the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Mar 2025
Updates of C-LSAT 2.1 and the development of high-resolution LSAT and DTR datasets
Abstract. High-resolution climate datasets are of critical importance for the comprehension of spatial and temporal variations in climate and hydrology. However, their development is significantly influenced by the availability, density, and quality of observational data. Using the China global Land Surface Air Temperature 2.0 (C-LSAT 2.0) station data as a foundation, we collected and integrated nearly 3000 additional station observations and conducted the quality control and homogenization processing to complete the update of the C-LSAT 2.1 dataset. The coverage of Tavg, Tmax, and Tmin in the C-LSAT 2.1 dataset has been significantly enhanced, further enhancing the representativeness of global land diurnal temperature range (DTR) data with greater spatial heterogeneity. Compared to C-LSAT 2.0, C-LSAT 2.1 shows consistent overall trends, except for a slight increase in LSAT anomaly observed in the Southern Hemisphere after 2010. Furthermore, we employ a "Thin Plate Spline (climatology) + Adjust Inverse Distance Weighted (anomaly fields)" technical framework to develop a high-resolution (0.5° × 0.5°) LSAT (C-LSAT HRv1) and DTR (C-LDTR HRv1) dataset from January 1901 to December 2023. Except for some differences existing during the period of 1901–1950 due to the limited number of observational stations, the C-LSAT HRv1 and C-LDTR HRv1 datasets effectively capture the corresponding variation patterns at both global and regional scales for the other periods. The C-LSAT 2.1 dataset can be downloaded from https://doi.org/10.6084/m9.figshare.28255394.v1 (Wei et al., 2025a), while the C-LSAT HRv1 and C-LDTR HRv1 datasets are available at https://doi.org/10.6084/m9.figshare.28255505.v1 (Wei et al., 2025c) and https://doi.org/10.6084/m9.figshare.28255568.v1 (Wei et al., 2025b), respectively. These can also be accessed at http://www.gwpu.net (last accessed: December 2024).
Competing interests: At least one of the (co-)authors is a member of the editorial board of Earth System Science Data.The authors also have no other competing interests to declare.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.- Preprint
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RC1: 'Comment on essd-2025-70', Zengyun Hu, 06 May 2025
This manuscript presents an important update and improvement based on a widely recognized dataset. I commend the authors for their excellent work. Building on the C-LSAT 2.0 dataset, the authors have integrated nearly 3,000 additional global observational stations to develop the C-LSAT 2.1, along with high-resolution (0.5°×0.5°) LSAT and DTR datasets. The new version significantly enhances data coverage, refines processing techniques, and provides valuable high-resolution products. The manuscript is comprehensive, logically structured, and the conclusions are sound. It fits well within the scope of Earth System Science Data (ESSD). However, the following minor revisions are recommended prior to publication:
- Please supplement the temporal coverage of observation stations at both annual and monthly scales.
- In section 4.1.2, the authors used the MAE, RMSE to evaluate the different datasets. However, sometimes, MAE and RMSE only provide one aspect performances of different datasets. DISO as a comprehensive performance evaluating index can illustrate the overall performance for all the datasets. Therefore, the DISO should be added in this study.
- If feasible, consider adding additional validation in representative regions, particularly in high-altitude or complex terrain areas.
- All variables in equations should be consistently italicized.
- Insert appropriate spaces between numbers, symbols, and units (e.g., in line 230).
- Add a space before parentheses in Table 1 for clarity.
- Please explain the observed increases in MAE and RMSE since 1990 as shown in Figure 7.
- The abnormal warming pattern in northern North America depicted in Figure 12(d) requires verification.
- The colorbars in Figures 11 and 17 hinder regional comparisons; consider revising them for clarity.
- There are citation errors that need correction (e.g., in line 776).
Overall, with these minor adjustments, the manuscript will make a valuable contribution to the climate data community.
Citation: https://doi.org/10.5194/essd-2025-70-RC1 -
AC1: 'Reply on RC1', Sihao Wei, 12 Jul 2025
We thank the reviewers for their efforts in reviewing our manuscript. Please find our author responses to the review comments in the attached document. We have revised our manuscript in response to these comments, with the improved manuscript provided separately.
-
RC2: 'Comment on essd-2025-70', Anonymous Referee #2, 29 May 2025
General comments
The manuscript describes the update of a valuable dataset of Land Surface Temperature through the inclusion of a significant number of additional stations, and the creation using the TSP method of high-resolution datasets of land surface temperature and diurnal temperature range. The manuscript is clear and scientifically sound. I have two main concerns that should be addressed before publication:
- it’s not clear whether the trend uncertainties (e.g Line 230) have been computed taking the serial correlation of the time series into account, as the time series (e.g. Figure 2) seem to display a significant autocorrelation/serial dependence, which biases downwards the uncertainties if not accounted for. This is something that should be clarified.
- the quality of the figures should be improved, to make them easier to understand, particularly in case of colour vision deficiencies.
Minor comments and suggestions are described below.
Specific comments:
Line 138: maybe give some more details on the filtering procedure
Figure 1: The colours in Figure 1 are very similar for some of the datasets and very difficult to distinguish – particularly for I suggest using more contrasting colours and different types of line (e.g. dashed, dotted) to make the figure clearer.
Line 166: maybe use : before “Any anomaly…” to make more obvious that the sentence is referring to the quality control process.
The same notation could be used to denote standard deviation, STD is used in line 177, and sigma in line 184
Table 1: the caption and text should be improved, to make clear what is exactly shown in the table – maybe instead of results of QC something like number of data values excluded during the QC procedure? The “unit:station month”could also be made clearer.
Line 221: enhance (instead of “ which significantly enhancing”)
Figure 2: same as for Figure 1, it’s hard to distinguish the different colours / lines
Line 230: please indicate if autocorrelation was taken into account in the estimation of trend uncertainties
Table: Ele was not defined for elevation in the text
Figures 6, 7: the figure would be clearer with a slightly larger size of the text in the axis and legend; using different line styles (dashed, etc…) would make the figure easier to perceive in case of colour vision deficiencies.
Figure 9: the colour scale should be improved, ensuring it is centred on zero and has improved perceptual properties
Figure 10: same as previous figures, using different line styles (dashed, etc…) would make the figure easier to perceive in case of colour vision deficiencies
Figures 11, 12: ideally, the diverging colormap scales should be symmetrical
Table 5: is the * denoting statistical significance? Was the uncertainty computed assuming any form of linear dependence or just white (uncorrelated) errors??
Figure 15: the colour scale should be improved – differences in colour between 20-25 ºC and 25-30ºC are not distinguishable
Citation: https://doi.org/10.5194/essd-2025-70-RC2 -
AC2: 'Reply on RC2', Sihao Wei, 12 Jul 2025
We thank the reviewers for their efforts in reviewing our manuscript. Please find our author responses to the review comments in the attached document. We have revised our manuscript in response to these comments, with the improved manuscript provided separately.
-
AC2: 'Reply on RC2', Sihao Wei, 12 Jul 2025
Status: closed
-
RC1: 'Comment on essd-2025-70', Zengyun Hu, 06 May 2025
This manuscript presents an important update and improvement based on a widely recognized dataset. I commend the authors for their excellent work. Building on the C-LSAT 2.0 dataset, the authors have integrated nearly 3,000 additional global observational stations to develop the C-LSAT 2.1, along with high-resolution (0.5°×0.5°) LSAT and DTR datasets. The new version significantly enhances data coverage, refines processing techniques, and provides valuable high-resolution products. The manuscript is comprehensive, logically structured, and the conclusions are sound. It fits well within the scope of Earth System Science Data (ESSD). However, the following minor revisions are recommended prior to publication:
- Please supplement the temporal coverage of observation stations at both annual and monthly scales.
- In section 4.1.2, the authors used the MAE, RMSE to evaluate the different datasets. However, sometimes, MAE and RMSE only provide one aspect performances of different datasets. DISO as a comprehensive performance evaluating index can illustrate the overall performance for all the datasets. Therefore, the DISO should be added in this study.
- If feasible, consider adding additional validation in representative regions, particularly in high-altitude or complex terrain areas.
- All variables in equations should be consistently italicized.
- Insert appropriate spaces between numbers, symbols, and units (e.g., in line 230).
- Add a space before parentheses in Table 1 for clarity.
- Please explain the observed increases in MAE and RMSE since 1990 as shown in Figure 7.
- The abnormal warming pattern in northern North America depicted in Figure 12(d) requires verification.
- The colorbars in Figures 11 and 17 hinder regional comparisons; consider revising them for clarity.
- There are citation errors that need correction (e.g., in line 776).
Overall, with these minor adjustments, the manuscript will make a valuable contribution to the climate data community.
Citation: https://doi.org/10.5194/essd-2025-70-RC1 -
AC1: 'Reply on RC1', Sihao Wei, 12 Jul 2025
We thank the reviewers for their efforts in reviewing our manuscript. Please find our author responses to the review comments in the attached document. We have revised our manuscript in response to these comments, with the improved manuscript provided separately.
-
RC2: 'Comment on essd-2025-70', Anonymous Referee #2, 29 May 2025
General comments
The manuscript describes the update of a valuable dataset of Land Surface Temperature through the inclusion of a significant number of additional stations, and the creation using the TSP method of high-resolution datasets of land surface temperature and diurnal temperature range. The manuscript is clear and scientifically sound. I have two main concerns that should be addressed before publication:
- it’s not clear whether the trend uncertainties (e.g Line 230) have been computed taking the serial correlation of the time series into account, as the time series (e.g. Figure 2) seem to display a significant autocorrelation/serial dependence, which biases downwards the uncertainties if not accounted for. This is something that should be clarified.
- the quality of the figures should be improved, to make them easier to understand, particularly in case of colour vision deficiencies.
Minor comments and suggestions are described below.
Specific comments:
Line 138: maybe give some more details on the filtering procedure
Figure 1: The colours in Figure 1 are very similar for some of the datasets and very difficult to distinguish – particularly for I suggest using more contrasting colours and different types of line (e.g. dashed, dotted) to make the figure clearer.
Line 166: maybe use : before “Any anomaly…” to make more obvious that the sentence is referring to the quality control process.
The same notation could be used to denote standard deviation, STD is used in line 177, and sigma in line 184
Table 1: the caption and text should be improved, to make clear what is exactly shown in the table – maybe instead of results of QC something like number of data values excluded during the QC procedure? The “unit:station month”could also be made clearer.
Line 221: enhance (instead of “ which significantly enhancing”)
Figure 2: same as for Figure 1, it’s hard to distinguish the different colours / lines
Line 230: please indicate if autocorrelation was taken into account in the estimation of trend uncertainties
Table: Ele was not defined for elevation in the text
Figures 6, 7: the figure would be clearer with a slightly larger size of the text in the axis and legend; using different line styles (dashed, etc…) would make the figure easier to perceive in case of colour vision deficiencies.
Figure 9: the colour scale should be improved, ensuring it is centred on zero and has improved perceptual properties
Figure 10: same as previous figures, using different line styles (dashed, etc…) would make the figure easier to perceive in case of colour vision deficiencies
Figures 11, 12: ideally, the diverging colormap scales should be symmetrical
Table 5: is the * denoting statistical significance? Was the uncertainty computed assuming any form of linear dependence or just white (uncorrelated) errors??
Figure 15: the colour scale should be improved – differences in colour between 20-25 ºC and 25-30ºC are not distinguishable
Citation: https://doi.org/10.5194/essd-2025-70-RC2 -
AC2: 'Reply on RC2', Sihao Wei, 12 Jul 2025
We thank the reviewers for their efforts in reviewing our manuscript. Please find our author responses to the review comments in the attached document. We have revised our manuscript in response to these comments, with the improved manuscript provided separately.
-
AC2: 'Reply on RC2', Sihao Wei, 12 Jul 2025
Data sets
China global Land Surface Air Temperature 2.1 (C-LSAT 2.1) Sihao Wei, Qingxiang Li, Qiya Xu, Zicheng Li, Hanyu Zhang, and Jiaxue Lin https://doi.org/10.6084/m9.figshare.28255394.v1
High-Resolution China global Land Diurnal Temperature Range version 1 (C-LDTR HRv1) Sihao Wei, Qingxiang Li, Qiya Xu, Zicheng Li, Hanyu Zhang, and Jiaxue Lin https://doi.org/10.6084/m9.figshare.28255568.v1
High-Resolution China global Land Surface Air Temperature version 1 (C-LSAT HRv1) Sihao Wei, Qingxiang Li, Qiya Xu, Zicheng Li, Hanyu Zhang, and Jiaxue Lin https://doi.org/10.6084/m9.figshare.28255505.v1
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