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Surface air temperature (t2m) data are essential for understanding climate dynamics and assessing the impacts of climate change. Reanalysis products, which combine observations with retrospective short‐range weather forecasts, can provide consistent and comprehensive datasets. ERA5 represents the state‐of‐the‐art in global reanalyses and supplies initial and boundary conditions for higher‐resolution regional reanalyses designed to capture finer‐scale atmospheric processes. However, these products require validation, especially in complex terrains like Italy. This study analyses the capability of different reanalysis products to reproduce t2m fields over Italy during the 1991–2020 period. The analyses encompass ERA5, ERA5‐Land, the MEteorological Reanalysis Italian DAtaset (MERIDA), the Copernicus European Regional ReAnalysis (CERRA), and the Very High‐Resolution dynamical downscaling of ERA5 REAnalysis over ITaly (VHR‐REA_IT). The validation we conduct pertains to both the spatial distribution of 30‐year seasonal and annual normal values and the daily anomaly records. Each reanalysis is compared with observations projected onto its respective grid positions and elevations, overcoming any model bias resulting from an inaccurate representation of the real topography. Key findings reveal that normal values in reanalyses closely match observational values, with deviations typically below 1°C. However, in the Alps, winter cold biases sometimes exceed 3°C and show a relation with the elevation. Similar deviations occur in the Apennines, Sicily, and Sardinia. Conversely, VHR‐REA_IT shows a warm bias in the Po Valley up to 3°C in summer. Daily anomalies generally exhibit lower errors, with MERIDA showing the highest accuracy and correlation with observational fields. Moreover, when aggregating daily anomalies to annual time scales, the errors in the anomaly records rapidly decrease to <0.5°C. The results of this study empower reanalysis users across multiple sectors to gain a more profound insight into the capabilities and constraints of different reanalysis products. The knowledge and the characterization of the reanalyses t2m bias against observations can indeed be crucial when incorporating these products into their research and practical applications. [ABSTRACT FROM AUTHOR]

Separating existing historical precipitation data into solid and liquid precipitation remains a challenge in the study of climate change, extreme precipitation, and hydrological modelling. Based on historical daily air temperature and precipitation data, as well as visual observations of precipitation phase (weather phenomena records) in China mainland, this study proposed a snow‐day direct definition method (SDDM) to determine the threshold air temperature (TAT) of rainfall and snowfall, and analysed the spatial pattern and its influential factors. The main findings include: (1) the TAT based on the SDDM varied from −1.2 to 6.3°C, with a mean value of 2.8°C for the entire study region; (2) TAT was generally higher and more variable in the low‐latitude areas, and the Qinghai‐Tibet Plateau was characterized by an abnormally high average TAT of 5.2°C, almost twice as large as that of the eastern monsoon region; (3) TAT exhibited a significant positive correlation with altitude and negative correlation with precipitation and relative humidity. The results presented in this paper have potential application for studies of large‐scale snowfall climatology and climate change, weather forecasting techniques, and hydrological model parameterization in areas with complex and diverse geographical and climatic conditions. [ABSTRACT FROM AUTHOR]

Extreme climate events are societally harmful and have increased in frequency and intensity in recent decades. Indices based on temperature and precipitation are a valuable way to quantify climate extremes. Certain indices are defined relative to percentiles, which are dependent on a climatological baseline period. In this study, indices computed using temperature and precipitation from the Modern Era Retrospective Analysis for Research and Applications, Version 2 are calculated using percentiles from three baseline periods: 1981–2010, 1991–2020 and 1981–2020. Updating the baseline period from 1981 to 2010 to 1991–2020 leads to significant changes in the quantification of temperature and precipitation extremes over the United States over 1980–2021. Using the later baseline period indicates more cold extremes, fewer warm extremes, and fewer but more intense precipitation extremes throughout the US, with regional variation. Changing the baseline period can mislead the public and decision makers, potentially undermining the appropriate response to climate‐related health risks. Plain Language Summary: Indices computed using 2‐m air temperature and precipitation are used to represent extreme climate events such as heat waves, cold waves, heavy precipitation, and drought. Some indices are defined relative to percentile‐based thresholds, which are computed using a baseline climatology period. The baseline climatology is typically a thirty‐year period and is updated every ten years. This study examines how updating the baseline climatology period from 1981–2010 to 1991–2020 affects the quantification of climate extremes in the United States over 1980–2021. In general, since the 1991–2020 period is warmer than 1981–2010 throughout the United States, there are fewer warm extremes detected and more cold extremes detected when it is used as the baseline. The differences are most notable in the southwest and northeast United States. The changes in the precipitation indices vary throughout the country, but in certain parts of the southern and central United States, updating the baseline period leads to the detection of fewer but heavier extreme precipitation events. It is important to communicate the choice of baseline climatology period to prevent misinterpretation of the extreme climate indices and the comparison of different studies. Key Points: Updating the baseline period from 1981–2010 to 1991–2020 leads to significant changes in percentile‐based extreme climate indices in the USTemperature indices show generally increased cold extremes and decreased warm extremes across the US when the baseline period is updatedFor precipitation indices, the later baseline period indicates fewer but more intense extreme events in the south and central US [ABSTRACT FROM AUTHOR]

Snowfall and snow accumulation play a crucial role in shaping ecosystems and human activities in the Alpine region. This resource is under threat as a consequence of the visible effects of global warming, and, therefore, it appears urgent to understand how snowfall trends have changed in time and space. In this context, we recovered data from over a hundred snowfall (HN) time series covering the period 1980–2020 over the mountain region of Trentino‐South Tyrol in the northeastern Italian Alps and analysed them to understand snowfall climatology in the region, recent trends and their dependence on elevation and timing of the season. Negative, although not always statistically significant, trends were found in the lowest elevation range (0–1,000 m a.s.l.) over the whole winter season, while some positive and even significant trends were found from January to March above 2,000 m a.s.l. The intermediate elevation range (1,000–2,000 m a.s.l.) exhibits a strong variability with no clear trend. Negative and statistically significant trends were found in April for all elevations. An attribution analysis was performed using precipitation (P), mean air temperature (TMEAN), and large‐scale synoptic descriptors, such as the North Atlantic Oscillation (NAO) and the Arctic Oscillation (AO) indices. The analysis shows that, overall, P is the driver that best explains the snowfall trends, but, for low elevations, especially during mid‐winter, TMEAN is more relevant. Low elevations are facing a clear decrease in HN due to a significant increase in mean temperatures, while high elevations during mid‐winter display a slight increase in HN, associated with a general increase in precipitation. NAO and AO indices exhibit no significant correlations with HN, except at the lowest elevations and at the beginning of the season. [ABSTRACT FROM AUTHOR]

Driven by ERA‐Interim reanalysis, two climate simulations are conducted from April to August for 20 years (1998–2017) during the East Asia summer monsoon with Model for Prediction Across Scale‐Atmosphere (MPAS‐A). The global variable‐resolution (MPAS‐VR) and regional (MPAS‐RCM) configurations are adopted under the same model framework with 92–25 km mesh refinement centered over Mainland China (MPAS‐A experiments hereafter). MPAS‐VR allows flexible interactions across scales. Constrained by the "reanalysis‐based" lateral boundary conditions (LBCs), MPAS‐RCM could well capture the evolution of large‐scale fields and becomes a proper reference for evaluating MPAS‐VR. The evaluations are conducted for precipitation, near‐surface air temperature, and circulation features against observed climate. Two MPAS‐A experiments present comparable results for the mean climatology and interannual variability except that MPAS‐VR presents wet biases along the tropical ocean and underestimation in the Yangtze–Huaihe River basin. Compared with MPAS‐VR, MPAS‐RCM presents better daily variations and rain belts' evolution, which demonstrates the crucial effects of the LBCs for the limited‐area model. The simulated circulation and moisture flux can explain most of the deviations. However, two MPAS‐A experiments show consistent results for the extreme precipitation frequency and the extreme indices over Mainland China. With the same dynamic core and model physics, those climate features are similarly resolved by the two simulations. Thus, the 92–25 km MPAS‐VR simulation is demonstrated as a promising tool for regional climate hindcast. Plain Language Summary: In this work, we conduct model experiments by two configurations during the East Asia summer monsoon using Model for Prediction Across Scale‐Atmosphere with refinement mesh centered over Mainland China. The global variable‐resolution configuration (MPAS‐VR hereafter) allows flexible interaction across global features to regional climate details. Constrained by the reanalysis (which blends simulated and observed results) around the boundary, the regional limited‐area configuration with the truncated mesh (MPAS‐RCM hereafter) is used for identifying the model's biases mostly related to its dynamic core and parameterization, which is a proper reference for MPAS‐VR. We evaluate the model's performances against the observed climate for the near‐surface air temperature and precipitation. Using MPAS‐RCM as the criterion, MPAS‐VR well captures the mean climatology, interannual variability, and extreme events, which demonstrates itself as a promising tool for regional climate details. The diverged circulation in the two experiments can explain most of the simulated biases. Extreme precipitation frequency and extreme indices over Mainland China suggest similar deficiencies in the two simulations due to a common model framework. Key Points: Dynamical downscaling is conducted by Model for Prediction Across Scale for Atmosphere (MPAS‐A) with a global variable‐resolution (MPAS‐VR) and truncated regional (MPAS‐RCM) configurationsTwo MPAS‐A experiments present comparable results with simulated circulation explaining most of their divergencesExtreme frequency and indices over Mainland China suggest similar deficiencies in the two simulations due to a common model framework [ABSTRACT FROM AUTHOR]

Despite the availability of global and continental climate datasets and climate change information, locally relevant quantification of historic trends in climate variables is still lacking in developing countries, especially at local scales. This is particularly true in the Department of Arequipa, Peru. An arid region with a booming population, substantial mining activities, and large irrigated agriculture, which is highly susceptible to climate change. This study aims to evaluate climate trends from 1988 to 2017 in the Arequipa Department and provide information that can facilitate stakeholders' adaptation to the rapid‐changing climate. The daily precipitation (Prec), and maximum (Tmax) and minimum (Tmin) daily air temperature data used in this study came from the Servicio Nacional de Meteorología e Hidrología del Perú (SENAMHI) and the National Ocean and Atmospheric Administration's (NOAA) Global Summary of the Day (GSOD). Data passed through a quality checking process for removal of implausible data, data gap filling, and inhomogeneity detection. The Mann–Kendall test, at a significance level of 0.10, was used to determine trends and the Theil–Sen slope (Sen's slope) was used to estimate the magnitude of the change. Sen's slope was also calculated spatially, using the gridded Arequipa Climate Maps (ACM) dataset. Results indicate that precipitation seasonality has been increasing, as the observed increase in annual precipitation is happening mostly in the rainy season (December–March) and the start and end of the rainy season are delayed. Positive temperature trends were dominant in the whole region. Tmin is increasing more than Tmax, especially at higher altitudes. Exceptions to increasing temperatures were found in areas influenced by irrigation projects that underwent great expansion. The effect of increasing temperature on glaciers was evaluated by mapping the change in the area with average annual temperature below 0°C between the decades of 1988–1997 and 2008–2017, which reduced by 73.2%, with small areas disappearing and larger contiguous areas shrinking. [ABSTRACT FROM AUTHOR]

Convective sea‐effect snowfall, in the form of snowbands, is observed over the northern Baltic Sea annually. Quasi‐stationary snowbands may last up to several days over the sea and, depending on the wind direction, move towards the coast. This study provides climatology of spatial and temporal occurrence of snowbands in Finland for a 48‐year period (1973–2020). We used a set of detection criteria together with ERA5 reanalysis at off‐shore areas and FMIClimGrid gridded observational data for on‐shore areas to find the days favouring snowband formation. Only those snowband days (SBD) when snow reached the Finnish coastal mainland were considered. The total annual number of SBDs in Finland varied from 6 to 40 with an average of 16. SBDs were detected most frequently over the Gulf of Bothnia near the western coast of the country. The largest increase in snow depth (SDI) during an SBD (67 cm/day) also took place on the western coast, although the long‐term mean of SDI (3–5 cm/day) was highest over the southern coast. Throughout the country, November and December showed the highest frequency of SBDs. However, between the periods 1973–1996 and 1997–2020, the seasonal cycle of SBDs shifted 1 month forward from late autumn to mid‐winter as the decrease in the number of SBDs during December as well as the increase during January and February were statistically significant in Finland. In northern Baltic Sea, long‐term increases in monthly means of sea surface temperature (SST) and air temperature at the atmospheric level of 850 hPa (T850) were in line with the decadal changes in the occurrence of SBDs. The increasing trend in SST favours the formation of snowbands but in late autumn the probability for snowband formation decreased because even larger increases in T850 resulted in diminishing differences between SST and T850. [ABSTRACT FROM AUTHOR]

Forty years (1980–2019) of reanalysis data were used to investigate climatology and trends of heat stress in the Caribbean region. Represented via the Universal Thermal Climate Index (UTCI), a multivariate thermophysiological‐relevant parameter, the highest heat stress is found to be most frequent and geographically widespread during the rainy season (August, September, and October). UTCI trends indicate an increase of more than 0.2°C·decade−1, with southern Florida and the Lesser Antilles witnessing the greatest upward rates (0.45°C·decade−1). Correlations with climate variables known to induce heat stress reveal that the increase in heat stress is driven by increases in air temperature and radiation, and decreases in wind speed. Conditions of heat danger, as depicted by the heat index (HI), have intensified since 1980 (+1.2°C) and are found to occur simultaneously to conditions of heat stress suggesting a synergy between heat illnesses and physiological responses to heat. This work also includes the analysis of the record‐breaking 2020 heat season during which the UTCI and HI achieved above average values, indicating that local populations most likely experienced heat stress and danger higher than the ones they are used to. These findings confirm the gradual intensification of heat stress in the Caribbean and aim to provide a guidance for heat‐related policies in the region. [ABSTRACT FROM AUTHOR]

Climate is a powerful driver of agricultural and natural systems, and spatial climate datasets are currently in great demand. This is especially true in the Arequipa Department of Peru, a region with low seasonal precipitation, remarkable topographic variability, and significant water demand in a highly managed water system. This paper presents the Arequipa Climate Maps (ACM) datasets, a high resolution (1 km) spatial 30‐year (1988–2017) climate dataset for the Arequipa Region, in Peru. Four interpolation methods, and combinations of those methods, were tested to produce 30 years of daily precipitation, maximum and minimum air temperature: Ordinary Kriging (OK), Thin Plate Splines (TPS), Regression Kriging (RK), and Regression Thin Plate Splines (RTPS). The mixed method RTPS‐TPS and RTPS using locally fitted polynomial and potential regressions were found to best represent the spatial variability of precipitation and daily extreme temperatures, respectively, and helped compensate the bias resulting from the lack of weather stations at higher elevations. These methods were then selected to create the ACM dataset, which contains climate maps of 30‐year annual and monthly climate normals (ACM–Normals) and 30 years of annual, monthly, and daily climate maps (ACM–YMD). In addition, insights on weather station gap filling in mountainous areas and bias corrections for avoidance of anomalous precipitation and to assure consistency between annual, monthly and daily data are presented, together with discussion about the quality and limitations of the dataset, and its comparison with other datasets. [ABSTRACT FROM AUTHOR]

The rise in the Earth's global average surface temperature and the increase of extreme weather events have been the focus of scientific discussion in the last decades. Extreme heat combined with other environmental extremes, like high concentrations of air pollution, may induce health problems—especially in socioeconomically vulnerable populations. Spring in South America requires particular attention due to its association with hot, dry weather and air pollution in most parts of the continent. This paper intends to better comprehend the behaviour of heatwaves during the austral winter–spring transition. We propose identifying the spatial coverage, frequency and intensity of heatwaves in homogeneous areas of maximum air temperature near the surface (Tmax). We employed cluster analyses during the period between July and October for 41 years (1979–2019) through the ERA5 reanalysis. Homogeneous Tmax areas in South America were defined by cluster analyses that indicated three homogeneous Tmax regions, as follows: a larger area of the continent including the tropical region (Area 1), eastern and southeastern South America (Area 2) and southernmost and steep areas in the Andes (Area 3). The heatwave events identified via ERA5 reanalysis were classified according to their intensity (intense, moderate and weak events). Spatial frequency and trend analyses were also performed regarding the intensity and persistence of heatwave episodes. These methods allowed the identification of the behavioural aspect of heatwaves spanning the last four decades. The Mann–Kendall statistical test (MK) was applied in order to analyse the heatwave trend with a statistical significance level of 5%. A total of 191 heatwave episodes were identified. Of this total, 47.12, 35.60 and 17.28% of episodes occurred in Areas 2, 3 and 1, respectively. The hotter area extending from northeast to southwest in central South America stood out by its largest frequencies of intense heatwave episodes. Across the continent there was a significant increase in the intensity and persistence of heatwaves over the period of 1979 through to 2019. [ABSTRACT FROM AUTHOR]

A key aim of climate monitoring is to place the current conditions of climate variables, such as surface air temperature and precipitation, in their historical context. In Europe, a leading provider of this information is the Copernicus Climate Change Service (C3S), which is implemented by the European Centre for Medium‐Range Weather Forecasts (ECMWF) on behalf of the European Commission. To undertake this activity in close‐to‐real time, C3S predominantly uses the ECMWF ERA5 reanalysis, and ERA5 precipitation is widely used as a proxy for observed precipitation for hydrological monitoring. However, for there to be confidence in the ERA5 data, it is essential to evaluate its ability to capture observed precipitation. In this study, we therefore evaluate ERA5 precipitation against observations globally to inform C3S monitoring activities and the broader climate science community. Using 24 hr precipitation observations at 5,637 stations from 2001 to 2020, results show that the smallest ERA5 random errors occur in the winter Extratropics and the largest ERA5 errors are in the Tropics. The errors grow in the summer Extratropics and the errors in the Tropics move with the intertropical convergence zone. These findings are mirrored in the stable equitable error in probability space (SEEPS) score, with the SEEPS signifying that ERA5 is more able to discriminate between different precipitation events in the Extratropics. In general, an ERA5 wet bias is also found. The assessment of annual maximum 1 day (RX1) precipitation accumulations and four extreme events shows that ERA5 cannot model the highest observed precipitation totals but that it can generally capture their locations and patterns. Furthermore, an evaluation of monthly precipitation corroborated that ERA5 is more skilful in the Extratropics. These results imply that users can have confidence in ERA5 precipitation in extratropical regions, and therefore it is recommended that ERA5 is mostly used for extratropical precipitation monitoring. [ABSTRACT FROM AUTHOR]

Heavy snowfall events of possible sea‐effect origin, occurring on the southern Baltic Sea coast, are described in this study. The analysed region is not very snowy, with an average of 40 days with snow cover in the winter season and a mean maximum snow depth of 8–12 cm. Twenty‐five snowfall events were selected using the threshold of 20 cm/2 days (i.e., 20 cm of snow depth increase during 2 days), and the synoptic climatology, as well as triggering factors of their occurrence, were defined. The bipolar pattern of sea level pressure with a high‐pressure over Scandinavia and a low‐pressure system southward of it was identified as favourable for sea‐effect snowfall on the southern Baltic Sea coast. This pressure field, causing northeastern circulation in the lower troposphere, brings cold air masses over the relatively warm sea basin, and the negative anomalies of air temperature at 850 hPa geopotential level, amounting to −9°C, encompass Scandinavia, as well as central and northeastern Europe. This makes the temperature difference between the air at the 850 hPa geopotential height and the sea surface exceeding 15°C. This large temperature difference establishes favourable conditions for convection in cold air masses, which become unstable while crossing over the warm sea. The formed clouds give snowfall in the coastal areas. In the further part of the study, three sea‐effect snowfall cases were chosen for detailed analysis, including atmospheric sounding, radar and satellite images, air parcel backward trajectories, and synoptic maps. They reveal high‐pressure wedges associated with low air temperature over Scandinavia and northern airflow over the Baltic Sea. The wind direction and the temperature of air masses are key components in the occurrence of sea‐effect snowfall, and global warming conditions seem to reduce the frequency of snow events over the Polish Baltic Sea coast. [ABSTRACT FROM AUTHOR]

Responses of quasi‐6‐day waves (Q6DWs) to the extreme 2019 Antarctic stratospheric sudden warming (SSW) have been widely reported in the ionosphere, while the excitation mechanism of the global Q6DWs is still unclear in the mesosphere and lower thermosphere (MLT) region. According to the Aura satellite measurements, the amplitudes of the Q6DWs in the upper MLT region are found to be higher than their climatological activities from 2005 to 2020 at mid‐latitudes in both Northern and Southern Hemispheres. Combined with the Modern‐Era Retrospective Analysis for Research and Applications, version 2 data, the trigger mechanisms of the global enhanced Q6DWs are investigated by tracing their sources and propagations. Our analysis indicates that the equatorward propagation induced by the SSW is the main reason for the enhanced Q6DWs in the upper MLT at mid‐latitudes in the Southern Hemisphere, and the main source of the Q6DWs in the Northern Hemisphere is the seasonal variability. Besides, interhemispheric propagations also contribute to the amplification of Q6DWs during the 2019 Antarctic SSW. Key Points: The sources and propagations of the global extremely excited quasi‐6‐day waves (Q6DWs) in September 2019 are revealedThe Antarctic warming and climatology are respectively responsible for the enhanced Q6DWs in the Southern and Northern HemispheresInterhemispheric propagations have additional contributions to the global enhanced Q6DWs [ABSTRACT FROM AUTHOR]

Freezing level height (FLH) is the altitude of a location where the air temperature is 0°C. The FLH denotes the approximate position for water phase transitions from liquid (or vapour) to solid but not vapour to liquid. Based on temperature and height data on the ground surface to heights with an atmospheric pressure of 400 hPa, the monthly FLH at 118 radiosonde stations located in China are derived for the 1958–2017 period. The monthly and annual climatology during 1981–2010 and the seasonal, intra‐annual and decadal variabilities of the FLH during 1958–2017, along with its long‐term trends, were quantitatively evaluated at the regional scale and for individual stations. FLH climatology is generally related to season, zone and topography. The annual FLH anomalies decline initially, followed by a rise, with synchronous changes in the temperature in the lower and middle troposphere; the anomalies in south‐eastern China are partly associated with El Niño–Southern Oscillation events. The increasing trends of FLH are closely associated with tropospheric warming and show evident regional and seasonal differences. Stronger increasing trends are observed in northern and western China in autumn or winter (particularly in recent decades). The changes in FLH have implications on long‐term trends in equilibrium line altitude (ELA) of glaciers in Tianshan Mountains, snow cover extent in melt season and hail frequency in China especially in high‐elevations. During recent decades, the evolution of the FLH and major composition in the cryosphere has accelerated as a response to tropospheric warming. The study on FLH change can provide valuable information for the evaluation of glaciers, snow cover extent and hail frequency in China from a new perspective; which is helpful for understanding regional hydrological processes and climate changes better. Further application of FLH in cryosphere monitoring can improve water management and flood forecasting in headwater regions in China. [ABSTRACT FROM AUTHOR]

The often neglected Guy Stewart Callendar (a steam engineer, for whom climate science was a hobby) was the first to bring together measurements of carbon dioxide concentration with how much the world had warmed from the late-nineteenth century in two key papers in I QJRMS i (in 1938 and 1961). David gave a brief history of climate science and how this has impacted UK Government Policy over the period since the late 1980s. Joanna also noted those who were sceptical of the importance of carbon dioxide (Knut Angstrom who erroneously stated that the carbon dioxide absorption bands were swamped by water vapour, and later ideas in the 1950s that carbon dioxide was being taken out of the atmosphere by the oceans much more than in reality). [Extracted from the article]

Land surface schemes (LSSs) in Earth System Models simulate how vegetation regulates land‐atmosphere fluxes of heat, water, carbon, and momentum. Despite advances in the spatial resolution of regional climate modeling, significant land cover changes occured at sub‐grid scale still not properly treated. Here, we investigate the response of evapotranspiration, representing the fluxes of heat and water, to deforestation in Southeast Asian Massif by employing three LSSs (Noah mosaic, Noah‐MP and Community Land Model (CLM)) with different approaches in representing sub‐grid variability, implemented in the Weather Research and Forecasting model. Two experiments, with and without satellite‐observed deforestation, were performed for each scheme. Results show that the simulations are highly sensitive to the subgrid‐scale approaches embedded in the LSSs. Compared with the observed historical climate, CLM outperforms other schemes and Noah mosaic shows the largest bias. However, if we target the simulation of the climate impacts of land cover change, the Noah mosaic scheme taking the sub‐grid approach can better capture the response of evapotranspiration to deforestation. The NoahMP and CLM schemes underestimate the evapotranspiration response in the grid cells where the dominant land cover type has not changed, but overestimate the response in those grid cells with dominant type changed, which is a characteristic of dominant‐grid approach. To improve our understanding of climate impacts induced by fine‐scale land cover change, future efforts to better represent subgrid‐scale variability and land‐atmosphere flux exchange in climate models are desirable. Key Points: Employing land surface schemes (LSSs) that take a sub‐grid approach is critical in simulating the climate impacts of fine‐scale land cover changeLSSs that take a dominant approach can largely misestimate the climate response to land cover changeThe theoretical tiling approach of Community Land Model was not active when it was coupled into the Weather Research and Forecasting model [ABSTRACT FROM AUTHOR]

Climate models are critical tools to study earth system processes and are often further downscaled to provide refined‐resolution data sets for regional water‐energy impact analyses. In this study, the Variable‐Resolution Community Earth System Model (VR‐CESM), a new dynamical downscaling method, is employed to generate 1/8° simulations for China and the western United States over 1970–2006. Precipitation, temperature, snowpack, radiation, and wind speed are compared with two 1° global climate models, regional climate model ensemble, and reference data sets to evaluate their fidelity. Simulated precipitation skill is dependent upon the reference data sets used, a slight (<+10%) and spatially consistent wet bias in China during summer and the western United States during winter. Precipitation bias in VR‐CESM is the smallest among models and demonstrates the added value provided by resolution refinement. However, temperature biases are generally greater than precipitation due to more complicated interactions among processes. VR‐CESM simulates +2°C warm bias in the low‐elevation regions in China but −2°C cold bias in mountains. However, DJF cold bias (−2.23°C to−3.37°C) and JJA warm bias (+1.13°C to +1.87°C) exist in the western United States. The modeling skills of precipitation and temperature at various topographies are similar in the two evaluated regions. The cold bias was likely affected by downward shortwave radiation deficit (−15%) and slow bias of surface wind speeds (−16% to −29%). Despite the wet and cold bias, snow water equivalent and snow cover are underestimated in mountains. We conclude that VR‐CESM outperforms other models evaluated and provides the best estimation of downscaled climate model data for water‐energy studies. Key Points: Variable‐Resolution Community Earth System Model (VR‐CESM) simulations at 14 km refined resolution are evaluated for future use in water‐energy nexus modeling and impacts studiesIn comparison with reference data sets, VR‐CESM outperforms other models in precipitation simulation but has significant bias in temperatureRefining grid resolution improves model skill in simulating precipitation, temperature, and snowpack in both China and the western United States [ABSTRACT FROM AUTHOR]

Land surface diurnal temperature cycle (LSDTC) is an important element in climate system. Diurnal temperature cycle (DTC) models are potential for deriving diurnal variation of land surface temperature (LST) over extended regions from satellite observations. To estimate the LSDTC at high spatial resolution from clear‐sky Moderate Resolution Imaging Spectrometer (MODIS) data, this study reduced the parameter number of a five‐parameter DTC model to only four by fitting the time of maximum temperature as a function of normalized differential vegetation index (NDVI), elevation and latitude. Using the ground‐based LSTs, the proposed four‐parameter DTC (FPD) model was evaluated for 48 and 4 available LST observations per day. The results showed that the root mean square errors (RMSEs) were 2.08 K, 0.88 K, 1.14 K, 1.31 K, 2.27 K, and 1.12 K for grassland, cropland, swamp meadow, desert steppe, desert shrub, and sand desert, respectively, for 48 observations. Comparatively, RMSEs for four observations corresponding to the MODIS overpass times were increased by less than 1 K, except for swamp meadow with an elevation greater than 3,500 m that the error was increased by 1.5 K. Applying the FPD model to MODIS LST product showed that the model could capture the variation of LSTs during a day, and the accuracy of the modeled LSTs depended on the accuracy of MODIS LST product. Generally, the proposed FPD model is simple and easy to be applied to MODIS four LSTs per day under clear skies to acquire LSDTC over extended regions with high spatial resolution. Key Points: The time of maximum temperature in the five‐parameter diurnal temperature cycle model is parameterizedA four‐parameter diurnal temperature cycle model is developed, and error of the predicted temperature versus the field data is less than 3 K [ABSTRACT FROM AUTHOR]

We quantified the relationships between seasonal air temperatures and ice phenology for a 0.54 km2 boreal lake in Northwestern Ontario, Canada using a 50‐year time series (1970–2019). Significant temporal trends in the duration of ice cover (−4.0 days decade−1) occurred over the study period and both ice‐on and ice‐off dates were highly predictable from seasonal air temperatures. While temporal trends in ice‐off dates were not significant, ice‐on dates trended later by 2.2 days decade−1, and both ice‐off dates and the duration of ice‐cover became increasingly variable over the study period. For three consecutive winter seasons, we also evaluated regional variations of ice‐phenology and snow and ice‐thickness from 9 to 30 boreal lakes across a lake size gradient (0.02–26 km2) using ground based and satellite observations. Regional variation of ice‐on dates (30–45 days), ice‐off dates (10–21 days), and total duration of ice‐cover (22–38 days) between lakes displayed significant nonlinear relationships to lake size across all years. Regional variation of ice‐off dates was structured according to lake size, and appeared driven by relationships between lake size, snow thickness, and ice thickness. Multiple linear regression and generalized additive modeling approaches to predict ice‐on and ice‐off dates at the regional scale by integrating the effects of air temperature and lake size had similar performance, explaining >90% of the variance of observed. Plain Language Summary: In this study, we used a continuous 50‐year data set of air temperature and ice phenology for a single boreal lake, and a combination of ground‐based and satellite observations to examine regional variation and drivers of ice phenology across 9–30 lakes near the IISD Experimental Lakes Area, Canada. Results indicated that ice‐cover duration declined by approximately 4 days per decade, and ice‐on and ice‐off dates were highly predictable based on seasonal air temperatures. Our regional analysis indicated that the duration of ice‐cover varied by approximately 1 month even for lakes in close proximity to one another and experienced similar weather conditions. This spatial variation of ice phenology was structured along a gradient of lake size. These results provide strong evidence of regional changes in climate since the 1970s and quantify the importance of air temperature and lake size in driving the duration of ice cover at a regional scale and can be incorporated with climate projections to estimate future ice‐conditions across lakes of varying sizes. Key Points: Long‐term declines in ice‐cover duration (4 days/decade) were highly predictable from seasonal air temperaturesRegional variation of ice‐cover duration was approximately 1 month and structured by factors associated with lake sizeLake‐size effects on ice‐off dates appeared driven by variations in snow and ice thickness [ABSTRACT FROM AUTHOR]

High‐resolution regional climate simulations were conducted for the past 30 years (1987–2016), focusing on the Euro‐Mediterranean region. The numerical simulations were used for computing the Canadian Fire Weather Index, for deriving the climatology of fire weather in the study area and investigating the presence of long‐term trends, with particular emphasis on extremes during the May to September period. Results suggest that the Euro‐Mediterranean fire weather follows a zonal pattern, characterized by adverse conditions occurring most frequently in the southern parts. Air temperature and relative humidity are the key drivers of fire weather in the study area, with precipitation and wind exerting less influence. Based on the conducted spatial trend analysis, extreme fire weather conditions have become more prevalent in the Iberian Peninsula and eastern Balkans, whereas declining trends occur in the Southeast Mediterranean basin. For both cases, the trends in fire weather extremes appear to coincide with trends in the input meteorological variables. Overall, the results of this study provide a new insight on the recent climatology and trends of fire weather in the Euro‐Mediterranean. Our dataset is publicly available on the Zenodo platform (DOI: 10.5281/zenodo.3713531). [ABSTRACT FROM AUTHOR]

Correlations of the North Atlantic Oscillation (NAO) with climate variables in continental Europe are strong but weak over Britain. This article presents evidence that interannual variations of British climate are explained more successfully by variations of the Icelandic Low (IL), the northern component of the NAO. The IL changes continuously in intensity as well as position, and these variations are not represented in the NAO. Monthly values of intensity and latitude and longitude positions for the IL are estimated from gridded sea level pressure data. These are used to analyse their impact on interannual variations of winter precipitation and temperature over Britain for the period 1981–2014. It is found that the primary influence on the interannual variations of winter precipitation over Britain are the pressure and the longitude position of the IL. When the IL is deeper or situated east of its mean location, there is above normal precipitation over Britain. The primary influence on air temperature is the meridional migration of the IL; it is warmer when the IL is situated north of its mean position. The impact of the IL on precipitation and air temperature are evaluated also for Scotland, North England and South England separately. [ABSTRACT FROM AUTHOR]

Mercury is found in Arctic marine mammals that are important in the diet of northern Indigenous peoples. The objectives of the present long‐term study, spanning a 45‐yr period, were to 1) investigate the temporal trends of total mercury (THg; muscle and liver) and selenium (Se; liver) in ringed seals (Pusa hispida) from different regions of the Canadian Arctic; and 2) examine possible relationships with age, diet, and climate parameters such as air temperature, precipitation, climatic indices, and ice‐coverage. Ringed seals were collected by hunters in northern communities in the Beaufort Sea, Central Arctic, Eastern Baffin Island, Hudson Bay, and Ungava/Nunatsiavut regions (Canada) between 1972 and 2017. Mercury levels did not change through time in seal liver, but THg levels in muscle decreased in seals from Hudson Bay (−0.91%/yr) and Ungava/Nunatsiavut (−1.30%/yr). Carbon stable isotope values in seal muscle decreased significantly through time in 4 regions. Selenium‐to‐THg ratios were found to be >1 for all years and regions. Variation partitioning analyses across regions indicated that THg trends in seals were mostly explained by age (7.3–21.7%), climate parameters (3.5–12.5%), and diet (up to 9%); climate indices (i.e., Arctic and North Atlantic Oscillations, Pacific/North American pattern) explained the majority of the climate portion. The THg levels had a positive relationship with Arctic Oscillation for multiple regions. Associations of THg with air temperature, total precipitation, and sea‐ice coverage, as well as with North Atlantic Oscillation and Pacific/North American pattern were found to vary with tissue type and geographical area. Environ Toxicol Chem 2020;39:2462–2474. © 2020 Her Majesty the Queen in Right of Canada. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. Reproduced with the permission of the Minister of Fisheries and Oceans Canada. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP), known as earth's "Third Pole," influences regional and even global weather and climate systems through its mechanical and thermal‐dynamical forcing. Near‐surface (2 m) air temperature (Ta) and surface (skin) temperature (Ts) are two crucial parameters of land–atmosphere interactions and climate change. Their difference (ΔT = Ts − Ta) determines the heating source over the TP that drives the Asian summer monsoon. This study focuses on climatology, inter‐annual variability, and long‐term trend of ΔT over the TP in the last four decades (1979–2018), based on four latest reanalysis datasets including ERA‐Interim, ERA5, MERRA2, and JRA55, along with observational data. We show that ΔT‐based different datasets display fairly different climatology in terms of seasonality, spatial distribution, and long‐term trend. ΔT exhibits a clear seasonality with negative value in winter and positive ones in summer despite different strengths and timings presented by the reanalyses. Along with global warming, all reanalyses except JRA55 exhibit obvious downwards trends of ΔT in a spatially non‐uniform way. The median ΔT among the four reanalyses features uniform decreases in all seasons, with the most distinct area on the northern TP, as well as the largest and least decreases in autumn and spring, respectively. Further analysis shows that the differences in ΔT are most likely associated with discrepancies in radiation forcing, snow cover, wind speed, and boundary layer height within the reanalyses. The present findings highlight the difficulty for the state‐of‐the‐art reanalyses to represent the climate change over the TP and point to possible factors behind the deficiencies identified. [ABSTRACT FROM AUTHOR]

This paper quantifies the potential benefit of limiting the global warming to 1.5°C above the preindustrial level on the high‐latitude climate of the Southern Hemisphere compared to 2.0°C and above. A set of Coupled Model Intercomparison Project Phase 5 global climate model experiments is used in order to determine the mean projected changes in surface air temperature, sea level pressure, surface winds and sea ice distribution over and around Antarctica. Alongside, changes in selected variables related to extremes (such as the annual number of extremely cold mornings and extremely cold days) are also quantified as a function of the Representative Concentration Pathway (RCP) considered reaching the given warming threshold. Physical mechanisms for the projected variations in sea ice are proposed, including surface air temperature, changes in low‐level air temperature advection and meridional winds. Results show that there would be significant differences in the atmospheric and sea ice variable changes depending on the warming threshold and RCP considered, and that indeed keeping the global warming under 1.5°C could have a noticeable impact particularly on the distribution of sea ice over the Southern Ocean. [ABSTRACT FROM AUTHOR]

The Svalbard archipelago in the Arctic North Atlantic is experiencing rapid changes in the surface climate and sea ice distribution, with impacts for the coupled climate system and the local society. This study utilizes observational data of surface air temperature (SAT) from 1980–2016 across the whole Svalbard archipelago, and sea ice extent (SIE) from operational sea ice charts to conduct a systematic assessment of climatologies, long‐term changes and regional differences. The proximity to the warm water mass of the West Spitsbergen Current drives a markedly warmer climate in the western coastal regions compared to northern and eastern Svalbard. This imprints on the SIE climatology in southern and western Svalbard, where the annual maxima of 50–60% area ice coverage are substantially less than 80–90% in the northern and eastern fjords. Owing to winter‐amplified warming, the local climate is shifting towards more maritime conditions, and SIE reductions of between 5 and 20% per decade in particular regions are found, such that a number of fjords in the west have been virtually ice‐free in recent winters. The strongest decline comes along with SAT forcing and occurs over the most recent 1–2 decades in all regions; while in the 1980s and 1990s, enhanced northerly winds and sea ice drift can explain 30–50% of SIE variability around northern Svalbard, where they had correspondingly lead to a SIE increase. With an ongoing warming it is suggested that both the meteorological and cryospheric conditions in eastern Svalbard will become increasingly similar to what is already observed in the western fjords, namely suppressed typical Arctic climate conditions. [ABSTRACT FROM AUTHOR]

As trusted voices, health leaders can lead a public conversation to convey the links between climate change, heatwaves, and heat-related deaths, which are more tangible than for many other climate, environment and health issues. Keywords: Climate change; Global warming; Health planning; Socioeconomic factors EN Climate change Global warming Health planning Socioeconomic factors 393 395 3 11/02/21 20211101 NES 211101 Recent climate-related heat events in the Northern Hemisphere are part of a global trend with immediate and long term implications for Australia's health system We have a climate on steroids. Fourth, extreme weather events and the most recent IPCC report have further strengthened arguments for a national climate and health strategy, including an evidence-based heat adaptation strategy and enhanced surveillance. [Extracted from the article]

Two skilful 2‐month‐leading hybrid downscaling prediction schemes in October for winter surface air temperature (SAT) over China are proposed in this paper. The schemes are based on the year‐to‐year increment approach and the coupled climate patterns between the winter SAT over China and its predictors. Observed North Pacific sea surface temperature (SST) from the preceding July to September, Arctic sea ice concentration (SIC) in the preceding August and winter sea level pressure (SLP) over pan Eurasia from version 2 of the Climate Forecast System (CFSv2) are selected as the predictors based on the fundamental physics. Individual‐predictor schemes (IP‐schemes), that is, SLP‐scheme, SST‐scheme, SIC‐scheme, indicate that these predictors exhibit prediction skills in different regions. Multi‐predictor scheme I (MP‐schemeI) is developed by combining three predictors. However, MP‐schemeI shows limited skill in predicting SAT over Northeast China (NECTA), due to the limited skill of CFSv2 over the extratropics. Thus, MP‐schemeII is established, in which a hybrid downscaling model for NECTA is constructed. These two MP‐schemes have comparable prediction skill over China, but MP‐schemeII outperforms MP‐schemeI over NEC. The temporal (spatial) anomaly correlation coefficient (ACC) increases from 0.23 (0.15) in MP‐schemeI to 0.36 (0.21) in MP‐schemeII, and the ratio of the same sign of anomalous years (Anomalous‐RSS) improves from 39.1% to 56.5% over NEC. For the winter SAT over China, the MP‐schemes greatly enhance the prediction compared with CFSv2 outputs and the IP‐schemes. The temporal (spatial) ACC increases from 0.29 (0.05) in CFSv2 to 0.52 (0.29) in the MP‐schemes, and the station‐average root‐mean‐square error decreases by about 48.0% compared with CFSv2. Moreover, the RSS (Anomalous‐RSS) of the winter SAT over China is 55.9% (47.4%) in CFSv2 and 64.7% (63.2%) in the MP‐schemes. This indicates that the MP‐schemes perform better in predicting anomalous winters. [ABSTRACT FROM AUTHOR]

The Eurasian boreal forest ecosystem is a strong sink in the global carbon cycle. Satellite observations show significant change in the ecosystem in recent decades, specifically an increase in vegetation productivity since 1982 and a hiatus after 1997. Previous studies attributed this enhanced vegetation growth (also known as greening) to air temperature increases and a longer growing season, and the recent greening hiatus as a result of a warmer and drier climate. However, using satellite data, we found observational evidence that increases in summer peak growth dominated the overall greening trend and that a wetting and cooling climate during the peak growing season was the primary cause of the hiatus. Plain Language Summary: Increases in vegetation growth or productivity is referred to as "greening." Scientists have found that the vegetation growth or productivity in Eurasian boreal forests increased from 1982. However, in 1997 that greening stagnated. Previous studies suggested that increases in air temperature prolonged the growing season, which may have been the driving force of the greening. Additionally, previous studies pointed to enhanced drought stress by warmer and drier summers as the reason for the greening hiatus. However, in revisiting this hypothesis we found evidence of an alternative explanation. First, we found that increases in summer peak growth, rather than a longer growing season, dominated the overall greening trends. Second, we found that wetting and cooling summer climates instigated the hiatus. Our findings suggest a more complex mechanism behind the shift in climate and vegetation growth in Eurasian boreal forests around 1997. This mechanism deserves further study. Key Points: Eurasian boreal forests experienced a greening trend from 1982 before stagnating in 1997Increases in summer peak growth, rather than prolongation of the growing season, dominated greening trends in earlier decadesA wetter and cooler, rather than a warmer and drier summer climate, was the primary cause of the recent greening hiatus [ABSTRACT FROM AUTHOR]

The quality of snow simulations in land surface models (LSMs) largely depends on the accuracy of the atmospheric forcing data, especially precipitation and air temperature. To investigate the sensitivities of snow simulations to atmosphere forcing, historical simulations from 1981–2010 were conducted using the Chinese Academy of Sciences land surface model (CAS‐LSM) with four atmospheric forcing data sets: third Global Soil Wetness Projects (GSWP3), the Water and Global Change (WATCH) Forcing Data (WFD/WFDEI), the Climate Research Unit ‐ National Centers for Environmental Prediction (CRU‐NCEP), and Princeton. A sensitivity index (Ψ) is utilized to quantify the sensitivity of the simulated snow cover fraction (SCF), snow water equivalent (SWE), and snow depth (SDP) to the uncertainties in forcing data. By comparing the simulated results with satellite‐based products and in situ observations, we find that CAS‐LSM generally captured the spatial and seasonal variations of the SCF, SWE, and SDP. The simulation based on GSWP3 produced more reasonable estimates than the other three simulations, particularly for the SCF and SDP. The sensitivity analysis suggested that the SWE and SDP suffered the most from the uncertainties in the atmospheric forcing data sets. The sensitivities of the SCF, SWE, and SDP to precipitation uncertainties had various seasonal cycles depending on the climate regime. The highest sensitivity in boreal climates appeared during January–March, while in warm temperate climates the highest sensitivity existed during April–June. On average, the strongest precipitation sensitivity was found for temperate climates with cold, dry winters. The sensitivities to uncertainties in air temperature showed similar patterns; however, air temperature sensitivity was generally dominant over precipitation sensitivity in boreal climates, while in warm temperate climates both of them were high. Plain Language Summary: We conducted four historical simulations using the land surface model Chinese Academy of Sciences land surface model (CAS‐LSM) with four state‐of‐the‐art atmospheric forcing data sets, that is, third Global Soil Wetness Project (GSWP3), the Water and Global Change (WATCH) Forcing Data (WFD/WFDEI), the Climate Research Unit ‐ National Centers for Environmental Prediction (CRU‐NCEP), and Princeton, to investigate the sensitivities of snow simulations to different atmosphere forcing data sets. The land surface model demonstrated its capability in reproducing temporal and spatial variations of historical snowpack over the Northern Hemisphere. Compared with the other three simulations, the simulation based on GSWP3 produced more reasonable estimates, particularly for the snow cover fraction and snow depth. Sensitivity analysis suggested that snow mass and snow depth suffered the most from the uncertainties in the atmospheric forcing data sets. The simulated snowpack is more sensitive to the uncertainties in precipitation and air temperature in temperate climates with cold, dry winters. This work highlighted that reliable forcing data, especially precipitation data, are of great significance to the accuracy of snow simulations. Key Points: Snow cover fraction (SCF), snow water equivalent (SWE), and snow depth (SDP) from simulations using different forcing data were evaluatedA sensitivity index, Ψ, was used to quantify the sensitivities of the SCF, SWE, and SDP to the uncertainties in atmospheric forcing dataSWE and SDP showed stronger sensitivities than SCF, with the highest sensitivity appearing for temperate climates with cold, dry winters [ABSTRACT FROM AUTHOR]

Stream temperatures are key indicators for aquatic ecosystem health, and are of particular concern in highly seasonal, water‐limited regions such as California that provide sensitive habitat for cold‐water species. Yet in many of these critical regions, the combined impacts of a warmer climate and urbanization on stream temperatures have not been systematically studied. We examined recent changes in air temperature and precipitation, including during the recent extreme drought, and compared the stream temperature responses of urban and nonurban streams under four climatic conditions and the 2008–2018 period. Metrics included changes in the magnitude and timing of stream temperatures, and the frequency of exceedance of ecologically relevant thresholds. Our results showed that minimum and average daily air temperatures in the region have increased by >1°C over the past 20 years, warming both urban and nonurban streams. Stream temperatures under drought warmed most (1°C–2°C) in late spring and early fall, effectively lengthening the summer warm season. The frequency of occurrence of periods of elevated stream temperatures was greater during warm climate conditions for both urban and nonurban streams, but urban streams experienced extreme conditions 1.5–2 times as often as nonurban streams. Our findings underscore that systematically monitoring and managing urban stream temperatures under climate change and drought is critically needed for seasonal, water‐limited urban systems. Research Impact Statement: Urban stream temperatures in the southern San Francisco Bay Area exhibit greater responses to drought and climate warming than those of nonurban streams, lengthening the summer warm season and resulting in greater frequencies of extremes. [ABSTRACT FROM AUTHOR]

In addition to a rise in mean air and water temperatures, more frequent and intense extreme climate events (such as heat waves) have been recorded around the globe during the past decades. These environmental changes are projected to intensify further in the future, and we still know little about how they will affect ecological processes driving harmful cyanobacterial bloom formation. Therefore, we conducted a long‐term experiment in 400‐L shallow freshwater mesocosms, where we evaluated the effects of a constant +4°C increase in mean water temperatures and compared it with a fluctuating warming scenario ranging from 0 to +8°C (i.e., including heat waves) but with the same +4°C long‐term elevation in mean water temperatures. We focused on investigating not only warming effects on cyanobacterial pelagic dynamics (phenology and biomass levels), but also on their recruitment from sediments—which are a fundamental part of their life history for which the response to warming remains largely unexplored. Our results demonstrate that (1) a warmer environment not only induces a seasonal advancement and boosts biomass levels of specific cyanobacterial species in the pelagic environment, but also increases their recruitment rates from the sediments, and (2) these species‐specific benthic and pelagic processes respond differently depending on whether climate warming is expressed only as an increase in mean water temperatures or, in addition, through an increased warming variability (including heat waves). These results are important because they show, for the first time, that climate warming can affect cyanobacterial dynamics at different life‐history stages, all the way from benthic recruitment up to their establishment in the pelagic community. Furthermore, it also highlights that both cyanobacterial benthic recruitment and pelagic biomass dynamics may be different as a result of changes in the variability of warming conditions. We argue that these findings are a critical first step to further our understanding of the relative importance of increased recruitment rates for harmful cyanobacterial bloom formation under different climate change scenarios. [ABSTRACT FROM AUTHOR]

Aim: The aim was to test the capacity of an ecologically focused microclimate model to capture the interaction between snow cover and soil temperature and use it to assess how historical and future climate warming affects exposure of organisms to stressfully cold conditions in shallow soil. Location: Continental USA. Time period: 1980–2017. Methods: A snow heat budget algorithm was developed and integrated with the general‐purpose microclimate model of the NicheMapR package. The gridMET daily historical weather grids were used as environmental forcing. The model was tested against hourly observations of soil temperature and snow cover for 590 Soil Climate Analysis Network/SNOw TELemetry sites across the USA. It was then used to simulate cold stress exposure of ectotherms and hibernation costs for endotherms. Results: The model captured soil and snow observations to within c. 10%–15% of the range (r c. near 0.85). Air temperature exhibited a mean warming rate of 0.37°C per decade across sites, but in snow‐affected regions the predicted shallow‐soil warming rates were one‐third lower. Biophysical analyses predicted an increase in the activity time of ectotherms as a result of contemporary climate change but little change in cold stress at most sites. For endotherms, hibernation costs were predicted to decline in general. However, both reduced and increased cold stress was simulated to occur for ectotherms and endotherms under simulated air temperature warming of ≤3 °C. Main conclusions: Many of the direct biological consequences of climate warming will be mediated through soil temperature. Microclimatic processes linked to snow cover mean that exposure to cold stress for both ectotherms and endotherms might stay constant, decrease or even increase under climate warming, depending on the local circumstances and species‐specific biology. The idiosyncratic and season‐specific decoupling of above‐ and below‐ground environments under climate warming will have important ecological consequences in snow‐affected regions and can now be computed mechanistically. [ABSTRACT FROM AUTHOR]

Late Pleistocene and Holocene ground ice are common throughout the Arctic. Some forms of relict ground ice preserve local meteoric water, and their stable oxygen‐ and hydrogen‐isotope ratios can be used to reconstruct past air temperatures. In this paper, we review the formation and sampling of two forms of relict ground ice—wedge ice and pore ice—and recent (2010–2019) advances in paleoclimatological studies of ground‐ice stable isotope records in the Arctic. Recent advances are attributed to better chronological constraints and refined understandings of the systematics and seasonality of relict wedge ice and pore ice. A rich network of ice‐wedge records has emerged, primarily from the Siberian Arctic, whereas pore‐ice records are less common. The ice‐wedge network depicts a robust pattern of late Pleistocene cooling, and remarkably similar temperature depressions during Marine Isotope Stages 3 and 2. Very high‐resolution wedge‐ and pore‐ice stable isotope chronologies have been established recently and used to reconstruct winter and summer climate histories and to assess seasonal dependencies in insolation‐forced climate. Reports of ancient (>125 ka BP) ground ice demonstrate its long‐term persistence, and its potential to expand our knowledge of Quaternary climate dynamics in the terrestrial Arctic. [ABSTRACT FROM AUTHOR]

Using the daily snow cover data at 24‐km resolution from the Interactive Multi‐sensor Snow and Ice Mapping System snow cover analysis, this study describes the variability in Tibetan Plateau (TP) snow cover (TPSC) at multiple time scales with a focus on the intraseasonal time scale (10–90 days). TPSC demonstrates variability over a wide range of temporal scales, but the annual cycle is generally dominant. Synoptic‐scale variability, seasonal variability and interannual and long‐term changes make small contributions to the total daily variability in TPSC. Intraseasonal variability (ISV) is dominant over most of the central and eastern TP and explains 22–40% of the total variability and leads to obvious variations in TPSC over periods shorter than a season. The ISV of TPSC is more active in the cold season than in the warm season. Specifically, the ISV over the Changtang Plateau explains approximately 50% of the total variability of snow cover in the cold season and is even more dominant than the annual cycle. Possible influences of regional atmospheric circulations on TPSC are also examined. TPSC variability is highly correlated with regional surface air temperature (SAT) and precipitation at an intraseasonal time scale. TPSC and SAT tend to have a simultaneous relationship, while anomalous precipitation leads to subsequent TPSC variations with a lag of approximately 5 days and a positive relationship. Such relationships are the result of intraseasonal variations in regional atmospheric circulation. The anomalous adiabatic heating induced by vertical ascending motion leads to tropospheric temperature variations. Furthermore, the horizontal advection of moisture and apparent moisture sink, which are induced by anomalous moisture supply and snow evaporation anomalies, respectively, lead to anomalous moisture associated with changes in the TPSC. [ABSTRACT FROM AUTHOR]

To determine hydrologic changes in a warmer climate, we impose precipitation and potential evaporation (Eo) perturbations on hydrologic response functions constructed from precipitation and satellite soil moisture observations across the United States. Despite nonlinearities in the evaporation (E) and drainage (D) responses and opposing‐sign perturbations, changes in individual fluxes are superposable. Empirical frameworks (Budyko) can misrepresent changes in E/D partitioning by neglecting shifts/trends in hydrologic regime and subseasonal precipitation dynamics. E/D both increase to balance mean precipitation (P¯) increases, and increased Eo reduces soil moisture. E and D are generally more elastic to changes in P¯ than Eo. The results suggest that (1) the impacts of regional hydrologic perturbations may allow for simple superposition/scaling, (2) changes in timing/intensity of precipitation may have substantial impacts on mean moisture states and fluxes, and (3) changes to the distribution of surface moisture states are likely more relevant for E/D partitioning than common aridity indices. Plain Language Summary: We use satellite observations of soil moisture and expected increase in air temperature to determine how evaporation and soil drainage (to groundwater recharge and rivers/streamflow) will change in a warmer climate. The impacts of drier air, more rainfall, and more extreme rainfall (drier dry days and wetter wet days) can largely be considered separately and then added, which will help when predicting a specific location's water balance using scenarios from global climate models. In typical scenarios, soils are likely to dry, evaporation is likely to increase, and—when precipitation increases—drainage to groundwater/streams is likely to increase as well. Evaporation and drainage are relatively more sensitive to changes in precipitation and humidity in the Western United States than the East, and the Corn Belt is particularly susceptible to changes in precipitation intensity. Common methods of determining changes in evaporation and drainage, which neglect changes in soil moisture, may have large errors in global change scenarios. Key Points: Aridity indices (Budyko) fail to capture effects of daily dynamics and precipitation extremes on long‐term hydroclimate changeClimate perturbations to evaporation/drainage are superposable despite nonlinear flux mechanisms and mixed‐sign effectsLong‐term soil drying tied to trends in increased evaporation, decreased drainage to groundwater and streamflow [ABSTRACT FROM AUTHOR]

This study examines projected changes in surface air temperature (SAT) and precipitation over mid–high latitudes of Eurasia during boreal spring in a 1.5/2°C warmer world (relative to pre‐industrial level) using historical, RCP4.5 and RCP8.5 simulations from 16 climate models of the Coupled Model Intercomparison Project Phase 5. Results show that spatial patterns of spring SAT and precipitation changes over Eurasia in the 1.5/2°C warmer world relative to the current climate (1979–2005) are similar between the two RCPs, but with a slightly larger amplitude in the RCP8.5. Spring SAT warmings over Eurasia are faster than the global annual mean. Amplitudes of the spring Eurasian SAT warmings are larger over higher latitudes. An additional 0.5°C global warming leads to extra spring Eurasian SAT warmings, with the magnitude larger than 0.5°C. Changes in the spring maximum temperature are comparable to the mean temperature changes both in the 1.5 and 2°C warming targets. Significant increases in the mean and extreme precipitation are only seen over parts of Eurasia. An additional 0.5°C global warming does not lead to obvious changes in the mean and extreme spring precipitation. Further analyses show that there are large uncertainties in the projected changes in the inter‐annual variability of the Eurasian spring SAT and precipitation. [ABSTRACT FROM AUTHOR]

Glaciers are of key importance to freshwater supplies in the Himalayan region. Their growth or decline is among other factors determined by an interaction of 2‐m air temperature (TAS) and precipitation rate (PR) and thereof derived positive degree days (PDD) and snow and ice accumulation (SAC). To investigate determining factors in climate projections, we use a model ensemble consisting of 36 CMIP5 general circulation models (GCMs) and 13 regional climate models (RCMs) of two Asian CORDEX domains for two different representative concentration pathways (RCP4.5 and RCP8.5). First, we downsize the ensemble in respect to the models' ability to correctly reproduce dominant circulation patterns (i.e., the Indian summer monsoon [ISM] and western disturbances [WDs]) as well as elevation‐dependent trend signals in winter. Within this evaluation, a newly produced data set for the Indus, Ganges and Brahmaputra catchments is used as observational data. The reanalyses WFDEI, ERA‐Interim, NCEP/NCAR and JRA‐55 are used to further account for observational uncertainty. In a next step, remaining TAS and PR data are bias corrected applying a new bias adjustment method, scale distribution mapping, and subsequently PDD and SAC computed. Finally, we identify and quantify projected climate change effects. Until the end of the century, the ensemble indicates a rise of PDD, especially during summer and for lower altitudes. Also TAS is rising, though the highest increases are shown for higher altitudes and between December and April (DJFMA). PRs connected to the ISM are projected to robustly increase, while signals for PR changes during DJFMA show a higher level of uncertainty and spatial heterogeneity. However, a robust decline in solid precipitation is projected over our research domain, with the exception of a small area in the high mountain Indus catchment where no clear signal emerges. [ABSTRACT FROM AUTHOR]

The impacts of including stochastic physics schemes (SPS) in the atmospheric component of the EC‐Earth climate model is studied by analyzing coupled simulations from 1850 to 2160. Sea ice in the Arctic retreats more slowly when SPS are included. An abrupt loss of winter sea ice is simulated 10 years later if the SPS are activated. However, this abrupt collapse is associated with a global surface air temperature (GSAT) of 17.4 ± 0.35 °C in all the cases. While the transient climate sensitivity is smaller if SPS are included up to the end of the 21st century, the opposite occurs in a warmer climate and in the absence of Arctic sea ice all the year‐round. These results are explained by the skewed interaction between stochastic perturbations and the process of condensation. Under a warmer climate, tropical convection is enhanced with SPS leading to the formation of high‐level clouds, which further increases GSAT. Plain Language Summary: Global climate models are a powerful tool to study the current climate and its future projections. Applying those models in high resolution is computationally demanding. A cheaper alternative to grid refinement is the use of stochastic physics schemes in which the unresolved subgrid processes are parameterized by introducing a term of randomness into the physical parameterizations. In this work, we study the impacts of including stochastic physics schemes in the atmospheric component of the EC‐Earth climate model by analyzing long‐term coupled simulations from 1850 to 2160. It was found that the global temperature increase is lower when the stochastic physics is included, which implies reduced climate sensitivity. However, with the melting of Arctic sea ice, the opposite occurs. An increase of high‐clouds due to the skewed interaction between stochastic perturbations and the process of condensation accelerates the increase in temperature in the simulations with stochastic physics. Key Points: Abrupt collapse of winter sea ice in the Arctic is simulated by EC‐Earth under RCP8.5 future scenarioEC‐Earth transient climate sensitivity is different when stochastic parameterizations are activatedTransient climate sensitivity in stochastic runs changes radically as a function of the global mean temperature [ABSTRACT FROM AUTHOR]

The main features of the urban boundary layer in the metropolitan region of São Paulo are estimated based on rawinsondes carried out (a) every 3 hr in two 10‐day field campaigns of the MCITY BRAZIL Project during the summer and winter of 2013 and (b) regularly once per day and continuously for 4 years from 2009 to 2013. On average, the boundary layer height showed a daytime maximum of 1476 ± 149 m in summer and 1122 ± 168 m in winter campaigns. The differences are related to seasonal variations in the (a) buoyancy flux at the surface, which was 30% larger in summer (4.7 ± 0.6 MJ m−2 day−1), and the (b) static stability of the free atmosphere, which was 15% smaller in summer (3.3 ± 0.1 K km−1). The average nighttime boundary layer height, estimated from equilibrium empirical expression, indicated maximum of 126 ± 13 m in summer and 122 ± 10 m in winter campaigns. The presence of a low‐level jet was identified in 80% of the field campaign nights, with intensity varying from 2.7 to 14 m s−1 and height ranging from 95 to 962 m. The nighttime residual‐mixing layer is well estimated using the air temperature gradient method and is a good indication of the maximum vertical evolution of the previous daytime boundary layer. The monthly average maximum boundary layer height varied from a minimum of 1,061 ± 77 m in September to maximum of 1,632 ± 96 m in May. Key Points: Main urban boundary layer features over the São Paulo megacity are estimated using 160 rawinsonde released in 2013 field campaignsSurface layer and entrainment layer thickness correspond to 10% (12%) and 16% (17%) of the urban boundary layer height in summer (winter)Four‐year climatology of boundary layer height estimated objectively yield a maximum (1,632 m) in May and a minimum (1,061 m) in September [ABSTRACT FROM AUTHOR]

This paper analyzes climate trends in satellite‐blended model reanalyses for the east Antilles islands of Guadeloupe, Dominica and Martinique (14–17°N, 62.5–60.5°W). Trends are studied for the dry winter (JFM) and wet summer (JAS) seasons, for parameters including: aerosol profile, tropospheric NO2 concentration, rainfall and hydrology, outgoing longwave radiation, potential evaporation, sea level, air and sea temperature and salinity in the upper ocean. Climate change emerges in the form of 2–4 and 6–8‐year surges in near‐surface temperature and mid‐tropospheric subsidence. While near‐surface specific humidity exhibits a rising trend especially in summer, the mid‐troposphere is drying. Coastal zones show little trend in rainfall, but cloud forests on the volcanic slopes are getting wetter. Faster rising trends are noted for sea surface height, and for sea temperatures in low salinity zones that affect tropical cyclone intensification. This work quantifies the spatial and temporal nature of climate variability in the east Antilles islands of Guadeloupe, Dominica and Martinique and advocates for increasing integration of mesoscale analysis into studies on local socio‐economic vulnerability. [ABSTRACT FROM AUTHOR]

Air temperature conditions affect various aspects of human life through diverse pathways, such as health, agriculture, labour productivity, infrastructure and building constructions, economic production, and so on. To address better efforts at resolving this issue, decision‐making tools need to be developed to identify and prioritize areas where appropriate actions are required. The present paper identifies vulnerable areas in Morocco with a focus on three different aspects: high temperatures, low temperatures and daily temperature amplitude. Maps were created using the National Oceanic and Atmospheric Administration (NOAA)‐developed Climate Forecast System Reanalysis (CFSR) 30 year‐long daily maximum and minimum temperature data set. Several indicators as well as vulnerability indexes were defined and calculated. Maps showing the spatial distribution of these indicators were created. The results show that 78% of Morocco is considered to be non‐vulnerable areas to areas with low vulnerability; and 19% is characterized by moderate vulnerability to the effects of temperature. The remaining areas are characterized by high to extreme vulnerability: the most vulnerable areas are located in the provinces of Figuig, Jerada and Boulemane in the eastern part of the country, as well as in the provinces of Khouribga, Fquih Ben Saleh and Settat in the central part. This implies, for example, that national‐level planners in the agriculture and transport/infrastructure sectors in the central part need to take into account temperature vulnerabilities as they develop sector strategies and plans. [ABSTRACT FROM AUTHOR]

To build healthy, resilient, and climate‐responsive cities, planners need ways to understand the local complexities of urban thermal climates. To assist in meeting this need, this study employs the simple classification of "local climate zones" (LCZs) to conduct a spatiotemporal thermal climatic analysis of the Toulouse Metropolitan Region (France) under warm and dry summer conditions. Simulations are performed using the mesoscale atmospheric model Méso‐NH. These simulations provide a city‐wide spatial coverage of 2‐m air temperature (T2M), mean radiant temperature (MRT), and Universal Thermal Climate Index (UTCI). Model parameters describing the urban morphology are initialized based on administrative databases and independent of LCZ maps, which allows for an evaluation of whether the distributions of the modelled thermal climatic parameters will differ between LCZs. The results show that different LCZs possess significantly different distributions of T2M and MRT, confirming the suitability of the LCZ scheme for discerning the thermal environment of Toulouse. Compact urban settings (LCZ 1/2/3) show the highest T2M throughout the day and a nocturnal temperature difference of up to 2.8 K compared to rural settings. The MRT of LCZ 1/2/3 in the late afternoon (1700–2000 LST (UTC + 2)) can be as much as 6.3 K lower than it is for LCZs with open settings due to shading by dense urban structures. Additional analysis reveals that the intra‐LCZ variabilities of T2M and MRT may be explained by the distance to the city centre. Finally, the thermal stress in different LCZs is assessed with the modelled UTCI. Among the built LCZs, the probability of strong heat stress is the highest for open high/mid‐rise (LCZ 4/5) and lowest for sparsely built (LCZ 9) and open low‐rise (LCZ 6) settings. For land cover type LCZs, dense trees (LCZ A) are the most favourable for daytime outdoor human thermal comfort. [ABSTRACT FROM AUTHOR]

The response of upper tropospheric clouds and relative humidity (RH) to warming is important to the overall sensitivity of the Earth to increasing greenhouse gas concentrations. Previous research has shown that changes in hydrologic fields should closely track rising isotherms in a warming climate. Here we show that the distribution of tropical clouds and RH in general circulation models is approximately constant under greenhouse warming when using temperature as a vertical coordinate. By assuming that these fields are an invariant function of atmospheric temperature and that temperature change follows a dilute moist adiabat, we are able to accurately predict cloud fraction and RH changes in the tropical upper troposphere (150–400 hPa) in 27 general circulation models. Our results indicate that intermodel spread in changes of tropical upper tropospheric clouds and RH is closely related to differences in model climatology and could be substantially reduced if model ensembles reliably reproduced observed climatologies. Plain Language Summary: As the Earth warms due to increasing greenhouse gas concentrations, many aspects of the climate adjust. Clouds, for example, tend to shift upward in response to surface warming. Such processes, known as climate feedbacks, alter the radiative energy that reaches the Earth's surface and, as a result, can amplify or damp global warming. One method for investigating climate feedbacks is to analyze their behavior in global climate models. Because clouds and relative humidity exhibit widely varying responses to warming in climate models, uncertainty in the Earth's sensitivity to changes in atmospheric carbon dioxide is exacerbated. Building on previous theoretical research, we demonstrate that the simulated response of clouds and relative humidity to global warming is closely associated with model representation of the present‐day climate. Theory, observations, and climate models indicate that moistening of the troposphere and rising of the highest cloud tops cause large positive feedbacks. Our research suggests that the substantial model spread in representing these feedbacks may be reduced by improving simulations of the observed present‐day climate. Key Points: Tropical upper tropospheric clouds and relative humidity in climate models track isotherms, which rise in response to greenhouse warmingIntermodel differences in cloud and relative humidity changes are related to model climatology, which shifts up with CO2‐mediated warmingObservations provide a constraint on simulated changes in tropical upper tropospheric clouds and relative humidity [ABSTRACT FROM AUTHOR]

The dataset is based on air temperature measurements from weather stations and sea-surface temperature measurements from buoys and ships in the Voluntary Observing Ship programme (Kent I et al i ., [22]). The central aim of the Paris Agreement (https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement) is ' I to strengthen the global response to the threat of climate change by keeping a global temperature rise this century well below 2 i °C I above pre-industrial levels and to pursue efforts to limit the temperature increase even further to 1.5 i °C'. Land areas with below-average temperature included Central Asia, central North America, and western South America, but only in a small portion of South America were the temperatures unusually low, that is, below the 10 SP th sp percentile of the 1961-1990 period. GLO:65TF/01oct19:wea3600-fig-0008.jpg PHOTO (COLOR): Surface air temperature anomalies for Europe and sea-surface temperature anomalies for neighbouring waters (degC, relative to 1961-1990) for December 2017 to February 2018, March to May 2018, June to August 2018 and September to November 2018. [Extracted from the article]

This paper has examined the relative significance of uncertainty in future climate projections from a subset of the coupled model intercomparison project phase 5 (CMIP5) global climate models for the Prairie Provinces of western Canada. This was undertaken by determining: (a) the contribution of model and scenario uncertainty and natural variability to the total variance of these future projections, and (b) the timing of climate signal emergence from the background noise of natural climate variability. We examined future projections of mean temperature, precipitation and summer climate moisture index (CMI). In this region, natural climate variability plays an important role in future uncertainty until the end of this century, particularly for precipitation and to a lesser extent, summer CMI. Model uncertainty also contributes to total uncertainty for these variables throughout this century, while scenario uncertainty becomes more important towards the end of the century. For the region as a whole, significant climate change (i.e., signal/noise >2) occurs earliest for summer mean temperature, with median time of emergence around 2035 for the RCP8.5 radiative forcing scenario. Although the median precipitation signal emerges from the noise (i.e., signal/noise >1) around the 2070s in winter and the 2080s in spring, significant values do not occur in any season for this variable before 2100. For summer CMI, the median time of emergence for significant change is around 2085. At the grid scale, signal‐to‐noise ratios are significant for all seasons for mean surface air temperature, with earliest times of emergence occurring in summer. In contrast, the summer precipitation signal is not significant this century; for summer CMI, significant values are obtained in the eastern half of the region, occurring from about 2065 onwards. Median times of emergence are towards the end of the century for summer CMI in western Saskatchewan and in Alberta, although some areas of Alberta do not exhibit significant signals this century. [ABSTRACT FROM AUTHOR]

The properties of initial leaders (ILs) of 1,056 flashes in two thunderstorms in Guangzhou, China, are analyzed. The median values of IL properties are 11.1 ms for duration, 2.2 km for vertical distance, 2.7 km for 3‐D distance, 1.9 × 105 m/s for vertical displacement speed, 2.4 × 105 m/s for 3‐D displacement speed, 28° for angle between the IL's displacement direction and the vertical direction, 1.1 per ms for pulse rate, 179 m for vertical step length, and 224 m for 3‐D step length. All the IL properties follow lognormal distributions. Strong monotonic relationships of IL duration with speed and pulse rate and pulse rate with speed and step length are revealed. With increasing initiation altitude, IL duration and step length tend to increase, and IL speed and pulse rate tend to decrease. The ILs initiated at approximately the −20 °C isotherm are more vertically orientated on average than those initiated at lower or higher levels. The ILs initiated in the strong kinematic areas generally had a greater speed and pulse rate but smaller duration, distance, and step length relative to those initiated in the weak kinematic areas. The possible impacts of the charge distribution patterns and density on the IL properties in different types of thunderstorms or thunderstorm areas are discussed. Key Points: The characteristics of multiple properties of initial leaders (ILs) during the preliminary breakdown processes of flashes are revealedWith increasing initiation altitude, the IL duration and step length tend to increase, and the IL speed and pulse rate tend to decreaseThe ILs had larger speed and pulse rate but smaller duration, distance, and step length in strong kinematic areas than in weak ones [ABSTRACT FROM AUTHOR]

This work deals with the assessment of the outdoor environment of a sanatorium site in mountainous conditions which were historically considered favourable in the treatment of tuberculosis patients in the pre‐antibiotic era. Thermal climate conditions are estimated through application of the thermal climate index (THI) to site‐specific air temperature and relative humidity data along with kata‐thermometer data recorded at the balconies outside of patients' rooms. Results show that the cold conditions dominate throughout the winter period at all observation times, whereas conditions of comfort prevail during summer, in morning and evening hours, and occur frequently in spring and autumn at noon. [ABSTRACT FROM AUTHOR]

This study implements a variable resolution version of Community Earth Systems Model (VR‐CESM; regionally refined at 1/8° resolution) to assess the improvement in simulating the seasonal climate across the Tibetan Plateau (TP) at higher horizontal resolutions compared to its uniform (UN) 1° counterpart (UN‐CESM). The Weather Research and Forecasting (WRF) model, run at 12‐km horizontal grid spacing, is compared to both CESM simulations. VR‐CESM and WRF are more comparable to satellite and surface‐based observations than UN‐CESM in simulating summertime (June to August) temperature across the TP and TP foothills, respectively. WRF and VR‐CESM also more accurately simulate precipitation than UN‐CESM across the region due to more accurate terrain treatment. The number of days in which effective (>2.5 mm/day) and heavy (>25 mm/day) precipitation events are generally better captured in VR‐CESM compared to UN‐CESM and WRF. Finally, snow cover fraction in VR‐CESM is better simulated for all months compared to UN‐CESM, with the largest improvements simulated from June to August, while WRF performs even better than VR‐CESM in simulating snow cover fraction. The improvements in simulated temperature, precipitation, and snow cover are due to WRF and VR‐CESM's ability to resolve more complex topographic features rather than time step differences between the UN‐CESM and VR‐CESM experiments. While it is fairly intuitive that improved topography accuracy should bring forth more accurate simulated meteorology, this evaluation suggests that VR‐CESM is competitive or may even be preferred over regional climate models (such as WRF) when examining internal and external climate variability, as it bypasses several drawbacks associated with regional climate models. Key Points: VR‐CESM more accurately captures Tibet's heterogeneous temperature, precipitation, and snow fields compared to tradition 1‐degreee CESMImprovements in simulated meteorology are brought about by better‐resolved mesoscale circulations, which regulate the surface energy budgetVR‐CESM better simulates moisture convergence and precipitation across Nepal, compared to 1‐degree CESM [ABSTRACT FROM AUTHOR]

As the forecasting community works toward skillfully applying the predictive power of periodic events, it is increasingly useful to quantify regional sensitivities to their influence. We have developed an index to quantify these regional Sensitivities To the Remote Influence of Periodic EventS (STRIPES). Here, periodic events are climate phenomena that smoothly evolve through their life cycles, such as the Madden‐Julian Oscillation (MJO). The STRIPES index compiles the information obtained through composite analysis of variables after a periodic event into a single number, which includes information about the life cycle of the event, and for a range of lags with respect to each stage of the event. The STRIPES index quantifies the strength and consistency of a region's response to the remote event. Because the index can be computed for a single point, it can be plotted on a map to show regional variations. We demonstrate the utility of the STRIPES index by applying it to observations of daily geopotential height, maximum temperature, minimum temperature, and precipitation totals for winters between 1974 and 2017. Results for geopotential height show strong sensitivities in the extratropics, indicative of the MJO's remote teleconnections. Results for surface stations over eastern North America highlight regions of enhanced sensitivity that agree well with previous studies. This sensitivity is robust in the sense that it is detectable and significant at the level of individual weather stations. The STRIPES index values for the MJO over North America are generally larger for daily maximum than for daily minimum temperature. Key Points: A new method is developed to quantify regional sensitivity to periodic weather eventsThe method is applied to global geopotential heights and North American weather stationsTemperatures at individual North American stations are significantly sensitive to the MJO [ABSTRACT FROM AUTHOR]

For some applications, long‐term coverage and climate consistency are important requirements in gridded climate data sets. This is often challenged by the inhomogeneity of measurements and by the variation of station networks over time. We describe the development of new monthly grid data sets of temperature and precipitation for Switzerland that range back as far as 1864 and satisfy high standards in long‐term consistency. To this end, data are incorporated from stations only with complete and homogenized series available. To compensate for the limited spatial coverage, our analysis integrates statistical information from high‐resolution grid data sets based on a dense network but over a limited period. For Switzerland, we demonstrate that the statistical reconstruction recovers mesoscale features not resolved by the stations alone. The method is fairly robust against representativity biases during calibration. The resulting trend maps are more plausible than those from common interpolation with more extensive but variable station networks. Typical errors in the analyses are 0.2 °C for monthly temperature and 10% for precipitation, but they can be larger in regions with low station density. In 75% of all months, the reconstructions explain more than 90% of the spatial variance for temperature and more than 60% for precipitation. From the new data sets, we infer a warming trend of around 1.3 °C per century in the period 1864–2017, increasing to more than 0.35 °C per decade for 1961–2017. Precipitation trends are not statistically significant, except in winter, when an increase of 1–3% per decade is found in the north and west of the country. Key Points: Long‐term consistent spatial analyses are needed and developed for climate monitoring and trend analyses for SwitzerlandThe reconstruction method is successfully applied and incorporates homogeneous and fully continuous time series onlyThe temperature trend is around 0.13 °C per decade in the period 1864–2017, increasing to more than 0.35 °C per decade for 1961–2017 [ABSTRACT FROM AUTHOR]