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.

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.

Homogenized Daily Mean/Maximum/Minimum Temperature Series for China from 1960-2008

Inhomogeneities in the daily mean/maximum/minimum temperature (T m /T max /T min) 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 Bei-jing (BJ),Wutaishan (WT),ürümqi (UR) and Henan (HN) stations.The homogenized dataset shows a mean warm-ing trend of 0.261/0.193/0.344oC/decade for the annual series of T m /T max /T min,slightly smaller than that of the original dataset by 0.006/0.009/0.007oC/decade.However,considerable differences between the adjusted and origi-nal datasets were found at the local scale.The adjusted T min series shows a significant warming trend almost eve-rywhere for all seasons,while there are a number of sta-tions with an insignificant trend in the original dataset.The adjusted T m 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 cen-tral 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 T max data exhibit cooling trends for summers at a number of stations in the mid-lower reaches of the Yangtze and Yellow Riv-ers and for springs and winters at a few stations in south-western 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 T m and T max 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.

Homogenised Monthly and Daily Temperature and Precipitation Time Series in China and Greece since 1960

In this paper,we describe and analyze two datasets entitled“Homogenised monthly and daily temperature and precipitation time series in China during 1960–2021”and“Homogenised monthly and daily temperature and precipitation time series in Greece during 1960–2010”.These datasets provide the homogenised monthly and daily mean(TG),minimum(TN),and maximum(TX)temperature and precipitation(RR)records since 1960 at 366 stations in China and 56stations in Greece.The datasets are available at the Science Data Bank repository and can be downloaded from https://doi.org/10.57760/sciencedb.01731 and https://doi.org/10.57760/sciencedb.01720.For China,the regional mean annual TG,TX,TN,and RR series during 1960–2021 showed significant warming or increasing trends of 0.27℃(10 yr)^(-1),0.22℃(10 yr)^(-1),0.35℃(10 yr)^(-1),and 6.81 mm(10 yr)-1,respectively.Most of the seasonal series revealed trends significant at the 0.05level,except for the spring,summer,and autumn RR series.For Greece,there were increasing trends of 0.09℃(10 yr)-1,0.08℃(10 yr)^(-1),and 0.11℃(10 yr)^(-1)for the annual TG,TX,and TN series,respectively,while a decreasing trend of–23.35 mm(10 yr)^(-1)was present for RR.The seasonal trends showed a significant warming rate for summer,but no significant changes were noted for spring(except for TN),autumn,and winter.For RR,only the winter time series displayed a statistically significant and robust trend[–15.82 mm(10 yr)^(-1)].The final homogenised temperature and precipitation time series for both China and Greece provide a better representation of the large-scale pattern of climate change over the past decades and provide a quality information source for climatological analyses.

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.

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.

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.

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.

Comparison of Daily Extreme Temperatures over Eastern China and South Korea between 1996–2005

This paper examined the decadal mean, seasonal cycle, and interannual variations of mean and extreme temperatures using daily temperature and relative humidity data from 589 stations over eastern China and South Korea between 1996-2005. The results show that the decadal mean Tm （mean daily mean temperature） and the TNn （minimum daily minimum temperature） increase from north to south; the opposite spatial gradient is found in the DTR （diurnal temperature range）; the value of the DTR over South Korea is in- between that over North China and the mid-low Yangtze River valley; the TXx （maximum daily maximum temperature） has a unique spatial distribution, with the largest value over eastern China. The highest standard deviation （STD） is located over northern China and the TNn has the largest area coverage of the high STD. The peak of the seasonal cycle for the Tm, TXx and TNn over South Korea （August） occurs one month later than that over eastern China （July）. The seasonal cycle of the DTR has two peaks （April and October）; the value in the middle-lower reaches of the Yangtze River valley is larger than that in South Korea during July and August owing to the seasonal northward jump of the major monsoon rain band. The interannual variations of summertime temperature indices including the Tin, TXx, and DTR over South Korea are consistent （opposite） to that over northern （southern） China. For the wintertime temperature indices however, the variation over South Korea is consistent with that over eastern China.

DETERMINATION OF WEIGHTED MEAN TROPOSPHERIC TEMPERATURE USING GROUND METEOROLOGICAL MEASUREMENTS

The weighted mean tropospheric temperature is a critical parameter in the conversion of wet zenith delay to precipitable water vapor in GPS Meteorology.This parameter can not be calculated from the radiosonde data in real time through the conventional methods.In this study,we first discuss the admissible error of weighted mean temperature to enable the accuracy of the conversion better than 1 mm,then summarize the performance of some of the existing methods. An empirical formula is established that satisfies the real_time requirement in GPS meteorology using Sequential Regression Analysis method.It is shown that this real_time formula as compared with other empirical methods is more accurate for local applications.

Relationships between Rainy Days,Mean Daily Intensity and Seasonal Rainfall in Normal,Flood and Drought Years over India

here are limitations in using the seasonal rainfall total in studies of Monsoon rainfall climatology. A correlation analysis of the individual station seasonal rainfall with all India seasonal mean rainfall has been made. After taking the significance test (strictly up to 5% level) the stations which are significantly correlated have been considered in this study in normal, flood and drought years respectively. Analysis of seasonal rainfall data of 50 stations spread over a period of 41 years suggests that a linear relationship fits better than the logarithmic relationship when seasonal rainfall versus number of rainy days is studied. The linear relationship is also found to be better in the case of seasonal rainfall versus mean daily intensity.

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.

The relation between ambulance transports due to heat stroke and air temperature using daily data in Okayama prefecture, Japan

The aim of this study was to investigate the link between ambulance transports due to heat stroke and air temperature by using daily data of ambulance transports in Okayama prefecture, Japan. Daily observations for ambulance transports due to heat stroke from July to September in 2010 in Okayama prefecture, Japan were obtained from Fire and Disaster Management Agency in Japan. Data of meteorological parameters in Okayama prefecture, Japan were also obtained from Japan Meteorological Agency. Effect of meteorological parameters on ambulance transports due to heat stroke was analyzed. A total of 1133 ambulance transports due to heat stroke were observed in from July to September of 2010 in Okayama prefecture, Japan. Ambulance transports due to heat stroke was significantly correlated with air temperature. In addition, number of subjects with ambulance transports due to heat stroke over 34°C in the highest air temperature was 21.2 ± 9.8 per day. Higher air temperature was closely associated with higher ambulance transports due to heat stroke by using daily data in Okayama, prefecture, Japan.

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.

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.

Applicability of Bevis Formula at Different Height Levels and Global Weighted Mean Temperature Model Based on Near-earth Atmospheric Temperature

Weighted mean temperature(T m)is a critical parameter in Global Navigation Satellite System(GNSS)technology to retrieve precipitable water vapor(PWV).It is convenient to obtain high-precision T m estimates near surface utilizing Bevis formula and surface temperature.However,some researches pointed out that the Bevis formula has large uncertainties in high-altitude regions.We investigate the applicability of the Bevis formula at different height levels and find that the Bevis formula has relatively high precision when the altitude is low,while with altitude increasing,the precision decreases gradually.To solve the problem,we analyze the relationship between T m and atmospheric temperature within the near-earth space range(the height range between 0~10 km)and find that they have a high correlation on a global scale.Accordingly,we build a global weighted mean temperature model based on near-earth atmospheric temperature.Validation results of the model show that this model can provide high-precision T m estimation at any height level in the near-earth space range.

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%.

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.

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 Qinghai-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 De-cember were the top three fast-warming months since the 1960s;while April,July and September presented dramatic warming tendency during the last decade.

基于分子晶体序参数与K-means聚类的TNT晶型转化有限温度弦研究

为揭示炸药转晶稀有事件的分子机制,分别构建了基于键距离(即分子间距离)与键取向和分子取向的两类序参数,借助基于Euclidean距离和密度权重的K-means聚类算法进行了序参数的增强采样。结果表明,基于分子晶体序参数与K-means聚类的增强采样改进了稀有事件常规有限温度弦方法,使自由能快速收敛。将该方法用于TNT晶型转化的研究,避免了分子晶体序参数“维数爆炸”,获得了平均力势面,验证了基于分子晶体序参数与K-means聚类有限温度弦方法在炸药晶型转化研究中的有效性,探明了TNT(O)与TNT(M)之间界面诱导、局部引发、多核非同步生长的晶型转变过程。