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.

Development of S-NPP VIIRS global surface type classification map using support vector machines

With the launch of the Joint Polar Satellite System(JPSS)/Suomi National Polar-orbiting Partnership(S-NPP)satellite in October 2011,many of the terrestrial remote sensing products generated from Moderate Resolution Imaging Spectroradiometer(MODIS),such as the global land cover map,have been inherited and expanded into the JPSS/S-NPP mission using the new Visible Infrared Imaging Radiometer Suite(VIIRS)data.In this study,an improved algorithm including the use of a new classifier support vector machines(SVM)classifier was proposed to produce VIIRS surface type maps.In addition to the new classification algorithm,a new post-processing strategy involving the use of new ancillary data to refine the classification output is implemented.As a result,the new global International Geosphere-Biosphere Programme(IGBP)map based on the 2014 VIIRS surface reflectance data was generated with a 78.5±0.6% overall classification accuracy.The new map was compared to a previously delivered VIIRS surface type map,and to the MODIS land cover product.Validation results including the error matrix,overall accuracy,and the user’s and producer’s accuracy suggest the new global surface type map provides similar classification accuracy compared to the old VIIRS surface type map,with higher accuracy achieved in agricultural types.

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.

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.

Is the global sea surface temperature rise accelerating?

This is an exploratory investigation to search for the presence of an acceleration in global sea surface temperature rise, which is essential to identify anthropogenic contributions to the climate change during the 20 th century. A weighted statistical model with an acceleration parameter was built progressively to reconstruct the variations in the global sea surface temperature data considering statistically significant confounders and autoregressive disturbances in the process. From the preliminary residual analysis of a weighted regression model, emerged a parsimonious model with first order autoregressive disturbances with a deterministic trend, acceleration and periodicity of 69 yr and its 138 yr subharmonic. The final model solution, selected from 29 alternative combinations of the model parameters using Mallows' s Cp metric, revealed a statistically significant deterministic trend, 0.40 ± 0.03C/c(p < 0.01), and acceleration, 0.67 ± 0.11C/c^2(p < 0.01) explaining 33% of the global sea surface temperature variations. The combined yearly trend and acceleration in global sea surface temperature as predicted by the model,exhibit a strong correlation with the yearly increase in the global CO^2 concentrations observed during the 20th century.

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.

基于全局响应面算法的Q235B钢的Johnson-Cook模型参数最优

This study investigates the mechanical properties of Q235B steel through quasi-static tests at both room temperature and elevated temperature.The initial values of the Johnson-Cook model parameters are determined using a fitting method.The global response surface algorithm is employed to optimize and calibrate the Johnson-Cook model parameters for Q235B steel under both room temperature and elevated temperature conditions.A simulation model is established at room temperature,and the simulated mechanical performance curves for displacement and stress are monitored.Multiple optimization algorithms are applied to optimize and calibrate the model parameters at room temperature.The global response surface algorithm is identified as the most suitable algorithm for this optimization problem.Sensitivity analysis is conducted to explore the impact of model parameters on the objective function.The analysis indicates that the optimized material model better fits the experimental values,aligning more closely with the actual test results of material strain mechanisms over a wide temperature range.

Quality Control and Analysis of Global Gauge-Based Daily Precipitation Dataset from 1980 to 2009

A series of quality control(QC) procedures were performed on a gauge-based global daily precipitation dataset from the Global Telecommunication System(GTS) for the period 1980-2009.A new global daily precipitation(NGDP) dataset was constructed by applying those QC procedures to eliminate erroneous records.The NGDP dataset was evaluated using the NOAA Climate Prediction Center Merged Analysis of Precipitation(CMAP) and the Global Precipitation Climatology Project(GPCP) precipitation datasets.The results showed that the frequency distribution and spatial distribution pattern of NGDP had a nice match with those from the CMAP and GPCP datasets.The global mean correlation coefficients with the CMAP and GPCP data increased from 0.24 for original GTS precipitation data to about 0.70 for NGDP data.Correspondingly,the root mean square errors(RMSE) decreased from 12 mm per day to 1 mm per day.The interannual variabilities of NGDP monthly precipitation are consistent with the CMAP and GPCP datasets in Asia.Meanwhile,the seasonal variabilities for most land areas on the Earth of NGDP dataset are also consistent with the CMAP and GPCP precipitation products.

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.

Land Surface Albedo Variations in Sanjiang Plain from 1982 to 2015: Assessing with GLASS Data

As a key parameter for indicating the fraction of surface-reflected solar incident radiation, land surface albedo plays an important role in the Earth’s surface energy budget(SEB). Since the Sanjiang Plain has been severely affected by human activities(e.g., reclamation and shrinking of wetlands), it is important to assess the spatiotemporal variations of surface albedo in this region using a long-term remote sensing dataset. In order to investigate the surface albedo climatology, trends, and mechanisms of change, we evaluated the surface albedo variations in the Sanjiang Plain, China from 1982 to 2015 using the Global LAnd Surface Satellite(GLASS) broadband surface albedo product. The results showed that: 1) an increasing annual trend(+0.000 58/yr) of surface albedo was discovered in the Sanjiang Plain based on the GLASS albedo dataset, with a much stronger increasing trend(+0.001 26/yr) occurring during the winter. Most of the increasing trends occurred over the cultivated land, unused land, and land use conversion types located in the northeastern Sanjiang Plain. 2) The increasing trend of land surface albedo in Sanjiang Plain can be largely explained by the changes of both snow cover extent and land use. The surface albedo in winter is highly correlated with the snow cover extent in the Sanjiang Plain, and the increasing trend of surface albedo can be further enhanced by the land use changes.

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.

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.

Preliminary analysis of spatiotemporal pattern of global land surface water

Land surface water(LSW) is one of the most important resources for human survival and development, and it is also a main component of global water recycling. A full understanding of the spatial distribution of land surface water and a continuous measuring of its dynamics can support to diagnose the global ecosystem and environment. Based on the Global Land 30-water 2000 and Global Land 30-water 2010 products, this research analyzed the spatial distribution pattern and temporal fluctuation of land surface water under scale-levels of global, latitude and longitude, continents, and climate zones. The Global Land 30-water products were corrected the temporal inconsistency of original remotely sensed data using MODIS time-series data, and then calculated the indices such as water area, water ration and coefficient of spatial variation for further analysis. Results show that total water area of land surface is about 3.68 million km2(2010), and occupies 2.73% of land area. The spatial distribution of land surface water is extremely uneven and is gathered mainly in mid- to high-latitude area of the Northern Hemisphere and tropic area. The comparison of water ratio between 2000 and 2010 indicates the overall fluctuation is small but spatially differentiated. The Global Land 30-water products and the statistics provided the fundamental information for analyzing the spatial distribution pattern and temporal fluctuation of land surface water and diagnosing the global ecosystem and environment.

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).

Satellite detection of increases in global land surface evapotranspiration during 1984-2007

As a key component of digital earth,remotely sensed data provides the compelling evidence that the amount of water vapour transferred from the entire global surface to the atmosphere increased from 1984 to 2007.The validation results from the earlier evapotranspiration(ET)estimation algorithm based on net radiation(Rn),Normalised Difference Vegetation Index(NDVI),air temperature and diurnal air temperature range(DTaR)showed good agreement between estimated monthly ET and ground-measured ET from 20 flux towers.Our analysis indicates that the estimated actual ET has increased on average over the entire global land surface except for Antarctica during 1984
2007.However,this increasing trend disappears after 2000 and the reason may be that the decline in net radiation and NDVI during this period depleted surface soil moisture.Moreover,the good correspondence between the precipitation trend and the change in ET in arid and semi-arid regions indicated that surface moisture linked to precipitation affects ET.The input parameters Rn,Tair,NDVI and DTaR show substantial spatio-temporal variability that is almost consistent with that of actual ET from 1984 to 2007 and contribute most significantly to the variation in actual ET.

Estimating global land surface broadband thermal-infrared emissivityusing advanced very high resolution radiometer optical data

An algorithm for retrieving global eight-day 5 km broadband emissivity (BBE)from advanced very high resolution radiometer (AVHRR) visible and nearinfrared data from 1981 through 1999 was presented. Land surface was dividedinto three types according to its normalized difference vegetation index (NDVI)values: bare soil, vegetated area, and transition zone. For each type, BBE at813.5 mm was formulated as a nonlinear function of AVHRR reflectance forChannels 1 and 2. Given difficulties in validating coarse emissivity products withground measurements, the algorithm was cross-validated by comparing retrievedBBE with BBE derived through different methods. Retrieved BBE was initiallycompared with BBE derived from moderate-resolution imaging spectroradiometer (MODIS) albedos. Respective absolute bias and root-mean-square errorwere less than 0.003 and 0.014 for bare soil, less than 0.002 and 0.011 fortransition zones, and 0.002 and 0.005 for vegetated areas. Retrieved BBE wasalso compared with BBE obtained through the NDVI threshold method. Theproposed algorithm was better than the NDVI threshold method, particularly forbare soil. Finally, retrieved BBE and BBE derived from MODIS data wereconsistent, as were the two BBE values.