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.

The rigorous adjustment of the global mean sea level budget during 2005-2015

Global mean sea level budget is rigorously adjusted during the period 2005-2015.The emphasis is to provide the best estimates for the linear rates of changes(trends)of the global mean sea level budget components during this period subject to the constraint:Earth’s hydrosphere conserves water.The newly simultaneously adjusted trends of the budget components suggest a larger correction for the global mean sea level trend implicated by the other budget components’trends under the budget constraint.The simultaneous estimation of the linear trends of the budget components subject to the constraint for closure improves their uncertainties and enables a holistic assessment of the global mean sea budget,which has implications for future sea level science studies,including the future Intergovernmental Panel on Climate Change(IPCC)Assessment Reports,and the US Climate Assessment Reports.

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.

Quantitative estimation of global mean precipitation throughout the Phanerozoic era

Quantitatively estimating the global mean precipitation(GMP)throughout Earth's history is crucial for enhancing our understanding of long-term climate evolution and the hydrological cycle.However,currently there is no established methodology for estimating global mean paleoprecipitation.Here,we present the first study that estimates GMP in the Phanerozoic era.The relationship between GMP and global mean surface temperature(GMST)is investigated by analyzing data from 23 models in the Coupled Model Intercomparison Project phase 6(CMIP6).The result reveals consistent and significant impact of temperature on precipitation,with a sensitivity range of 2–3%K^(-1).Additionally,we propose a method for accessing latitudinal variations in precipitation caused by land area distributions and paleo-Koppen climatic belts.These climatic belts are determined based on geological indicators such as coals,evaporites,and glacial deposits.The GMP is thus quantitatively estimated by combining variations in GMST,land area distributions,and paleo-koppen climatic belts,spanning from 540 Ma to the present day.The quantitative GMP curve demonstrates fluctuations in GMP about 500 mm yr^(-1),with values ranging from 948 to1442 mm yr-1over the Phanerozoic era.This curve aligns closely with findings derived from numerical simulations.The presented paleoprecipitation variations facilitate a more comprehensive understanding of the interconnected geological and paleoclimatic developments.

Trend of Santonian (Late Cretaceous) atmospheric CO_2 and global mean land surface temperature: Evidence from plant fossils

Quantitative reconstructions of atmospheric CO2 by using terrestrial and marine records are critical for understanding the so-called ＂greenhouse＂ conditions in the Cretaceous, but data from terrestrial plants for several stages of this period remain quite limited. Using the stomatal index （SI） technique, here we estimate the Santonian （Late Cretaceous） CO2 contents based on a sequence of fossil cuticles of Ginkgo adiantoides （Ung.） Heer from three beds of the Yong＇ancun Formation in Jiayin, Heilongjiang Province, northeastern China. By the regress function, Sis of Ginkgo fossils reveal a pronounced CO2 reduction from the early to late Santonian （-661 and -565 ppm, respectively）. The relatively high CO2 levels provide additional evidence for paleoclimatic warmth in this interval. Moreover, available paleobotanical data illustrate a decline trend of CO2 contents throughout the Late Cretaceous, punctuated by several fluctuations in particular episodes with different magnitudes. The CO2 contents shifted notably in the late Cenomanian, Turonian, early Santonian, late Campanian, and probably latest Maastrichtian. Furthermore, a comprehensive study based on CO2 data shows that the global mean land surface temperature （GMLST） fluctuated several times accordingly. The change ratios of GMLST （AT） increased from -3℃ in late Cenomanian to -4.7℃ in mid Turonian, and then dramatically reduced to -2.2℃ in mid Coniacian. From the Santonian onward, it appears that the temperature gradually decreased with a few minor fluctuations.

Potential Impact of Tonga Volcano Eruption on Global Mean Surface Air Temperature

The undersea volcano,located in the South Pacific island nation of Tonga,violently erupted from 14 to 15 January 2022.The Tonga volcano eruption has aroused extensive discussion in the climate change field.Some climatologists believe that this event will cause little effect on global climate change while others insist that it will trigger“the year without a summer”as the Tambora eruption did in 1815.How will the Tonga volcano eruption affect global climate change?Based on the indices of past volcanic eruptions and the eruption data of El Chichón volcano in 1982,we use a simplified radiation equilibrium model to quantify the stratospheric aerosol radiative forcing and the change in global mean surface air temperature(Ts)caused by the Tonga volcano eruption.The results show that the global average Ts will decrease by about 0.0315-0.1118℃in the next 1-2 years.The Tonga eruption will slightly slow down the global warming in a short period of time,but it will not change the global warming trend in the long term.In addition,we propose a generalized approach for estimating the impact of future volcanic eruption on global mean T_(s).

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.

The global mean sea level rise,still accelerated

Subject Code:D06A collaborative study partly sponsored by the National Natural Science Foundation of China indicates that the global mean sea level is still accelerated rising,with much of the increase from the accelerated Greenland ice sheet melting during 1993—2014.The work is coauthored by Prof.Chen Xianyao(陈显尧)

A physical model for the global mean surface air temperature anomalies over the past century

OBSERVATIONS show that the global mean surface air temperature has risen by about 0.62℃over the past century.This rise is widely thought to be the result of increasing atmosphericconcentrations of greenhouse gases such as carbon dioxide,methane,nitrous oxide and chlo-

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.

Global Warming in Japanese Cities from 1960 to 2019 Using Machine Learning

In this study, we investigated the variations in warming between Japanese cities for 1960-1989, and 1990-2019 using principal component analysis (PCA) and k-means clustering. The precipitation and sunshine hours exhibited opposite tendencies in the PCA results. It was found that 1960M and 1990M had a correlation (r = 0.51). The 1960M and 1990M are the mean temperature anomalies in Japanese cities for 1960-1989 and 1990-2019, respectively. There was a strong correlation between temperature and precipitation (r = 0.62). There was an inverse correlation between 1960M and sunshine hours (r = −0.25), but a correlation between 1990M and sunshine hours (r = 0.11). Sunshine hours had less effect on the 1960M but more impact on the 1990M. The k-means clustering for 1960M and 1990M can be classified into four types: high 1960M and high 1990M, which indicates that global warming is progressing rapidly (Sapporo, Tokyo, Kyoto, Osaka, Fukuoka, Nagasaki), low 1960M and low 1990M, global warming is progressing slowly (Nemuro, Ishinomaki, Yamagata, Niigata, Fushiki, Nagano, Karuizawa, Mito, Suwa, Iida, Hamada, Miyazaki, Naha), low 1960M and high 1990M, global warming has accelerated since 1990 (Utsunomiya, Kofu, Okayama, Hiroshima), and normal 1960M and normal 1990M, the rate of warming is normal among the 38 cities (Asahikawa, Aomori, Akita, Kanazawa, Maebashi, Matsumoto, Yokohama, Gifu, Nagoya, Hamamatsu, Kochi, Kagoshima). Higher annual temperatures were correlated with higher annual precipitation according to the k-means clustering of temperature and precipitation. Two of the four categories consisted of places with high annual temperatures and high precipitation (Fushiki, Kanazawa, Kochi, Miyazaki, Kagoshima, Naha, Ishigakijima), and places with low annual temperatures and low precipitation (Asahikawa, Nemuro, Sapporo, Karuizawa).

An Observational Analysis of the Oceanic and Atmospheric Structure of Global-Scale Multi-decadal Variability

The aim of the present study was to identify multi-decadal variability （MDV） relative to the current centennial global warming trend in available observation data.The centennial global wanning trend was first identified in the global mean surface temperature （STgm） data.The MDV was identified based on three sets of climate variables,including sea surface temperature （SST）,ocean temperature from the surface to 700 m,and the NCEP and ERA40 reanalysis datasets,respectively.All variables were detrended and low-pass filtered.Through three independent EOF analyses of the filtered variables,all results consistently showed two dominant modes,with their respective temporal variability resembling the Pacific Decadal Oscillation/Inter-decadal Pacific Oscillation （PDO/IPO） and the Atlantic Multi-decadal Oscillation （AMO）.The spatial structure of the PDO-like oscillation is characterized by an ENSO-like structure and hemispheric symmetric features.The structure associated with the AMO-like oscillation exhibits hemispheric asymmetric features with anomalous warm air over Eurasia and warm SST in the Atlantic and Pacific basin north of 10°S,and cold SST over the southern oceans.The Pacific and Atlantic MDV in upper-ocean temperature suggest that they are mutually linked.We also found that the PDO-like and AMO-like oscillations are almost equally important in global-scale MDV by EOF analyses.In the period 1975-2005,the evolution of the two oscillations has given rise to strong temperature trends and has contributed almost half of the STgm warming.Hereon,in the next decade,the two oscillations are expected to slow down the global warming trends.

基于全局K-means的谱聚类算法

谱聚类算法是近年来研究得比较多的一种聚类算法。但谱聚类是对初始化敏感的,针对这种缺陷,提出一种基于全局K-means的谱聚类算法(GKSC),引入对初值不敏感的全局K-means算法来改善。通过仿真实验表明:GKSC与传统谱聚类算法相比更能得到稳定的聚类结果和更高的聚类精确度。

改进的全局K′-means算法及其在数据分类中的应用

为了解决初始聚类中心的选择、簇个数的确定以及孤立点的避免等问题,提出了一种改进的全局K′-means算法.改进的算法不仅能够利用辅助聚类函数来计算初始点,而且能够利用目标函数在没有预定义聚类个数的前提下,找到实际的聚类中心个数,同时避免了孤立点问题.将改进的算法应用到实际数据集的分类中,并与改进的全局K-means算法以及K′-means算法进行了比较,实验结果证明所提出的算法能获得更好的聚类结果.

基于全局K-means算法的超像素分割方法

为了提高超像素分割的性能,提出了一种基于全局K-means算法的超像素分割方法。该算法利用全局K-means算法计算超像素聚类,优化了超像素的分割质量,并根据聚类后的超像素结果从图像中分割出目标.实验数据来自于国际公认的PASCAL VOC 2007数据集.实验结果表明,与Wang算法和ISLIC算法相比,提出的分割算法的PRI、CR、VOI及运行时间4个指标分别平均提高9.38%、17.13%、21.67%、10.50%,可以实现更佳的图像分割效果.

基于全局K-means算法的高校学生成绩分析

采用无监督聚类算法实现对学生成绩的评价分析。在给定的聚类个数区间内,全局K-means聚类算法对学生成绩数据进行聚类,得到不同聚类个数下的成绩分类结果;结合聚类有效性指标自动地确定出最佳聚类数目,实现对学生多科成绩数据的无监督分类。实验结果表明,本方法能够发现不同成绩聚类结果之间的差异,揭示影响数据分布的主要因素,平衡了数据的所有属性对最终的分析结果的影响,同时避免了手动分析中结果容易产生较大偏差的问题。对指导学生选修课程、教师对个人的教学方法进行调整以及改善学校教学质量和提升学生成绩都具有重要作用。

粒子群K-means聚类算法的改进

粒子群(PSO)与K-means结合是聚类分析中的重要方法之一,但都未考虑粒子更新导致的空类问题。提出基于多子群粒子群伪均值(PK-means)聚类算法,为该问题的解决提供一种有效途径,并与粒子群K均值(PSOK-means),K-means算法进行比较。理论分析和实验表明,该算法不但可以防止空类出现,而且同时还具有非常好的全局收敛性和局部寻优能力,并且在孤立点问题的处理上也具有很好的效果。

融合快速全局K-means与区域合并的图像分割

提出一种融合快速全局K-means与区域合并的图像分割方法。该方法利用中值滤波方法对图像去噪;运用快速全局K-means算法对图像的颜色空间进行聚类分析;结合区域合并准则,对初始分割合并得到最终的分割结果。实验表明,与同类算法比较,该方法的分割结果在图像细节方面能够很好地满足人的主观视觉。