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Meteorological observations of surface air temperature have provided fundamental data for climate change detection and attribution. However, the weather stations are unevenly distributed, and are still very sparse in remote regions. The possible sampling error is well known, but not well quantified because we are lack of the adequate and regularly distributed measurements. The high resolution of satellite land surface temperature retrieval during night time provide a nice proxy for near surface temperature as both temperatures controlled by surface longwave radiative cooling and the nocturnal temperature inversion depress land‐atmosphere turbulent exchange. The sampling error of mean value and trend were assessed by comparing station point measurements (pixel of ∼0.01°) with grid (1°) mean and national mean from 2001 to 2021. This method permits us to make the first assessment of under‐sampling error and spatial representative error on both national mean and trend of air temperature during nighttime collected at ∼2,400 weather stations over China. The sampling error in national mean temperature is more than 3°C. The under‐sampling error due to lack of observation explains two thirds and the spatial representative error due to the difference between station and grid/regional mean elevation contribute the other one third. The sampling error in trend account for one third of the national mean trend. The urban heat island effect associated with urbanization around the weather stations (spatial representative error) can explain four fifths of the sampling error in trend, which is consistent with existing studies based on air temperature collected at paired weather station. Plain Language Summary: Meteorological observations provide fundamental measurements of surface air temperature. These station‐based temperature records are usually processed to grid data sets considering the uneven distribution of meteorological stations. The gridding procedure represents a non‐uniformly sampling. In this paper, we quantified the sampling error over China using remote sensed temperature record as proxy of near surface temperature during nighttime. The causes of sampling error both in mean value and trend were both quantified. Key Points: Sampling error of mean and trend of temperature collected at weather stations at nighttime over China were investigatedThe sampling error in national mean temperature were found to be more than 3°C due to uneven distribution of weather stationsThe sampling error in temperature trend account for one thirds of the national mean temperature trend due to local urbanization [ABSTRACT FROM AUTHOR]

In this paper, we describe the results of a climate data rescue (DARE) activity conducted at the Ukrainian Hydrometeorological Institute (Kyiv, Ukraine) in cooperation with the Justus Liebig University Giessen (Giessen, Germany), the World Meteorological Organization (WMO) and Met Office Hadley Centre (Exeter, United Kingdom). The focus of our work is on pre‐1850 sub‐daily meteorological observations conducted on the territory of modern Ukraine. Data from eight stations (stored in special hard copy tables/books, with handwritten records) have been digitized during the DARE process, resulting in 291,103 rescued values in total. More than half of this number (165,980, ~57%) are related to air temperature data, 124,376 data values (~42.7%) concern atmospheric pressure records and 747 values (~0.3%) are precipitation data. Simple quality control of the digitized data has been conducted, including an intercomparison between stations as well as comparison with monthly temperature data previously digitized from other paper sources. The quality control procedures revealed fairly good agreement between rescued time series on the monthly time scale as well as with the monthly data from independent sources. However, several periods for a few stations should be used with caution due to relatively large discrepancies that were found. The rescued digital database can be used for extreme value analysis for the pre‐1850 period in comparison with today's climate, regional climatological studies and will be used for future Reanalysis. In addition, for the first time, we show that subdaily temperature from Kyiv during 'the year without summer' 1816 in Western and Central Europe were not anomalous in this part of Eastern Europe. [ABSTRACT FROM AUTHOR]

Long‐term climate monitoring by universities provides fundamental data for various disciplines in the natural sciences. Kyoto University once managed 10 university forest stations and is currently managing five. At these stations, climate data have been monitored almost since the establishment of each station until today, with the exception of the stations in foreign countries. We compiled and report the monthly climate data at these forest stations from the start of monitoring until December 2018. These data are important for investigating environmental changes in East Asia, including China (Taiwan), South Korea (Chosen) and Russia (Sakhalin, Karafuto) over the last 100 years. Long‐term monitoring often involves minor and major changes in the monitoring procedures, devices and even monitoring sites due to unavoidable circumstances, such as technological advances, changes in standard methods and climatic and anthropogenic disasters. Therefore, associated metadata on the monitoring methods are also important. We also compiled metadata on changes in monitoring methods. In addition, to examine the effects of changes in the methods on the observed climate data, we compared the values recorded before and after the change for each climate variable at each monitoring site. Methodological changes affected only a few variables, with the exception of humidity. Careful interpretation may be required when a researcher uses humidity data in any analysis. The complete data set for this abstract published in the Data Paper section of the journal is available in electronic format in MetaCat in JaLTER at http://db.cger.nies.go.jp/JaLTER/metacat/metacat/ERDP-2020-03.1/jalter-en. [ABSTRACT FROM AUTHOR]

Arctic glaciers have been, and are predicted to remain, an important sea‐level rise contributor over the 20th and 21st centuries. However, multi‐annual observations of their basic meteorological characteristics, which drive melt processes, are very sparse, contributing to uncertainties in sea‐level rise projections. This paper presents a methodology to process and analyse 6 years (September 2011–September 2017) of glacier meteorology data collected by an automatic weather station on the ablation zone of a small valley glacier in Svalbard (~79°N). The study focuses on the microclimate of the study glacier and its differences with that of large glaciers in the region, namely slightly increased summer air temperature with steep near‐surface lapse rates, frequently superadiabatic, reduced wind speed and incoming shortwave radiation flux. These differences are likely related to the adjacent complex relief and non‐ice‐covered surfaces within the basin of the study glacier and, as such, pose a challenge for accurate simulation of alpine glacier microclimates in regional melt assessments in the Arctic. The processing routine applied in this paper might serve as a reference for future similar studies. [ABSTRACT FROM AUTHOR]

This paper presents a new dataset of quality controlled and homogenized monthly mean air temperature for Belgium. After in‐depth data quality control, monthly maximum and minimum temperature time series have been homogenized with the HOMER software on the basis of available metadata. Homogenization results are provided for 61 series over the period 1954–2015 and for 16 series starting before 1931 including eight covering the full time period 1880–2015. The new dataset of monthly homogenized series allows to make a robust analysis of the temperature evolution in Belgium at a local scale from 1880 to nowadays. Trends in maximum and minimum temperatures indicate on average a similar and statistically significant warming of about 0.3 °C/decade from 1954 and of about 0.15 °C/decade from 1880 with moderate spatial variations. An abrupt warming is observed at the end of the 1980s, especially in summer and spring seasons. This paper presents a new dataset of quality controlled and homogenized monthly mean air temperature for Belgium. It allows to make, for the first time, a robust analysis of the temperature evolution in Belgium at a local scale from 1880 to nowadays. Before this analysis, the quality control and the homogenization of the data are described in detail. [ABSTRACT FROM AUTHOR]

This paper presents changes in the ice regime of lakes located in the northern part of Poland. It has been established that in the period 1960–2019, there was a successive decline in the ice extent of lakes, clearly visible in individual parameters; formation of ice cover is delayed by 3.0 days/decade (on average for all lakes), its decline occurred earlier by 3.8 days/decade, and the length of ice cover is shorter by 6.8 days/decade. Moreover, the maximum thickness of the ice cover is becoming thinner, and this process has progressed at an average rate of 2.3 cm/decade. The aforementioned situation shows strong relations with air temperature. It was particularly evident in the late 1980s, when a change in the thermal regime took place, which was documented in most of the analysed parameters, as demonstrated in the study. It has been established that from that period up to now, the decline of ice cover tends to start 19 days earlier, the duration of ice cover is shorter by 22 days, and the ice cover is 8 cm thinner, on average. The observed transformation affects the imbalance of the existing equilibrium in lakes, which can be related to both biotic and abiotic processes. [ABSTRACT FROM AUTHOR]

This paper investigates the elevation‐warming relationships across the Carpathian Mountains, using the 0.1° × 0.1° gridded daily air temperature dataset developed within the CARPATCLIM project, in order to understand the spatial patterns of annual and seasonal temperature trends and test the hypothesis of enhanced warming with elevation. Temperature trend and elevation‐warming analyses were conducted over the 50 years of the dataset (1961–2010). The vertical variations of five key isotherms relevant for the development of cold‐climate weathering, presence of permafrost (−2°C, 0°C, +2°C, +3°C) and spread of forest vegetation (July +10°C) were also examined. The Carpathian Mountains are under visible annual and seasonal warming (stronger in the summer and winter), both daytime and nighttime. The warming process shows a great spatial inhomogeneity, but is more pronounced in the lowlands of the region, generally below 1,000–1,200 m. The correlations between elevation and warming rates across the Carpathians have been found both to be positive and negative, while mostly significant at the 5% level. Evidence of enhanced warming with elevation was found to be related to the minimum temperature increase mostly across the NW and SW Carpathians, at both annual and seasonal scales (winter, summer and spring). Less prominent EDW signals have been identified in the NE, E and S Carpathians (summer), as well as in the NE Carpathians (spring). Parts of the detected temperature trends and EDW signals have been considered to be an effect of the changing atmospheric circulation (i.e. westerly intensification especially in the winter), although other mechanisms and processes could be involved (e.g. snow‐albedo feedbacks in the spring). These results could serve as a reference for further investigations of climate warming effects across the Carpathian Mountains region (e.g. ecological and geomorphic), especially in relation to the augmented summer heat stress (e.g. more frequent heatwaves) and milder winters (e.g. less freezing days). [ABSTRACT FROM AUTHOR]

The current study focuses on the RegCM4.5 model and specifically on a comparison of hydrostatic and non‐hydrostatic approaches as well as on different microphysical parameterisations and planetary boundary layer (PBL) schemes. The main goal of the paper is to simulate the historical regional precipitation characteristics of the Carpathian region as reliably as possible. For this purpose, seven different model experiments at a 10 km horizontal resolution were completed for a 10‐year period (1981–1990) using ERA‐Interim reanalysis data (with 0.75° resolution) as initial and boundary conditions. Our simulation matrix consists of hydrostatic and non‐hydrostatic runs together with different treatments of moisture, namely, the SUBEX and the NogTom schemes. In addition, two PBL schemes are tested, the Holtslag and the UW‐PBL scheme. In this detailed validation study, RegCM outputs (e.g., temperature, global radiation, cloud cover, precipitation) are compared to the homogenized, gridded CarpatClim data (available with 0.1° resolution) that are based on measurements at regular meteorological station sites. The validation considers seasonal and monthly means, as well as extreme climatic events. On the basis of the results we can conclude that the role of the non‐hydrostatic core can be clearly recognized for precipitation, particularly over mountains. Moreover, it was also found that the UW‐PBL scheme performs with a negative bias regarding atmospheric boundary layer thickness and temperature and it reduces the wet/dry biases of the Holtslag PBL scheme. Regarding microphysical schemes, the NogTom scheme performs better than the SUBEX scheme, but the modified SUBEX (SUB4.3) can also reduce the precipitation over mountainous areas. [ABSTRACT FROM AUTHOR]

In order to improve the prediction accuracy of air temperature forecasting, a temperature prediction model based on the hybrid SARIMA (seasonal autoregressive integrated moving average)‐LSTM (long short‐term memory) model is constructed. First, this method decomposes the temperature series into three series of trend, seasonal, and residual through seasonal‐trend decomposition procedure based on Loess decomposition method. It establishes SARIMA to predict the trend and seasonal series and extracts the linear information contained in the time series to the maximum extent. Then, the LSTM model is used to fit the residual series and the hidden nonlinear information is further extracted. Finally, the prediction results of two parts are added in series to obtain the prediction result of the final hybrid model. Three indexes, namely, root mean square error, mean absolute error, and mean absolute percentage error are evaluated to calculate the prediction accuracy about single models including ARIMA, SARIMA, and LSTM and the hybrid models ARIMA‐LSTM and SARIMA‐LSTM. Also the Kupiec index is used to show tail performance. The empirical results show that the SARIMA‐LSTM combination model is more accurate than the single prediction methods and other combination model. Its accuracy increases by 10.0–27.7%. [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]

The 1980 volcanic eruption of Mount St. Helens had profound impacts on the geology, hydrology, and ecology of its surrounding landscapes. Consequently, the event provided a unique opportunity to study ecological change over time in relation to abiotic factors. To better assess the role localized environmental conditions play in these larger processes, we have monitored micrometeorological conditions across six disturbance zones on Mount St. Helens created by the eruption. We deployed 823 environmental sensors at 191 sites from 1997 to 2022 to measure the temperature and relative humidity of aquatic (temperature only) and terrestrial habitats in these areas, collecting over 4.2 million measurements in total. Measurements were typically recorded every 30 min from late spring through mid‐fall, with the exception being Spirit Lake, where temperatures have been measured hourly on a year‐round basis since 2002. These data have been used to address two broad research questions: (1) how small‐scale environmental conditions influence patterns of survivorship and/or establishment on Mount St. Helens post‐eruption for a range of organisms, including plants, small mammals, birds, amphibians, arthropods, fish, and other aquatic biota, and (2) to quantify and compare these environmental conditions across different disturbance zones, which vary in disturbance type, intensity, and history of post‐eruption secondary disturbances. Due to the repeatability of these measurements over many years, these data lend themselves to exploring the relationship between forest succession and microclimate, especially with respect to forest‐dwelling organisms whose spread and demography are sensitive to temperature and relative humidity. In addition, this dataset could be used to investigate additional questions related to early succession, disturbance ecology, climate change, or volcano ecology. This dataset is available in the R data package MSHMicroMetR, which also includes an R Shiny data visualization and exploration tool. There is no copyright on the data; please cite this data paper Ecology when using these data. [ABSTRACT FROM AUTHOR]

The Arctic has experienced step changes in climate conditions, which are still not fully understood. Particularly intriguing was the Early Twentieth‐Century Arctic Warming (ETCAW), which occurred prior to anthropogenic interference in the environment. This paper quantitatively assesses the differences in atmospheric circulation patterns during periods of warming and cooling on Spitsbergen with a particular focus on ETCAW. The patterns of atmospheric circulation were taken from the catalog of day‐by‐day circulation types created for Svalbard. The catalog includes 21 circulation types, assigned to every day between 1920 and 2017. The research focused on winter and autumn, the seasons exhibiting pronounced ETCAW on Spitsbergen. The long‐term variability in autumn and winter air temperatures on Spitsbergen significantly depends on the frequency of air advection from clearly defined directions and reveals some seasonal differentiation. Warming was related in autumn to southern and south‐eastern advection, in winter to southern advection, and in both cyclonic and anticyclonic conditions. These "warm" types explained up to 21% (September–November) or 25% (December–February) of the variability in air temperature. In winter, cooling was significantly related to anticyclonic type with air advection from the northeast (NEa: 24% of variability): in autumn to cyclonic northern (Nc) and northeastern (NEc) types. During ETCAW, the frequency of warm circulation types was significantly higher, and that of "cold" circulation types significantly lower. No relevant differences in patterns were found between ETCAW and Recent Arctic Warming; however, the frequency of warm and cold types was higher and lower, respectively, during ETCAW. Key Points: A 10% change in the circulation types frequency can raise DJF air temperature by 2.5 °C (S types) or reduce them by 2.1 °C (N types)Southern CTs explain up to 46%, and northern up to 41% of variability in DJF air temperaturesETCAW was marked by a significantly higher frequency of warm southern CTs and a lower frequency of cold northern CTs, both by >20% [ABSTRACT FROM AUTHOR]

Studies assessing air temperature variations and their dependence on altitude from the eastern side of the Antarctic Peninsula are sparse. In this paper, we analyse air temperature and near‐surface lapse rates from the Ulu Peninsula (James Ross Island, Antarctic Peninsula). The temperature data were acquired from nine sites both in ice‐free areas and glaciers of the Ulu Peninsula in 2013–2016. The most important factors influencing air temperature differences were sea‐ice conditions, topography and ground surface properties. During the study period, air temperature decreased with height with the mean lapse rate of 0.40 °C 100 m−1 for the ice‐free sites (10–375 m a.s.l.) and 0.43 °C 100 m−1 for the glaciated sites (268–539 m a.s.l.). However, the values were lower in winter (ice‐free: 0.02 °C 100 m−1 and glacier: 0.31 °C 100 m−1) than in summer (ice‐free: 0.62 °C 100 m−1 and glacier: 0.51 °C 100 m−1) for both categories. The air layer between 10 and 56 m a.s.l. in the ice‐free area revealed an almost year‐round air temperature inversion resulting in a mean lapse rate of −0.79 °C 100 m−1 and an air temperature inversion frequency reaching up to 43%. The temperature inversion frequency was 10% lower for the lowest air layer for the glacier category, which was between 268 and 356 m a.s.l. The land surface air temperature was the highest when the wind was from the northwest and lowest when it was from the south–southwest. The near‐surface lapse rate–wind direction relationship was the most pronounced for the air layer between 10 and 56 m a.s.l. with the lowest mean lapse rate for west–southwest wind (−2.83 °C 100 m−1) and the highest for east–northeast wind (0.41 °C 100 m−1). Spatial variability of air temperature on the Ulu Peninsula, James Ross Island, from 2013–2016 was analysed based on data from nine sites. The sites were divided into two categories (ice‐free and glacier sites) and each category was analysed separately. Special attention was given to the dependence on altitude by calculating near‐surface lapse rates and air temperature inversion relative frequency as well as to the effect of wind direction. [ABSTRACT FROM AUTHOR]

Shrink-swell soils can cause distresses in buildings, and every year, the economic loss associated with this problem is huge. This paper presents a comprehensive system for simulating the soil-foundation-building system and its response to daily weather conditions. Weather data include rainfall, solar radiation, air temperature, relative humidity, and wind speed, all of which are readily available from a local weather station or the Internet. These data are used to determine simulation flux boundary conditions. Different methods are proposed to simulate different boundary conditions: bare soil, trees, and vegetation. A coupled hydro-mechanical stress analysis is used to simulate the volume change of shrink-swell soils due to both mechanical stress and water content variations. Coupled hydro-mechanical stress-jointed elements are used to simulate the interaction between the soil and the slab, and general shell elements are used to simulate structural behavior. All the models are combined into one finite element program to predict the entire system's behavior. This paper first described the theory for the simulations. A site in Arlington, Texas, is then selected to demonstrate the application of the proposed system. Simulation results are shown, and a comparison between measured and predicted movements for four footings in Arlington, Texas, over a 2-year period is presented. Finally, a three-dimensional simulation is made for a virtual residential building on shrink-swell soils to identify the influence of various factors. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In light of global climate change, ecological studies increasingly address effects of temperature on organisms and ecosystems. To measure air temperature at biologically relevant scales in the field, ecologists often use small, portable temperature sensors. Sensors must be shielded from solar radiation to provide accurate temperature measurements, but our review of 18 years of ecological literature indicates that shielding practices vary across studies (when reported at all), and that ecologists often invent and construct ad hoc radiation shields without testing their efficacy. We performed two field experiments to examine the accuracy of temperature observations from three commonly used portable data loggers ( HOBO Pro, HOBO Pendant, and iButton hygrochron) housed in manufactured Gill shields or ad hoc, custom-fabricated shields constructed from everyday materials such as plastic cups. We installed this sensor array (five replicates of 11 sensor-shield combinations) at weather stations located in open and forested sites. HOBO Pro sensors with Gill shields were the most accurate devices, with a mean absolute error of 0.2°C relative to weather stations at each site. Error in ad hoc shield treatments ranged from 0.8 to 3.0°C, with the largest errors at the open site. We then deployed one replicate of each sensor-shield combination at five sites that varied in the amount of urban impervious surface cover, which presents a further shielding challenge. Bias in sensors paired with ad hoc shields increased by up to 0.7°C for every 10% increase in impervious surface. Our results indicate that, due to variable shielding practices, the ecological literature likely includes highly biased temperature data that cannot be compared directly across studies. If left unaddressed, these errors will hinder efforts to predict biological responses to climate change. We call for greater standardization in how temperature data are recorded in the field, handled in analyses, and reported in publications. [ABSTRACT FROM AUTHOR]

ABSTRACT The purpose of this paper is to describe the variability of sunshine duration and air temperature and to assess the relationship between these variables. The paper is based on daily means of air temperature and daily sunshine duration totals from the 1884-2012 period. The variability analyses of these variables for the whole period is supplemented by additional trend analyses for two separate time periods described in the literature as dimming (1958-1985) and brightening (1985-2012). All the data come from the Jagiellonian University Research Station in Krakow. In Krakow, there is a decrease in the number of sunshine hours in the 1950s to 1980s period followed by an increase in the last two decades of the 20th century. The long-term course of air temperature in Krakow confirms the results obtained in other places in the world and exhibits increasing warming of the climate. The temperature increase is visible in each month but is highest in winter (exceeding 1.5 °C per 100 years). Therefore, it may be assumed that the increase in air temperature, which has been particularly strong since the 1980s, showed the full effect of global warming in the brightening period that was previously masked by global dimming as shown in the sunshine duration records. The time course of air temperature for the whole study period (1884-2012) shows a statistically significant positive linear trend. The present warming is influenced probably by the air-quality improvement (brightening period) and an increase in sunshine duration in the years 1980-2012. [ABSTRACT FROM AUTHOR]

Urban heat island ( UHI) effect is an important impact factor of the regional climate and ecological environment. How to observe and analyse the spatial distribution of UHI has become an important issue of urban environmental research. In this paper, the near-surface air temperature of Beijing was derived based on the Landsat/ TM satellite imagery on 26 July 2011 to study the near-surface UHI. A statistical model at 195-m window size was established to estimate the air temperature, using land surface temperature, normalized difference vegetation index, altitude, and surface albedo as independent variables. The mean absolute error ( MAE) of the model was 0.87°C, and the R2 was 0.66, indicating that the method can be used to effectively estimate the air temperature. The air temperature distribution obtained from remote sensing revealed that the UHI effect in Beijing was very significant and showed a concentrated pattern. The heat island intensity was stronger in the southern part than in the northern part of the city. In addition, the relationship between the air temperature and impervious surfaces was analysed. The air temperature increased with increasing impervious surface coverage, and the rate of change depended on the impervious surface coverage. When the impervious surface coverage was below 40 per cent, the temperature increased rapidly with increasing impervious surface coverage, and when the impervious surface coverage was above 40 per cent, the temperature increased slowly. This study provides a new approach to monitor near-surface UHI and reveals its relationship with impervious surface, providing a scientific reference for urban planning and environmental assessment. [ABSTRACT FROM AUTHOR]

Soil respiration (SR) is commonly modeled by a Q10 (an indicator of temperature sensitivity) function in ecosystem models. Q10 is usually treated as a constant of 2 in these models, although Q10 value of SR often decreases with increasing temperatures. It remains unclear whether a general temperature-dependent Q10 model of SR exists at biome and global scale. In this paper, we have compiled the long-term Q10 data of 38 SR studies ranging from the Boreal, Temperate, to Tropical/Subtropical biome on four continents. Our analysis indicated that the general temperature-dependent biome Q10 models of SR existed, especially in the Boreal and Temperate biomes. A single-exponential model was better than a simple linear model in fitting the average Q10 values at the biome scale. Average soil temperature is a better predictor of Q10 value than average air temperature in these models, especially in the Boreal biome. Soil temperature alone could explain about 50% of the Q10 variations in both the Boreal and Temperate biome single-exponential Q10 model. Q10 value of SR decreased with increasing soil temperature but at quite different rates among the three biome Q10 models. The k values (Q10 decay rate constants) were 0.09, 0.07, and 0.02/oC in the Boreal, Temperate, and Tropical/Subtropical biome, respectively, suggesting that Q10 value is the most sensitive to soil temperature change in the Boreal biome, the second in the Temperate biome, and the least sensitive in the Tropical/Subtropical biome. This also indirectly confirms that acclimation of SR in many soil warming experiments probably occurs. The k value in the “global” single-exponential Q10 model which combined both the Boreal and Temperate biome data set was 0.08/°C. However, the global general temperature-dependent Q10 model developed using the data sets of the three biomes is not adequate for predicting Q10 values of SR globally. The existence of the general temperature-dependent Q10 models of SR in the Boreal and Temperate biome has important implications for modeling SR, especially in the Boreal biome. More detail model runs are needed to exactly evaluate the impact of using a fixed Q10 vs a temperature-dependent Q10 on SR estimate in ecosystem models (e.g., TEM, Biome-BGC, and PnET). (Managing editor: Ya-Qin HAN) [ABSTRACT FROM AUTHOR]

The fragile ecological environment of the Mongolian Plateau (MP) is sensitive to climate change. It is necessary to fully understand the temperature and precipitation changes on the MP to ensure regional sustainable development. Most existing studies are conducted based on station observations which suffer from sparse distribution and limited spatial representativeness and cannot perfectly represent the climatic conditions of the whole region. By contrast, the long‐term, spatially continuous reanalysis products offer new opportunities for MP climate change research. However, the coarse spatial resolution and large uncertainties of reanalysis data limit their applicability to provide reliable climate information at finer scales. Within the delta downscaling framework, we downscale and correct the state‐of‐the‐art European Centre for Medium‐range Weather Forecasts ReAnalysis 5 land portion (ERA5‐Land) using a high‐resolution WorldClim reference climatology and generate a long‐term (1950–2020) 1‐km monthly air temperature and precipitation downscaled dataset. During the downscaling process, the results obtained by nearest neighbour, bilinear and bicubic interpolation methods are evaluated and compared. Air temperature result of bilinear method and precipitation result of nearest neighbour method, which have the best accuracy, are taken as the final downscaled data. The evaluation shows that the downscaled data has acceptable accuracy (overall MBE = −0.41°C, Corr = 0.997, RMSE = 1.51°C for air temperature, and MBE = 1.58 mm·month−1, Corr = 0.747, RMSE = 24.24 mm·month−1 for precipitation), and outperforms the original ERA5‐Land and widely used Climatic Research Unit (CRU) data. In addition, the downscaled data can also provide accurate and detailed temperature and precipitation change trends over the MP. With fine spatial resolution, long‐time span and good spatial continuity, the downscaled air temperature and precipitation will be a useful data source to explore climate change over the MP. [ABSTRACT FROM AUTHOR]

Understanding the riverine thermal regimes is challenging due to sparse spatiotemporal data of river water temperatures (RWTs). The development of systematic models combined with machine learning models under data‐limited context has not been intensively studied for the prediction of RWT. The present study developed hybrid models using long short‐term memory (LSTM), integrated with (a) k‐nearest neighbor (k‐NN) bootstrap resampling algorithms (kNN‐LSTM) to address the data limitations of RWT prediction and (b) discrete wavelet transform (WT) approach (WT‐LSTM) to address the time–frequency localized features of RWT prediction. The study assessed the climate change impacts on RWT using an ensemble of National Aeronautics Space Administration Earth Exchange Global Daily Downscaled Projections of air temperature with Representative Concentration Pathway scenarios 4.5 and 8.5 for seven major polluted river catchments of India. The hybrid kNN‐LSTM has effectively predicted RWT at monthly time scales under data limitations and outperformed the standalone LSTM, WT‐LSTM, and hybrid three‐parameter version air2stream models. The RWT increase for Tunga‐Bhadra, Musi, Ganga, and Narmada basins is predicted as 3.0, 4.0, 4.6, and 4.7°C, respectively, for 2071–2100. Key Points: Hybrid deep learning models coupled with resampling algorithms and wavelet transforms to predict river water temperatures under sparse dataThe hybrid models effectively predicted river water temperatures at monthly time scales under data limitationsAssessed the climate change impacts on river water temperatures in seven major polluted river catchments of India [ABSTRACT FROM AUTHOR]

New Findings: What is the central question of this study?What are the independent effects of air temperature and humidity on performance, physiological and perceptual responses during endurance exercise?What is the main finding and its importance?When examined independently, elevated air temperature increased heat strain and impaired aerobic exercise performance, but to a lesser extent than has been reported previously. These findings highlight the importance of absolute humidity relative to temperature when exercising or working under severe heat stress. Many studies have reported that ambient heat stress increases physiological and perceptual strain and impairs endurance exercise, but effects of air temperature per se remain almost unexamined. Most studies have used matched relative humidity, thereby exponentially increasing absolute humidity (water content in air) concurrently with temperature. Absolute (not relative) humidity governs evaporative rate and is more important at higher work rates and air temperatures. Therefore, we examined the independent effects of air temperature and humidity on performance, thermal, cardiovascular and perceptual measures during endurance exercise. Utilizing a crossover design, 14 trained participants (7 females) completed 45 min fixed‐intensity cycling (70% V̇O2peak${\dot V_{{{\rm{O}}_{\rm{2}}}{\rm{peak}}}}$) followed by a 20‐km time trial in each of four environments: three air temperatures at matched absolute humidity (Cool, 18°C; Moderate, 27°C; and Hot, 36°C; at 1.96 kPa, air velocity ∼4.5 m/s), and one at elevated humidity (Hot Humid, 36°C at 3.92 kPa). Warmer air caused warmer skin (0.5°C/°C; P < 0.001), higher heart rate (1 bpm/°C; P < 0.001), sweat rate (0.04 l/h/°C; P < 0.001) and thermal perceptions during fixed‐intensity exercise, but minimally affected core temperature (<0.01°C/°C; P = 0.053). Time‐trial performance was comparable between Cool and Moderate (95% CI: –1.4, 5.9%; P = 0.263), but 3.6–6% slower in Hot (95% CI: ±2.4%; P ≤ 0.006). Elevated humidity increased core temperature (P < 0.001), perceived temperature and discomfort but not skin temperature or heart rate, and reduced mean blood pressure (P = 0.046) during fixed‐intensity exercise. Elevated humidity impaired time‐trial performance by 3.4% (95% CI: ±2.2%; P = 0.006). In conclusion, these findings quantify the importance of absolute humidity alongside air temperature when exercising under severe heat stress. [ABSTRACT FROM AUTHOR]

Anthropogenic activities that degrade natural ecosystems may also impact human health. However, research on the links between human and environmental health has most often been conducted at small scales (e.g. individual cities) and cannot easily be extrapolated to larger scales.We created a macrosystems ecology framework to identify associations between human and environmental health by combining human mortality and socioeconomic data for the conterminous United States with spatially aligned data on the physicochemical characteristics of river basins.Principal component analysis was first used to reduce a list of 596 environmental variables to a subset of 64 environmental covariates, representing six main environmental themes (climate, geology, hydrology, land use, river basin morphology and pollution). Independent, spatially aligned information was then obtained for 12 socioeconomic covariates.Random forest modelling was used to predict age‐adjusted mortality rate as a function of the environmental and socioeconomic covariates. An independent data subset (random 75:25 model building vs. testing split) was also used for model validation. The coefficient of determination between predicted and observed mortality rates was 0.76 for the validation data. Furthermore, model residuals (predicted − observed mortality) were centered near zero and normally distributed (1 SD = 62.26), suggesting high model accuracy and precision.Socioeconomic covariates were consistently the most influential predictors of mortality rate. Smoking, food insecurity, and lack of physical activity were particularly important. However, environmental covariates accounted for 5 of the 10 strongest predictors overall, with air temperature and precipitation being most influential among environmental variables.This proof‐of‐concept study demonstrates the utility of a modelling framework that combines environmental and human health data at macroscales. We suggest that further application of macrosystems ecology tools will improve the capacity to anticipate human health responses to ongoing environmental change. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Knowledge of the difference between soil and air temperatures (ΔT) is helpful to improve our understanding on the land‐atmosphere thermal interactions and temperature‐dependent soil processes. Based on 272 stations across China, this study investigated the spatiotemporal variations of the annual and seasonal ΔT (difference between soil temperature at a depth of 0.4 m and air temperature) from 1981 to 2014, and quantified the relative contributions of multiple environmental variables (snow cover, precipitation, vegetation, soil moisture, and solar radiation) to ΔT variation for the first time. Air temperature primarily controls soil temperature dynamics, but the asynchronous trends of soil and air temperatures may lead to the complexity of the land‐atmosphere relationship. Almost no apparent trends in ΔT were detected for the entire China (except in summer), but the spatial heterogeneity of trends was evident. Snow cover conditions greatly dominated the ΔT dynamics both annually and seasonally (except in summer). The relative contribution of snow cover duration to ΔT variation was significantly greater than that of mean snow depth for the entire China, but the regional differences in the contributions of the two variables were noticeable at different seasons. The greening of vegetation closely associated with the ΔT variation in annual, autumn and winter, and soil moisture exerted a great influence on summer ΔT, associated with sunshine duration (a proxy for surface solar radiation). The amount of precipitation made a slight impact on ΔT at either seasonal or annual scales. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper presents an attempt to obtain high-quality data series of monthly air temperature for Slovenian stations network in the period from 1961 to 2011. Intensive quality control procedure was applied to mean, maximum and minimum air temperature datasets from the Slovenian Environment Agency. Recently developed semi-automatic homogenization tool HOMER (HOMogenisation softwarE in R) was used to homogenize the selected high-quality datasets. To estimate the reliability of homogenized datasets, three to six experts independently homogenized the same datasets or their subsets. Different homogenization parameter settings were used by each of the experts, thus comprising ensemble homogenization experiment. Resulting datasets were compared by break statistics, root-mean-squared-difference (RMSD) of monthly and annual values, and RMSD of the long-term trend. This semi-automatic homogenization approach based on metadata gave more reliable homogenization results than a fully automatic approach without metadata. While the network-wide linear trend of the dataset did not change after semi-automatic homogenization was applied, the distribution of the trends of individual stations became spatially more uniform. The arithmetic mean of the homogenized datasets of three experts was assigned as a reference homogenized dataset and it was compared with some publicly available homogenized datasets. The calculated linear trend on an annual level for Slovenia is strongly positive in all datasets, though the trend values are significantly different between the datasets. We conclude that the warming trend of near-surface air temperature in Slovenia in 1961-2011 is significant and unequivocal in all seasons, except for autumn. Mean, maximum and minimum temperature series indicate linear trend of around 0.3-0.4 °C decade-1 on an annual level. [ABSTRACT FROM AUTHOR]

In recent decades, the world has been confronted with the consequences of global warming; however, this phenomenon is not reflected equally in every part of the globe. Thus, the warming phenomenon must be monitored in a more regional or local scale. This paper analyzes monthly long‐term time series of air temperatures in three Portuguese cities: Lisbon, Oporto, and Coimbra. We propose a periodic state‐space framework, associated with a suitable version of the Kalman filter; which allows for the estimation of monthly warming rates taking into account the seasonal behavior and serial correlation. Results about the monthly mean of the daily midrange temperature time series show that there are different monthly warming rates. The greatest annual mean rise was found in Oporto with 2.17°C, whereas in Lisbon and Coimbra, it was respectively 0.62°C and 0.55°C per century. [ABSTRACT FROM AUTHOR]

Epidemiological studies investigating the relationship between air temperature or heat and health, still, by and large, rely on either information from the nearest weather station or on coarse gridded temperature predictions, thereby ignoring small‐scale intra‐urban variations. Recent methodological advances show promise in achieving high spatiotemporal temperature predictions, thus improving the characterization of spatial variations in temperature and decreasing bias in health studies. Here, we applied a two‐stage approach using random forest to (a) impute missing moderate resolution imaging spectroradiometer (MODIS) land surface temperature at a 1 × 1 km resolution and (b) to use the gap‐filled MODIS data to explain spatiotemporal variation in the measured ground‐based air temperature data at a 100 × 100 m resolution across Switzerland using a range of predictor variables, including meteorological parameters, normalized difference vegetation index, impervious surface and altitude. Models presented here managed to capture temporal and spatial variations in air temperature in Switzerland from 2003 to 2018 at a fine spatial resolution of 100 × 100 m. Stage 1 models achieved an overall R2 of 0.98 and a root mean squared error (RMSE) of 1.49°C (independent validation), and the stage 2 model performed well for all years with R2 and RMSE ranging from 0.94 to 0.99 and 1.05 to 1.86°C, respectively. We were also able to capture the urban heat island effect and some typical weather phenomena caused by Switzerland's complex topography, like the foehn effect and inversion conditions. The resulting daily temperature surfaces for 2003–2018 will facilitate ongoing epidemiological research investigating the health effects of heat. [ABSTRACT FROM AUTHOR]

Summary: Portable gas exchange analysers provide critical data for understanding plant‐atmosphere carbon and water fluxes, and for parameterising Earth system models that forecast climate change effects and feedbacks.We characterised temperature measurement errors in the Li‐Cor LI‐6400XT and LI‐6800, and estimated downstream errors in derived quantities, including stomatal conductance (gsw) and leaf intercellular CO2 concentration (Ci).The LI‐6400XT exhibited air temperature errors (differences between reported air temperature and air temperature measured near the leaf) up to 7.2°C, leaf temperature errors up to 5.3°C, and relative errors in gsw and Ci that increased as temperatures departed from ambient. This caused errors in leaf‐to‐air temperature relationships, assimilation–temperature curves and CO2 response curves. Temperature dependencies of maximum Rubisco carboxylation rate (Vcmax) and maximum RuBP regeneration rate (Jmax) showed errors of 12% and 35%, respectively. These errors are likely to be idiosyncratic and may differ among machines and environmental conditions. The LI‐6800 exhibited much smaller errors.Earth system model predictions may be erroneous, as much of their parametrisation data were measured on the LI‐6400XT system, depending on the methods used. We make recommendations for minimising errors and correcting data in the LI‐6400XT. We also recommend transitioning to the LI‐6800 for future data collection. [ABSTRACT FROM AUTHOR]

The aim of the study was to determine changes in maximum air temperature (Tmax) in summer, and occurrence of hot days in the period 1966–2020, as well as to forecast the direction and rate of changes in near and far future. The research showed an increase in Tmax in the summer season. In the analysed period, the changes intensified over the last 20 years, as suggested by, among others, the occurrence of the 10 warmest years in the study period, mostly after 2000. According to model projections, an increase in Tmax will be recorded both in the near and far future. The consequence of the current increase in Tmax has been an increase in the frequency of hot days. Further increase in the number of hot days in Poland is projected for the upcoming decades. The smallest changes are projected for areas with most intensive changes in Tmax, and the largest in areas with moderate changes in the Tmax. [ABSTRACT FROM AUTHOR]

ABSTRACT The immense and towering Tibetan Plateau ( TP) acts as a heating source and shapes the climate of not only the Eurasian continent but also the entire world. The mass elevation effect of the TP was first observed in the 1950s; however, due to the scarcity of meteorological observation stations and limited climatic data, little information on the mass elevation effect of the plateau and its implications for the position of Alpine treelines in the southeastern part of the TP is quantitatively known. This paper compares monthly mean air temperature differences at elevations of 4000, 4500, 5000, 5500 and 6000 m between the main plateau, the Qilian Mts. in the northeastern corner of the plateau and the Sichuan Basin to the east of the plateau to quantify the mass elevation effect of the plateau. The TP air temperature data are retrieved from Terra moderate-resolution imaging spectroradiometer ( MODIS) land surface temperature ( LST), and the free-air temperatures over the westernmost Sichuan Basin are estimated using the measured lapse rate from Mt. Emei, which is located in the western portion of the Sichuan Basin. The results demonstrate the following important characteristics. (1) Owing to the mass elevation effect, air temperatures gradually increase from the eastern edge to the interior main TP. The monthly mean air temperature in the interior main plateau is approximately 2-7 °C higher than in the surrounding mountains and adjacent lowland areas. At an elevation of 4500 m (corresponding to the mean altitude of the TP), the monthly mean temperature differences between the plateau and the Sichuan Basin range from 3.58 °C (April) to 6.63 °C (June); the monthly temperature differences between the plateau and the Qilian Mts. range from 1.6 °C (July) to 7.7 °C (March). (2) The mass elevation effect of the plateau pushes the 10 °C isotherm upward in the warmest month and is indicative of a warmth index of 15 °C month up to elevations of 4600-4700 m, which enables the treeline altitude in the interior TP 500-1000 m higher than along the eastern edge. Therefore, mass elevation effect contributes to the occurrence of the highest treeline in the Northern Hemisphere, which is present on the southeastern TP. [ABSTRACT FROM AUTHOR]

Lake ice loss has been detected worldwide due to recent climate warming, yet spatially and temporally detailed information on the changes in global ice phenology does not exist. Here, we build a global lake ice phenology database comprising three lake ice phenologies—freeze‐up, break‐up, and ice duration—for each year across two centuries (1900–2099). The timing of all three phenologies experienced mild but statistically significant warming trends in the 20th century; continued warming trends were detected in ∼60% of the lakes from 2001 to 2020. Under a high emissions scenario (RCP 8.5), future global median ice duration would be shortened by 49.9 days by the end of the 21st century; such change can be substantially reduced under lower emission scenarios. We revealed continuous loss of global lake ice during the observed period, our generated database provides critical baseline information to evaluate the consequences of historical and future lake ice changes. Plain Language Summary: We provide the first spatially and temporally detailed quantification of the changes in global lake ice across two centuries (1900–2099). We found global lake ice changes toward later freeze‐ups, earlier break‐ups, or shorter ice durations over the past century, in response to global warming. Our projections revealed that continued lake ice loss would occur in the future, and the magnitude of changes increases with the increase in greenhouse gas emissions. Our analysis contributes the first comprehensive insight into how global lake ice changes over two centuries, and the data set provided here is critical for the evaluation of the consequences of historical and future lake ice changes. Key Points: We provide the first characterization of the changes in global lake ice phenology across two centuries (1900–2099) at lake levelWe examine >30,000 lakes across the Northern Hemisphere, accounting for 67% of the total area of frozen lakes globallyWe quantify the responses of global lake ice phenology to anthropogenic climate change over the past century [ABSTRACT FROM AUTHOR]

This study presents δ26Mg values, Mg2+ concentrations, δ18O and δ2H values of monthly collected rain and snow samples between 2014 and 2017 from north‐west Germany. The δ26Mg values of snow are similar to those of rain. Unlike δ18O and δ2H, neither δ26Mgprecipitation values nor Mg2+ concentrations display a relationship with air temperature. In contrast, δ26Mgprecipitation values and Mg2+ concentrations are correlated with precipitation amount. As previously suggested by other studies but now based on a larger data set, it is proposed that the δ26Mg values and Mg2+ concentrations are affected by the mineralogical composition and amount of airborne dust. Consequently, a small number of random precipitation samples cannot represent the complex processes influencing the δ26Mgprecipitation values. δ26Mgprecipitation can be used to differentiate between seawater aerosols, carbonate and silicate dust. Silicate dust is typified by higher Mg concentrations and δ26Mg values compared to carbonate dust. Concerning carbonates, dolostone dust has higher Mg concentrations and δ26Mg values, while limestone dust is Mg‐lean with lower δ26Mg values. Dust of anthropogenic origin in the region can come from active limestone and dolostone quarries, surface mining of siliciclastics, and agriculture on silicate‐rich soils. Although dust of this mineralogy can also be of natural origin, most natural dust consists of airborne silicates transported by wind systems from the Sahara to Europe. Higher δ26Mgprecipitation values would indicate silicate or dolostone dust influx. Lower δ26Mgprecipitation values suggest lower dust concentrations and a predominantly calcitic dust mineralogy. It appears that δ26Mgprecipitation is most probably influenced by changes in weather conditions and natural and anthropogenic dust origin. This study implies that δ26Mgprecipitation is a potential proxy for dust mineralogy and fluxes. Nevertheless, further research should include the determination of atmospheric dust mineralogy and their fractions as well as air mass back trajectory analysis to identify the source(s) of dust. [ABSTRACT FROM AUTHOR]

Results and accuracy of air temperature measurements made by meteorological thermometers hosted in thermometer screens strongly depend on the structure of the thermometer screen itself. In this study, the experiment was designed and conducted to understand the effect of radiation on air temperature observation. In order to control radiation sensitivity of thermometer screen, an umbrella made of Al alloy was installed over the thermometer screen (KTU). Air temperatures measured from five thermometer screens have been studied for several weather conditions using a 2-month field experiment at Korea Research Institute of Standards and Science (KRISS). The experiments comprised two version of KRISS type, which artificially ventilated thermometer screen (KTE and KTU) and three naturally ventilated round-shaped multi-plate thermometer screens (BT1, BT2, VT). The results showed that the air temperature of the KTU was approximately 0.10-0.50°C lower than the other group of thermometer screens without an umbrella, which blocked direct solar radiation. During the night, the umbrella minimizes the longwave energy from the KTU to the atmosphere. So the air temperature was roughly 0.10-0.18°C higher than that of the other group without the umbrella. It is confirmed that the umbrella installed over the thermometer screen minimizes the radiation sensitivity of the thermometer screen, and it has a significant effect on air temperature measurements. [ABSTRACT FROM AUTHOR]

High ground‐level ozone concentrations and high air temperatures present two health‐relevant natural hazards. The most severe health outcomes are generally associated with concurrent elevated levels of both variables, representing so‐called compound ozone and temperature (o‐t‐) events. These o‐t‐events, their relationship with identified main meteorological and synoptic drivers, as well as ozone and temperature levels themselves and the linkage between both variables, vary temporally and with the location of sites. Due to the serious health burden and its spatiotemporal variations, the analysis of o‐t‐events across the European domain represents the focus of the current work. The main objective is to model and project present and future o‐t‐events, taking region‐specific differences into account. Thus, a division of the European domain into six o‐t‐regions with homogeneous, similar ground‐level ozone and temperature characteristics and patterns built the basis of the study. In order to assess region‐specific main meteorological and synoptic drivers of o‐t‐events, statistical downscaling models were developed for selected representative stations per o‐t‐region. Statistical climate change projections for all central European o‐t‐regions were generated to assess potential frequency shifts of o‐t‐events until the end of the 21st century. The output of eight Earth System Models from the sixth phase of the Coupled Model Intercomparison Project considering SSP245 and SSP370 scenario assumptions was applied. By comparing midcentury (2041–2060) and late century (2081–2100) time slice differences with respect to a historical base period (1995–2014), substantial increases of the health‐relevant compound o‐t‐events were projected across all central European regions. Plain Language Summary: Compound events with concurrent high levels of ozone, being a major pollutant present in the air near Earth's surface, and of air temperature, have strong negative impacts on the health of humans. In this study, these compound events were investigated under present and future European climate. Six regions of similar ozone and temperature characteristics and patterns were defined. For each region, representative stations being typical examples for the overall region were extracted. Models for these stations were developed and their results analyzed to define factors that highly influence the occurrence of compound ozone and temperature events in each region, for example, mean air temperature or humidity levels. The generated models were later applied to project future frequency shifts of these compound events under climate change in central Europe. As a major result of the study, the future health‐relevant compound ozone and temperature events were projected to occur more frequently in the middle as well as at the end of the 21st century across all central European regions. Key Points: A clustering approach based on ground‐level ozone and air temperature leads to a division of Europe into six ozone and temperature regionsStatistical downscaling models identify region‐specific main meteorological and synoptic drivers of compound ozone and temperature eventsClimate change projections point to an increase in these compound events until the end of the 21st century in central Europe [ABSTRACT FROM AUTHOR]

The aims of the study were to assess the multiannual variability and trends of cold episodes in summer in Central Europe and to determine circulation conditions favouring their occurrence. The research was conducted based on data obtained for stations from Germany and Poland during the period 1966–2015. Cold episodes were understood as single cold days and cold spells. A cold day is defined as a day on which the maximum air temperature is lower than the 5th percentile of the local function of probability density determined from observation, and a cold spell was defined as at least two consecutive cold days. The observed increase in the air temperature in summer translated into a decrease in the number of cold episodes. The greatest changes in their number were recorded in the southern regions of the study area. On average, during the occurrence of cold episodes in summer in Central Europe, lower than usual sea level pressure was recorded, as well as lower persistence of the 300 hPa geopotential level, indicating the presence of cold air masses. Based on the minimum variance method, known as the Ward method, three circulation types were designated to cause the occurrence of cold episodes. The designated baric conditions caused advection of cold air masses from the northern sector. In each type, the largest T850 anomalies were recorded in Central Europe, reaching even less than −5°C. [ABSTRACT FROM AUTHOR]

Ascension Island has had a long intermittent record of instrumental weather recording. Here, we develop monthly records of mean temperature and precipitation totals (for 1924–2020 when an almost complete record is available) from the three principal recording sites: the capital Georgetown and two gauges at Wideawake Airfield. Although some of the data are in global climate databases, we have sourced as much data as possible from the primary sources in the United Kingdom, the United States and from the island itself. Air temperature shows statistically significant warming since 1950 of 0.54°C and since 1979 of 0.40°C, and agrees closely with sea surface temperatures, taken from the seas around the island, back to the start of the island series in 1924. Although the island is too small to be in Reanalyses, the warming trends of air temperatures from these products also agree, but the absolute air temperature values are about 1°C cooler than measured on the island. Annual precipitation on the island indicates it is very arid, with a long‐term average of only 165 mm. Occasionally, heavy monthly precipitation totals occur (always between February and June) which bring severe damage to the island's infrastructure and ecosystems. The heaviest monthly total was 334 mm for April 1985. [ABSTRACT FROM AUTHOR]

The article discusses the effects of weather condition on soil erodibility. The paper aims to consider whether climate affects the condition of soils to water erosion. The study collected soil erodibility data using measured soil loss from standard run-off plots. It illustrates the strong climate effect on soil by calculating seasonal variation of mean monthly soil erodibility. Soil erodibilities are divided into two large groups, one corresponding to soils in cool climate and another to soils located in warm climates.

BACKGROUND: The loss of coffee leaves caused by the attack of pests and diseases significantly reduces its production and bean quality. Thus this study aimed to estimate foliation for regions with the highest production of arabica coffee in Brazil using nonlinear models as a function of climate. A 25‐year historical series (1995–2019) of Coffea arabica foliation (%) data was obtained by the Procafé Foundation in cultivations with no phytosanitary treatment. The climate data were obtained on a daily scale by NASA/POWER platform with a temporal resolution of 33 years (1987–2019) and a spatial resolution of approximately 106 km, thus allowing the calculation of the reference evapotranspiration (PET). Foliation estimation models were adjusted through regression analysis using four‐parameter sigmoidal logistic models. The analysis of the foliation trend of coffee plantations was carried out from degrees‐day for 70 locations. RESULTS: The general model calibrated to estimate the arabica coffee foliation was accurate (mean absolute percentage error = 2.19%) and precise (R2adj = 0.99) and can be used to assist decision‐making by coffee growers. The model had a sigmoidal trend of reduction, with parameters ymax = 97.63%, ymin = 9%, Xo = 3517.41 DD, and p = 6.27%, showing that foliation could reach 0.009% if the necessary phytosanitary controls are not carried out. CONCLUSION: Locations with high air temperatures over the year had low arabica coffee foliation, as shown by the correlation of −0.94. Therefore, coffee foliation can be estimated using degree days with accuracy and precision through the air temperature. This represents great convenience because crop foliation can be obtained using only a thermometer. © 2021 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

River water temperature is known to be important for water quality and ecosystem processes. We quantified the degree to which lower river flows are associated with warmer river water, after accounting for seasonality and meteorological variability. We applied a systematic methodology to analyse observed mean daily river water temperature and mean daily river flow from 47 sites draining mountain, hill, and lowland catchments across the Canterbury region of Aotearoa New Zealand. We fitted regression models to remove seasonal patterns from all variables, then removed correlations between water temperature and each of three meteorological variables (solar radiation, air temperature, and earth temperature) before quantifying water temperature‐river flow relationships. Strong seasonal patterns were present in water temperature and each meteorological variable across all sites. Many sites also showed strong seasonal patterns in river flows. We demonstrated that seasonal patterns must be accounted for before day‐to‐day associations between water temperature and meteorological variables or river flow can be characterised. Higher water temperatures were associated with lower flows for 46 of 47 sites, even after having accounted for seasonality and associations with each meteorological variable. Increases in water temperature associated with a hypothetical reduction in river flow from the median to the fifth percentile varied with the site but were 0.5°C on average. This finding has important implications for river flow management because it indicates that increased river water temperatures would accompany reduced river flows regardless of site or catchment characteristics. [ABSTRACT FROM AUTHOR]

Uneven and sparse surface air temperature (Ta) observations challenge the understanding of the atmosphere‐land interactions and global environmental changes. Lapse rate (γ) is thought of with great potential to obtain accurate long‐term and high‐resolution Ta dataset. However, previous studies on γ over China were solely based on observed data, which limited our understanding since few stations are distributed in western China. In this study, an improved procedure for estimating γ by combining sparse observations and newly released ERA5 reanalysis data was proposed. The spatial heterogeneity in γ over China and its influencing factors (e.g., topography, climate, and vegetation) were then explored. A re‐examination of long‐term spatiotemporal variations in γ and its potential driving forces over China was also performed at grid scales. The results demonstrated that our γ estimations reproduced similar patterns to those derived from observations. The subsequent analyses revealed that the annual and seasonal variability in γ varied substantially among different altitude gradients, land cover types, and climate zones. The vital role played by vegetation covers in regulating local γ was emphasized in this study, which was poorly assessed previously. Moreover, γ in low altitude zones showed lower values but strong temporal fluctuations and contributed more to the interannual variability in γ over China, which could be mainly attributed to human activities. The present findings can provide an alternative method for estimating γ and be further used to assess regional or global temperature changes. [ABSTRACT FROM AUTHOR]

Gross primary productivity (GPP) of wooded ecosystems (forests and savannas) is central to the global carbon cycle, comprising 67%–75% of total global terrestrial GPP. Climate change may alter this flux by increasing the frequency of temperatures beyond the thermal optimum of GPP (Topt). We examined the relationship between GPP and air temperature (Ta) in 17 wooded ecosystems dominated by a single plant functional type (broadleaf evergreen trees) occurring over a broad climatic gradient encompassing five ecoregions across Australia ranging from tropical in the north to Mediterranean and temperate in the south. We applied a novel boundary‐line analysis to eddy covariance flux observations to (a) derive ecosystem GPP–Ta relationships and Topt (including seasonal analyses for five tropical savannas); (b) quantitatively and qualitatively assess GPP–Ta relationships within and among ecoregions; (c) examine the relationship between Topt and mean daytime air temperature (MDTa) across all ecosystems; and (d) examine how down‐welling short‐wave radiation (Fsd) and vapour pressure deficit (VPD) influence the GPP–Ta relationship. GPP–Ta relationships were convex parabolas with narrow curves in tropical forests, tropical savannas (wet season), and temperate forests, and wider curves in temperate woodlands, Mediterranean woodlands, and tropical savannas (dry season). Ecosystem Topt ranged from 15℃ (temperate forest) to 32℃ (tropical savanna—wet and dry seasons). The shape of GPP–Ta curves was largely determined by daytime Ta range, MDTa, and maximum GPP with the upslope influenced by Fsd and the downslope influenced by VPD. Across all ecosystems, there was a strong positive linear relationship between Topt and MDTa (Adjusted R2: 0.81; Slope: 1.08) with Topt exceeding MDTa by >1℃ at all but two sites. We conclude that ecosystem GPP has adjusted to local MDTa within Australian broadleaf evergreen forests and that GPP is buffered against small Ta increases in the majority of these ecosystems. [ABSTRACT FROM AUTHOR]

Many organisms live in environments in which temperatures differ substantially from those measured by standard weather stations. The last decade has witnessed a paradigm shift in efforts to quantify these differences and to understand their ecological, functional and evolutionary implications. This renewed interest in microclimate ecology has been accompanied by the development of various compact temperature sensors and radiation shields. However, it is clear that there are many pitfalls when measuring temperature using these devices.Here we address the problem of measuring temperatures in these microenvironments accurately. We first discuss the theory of measuring surface, ground and air temperatures with reference to energy fluxes and how these are modified by material, reflective properties and size of the device. We highlight the particular difficulties associated with measuring air temperature. We then report on the results of a series of experiments in which air temperatures recorded by various commonly used microclimate temperature loggers are compared to those obtained using research‐grade instruments and synoptic weather stations.While accurate measurements of surface and ground temperatures and air temperatures at night and in shaded environments can be relatively easily obtained, we show substantial errors are to be expected when measuring air temperatures in environments exposed to sunlight. Most standard sensors yield large errors, which can reach 25°C due to radiative fluxes operating on the thermometer. This problem cannot be wholly overcome by shielding the thermometer from sunlight, as the shield itself will influence both the temperatures being measured and the accuracy of measurement.We demonstrate that reasonably accurate estimates of air temperature can be obtained with low‐cost and unshielded ultrafine‐wire thermocouples that possess low thermal emissivity and a highly reflective surface. As the processes that create microclimatic temperature variation are the same as those that cause errors, other logger types should be used with care, and generally avoided in environments exposed to sunlight and close to the ground where wind speeds are lower. We urge researchers interested in microclimates and their effects to pay greater heed to the physics of heat exchange when attempting to measure microclimate temperatures and to understand the trade‐offs that exist in doing so. [ABSTRACT FROM AUTHOR]

Future climate may profoundly impact the functioning of terrestrial ecosystems. However, we do not know well how the functioning of different types of grassland ecosystems is associated with variation in temperature and precipitation. Here, we used long‐term field measurements to examine how climatic changes between the 1980s and the 2010s (i.e., growing season temperature, precipitation, habitat moisture index, solar radiation, and sunshine duration) have affected aboveground net primary productivity (ANPP) for all major grassland types in northern China. We found that ANPP consistently declined over the 30‐year period across all types of grassland, on average by about 6.1%. Warming, associated with increased solar radiation and, hence, soil temperature, was the primary factor driving the decrease of ANPP. We further show that ANPP was more sensitive to climate change in alpine and lowland grasslands than in temperate grasslands. Together, our findings indicate that climate warming consistently reduces plant productivity of different types of grassland ecosystems, and emphasize the importance of soil temperature in driving the decline in grassland productivity under climate change. Plain Language Summary: Future climate may profoundly impact the functioning of terrestrial ecosystems. However, we do not know well how the functioning of different types of grassland ecosystems is associated with variation in temperature and precipitation. Here, we used long‐term field measurements to examine how climatic changes between the 1980s and the 2010s (i.e., growing season temperature, precipitation, habitat moisture index, solar radiation, and sunshine duration) have affected aboveground net primary productivity (ANPP) for all major grassland types in northern China. We found that ANPP consistently declined over the 30‐year period across all types of grassland, on average by about 6.1%. Warming, associated with increased solar radiation and, hence, soil temperature, was the primary factor driving the decrease of ANPP. We further show that ANPP was more sensitive to climate change in alpine and lowland grasslands than in temperate grasslands. Together, our findings indicate that climate warming consistently reduces plant productivity of different types of grassland ecosystems, and emphasize the importance of soil temperature in driving the decline in grassland productivity under climate change. Key Points: There is a universal decrease in aboveground net primary productivity (ANPP) over 30 years across different grassland types, and the magnitude of ANPP decline differed between typesThe air temperature and soil temperature rather than precipitation were the primary drivers of ANPP decline to climate changeThe increased air temperature and soil temperature from increased total solar radiation were the main drivers for ANPP decline [ABSTRACT FROM AUTHOR]

With the purpose of revising World Meteorological Organization's Commission for Instruments and Methods of Observation (WMO/CIMO) Guide #8 on weather stations siting, an experiment to evaluate metrologically the maximum influence of a paved road on 2‐m air temperature measurements ("road siting effect") has been designed, installed and run in Italy. It consists of a 100‐m long array of seven measurement stations, at increasing distances from a local road, equipped with shielded Pt100 thermometers and ancillary sensors. Data coming from 1 year of observations have been analysed for daily climatological metrics, finding that the road mostly effects minimum temperatures, with average values of ~0.30 ± 0.18 °C at a distance of 1 m; then, in order to quantify the instrumental effect on the measurement, data were filtered by applying a generalized additive model, selecting only times when the effect is more intense (during nights, in presence of low winds coming from the road), and the road siting effect has been calculated by modelling the maximum temperature differences by using extreme values analysis. The 1‐year return value on 10‐min measurements is 1.22 ± 0.30 °C at 1 m from the road, with a gradual decline (~0.1 °C·m−1), while an extrapolation to 100‐year return level gives a value of 1.71 ± 0.79 °C with a decline rate of about 0.17 °C·m−1. This is a first step towards a redefinition of the weather station classification scheme of WMO/CIMO Guide #8, together with building and tree effects experiments which have been run in parallel with the road siting experiment here presented and which will be presented elsewhere. [ABSTRACT FROM AUTHOR]

Snow and glacier are important components in the hydrological cycle of the Tibetan Plateau (TP). Air temperature, as the main driver in freezing and thawing processes, becomes vital for hydrological modelling and prediction in this region. Due to a sparse ground gauging network, spatial density of air temperature measurement is insufficient for hydro‐meteorological studies. Therefore, the aim of this study is to identify the best representative temperature data for hydrological applications from four widely used reanalysis products, including ERA‐Interim, ERA‐5, GLDAS‐2.1 and NCEP‐R2, with reference to in situ measurements and gridded snow depth from the year 2008–2017 over the entire TP. To reduce errors, Bayesian Joint Probability (BJP) approach based on K‐Nearest Neighbour (KNN) classification algorithm (KNN‐BJP) is proposed to post‐process gridded reanalyses. The results indicate that all the reanalysis datasets provide highly correlated but cold biased air temperature. The correlation ecoefficiency is greater than 0.85. The cold biases are near −3 ° C and mainly distributed in the southeastern TP. Bias in daily maximum temperature during Spring is greater than −8 ° C for most stations. ERA‐Interim is found to have the closest agreement with in situ measurements, closely followed by GLDAS‐2.1. KNN‐BJP is found to be effective within a distance smaller than 5°. After post‐processing, the prominent underestimation is efficiently corrected with Bias near 0. RMSE is markedly reduced to be smaller than 2.5 ° C. The post‐processed ERA‐5 and GLDAS‐2.1 are as accurate as ERA‐Interim, but able to provide more detailed information for extreme events due to their finer spatial resolution. Thus, ERA‐5 and GLDAS‐2.1 are more recommended to represent air temperature in the TP. Snow depth as complementary reference data is able to present spatial variance of air temperature. Our study can help alleviate the problem of sparse air temperature data over the TP. [ABSTRACT FROM AUTHOR]

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This study proposed that due to their high standing water tables that peatlands would be cold humid islands within their landscape, and especially so relative to farmland on mineral soils. To test this hypothesis, we measured air temperature and humidity at 17 locations along a 7.8 km transect across the UK's largest lowland raised bog from February 2018 to January 2019. Air temperature and humidity were measured hourly for 1 year and supported with spot albedo measurements. The study represented a factorial experiment with respect to sites of measurement, the type of land use (peat vs. arable land) and time of sampling over both the seasonal and diurnal cycles. We show that: (a) That although mean annual temperature was not significantly different between arable and peatlands, the arable land showed a decreased amplitude to its seasonal cycle – this is the reverse of the expected pattern. (b) The albedo of the peatland was significantly lower than that of arable land showing that vegetated peatland still absorbed more solar radiation. (c) The specific humidity was lower on the peatland than on the surrounding arable land. The study showed that while shrubby vegetation exists over a peatland then energy budgets are more likely to be dominated by the lower aerodynamic resistance and lower albedo of the vegetated peatland relative to arable land. Thus, shrub‐dominated peatlands will not be a cold humid island in their landscape. [ABSTRACT FROM AUTHOR]

Rising global temperatures over the last decades have increased heat exposure among populations worldwide. An accurate estimate of the resulting impacts on human health demands temporally explicit and spatially resolved monitoring of near‐surface air temperature (Ta). Neither ground‐based nor satellite‐borne observations can achieve this individually, but the combination of the two provides synergistic opportunities. In this study, we propose a two‐stage machine learning‐based hybrid model to estimate 1 × 1 km2 gridded intra‐daily Ta from surface skin temperature (Ts) across the complex terrain of Israel during 2004–2016. We first applied a random forest (RF) regression model to impute missing Ts from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra satellites, integrating Ts from the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI) satellite and synoptic variables from European Centre for Medium‐Range Weather Forecasts' (ECMWF) ERA5 reanalysis data sets. The imputed Ts are in turn fed into the Stage 2 RF‐based model to estimate Ta at the satellite overpass hours of each day. We evaluated the model's performance applying out‐of‐sample fivefold cross validation. Both stages of the hybrid model perform very well with out‐of‐sample fivefold cross validated R2 of 0.99 and 0.96, MAE of 0.42°C and 1.12°C, and RMSE of 0.65°C and 1.58°C (Stage 1: imputation of Ts, and Stage 2: estimation of Ta from Ts, respectively). The newly proposed model provides excellent computationally efficient estimation of near‐surface air temperature at high resolution in both space and time, which helps further minimize exposure misclassification in epidemiological studies. [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]

Guidelines in the Guide to Meteorological Instruments and Methods of Observation (the CIMO guide) of the World Meteorological Organization (WMO, published 2014, updated 2017, section 2.1.3.3, Response times of thermometers) recommend that the 63% response time τ for an air temperature sensor be 20 s, although – as airflow speed influences response time – the minimum airflow speed at which this applies should also be specified in the document. A 63% response time τ63 = 20 s implies that 95% of a step change be registered within 3τ63 or 60 s, the WMO recommended averaging interval for air temperature: rapid air temperature changes on this time‐scale are not uncommon, often associated with convective squalls, frontal systems or sea breeze circulations. An alternative way of expressing the effect of the time constant is that in air whose temperature is changing at 0.1 K·min−1 the thermometer would lag by approximately 0.03 K. To assess whether this response time specification was realistic, we have undertaken an experimental and theoretical study of the time constants of meteorological thermometers. Laboratory wind tunnel tests were undertaken to quantify 63% and 95% response times of 25 commercial 100 Ω platinum resistance thermometers (PRTs) of various sizes (length and sheath diameter) from five manufacturers. The test results revealed a fourfold difference in response times between different sensors: none of the PRTs tested met the CIMO response time guideline at a ventilation speed of 1 m·s−1 assumed typical of passively ventilated thermometer shields such as Stevenson‐type thermometer screens. A theoretical model of the sensors was devised which matched the experimental behaviour with regard to the most important contributing factors, namely ventilation rate and sensor diameter. Finally, suggestions and recommendations for operational air temperature sensor adoption and future sensor development are included. [ABSTRACT FROM AUTHOR]

The correlation between the thickness of the troposphere (THK) and observed temperature over Iran is investigated. To achieve this goal, the monthly observed temperature data for the period between January 1, 1979 and December 31, 2013, from 42 meteorological stations throughout Iran were used. Atmospheric thickness, defined as the vertical distances between the 500, 700, 850 and 1,000 hPa pressure surfaces, is directly related to near‐surface air temperature. The results show that the magnitude of the correlation between near‐surface air temperature and the THK vary in different months and from region to region. The strongest significant correlation was observed between December and March when air temperature is low. The correlation was particularly high in the eastern part of the study area at layer 1,000–850 hPa, while the strongest correlation was observed at layer 1,000–700 hPa in the mountainous western parts of the country. The study area's average correlation co‐efficient in March fluctuated between 0.900 at layer 1,000–500 hPa to 0.918 at layer 1,000–700 hPa. The magnitude of the correlation co‐efficients and the spatial extent of the significant positive correlation gradually decreased with increasing air temperature, reaching a minimum during the summer. Low‐level troposphere thickness (1,000–700 hPa) plays an important role in impacting near‐surface temperature comparable with the effect of mid‐level troposphere thickness. [ABSTRACT FROM AUTHOR]