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.     

The identification of distinct patterns in California temperature trends

Regional changes in California surface temperatures over the last 80 years are analyzed using station data from the US Historical Climate Network and the National Weather Service Cooperative Network. Statistical analyses using annual and seasonal temperature data over the last 80 years show distinctly different spatial and temporal patterns in trends of maximum temperature (Tmax) compared to trends of minimum temperature (Tmin). For trends computed between 1918 and 2006, the rate of warming in Tmin is greater than that of Tmax. Trends computed since 1970 show an amplified warming rate compared to trends computed from 1918, and the rate of warming is comparable between Tmin and Tmax. This is especially true in the southern deserts, where warming trends during spring (March?CMay) are exceptionally large. While observations show coherent statewide positive trends in Tmin, trends in Tmax vary on finer spatial and temporal scales. Accompanying the observed statewide warming from 1970 to 2006, regional cooling trends in Tmax are observed during winter and summer. These signatures of regional temperature change suggest that a collection of different forcing mechanisms or feedback processes must be present to produce these responses.     

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.     

Analysis of Sampling Error Uncertainties and Trends in Maximum and Minimum Temperatures in China简

In this paper we report an analysis of sampling error uncertainties in mean maximum and minimum temperatures （Tmax and Tmin） carried out on monthly,seasonal and annual scales,including an examination of homogenized and original data collected at 731 meteorological stations across China for the period 1951-2004.Uncertainties of the gridded data and national average,linear trends and their uncertainties,as well as the homogenization effect on uncertainties are assessed.It is shown that the sampling error variances of homogenized Tmax and Tmin,which are larger in winter than in summer,have a marked northwest-southeast gradient distribution,while the sampling error variances of the original data are found to be larger and irregular.Tmax and Tmin increase in all months of the year in the study period 1951-2004,with the largest warming and uncertainties being 0.400℃ （10 yr）-1 ＋ 0.269℃ （10 yr）-1 and 0.578℃ （10 yr）-1 ＋ 0.211℃ （10 yr）-1 in February,and the least being 0.022℃ （10 yr）-1 ＋ 0.085℃ （10 yr）-1 and 0.104℃ （10 yr）-1 ＋0.070℃ （10 yr）-1 in August.Homogenization can remove large uncertainties in the original records resulting from various non-natural changes in China.     

Changes in daily climate extremes in the arid area of northwestern China

There has been a paucity of information on trends in daily climate and climate extremes, especially for the arid region. We analyzed the changes in the indices of climate extremes, on the basis of daily maximum and minimum air temperature and precipitation at 59 meteorological stations in the arid region of northwest China over the period 1960–2003. Twelve indices of extreme temperature and six indices of extreme precipitation are examined. Temperature extremes show a warming trend with a large proportion of stations having statistically significant trends for all temperature indices. The regional occurrence of extreme cool days and nights has decreased by ?0.93 and ?2.36 days/decade, respectively. Over the same period, the occurrence of extreme warm days and nights has increased by 1.25 and 2.10 days/decade, respectively. The number of frost days and ice days shows a statistically significant decrease at the rate of ?3.24 and ?2.75 days/decade, respectively. The extreme temperature indices also show the increasing trend, with larger values for the index describing variations in the lowest minimum temperature. The trends of Min Tmin (Tmax) and Max Tmin (Tmax) are 0.85 (0.61) and 0.32 (0.17)?°C/decade. Most precipitation indices exhibit increasing trends across the region. On average, regional maximum 1-day precipitation, annual total wet-day precipitation, and number of heavy precipitation days and very wet days show insignificant increases. Insignificant decreasing trends are also found for consecutive dry days. The rank-sum statistic value of most temperature indices exhibits consistent or statistically significant trends across the region. The regional medians after 1986 of Min Tmin (Tmax), Max Tmin (Tmax), warm days (nights), and warm spell duration indicator show statistically more larger than medians before 1986, but the frost days, ice days, cool days (nights), and diurnal temperature range reversed. The medians of precipitation indices show insignificant change except for consecutive dry days before and after 1986.     

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.     

Time trend analysis of some agroclimatic variables during the last half century over Iran

Theoretical and Applied Climatology - We assessed the trends of precipitation, maximum and minimum temperature (Tmax and Tmin), diurnal temperature range (DTR), water requirement of autumn-planted...     

Climate changes in the Lhasa River basin,Tibetan Plateau: irrigation-induced cooling along with a warming trend

Characterizing the response of temperature variables to agricultural irrigation is expected to be an important challenge for understanding the full impact of water management on regional climate change. In this paper, the trend analysis and abrupt change test were applied to detect the global warming effect. Then, the quantitative irrigation-induced cooling effects on temperature variables between April and August from 1970 to 2010 in the Lhasa River basin were estimated using historical time series of gridded meteorological records and a map of the area equipped for irrigation. Trends in the maximum temperature (Tmax) were statistically positive, and a significant increasing trend for the minimum temperature (Tmin) was detected at the 0.01 and 0.05 confidence levels. No abrupt changing point of warming was detected in the time series for Tmax. The abrupt changes in Tmin in the irrigation concentration period took place in 1995, 5 years later than the corresponding change in April. Affected by global warming, the increase in temperature was the largest in July and August, when the irrigation-induced cooling effect was also the most significant. The irrigation-induced cooling effect for Tmax and Tmin in April–August (except for June) ranged from − 0.017 to − 0.009 °C/decade and from − 0.011 to − 0.001 °C/decade, respectively, and the cooling effect for diurnal temperature range (DTR) ranged from − 0.011 to 0 °C/decade. The cooling effect on temperature reached above 0.01 °C in July and August, but for the growing seasons, the effect was weak, only 0.001 °C. The Tmax and Tmin trends during the whole growing seasons decreased by both 0.002 °C/decade, respectively, with a 10% increase in irrigation land proportion. Even in July and August, the trends were expected to decrease by about 0.005 °C/decade with a 10% increase in irrigation land proportion. The irrigation-induced cooling effect could partially slow global warming.     

Multi-Fractal Analysis 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, Tmaxand Tminof 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 Tmaxmultifractal strength of Jiangsu is stronger than that of Liaoning, while the Tavgand Tminmultifractal strength of Jiangsu is weaker than that of Liaoning, showing that Jiangsu has a larger internal Tmaxfluctuation than Liaoning does, while it has a smaller fluctuation of Tavgand Tminthan Liaoning does. Guangdong and Liaoning both show the strongest Tminmultifractal strength, followed by Tavgmultifractal 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.     

CHARACTERISTICS AND TRENDS OF CLIMATIC EXTREMES IN CHINA DURING 1959–2014

The spatial and temporal variations of daily maximum temperature(Tmax), daily minimum temperature(Tmin), daily maximum precipitation(Pmax) and daily maximum wind speed(WSmax) were examined in China using Mann-Kendall test and linear regression method. The results indicated that for China as a whole, Tmax, Tmin and Pmax had significant increasing trends at rates of 0.15℃ per decade, 0.45℃ per decade and 0.58 mm per decade,respectively, while WSmax had decreased significantly at 1.18 m·s~(-1) per decade during 1959—2014. In all regions of China, Tmin increased and WSmax decreased significantly. Spatially, Tmax increased significantly at most of the stations in South China(SC), northwestern North China(NC), northeastern Northeast China(NEC), eastern Northwest China(NWC) and eastern Southwest China(SWC), and the increasing trends were significant in NC, SC, NWC and SWC on the regional average. Tmin increased significantly at most of the stations in China, with notable increase in NEC, northern and southeastern NC and northwestern and eastern NWC. Pmax showed no significant trend at most of the stations in China, and on the regional average it decreased significantly in NC but increased in SC, NWC and the mid-lower Yangtze River valley(YR). WSmax decreased significantly at the vast majority of stations in China, with remarkable decrease in northern NC, northern and central YR, central and southern SC and in parts of central NEC and western NWC. With global climate change and rapidly economic development, China has become more vulnerable to climatic extremes and meteorological disasters, so more strategies of mitigation and/or adaptation of climatic extremes,such as environmentally-friendly and low-cost energy production systems and the enhancement of engineering defense measures are necessary for government and social publics.     

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     

Prediction of temperature and precipitation in Sudan and South Sudan by using LARS-WG in future

Global warming has brought great pressure on the environment and livelihood conditions in Sudan and South Sudan. It is desirable to analyze and predict the change of critical climatic variables, such as temperature and precipitation, which will provide valuable reference results for future water resources planning and management in the region. The aims of this study are to test the applicability of the Long Ashton Research Station Weather Generator (LARS-WG) model in downscaling daily precipitation and daily maximum (Tmax) and daily minimum (Tmin) temperatures in Sudan and South Sudan and use it to predict future changes of precipitation; Tmin and Tmax for nine stations in Sudan and South Sudan are based on the SRA2 scenario of seven General Circulation Models (GCMs) outputs for the periods of 2011–2030, 2046–2065, and 2080–2099. The results showed that (1) the LARS-WG model produces good performance in downscaling daily precipitation and excellent performance in downscaling Tmax and Tmin in the study region; (2) downscaled precipitation from the prediction of seven GCMs showed great inconsistency in these two regions, which illustrates the great uncertainty in GCMs' results in the regions; (3) predicted precipitation in rainy season JJA (June, July, and August) based on the ensemble mean of seven GCMs showed a decreasing trend in the periods of 2011–2030, 2046–2065, and 2080–2099 in Sudan; however, an increasing trend can be found in SON (September, October, and November) in the future; (4) precipitation in South Sudan has an increasing trend in most seasons in the future except in MAM (March, April, and May) season in 2011–2030; and (5) predictions from seven GCMs showed a similar and continuous increasing trend for Tmax and Tmin in all three future periods, which will bring severe negative influence on improving livelihoods and reducing poverty in Sudan and South Sudan.     

Historical and future changes in maximum and minimum temperature records over Europe

Recent studies examining changes in temperature record frequency over the continental United States have reported that the number of Tmax records has been increasing over the past 50 years and occurring at twice the frequency of Tmin records. In a stationary climate, the number of records should decrease with time as 1/n, where n is the number of years of record-keeping. Here we seek to understand how European temperature records have changed during the late 20th century and how they are expected to change as greenhouse gases increase during the 21st century, using a new ensemble method to filter out the effect of the starting year in the calculation of the records. We find that until 1980, the ratio of Tmax to Tmin records remains close to one, indicating that the climate was relatively stationary. After 1980, there is a distinct positive trend where the observed ratio averages around four during the early part of the 21st century, indicative of a warming trend. We note considerable spatial variability in the observations. Further, the ratio of Tmax to Tmin records set by the year 2100 as simulated by five RCM simulations reaches values of up to several hundred by the end of the 21st century. However, the changes in record frequency vary spatially over Europe. The models project the highest numbers of Tmax records over the Mediterranean during summer, and Scandinavia during the spring and fall. Tmin records decrease most substantially over eastern Europe and western Russia, and the Mediterranean. Our analysis confirms the value of the use of maximum and minimum temperature records in regional climate change studies.     

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.     

The inter-annual variations and the long-term trends of monthly air temperatures in Iraq over the period 1941–2013

Theoretical and Applied Climatology - Mean annual and monthly averages of mean (Tmean), maximum (Tmax) and minimum (Tmin) air temperature from seven stations in Iraq were analysed to detect the...     

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。     

Observed (1970–1999) climate variability in Central America using a high-resolution meteorological dataset with implication to climate change studies

High spatial resolution of precipitation (P) and average air temperature (Tavg) datasets are ideal for determining the spatial patterns associated with large-scale atmospheric and oceanic indexes, and climate change and variability studies, however such datasets are not usually available. Those datasets are particularly important for Central America because they allow the conception of climate variability and climate change studies in a region of high climatic heterogeneity and at the same time aid the decisionmaking process at the local scale (municipalities and districts). Tavg data from stations and complementary gridded datasets at 50 km resolution were used to generate a high-resolution (5 km grid) dataset for Central America from 1970 to 1999. A highresolution P dataset was used along with the new Tavg dataset to study climate variability and a climate change application. Consistently with other studies, it was found that the 1970-1999 trends in P are generally non-significant, with the exception of a few small locations. In the case of Tavg, there were significant warming trends in most of Central America, and cooling trends in Honduras and northern Panama. When the sea surface temperature anomalies between the Tropical Pacific and the Tropical Atlantic have different (same) sign, they are a good indicator of the sign of P (Tavg) annual anomalies. Even with non-significant trends in precipitation, the significant warming trends in Tavg in most of Central America can have severe consequences in the hydrology and water availability of the region, as the warming would bring increases in evapotranspiration, drier soils and higher aridity.     

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.     

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.     

Significant Difference in Dimming Magnitude Between Urban and Rural Regions in China and Its Implication for Surface Temperature

The climatology and long term trends of sunshine duration (SSD), total cloud amount (TCC) and surface temperature are studied based on surface observations at 84 stations across China from 1961 to 2009. These stations were classified into metropolis, large city, small city and rural station based on their populations and specific station locations. Difference in SSD and its implication for surface temperature among four station categories are analyzed. Different SSD decreasing rates among four station categories were found. The maximum decreasing rate occurred at metropolis stations (-89.2 h per decade) and the minimum rate occurred at rural station (-54.1 h per decade). TCC also showed a negative trend. The correlation coefficients between decadal variability of SSD and TCC were significantly positive, which implied that the dimming during this period was apparently not explained by TCC. Among all station categories, the maximum temperatures (Tmax) showed a similar positive trend, however, the minimum temperatures (Tmin) increased much faster at urban stations than at rural stations. This suggested that the faster decline of SSD at urban stations could partly dampen the effect of urban heat island on Tmax.