maximum in summer (66.4%) and minimum in winter (44.9%). Compared with observations, NCEP/NCAR overestimates RH in all seasons, especially in spring (18.2%) and winter (17.8%). Mean annual regional surface RH has decreased by −0.23% decade−1 and even more rapidly in summer (−0.60% decade−1) and autumn (−0.39% decade−1). The reduction of surface RH is also captured by the NCEP/NCAR reanalysis at the surface, 400, 500, and 600 hPa. A particularly sharp reduction of RH since the mid-1990s is evident in both data sets, in line with rapid warming over the plateau. This suggests that moisture supply to the plateau from the Arabian Sea and the Bay of Bengal is limited and that variability and trends of surface RH over the TP are not uniquely driven by the Clausius-Clapeyron relationship.

Snowfall is a critical part of the hydrological system in high-altitude regions and strongly impacted by climate change. This study uses a threshold temperature method to estimate spatial and temporal variations of snowfall at 71 stations across the Tibetan Plateau from 1960 to 2014. Regional air temperature and precipitation have increased by 0.039°C/yr, and 1.43 mm/yr, respectively. While warming rates have been fairly uniform across the plateau, spatial variations in snowfall trends are large, with decreases in the eastern and northeastern areas but increases at higher elevations in the center and west. Region-wide snowfall increased during 1961–1990 and 1971–2000 but decreased in 1981–2010 and 1991–2014. Wintertime snowfall has increased, but summer snowfall has decreased. These divergent trends can be explained because maximum snowfall is recorded at temperatures between 1 and 2°C. Above/below this threshold snowfall usually decreases/increases with increased warming. Although maximum snowfall temperature is a key factor to understand future snowfall changes, concurrent influences such as changing moisture sources and atmospheric circulation patterns require further research.

Heat waves (HWs) can have disastrous impacts on human activities and natural systems, and are one of the current foci of scientific research, particularly in the context of global warming. However, there is no standard definition of a HW, which makes assessment of temporal trends a challenge. In this study, based on daily mean, maximum and minimum temperature, and relative humidity datasets from China Meteorological Administration, the patterns, trends and variations of HW in China during 1961–2014 are investigated. Sixteen previously published HW indices (HIs) are calculated, which are divided into two types using relative and absolute threshold temperatures, respectively. During 1961–2014, both relative and absolute threshold HIs show the highest number of HW in Jianghua and South China, geographically consistent with the climate characteristics of China. The majority of HIs shows negative/positive trends of HW days before/after 1990 over the whole of China, but especially in Jianghua and South China, which reflects rapid warming since 1990. There are significant correlations among different HIs in the same type (both absolute and relative), but correlations are weak between relative and absolute threshold HIs. Because relative and absolute HIs show contrasting trends, the choice of HI is therefore critical for future analysis

Based on intersatellite-calibrated high-resolution infrared radiation sounder (HIRS) upper tropospheric water vapour (UTWV) brightness temperatures, the width of the tropical belt is defined as the distance between the latitudes at which maximum HIRS UTWV brightness temperatures are recorded in both hemispheres. Poleward expansion of the tropical belt is evident during 1979–2013 on an annual basis, with an average global magnitude of 1.57° latitude per decade. Most rapid widening is evident in the west Pacific, in agreement with the strengthening of the Walker Circulation over time. This research suggests that the HIRS UTWV brightness temperatures are a good proxy to investigate the expansion of the tropical circulation in low latitudes.

The heights of the atmospheric freezing level have increased over most glacierized areas of High Asia during 1971–2010, especially in the Altai Mountains, the eastern Tianshan Mountains, and the northeastern margins of the Tibetan Plateau. The systematic increase of freezing level heights (FLHs) is evidenced from both radiosonde and National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis data. Eleven glaciers with long-term observations are selected in typical high-elevation mountain ranges to examine the relationship between changes in FLHs and cryospheric response. Long-term trends in glacier mass balance and equilibrium line altitude (ELA) show significant correlations with changes in FLHs. A rise of 10 m in summer FLH causes mass balance of reference glaciers in High Asia to decrease by between 7 and 38 mm (water equivalent) and ELA to increase by between 3.1 and 9.8 m, respectively, depending on location. Both relationships are statistically significant (p

Climate warming on the Tibetan Plateau (TP) potentially influences many climate parameters other than temperature including wind speed, cloudiness and precipitation. Temporal trends of surface wind speed at 71 stations above 2000 m above sea level in the TP are examined during 1980–2005. To uncover causes of observed trends in wind speed, relationships with surface temperature, a TP index and sunshine duration are also analysed. The TP index is calculated as the accumulated 500 hPa geopotential height above 5000 m over the region of 30°N–40°N, 75°E–105°E from NCEP/NCAR reanalysis. The annual mean wind speed patterns during 1980–2005 are similar to those in different seasons, with higher wind speeds in the northern and western parts of the TP. Highest mean wind speeds occur in spring and lowest in autumn. During 1980–2005, annual and seasonal mean wind speeds show statistically decreasing trends at most stations. The mean trend magnitude for annual mean wind speed is −0.24 m s−1 decade−1, with the maximum decline in spring (−0.29 m s−1 decade−1) and minimum in autumn (−0.19 m s−1 decade−1). Both annually and in different seasons, wind speed is significantly negatively correlated with mean temperature, minimum temperature, maximum temperature, and the TP index, but significantly positively correlated with sunshine duration. Wind speed trends fail to show a simple elevation dependency but speeds are positively correlated with meridional surface temperature/pressure gradients. Warming in the TP may weaken the latitudinal gradients of both regional temperature and surface pressure, thus altering the regional atmospheric circulation and accounting in part for the observed decline of wind speed.