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The seasonal cycle (SC) of surface air temperature is a substantial element in climatology. Some basic and important topics in climate change studies are based on accurate and reliable estimation of SCs, such as percentile‐based indices of extremes and probability density function (PDF) changes of daily temperatures. For both of them, most studies characterized SCs using averages of multi‐days windows, which is not smooth and accurate enough representing the extreme thresholds and climatological normal of SCs. It is necessary to construct smooth and reasonable SCs for more accurate estimation of temperature changes on extreme thresholds and PDFs. In this study, we propose a flexible method based on generalized additive models for location scale and shape and penalized b‐spline smoothing technique with respective distributions to construct smooth SCs and SC for extreme temperatures (SCETs). The accuracy of the constructed smooth SCETs is good with the estimation biases of percentiles tending to be zero. The constructed smooth SCET also exhibits good stability over time, such that the magnitude changes of temperatures on each calendar day are close to climatic changes of mean temperature when the concerned period shifts. Based on the constructed smooth SCs, climatic changes by seasons and PDFs between two periods, 1961–1990 and 1991–2020, are examined over China. The increase of thresholds for hot extremes in spring during the recent period is prominent, while the increase of thresholds for daytime cold extremes in summer over a part of central to southern China is also notable. The smooth SCs and SCETs based on our flexible statistical modelling framework can characterize daily extreme temperatures reasonably and accurately, and should be expected to have more applications for a better understanding of climate changes related to distributions and seasonal cycles. [ABSTRACT FROM AUTHOR]

A set of homogenized monthly mean surface air temperature (SAT) series at 32 stations in China back to the 19th century had previously been developed based on the RHtest method by Cao et al., but some inhomogeneities remained in the dataset. The present study produces a further-adjusted and updated dataset based on the Multiple Analysis of Series for Homogenization (MASH) method. The MASH procedure detects 33 monthly temperature records as erroneous outliers and 152 meaningful break points in the monthly SAT series since 1924 at 28 stations. The inhomogeneous parts are then adjusted relative to the latest homogeneous part of the series. The new data show significant warming trends during 1924-2016 at all the stations, ranging from 0.48 to 3.57°C (100 yr)−1, with a regional mean trend of 1.65°C (100 yr)−1; whereas, the previous results ranged from a slight cooling at two stations to considerable warming, up to 4.5°C (100 yr)−1. It is suggested that the further-adjusted data are a better representation of the large-scale pattern of climate change in the region for the past century. The new data are available online at http://www.dx.doi.org/10.11922/sciencedb.516. [ABSTRACT FROM AUTHOR]

ABSTRACT: The geographical extent, magnitude, and uncertainty of global climate change have been widely discussed and have critical policy implications at both global and local scales. In this study, a new analysis of annual mean global land surface air temperature since 1880 was generated, which has greater coverage and lower uncertainty than previous distributions. The Biased Sentinel Hospitals Areal Disease Estimation (BSHADE) method, used in this study, makes a best linear unbiased estimation (BLUE) when a sample is small and biased to a spatially heterogeneous population. For the period of 1901–2010, the warming trend was found to be 0.109 °C decade−1 with 95% confidence intervals between 0.081 °C and 0.137 °C. Additionally, warming exhibited different spatial patterns in different periods. In the early 20th century (1923–1950), warming occurred mainly in the mid‐high latitudes of the Northern Hemisphere, whereas in the most recent decades (1977–2014), warming was more spatially extensive across the global land surface. Compared with other common methods, the difference in results appears in the areas with few stations and in the early years, when stations had sparse coverage and were unevenly distributed. Validation, which was performed using real data that simulated the historic situation, showed a smaller error in the BSHADE estimate than in other methods. This study produced a new database with greater coverage and less uncertainty that will improve the understanding of climate dynamics on the Earth since 1880, especially in isolated areas and early periods, and will benefit the assessment of climate‐change‐related issues, such as the effects of human activities. [ABSTRACT FROM AUTHOR]

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Global warming exerts a lengthening effect on the growing season, with observational evidences emerging from different regions over the world. However, the difficulty for a global overview of this effect for the last century arises from limited availability of the long-term daily observations. In this study, we find a good linear relationship between the start (end) date of local growing season (LGS) and the monthly mean temperature in April (October) using the global gridded daily temperature dataset for 1960-1999. Using homogenized daily temperature records from nine stations where the time series go back to the beginning of the twentieth century, we find that the rate of change in the start (end) date of the LGS for per degree warming in April (October) mean temperature keeps nearly constant throughout the time. This enables us to study LGS changes during the last century using global gridded monthly mean temperature data. The results show that during the period 1901-2009, averaged over the observation areas, the LGS length has increased by a rate of 0.89 days decade, mainly due to an earlier start (−0.58 days decade). This is smaller than those estimates for the late half of the twentieth century, because of multidecadal climate variability (MDV). A MDV component of the LGS index series is extracted by using Ensemble Empirical Mode Decomposition method. The MDV exhibits significant positive correlation with the Atlantic Multi-decadal Oscillation (AMO) over most of the Northern Hemisphere lands, but negative in parts of North America and Western Asia for start date of LGS. These are explained by analyzing differences in atmospheric circulation expressed by sea level pressure departures between the warm and cool phases of AMO. It is suggested that MDV in association with AMO accelerates the lengthening of LGS in Northern Hemisphere by 53 % for the period 1980-2009. [ABSTRACT FROM AUTHOR]

Two approaches of statistical downscaling were applied to indices of temperature extremes based on percentiles of daily maximum and minimum temperature observations at Beijing station in summer during 1960-2008. One was to downscale daily maximum and minimum temperatures by using EOF analysis and stepwise linear regression at first, then to calculate the indices of extremes; the other was to directly downscale the percentile-based indices by using seasonal large-scale temperature and geo-potential height records. The cross-validation results showed that the latter approach has a better performance than the former. Then, the latter approach was applied to 48 meteorological stations in northern China. The cross-validation results for all 48 stations showed close correlation between the percentile-based indices and the seasonal large-scale variables. Finally, future scenarios of indices of temperature extremes in northern China were projected by applying the statistical downscaling to Hadley Centre Coupled Model Version 3 (HadCM3) simulations under the Representative Concentration Pathways 4.5 (RCP 4.5) scenario of the Fifth Coupled Model Inter-comparison Project (CMIP5). The results showed that the 90th percentile of daily maximum temperatures will increase by about 1.5°C, and the 10th of daily minimum temperatures will increase by about 2°C during the period 2011-35 relative to 1980-99. [ABSTRACT FROM AUTHOR]

The temperature thresholds and timings of the 24 climatic Solar Terms in China are determined from a homogenized dataset of the surface air temperature recorded at 549 meteorological stations for the period 1960-2008 employing the ensemble empirical mode decomposition method. Changes in the mean temperature and timing of the climatic solar terms are illustrated. The results show that in terms of the mean situation over China, the number of cold days such as those of Slight Cold and Great Cold has decreased, especially by 56.8% for Great Cold in the last 10 years (1998-2007) compared with in the 1960s. The number of hot days like those of Great Heat has increased by 81.4% in the last 10 years compared with in the 1960s. The timings of the climatic Solar Terms during the warming period (around spring) in the seasonal cycle have advanced significantly by more than 6 d, especially by 15 d for Rain Water, while those during the cooling period (around autumn) have delayed significantly by 5-6 d. These characteristics are mainly due to a warming shift of the whole seasonal cycle under global warming. However, the warming shift affects the different Solar Terms to various extents, more prominently in the spring than in the autumn. The warming tendencies for Rain Water, the Beginning of Spring, and the Waking of Insects are the largest, 2.43°C, 2.37°C, and 2.21°C, respectively, for the period 1961-2007 in China as a whole. Four particular phenology-related climatic Solar Terms, namely the Waking of Insects, Pure Brightness, Grain Full, and Grain in Ear, are found to have advanced almost everywhere. In semi-arid zones in northern China, advances of the timings of these four climatic Solar Terms are significant, 12-16, 4-8, 4-8, and 8-12 d, respectively, for the period 1961-2007. These quantitative results provide a scientific base for climate change adaptation, especially in terms of agricultural planning and energy-saving management throughout a year. [ABSTRACT FROM AUTHOR]

Heatwaves have considerable socioeconomic impact. Because of the urban heat island effect, risks associated with heatwaves are exacerbated in urban areas compared with surrounding regions. This study investigated the behaviors of local-scale urban heat island during a heatwave in Beijing using in situ observations and kilometer-scale simulations based on the Weather Research and Forecasting model. Results showed the observed urban heat island was enhanced by 0.78 °C during the heatwave episode, with greater increase at night than during daytime. The model with gridded urban canopy parameters was able to capture the spatiotemporal variation of surface air temperature, although it overestimated the magnitude. Spatial variation of urban canopy parameters could improve simulation of the spatiotemporal variations of near-surface air temperature in urban areas. Using a biophysical factorization method, urbanization-induced temperature changes were attributed to different driving factors. Results suggested that heatwave-related enhancement of urban heat island during daytime is primarily because of increased urban–rural contrast in surface evapotranspiration, whereas such enhancement at night is associated mainly with greater anthropogenic heat and enhanced warm advection. Since climate change make heatwaves more frequent, intense and persistent, targeted implementation of adaptation measures are needed to reduce the negative effects of urban heat island and to minimize their related risks to public health, power grids, and infrastructure. • This paper demonstrates the enhancement of urban heat island (UHI) due to heat wave using observations and numerical model. • Observed UHI in Beijing was enhanced by 0.78 °C during a heatwave episode, more profoundly during night than during daytime. • Enhanced UHI is due to increased urban–rural contrasts in surface evapotranspiration, anthropogenic heat and warm advection. • Synergistic interactions between UHI and heatwave present further heat-related risks for urban society and residents. [ABSTRACT FROM AUTHOR]