| Climate Change:Observation and Modeling
China-LSAT
China-MST
C-LAPT
C-LSAR_HR

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.


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

CLSAT2.0_tavg.nc

CLSAT2.0_tmax.nc

CLSAT2.0_tmin.nc









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

        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(Table1) during reconstruction, with a total of 756 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.


Reconstructed-CLSAT2.0:

reconstructed_CLSAT2.0_tavg.nc

reconstructed_CLSAT2.0_tmax.nc

reconstructed_CLSAT2.0_tmin.nc








                                         Reconstructed_CLSAT2.0 temperature anomaliesrelative to 1961-1990 averagestime series

        If you are interested in the data with other parameters, you can download the compression files. Because the data is too large, we split the data intoseveral parts to upload. You’d better download them all and decompress them together.


Reconstructed_CLSAT2.0_tavg_ice is the average land surface temperature with maximun ice surface area added.

reconstructed_CLSAT2.0_tavg_ice.part01.rar

reconstructed_CLSAT2.0_tavg_ice.part02.rar

reconstructed_CLSAT2.0_tavg_ice.part03.rar

reconstructed_CLSAT2.0_tavg_ice.part04.rar

reconstructed_CLSAT2.0_tavg_ice.part05.rar

reconstructed_CLSAT2.0_tavg_ice.part06.rar

reconstructed_CLSAT2.0_tavg_ice.part07.rar

reconstructed_CLSAT2.0_tavg_ice.part08.rar

reconstructed_CLSAT2.0_tavg_ice.part09.rar

reconstructed_CLSAT2.0_tavg_ice.part10.rar

reconstructed_CLSAT2.0_tavg_ice.part11.rar

reconstructed_CLSAT2.0_tavg_ice.part12.rar

reconstructed_CLSAT2.0_tavg_ice.part13.rar

reconstructed_CLSAT2.0_tavg_ice.part14.rar


Reconstructed_CLSAT2.0_tavg is the average land surface temperature without ice surface area.

reconstructed_CLSAT2.0_tavg.part01.rar

reconstructed_CLSAT2.0_tavg.part02.rar

reconstructed_CLSAT2.0_tavg.part03.rar


And the reconstructed_CLSAT2.0_tmax and reconstructed_CLSAT2.0_tmin are the maximum temperature and minimum temperature respectively.

reconstructed_CLSAT2.0_tmax.part01.rar

reconstructed_CLSAT2.0_tmax.part02.rar

reconstructed_CLSAT2.0_tmax.part03.rar

reconstructed_CLSAT2.0_tmin.part01.rar

reconstructed_CLSAT2.0_tmin.part02.rar

reconstructed_CLSAT2.0_tmin.part03.rar


        When you decompress the dataset, you'll find :

a)   There are seven folders and they represent the data from seven kinds of EOTs training periods and spatial scales.

b)   The NetCDF files in the folders are named:

masked_reSAT_αyr_βminyrfilter_γcrit.nc

c)   If you read the NetCDF files, you will find there are three sets of data in one NetCDF file: masked_reSAT, masked_reSAT2 and masked_reSAT3.

The meaning of them are as follow


Parameter

Operational   options

Alternative   options

masked_reSAT

masked_reSAT2

masked_reSAT3

Minimum number of months

annual average


2 months


1,   2, 3 months

α

LF filter periods

15 years

10, 15, 20 years

β

Min number of years for LF filter

2 years

1, 2, 3 years

γ

EOTs acceptance criterion

0.2

0.10, 0.15, 0.20, 0.25

1979-2018_modes_option1

1979-2018_modes_option2

1979-2018_modes_operational

1979-2008_modes_option

1989-2018_modes_option

evenyear_modes_option

oddyear_modes_option



EOTs training periods and

spatial scales



1979-2018,   

Lx=4000, 3000, 2500,

Ly=2500

1979-2018: Lx=3000,2000,1500, Ly=1500;

1979-2018: Lx=5000,4000,3500, Ly=3500;

1979-2018: Lx=4000,3000,2500, Ly=2500;

1979-2008: Lx=4000,3000,2500, Ly=2500;

1989-2018: Lx=4000,3000,2500, Ly=2500;

even year: Lx=4000,3000,2500, Ly=2500;

odd year: Lx=4000,3000,2500, Ly=2500

Table1   Parameter settings used for reconstruction scenarios and the operational option

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:liqingx5@mail.sysu.edu.cn

       We also constructed the Polar temperatures by using a combination of epitaxial difference and high- and low- frequency reconstruction to assess the magnitude and uncertainty of warming at high latitudes and global area, as well as the effect of polar amplification on global warming. After that, the accuracy of the data has been further improved, and the datasets will make better contributions to further accurate estimates and assessments of uncertainty about the magnitude of global warming.

       In China-MST2.0, we designed two experiments:

      (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;








China-MST2.0-Imax temperature anomaliesrelative to 1961-1990 averagestime series

       The China-MST-Interim is the reconstructed global dataset which is created by merging the Reconstructed China-LSAT2.0 ensemble with ERSSTv5. However, when there is sea ice cover in the Arctic region, the ERRSTv5 sets the sea ice covered area as the default value, which may lead to an underestimation of global warming trends according to the polar amplification (IPCC,2021). In order to solve this problem and improve the coverage of China-MST-Interim in the Arctic region, we improved the temperature calculation method of the Arctic region, using the air temperature of the ice surface (considering the physical properties of ice and land are similar, we regarded the ice area as the landarea) to represent the surface temperature of the Arctic region. China-MST-Interim has been used in IPCC AR6.






                                                                       
                                                 China-MST-Interim temperature anomalies(relative to 1961-1990 averagestime 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.







China-MST2.0-Imin temperature anomaliesrelative to 1961-1990 averagestime series

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 Temperature and Surface Temperature trends based on CLSAT and CMST,ClimateDynamics, 56:635-650,DOI: 10.1007/s00382-020-05502-0, 2021

[7]Sun, W., Yang, Y., Chao, L., Dong, W., Huang, B., Jones, P., and Li, Q.: Description of the China global Merged Surface Temperature version 2.0, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2021-447, in review, 2022.


Contact

Emailliqingx5@mail.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).

       Due to the low resolution of the land surface temperature data (5°×5°), the Thin Plate Spline method (Hutchinson, 1991) has been used in the early stage to interpolate the data to a resolution of 0.5°×0.5°. The comparative evaluation shows that the interpolation reflects well trend characteristics of temperature change at a higher resolution (Cheng et al., 2020). China-MST 2.0 is the latest version of China-MST (Sun et al., 2022).


       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.








China-MST-Nrec temperature anomaliesrelative to 1961-1990 averagestime series

        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

         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:liqingx5@mail.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:liqingx5@mail.sysu.edu.cn