Multi-Decadal Trends of Global Surface Temperature:A Broken Line with Alternating~30 yr Linear Segments?

We investigate global temperature data produced by the Climate Research Unit at the University of East Anglia (CRU) and the Berkeley Earth Surface Temperature consortium (BEST). We first fit the 1850-2010 data with polynomials of degrees 1 to 9. A significant ~60-yr oscillation is accounted for as soon as degree 4 is reached. This oscillation is even better modeled as a broken line, more precisely a series of ~30-yr long linear segments, with slope breaks (singularities) in ~1904, ~1940, and ~1974 (±3 yr), and a possible recent occurrence at the turn of the 20th century. Oceanic indices PDO (Pacific Decadal Oscillation) and AMO (Atlantic Multidecadal Oscillation) have undergone major changes (respectively of sign and slope) roughly at the same times as the temperature slope breaks. This can be interpreted with a system of oceanic non-linear coupled oscillators with abrupt mode shifts. Thus, the Earth’s climate may have entered a new mode (a new ~30-yr episode) near the turn of the 20th century: no further temperature increase, a dominantly negative PDO index and a decreasing AMO index might be expected for the next decade or two.

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.

THE IDENTIFICATION OF THE VOLCANIC SIGNAL IN GLOBAL SURFACE TEMPERATURE RECORDS

In an attempt to clearly separate the volcanic signal,we use a mixture of principal component analysis(PCA)and superposed epoch analysis to identify volcanic signal in the global surface tem- perature field.In this way,the spatial and temporal pattern of volcanic signals is identified in the global surface temperature records.Our results show that the strongest ENSO and volcanic signals are related with the first and the third principal components respectively.Both ENSO and volcanic signals have responses in the second principal component.

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.

Evolution of Surface Sensible Heat over the Tibetan Plateau Under the Recent Global Warming Hiatus

Based on regular surface meteorological observations and NCEP/DOE reanalysis data, this study investigates the evolution of surface sensible heat（SH） over the central and eastern Tibetan Plateau（CE-TP） under the recent global warming hiatus. The results reveal that the SH over the CE-TP presents a recovery since the slowdown of the global warming. The restored surface wind speed together with increased difference in ground-air temperature contribute to the recovery in SH.During the global warming hiatus, the persistent weakening wind speed is alleviated due to the variation of the meridional temperature gradient. Meanwhile, the ground surface temperature and the difference in ground-air temperature show a significant increasing trend in that period caused by the increased total cloud amount, especially at night. At nighttime, the increased total cloud cover reduces the surface effective radiation via a strengthening of atmospheric counter radiation and subsequently brings about a clear upward trend in ground surface temperature and the difference in ground-air temperature.Cloud–radiation feedback plays a significant role in the evolution of the surface temperature and even SH during the global warming hiatus. Consequently, besides the surface wind speed, the difference in ground-air temperature becomes another significant factor for the variation in SH since the slowdown of global warming, particularly at night.

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.

The Space and Time Features of Global SST Anomalies Studied by Complex Principal Component Analysis

In this paper, the variability characteristics of the global field of sea surface temperature (SST) anomaly are studied by complex principal component (c.p.c.) analysis, whose results are also compared with those of real p.c. analysis. The data consist of 40 years of global SST monthly averages over latitudes from 42 5°S to 67 5°N. In the spatial domain, it is found that the distribution of the first complex loading amplitude is characterized by three areas of large values: the first one in the eastern and central equatorial Pacific Ocean, the second one in the northern tropical Indian Ocean and South China Sea, the third one in the northern Pacific Ocean. As it will be explained, this pattern may be considered as representative of El Nio mode. The first complex loading phase pattern shows a stationary wave in the Pacific (also revealed by real p.c. analysis) superimposed to an oscillating disturbance, propagating from the Pacific to Indian or the opposite way. A subsequent correlation analysis among different spatial points allows revealing disturbances actually propagating westward from the Pacific to the Indian Ocean, which could therefore represent reflected Rossby waves, i.e. the west phase of the signals that propagate disturbances of thermal structure in the tropical Pacific Ocean. In the time domain, a relation between the trend of the first complex principal component and the ENSO cycle is also established.

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).

Multiple ocean surface temperature changes induced multi-decadal global warming rate

Subject Code:D05Under support of the National Natural Science Foundation of China,a concerted study by Dr.Huang Gang(黄刚)from the Institute of Atmospheric Physics,Chinese Academy of Sciences,climate scientist Jing-Jia Luo from the Australian Bureau of Meteorology,Dr.Yao Shuailei(姚帅磊)from the Institute

Perceiving the Trend of Terrestrial Climate Change during the Past 40 year(1978-2018)

In past few decades,climate has manifested numerous shifts in its trend.Various natural and anthropogenic factors have influenced the dynamics and the trends of climate change at longer time scale.To understand the long term climate fluctuations,we have analyzed forty years(1978-2018)data of ten climatic parameters that are responsible to influence the climate dynamics.The parameters involved in the present study are total solar irradiance(TSI),ultra violet(UV)index,cloud cover,carbon dioxide(CO2)abundances,multivariate(ENSO)index,volcanic explosivity index(VEI),global surface temperature(GST)anomaly,global sea ice extent,global mean sea level and global precipitation anomaly.Using the above mentioned climate entities;we have constructed a proxy index to study the quantitative measure of the climate change.In this process these indicators were aggregated to a single proxy index as global climate index(GCI)that has measured the strength of present climate change in semblance with the past natural variability.To construct GCI,the principal component analysis(PCA)has been used on yearly based data for the period 1978-2018.Actually PCA is a statistical tool with which we can reduce the dimensionality of the data and it retains most of the variation in the new data set.Further,we have confined our study to natural climate drivers and anthropogenic climate drivers.Our result has indicated that the strongest climate change has been occurred globally by the end of the year 2018 in comparison to late 1970’s natural variability.

Interdecadal Variability of the East Asian Summer Monsoon in an AGCM

It is well known that significant interdecadal variation of the East Asian summer monsoon （EASM） occurred around the end of the 1970s. Whether these variations can be attributed to the evolution of global sea surface temperature （SST） and sea ice concentration distribution is investigated with an atmospheric general circulation model （AGCM）. The model is forced with observed monthly global SST and sea ice evolution through 1958-1999. A total of four integrations starting from different initial conditions are carried out. It is found that only one of these reproduces the observed interdecadal changes of the EASM after the 1970s, including weakened low-level meridional wind, decreased surface air temperature and increased sea level pressure in central China, as well as the southwestward shift of the western Pacific subtropical high ridge and the strengthened 200-hPa westerlies. This discrepancy among these simulated results suggests that the interdecadal variation of the EASM cannot be accounted for by historical global SST and sea ice evolution. Thus, the possibility that the interdecadal timescale change of monsoon is a natural variability of the coupled climate system evolution cannot be excluded.

Contributions of internal climate variability in driving global and ocean temperature variations using multi-layer perceptron neural network

Roles of internal climate variabilities regulating global and ocean temperature changes is a hot but complex issue of scientific concern,infuencing the comprehensive policy-making in response to global and regional warming.In this study,the time series of monthly global and ocean mean surface temperature(GST and OST,respectively)since 1866 is successflly reconstructed via natural and anthropogenic forcing factors and internal climate variability by using a Multi-Layer Perceptron(MLP)neural network technique.The MLP demonstrates prominent monthly GST and OST reconstruction skills on both interannual and annual time scales.Most of the warming in GST and OST since 1866 is found to be attributable to anthropogenic forcing,while the multidecadal and interannual GST and OST variations are considerably dominated by Atlantic Multidecadal Oscillation(AMO).Internal climate variabilities like Interdecadal Pacific Oscillation(IPO)can amplify the GST and OST changes and explain the global warming slowdown since 1998.Southern Oscillation Index(SOI)performs a similar role as IPO but to a lesser extent.Changes in OST caused by solar forcing are more considerable than those in GST.Moreover,the"biased warmth"during the Second World War is successfully reconstructed in MLP.AMO and IPO can explain most annual and even sub-annual temperature variations during this period,offering an explanation for the existence of this abnormal warm period other than that it was entirely caused by instrumental errors.The generally high accuracy of reconstructions on interannual and annual time scales can enhance the ability to monitor the prompt feedback of specific external radiative forcings and internal variabilities to changes in climate.