A neural network method for estimating weighted mean temperature over China and adjacent areas

To improve the applicability of the global pressure and temperature 2 wet(GPT2w)model in estimating the weighted mean temperature in China and adjacent areas,the error compensation technology based on the neural network was proposed,and a total of 374800 meteorological profiles measured from 2006 to 2015 of 100 radiosonde stations distributed in China and adjacent areas were used to establish an enhanced empirical model for estimating the weighted mean temperature in this region.The data from 2016 to 2018 of the remaining 92 stations in this region was used to test the performance of the proposed model.Results show that the proposed model is about 14.9%better than the GPT2w model and about 7.6%better than the Bevis model with measured surface temperature in accuracy.The performance of the proposed model is significantly improved compared with the GPT2w model not only at different height ranges,but also in different months throughout the year.Moreover,the accuracy of the weighted mean temperature estimation is greatly improved in the northwestern region of China where the radiosonde stations are very rarely distributed.The proposed model shows a great application potential in the nationwide real-time ground-based global navigation satellite system(GNSS)water vapor remote sensing.

Novel Analytical Model of Mean Temperature and Experimental Research on the Rail Universal Rolling

For building the analytical model of mean temperature in rail universal rolling, the cross-section of workpieces and the profile of horizontal roll and vertical roll are simplified rationally. The mean temperature of the web of rail, the top of rail and the base of rail are considered individually. The temperature rises for plastic deformation and friction incorresponding deformation zone, the temperature drop for contact is calculated on the base of variation principle and energy conservation law. Then the mean temperature is obtained. For verifying the theoretical model, the 18 kg/m light rail universal rolling experiments are accomplished in Yanshan University Rolling Laboratory, China and the surface temperature is measured. The surface temperature is not exact enough to express the true temperature and the mean temperature can show the status of the true temperature basically. So the mean temperature can be used to express the true temperature and this theoretical model and its results can be applied as an important reference to control the temperature of rail universal rolling and the heat treatment of the rolled rail.

Comparison of Global Mean Temperature Series

Studies on the reconstruction of global mean temperature series are reviewed by introducing three series, Had- CRUT3, NCDC, and GISS in details. Satellite data have been used since 1982 in NCDC and GISS series. NCDC series has the most complete spatial coverage among the three by using statistic interpolation technique. The weakened global warming in 2000-2009 as revealed in HadCRUT3 data is possibly caused by the lack of data coverage of this dataset over the Arctic. GISS and NCDC series showed much stronger warming trends during the last 10 years （-0.1 ℃ per 10 years）. Three series yielded almost the same warming trend for 1910-2009 （ 0.70-0.75 ℃ per 100 years）.

LOW FREQUENCY VARIABILITY OF INTERANNUAL CHANGE PATTERNS FOR GLOBAL MEAN TEMPERATURE DURING THE RECENT 100 YEARS

The TEEOF method that expands temporally is used to conduct a diagnostic study of the variation patterns of 1, 3, 6 and 10 years with regard to mean air temperature over the globe and Southern and Northern Hemispheres over the course of 100 years. The results show that the first mode of TEEOF takes up more than 50% in the total variance, with each of the first mode in the interannual oscillations generally standing for annually varying patterns which are related with climate and reflecting long-term tendency of change in air temperature. It is particularly true for the first mode on the 10-year scale, which shows an obvious ascending trend concerning the temperature in winter and consistently the primary component of time goes in a way that is very close to the sequence of actual temperature. Apart from the first mode of all time sections of TEEOF for the globe and the two hemispheres and the second mode of the 1-year TEEOF, interannual variation described by other characteristic vectors are showing various patterns, with corresponding primary components having relation with long-term variability of specific interannual quasi-periodic oscillation structures. A 2T test applied to the annual variation pattern shows that the abrupt changes for the Southern Hemisphere and the globe come closer to the result of a uni-element t test for mean temperature than those for the Northern Hemisphere do. It indicates that the 2Ttest, when carried out with patterns of multiple variables, seems more reasonable than the t test with single elements.

Empirical model for mean temperature and assessment of precipitable water vapor derived from GPS

The estimation of Precipitable Water Vapor （PWV） derived from Global Positioning System （GPS） data at the IGS site WUHN is assessed by comparing with PWV obtained from radiosonde data （No.57494） in Wuhan. The applicability of Saastamoinen （SAAS）, Hopfield and Black models used for estimating Zenith Hydrostatic Delay （ZHD） and Zenith Wet Delay （ZWD） and different models is verified in the estimation of GPS-derived PWV for the applied area. The experimental results demonstrated that ： 1 ） the precision of PWV estimated from Black model used for calculating ZHD （ ZHDs ） is lower than that of SAAS （ ZHDsAAs ） model and Hopfield model （ZHDn） with the RMS of 4. 16 ram; 2） the RMS of PWV estimated from SAAS model used for calculating ZWD （SAAS） is 3.78 ram; 3 ） the well-known Bevis model gives similar accuracy compared with the site-specific models for Tm in terms of surface temperature （ Ts ） and surface pressure （Ps）, which can reach the accuracy inside 1 mm in the GPS-derived PWV estimates.

El Niño and the AMO Sparked the Astonishingly Large Margin of Warming in the Global Mean Surface Temperature in 2023

In 2023,the majority of the Earth witnessed its warmest boreal summer and autumn since 1850.Whether 2023 will indeed turn out to be the warmest year on record and what caused the astonishingly large margin of warming has become one of the hottest topics in the scientific community and is closely connected to the future development of human society.We analyzed the monthly varying global mean surface temperature(GMST)in 2023 and found that the globe,the land,and the oceans in 2023 all exhibit extraordinary warming,which is distinct from any previous year in recorded history.Based on the GMST statistical ensemble prediction model developed at the Institute of Atmospheric Physics,the GMST in 2023 is predicted to be 1.41℃±0.07℃,which will certainly surpass that in 2016 as the warmest year since 1850,and is approaching the 1.5℃ global warming threshold.Compared to 2022,the GMST in 2023 will increase by 0.24℃,with 88%of the increment contributed by the annual variability as mostly affected by El Niño.Moreover,the multidecadal variability related to the Atlantic Multidecadal Oscillation(AMO)in 2023 also provided an important warming background for sparking the GMST rise.As a result,the GMST in 2023 is projected to be 1.15℃±0.07℃,with only a 0.02℃ increment,if the effects of natural variability—including El Niño and the AMO—are eliminated and only the global warming trend is considered.

Record-breaking High-temperature Outlook for 2023: An Assessment Based on the China Global Merged Temperature(CMST) Dataset

According to the latest version(version 2.0) of the China global Merged Surface Temperature(CMST2.0) dataset, the global mean surface temperature(GMST) in the first half of 2023 reached its third warmest value since the period of instrumental observation began, being only slightly lower than the values recorded in 2016 and 2020, and historically record-breaking GMST emerged from May to July 2023. Further analysis also indicates that if the surface temperature in the last five months of 2023 approaches the average level of the past five years, the annual average surface temperature anomaly in 2023 of approximately 1.26°C will break the previous highest surface temperature, which was recorded in 2016of approximately 1.25°C(both values relative to the global pre-industrialization period, i.e., the average value from 1850 to1900). With El Ni?o triggering a record-breaking hottest July, record-breaking average annual temperatures will most likely become a reality in 2023.

Meridional Distributions of Historical Zonal Averages and Their Use to Quantify the Global and Spheroidal Mean Near-Surface Temperature of the Terrestrial Atmosphere

The zonal averages of temperature (the so-called normal temperatures) for numerous parallels of latitude published between 1852 and 1913 by Dove, Forbes, Ferrel, Spitaler, Batchelder, Arrhenius, von Bezold, Hopfner, von Hann, and Börnstein were used to quantify the global (spherical) and spheroidal mean near-surface temperature of the terrestrial atmosphere. Only the datasets of Dove and Forbes published in the 1850s provided global averages below 〈T〉=14°C, mainly due to the poor coverage of the Southern Hemisphere by observations during that time. The global averages derived from the distributions of normal temperatures published between 1877 and 1913 ranged from 〈T〉=14.0°C (Batchelder) to 〈T〉=15.1°C (Ferrel). The differences between the global and the spheroidal mean near-surface air temperature are marginal. To examine the uncertainty due to interannual variability and different years considered in the historic zonal mean temperature distributions, the historical normal temperatures were perturbed within ±2σ to obtain ensembles of 50 realizations for each dataset. Numerical integrations of the perturbed distributions indicate uncertainties in the global averages in the range of ±0.3°C to ±0.6°C and depended on the number of available normal temperatures. Compared to our results, the global mean temperature of 〈T〉=15.0°C published by von Hann in 1897 and von Bezold in 1901 and 1906 is notably too high, while 〈T〉=14.4°C published by von Hann in 1908 seems to be more adequate within the range of uncertainty. The HadCRUT4 record provided 〈T〉≌?13.7°C for 1851-1880 and 〈T〉=13.6°C for 1881-1910. The Berkeley record provided 〈T〉=13.6°C and 〈T〉≌?13.5°C for these periods, respectively. The NASA GISS record yielded 〈T〉=13.6°C for 1881-1910 as well. These results are notably lower than those based on the historic zonal means. For 1991-2018, the HadCRUT4, Berkeley, and NASA GISS records provided 〈T〉=14.4°C, 〈T〉=14.5°C, and 〈T〉=14.5°C, respectively. The comparison of the 1991-2018 globally averaged near-surface temperature with those derived from distributions of zonal temperature averages for numerous parallels of latitude suggests no change for the past 100 years.

Evaluation model of individual thermal comfort based on mean skin temperature

The local skin temperatures of 22 subjects at air temperatures of 21,24,26,29 ℃ are measured,and the mean skin temperatures are calculated by ten skin temperature measuring points.The thermal comfort levels and the thermal sensations of these subjects are also investigated by a questionnaire.The Mahalanobis distance discrimination method is applied to establish the evaluation model for the thermal comfort based on the mean skin temperature.The experimental results indicate that the difference of the mean skin temperatures between the comfort level and the discomfort level is significant.Using the evaluation model,the mean skin temperature at the thermal comfort level is 32.6 to 33.7 ℃,and the thermal comfort levels of 72% of the subjects are correctly evaluated.The accuracy of the evaluation model can be improved when the effects of sex of the subject on the mean skin temperature and the thermal comfort are considered.It can be concluded that the mean skin temperature can be used as an effective physiological indicator to evaluate human thermal comfort in a steady thermal environment.

Analysis of the Weighted Mean Temperature of China Based on Sounding and ECMWF Reanalysis Data

Weighted mean temperature （Tin） is one of the most important conversion parameters for calculating precipitable water vapor by the signal path wet delay in ground-based GPS meteorology. This paper first discusses the Tm regression models for Hong Kong （HK） and the associated error statistics relative to the true values of Tm from the numerical method. The results show that there is little difference in precision between annual and seasonal Tm regression models for HK. The Bevis Tm-Ts （surface temperature） regression model is more suitable for northeastern China and the Qinghai-Tibetan Plateau than the local models. For areas lack of historical sounding data, the Kriging interpolation method and the ECMWF reanalysis product ERA-interim were employed to set up local Tm-Ts models. The results indicate that the Tm derived by the ERA-interim data coincides well with that by the sounding data, and the Kriging interpolation method can successfully obtain the coefficients of local Tm-Ts models, suggesting that these two approaches may serve as effective ways in the acquisition and localization of Tin.

ESTIMATES OF CLIMATE NOISES OF ANNUAL MEAN TEMPERATURES OVER CHINA

In this paper,a design to estimate climate noise of annual mean temperature has been made by means of the mini- mum interannual variance and effectively independent observations in time series.By using it the climate noises of annu- al mean surface air temperatures have been estimated based on the data from 1960 to 1991 in this country.The low val- ues of climate noises of annual mean temperatures are found in the southeastern Tibet Plateau,Yunnan,the Sichuan Ba- sin and south of the middle and lower reaches of the Changjiang River Valley.The high values are seen in the northwestern and northeastern China and the rest of the Tibet Plateau.A relatively low value region is in the southern Xinjiang.

Monthly Mean Temperature Prediction Based on a Multi－level Mapping Model of Neural Network BP Type

In terms of 34-year monthly mean temperature series in 1946-1979,the multi-level maPPing model of neural netWork BP type was applied to calculate the system's fractual dimension Do=2'8,leading tO a three-level model of this type with ixj=3x2,k=l,and the 1980 monthly mean temperture predichon on a long-t6rm basis were prepared by steadily modifying the weighting coefficient,making for the correlation coefficient of 97% with the measurements.Furthermore,the weighhng parameter was modified for each month of 1980 by means of observations,therefore constrcuhng monthly mean temperature forecasts from January to December of the year,reaching the correlation of 99.9% with the measurements.Likewise,the resulting 1981 monthly predictions on a long-range basis with 1946-1980 corresponding records yielded the correlahon of 98% and the month-tO month forecasts of 99.4%.

Detection of Chaoticity in Daily Mean Temperature Time Series

The chaoticity of daily mean temperature time series is investigated with complex systems theory. The data set that has been used in this analysis consists of daily mean temperature recorded at four stations in China. The power spectrums axe used to obtain some preliminaxy information regaxding the temperature time series. R/S analysis provides evidence for fractaiity in temperature time series. Cao method and correlation dimension, as well as the largest Lyapunov exponent, give consistent results, which does not exclude the possibility of deterministic chaos for the four daily mean temperature series. The research provides a principled basis for further study of temperature data with nonlinear dynamical methods.

Temporal and spatial variation of annual mean air temperature in arid and semiarid region in northwest China over a recent 46 year period

We analyzed the 1961-2006 mean surface air temperature data of 138 stations in China’s northwest arid and semi-arid areas(CNASA),to measure climate change in terms of annual mean air temperature changes.We used methods of linear regression analysis,multinomial fitting,Empirical Or-thogonal Function(EOF),Rotated Empirical Orthogonal Function(REOF),Mann-Kendall,Glide T-examination,wavelet analysis and power spectrum analysis.The results show that(1) the warming rate of the annual mean air temperature in CNASA was 0.35oC/10a during the 1961-2006 study period.Some places in the west part of Xinjiang and east part of the Qinghai plateau,which is impacted by the terrain of leeward slope,exhibit smaller increasing trends.However,the majority of region has shown distinct warming in line with general global warming;(2) The standard deviation of the annual mean temperature distribution is non-uniform.The south Xinjiang and east Qinghai-south Gansu areas show relatively small standard deviations,but the inter-annual variation in annual mean air temperature in the greater part of the region is high;(3) Inner Mongolia,Shaanxi,Gansu,Ningxia and Tarim Basin are the areas where the temperature changes are most sensitive to the environment.The degree of uniformity in annual mean air temperature increase is higher in the arid and semi-arid area.From the early 1970s,the trend in tempera-ture changed from a decrease to an increase,and there was a marked increase in mean temperature in 1986.After that mean temperature went through a period of rapid increase.The entire area’s 10 hottest years all occurred in or since the 1990s,and 90% of various sub-districts’ hottest years also occurred after 1990.The process of temperature change appears to have a roughly 5-year and a 10-year cycle;(4) An-nual mean air temperature variation has regional differences.In Inner Mongolia-Xinjiang and Shaanxi-Gansu-Ningxia-Qinghai areas,the temperature variation in their northern areas was very different from that in their southern areas;(5) Using the REOF method we divided the region into 4 sub-regions:the Northern region,the Plateau region,the Southern Xinjiang region and the Eastern region.The region’s annual mean air temperature transition has regional differences.The Plateau and Southern Xinjiang re-gions got warmer steadily without any obvious acceleration in the rate of warming.The Northern region’s warming started about 5-years earlier than that of the low latitude Eastern region.The ’Startup region’ of the Qinghai-Tibet Plateau,appears to undergo temperature changes 3 to 10 years earlier than the other regions,and exhibits inter-decadal variations 1 to 2 years ahead of the other regions.

Homogenized Daily Mean/Maximum/Minimum Temperature Series for China from 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 in the results.

Characteristics of Spatial and Temporal Variations of Monthly Mean Surface Air Temperature over Qinghai-Tibet Plateau

The recorded meteorological data of monthly mean surface air temperature from 72 meteorological stations over the Qinghal-Tibet Plateau in the period of 1960-2003 have been analyzed by using Empirical Orthogonal Function （EOF） method, to understand the detailed features of its temporal and spatial variations. The results show that there was a high consistency of the monthly mean surface air temperature, with a secondarily different variation between the north and the south of the plateau. Warming trend has existed at all stations since the 1960s, while the warming rates were different in various zones. The source regions of big rivers had intense warming tendency. June, November and December were the top three fast-warming months since the 1960s; while April, July and September presented dramatic warming tendency during the last decade.

Study on the Influence of Piloti on Mean Radiant Temperature in Residential Blocks by 3-D Unsteady State Heat Balance Radiation Calculation

Piloti is commonly used in tropical and subtropical climate zones to get high wind velocity and create shadowed areas in order to optimize the living environment of residential blocks,but there are few studies to reveal the influence of piloti on the radiant environment of residential blocks systematically. Taking the city of Guangzhou as an example,using 3-D Unsteady State Heat Balance Radiation Calculation Method,this paper shows that the mean radiant temperature( MRT) under piloti area increases with the increase of piloti ratio,and especially when piloti ratio is equal to 100%,the MRT increase trend becomes sharp. The MRT of exposed area decreases with the increase of piloti ratio,especially when piloti ratio reaches 100%,the decrease trend of MRT becomes sharp,which offers the reference for the study on piloti design in subtropical climate zones and further research on living environment by CFD simulation in residential blocks.

Global Annual Mean Surface Air Temperature Anomalies and Their Link with Indian Summer Monsoon Failures

Analysis of the global mean annual temperature anomalies based on land and marine data for the last 88 years (1901-1988) of this century has been carried out with a view to find any relationship with failures in Indian summer monsoon rainfall. On the climatological scale (i.e. 30 years) it has been noticed that there is an abnormal increase in the frequency of drought years during epochs of global warming and cooling, while it is considerably less when global temperatures are near normal. Results are unchanged even when the data are filtered out for ENSO (El-Nino Southern Oscillation) effect.It has also been noticed that during warm and cold epochs in global temperatures the amount of summer monsoon rainfall decreases as compared to the rainfall during a normal temperature epoch.

Asymmetric Mean Annual Temperature Wavelets Surface Air Layer of Berlin for 1701-2021

The regularities of the dynamics of the average annual temperature of Ber­lin from 1701 to 2021 are revealed.A total of 65 wavelets were received.The temperature has a high quantum certainty,and the change in the aver­age annual temperature of Berlin was identified by a model that contains only two components for prediction.The basis of the forecast at 320 years makes it possible to look into the future until the year 2340.The forecast confirms the conclusions made in the CMIP5 report on global warming.With an increase in the number of components in the model up to five,the forecast is possible only until 2060.Therefore,the model with only two components is workable.The trend is characterized by a modified Man­delbrot equation showing exponential growth with a high growth rate of 1.47421.The wave equation also has an amplitude in the form of the Man­delbrot law(in mathematics,the Laplace law,in biology,the Zipf-Pearl law,in econometrics,the Pareto law),when the exponential growth activity is equal to 1.For 1701,the period of oscillation was 2×60.33333≈120.7 years.By 2021,the period decreased and became equal to 87.6 years.The trend is such that by 2340 the period of oscillation will decrease to 30.2 years.Such an increase in fluctuations indicates an imbalance in climate disturbances in temperature in Berlin.For Berlin,the last three years are characterized by sharp decreases in the average annual temperature from 11.8℃ to 10.5℃,i.e.by 12.4% in 2021.Therefore,the forecast is still unstable,as a further decrease in the average annual temperature of Berlin in the near future may change the picture of the forecast.

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.