China-LSAT
China-MST
C-LAPT
C-LSAT_HR
SSR
CMST-AI

The China global Land Surface Air Temperature (China-LSAT/C-LSAT) is a new dataset of integrated and homogenized monthly land surface air

temperature (site and gridded).

        The dataset has totally collected and integrated 14 data sources, including three global (CRUTEM4, GHCM, and BEST), three regional and eight national sources. The most substantial advance in this dataset is the improved station coverage in most countries of Asia, especially in China and its neighboring regions. C-LSAT is based on CMA-LSAT1.0 (1900-2015) (Xu et al, 2018), which was published in 2018. For some reason, maintenance and updates were stopped for a short time. In 2019, C-LSAT was upgraded to C-LSAT1.3 (1900-), and regularly maintained in Sun Yat-sen University. In 2021, the length of data was extended to January 1850 and upgraded to C-LSAT2.0 (Li et al, 2021).

        In 2019, C-LSAT1.3 integrated the ERSSTv5, which was developed by experts from National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI), to form the China global Merged Surface Temperature, version 1.0 (CMST/China-MST 1.0) (Yun et al., 2019).

        The latest version in 2021 is China-LSAT /C-LSAT2.0. It includes China-LSAT2.0/C-LSAT2.0 optional and China-LSAT2.0/C-LSAT2.0 ensemble (Sun et al, 2021). The dataset was considered "fully meeting the requirements of IPCC" in the sixth scientific assessment report (Ar6) of the Intergovernmental Panel on climate change (IPCC) released in August 2021. Now C-LSAT2.0 was included and used in the report.


       On the basis of China-LSAT/C-LSAT, we reconstructed surface air temperature data which is called the Reconstructed China-LSAT2.0/ C-LSAT2.0 ensemble with higher coverage by using the method of high- and low- frequency component reconstruction and observation constraint masking. (Sun et al.,2021) In there construction method of high- and low-frequency components, we first solved the low-frequency reconstruction which is relatively easy. Then referring to the most advanced practice, we used ERA5 reanalysis data to train the empirical orthogonal remote correlation mode (EOT) of global temperature and adopted different parameters to carry out ensemble reconstruction of global temperature data. Base on the assessment of AR6, the Reconstructed C-LSAT2.0 ensemble has completely met the criteria of the AR6 and has been included in the table 2.3 and table 2.4 of the report.

        When China-LSAT2.0/C-LSAT2.0 is reconstructed, the selection of different parameters will lead to specific uncertainties. We used different parameter settings during reconstruction, with a total of 252 ensemble members. Among them, the operational options use the intermediate values of various parameters (so-called “optimal” parameters). The dataset reconstructed with the operational optimal parameters is the basis for our daily product evaluation and scientific applications.

       Note that the current update to 2023 uses a new algorithm of observational constraints with better regional representation, and is slightly different from the previous data.


China-LSAT2.0/C-LSAT2.0:

China-LSAT2.0_tavg.nc (1850-2023)

China-LSAT2.0_tmax.nc(1900-2020)

China-LSAT2.0_tmin.nc (1900-2020)










                                                  China-LSAT2.0 temperature anomaliesrelative to 1961-1990 averagestime series


China-LSAT2.0/C-LSAT2.0 (No reconstructed):

China-LSAT2.0_tavg-Nrec.nc (1850-2023)

China-LSAT2.0_tmax-Nrec.nc(1900-2020)

China-LSAT2.0_tmin-Nrec.nc (1900-2020)


        If you are interested in the data with other parameters, you can click the China-LSAT2.0 large ensemble data.

If you want to know more about the data, you can read the following articles:

[1]Xu, W., Li, Q.*, Jones, P., Wang, X. L., Trewin, B.,Yang, S., Zhu, C., Zhai, P., Wang, J., Vincent, L., Dai, A., Gao, Y. and Ding,Y.: A new integrated and homogenized global monthly land surface air temperature dataset for the period since 1900, Climate Dynamics, 50, 2513-2536,doi: 10.1007/s00382-017-3755-1, 2018
[2]Yun, X., Huang, B., Cheng, J., Xu, W., Qiao, S. andLi, Q.*: A new merge of global surface temperature datasets since the start of the 20th century, Earth System Science Data, 11, 1629-1643, doi:10.5194/essd-11-1629-2019, 2019
[3]Li Q, Sun W, Huang B, Dong W, Wang X, Zhai P and PhilJones, Consistency of global warming trends strengthened since 1880s, ScienceBulletin, https://doi.org/10.1016/j.scib.2020.06.009, 2020
[4]Cheng J,Li Q*, Chao L, Suman M, Huang B, Jones P,2020, Development of a high-resolution and homogenized gridded land surface air temperature data: a case study over pan East Asia, Frontiers in EnvironmentalScience, DOI: 10.3389/fenvs.2020.588570
[5]Sun, W., Li, Q.*, Huang, B., Cheng, J., Song, Z., Li,H., Dong, W., Zhai, P. and Jones, P.: The Assessment of Global Surface Temperature Change from 1850s: The C-LSAT2.0 Ensemble and the CMST-Interim Datasets, Advances in Atmospheric Sciences, 38, 875-888, doi:10.1007/s00376-021-1012-3, 2021
[6]Li Q, Sun W, Yun X, Huang B, Dong W, Wang X, Zhai Pand Phil Jones, An updated evaluation of the global mean Land Surface Air Te
mperature and Surface Temperature trends based on CLSAT and CMST,ClimateDynamics, 56:635-650,DOI: 10.1007/s00382-020-05502-0, 2021

Contact:
Email:lizch9@mail2.sysu.edu.cn
The China global Merged Surface Temperature (China-MST/CMST) dataset is  the fifth global surface temperature benchmarking dataset.

       It is based on a merge of surface air temperature data from China-Land Surface Air Temperature(C-LSAT) and SST data from Extended Reconstructed Sea SurfaceTemperature version 5 (ERSSTv5) released by the National Oceanic and Atmospheric Administration/National Centers for Environmental Information(NOAA/NCEI) (Huang et al., 2017). Comparative analysis shows that the consistency of the global warming trend derived from CMST has been strengthened with other similar datasets since 1880 (Li et al., 2020). In particular, during the debated “warming hiatus” period from 1998 to 2012, the warming trend estimated from the China-MST agrees well with that calculated from data sets in which northern high latitudes are filled with satellite data, buoy observations, and reanalysis data (Li et al., 2021).

       China-MST2.0 (5°×5°) is the latest version of China MST, including three different versions: China-MST2.0-Nrec (no reconstruction), China-MST2.0-Imax, and China-MST2.0-Imin (differentiated by the sea ice extent of reconstructed Arctic surface air temperature), for professional users to choose according to their research needs. China-MST2.0-Imax is recommended for public users. Note that the current update of land air temperature to 2023 uses a new algorithm of observational constraints with better regional representation, and is slightly different from the previous data.

   

       (1) We used the land temperature to represent the temperature in the 65°N-90°N region to simulate the temperature at the maximum arctic sea ice cover in 1983 and we got China-MST2.0-Imax;

        Download: China-MST2.0-Imax.nc (1850-2023)









China-MST-Imax temperature anomalies (relative to 1961-1990 averages) time series


      (2) We used the land temperature to represent the temperature in the 80°N-90°N region to represent the temperature at the time of the minimum sea ice cover in the Arctic in 2012 and got China-MST2.0-Imin.
        Download: China-MST2.0-Imin.nc (1850-2023)

       (3) China-MST2.0 Nrec is the homogeneous grid dataset which is based on observed data. It is created by merging C-LSAT2.0 with ERSSTv5.

       Download: China-MST2.0-Nrec.nc  (1850-2023)


       The China-MST-Interim is the reconstructed global dataset which is created by merging China-LSAT2.0 which does not reconstruct Arctic air temperature with ERSSTv5 (Sun et al., 2021). China-MST-Interim has been used in IPCC AR6. Updates are currently discontinued.
       Download: China-MST-Interim.nc (1850-2020)

If you want to know more about the data, you can read the following articles:


[1]  Sun, W., Yang, Y., Chao, L., Dong, W., Huang, B., Jones, P., and Li, Q.*, 2022, Description of the China global Merged Surface Temperature version 2.0, Earth Syst. Sci. Data ., 14, 1677-1693, https://doi.org/10.5194/essd-14-1677-2022

[2] Li Z., Sun W., Liang C., Xing X., Li Q*. Arctic warming trends and their uncertainties based on surface temperature reconstruction under different sea ice extent scenarios. Adv. Clim. Change Res.2023,14: 335-346. https://doi.org/10.1016/j.accre.2023.06.003.

[3] Li Z, Li Q*, Chen T.,2023,Record breaking high temperature outlook for 2023:An assessment from CMST, Adv. Atmos. Sci., 2024, 41:369-376, doi: 10.1007/s00376-023-3200-9

[4] Xu, W., Li, Q.*, Jones, P., Wang, X. L., Trewin, B.,Yang, S., Zhu, C., Zhai, P., Wang, J., Vincent, L., Dai, A., Gao, Y. and Ding,Y.: A new integrated and homogenized global monthly land surface air temperature dataset for the period since 1900, Climate Dynamics, 50, 2513-2536,doi: 10.1007/s00382-017-3755-1, 2018

[5] Yun, X., Huang, B., Cheng, J., Xu, W., Qiao, S. andLi, Q.*: A new merge of global surface temperature datasets since the start of the 20th century, Earth System Science Data, 11, 1629-1643, doi:10.5194/essd-11-1629-2019, 2019

[6]Li Q, Sun W, Huang B, Dong W, Wang X, Zhai P and PhilJones, Consistency of global warming trends strengthened since 1880s, ScienceBulletin, https://doi.org/10.1016/j.scib.2020.06.009, 2020

[7]Cheng J,Li Q*, Chao L, Suman M, Huang B, Jones P,2020, Development of a high-resolution and homogenized gridded land surface air temperature data: a case study over pan East Asia, Frontiers in EnvironmentalScience, DOI: 10.3389/fenvs.2020.588570

[8]Sun, W., Li, Q.*, Huang, B., Cheng, J., Song, Z., Li,H., Dong, W., Zhai, P. and Jones, P.: The Assessment of Global Surface Temperature Change from 1850s: The C-LSAT2.0 Ensemble and the CMST-Interim Datasets, Advances in Atmospheric Sciences, 38, 875-888, doi:10.1007/s00376-021-1012-3, 2021

[9]Li Q, Sun W, Yun X, Huang B, Dong W, Wang X, Zhai Pand Phil Jones, An updated evaluation of the global mean Land Surface Air Temperature and Surface Temperature trends based on CLSAT and CMST,ClimateDynamics, 56:635-650,DOI: 10.1007/s00382-020-05502-0, 2021


Contact

Emaillizch9@mail2.sysu.edu.cn

        The global land gridded dataset of near-surface apparent temperature (Huang et al., 2021) is a hybrid of near-surface air temperature from C-LSAT 2.0 (Sun et al., 2021) and near-surface relative humidity and near-surface wind speed from 20CRv3 (Slivinski et al., 2021).

        Download: C-LAPT.nc

         Temporal coverage: 1850-2015.          Spatial resolution: 5°x 5°          Variable: Monthly mean near-surface apparent temperature.










C-LAPT temperature anomalies(relative to 1961-1990 averages) time series

If you want to know more about the data, you can read the following articles:

[1]Huang, J., Li, Q., and Song, Z., 2021. Historical global land surface air apparent temperature and its future changes based on CMIP6 projections. Sci. Total Environ.  151656. https://doi.org/10.1016/j.scitotenv.2021.151656.

[2]Slivinski, L.C., Compo, G.P., Sardeshmukh, P.D., Whitaker, J.S., McColl, C., Allan, R.J., Brohan, P., Yin, X., Smith, C.A., Spencer, L.J., Vose, R.S., Rohrer, M., Conroy, R.P., Schuster, D.C., Kennedy, J.J., Ashcroft, L., Brönnimann, S., Brunet, M., Camuffo, D., Cornes, R., Cram, T.A., Domínguez-Castro, F., Freeman, J.E., Gergis, J., Hawkins, E., Jones, P.D., Kubota, H., Lee, T.C., Lorrey, A.M., Luterbacher, J., Mock, C.J., Przybylak, R.K., Pudmenzky, C., Slonosky, V.C., Tinz, B., Trewin, B., Wang, X.L., Wilkinson, C., Wood, K., and Wyszyński, P., 2021. An Evaluation of the Performance of the Twentieth Century Reanalysis Version 3. J. Clim. 34 (4), 1417-1438. https://doi.org/10.1175/jcli-d-20-0505.1.

[3]Sun, W., Li, Q., Huang, B., Cheng, J., Song, Z., Li, H., Dong, W., Zhai, P., and Jones, P., 2021. The Assessment of Global Surface Temperature Change from 1850s: The C-LSAT2.0 Ensemble and the CMST-Interim Datasets. Adv. Atmos. Sci. https://doi.org/10.1007/s00376-021-1012-3.


Contact:
Email:liaolsh5@mail2.sysu.edu.cn

        C-LSAT_HR (High-Resolution China-Land Surface Air Temperature) is a high-resolution version of the global land surface air temperature dataset C-LSAT2.0. The data are divided into two parts: climatology (1961–1990 average) and anomaly. Based on C-LSAT station data and GMTED2010 dataset, the Thin Plate Spline method was used for interpolation of the climatology field. We used Adjusted Inverse Distance Weighted method to interpolate the anomaly data. By adding up the two, C-LSAT HR dataset can be obtained.

        Download: C-LSAT_HR

        Temporal coverage: 1901-2018

        Spatial resolution: 0.5°x 0.5°















Spatial distribution of trends in global annual mean land surface air temperature (relative to the 1961-1990 averages) over different periods and zonal mean trends per 5 degrees

If you want to know more about the data, you can read the following articles:

[1] Cheng J, Li Q*, Chao L, Suman M, Huang B, Jones P, 2020, Development of a high-resolution and homogenized gridded land surface air temperature data: a case study over pan East Asia, Frontiers in Environmental Science, DOI: 10.3389/fenvs.2020.588570

[2] Li Q, Sun W, Yun X, Huang B, Dong W, Wang X, Zhai P and Phil Jones, An updated evaluation of the global mean Land Surface Air Temperature and Surface Temperature trends based on CLSAT and CMST, Climate Dynamics, 56:635-650, DOI: 10.1007/s00382-020-05502-0, 2021

[3] Xu, W., Li, Q.*, Jones, P., Wang, X. L., Trewin, B., Yang, S., Zhu, C., Zhai, P., Wang, J., Vincent, L., Dai, A., Gao, Y. and Ding, Y.: A new integrated and homogenized global monthly land surface air temperature dataset for the period since 1900, Climate Dynamics, 50, 2513-2536, doi: 10.1007/s00382-017-3755-1, 2018


Contact:
Email:weish29@mail2.sysu.edu.cn


         The globally integrated and homogenized solar surface radiation dataset includes the homogenized SSR dataset (SSRIHgrid) and the AI-reconstructed SSR dataset (SSRIH20CR).

        SSRIHgrid is a grid boxes version of the homogenized global monthly SSR anomalies dataset.

         Download: SSRIHgrid.nc

         Temporal coverage: 1955-2018

         Spatial resolutio

n: 5°x

         SSRIH20CR is a full-coverage monthly land (except for Antarctica) SSR anomalies reconstructed dataset based on the 20CRv3 AI model.

         Download: SSRIH20CR.nc

         Temporal coverage: 1955-2018

         Spatial resolution: 2.5°x

If you want to know more about the data, you can read the following articles:

[1] Jiao, B., Su, Y., Li, Q., Manara, V., and Wild, M.: An integrated and homogenized global surface solar radiation dataset and its reconstruction based on  a convolutional neural network approach, Earth Syst. Sci. Data, accpted, 2023.


Contact:
Email:jiaoby3@mail2.sysu.edu.cn
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Global land (except for Antarctica) annual SSR anomaly variations (relative to 1971-2000) before/after reconstruction. The Black solid line represents the SSRIHgrid annual anomalies. The solid blue line represents the SSRIH20CR annual anomalies. The histograms represent the decadal trends of the SSRIHgrid /SSRIH20CR (unit: W/m2 per decade) and their 95% uncertainty range from 1955 to 1991, 1991-2018 and 1955-2018

ABUIABACGAAg78e0qQYozLTL9wUwzDo46jA

Regional land (except for Antarctica) annual SSR anomaly variations (relative to 1971-2000) before/after reconstruction. The Black solid line represents the SSRIHgrid annual anomalies. The solid blue line represents the SSRIH20CR annual anomalies. The green colour filling diagram represents the variation in grid box coverage (before reconstruction)

         CMST-AI is a reconstructed dataset that fills the missing monthly values and reconstructs the surface temperature dataset based on the newly developed CMST2.0 dataset from 1850 to 2020, using the partial Convolutional Neural Network approach (deep learning) combined with several atmospheric reanalysis datasets.      

         re_nrec_10_1000000.nc : validation set: the first member in the 20CR dataset; training sets: other 9 out of 10 ensemble member

         re_nrec_80_1000000.nc : validation set: the first member in the CERA dataset; training sets: other 79 out of 80 ensemble member

         Temporal coverage: 1900-2020

         Spatial resolution: 2.5°x



















Distribution of global surface temperature anomaly (with respect to the 1961-1990 climatology) at several typical times (January 1881, January 1921, January 1961, and January 2001) based on 10-member 20CR dataset















Distribution of global surface temperature anomaly (with respect to the 1961-1990 climatology) at several typical times (January 1881, January 1921, January 1961, and January 2001) based on 80-member CERA dataset


















Global annual mean temperature anomaly time series for 1850-2020 based on different datasets (with respect to the 1961-1990 climatology)
If you want to know more about the data, you can read the following articles:

[1] Sun, W., Yang, Y., Chao, L., Dong, W., Huang, B., Jones, P., and Li, Q.*, 2022, Description of the China global Merged Surface Temperature version 2.0, Earth Syst. Sci. Data ., 14, 1677-1693, https://doi.org/10.5194/essd-14-1677-2022

[2] Cao, Y., Jiao, B., Lan, X., Tan, J., Yang, Y., Sun, W., Li, Z., Luo, J.and Li, Q.*, 2022. Reconstruction of global surface temperature based on an artificial intelligence approach. Submitted to Environmental Research Letters.


Contact:
Email:liqingx5@mail.sysu.edu.cn
Personal homepage
http://atmos.sysu.edu.cn/teacher/350
E-mail
zhoux288@mail.sysu.edu.cn(Miss Zhou)
liqingx5@mail.sysu.edu.cn(Prof. Li)
Address
Sun Yat sen University Zhuhai Campus, Tangjiawan Town, Zhuhai City, Guangdong Province, 519082, P. R. China