A new global land-ocean merged surface temperature dataset since the 1850s: the CMA-GMST dataset

A new global land-ocean merged surface temperature dataset since the 1850s: the CMA-GMST dataset | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A new global land-ocean merged surface temperature dataset since the 1850s: the CMA-GMST dataset Lifan Chen, Wenhui Xu, Zijiang Zhou, Lijuan Cao, Su Yang, Chengdong Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3999517/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Apr, 2025 Read the published version in Climate Dynamics → Version 1 posted 4 You are reading this latest preprint version Abstract A new global land-ocean merged surface temperature dataset, China Meteorological Administration global merged surface temperature (CMA-GMST), is developed. It is constructed from the monthly China Meteorological Administration global reconstructed land surface temperature (CMA-GLST) and sea surface temperature (CMA-SST) analyses that benefit from the improved in-situ observation coverage. Besides, the Arctic ice covered area is also reconstructed based on air temperatures and merged into CMA-GMST. This dataset provides a spatial complete and homogeneous surface temperature anomaly field in 2°×2° resolution for each month since 1850, and covers the majority of the earth’s surface: reaches 90% in the middle 1950s and exceeds 99% from the late 1970s. Assessments show that the observed global and regional (terrestrial, oceanic and hemispheric) trends of the annual average anomalies from CMA-GMST agree well with the ranges of trends computed from other published surface temperature analyses. The trends over the different latitudinal zones are also broadly in line with other published surface temperature analyses, while there are some differences in regions with limited observations among the datasets, such as the region of 90S–60ºS. Besides, evaluations by CMA-GMST show that the year 2023 was the warmest year on record and each month from July 2023 to December 2023 ranked as the globe's hottest month in recorded history, which agree well with the evaluations from other published surface temperature analyses. global surface temperature dataset bias correction reconstruction climate change Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1 Introduction While there are many indicators of climate change, the long-term evolution of global near surface temperature (ST) is an important one that is easy to understand. Global ST analyses, based on combination of traditional land air temperature (LAT) and sea surface temperature (SST) observations, are among the longest instrumental records that are well measured with reliable data extending back to circa 1850. These analyses are commonly used to assess the changes in the Earth’s climate, such as the onset of industrial-era warming (Morice et al. 2021 ; Rohde and Hausfather 2020 ). There are now multiple peer-reviewed global ST analyses available, including the Met Office Hadley Centre/Climatic Research Unit global ST (HadCRUT) dataset (Morice et al. 2021 ), the Goddard Institute for Space Studies ST (GISTEMP) dataset (Lenssen et al. 2019 ), the NOAA Merged Global Land-Ocean ST (NOAAGlobalTemp) dataset (Vose et al. 2021 ), the Berkeley Earth Land/Ocean ST (BEST) dataset (Rohde and Hausfather 2020 ), the China Merged ST (CMST) dataset (Sun et al. 2022 ), and some postprocessed analyses based on HadCRUT4 by spatial interpolation (Cowtan and Way 2014 ; Karl et al. 2015 ). In general, these products use somewhat different data sources or different approaches between each other. For instance, NOAAGlobalTemp and GISTEMP utilize Global Historical Climatological Network-monthly database (GHCNm) (Menne et al. 2018 ) and NOAA’s Extended Reconstructed SST (ERSST) (Huang et al. 2017a ) for land (LAT) and ocean (SST) temperature records, while HadCRUT adopts the Climatic Research Unit Temperature (CRUTEM) (Osborn et al. 2020 ) and the Met Office Hadley Centre's SST (HadSST) (Kennedy et al. 2019 ). The bias correction methods of ERSST and HadSST are essentially different, the former adopts the large-scale statistical adjustment technique that was developed by Smith and Reynolds ( 2002 , thereafter SR02), while the latter applies physics-based model (Kent et al. 2017 ). In addition, NOAAGlobalTemp adopts a low- and high-frequency components reconstruction method that falls within the category of reduced space algorithms, while HadCRUT5 analysis and GISTEMP apply a Gaussian process and a distance-weighted average method, to infill the in-situ observation gaps (Morice et al. 2021 ). This series of ST datasets by different groups has gradually helped to increase knowledge of the global temperature and its variation. However, there are still uncertainties in these datasets. For example, intercomparison among land components of BEST, GISTEMP, NOAAGlobalTemp and HadCRUT at local/regional scales has showed remarkable differences of the mean land surface air temperature anomalies (LSTA) and even disagreement on sigh of their changing trends, which are associated with the availability of in-situ observations and the use of infilling techniques. Henceforth, developing new datasets or improving the existing products with more data sources and reassessing the differences with the advent of them are suggested (Rao et al. 2018 ). In addition, it has been pointed out that the improvements including methodological advances in ST products and new datasets since the fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) contributed approximately 0.1°C to the updated estimate of warming in AR6, which emphasizes the development and update of the ST datasets help to improve the understanding of modern climate change in climate history (IPCC 2021 ). Therefore, the optimization and improvement of observed climate data as a reference base for climate change research and verification benchmark for other climatic data products is a long-term task. In particularly, although the existing ST analyses including BEST, HadCRUT, GISTEMP and NOAAGlobalTemp have a high degree of agreements in average temperatures for regions such as the United States and Europe, they are found to have significant trend differences over Asia, which is associated with their insufficient observations over Asia (especially for large countries such as China and India) compared with the United States and Europe (Rao et al. 2018 ; Xu et al. 2018a ). The differences might cause confusion on the objective assessments of warming over Asia, where temperatures are increasing faster than the global average (WMO 2023 ). Fortunately, the China Meteorological Administration (CMA) hosts the exchanged meteorological data between the members of the World Meteorological Organization Regional Association II (WMO RA II), and is also in charge of meteorological data including the digitized historical paper data archives in China. To enhance the service capability and value of these data sources, the National Meteorological Information Center (NMIC) of CMA has continuously involved in developing and updating a collection of high-quality global integrated datasets (Jiang et al. 2021 ; Xu et al. 2018a ; Chen et al. 2021 ). The developed station databases have obvious advantage in improved LAT coverage over Asia and SST observation coverage along the east coastline of Asia, especially for China with more than 2000 stations in the latest version. Moreover, the operational effectiveness and stability of the developed datasets help to guarantee the capacity of real-time monthly climate monitor by statistical products based on them, such as the monthly ST analysis dataset. Hence, a new global monthly land-ocean merged ST dataset of 2°×2° resolution since the 1850s, CMA global merged ST (CMA-GMST), is presented in this study. It is based on the recently developed CMA global reconstructed land ST (CMA-GLST) and SST (CMA-SST) analyses (Chen et al. 2021 ), which are constructed from the data sources with improved coverage of observations especially over Asia. The STs in the Arctic ice covered regions are also reconstructed and merged in this new dataset. The details about the input data sources and the applied analysis techniques are given in section 2 , the assessment results of the ST analysis are given in section 3 and the conclusion and discussion are given in section 4 . 2 Data and method 2.1 ST over land Monthly averages of near-surface air temperature measured at weather stations over land since 1850 are obtained from a newly developed integrated global land surface dataset (Jiang et al. 2021 ), the hourly database is a collection of station series from four global sources and one regional source. The monthly average temperatures from stations are subjected to quality control and have been homogenized through the same approach as Xu et al. ( 2018a ). The reconstruction of land surface air temperature adopts the method of the point interpolation based on Biased Sentinel Hospitals Areal Disease Estimation (P-BSHADE). The method can be used to remedy the station bias resulting from sparse coverage, and it considers the characteristics of spatial autocorrelation and nonhomogeneity of the temperature distribution to obtain unbiased and minimum error variance estimates. The method has been used to interpolate 1-km grids of monthly surface air temperatures in the historical period of 1900–1950 in China, and it was proven that the method has the smallest error compared with the widely used methods including kriging, inverse distance weighting (IDW), and a combined spline with kriging (TPS-KRG) method, both theoretically and empirically (Xu et al. 2018b ). There are four steps to establish the global land surface air temperature reconstructed analysis dataset by P-BSHADE. First, calculate the correlation coefficient ( R ij ), covariance ( C ij ) and ratio ( b i ) of temperatures between each two stations for each calendar month of the reference period 1961–1990; Second, monthly average station data for each month of the period 1961–1990 is interpolated into 1°×1° grids using TPS-KRG method, thereby the correlation coefficients between the grid box and the nearby stations for each calendar month could be obtained. Third, using the above parameters from the reference period, the weight ( w i ) for each neighboring observation station could be calculated by the unbiased condition constrain as: \(\sum\nolimits_{{i=1}}^{n} {{w_i}{b_i}=1}\) We select five neighboring observation stations holding highest correlations with the target grid, positive weights and smallest estimated error variances for reconstruction. Last, the estimated temperature y 0 at the target grid is obtained as: \({y_0}=\sum\nolimits_{{i=1}}^{n} {{w_i}{y_i}}\) Where y i is the temperature record of the i th neighboring station. Thus, a global land surface air temperature reconstructed analysis dataset (CMA-GLST) with 1°×1° resolution since 1850 has been developed. 2.2 ST over ocean A new global monthly reconstructed SST analysis dataset (CMA-SST) with 2°×2° resolution since 1900 was developed. It was constructed based on a newly developed hourly dataset integrating multiple sources, and by SR02 method to adjust the systematic biases of ship SSTs and a low- and high-frequency components reconstruction method to full-fill the SSTs over ocean (Chen et al. 2021 ). In this study, the SST dataset is improved in two aspects to generate the ST analysis. The SST dataset is forward extended to 1850, to enable it to investigate the global warming above pre-industrial levels. Moreover, as different changing trends between sea surface and atmospheric temperatures have been detected (Christy et al. 2001 ), the bias estimates of ship SSTs before 2010 based on SR02 method by comparing SST with night marine air temperature (NMAT) data are revised, to address the potential biases of the estimates caused by the assumption of SR02 that SST–NMAT differences keep nearly constant over multi-decadal scales. The bias estimates of ship SSTs before 2010 are revised by ancillary information from HadSST4 (Kennedy et al. 2019 ) and the Met Office Hadley Centre's monthly NMAT dataset Version 2 (HadNMAT2) (Kent et al. 2013 ), which are strictly adjusted for changes in observation instruments and heights respectively. Firstly, the climatic SST–NMAT differences for each calendar month are computed by averaging HadSST4–HadNMAT2 differences over the recent 30-yr base period and interpolated over ocean by optimum interpolation (OI). And then, the monthly SST–NMAT differences before 2010 are estimated by best-fit of the observed monthly HadSST4–HadNMAT2 differences to its climatic pattern (Smith and Reynolds 2002 ; Chen et al. 2021 ). Secondly, the monthly estimated SST–NMAT differences are compared to its calendar month’s climatic field to assess their changes over time. Finally, the changes of SST–NMAT differences are subtracted from the SST biases estimated by SR02 method. As HadSST4 is bias corrected by physics-based model, the revised SST bias estimations synthesize the advantages of both the physics-based model and the large-scale statistical method (Kent et al. 2017 ). 2.3 ST over the Arctic ice covered area LAT is applied to reconstruct the STs of the Arctic ice covered region in CMA-GMST, likewise other published global ST products, to avoid the underestimation of the average global temperatures caused by the missing data in the Arctic (Huang et al. 2017b ; Sun et al. 2022 ). A reconstruction scheme synthesizing IDW extrapolation and low- and high-frequency components reconstruction technique is adopted. The details are: (1) calculate the monthly LSTAs (relative to 1961–1990 average) for each station based on the homogenized monthly air temperature applied in CMA-GLST, and then arithmetically average them to the 2º×2º grids of SST analysis; (2) interpolate the gridded LSTAs by IDW technique, to extend the temperature distribution by observed neighbors within 300–500 km; (3) perform the low- and high-frequency components reconstruction on the interpolated data. A maximum Arctic ice covered region is defined by the monthly sea ice concentrations at 1º×1º resolution from Met Office Hadley Centre's Sea Ice and SST (HadISST2) for the period 1900–2015 (Titchner and Rayner 2014 ) and NOAA’s optimum interpolation SST (OISSTV2) from 2016 (Banzon et al. 2016 ), and applied while the reconstruction of the high-frequency component. A similar method as Chen et al. ( 2021 ) is conducted to address the discontinuity between HadISST2 and OISSTV2 due to their different development schemes. After that, a value of one is set when the sea ice concentration is greater than 15% and zero otherwise for each monthly 1º×1º HadISST2/OISSTV2 grid, and then the area coverage of sea ice in 2º×2º resolution are obtained as the average of the reassigned sea ice concentration. The maximum Arctic ice covered region is treated as all the 2º×2º grids with sea ice area coverage greater than 0 from 1850 (Morice et al. 2021 ). Similarly to the SST analysis (Chen et al. 2021 ), the low-frequency component is calculated by running average over space and time, specifically: (1) calculate a 18°×26° spatial mean of the monthly LSTAs for latitude and longitude respectively; (2) define the annual mean LSTA for grids with at least 2 months of the year, and perform a 15-yr median filter to the annual values; (3) run a 14°×26° spatial mean for latitude and longitude, and then a nine-point binomial spatial filter and a three-point binomial temporal filter, until filling the polar regions; and (4) run a 14°×26° spatial mean for latitude and longitude to smooth the reconstructed data. The high-frequency component is obtained by subtracting the reconstructed low-frequency component from the LSTAs and then reconstructed by (1) training the 53 leading empirical orthogonal teleconnection (EOT) modes within the maximum Arctic ice covered region by ECMWF Reanalysis v5 (ERA5) (Simmons et al. 2017 ). The modes are restricted within 3000 and 6000 km for latitude and longitude; (2) linearly fitting the high-frequency LSTAs to the trained EOTs in a least-squares sense, in which the EOT modes are applied if the observations support more than 0.1 of their variance ratios. Finally, the monthly STs over Arctic are obtained by summing the reconstructed low- and high-frequency components, and masked by the monthly sea ice area coverages that are greater than 0. 2.4 Merging analysis The STs over land, ocean and the Arctic ice covered area are merged by area-weighted average to create CMA-GMST dataset. First, the monthly ST anomalies (relative to a 1961–1990 baseline period) over land and ocean are derived from the reconstructed LAT and SST analyses, and then the anomalies with 1º×1º resolution over land are resampled to 2º×2 grids of SST. The land/ocean area weights used to merge the land/ocean anomalies are derived from a 0.25º×0.25º land/ocean mask (Jet Propulsion Laboratory 2013 ). The land weight is defined as the proportion of 0.25º×0.25º grids labeled as land type within each 2º×2º grid, and the ocean weight is defined as 1–land weight. Besides, anomalies over the Arctic ice covered area are treated as if they are land temperatures, and the monthly area coverages of sea ice are treated as land weights. 3 Result 3.1 Spatial coverage Figure 1 shows the annual coverage comparison among CMA-GMST, HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP (1200 km smoothing, so is thereafter), BEST and CMST (the average of the Imax and Imin datasets, so is thereafter) from 1850 to 2022. The latest version up to 2023 of each published dataset is applied here, such as V5.1 of NOAAGlobalTemp. It could be found that the spatial coverages differ among these datasets, especially in the periods with sparse observations. For example, NOAAGlobalTemp is the only one with complete coverage of all land and ocean areas for the entire period of record, while the data coverages of HadCRUT5 analysis and BEST have increased over time and have three obvious drops in the 1860s, 1910s and 1940s. By comparison, time-varying characteristics of CMA-GMST coverage are primarily consistent with those of HadCRUT5 analysis and BEST, and its coverage magnitude is between the two published datasets and is somewhat higher than that of HadCRUT5 analysis. This is likely related to: (1) the land component of CMA-GMST is generated based on the stations with data length ≥ 20 year, and by local statistical interpolation with a 1000 km radius which is close to that of HadCRUT5 analysis (1300 km). These parameters are stricter than those adopted in the other published datasets, such as BEST (Rohde and Hausfather 2020 ; Morice et al. 2021 ); (2) although the ocean component of CMA-CMST is interpolated by the same technique as that adopted in NOAAGlobalTemp, a geographic masking is postprocessed to prevent global averages from depending heavily upon the highly smoothed extrapolation estimates (Vose et al. 2012 ), which however is omitted in NOAAGlobalTempV5.1 in order to get the full coverage (Vose et al. 2021 ). Specifically, the areal coverage in the CMA-GMST grids reaches 90% in the middle 1950s and exceeds 99% from the late 1970s, similarly to HadCRUT5 analysis. Moreover, consistent with HadCRUT5 analysis and BEST, an obvious higher data coverage of the Northern Hemisphere (NH) than the Southern Hemisphere (SH) is detected in CMA-GMST especially before the 1950s, and two drops in the 1910s and 1940s are found in the SH associated with the drops of SST observations while the two world wars (Morice et al. 2021 ). Quantitatively, both the NH coverages of CMA-GMST and HadCRUT5 analysis exceed 95% in the early 1920s and reach 100% in the mid-1950s. However, both the SH coverages of CMA-GMST and HadCRUT5 analysis exceed 95% in the early 1970s. In addition, the NH coverage of CMA-GMST is persistently higher than that of HadCRUT5 analysis, while a somewhat lower SH coverage of CMA-GMST is found around the period 1957–1972. Nevertheless, the corresponding SH coverage of CMA-GMST is obviously higher than that of CMST. 3.2 Correlation with the published products Figure 2 shows the annual average ST anomaly spatial correlations between CMA-GMST and HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP, BEST and CMST across the globe. CMA-GMST is resampled to match the spatial resolution of each reference product. The spatial correlations between CMA-GMST and each of the existing products are calculated for the monthly data first and then averaged over the 12 months of the year to get the annual values, to assess the spatial structure similarity of temperatures between CMA-GMST and the existing datasets. It could be found that the spatial correlation of CMA-GMST with HadCRUT5 analysis is highest, it increases from about 0.65 in 1850 to around 0.8 from the early 1880s, and keeps 0.8–0.9 to present. The persistent higher correlation of CMA-GMST with HadCRUT5 analysis than those with the other published products is reasonable due to their close parameters applied to screen the land stations with enough record length and control the data extrapolation in an appropriate distance range, which have been pointed out in the coverage comparison part. Besides, the revised SST bias estimations by historical HadSST4 data might also be a potential contributor to the higher correlation with HadCRUT5 analysis. By comparison, the spatial correlations with NOAAGlobalTemp, GISTEMP, BEST and GMST are smaller by about 0.1–0.2 relative to that with HadCRUT5 analysis before the 1900s, and keep ≥ 0.7 after the 1950s, except for a retreat of correlation to about 0.6–0.7 from the mid-1950s to the early 2000s with NOAAGlobalTemp. Figure 3 shows the spatial distribution of ST anomaly correlations between CMA-GMST and HadCRUT5 analysis, BEST and CMST over time from 1880 to 2022. Only HadCRUT5 analysis, BEST and CMST are applied to compare in this part, because they provide the spatial field of anomalies relative to a same 1961–1990 baseline period as CMA-GMST, or provide additional climate fields for conversion such as BEST. Correlations have been calculated where paired data are present for at least 70% of all months during the statistical period. It could be found that the correlations in terrestrial area are obviously higher in regions including the North America, Europe excluding Greenland and the middle-to-high latitudes of Asia, where the correlations of CMA-GMST with the published products universally concentrate in > 0.9. Similarly, the coherently higher correlations > 0.9 in oceanic area occur in parts of the North Atlantic. Beyond that, the correlations of CMA-GMST with the published datasets are mainly > 0.8, except for regions with limited observations, such as Africa, South America, the Arctic ice covered area and the high latitudes of SH (Morice et al. 2021 ). Albeit the consistency in most regions, some regional differences are also found, for example, the correlations for Australia and the middle-to-low latitudes of Asia are overall higher with HadCRUT5 analysis and BEST, while the correlations for the Equatorial East Pacific region with CMST are mainly > 0.9 and higher than those with HadCRUT5 analysis and BEST at about 0.8–0.9. Reconstruction technique might be a potential contributor to these differences. Specifically, CMA-GMST, HadCRUT5 analysis and BEST conduct reconstruction for terrestrial area only referring to the observations from spatial neighbors, while a low- and high-frequency components reconstruction by referring the observations not only from the spatial but also the temporal neighbors is applied by CMST and CMA-GMST for oceanic reconstruction (Morice et al. 2021 ; Sun et al. 2022 ). 3.3 Annual to decadal climate monitoring Figure 4 shows the comparisons of the annual and decadal average global ST anomalies (relative to a 1961–1990 baseline period) among CMA-GMST, HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP, BEST and CMST from 1850 to 2022. The ST anomalies for each year are calculated as the mean of the area-weighted average for the NH and SH. The anomalies for each decade are calculated as the mean of each 10 yearly values. Overall, the annual average global ST anomalies and their interannual variations of CMA-GMST are comparable to those of the published datasets. Fluctuations with no obvious changing trend of the global STs before the 1900s, and thereafter a temperature decline in the 1900s are detected by all the datasets including CMA-GMST. From the 1920s, the average global STs in CMA-GMST and all the published products undergo two obvious warming periods, including the one from the 1920s to the middle 1940s and the other from the mid-1960s to present. Between these two periods, there is a slight retreat of the increasing temperatures. Quantitatively, the fitted linear trends of the global temperature anomalies are approximately 0.84, 1.69 and 1.87°C/100 year for the periods 1880–2022, 1960–2022 and 1980–2022 respectively, and they agree well with the range of trends computed from other published ST analyses (Table 1 ). Meanwhile, appreciable differences among the six datasets in the period 1850–1900 are found. The differences are reasonable due to the larger uncertainties of the ST analyses associated with the sparse observations in the earlier periods (Morice et al. 2021 ), however, they will pose confusion while the assessment of the global warming above pre-industrial levels owing to the mean temperature over the period 1850–1900 is used as the pre-industrial baseline (Schurer et al. 2017 ). Quantitative comparison shows that the mean value of the annual records over the period 1850–1900 is − 0.377°C for CMA-GMST, it is close to HadCRUT5 analysis with − 0.357°C and BEST with − 0.370°C, due to the closer coverages and higher anomaly correlations of CMA-GMST with HadCRUT5 analysis and BEST. Comparatively, the mean anomalies over the period 1850–1900 for NOAAGlobaltemp and CMST are higher as − 0.299 and − 0.254°C. Figure 5 shows the comparisons of the annual area-weighted average ST anomalies (relative to a 1961–1990 baseline period) for land, ocean, NH and SH from 1850 to 2022. The same as the assessment result on global scale, the annual average ST anomalies and their inter-annual variations of CMA-GMST are consistent with the published datasets. Quantitatively, the fitted linear trends of land/ocean are 1.08/0.77, 2.43/1.54 and 2.90/1.82°C/100 year, and the trends of NH/SH are 0.95/0.74, 2.15/1.22 and 2.76/0.98°C/100 year for the periods 1880–2022, 1960–2022 and 1980–2022 respectively. Comparison assessments indicate that the regional observed trends of CMA-GMST agree well with the range of trends computed from other published ST analyses, no matter for land, ocean, or hemispheres. In addition, higher warming rates of land than ocean and higher warming rates of NH than SH for the various periods, and the higher warming rates of recent period for the globe, land, ocean and NH are detected in all the ST datasets including CMA-GMST. Moreover, the warming rate differences between land and ocean, and those between NH and SH are also detected to increase in the recent period by all the ST datasets including CMA-GMST (Table 1 ). Table 1 Observed trends (°C/100 year) in CMA-GMST, HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP, BEST and CMST for periods 1880–2022, 1960–2022 and 1980–2022 over the globe, land, ocean and hemispheres. Trend values are calculated based on the annual average anomalies with ordinary least squares Region Time period CMA-GMST HadCRUT5 analysis NOAAGlobalTemp GISTEMP BEST CMST Globe 1880–2022 0.84 0.83 0.78 0.78 0.86 0.74 1960–2022 1.69 1.77 1.67 1.74 1.85 1.60 1980–2022 1.87 1.90 1.82 1.92 1.95 1.72 Land 1880–2022 1.08 1.04 1.15 1.09 1.10 1.06 1960–2022 2.43 2.47 2.53 2.61 2.50 2.40 1980–2022 2.90 2.88 2.89 3.00 2.83 2.76 Ocean 1880–2022 0.77 0.77 0.65 0.70 0.79 0.65 1960–2022 1.54 1.65 1.50 1.57 1.76 1.42 1980–2022 1.82 1.84 1.79 1.87 1.97 1.68 NH 1880–2022 0.95 0.96 0.93 0.94 0.97 0.90 1960–2022 2.15 2.29 2.22 2.25 2.35 2.14 1980–2022 2.76 2.78 2.77 2.82 2.87 2.70 SH 1880–2022 0.74 0.70 0.62 0.63 0.76 0.59 1960–2022 1.22 1.25 1.12 1.23 1.34 1.05 1980–2022 0.98 1.03 0.88 1.01 1.03 0.75 Figure 6 shows the annual average ST anomalies for each 30° latitude bands. It shows that the differences between the six datasets are pronounced in 90S–60ºS, which is related to the large uncertainty mainly due to the sparse observations in this region (Morice et al. 2021 ). Quantitatively, the annual average anomalies of NOAAGlobalTemp and CMST are relatively smooth with standard deviations (SDs) of 0.191 and 0.213 over the period 1850–2022 in 90S–60ºS, while BEST has the largest variability with SD of 0.583, and the corresponding SDs of CMA-GMST and HadCRUT5 analysis are between them by 0.257 and 0.384. In addition, it is obviously cooler in HadCRUT5 analysis and BEST than in NOAAGlobalTemp and CMST before the 1880s in 60S–30ºS, and by comparison, CMA-GMST is closer to HadCRUT5 analysis and BEST. Apart from the appreciable differences that are mainly located in regions and periods with less observations, the observed trends of the annual average anomalies over the different latitudinal zones basically agree well with each other between the six ST datasets. Figure 7 shows the spatially resolved trends (°C/10 year) of CMA-GMST (a), HadCRUT5 analysis (b), NOAAGlobalTemp (c), GISTEMP (d), BEST (e) and CMST (f) over 1900–1980. Trends have been calculated where data are present in both the first and last decade and for at least 70% of all years within the period, referenced to IPCC AR6 report (IPCC 2021 ). All the datasets including CMA-GMST show that the temperature trends focus on − 0.15–0.15°C/10 year over 1900–1980 and are dominated by the positive trends. Whatever, some regional differences of the trends among the six ST datasets are found, for example, cooling trends in the central region of South America are detected by HadCRUT5 analysis, while corresponding warming trends are detected by BEST, CMST and CMA-GMST, although the overall higher correlation of CMA-GMST with HadCRUT5 analysis has been mentioned. When it goes to the period 1981–2022 (Fig. 8 ), all the datasets including CMA-GMST show significant increases of warming rates for most regions of the world. The strongest warming rates over 1981–2022 occur in the Arctic ice covered area, where the warming trends are detected ≥ 0.6°C/10 year by all the six datasets. Followed are the warming trends in Eurasia, where large areas are detected with warming rates ≥ 0.3°C/10 year by the datasets except for NOAAGlobalTemp with cooling trends over Greenland. Comparatively, the warming trends over ocean are lower, they are donated by 0–0.3°C/10 year, and moreover all the datasets including CMA-GMST show cooling trends in the high-latitudes of the SH and the near-equatorial region of the South Pacific. Except for the overall consistency, some regional differences are also detected, for example, some small regions with warming trends ≥ 0.45°C/10 year over 1981–2022 are detected in the eastern Asia by CMA-GMST, while the local higher warming is relatively inconspicuous in the published datasets. The impacts of the regional differences detected in Fig. 7 and Fig. 8 on the assessment of global temperature and its trends might be negligible, however, it is important for regional assessment and emphasizes the necessity of conducting multiple products comparison while assessing the temperature changes in small regions. 3.4 Monthly to seasonal climate monitoring The statements have been issued by several institutions that 2023 was the warmest year on record since meteorological records began. Our quantitative evaluation indicates that the average global temperatures of 2023 above the 1991–2020 monthly climatology reached 0.54, 0.56, 0.55, 0.55, 0.55 and 0.51°C for CMA–GMST, HadCRUT5, NOAAGlobalTemp, GISTEMP, BEST and CMST respectively. Moreover, the ranking of the average global temperature for each calendar month also shows that each month from July 2023 to December 2023 ranked as the globe's hottest month in recorded history (Table 2 ). Table 2 The year with historical highest temperature for each calendar month and annual average up to 2023 Date CMA-GMST HadCRUT5 analysis NOAAGlobalTemp GISTEMP BEST CMST Jan 2016 2016 2016 2016 2016 2016 Feb 2016 2016 2016 2016 2016 2016 Mar 2016 2016 2016 2016 2016 2016 Apr 2016 2020 2016 2020 2020 2016 May 2020 2020 2016 2020 2016 2023 Jun 2023 2023 2023 2023 2023 2023 Jul 2023 2023 2023 2023 2023 2023 Aug 2023 2023 2023 2023 2023 2023 Sep 2023 2023 2023 2023 2023 2023 Oct 2023 2023 2023 2023 2023 2023 Nov 2023 2023 2023 2023 2023 2023 Dec 2023 2023 2023 2023 2023 2023 Yearly 2023 2023 2023 2023 2023 2023 Figure 9 shows the average ST anomalies (relative to a 1991–2020 monthly climatology) for the JJA (June, July and August) season over the globe and hemispheres from 1850 to 2023 in CMA-GMST, HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP, BEST and CMST. It is shown that the JJA season 2023 ranked as the warmest June–August period in the 174-year record over the globe. The average global STs of the JJA season 2023 were about 0.61, 0.55, 0.57, 0.61 and 0.55°C above the 1991–2020 monthly climatology in HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP, BEST and CMST, and it was comparable as 0.60°C in CMA-GMST. Moreover, all the datasets including CMA-GMST indicate that the JJA season 2023 was the NH’s hottest meteorological summer on record, at about 0.71, 0.74, 0.73, 0.69 and 0.69°C above the 1991–2020 monthly climatology in HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP, BEST and CMST, and about 0.74°C in CMA-GMST. The JJA season 2023, which also marked the SH’s winter, was the SH’s warmest winter, it was on record at 0.51, 0.36, 0.42, 0.53 and 0.41°C above the 1991–2020 monthly climatology in HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP, BEST and CMST and was comparable as 0.45°C in CMA-GMST. Figure 10 is the same as Fig. 9 , but for the SON (September, October and November) season. It is also shown that the SON season 2023 ranked as the warmest September–November period in the 174-year record over the globe. The average global STs of the SON season 2023 above the 1991–2020 monthly climatology were about 0.79, 0.78, 0.79, 0.80, 0.79 and 0.72°C in CMA-GMST, HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP, BEST and CMST respectively and higher than those of the JJA season. A significantly higher average ST of the SON season 2023 above the 1991–2020 monthly climatology than that of the JJA season was also found in all the six datasets including CMA-GMST in the NH, the SON season 2023 was the NH’s hottest meteorological autumn on record, at about 1.08, 1.04, 1.15, 1.11, 1.07 and 1.03°C above the 1991–2020 monthly climatology in CMA-GMST, HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP, BEST and CMST. Comparatively, the SON season 2023, which also marked the SH’s spring, was the SH’s warmest spring, it was on record at 0.49, 0.52, 0.43, 0.49, 0.52 and 0.40°C above the 1991–2020 monthly climatology in CMA-GMST, HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP, BEST and CMST, and were comparable as those of the JJA season. 4 Conclusion and discussion It has been emphasized that although global temperature series derived from the exiting ST analyses are in good agreement, their differences in small regions such as Asia might be significant (Rao et al. 2018 ). Motivated by the improved observation coverage over Asia of the multi-source integrated datasets developed by CMA ( Jiang et al. 2021 ; Xu et al. 2018a ), a new global ST dataset, CMA-GMST, is presented in this paper. The dataset is generated by the similar process flow to the existing ST analyses. First, the LAT observations are homogenized using the same method as Xu et al. ( 2018a ) and reconstructed by P-BSHADE technique (Xu et al. 2018b ) to cover the land component of ST analysis, and the SST observations are bias corrected by revised technique based on SR02 method and a low- and high-frequency components reconstruction method (Chen et al. 2021 ) to cover the ocean component. Moreover, reconstruction of air temperatures over the Arctic ice covered area is also carried out to consider the polar amplification effect on global climate change (Sun et al. 2022 ). After that, the reconstructed land, ocean and Arctic components are merged to provide the global monthly temperature anomaly (relative to a 1961–1990 baseline period) in 2º×2º resolution since 1850. Spatial coverage of CMA-GMST and its anomaly correlation with the published products are assessed, because not only the anomaly value itself but also its coverage are important factors in the calculations of global or regional average temperature series (Morice et al. 2021 ; Rao et al. 2018 ). Comparisons show that the time-varying characteristics of CMA-GMST’s data coverage are primarily consistent with those of HadCRUT5 analysis and BEST, and its coverage magnitude is between them and is somewhat higher than that of HadCRUT5 analysis. Generally, both the global coverages of CMA-GMST and HadCRUT5 analysis reach 90% in the middle 1950s and exceed 99% in the late 1970s. Assessments also show that the global spatial correlation of CMA-GMST with HadCRUT5 analysis is overall higher than those with the other existing products. The close relationship between CMA-GMST and HadCRUT5 analysis might be related to (1) their close parameters applied to screen the land stations with enough record length and control the data extrapolation in an appropriate distance range; (2) the revised SST bias estimations by historical HadSST4 data. Further assessments show that the ability of CMA-GMST to monitor temperature variation characteristics at multiple temporal and spatial scales is generally comparable to the existing datasets. For example, (1) the interannual variations of the annual average ST anomalies calculated by CMA-GMST are consistent with those of the published datasets, and its observed trends for the various periods including 1880–2022, 1960–2022 and 1980–2022 are all between the published datasets, whether for the globe, land, ocean, or hemispheres; moreover, the regional trends of the annual average anomalies over 60N–90ºN, 30N–60ºN, EQ–30ºN and 30ºS–EQ agree well with the published datasets; (2) the global spatial distribution of the resolved trends indicate that the regions with accelerated warming from 1900–1980 to 1981–2022 and some regions mainly located in the SH with cooling trends over 1981–2022 in CMA-GMST are broadly consistent with those of the published datasets; and (3) all the datasets including CMA-GMST reflect that the year 2023 was the warmest year on record, and each month from July 2023 to December 2023 was the globe's hottest month in recorded history. Whatever, some differences are also detected and mostly caused by the factors affecting the uncertainties of the ST analyses. For example, (1) the correlations for North America and Europe are persistently higher than those for Africa and South America, no matter which existing product CMA-GMST is related to, which reflects the fundamental role of observation coverage in the ST analysis; (2) the correlations for the middle-to-low latitudes of Asia are overall higher with HadCRUT5 analysis and BEST than those with CMST, in contrast, higher correlations with CMST for the Equatorial East Pacific region are found. It might be attributed to the extrapolation by spatial neighbors for land component of CMA-GMST, HadCRUT5 analysis and BEST, while by both temporal and spatial neighbors for ocean component of CMA-GMST and CMST, which indicates the importance of reconstruction technique on the uncertainty of ST analysis; (3) cooler temperatures of HadCRUT5 analysis and BEST than NOAAGlobalTemp and CMST are found before the 1880s in 60S–30ºS that is mainly covered with sea water, and comparatively, corresponding temperatures of CMA-GMST are closer to the former two albeit its reconstruction technique for the ocean component is more similar to the latter two. The revised SST bias estimations by historical HadSST4 of CMA-GMST might be a potential contributor, which indicates the influence of bias technique to the uncertainty of ST analysis. Last but not least, further work is still needed to improve the understanding of CMA-GMST, including (1) a comprehensive set of uncertainty estimates, including estimates of temperature bias effects and the effect of reconstruction technique will be needed to further measure the reliability of CMA-GMST and to strength the understanding of its temperature series’ uncertainties; (2) ongoing investigations to supplement data sources especially in regions with limited observations and technically refine methods are needed, which are evidently manifested by the appreciable differences of linear trends among the six ST datasets including CMA-GMST over 90S–60ºS even in modern years. In particular, artificial neural network method has been implemented in NOAAGlobalTempV6.0.0 to improve the ST reconstruction over land (Huang et al. 2022 ) and the updated ST analysis was released recently; and (3) the studies over small regions are expected, for example, the positive feedback of improved coverage over Aisa on the somewhat higher warming rates of the eastern Asia over 1981–2022 in CMA-GMST needs further evaluation, such as by comparative experiments through controlling the input in-situ observations or intercomparison with the supplementary data sources. Declarations Data availability The dataset generated in this study is available from the corresponding author upon reasonable request. The datasets used for comparative evaluations in this study are accessible online, specifically, HadCRUT5 is available from https://www.metoffice.gov.uk/hadobs/hadcrut5/, NOAAGlobalTemp is available from https://www.ncei.noaa.gov/products/land-based-station/noaa-global-temp, GISTEMP is available from https://data.giss.nasa.gov/gistemp/, BEST is available from https://berkeleyearth.org/data/, and CMST is available from http://www.gwpu.net/h-col-103.html. The datasets used for defining the ice covered region in this study are accessible online, specifically, HadISST2 is available from https://www.metoffice.gov.uk/hadobs/hadisst2/, monthly OISSTV2 is available from https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.html. Acknowledgements We like to thank Panmao Zhai (Chinese Academy of Meteorological Sciences) for the discussions and constructive suggestions during the realisation of this work. We would like to express our gratitude to National Climate Center of China Meteorological Administration for their operational application of the newly developed dataset, which has helped to monitor the quality and operational stability of the dataset. Funding This study was supported by the Innovation and Development Project of China Meteorological Administration (Grant No. CXFZ2023J049). Author information Authors and Affiliations National Meteorological Information Center, China Meteorological Administration, Beijing, China Lifan Chen, Wenhui Xu, Zijiang Zhou, Lijuan Cao & Su Yang State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China Chengdong Xu Contributions Lifan Chen took the lead in writing the main manuscript text, Wenhui Xu provided critical feedback and helped shape the analysis and manuscript. 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Journal of Climate 31:1757–1770. https://doi.org/10.1175/JCLI-D-17-0150.1 Cite Share Download PDF Status: Published Journal Publication published 09 Apr, 2025 Read the published version in Climate Dynamics → Version 1 posted Reviewers agreed at journal 04 Mar, 2024 Reviewers invited by journal 28 Feb, 2024 Editor assigned by journal 28 Feb, 2024 First submitted to journal 27 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3999517","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":275667047,"identity":"edd0bd1b-6c9e-4b03-8868-d4bd051d9b47","order_by":0,"name":"Lifan 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1","display":"","copyAsset":false,"role":"figure","size":1415666,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual coverage comparison among CMA-GMST, HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP, BEST and CMST for\u003cstrong\u003e \u003c/strong\u003ethe \u003cstrong\u003ea\u003c/strong\u003e globe, \u003cstrong\u003eb\u003c/strong\u003e NH and \u003cstrong\u003ec\u003c/strong\u003e SH from 1850 to 2022\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-3999517/v1/b78c8053874951a6c8895536.png"},{"id":51973274,"identity":"79478095-f302-4e9a-9df8-204876b4018b","added_by":"auto","created_at":"2024-03-04 18:59:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":887215,"visible":true,"origin":"","legend":"\u003cp\u003eThe annual average ST anomaly spatial correlations between CMA-GMST and HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP, BEST and CMST across the globe from 1850 to 2022\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-3999517/v1/5d6dddd48b46c8e116698989.png"},{"id":51973279,"identity":"1cd742f2-fea5-48f1-81d0-d510dc923b02","added_by":"auto","created_at":"2024-03-04 18:59:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1124364,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial distribution of ST anomaly correlations between CMA-GMST and \u003cstrong\u003ea\u003c/strong\u003e HadCRUT5 analysis, \u003cstrong\u003eb\u003c/strong\u003e BEST and \u003cstrong\u003ec\u003c/strong\u003e CMST over time from 1880 to 2022\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-3999517/v1/eb6c5a3cef574d6f56c736ba.png"},{"id":51973276,"identity":"9f885bbe-e958-450d-932b-d9749a2aea49","added_by":"auto","created_at":"2024-03-04 18:59:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1345260,"visible":true,"origin":"","legend":"\u003cp\u003eComparisons of the \u003cstrong\u003ea\u003c/strong\u003e annual and \u003cstrong\u003eb\u003c/strong\u003e decadal average global ST anomalies (relative to a 1961–1990 baseline period) among CMA-GMST, HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP, BEST and CMST from 1850 to 2022\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-3999517/v1/23442310f6d076c5bd593b0d.png"},{"id":51973281,"identity":"983717e8-6371-4761-bbfd-e5d9fdc9ad0c","added_by":"auto","created_at":"2024-03-04 18:59:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3019014,"visible":true,"origin":"","legend":"\u003cp\u003eComparisons of the annual area-weighted average ST anomalies (relative to a 1961–1990 baseline period) among CMA-GMST, HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP, BEST and CMST for \u003cstrong\u003ea\u003c/strong\u003eland, \u003cstrong\u003eb\u003c/strong\u003e ocean, \u003cstrong\u003ec\u003c/strong\u003e NH and \u003cstrong\u003ed\u003c/strong\u003e SH from 1850 to 2022\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-3999517/v1/e9d4085dcc8f4abdcb380015.png"},{"id":51973282,"identity":"23064933-16a2-46a1-bd4a-b2e4a1b519a2","added_by":"auto","created_at":"2024-03-04 18:59:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":5901770,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig.5, but for each 30° latitude band including \u003cstrong\u003ea\u003c/strong\u003e 60N–90ºN, \u003cstrong\u003eb\u003c/strong\u003e 90S–60ºS, \u003cstrong\u003ec\u003c/strong\u003e 30N–60ºN, \u003cstrong\u003ed\u003c/strong\u003e 60S–30ºS, \u003cstrong\u003ee\u003c/strong\u003e EQ–30ºN and \u003cstrong\u003ef\u003c/strong\u003e 30ºS–EQ\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-3999517/v1/8eae572eb7fd391f0825bf92.png"},{"id":51974357,"identity":"fdb175d6-5fdf-4ce9-bcc6-35cf405f668e","added_by":"auto","created_at":"2024-03-04 19:07:48","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2377842,"visible":true,"origin":"","legend":"\u003cp\u003eSpatially resolved trends (℃/10yr) of \u003cstrong\u003ea\u003c/strong\u003e CMA-GMST, \u003cstrong\u003eb\u003c/strong\u003e HadCRUT5 analysis, \u003cstrong\u003ec \u003c/strong\u003eNOAAGlobalTemp, \u003cstrong\u003ed\u003c/strong\u003e GISTEMP, \u003cstrong\u003ee\u003c/strong\u003e BEST and \u003cstrong\u003ef\u003c/strong\u003e CMST over 1900–1980\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-3999517/v1/e7d563e2f5cce06e9df2b513.png"},{"id":51973278,"identity":"84fd0ecc-74cb-461d-9628-7cc2772676f1","added_by":"auto","created_at":"2024-03-04 18:59:48","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1956117,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig.7, but over 1981–2022\u003c/p\u003e","description":"","filename":"Fig.8.png","url":"https://assets-eu.researchsquare.com/files/rs-3999517/v1/4f882899cdadf7b1e3e3a5ab.png"},{"id":51973283,"identity":"6e434d24-fbae-431e-85ac-a0b5cfa0256b","added_by":"auto","created_at":"2024-03-04 18:59:48","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":5089217,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the average ST anomalies (relative to a 1991–2020 monthly climatology) for the JJA season over the \u003cstrong\u003ea\u003c/strong\u003eglobe, \u003cstrong\u003eb\u003c/strong\u003e NH and \u003cstrong\u003ec\u003c/strong\u003e SH from 1850 to 2023 among CMA-GMST, HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP, BEST and CMST\u003c/p\u003e","description":"","filename":"Fig.9.png","url":"https://assets-eu.researchsquare.com/files/rs-3999517/v1/7f085bc03807df6ed0a757fc.png"},{"id":51973277,"identity":"d2883845-daf4-4249-9653-f4209ecbed26","added_by":"auto","created_at":"2024-03-04 18:59:48","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":4605649,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig.9, but for the SON season\u003c/p\u003e","description":"","filename":"Fig.10.png","url":"https://assets-eu.researchsquare.com/files/rs-3999517/v1/e5dd731b10875d759236e3e9.png"},{"id":80558229,"identity":"45fc9f2b-3bc8-4903-b3f1-ff4dfc473b40","added_by":"auto","created_at":"2025-04-14 16:13:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":28445029,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3999517/v1/2b297659-3861-47b6-a5f1-86615e2ba8fb.pdf"}],"financialInterests":"","formattedTitle":"A new global land-ocean merged surface temperature dataset since the 1850s: the CMA-GMST dataset","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eWhile there are many indicators of climate change, the long-term evolution of global near surface temperature (ST) is an important one that is easy to understand. Global ST analyses, based on combination of traditional land air temperature (LAT) and sea surface temperature (SST) observations, are among the longest instrumental records that are well measured with reliable data extending back to circa 1850. These analyses are commonly used to assess the changes in the Earth\u0026rsquo;s climate, such as the onset of industrial-era warming (Morice et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rohde and Hausfather \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere are now multiple peer-reviewed global ST analyses available, including the Met Office Hadley Centre/Climatic Research Unit global ST (HadCRUT) dataset (Morice et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the Goddard Institute for Space Studies ST (GISTEMP) dataset (Lenssen et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), the NOAA Merged Global Land-Ocean ST (NOAAGlobalTemp) dataset (Vose et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the Berkeley Earth Land/Ocean ST (BEST) dataset (Rohde and Hausfather \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the China Merged ST (CMST) dataset (Sun et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and some postprocessed analyses based on HadCRUT4 by spatial interpolation (Cowtan and Way \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Karl et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In general, these products use somewhat different data sources or different approaches between each other. For instance, NOAAGlobalTemp and GISTEMP utilize Global Historical Climatological Network-monthly database (GHCNm) (Menne et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and NOAA\u0026rsquo;s Extended Reconstructed SST (ERSST) (Huang et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e) for land (LAT) and ocean (SST) temperature records, while HadCRUT adopts the Climatic Research Unit Temperature (CRUTEM) (Osborn et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and the Met Office Hadley Centre's SST (HadSST) (Kennedy et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The bias correction methods of ERSST and HadSST are essentially different, the former adopts the large-scale statistical adjustment technique that was developed by Smith and Reynolds (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, thereafter SR02), while the latter applies physics-based model (Kent et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In addition, NOAAGlobalTemp adopts a low- and high-frequency components reconstruction method that falls within the category of reduced space algorithms, while HadCRUT5 analysis and GISTEMP apply a Gaussian process and a distance-weighted average method, to infill the in-situ observation gaps (Morice et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis series of ST datasets by different groups has gradually helped to increase knowledge of the global temperature and its variation. However, there are still uncertainties in these datasets. For example, intercomparison among land components of BEST, GISTEMP, NOAAGlobalTemp and HadCRUT at local/regional scales has showed remarkable differences of the mean land surface air temperature anomalies (LSTA) and even disagreement on sigh of their changing trends, which are associated with the availability of in-situ observations and the use of infilling techniques. Henceforth, developing new datasets or improving the existing products with more data sources and reassessing the differences with the advent of them are suggested (Rao et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In addition, it has been pointed out that the improvements including methodological advances in ST products and new datasets since the fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) contributed approximately 0.1\u0026deg;C to the updated estimate of warming in AR6, which emphasizes the development and update of the ST datasets help to improve the understanding of modern climate change in climate history (IPCC \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, the optimization and improvement of observed climate data as a reference base for climate change research and verification benchmark for other climatic data products is a long-term task.\u003c/p\u003e \u003cp\u003eIn particularly, although the existing ST analyses including BEST, HadCRUT, GISTEMP and NOAAGlobalTemp have a high degree of agreements in average temperatures for regions such as the United States and Europe, they are found to have significant trend differences over Asia, which is associated with their insufficient observations over Asia (especially for large countries such as China and India) compared with the United States and Europe (Rao et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e). The differences might cause confusion on the objective assessments of warming over Asia, where temperatures are increasing faster than the global average (WMO \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Fortunately, the China Meteorological Administration (CMA) hosts the exchanged meteorological data between the members of the World Meteorological Organization Regional Association II (WMO RA II), and is also in charge of meteorological data including the digitized historical paper data archives in China. To enhance the service capability and value of these data sources, the National Meteorological Information Center (NMIC) of CMA has continuously involved in developing and updating a collection of high-quality global integrated datasets (Jiang et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The developed station databases have obvious advantage in improved LAT coverage over Asia and SST observation coverage along the east coastline of Asia, especially for China with more than 2000 stations in the latest version. Moreover, the operational effectiveness and stability of the developed datasets help to guarantee the capacity of real-time monthly climate monitor by statistical products based on them, such as the monthly ST analysis dataset.\u003c/p\u003e \u003cp\u003eHence, a new global monthly land-ocean merged ST dataset of 2\u0026deg;\u0026times;2\u0026deg; resolution since the 1850s, CMA global merged ST (CMA-GMST), is presented in this study. It is based on the recently developed CMA global reconstructed land ST (CMA-GLST) and SST (CMA-SST) analyses (Chen et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which are constructed from the data sources with improved coverage of observations especially over Asia. The STs in the Arctic ice covered regions are also reconstructed and merged in this new dataset. The details about the input data sources and the applied analysis techniques are given in section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the assessment results of the ST analysis are given in section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e3\u003c/span\u003e and the conclusion and discussion are given in section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e"},{"header":"2 Data and method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 ST over land\u003c/h2\u003e \u003cp\u003eMonthly averages of near-surface air temperature measured at weather stations over land since 1850 are obtained from a newly developed integrated global land surface dataset (Jiang et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the hourly database is a collection of station series from four global sources and one regional source. The monthly average temperatures from stations are subjected to quality control and have been homogenized through the same approach as Xu et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e). The reconstruction of land surface air temperature adopts the method of the point interpolation based on Biased Sentinel Hospitals Areal Disease Estimation (P-BSHADE). The method can be used to remedy the station bias resulting from sparse coverage, and it considers the characteristics of spatial autocorrelation and nonhomogeneity of the temperature distribution to obtain unbiased and minimum error variance estimates. The method has been used to interpolate 1-km grids of monthly surface air temperatures in the historical period of 1900\u0026ndash;1950 in China, and it was proven that the method has the smallest error compared with the widely used methods including kriging, inverse distance weighting (IDW), and a combined spline with kriging (TPS-KRG) method, both theoretically and empirically (Xu et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere are four steps to establish the global land surface air temperature reconstructed analysis dataset by P-BSHADE.\u003c/p\u003e \u003cp\u003eFirst, calculate the correlation coefficient (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e), covariance (\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e) and ratio (\u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e) of temperatures between each two stations for each calendar month of the reference period 1961\u0026ndash;1990; Second, monthly average station data for each month of the period 1961\u0026ndash;1990 is interpolated into 1\u0026deg;\u0026times;1\u0026deg; grids using TPS-KRG method, thereby the correlation coefficients between the grid box and the nearby stations for each calendar month could be obtained. Third, using the above parameters from the reference period, the weight (\u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e) for each neighboring observation station could be calculated by the unbiased condition constrain as:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\sum\\nolimits_{{i=1}}^{n} {{w_i}{b_i}=1}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003eWe select five neighboring observation stations holding highest correlations with the target grid, positive weights and smallest estimated error variances for reconstruction. Last, the estimated temperature \u003cem\u003ey\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e at the target grid is obtained as:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({y_0}=\\sum\\nolimits_{{i=1}}^{n} {{w_i}{y_i}}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003eWhere \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the temperature record of the \u003cem\u003ei\u003c/em\u003eth neighboring station. Thus, a global land surface air temperature reconstructed analysis dataset (CMA-GLST) with 1\u0026deg;\u0026times;1\u0026deg; resolution since 1850 has been developed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 ST over ocean\u003c/h2\u003e \u003cp\u003eA new global monthly reconstructed SST analysis dataset (CMA-SST) with 2\u0026deg;\u0026times;2\u0026deg; resolution since 1900 was developed. It was constructed based on a newly developed hourly dataset integrating multiple sources, and by SR02 method to adjust the systematic biases of ship SSTs and a low- and high-frequency components reconstruction method to full-fill the SSTs over ocean (Chen et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this study, the SST dataset is improved in two aspects to generate the ST analysis. The SST dataset is forward extended to 1850, to enable it to investigate the global warming above pre-industrial levels. Moreover, as different changing trends between sea surface and atmospheric temperatures have been detected (Christy et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), the bias estimates of ship SSTs before 2010 based on SR02 method by comparing SST with night marine air temperature (NMAT) data are revised, to address the potential biases of the estimates caused by the assumption of SR02 that SST\u0026ndash;NMAT differences keep nearly constant over multi-decadal scales.\u003c/p\u003e \u003cp\u003eThe bias estimates of ship SSTs before 2010 are revised by ancillary information from HadSST4 (Kennedy et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and the Met Office Hadley Centre's monthly NMAT dataset Version 2 (HadNMAT2) (Kent et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), which are strictly adjusted for changes in observation instruments and heights respectively. Firstly, the climatic SST\u0026ndash;NMAT differences for each calendar month are computed by averaging HadSST4\u0026ndash;HadNMAT2 differences over the recent 30-yr base period and interpolated over ocean by optimum interpolation (OI). And then, the monthly SST\u0026ndash;NMAT differences before 2010 are estimated by best-fit of the observed monthly HadSST4\u0026ndash;HadNMAT2 differences to its climatic pattern (Smith and Reynolds \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Secondly, the monthly estimated SST\u0026ndash;NMAT differences are compared to its calendar month\u0026rsquo;s climatic field to assess their changes over time. Finally, the changes of SST\u0026ndash;NMAT differences are subtracted from the SST biases estimated by SR02 method. As HadSST4 is bias corrected by physics-based model, the revised SST bias estimations synthesize the advantages of both the physics-based model and the large-scale statistical method (Kent et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 ST over the Arctic ice covered area\u003c/h2\u003e \u003cp\u003eLAT is applied to reconstruct the STs of the Arctic ice covered region in CMA-GMST, likewise other published global ST products, to avoid the underestimation of the average global temperatures caused by the missing data in the Arctic (Huang et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A reconstruction scheme synthesizing IDW extrapolation and low- and high-frequency components reconstruction technique is adopted. The details are: (1) calculate the monthly LSTAs (relative to 1961\u0026ndash;1990 average) for each station based on the homogenized monthly air temperature applied in CMA-GLST, and then arithmetically average them to the 2\u0026ordm;\u0026times;2\u0026ordm; grids of SST analysis; (2) interpolate the gridded LSTAs by IDW technique, to extend the temperature distribution by observed neighbors within 300\u0026ndash;500 km; (3) perform the low- and high-frequency components reconstruction on the interpolated data.\u003c/p\u003e \u003cp\u003eA maximum Arctic ice covered region is defined by the monthly sea ice concentrations at 1\u0026ordm;\u0026times;1\u0026ordm; resolution from Met Office Hadley Centre's Sea Ice and SST (HadISST2) for the period 1900\u0026ndash;2015 (Titchner and Rayner \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and NOAA\u0026rsquo;s optimum interpolation SST (OISSTV2) from 2016 (Banzon et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and applied while the reconstruction of the high-frequency component. A similar method as Chen et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) is conducted to address the discontinuity between HadISST2 and OISSTV2 due to their different development schemes. After that, a value of one is set when the sea ice concentration is greater than 15% and zero otherwise for each monthly 1\u0026ordm;\u0026times;1\u0026ordm; HadISST2/OISSTV2 grid, and then the area coverage of sea ice in 2\u0026ordm;\u0026times;2\u0026ordm; resolution are obtained as the average of the reassigned sea ice concentration. The maximum Arctic ice covered region is treated as all the 2\u0026ordm;\u0026times;2\u0026ordm; grids with sea ice area coverage greater than 0 from 1850 (Morice et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilarly to the SST analysis (Chen et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the low-frequency component is calculated by running average over space and time, specifically: (1) calculate a 18\u0026deg;\u0026times;26\u0026deg; spatial mean of the monthly LSTAs for latitude and longitude respectively; (2) define the annual mean LSTA for grids with at least 2 months of the year, and perform a 15-yr median filter to the annual values; (3) run a 14\u0026deg;\u0026times;26\u0026deg; spatial mean for latitude and longitude, and then a nine-point binomial spatial filter and a three-point binomial temporal filter, until filling the polar regions; and (4) run a 14\u0026deg;\u0026times;26\u0026deg; spatial mean for latitude and longitude to smooth the reconstructed data. The high-frequency component is obtained by subtracting the reconstructed low-frequency component from the LSTAs and then reconstructed by (1) training the 53 leading empirical orthogonal teleconnection (EOT) modes within the maximum Arctic ice covered region by ECMWF Reanalysis v5 (ERA5) (Simmons et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The modes are restricted within 3000 and 6000 km for latitude and longitude; (2) linearly fitting the high-frequency LSTAs to the trained EOTs in a least-squares sense, in which the EOT modes are applied if the observations support more than 0.1 of their variance ratios. Finally, the monthly STs over Arctic are obtained by summing the reconstructed low- and high-frequency components, and masked by the monthly sea ice area coverages that are greater than 0.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Merging analysis\u003c/h2\u003e \u003cp\u003eThe STs over land, ocean and the Arctic ice covered area are merged by area-weighted average to create CMA-GMST dataset. First, the monthly ST anomalies (relative to a 1961\u0026ndash;1990 baseline period) over land and ocean are derived from the reconstructed LAT and SST analyses, and then the anomalies with 1\u0026ordm;\u0026times;1\u0026ordm; resolution over land are resampled to 2\u0026ordm;\u0026times;2 grids of SST. The land/ocean area weights used to merge the land/ocean anomalies are derived from a 0.25\u0026ordm;\u0026times;0.25\u0026ordm; land/ocean mask (Jet Propulsion Laboratory \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The land weight is defined as the proportion of 0.25\u0026ordm;\u0026times;0.25\u0026ordm; grids labeled as land type within each 2\u0026ordm;\u0026times;2\u0026ordm; grid, and the ocean weight is defined as 1\u0026ndash;land weight. Besides, anomalies over the Arctic ice covered area are treated as if they are land temperatures, and the monthly area coverages of sea ice are treated as land weights.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Result","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Spatial coverage\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the annual coverage comparison among CMA-GMST, HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP (1200 km smoothing, so is thereafter), BEST and CMST (the average of the Imax and Imin datasets, so is thereafter) from 1850 to 2022. The latest version up to 2023 of each published dataset is applied here, such as V5.1 of NOAAGlobalTemp. It could be found that the spatial coverages differ among these datasets, especially in the periods with sparse observations. For example, NOAAGlobalTemp is the only one with complete coverage of all land and ocean areas for the entire period of record, while the data coverages of HadCRUT5 analysis and BEST have increased over time and have three obvious drops in the 1860s, 1910s and 1940s. By comparison, time-varying\u0026ensp;characteristics of CMA-GMST coverage are primarily consistent with those of HadCRUT5 analysis and BEST, and its coverage magnitude is between the two published datasets and is somewhat higher than that of HadCRUT5 analysis. This is likely related to: (1) the land component of CMA-GMST is generated based on the stations with data length\u0026thinsp;\u0026ge;\u0026thinsp;20 year, and by local statistical interpolation with a 1000 km radius which is close to that of HadCRUT5 analysis (1300 km). These parameters are stricter than those adopted in the other published datasets, such as BEST (Rohde and Hausfather \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Morice et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); (2) although the ocean component of CMA-CMST is interpolated by the same technique as that adopted in NOAAGlobalTemp, a geographic masking is postprocessed to prevent global averages from depending heavily upon the highly smoothed extrapolation estimates (Vose et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), which however is omitted in NOAAGlobalTempV5.1 in order to get the full coverage (Vose et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpecifically, the areal coverage in the CMA-GMST grids reaches 90% in the middle 1950s and exceeds 99% from the late 1970s, similarly to HadCRUT5 analysis. Moreover, consistent with HadCRUT5 analysis and BEST, an obvious higher data coverage of the Northern Hemisphere (NH) than the Southern Hemisphere (SH) is detected in CMA-GMST especially before the 1950s, and two drops in the 1910s and 1940s are found in the SH associated with the drops of SST observations while the two world wars (Morice et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Quantitatively, both the NH coverages of CMA-GMST and HadCRUT5 analysis exceed 95% in the early 1920s and reach 100% in the mid-1950s. However, both the SH coverages of CMA-GMST and HadCRUT5 analysis exceed 95% in the early 1970s. In addition, the NH coverage of CMA-GMST is persistently higher than that of HadCRUT5 analysis, while a somewhat lower SH coverage of CMA-GMST is found around the period 1957\u0026ndash;1972. Nevertheless, the corresponding SH coverage of CMA-GMST is obviously higher than that of CMST.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Correlation with the published products\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the annual average ST anomaly spatial correlations between CMA-GMST and HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP, BEST and CMST across the globe. CMA-GMST is resampled to match the spatial resolution of each reference product. The spatial correlations between CMA-GMST and each of the existing products are calculated for the monthly data first and then averaged over the 12 months of the year to get the annual values, to assess the spatial structure similarity of temperatures between CMA-GMST and the existing datasets. It could be found that the spatial correlation of CMA-GMST with HadCRUT5 analysis is highest, it increases from about 0.65 in 1850 to around 0.8 from the early 1880s, and keeps 0.8\u0026ndash;0.9 to present. The persistent higher correlation of CMA-GMST with HadCRUT5 analysis than those with the other published products is reasonable due to their close parameters applied to screen the land stations with enough record length and control the data extrapolation in an appropriate distance range, which have been pointed out in the coverage comparison part. Besides, the revised SST bias estimations by historical HadSST4 data might also be a potential contributor to the higher correlation with HadCRUT5 analysis. By comparison, the spatial correlations with NOAAGlobalTemp, GISTEMP, BEST and GMST are smaller by about 0.1\u0026ndash;0.2 relative to that with HadCRUT5 analysis before the 1900s, and keep\u0026thinsp;\u0026ge;\u0026thinsp;0.7 after the 1950s, except for a retreat of correlation to about 0.6\u0026ndash;0.7 from the mid-1950s to the early 2000s with NOAAGlobalTemp.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the spatial distribution of ST anomaly correlations between CMA-GMST and HadCRUT5 analysis, BEST and CMST over time from 1880 to 2022. Only HadCRUT5 analysis, BEST and CMST are applied to compare in this part, because they provide the spatial field of anomalies relative to a same 1961\u0026ndash;1990 baseline period as CMA-GMST, or provide additional climate fields for conversion such as BEST. Correlations have been calculated where paired data are present for at least 70% of all months during the statistical period. It could be found that the correlations in terrestrial area are obviously higher in regions including the North America, Europe excluding Greenland and the middle-to-high latitudes of Asia, where the correlations of CMA-GMST with the published products universally concentrate in \u0026gt;\u0026thinsp;0.9. Similarly, the coherently higher correlations\u0026thinsp;\u0026gt;\u0026thinsp;0.9 in oceanic area occur in parts of the North Atlantic. Beyond that, the correlations of CMA-GMST with the published datasets are mainly\u0026thinsp;\u0026gt;\u0026thinsp;0.8, except for regions with limited observations, such as Africa, South America, the Arctic ice covered area and the high latitudes of SH (Morice et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlbeit the consistency in most regions, some regional differences are also found, for example, the correlations for Australia and the middle-to-low latitudes of Asia are overall higher with HadCRUT5 analysis and BEST, while the correlations for the Equatorial East Pacific region with CMST are mainly\u0026thinsp;\u0026gt;\u0026thinsp;0.9 and higher than those with HadCRUT5 analysis and BEST at about 0.8\u0026ndash;0.9. Reconstruction technique might be a potential contributor to these differences. Specifically, CMA-GMST, HadCRUT5 analysis and BEST conduct reconstruction for terrestrial area only referring to the observations from spatial neighbors, while a low- and high-frequency components reconstruction by referring the observations not only from the spatial but also the temporal neighbors is applied by CMST and CMA-GMST for oceanic reconstruction (Morice et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Annual to decadal climate monitoring\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the comparisons of the annual and decadal average global ST anomalies (relative to a 1961\u0026ndash;1990 baseline period) among CMA-GMST, HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP, BEST and CMST from 1850 to 2022. The ST anomalies for each year are calculated as the mean of the area-weighted average for the NH and SH. The anomalies for each decade are calculated as the mean of each 10 yearly values. Overall, the annual average global ST anomalies and their interannual variations of CMA-GMST are comparable to those of the published datasets. Fluctuations with no obvious changing trend of the global STs before the 1900s, and thereafter a temperature decline in the 1900s are detected by all the datasets including CMA-GMST. From the 1920s, the average global STs in CMA-GMST and all the published products undergo two obvious warming periods, including the one from the 1920s to the middle 1940s and the other from the mid-1960s to present. Between these two periods, there is a slight retreat of the increasing temperatures. Quantitatively, the fitted linear trends of the global temperature anomalies are approximately 0.84, 1.69 and 1.87\u0026deg;C/100\u0026nbsp;year for the periods 1880\u0026ndash;2022, 1960\u0026ndash;2022 and 1980\u0026ndash;2022 respectively, and they agree well with the range of trends computed from other published ST analyses (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMeanwhile, appreciable differences among the six datasets in the period 1850\u0026ndash;1900 are found. The differences are reasonable due to the larger uncertainties of the ST analyses associated with the sparse observations in the earlier periods (Morice et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), however, they will pose confusion while the assessment of the global warming above pre-industrial levels owing to the mean temperature over the period 1850\u0026ndash;1900 is used as the pre-industrial baseline (Schurer et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Quantitative comparison shows that the mean value of the annual records over the period 1850\u0026ndash;1900 is \u0026minus;\u0026thinsp;0.377\u0026deg;C for CMA-GMST, it is close to HadCRUT5 analysis with \u0026minus;\u0026thinsp;0.357\u0026deg;C and BEST with \u0026minus;\u0026thinsp;0.370\u0026deg;C, due to the closer coverages and higher anomaly correlations of CMA-GMST with HadCRUT5 analysis and BEST. Comparatively, the mean anomalies over the period 1850\u0026ndash;1900 for NOAAGlobaltemp and CMST are higher as \u0026minus;\u0026thinsp;0.299 and \u0026minus;\u0026thinsp;0.254\u0026deg;C.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the comparisons of the annual area-weighted average ST anomalies (relative to a 1961\u0026ndash;1990 baseline period) for land, ocean, NH and SH from 1850 to 2022. The same as the assessment result on global scale, the annual average ST anomalies and their inter-annual variations of CMA-GMST are consistent with the published datasets. Quantitatively, the fitted linear trends of land/ocean are 1.08/0.77, 2.43/1.54 and 2.90/1.82\u0026deg;C/100\u0026nbsp;year, and the trends of NH/SH are 0.95/0.74, 2.15/1.22 and 2.76/0.98\u0026deg;C/100\u0026nbsp;year for the periods 1880\u0026ndash;2022, 1960\u0026ndash;2022 and 1980\u0026ndash;2022 respectively. Comparison assessments indicate that the regional observed trends of CMA-GMST agree well with the range of trends computed from other published ST analyses, no matter for land, ocean, or hemispheres. In addition, higher warming rates of land than ocean and higher warming rates of NH than SH for the various periods, and the higher warming rates of recent period for the globe, land, ocean and NH are detected in all the ST datasets including CMA-GMST. Moreover, the warming rate differences between land and ocean, and those between NH and SH are also detected to increase in the recent period by all the ST datasets including CMA-GMST (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eObserved trends (\u0026deg;C/100\u0026nbsp;year) in CMA-GMST, HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP, BEST and CMST for periods 1880\u0026ndash;2022, 1960\u0026ndash;2022 and 1980\u0026ndash;2022 over the globe, land, ocean and hemispheres. Trend values are calculated based on the annual average anomalies with ordinary least squares\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003cp\u003eperiod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCMA-GMST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHadCRUT5 analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNOAAGlobalTemp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGISTEMP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBEST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCMST\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGlobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1880\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1960\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1980\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1880\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1960\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1980\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOcean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1880\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1960\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1980\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1880\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1960\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1980\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1880\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1960\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1980\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the annual average ST anomalies for each 30\u0026deg; latitude bands. It shows that the differences between the six datasets are pronounced in 90S\u0026ndash;60\u0026ordm;S, which is related to the large uncertainty mainly due to the sparse observations in this region (Morice et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Quantitatively, the annual average anomalies of NOAAGlobalTemp and CMST are relatively smooth with standard deviations (SDs) of 0.191 and 0.213 over the period 1850\u0026ndash;2022 in 90S\u0026ndash;60\u0026ordm;S, while BEST has the largest variability with SD of 0.583, and the corresponding SDs of CMA-GMST and HadCRUT5 analysis are between them by 0.257 and 0.384. In addition, it is obviously cooler in HadCRUT5 analysis and BEST than in NOAAGlobalTemp and CMST before the 1880s in 60S\u0026ndash;30\u0026ordm;S, and by comparison, CMA-GMST is closer to HadCRUT5 analysis and BEST. Apart from the appreciable differences that are mainly located in regions and periods with less observations, the observed trends of the annual average anomalies over the different latitudinal zones basically agree well with each other between the six ST datasets.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the spatially resolved trends (\u0026deg;C/10\u0026nbsp;year) of CMA-GMST (a), HadCRUT5 analysis (b), NOAAGlobalTemp (c), GISTEMP (d), BEST (e) and CMST (f) over 1900\u0026ndash;1980. Trends have been calculated where data are present in both the first and last decade and for at least 70% of all years within the period, referenced to IPCC AR6 report (IPCC \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). All the datasets including CMA-GMST show that the temperature trends focus on \u0026minus;\u0026thinsp;0.15\u0026ndash;0.15\u0026deg;C/10\u0026nbsp;year over 1900\u0026ndash;1980 and are dominated by the positive trends. Whatever, some regional differences of the trends among the six ST datasets are found, for example, cooling trends in the central region of South America are detected by HadCRUT5 analysis, while corresponding warming trends are detected by BEST, CMST and CMA-GMST, although the overall higher correlation of CMA-GMST with HadCRUT5 analysis has been mentioned.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen it goes to the period 1981\u0026ndash;2022 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), all the datasets including CMA-GMST show significant increases of warming rates for most regions of the world. The strongest warming rates over 1981\u0026ndash;2022 occur in the Arctic ice covered area, where the warming trends are detected\u0026thinsp;\u0026ge;\u0026thinsp;0.6\u0026deg;C/10\u0026nbsp;year by all the six datasets. Followed are the warming trends in Eurasia, where large areas are detected with warming rates\u0026thinsp;\u0026ge;\u0026thinsp;0.3\u0026deg;C/10\u0026nbsp;year by the datasets except for NOAAGlobalTemp with cooling trends over Greenland. Comparatively, the warming trends over ocean are lower, they are donated by 0\u0026ndash;0.3\u0026deg;C/10\u0026nbsp;year, and moreover all the datasets including CMA-GMST show cooling trends in the high-latitudes of the SH and the near-equatorial region of the South Pacific. Except for the overall consistency, some regional differences are also detected, for example, some small regions with warming trends\u0026thinsp;\u0026ge;\u0026thinsp;0.45\u0026deg;C/10\u0026nbsp;year over 1981\u0026ndash;2022 are detected in the eastern Asia by CMA-GMST, while the local higher warming is relatively inconspicuous in the published datasets. The impacts of the regional differences detected in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e on the assessment of global temperature and its trends might be negligible, however, it is important for regional assessment and emphasizes the necessity of conducting multiple products comparison while assessing the temperature changes in small regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Monthly to seasonal climate monitoring\u003c/h2\u003e \u003cp\u003eThe statements have been issued by several institutions that 2023 was the warmest year on record since meteorological records began. Our quantitative evaluation indicates that the average global temperatures of 2023 above the 1991\u0026ndash;2020 monthly climatology reached 0.54, 0.56, 0.55, 0.55, 0.55 and 0.51\u0026deg;C for CMA\u0026ndash;GMST, HadCRUT5, NOAAGlobalTemp, GISTEMP, BEST and CMST respectively. Moreover, the ranking of the average global temperature for each calendar month also shows that each month from July 2023 to December 2023 ranked as the globe's hottest month in recorded history (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe year with historical highest temperature for each calendar month and annual average up to 2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMA-GMST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHadCRUT5 analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNOAAGlobalTemp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGISTEMP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBEST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCMST\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNov\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYearly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows the average ST anomalies (relative to a 1991\u0026ndash;2020 monthly climatology) for the JJA (June, July and August) season over the globe and hemispheres from 1850 to 2023 in CMA-GMST, HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP, BEST and CMST. It is shown that the JJA season 2023 ranked as the warmest June\u0026ndash;August period in the 174-year record over the globe. The average global STs of the JJA season 2023 were about 0.61, 0.55, 0.57, 0.61 and 0.55\u0026deg;C above the 1991\u0026ndash;2020 monthly climatology in HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP, BEST and CMST, and it was comparable as 0.60\u0026deg;C in CMA-GMST. Moreover, all the datasets including CMA-GMST indicate that the JJA season 2023 was the NH\u0026rsquo;s hottest meteorological summer on record, at about 0.71, 0.74, 0.73, 0.69 and 0.69\u0026deg;C above the 1991\u0026ndash;2020 monthly climatology in HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP, BEST and CMST, and about 0.74\u0026deg;C in CMA-GMST. The JJA season 2023, which also marked the SH\u0026rsquo;s winter, was the SH\u0026rsquo;s warmest winter, it was on record at 0.51, 0.36, 0.42, 0.53 and 0.41\u0026deg;C above the 1991\u0026ndash;2020 monthly climatology in HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP, BEST and CMST and was comparable as 0.45\u0026deg;C in CMA-GMST.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e is the same as Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, but for the SON (September, October and November) season. It is also shown that the SON season 2023 ranked as the warmest September\u0026ndash;November period in the 174-year record over the globe. The average global STs of the SON season 2023 above the 1991\u0026ndash;2020 monthly climatology were about 0.79, 0.78, 0.79, 0.80, 0.79 and 0.72\u0026deg;C in CMA-GMST, HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP, BEST and CMST respectively and higher than those of the JJA season. A significantly higher average ST of the SON season 2023 above the 1991\u0026ndash;2020 monthly climatology than that of the JJA season was also found in all the six datasets including CMA-GMST in the NH, the SON season 2023 was the NH\u0026rsquo;s hottest meteorological autumn on record, at about 1.08, 1.04, 1.15, 1.11, 1.07 and 1.03\u0026deg;C above the 1991\u0026ndash;2020 monthly climatology in CMA-GMST, HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP, BEST and CMST. Comparatively, the SON season 2023, which also marked the SH\u0026rsquo;s spring, was the SH\u0026rsquo;s warmest spring, it was on record at 0.49, 0.52, 0.43, 0.49, 0.52 and 0.40\u0026deg;C above the 1991\u0026ndash;2020 monthly climatology in CMA-GMST, HadCRUT5 analysis, NOAAGlobalTemp, GISTEMP, BEST and CMST, and were comparable as those of the JJA season.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Conclusion and discussion","content":"\u003cp\u003eIt has been emphasized that although global temperature series derived from the exiting ST analyses are in good agreement, their differences in small regions such as Asia might be significant (Rao et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Motivated by the improved observation coverage over Asia of the multi-source integrated datasets developed by CMA \u003cb\u003e(\u003c/b\u003eJiang et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e), \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003ea\u003c/span\u003e new global ST dataset, CMA-GMST, is presented in this paper. The dataset is generated by the similar process flow to the existing ST analyses. First, the LAT observations are homogenized using the same method as Xu et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e) and reconstructed by P-BSHADE technique (Xu et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e) to cover the land component of ST analysis, and the SST observations are bias corrected by revised technique based on SR02 method and a low- and high-frequency components reconstruction method (Chen et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to cover the ocean component. Moreover, reconstruction of air temperatures over the Arctic ice covered area is also carried out to consider the polar amplification effect on global climate change (Sun et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). After that, the reconstructed land, ocean and Arctic components are merged to provide the global monthly temperature anomaly (relative to a 1961\u0026ndash;1990 baseline period) in 2\u0026ordm;\u0026times;2\u0026ordm; resolution since 1850.\u003c/p\u003e \u003cp\u003eSpatial coverage of CMA-GMST and its anomaly correlation with the published products are assessed, because not only the anomaly value itself but also its coverage are important factors in the calculations of global or regional average temperature series (Morice et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rao et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Comparisons show that the time-varying\u0026ensp;characteristics of CMA-GMST\u0026rsquo;s data coverage are primarily consistent with those of HadCRUT5 analysis and BEST, and its coverage magnitude is between them and is somewhat higher than that of HadCRUT5 analysis. Generally, both the global coverages of CMA-GMST and HadCRUT5 analysis reach 90% in the middle 1950s and exceed 99% in the late 1970s. Assessments also show that the global spatial correlation of CMA-GMST with HadCRUT5 analysis is overall higher than those with the other existing products. The close relationship between CMA-GMST and HadCRUT5 analysis might be related to (1) their close parameters applied to screen the land stations with enough record length and control the data extrapolation in an appropriate distance range; (2) the revised SST bias estimations by historical HadSST4 data.\u003c/p\u003e \u003cp\u003eFurther assessments show that the ability of CMA-GMST to monitor temperature variation characteristics at multiple temporal and spatial scales is generally comparable to the existing datasets. For example, (1) the interannual variations of the annual average ST anomalies calculated by CMA-GMST are consistent with those of the published datasets, and its observed trends for the various periods including 1880\u0026ndash;2022, 1960\u0026ndash;2022 and 1980\u0026ndash;2022 are all between the published datasets, whether for the globe, land, ocean, or hemispheres; moreover, the regional trends of the annual average anomalies over 60N\u0026ndash;90\u0026ordm;N, 30N\u0026ndash;60\u0026ordm;N, EQ\u0026ndash;30\u0026ordm;N and 30\u0026ordm;S\u0026ndash;EQ agree well with the published datasets; (2) the global spatial distribution of the resolved trends indicate that the regions with accelerated warming from 1900\u0026ndash;1980 to 1981\u0026ndash;2022 and some regions mainly located in the SH with cooling trends over 1981\u0026ndash;2022 in CMA-GMST are broadly consistent with those of the published datasets; and (3) all the datasets including CMA-GMST reflect that the year 2023 was the warmest year on record, and each month from July 2023 to December 2023 was the globe's hottest month in recorded history.\u003c/p\u003e \u003cp\u003eWhatever, some differences are also detected and mostly caused by the factors affecting the uncertainties of the ST analyses. For example, (1) the correlations for North America and Europe are persistently higher than those for Africa and South America, no matter which existing product CMA-GMST is related to, which reflects the fundamental role of observation coverage in the ST analysis; (2) the correlations for the middle-to-low latitudes of Asia are overall higher with HadCRUT5 analysis and BEST than those with CMST, in contrast, higher correlations with CMST for the Equatorial East Pacific region are found. It might be attributed to the extrapolation by spatial neighbors for land component of CMA-GMST, HadCRUT5 analysis and BEST, while by both temporal and spatial neighbors for ocean component of CMA-GMST and CMST, which indicates the importance of reconstruction technique on the uncertainty of ST analysis; (3) cooler temperatures of HadCRUT5 analysis and BEST than NOAAGlobalTemp and CMST are found before the 1880s in 60S\u0026ndash;30\u0026ordm;S that is mainly covered with sea water, and comparatively, corresponding temperatures of CMA-GMST are closer to the former two albeit its reconstruction technique for the ocean component is more similar to the latter two. The revised SST bias estimations by historical HadSST4 of CMA-GMST might be a potential contributor, which indicates the influence of bias technique to the uncertainty of ST analysis.\u003c/p\u003e \u003cp\u003eLast but not least, further work is still needed to improve the understanding of CMA-GMST, including (1) a comprehensive set of uncertainty estimates, including estimates of temperature bias effects and the effect of reconstruction technique will be needed to further measure the reliability of CMA-GMST and to strength the understanding of its temperature series\u0026rsquo; uncertainties; (2) ongoing investigations to supplement data sources especially in regions with limited observations and technically refine methods are needed, which are evidently manifested by the appreciable differences of linear trends among the six ST datasets including CMA-GMST over 90S\u0026ndash;60\u0026ordm;S even in modern years. In particular, artificial neural network method has been implemented in NOAAGlobalTempV6.0.0 to improve the ST reconstruction over land (Huang et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and the updated ST analysis was released recently; and (3) the studies over small regions are expected, for example, the positive feedback of improved coverage over Aisa on the somewhat higher warming rates of the eastern Asia over 1981\u0026ndash;2022 in CMA-GMST needs further evaluation, such as by comparative experiments through controlling the input in-situ observations or intercomparison with the supplementary data sources.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe dataset generated in this study is available from the corresponding author upon reasonable request. The datasets used for comparative evaluations in this study are accessible online, specifically, HadCRUT5 is available from https://www.metoffice.gov.uk/hadobs/hadcrut5/, NOAAGlobalTemp is available from https://www.ncei.noaa.gov/products/land-based-station/noaa-global-temp, GISTEMP is available from https://data.giss.nasa.gov/gistemp/, BEST is available from https://berkeleyearth.org/data/, and CMST is available from http://www.gwpu.net/h-col-103.html. The datasets used for defining the ice covered region in this study are accessible online, specifically, HadISST2 is available from https://www.metoffice.gov.uk/hadobs/hadisst2/, monthly OISSTV2 is available from https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.html.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe like to thank Panmao Zhai (Chinese Academy of Meteorological Sciences) for the discussions and constructive suggestions during the realisation of this work. We would like to express our gratitude to National Climate Center of China Meteorological Administration for their operational application of the newly developed dataset, which has helped to monitor the quality and operational stability of the dataset.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Innovation and Development Project of China Meteorological Administration (Grant No. CXFZ2023J049).\u003c/p\u003e\n\u003cp\u003eAuthor information\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003eNational Meteorological Information Center, China Meteorological Administration, Beijing, China\u003c/p\u003e\n\u003cp\u003eLifan Chen, Wenhui Xu, Zijiang Zhou, Lijuan Cao \u0026amp; Su Yang\u003c/p\u003e\n\u003cp\u003eState Key Laboratory of Resources and Environmental Information System, Institute\u0026nbsp;of\u0026nbsp;Geographic\u0026nbsp;Sciences\u0026nbsp;and\u0026nbsp;Natural\u0026nbsp;Resources\u0026nbsp;Research,\u0026nbsp;Chinese\u0026nbsp;Academy of\u0026nbsp;Sciences, Beijing, China\u003c/p\u003e\n\u003cp\u003eChengdong Xu\u003c/p\u003e\n\u003cp\u003eContributions\u003c/p\u003e\n\u003cp\u003eLifan Chen took the lead in writing the main manuscript text, Wenhui Xu provided critical feedback and helped shape the analysis and manuscript. All the authors provide valuable information on key techniques applied in the newly developed dataset.\u003c/p\u003e\n\u003cp\u003eCorresponding author\u003c/p\u003e\n\u003cp\u003eCorrespondence to Wenhui Xu.\u003c/p\u003e\n\u003cp\u003eEthics declarations\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBanzon V, Smith TM, Chin TM, Liu C, Hankins W (2016) A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies. Earth System Science Data 8:165\u0026ndash;176. https://doi.org/10.5194/essd-8-165-2016\u003c/li\u003e\n\u003cli\u003eChen L, Cao L, Zhou Z, Zhang D, Liao J (2021) A new globally reconstructed sea surface temperature analysis dataset since 1900. 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Journal of Climate 31:1757\u0026ndash;1770. https://doi.org/10.1175/JCLI-D-17-0150.1\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"climate-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cldy","sideBox":"Learn more about [Climate Dynamics](https://www.springer.com/journal/382)","snPcode":"382","submissionUrl":"https://submission.nature.com/new-submission/382/3","title":"Climate Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"global surface temperature, dataset, bias correction, reconstruction, climate change","lastPublishedDoi":"10.21203/rs.3.rs-3999517/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3999517/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA new global land-ocean merged surface temperature dataset, China Meteorological Administration global merged surface temperature (CMA-GMST), is developed. It is constructed from the monthly China Meteorological Administration global reconstructed land surface temperature (CMA-GLST) and sea surface temperature (CMA-SST) analyses that benefit from the improved in-situ observation coverage. Besides, the Arctic ice covered area is also reconstructed based on air temperatures and merged into CMA-GMST. This dataset provides a spatial complete and homogeneous surface temperature anomaly field in 2\u0026deg;\u0026times;2\u0026deg; resolution for each month since 1850, and covers the majority of the earth\u0026rsquo;s surface: reaches 90% in the middle 1950s and exceeds 99% from the late 1970s. Assessments show that the observed global and regional (terrestrial, oceanic and hemispheric) trends of the annual average anomalies from CMA-GMST agree well with the ranges of trends computed from other published surface temperature analyses. The trends over the different latitudinal zones are also broadly in line with other published surface temperature analyses, while there are some differences in regions with limited observations among the datasets, such as the region of 90S\u0026ndash;60\u0026ordm;S. Besides, evaluations by CMA-GMST show that the year 2023 was the warmest year on record and each month from July 2023 to December 2023 ranked as the globe's hottest month in recorded history, which agree well with the evaluations from other published surface temperature analyses.\u003c/p\u003e","manuscriptTitle":"A new global land-ocean merged surface temperature dataset since the 1850s: the CMA-GMST dataset","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-04 18:59:43","doi":"10.21203/rs.3.rs-3999517/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-03-04T16:54:47+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-28T16:15:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-28T09:48:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Climate Dynamics","date":"2024-02-28T03:22:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"climate-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cldy","sideBox":"Learn more about [Climate Dynamics](https://www.springer.com/journal/382)","snPcode":"382","submissionUrl":"https://submission.nature.com/new-submission/382/3","title":"Climate Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f59adbb3-7a37-4cde-8509-9a03ca6449ab","owner":[],"postedDate":"March 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-14T16:06:58+00:00","versionOfRecord":{"articleIdentity":"rs-3999517","link":"https://doi.org/10.1007/s00382-025-07614-x","journal":{"identity":"climate-dynamics","isVorOnly":false,"title":"Climate Dynamics"},"publishedOn":"2025-04-09 16:04:57","publishedOnDateReadable":"April 9th, 2025"},"versionCreatedAt":"2024-03-04 18:59:43","video":"","vorDoi":"10.1007/s00382-025-07614-x","vorDoiUrl":"https://doi.org/10.1007/s00382-025-07614-x","workflowStages":[]},"version":"v1","identity":"rs-3999517","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3999517","identity":"rs-3999517","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}