Dynamic thermo-mechanical responses of road-soft ground system under vehicle load and daily temperature variation

A complete road-soft ground model is established in this paper to study the dynamic responses caused by vehicle loads and/or daily temperature variation.A dynamic thermo-elastic model is applied to capturing the behavior of the rigid pavement,the base course,and the subgrade,while the soft ground is characterized using a dynamic thermo-poroelastic model.Solutions to the road-soft ground system are derived in the Laplace-Hankel transform domain.The time domain solutions are obtained using an integration approach.The temperature,thermal stress,pore water pressure,and displacement responses caused by the vehicle load and the daily temperature variation are presented.Results show that obvious temperature change mainly exists within 0.3 m of the road when subjected to the daily temperature variation,whereas the stress responses can still be found in deeper places because of the thermal swelling/shrinkage deformation within the upper road structures.Moreover,it is important to consider the coupling effects of the vehicle load and the daily temperature variation when calculating the dynamic responses inside the road-soft ground system.

Application of Multiple Analysis of Series for Homogenization to Beijing Daily Temperature Series (1960-2006)

Homogenization of climate observations remains a challenge to climate change researchers, especially in cases where metadata （e.g., probable dates of break points） are not always available. To examine the inffuence of metadata on homogenizing climate data, the authors applied the recently developed Multiple Analysis of Series for Homogenization （MASH） method to the Beijing （BJ） daily temperature series for 1960- 2006 in three cases with different references： （1） 13M-considering metadata at BJ and 12 nearby stations; （2） 13NOM-considering the same 13 stations without metadata; and （3） 21NOM-considering 20 further stations and BJ without metadata. The estimated mean annual, seasonal, and monthly inhomogeneities are similar between the 13M and 13NOM cases, while those in the 21NOM case are slightly different. The detected biases in the BJ series corresponding to the documented relocation dates are as low as -0.71~0C, -0.79~0C, and -0.5~0C for the annual mean in the 3 cases, respectively. Other biases, including those undocumented in metadata, are minor. The results suggest that any major inhomogeneity could be detected via MASH, albeit with minor differences in estimating inhomogeneities based on the different references. The adjusted annual series showed a warming trend of 0.337, 0.316, and 0.365~0C （10 yr）^（-1） for the three cases, respectively, smaller than the estimate of 0.453~0C （10 yr）^（-1） in the original series, mainly due to the relocation-induced biases. The impact of the MASH-type homogenization on estimates of climate extremes in the daily temperature series is also discussed.

Modeling Daily Average Temperatures in a Coastal Site of Central Africa: An Analysis of Seasonal Divisions

The seasonality and day-to-day variation of near-surface temperature patterns can greatly control nearly all physical and biological processes though temperature predictions at such scales remain challenging. This paper implements a simple analytical approach in order to generate daily average temperatures which implicitly accounts for surface heating and drivers through a comprehensive representation of station-based temperature records on a universal standard calendar propagated by the earth’s dynamics features. The modeled and observed pattern of daily temperatures exhibits a close agreement with the level of strength agreement exceeding 0.56. The extreme high and low values of the observed temperature patterns are equally well captured although model underestimates the probability of temperatures around the two modal peaks (~25.6℃ and 27.5℃). Additionally, a theoretical thermal-based division led to the identification of six seasons, including two hot and cold periods along with two pairs of mixed hot-cold. The theoretical division proposed here appears to be a good approximation for the understanding of rainfall seasonality in this area.

Effects of urbanization on daily temperature extremes in North China

The regional changes of daily temperature extremes in North China caused by ur- banization are studied further from observed facts and model estimates on the basis of ho- mogenized daily series of maximum and minimum temperature observations from 268 mete- orological stations, NCEP/DOE AMIP- Ⅱ reanalysis data （R-2）, and the data of simulations by regional climate model （RegCM3）. The observed facts of regional warming on long time scales are obtained by analyzing the indices of temperature extremes during two time periods of 1961-2010 and 1951-2010. For urbanization effect, the contributions to decreases in an- nual and winter diurnal temperature range （DTR） are 56.0% and 52.9%, respectively, and increases in the lowest minimum temperature （TNn） are 35.7% and 26.2% by comparison of urban and rural observations. Obtained by R-2 data with observations for contrast, on the other hand, increase in the number of annual warm nights （TN90p） contributed by urbaniza- tion is 60.9%. And observed facts of regional warming in daily temperature extremes are also reflected in the simulations, but what difference is urbanization progress at rural areas in North China would be prominent in the next few years relative to urban areas to some extent from model estimates.

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.

The efficacy of Kriging spatial interpolation for filling temporal gaps in daily air temperature data: A case study in northeast China

Quality-controlled and serially complete daily air temperature data are essential to evaluating and modelling the influences of climate change on the permafrost in cold regions. Due to malfunctions and location changes of observing stations, temporal gaps （i.e., missing data） are common in collected datasets. The objective of this study was to assess the efficacy of Kriging spatial interpolation for estimating missing data to fill the temporal gaps in daily air temperature data in northeast China. A cross-validation experiment was conducted. Daily air temperature series from 1960 to 2012 at each station were estimated by using the universal Kriging （UK） and Kriging with an external drift （KED）, as appropriate, as if all the ob-servations at a given station were completely missing. The temporal and spatial variation patterns of estimation uncertainties were also checked. Results showed that Kriging spatial interpolation was generally desirable for estimating missing data in daily air temperature, and in this study KED performed slightly better than UK. At most stations the correlation coefficients （R2） between the observed and estimated daily series were 〉0.98, and root mean square errors （RMSEs） of the estimated daily mean （Tmean）, maximum （Tmax）, and minimum （Tmin） of air temperature were 〈3 ℃. However, the estimation quality was strongly affected by seasonality and had spatial variation. In general, estimation uncertainties were small in summer and large in winter. On average, the RMSE in winter was approximately 1 ℃ higher than that in summer. In addition, estimation uncertainties in mountainous areas with complex terrain were significantly larger than those in plain areas.

Study on Refined Forecast Method of Daily Maximum Temperature in Wugang City from July to September

[Objective] The aim was to study the refined forecast method of daily highest temperature in Wugang City from July to September. IM[ethod] By dint of ECMWF mode product and T231 in 2009 and 2010 and daily maximum temperature in the station in corresponding period, multi-factors similar forecast method to select forecast sample, multivariate regression multi-mode integration MOS method, after dynamic corrected mode error and regression error, dynamic forecast equation was concluded to formulate the daily maximum temperature forecast in 24 -120 h in Wugang City from July to September. [ Result] Through selection, error correction, the daily maximum temperature equation in Wugang City from July to September was concluded. Through multiple random sampling, F test was made to pass test with significant test of 0.1. [ Conclusionl The method integrated domestic and foreign forecast mode, made full use of useful information of many modes, absorbed each others advantages, con- sidered local regional environment, lessen mode and regression error, and improved forecast accuracy.

Diurnal Temperature Range and Daily Emergency Room Admissions among the Elderly in Beijing,China

In recent years, more attentions have been paid to the association between climate change and human health. Increasing and more variable global surface temperature is one of the key climatic change factors which have been consistently reported about the effect on human health. So far, more researches have revealed that temperature lead not only to direct deaths and illnesses but also to aggravation of cardiovascular and respiratory diseases. Typically, the relationship between temperature and mortality or morbidity is V-, U-, or J- shaped, with optimum temperature corresponding to the lowest point in the temperature mortality curve.

Quantification of relationship between annual daily maximum temperature and annual daily maximum rainfall in South Australia

The empirical relationship between annual daily maximum temperature(ADMT)and annual daily maximum rainfall(ADMR)was investigated.The data were collected from four weather stations located in Adelaide,South Australia,from 1988 to 2017.Due to the influence of sea surface temperature on rainfall and temperature,the distance from the weather station to the sea was considered in the selection of weather stations.Two weather stations near the sea and two inland weather stations were selected.Three non-parametric statistical tests(Kruskal–Wallis,Mann–Whitney,and correlation)were applied to perform statistical analysis on the ADMT and ADMR data.It was revealed that the temperature and rainfall in South Australia varies according to weather station location.The distance from the sea to the weather station was found to have limited influence on temperature and rainfall.Meanwhile,with the 0.05 level of significance,the association between ADMT and ADMR near sea stations is not as significant as the association between the two inland weather stations.It is relatively unrealistic to use ADMR to predict ADMT,or vice versa,since their correlation is not statistically significant(Spearman’s rank correlation coefficient:−0.106).

Climatic characteristics of high temperature in East China during 1961-2005

Based on the daily maximum temperature data covering the period 1961-2005, temporal and spatial characteristics and their changing in mean annual and monthly high temperature days（HTDs）and the mean daily maximum temperature（MDMT）during annual and monthly HTDs in East China were studied.The results show that the mean annual HTDs were 15.1 and the MDMT during annual HTDs was 36.3℃in the past 45 years.Both the mean annual HTDs and the MDMT during annual HTDs were negative anomaly in the1980s and positive anomaly in the other periods of time,oscillating with a cycle of about 12-15 years.The mean annual HTDs were more in the southern part,but less in the northern part of East China.The MDMT during annual HTDs was higher in Zhejiang,Anhui and Jiangxi provinces in the central and western parts of East China.The high temperature process（HTP） was more in the southwestern part,but less in northeastern part of East China.Both the HTDs and the numbers of HTP were at most in July,and the MDMT during monthly HTDs was also the highest in July.In the first 5 years of the 21st century,the mean annual HTDs and the MDMT during annual HTDs increased at most of the stations,both the mean monthly HTDs and the MDMT during monthly HTDs were positive anomalies from April to October,the number of each type of HTP generally was at most and the MDMT in each type of HTP was also the highest.

The Effect of High Day and Low Night Temperature on Pollen Production, Pollen Germination and Postharvest Quality of Tomatoes

Temperature integration where high day temperatures are compensated by lower night temperatures is one strategy that can be used to reduce energy consumption in greenhouses. Crop tolerance to temperature variation is a prerequisite for using such a strategy. Greenhouse experiments were conducted on tomatoes cvs, Capricia, Mecano and Cederico in order to investigate the effect of different day/night temperature regimes (24/17, 27/14 and 30/11℃) where the same mean temperature was maintained for the production and germination of pollen. In addition, fruit quality as determined by fruit firmness, dry matter content, soluble solids, titratable acids, and pH was examined at harvest and after seven and 14 days of storage. The 30/11℃ treatment significantly increased pollen production and germination compared to the 24/17℃ treatment, while the 27/14℃ treatment was generally in between the other two treatments. Fruits grown at the 27/14℃ treatment were significantly firmer, while fruits grown at 24/17℃ had higher dry matter content, soluble solids, and titratable acids compared to the other treatments. There were significant differences between cultivars with respect to firmness, dry matter, titratable acidity, and pH. The quality of the fruits changed during storage, but the storability of the tomatoes was not affected by preharvest temperature treatments. The overall conclusion was that the 27/14℃ treatment was superior to the other two temperature treatments with respect to the studied parameters.

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 min- imum 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 cMculating 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.

观测序列的不均一性对估算北京和上海的平均温度与极端温度变化趋势的影响(英文)

Inhomogeneities in the temperature series from Beijing and Shanghai are analyzed, using the detailed histories of both sets of observations. The major corrections for different periods range from ?0.33 to 0.6°C for Beijing and ?0.33 to 0.3°C for Shanghai, Annual mean and extreme temperature series are deduced from the daily observations and trends in the adjusted and unadjusted series are compared. The adjusted yearly mean temperatures show a warming trend of 0.5°C/ century since the turn of this century and an enhanced one of 2.0°C/ century since the 1960s. In contrast, the unadjusted data show a twice this value trend for Shanghai but little trend for Beijing at the long-term scale and overestimate the recent warming by 50%–130%. Beijing experienced a decrease of frequency of the extremes together with a cooling during the 1940s–1970s and an increase of frequency of extremes together with a warming since then. The trends of frequency of extremes at Shanghai were more or less opposite. It is implied that the regional trends of strong weather variations may be different even when the regional mean temperatures coherently change. Key words Inhomogeneity - Daily temperature series - Climatic warming - Extreme temperature The study was supported by the China NKBRSF Project G 1999043400, IAP/ DF and CAS project (KZ951-A1-402).

Variations of the thermal growing season during the period 1961–2015 in northern China

Researching into changes in thermal growing season has been one of the most important scientific issues in studies of the impact of global climate change on terrestrial ecosystems. However, few studies investigated the differences under various definitions of thermal growing season and compared the trends of thermal growing season in different parts of China. Based on the daily mean air temperatures collected from 877 meteorological stations over northern China from 1961 to 2015, we investigated the variations of the thermal growing season parameters including the onset, ending and duration of the growing season using the methods of differential analysis, trend analysis, comparative analysis, and Kriging interpolation technique. Results indicate that the differences of the maximum values of those indices for the thermal growing season were significant, while they were insignificant for the mean values. For indices with the same length of the spells exceeding 5°C, frost criterion had a significant effect on the differences of the maximum values. The differences of the mean values between frost and non-frost indices were also slight, even smaller than those from the different lengths of the spells. Temporally, the starting date of the thermal growing season advanced by 10.0–11.0 days, while the ending dates delayed by 5.0–6.0 days during the period 1961–2015. Consequently, the duration of the thermal growing season was prolonged 15.0–16.0 days. Spatially, the advanced onset of the thermal growing season occurred in the southwestern, eastern, and northeastern parts of northern China, whereas the delayed ending of the thermal growing season appeared in the western part, and the length of the thermal growing season was prolonged significantly in the vast majority of northern China. The trend values of the thermal growing season were affected by altitude. The magnitude of the earlier onset of the thermal growing season decreased, and that of the later ending increased rapidly as the altitude increased, causing the magnitude of the prolonged growing season increased correspondingly. Comparing the applicability of selected indices and considering the impacts of frost on the definitions are important and necessary for determining the timing and length of the thermal growing season in northern China.

Opposing Trends of Winter Cold Extremes over Eastern Eurasia and North America under Recent Arctic Warming

Under recent Arctic warming,boreal winters have witnessed severe cold surges over both Eurasia and North America,bringing about serious social and economic impacts.Here,we investigated the changes in daily surface air temperature(SAT)variability during the rapid Arctic warming period of 1988/89–2015/16,and found the daily SAT variance,mainly contributed by the sub-seasonal component,shows an increasing and decreasing trend over eastern Eurasia and North America,respectively.Increasing cold extremes(defined as days with daily SAT anomalies below 1.5 standard deviations)dominated the increase of the daily SAT variability over eastern Eurasia,while decreasing cold extremes dominated the decrease of the daily SAT variability over North America.The circulation regime of cold extremes over eastern Eurasia(North America)is characterized by an enhanced high-pressure ridge over the Urals(Alaska)and surface Siberian(Canadian)high.The data analyses and model simulations show the recent strengthening of the high-pressure ridge over the Urals was associated with warming of the Barents–Kara seas in the Arctic region,while the high-pressure ridge over Alaska was influenced by the offset effect of Arctic warming over the East Siberian–Chukchi seas and the Pacific decadal oscillation(PDO)–like sea surface temperature(SST)anomalies over the North Pacific.The transition of the PDO-like SST anomalies from a positive to negative phase cancelled the impact of Arctic warming,reduced the occurrence of extreme cold days,and possibly resulted in the decreasing trend of daily SAT variability in North America.The multi-ensemble simulations of climate models confirmed the regional Arctic warming as the driver of the increasing SAT variance over eastern Eurasia and North America and the overwhelming effect of SST forcing on the decreasing SAT variance over North America.Therefore,the regional response of winter cold extremes at midlatitudes to the Arctic warming could be different due to the distinct impact of decadal SST anomalies.

Analysis of Spatial Distribution of Agricultural Meteorological Conditions in Sanjiang Plain during Nearly 50 Years

[ Objective] The study aimed to analyze the spatial distribution of agricultural meteorological conditions in Sanjiang Plain during nearly 50 years. [ Method] Accumulated temperature of Sanjiang Plain was computed based on meteorological observation data from different meteorological stations in Sanjiang Plain, including temperature, precipitation, sunshine time, etc. A spatial interpolation map involving varieties of meteorological elements in neady 50 years was generated based on the Kriging interpolation, and the spatial distribution characteristics of those meteorological ele- ments were analyzed. [ Result] Temperature of Sanjiang Plain decreased with the increase of latitude and altitude, and the annual average temper- atura varied from 2.5 to 4.5 ~（3 generally, showing a zonal distribution. Precipitation of Sanjiang Plain changed spatially and the annual average pre- cipitation varied from 500 to 600 mm symmetrically in northwest-southeast direction. Spatial distribution of the annual average wind speed in San- jiang Plain was identical with the spatial pattern of topography here, and the annual average wind speed changed from 3.0 to 3.6 rn/s in most re- gions. Relative air humidity of Sanjiang Plain in summer half year was relatively high and always above 65%. The maximum sunshine hours of San- jiang Plain in one year distributed similarly to the annual changing curve of solar declination, and both of them presented a normal distribution and changed with geographic latitude. The days from the beginning to the end of daily average temperature ~〉 10 ~C in Sanjiang Plain were 135 -146 d, and its distribution presented a latitudinal trend, with certain vertical zonality. [ Conclusion] The research could provide scientific references for the reasonable arrangement of agricultural production and effective prevention of meteorological disasters in Sanjiang Plain.

Bias Adjustment and Analysis of Chinese Daily Historical Radiosonde Temperature Data

The discontinuities in historical Chinese radiosonde datasets are attributed to artificial errors. In order to reflect more realistically basic conditions of the atmosphere over China and provide more reasonable radiosonde data as input to climate change analysis and to atmospheric reanalysis data assimilation systems, this paper proposes a scheme to identify breakpoints and adjust biases in daily radiosonde observations. The ongoing ECMWF Re Analysis-Interim(ERA-Interim) 12-h forecasts are used as reference series in the scheme, complemented by the ECMWF Twentieth Century Reanalysis(ERA-20 C). A series of breakpoint identification schemes are developed and combined with metadata to detect breakpoints. The Quantile-Matching(QM) method is applied to test and adjust daily radiosonde data on 12 mandatory pressure levels collected at 80 sounding stations during 1979–2013. The adjusted temperatures on mandatory levels are interpolated to significant levels for temperature adjustment on these levels. The adjustment scheme not only solves the data discontinuity problem caused by changes in observational instruments and bias correction methods, but also solves the discontinuity problem in the 1200 minus 0000 UTC temperature time series on mandatory levels at individual sounding stations. Before the adjustment, obvious discontinuities can be found in the deviation field between the raw radiosonde data and ERA-Interim reanalysis with relatively large deviations before 2001. The deviation discontinuity is mainly attributed to the nationwide upgrade of the radiosonde system in China around 2001. After the adjustment, the time series of deviations becomes more continuous. In addition, compared with the adjusted temperature data on mandatory levels over 80 radiosonde stations in China contained in the Radiosonde Observation Correction Using Reanalyses(RAOBCORE) 1.5, the dataset adjusted by the method proposed in the present study exhibits higher quality than RAOBCORE 1.5, while discontinuities still exist in the time series of temperature at 0000, 1200, and 1200 minus 0000 UTC in RAOBCORE 1.5.

Spatial and Temporal Variations of Climate in Europe

Temperature series of the original individual measurements of the minimum and maximum daily temperatures from 24 stations located in different regions of Europe are considered with the objective of studying the stability or the variability of the “climate” in time and space. The patterns of the temperature statistics, the shapes of probability density functions, in particular, at different places and times allow comparisons based on the Kolmogorov-Smirnov test, the Shannon entropy, and cluster analysis, used separately or in combination for the purposes of quantitative climate classification.