Trends and Uncertainties in Surface Air Temperature over the Tibetan Plateau,1951–2013

Trends and uncertainties of surface air temperature over the Tibetan Plateau(TP)are evaluated by using observations at 100 meteorological stations during the period 1951–2013.The sampling error variances of gridded monthly data are estimated for every month and every grid box of data.The gridded data and their sampling error variances are used to calculate TP averages,their trends,and associated uncertainties.It is shown that large sampling error variances dominate northern and western TP,while small variances appear over southern and eastern TP.Every month from January to December has a positive linear trend during the study period.February has the largest trend of 0.34±0.18°C(10 yr)~(–1),and April the smallest at 0.15±0.11°C(10 yr)~(–1).The uncertainties decrease steadily with time,implying that they are not large enough to alter the TP warming trend.     

中国均一化日平均温、最高温和最低温序列1960-2008

Inhomogeneities in the daily mean/maximum/ minimum temperature （Tm/Tmax/Tmin） series from 1960- 2008 at 549 National Standard Stations （NSSs） in China were analyzed by using the Multiple Analysis of Series for Homogenization （MASH） software package. Typical biases in the dataset were illustrated via the cases of Beijing （B J）, Wutaishan （WT）, Urumqi （UR） and Henan （HN） stations. The homogenized dataset shows a mean warming trend of 0.261/0.193/0.344℃/decade for the annual series of Tm/Tmax/Tmin, slightly smaller than that of the original dataset by 0.006/0.009/0.007℃/decade. However, considerable differences between the adjusted and original datasets were found at the local scale. The adjusted Tmin series shows a significant warming trend almost everywhere for all seasons, while there are a number of stations with an insignificant trend in the original dataset. The adjusted Tm data exhibit significant warming trends annually as well as for the autumn and winter seasons in northern China, and cooling trends only for the summer in the middle reaches of the Yangtze River and parts of central China and for the spring in southwestern China, while the original data show cooling trends at several stations for the annual and seasonal scales in the Qinghai, Shanxi, Hebei, and Xinjiang provinces. The adjusted Tmax data exhibit cooling trends for summers at a number of stations in the mid-lower reaches of the Yangtze and Yellow Rivers and for springs and winters at a few stations in southwestern China, while the original data show cooling trends at three/four stations for the annual/autumn periods in the Qinghai and Yunnan provinces. In general, the number of stations with a cooling trend was much smaller in the adjusted Tm and Tmax dataset than in the original dataset. The cooling trend for summers is mainly due to cooling in August. The results of homogenization using MASH appear to be robust; in particular, different groups of stations with consideration of elevation led to minor effects i     

Impact of Urbanization on Low-Temperature Precipitation in Beijing during 1960–2008简

Daily precipitation and temperature records at 13 stations for the period 1960-2008 were analyzed to identify climatic change and possible effects of urbanization on low-temperature precipitation [LTP, precipitation of ≥ 0.1 mm d^-1 occurring under a daily minimum temperature （Tmin） of ≤ 0℃] in the greater Beijing region （B JR）, where a rapid process of urbaniza tion has taken place over the last few decades. The paper provides a climatological overview of LTP in B JR. LTP contributes 61.7% to the total amount of precipitation in B JR in the cold season （November-March）. There is a slight increasing trend [1.22 mm （10 yr）^-1] in the amount of total precipitation for the cold season during 1960-2008. In contrast, the amount of LTP decreases by 0.6 mm （10 yr）^-1. The warming rate of Train in B JR is 0.66℃ （10 yr）^-1. Correspondingly, the frequency of LTP decreases with increasing Tmin by -0.67 times per ℃. The seasonal frequency and amount of LTP in southeast B JR （mostly urban sites） are 17%-20% less than those in the northwestern （rural and montane sites）. The intensity of LTP for the urban sites and northeastern B JR exhibited significant enhancing trends [0.18 and 0.15 mm d^- 1 （10 yr）^- 1, respectively]. The frequency of slight LTP （〈0.2 mm d^-1） significantly decreased throughout B JR [by about -15.74% （10 yr）^-1 in the urban area and northeast B JR], while the contribution of the two heaviest LTP events to total LTP amount significantly increased by 3.2% （10 yr） ^-1.     

Changes in Seasonal Cycle and Extremes in China during the Period 1960–2008

Recent trends in seasonal cycles in China are analyzed, based on a homogenized dataset of daily temperatures at 541 stations during the period 1960–2008. Several indices are defined for describing the key features of a seasonal cycle, including local winter/summer (LW/LS) periods and local spring/autumn phase (LSP/LAP). The Ensemble Empirical Mode Decomposition method is applied to determine the indices for each year. The LW period was found to have shortened by 2–6 d (10 yr)-1, mainly due to an earlier end to winter conditions, with the LW mean temperature having increased by 0.2°C–0.4°C (10 yr)?1, over almost all of China. Records of the most severe climate extremes changed less than more typical winter conditions did. The LS period was found to have lengthened by 2–4 d (10 yr)?1, due to progressively earlier onsets and delayed end dates of the locally defined hot period. The LS mean temperature increased by 0.1°C–0.2°C (10 yr)-1 in most of China, except for a region in southern China centered on the mid-lower reaches of the Yangtze River. In contrast to the winter cases, the warming trend in summer was more prominent in the most extreme records than in those of more typical summer conditions. The LSP was found to have advanced significantly by about 2 d (10 yr)-1 in most of China. Changes in the autumn phase were less prominent. Relatively rapid changes happened in the 1980s for most of the regional mean indices dealing with winter and in the 1990s for those dealing with summer.     

Updated Homogenized Chinese Temperature Series with Physical Consistency

Most methods of homogenization of climate data are applied to time series of a single variable, such as daily maximum temperature(Tmax) or daily minimum temperature(Tmin). Consequently, the physical relationship among different variables, e.g., TmaxTmin, may be distorted after homogenization of climate series of individual variables. The authors develop a solution to improve consistency among diurnal temperature records, while using the Multiple Analysis of Series for Homogenization(MASH) method to homogenize the observation series of daily mean temperature(Tm), Tmin, and Tmax at 545 stations in China for the period 1960–2011, called CHTM2.0. In the previous version of this homogenized dataset based on MASH(CHTM1.0) for the period 1960–2008, there are a few records(0.039% of the total) that are physically inconsistent. For developing CHTM2.0, the authors apply additional adjustments for each day with inconsistent temperature records, in order to hold TmaxTmTmin. Although the additional adjustments are barely influential for estimating long-term climate trends in China as a whole(because very few records are additionally adjusted), the newly introduced solution improves the physical consistency throughout the dataset. It is also helpful for developing more reasonable homogenized climate datasets with regard to physical consistency among multiple variables. Based on CHTM2.0, the annual Tmax/Tm/Tmin series averaged over China for the period 1960–2011 show significant warming trends of about 0.19/0.25/0.34°C per decade, respectively. Large warming trends of up to 0.425/0.596/ 0.704°C per decade occur in northeastern and northwestern China.     

Extreme Temperature Change of the Last 110 Years in Changchun,Northeast China

In China and East Asia,the long-term continuous observational data at daily resolution are insufficient,and thus there is a lack of good understanding of the extreme climate variation over the last 100 years plus.In this study,the extreme temperature indices defined by ETCCDI(Expert Team on Climate Change Detection and Indices)and local meteorological administrations were analyzed for Changchun City,Northeast China,by using the daily maximum temperature(Tmax)and daily minimum temperature(Tmin)over 1909?2018.The results showed that extreme cold events,such as cold days,cold nights,frost days,icing days,and low temperature days,decreased significantly at rates of?0.41 d(10 yr)^?1,?1.45 d(10 yr)^?1,?2.28 d(10 yr)^?1,?1.16 d(10 yr)?1 and?1.90 d(10 yr)^?1,respectively.Warm nights increased significantly at a rate of 1.71 d(10 yr)^?1,but warm days decreased slightly and the number of high temperature days decreased at a rate of?0.20 d(10 yr)?1.The frequency of cold surge events increased significantly at a rate of 0.25 d(10 yr)^?1,occurring mainly from the mid-1950s to late-1980s.The average Tmax,average Tmin and extreme Tmin increased at rates of 0.09℃(10 yr)^?1,0.36℃(10 yr)^?1 and 0.54℃(10 yr)^?1,respectively;and extreme Tmax decreased significantly at a rate of?0.17℃(10 yr)^?1.In 1909?2018,1951?2018 and 1979?2018,the indices related to cold events decreased,while the trends of the indices related to warm events were different for different periods.     

URBAN HEAT ISLAND EFFECT AND ITS CONTRIBUTION TO OBSERVED TEMPERATURE INCREASE AT WUHAN STATION, CENTRAL CHINA

Based on an in-homogeneity adjusted dataset of the monthly mean temperature, minimum and maximum temperature, this paper analyzes the temporal characteristics of Urban Heat Island (UHI) intensity at Wuhan Station, and its impact on the long-term trend of surface air temperature change recorded during 1961–2015 by using an urban-rural method. Results show that UHI effect is obvious near Wuhan Station in the past 55 years, especially for minimum temperature. The strongest UHI intensity occurs in summer and the weakest in winter. For the period 1961–2004, UHI intensity undergoes a significant increase near the urban station, with the increase especially large for the period 1988–2004, but the last 10 years witness a significant decrease, with the decrease in minimum temperature being more significant than that of maximum temperature. The annual mean urban warming and its contribution to overall warming are 0.18?C/10yr and 48.8% respectively for the period 1961–2015, with a more significant and larger urbanization effect seen in Tmin than Tmax. Thus, a large proportion warming, about half of the overall increase in annual mean temperature, as observed at the urban station, can be attributed to the rapid urbanization in the past half a century.     

Multimodel ensemble forecasts for precipitations in China in 1998

Different multimodel ensemble methods are used to forecast precipitations in China, 1998, and their forecast skills are compared with those of individual models. Datasets were obtained from monthly simulations of eight models during the period of January 1979 to December 1998 from the “Climate of the 20th Century Experiment” （20C3M） for the Fourth IPCC Assessment Report. Climate Research Unit （CRU） data were chosen for the observation analysis field. Root mean square （RMS） error and correlation coeffi-cients （R） are used to measure the forecast skills. In addition, superensemble forecasts based on different input data and weights are analyzed. Results show that for original data, superensemble forecasting based on multiple linear regression （MLR） performs best. However, for bias-corrected data, the superensemble based on singular value decomposition （SVD） produces a lower RMS error and a higher R than in the MLR superensemble. It is an interesting result that the SVD superensemble based on bias-corrected data performs better than the MLR superensemble, but that the SVD superensemble based on original data is inferior to the corresponding MLR superensemble. In addition, weights calculated by different data formats are shown to affect the forecast skills of the superensembles. In comparison with the MLR superensemble, a slightly significant effect is present in the SVD superensemble. However, both the SVD and MLR superensembles based on different weight formats outperform the ensemble mean of bias-corrected data.     

Homogenized Daily Relative Humidity Series in China during 1960?2017

Surface relative humidity(RH)is a key element for weather and climate monitoring and research.However,RH is not as commonly applied in studying climate change,partly because the observation series of RH are prone to inhomogeneous biases due to non-climate changes in the observation system.A homogenized dataset of daily RH series from 746 stations in Chinese mainland for the period 1960–2017,ChinaRHv1.0,has been developed.Most(685 or 91.82%of the total)station time series were inhomogeneous with one or more break points.The major breakpoints occurred in the early 2000s for many stations,especially in the humid and semi-humid zones,due to the implementation of automated observation across the country.The inhomogeneous biases in the early manual records before this change are positive relative to the recent automatic records,for most of the biased station series.There are more break points detected by using the MASH(Multiple Analysis of Series for Homogenization)method,with biases mainly around?0.5%and 0.5%.These inhomogeneous biases are adjusted with reference to the most recent observations for each station.Based on the adjusted observations,the regional mean RH series of China shows little long-term trend during 1960–2017[0.006%(10 yr)^?1],contrasting with a false decreasing trend[?0.414%(10 yr)?1]in the raw data.It is notable that ERA5 reanalysis data match closely with the interannual variations of the raw RH series in China,including the jump in the early 2000s,raising a caveat for its application in studying climate change in the region.     

Re-Assessing Climatic Warming in China since 1900

The regional mean surface air temperature(SAT)in China has risen with a rate of 1.3–1.7℃(100 yr)^-1 since 1900,based on the recently developed homogenized observations.This estimate is larger than those[0.5–0.8℃(100 yr)^-1]adopted in the early National Reports of Climate Change in China.The present paper reviews the studies of the longterm SAT series of China,highlighting the homogenization of station observations as the key progress.The SAT series of China in early studies showed a prominent warm peak in the 1940s,mainly due to inhomogeneous records associated with site-moves of a number of stations from urban to outskirts in the early 1950s,thus leading to underestimates of the centennial warming trend.Parts of China were relatively warm around the 1940s but with differentphase interdecadal variations,while some parts were even relatively cool.This fact is supported by proxy data and could partly be explained by interdecadal changes in large-scale circulation.The effect of urbanization should have a minor contribution to the observed warming in China,although the estimates of such contributions for individual urban stations remain controversial.Further studies relevant to the present topic are discussed.     

Trends and uncertainties in surface air temperature over the Tibetan Plateau, 1951–2013

Trends and uncertainties of surface air temperature over the Tibetan Plateau (TP) are evaluated by using observations at 100 meteorological stations during the period 1951–2013. The sampling error variances of gridded monthly data are estimated for every month and every grid box of data. The gridded data and their sampling error variances are used to calculate TP averages, their trends, and associated uncertainties. It is shown that large sampling error variances dominate northern and western TP, while small variances appear over southern and eastern TP. Every month from January to December has a positive linear trend during the study period. February has the largest trend of 0.34 ± 0.18°C (10 yr)^–1, and April the smallest at 0.15 ± 0.11°C (10 yr)^–1. The uncertainties decrease steadily with time, implying that they are not large enough to alter the TP warming trend.     

Effect of data homogenization on estimate of temperature trend: a case of Huairou station in Beijing Municipality

Daily minimum temperature (Tmin) and maximum temperature (Tmax) data of Huairou station in Beijing from 1960 to 2008 are examined and adjusted for inhomogeneities by applying the data of two nearby reference stations. Urban effects on the linear trends of the original and adjusted temperature series are estimated and compared. Results show that relocations of station cause obvious discontinuities in the data series, and one of the discontinuities for Tmin are highly significant when the station was moved from downtown to suburb in 1996. The daily Tmin and Tmax data are adjusted for the inhomogeneities. The mean annual Tmin and Tmax at Huairou station drop by 1.377°C and 0.271°C respectively after homogenization. The adjustments for Tmin are larger than those for Tmax, especially in winter, and the seasonal differences of the adjustments are generally more obvious for Tmin than for Tmax. Urban effects on annual mean Tmin and Tmax trends are ?0.004°C/10 year and ?0.035°C/10 year respectively for the original data, but they increase to 0.388°C/10 year and 0.096°C/10 year respectively for the adjusted data. The increase is more significant for the annual mean Tmin series. Urban contributions to the overall trends of annual mean Tmin and Tmax reach 100% and 28.8% respectively for the adjusted data. Our analysis shows that data homogenization for the stations moved from downtowns to suburbs can lead to a significant overestimate of rising trends of surface air temperature, and this necessitates a careful evaluation and adjustment for urban biases before the data are applied in analyses of local and regional climate change.     

Comparison of two homogenized datasets of daily maximum/mean/minimum temperature in China during 1960–2013

Two homogenized datasets of daily maximum temperature (Tmax), mean temperature (Tm), and minimum temperature (Tmin) series in China have recently been developed. One is CHTM3.0, based on the Multiple Analysis of Series for Homogenization (MASH) method, and includes 753 stations for the period 1960–2013. The other is CHHTD1.0, based on the Relative Homogenization test (RHtest), and includes 2419 stations over the period 1951–2011. The daily Tmax/Tm/Tmin series at 751 stations, which are in both datasets, are chosen and compared against the raw dataset, with regard to the number of breakpoints, long-term climate trends, and their geographical patterns. The results indicate that some robust break points associated with relocations can be detected, the inhomogeneities are removed by both the MASH and RHtest method, and the data quality is improved in both homogenized datasets. However, the differences between CHTM3.0 and CHHTD1.0 are notable. By and large, in CHHTD1.0, the break points detected are fewer, but the adjustments for inhomogeneities and the resultant changes of linear trend estimates are larger. In contrast, CHTM3.0 provides more reasonable geographical patterns of long-term climate trends over the region. The reasons for the differences between the datasets include: (1) different algorithms for creating reference series for adjusting the candidate series—more neighboring stations used in MASH and hence larger-scale regional signals retained; (2) different algorithms for calculating the adjustments—larger adjustments in RHtest in general, partly due to the individual local reference information used; and (3) different rules for judging inhomogeneity—all detected break points are adjusted in CHTM3.0, based on MASH, while a number of break points detected via RHtest but without supporting metadata are overlooked in CHHTD1.0. The present results suggest that CHTM3.0 is more suitable for analyses of large-scale climate change in China, while CHHTD1.0 contains more original information regarding station temperature records.     

Assessment of Air Temperature Trends in the Source Region of Yellow River and Its Sub-Basins,China

Changes in climatic variables at the sub-basins scale (having different features of land cover) are crucial for planning, development and designing of water resources infrastructure in the context of climate change. Accordingly, to explore the features of climate changes in sub-basins of the Source Region of Yellow River (SRYR), absolute changes and trends of temperature variables, maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tavg) and diurnal temperature range (DTR), were analyzed annually and seasonally by using daily observed air temperature dataset from 1965 to 2014. Results showed that annual Tmax, Tmin and Tavg for the SRYR were experiencing warming trends respectively at the rate of 0.28, 0.36 and 0.31°C (10 yr)^?1. In comparison with the 1st period (1965-1989), more absolute changes and trends towards increasing were observed during the 2nd period (1990-2014). Apart from Tangnaihai (a low altitude sub-basin), these increasing trends and changes seemed more significant in other basins with highest magnitude during winter. Among sub-basins the increasing trends were more dominant in Huangheyan compared to other sub-basins. The largest increase magnitude of Tmin, 1.24 and 1.18°C (10 yr)^?1, occurred in high altitude sub-basins Jimai and Huangheyan, respectively, while the smallest increase magnitude of 0.23°C (10 yr)^?1 occurred in a low altitude sub-basin Tangnaihai. The high elevation difference in Tangnaihai probably was the main reason for the less increase in the magnitude of Tmin. In the last decade, smaller magnitude of trend for all temperature variables signified the signal of cooling in the region. Overall, changes of temperature variables had significant spatial and seasonal variations. It implies that seasonal variations of runoff might be greater or different for each sub-basin.     

On the Contrasting Decadal Changes of Diurnal Surface Temperature Range between the Tibetan Plateau and Southeastern China during the 1980s–2000s

The diurnal surface temperature range(DTR) has become significantly smaller over the Tibetan Plateau(TP) but larger in southeastern China, despite the daily mean surface temperature having increased steadily in both areas during recent decades.Based on ERA-Interim reanalysis data covering 1979–2012, this study shows that the weakened DTR over TP is caused by stronger warming of daily minimum surface temperature(Tmin) and a weak cooling of the daily maximum surface temperature(Tmax); meanwhile, the enhanced DTR over southeastern China is mainly associated with a relatively stronger/weaker warming of Tmax/Tmin. A further quantitative analysis of DTR changes through a process-based decomposition method—the Coupled Surface–Atmosphere Climate Feedback Response Analysis Method(CFRAM)—indicates that changes in radiative processes are mainly responsible for the decreased DTR over the TP. In particular, the increased low-level cloud cover tends to induce the radiative cooling/warming during daytime/nighttime, and the increased water vapor helps to decrease the DTR through the stronger radiative warming during nighttime than daytime. Contributions from the changes in all radiative processes(over-2?C) are compensated for by those from the stronger decreased surface sensible heat flux during daytime than during nighttime(approximately 2.5?C), but are co-contributed by the changes in atmospheric dynamics(approximately-0.4?C) and the stronger increased latent heat flux during daytime(approximately-0.8?C). In contrast, the increased DTR over southeastern China is mainly contributed by the changes in cloud, water vapor and atmospheric dynamics. The changes in surface heat fluxes have resulted in a decrease in DTR over southeastern China.     

1961~2014年中国冬季极端低温变化特征分析

利用中国691个无缺测站点的经均一化处理及质量控制的逐日最高、最低气温资料,基于冷昼日数、冷夜日数、霜冻日数、冰冻日数、月最低气温极大值以及月最低气温极小值等6个由世界气象组织定义的极端气温指数,分析了1961~2014年中国冬季的极端低温变化特征。结果表明:冷昼日数、冷夜日数、霜冻日数以及冰冻日数在全国大部分地区均呈现下降的趋势,下降趋势较为明显的区域集中在东北南部、华北、西北东部、华东、华中、西南及高原地区,全国整体上下降幅度分别为-0.9 d/10 a、-1.7 d/10 a、-1.5 d/10a和-1.4 d/10 a。最低气温极大值和最低气温极小值在全国范围内则主要呈现上升的趋势,全国整体上分别为0.4℃/10 a和0.6℃/10 a;极端低温天数在20世纪60年代至70年代中后期呈现波动状,随后自20世纪70年代末80年代初至21世纪初呈明显下降趋势,从2006年左右以后其下降趋势较之前有所减缓,是对全球变暖减缓背景下的气候响应;与其他时间段相比,20世纪60年代至70年代为冬季极端低温事件较为频发的时间段,这可能与该时段陆地冷高压频繁活动有关。     

Estimation of sampling error uncertainties in observed surface air temperature change in China

This study examines the sampling error uncertainties in the monthly surface air temperature (SAT) change in China over recent decades, focusing on the uncertainties of gridded data, national averages, and linear trends. Results indicate that large sampling error variances appear at the station-sparse area of northern and western China with the maximum value exceeding 2.0 K² while small sampling error variances are found at the station-dense area of southern and eastern China with most grid values being less than 0.05 K². In general, the negative temperature existed in each month prior to the 1980s, and a warming in temperature began thereafter, which accelerated in the early and mid-1990s. The increasing trend in the SAT series was observed for each month of the year with the largest temperature increase and highest uncertainty of 0.51 ± 0.29 K (10 year)⁻¹ occurring in February and the weakest trend and smallest uncertainty of 0.13 ± 0.07 K (10 year)⁻¹ in August. The sampling error uncertainties in the national average annual mean SAT series are not sufficiently large to alter the conclusion of the persistent warming in China. In addition, the sampling error uncertainties in the SAT series show a clear variation compared with other uncertainty estimation methods, which is a plausible reason for the inconsistent variations between our estimate and other studies during this period.

     

Multi-Fractal Analy is of Daily Air Temperature Time Series in Coastal Areas of China

In this article, the Multi-Fractal Detrended Fluctuation Analysis (MF-DFA) method is adopted to study the temperature, i. e., the maximum temperature (Tmax), mean temperature (Tavg) and minimum (Tmin) air temperature, multifractal characteristics and their formation mechanism, in the typical temperature zones in the coastal regions in Guangdong, Jiangsu and Liaoning Provinces. Following are some terms and concepts used in the present study. Multifractality is defined as a term that characterizes the complexity and self-similarity of objects, and fractal characteristics depict the distribution of probability over the whole set caused by different local conditions or different levels in the process of evolution. Fractality strength denotes the fluctuation range of the data set, and long-range correlation (LRC) measures the stability of the climate system and the trend of climate change in the future. In this research, it is found that the internal stability and feedback mechanism of climate systems in different regions show regional differences. Furthermore, the research also proves that the Tavg, Tmax and Tmin of the above three provinces are highly multifractal. The temperature series multifractality of each province decreases in the order of temperature series multifractality of Liaoning > temperature series multifractality of Guangdong > temperature series multifractality of Jiangsu, and the corresponding long-range correlations follow the same order. It reveals that the most stable temperature series is that of Liaoning, followed by the temperature series of Guangdong, and the most unstable one is that of Jiangsu. Liaoning has the most stable climate system, and it will thus be less responsive to the future climate warming. The stability of the climate system in Jiangsu is the weakest, and its temperature fluctuation will continue to increase in the future, which will probably result in the meteorological disasters of high temperature and heat wave there. Guangdong possesses the strongest degree of multifractal strength, which indicates that its internal temperature series fluctuation is the largest among the three regions. The Tmax multifractal strength of Jiangsu is stronger than that of Liaoning, while the Tavg and Tmin multifractal strength of Jiangsu is weaker than that of Liaoning, showing that Jiangsu has a larger internal Tmax fluctuation than Liaoning does, while it has a smaller fluctuation of Tavg and Tmin than Liaoning does. Guangdong and Liaoning both show the strongest Tmin multifractal strength, followed by Tavg multifractal strength, and the weakest Tmax multifractal strength. However, Jiangsu has the strongest Tmax, followed by Tavg, and the weakest Tmin. The research findings show that these phenomena are closely related to solar radiation, monsoon strength, topography and some other factors. In addition, the multifractality of the temperature time series results from the negative power-law distribution and long-range correlation, in which the long-range correlation influence of temperature series itself plays the dominant role. With the backdrop of global climate change, this research can provide a theoretical basis for the prediction of the spatial-temporal air temperature variation in the eastern coastal areas of China and help us understand its characteristics and causes, and thus the present study will be significant for the environmental protection of coastal areas.