the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
CHELSA-W5E5: Daily 1 km meteorological forcing data for climate impact studies
Dirk Nikolaus Karger
Stefan Lange
Chantal Hari
Christopher P. O. Reyer
Olaf Conrad
Niklaus E. Zimmermann
Katja Frieler
Abstract. Current changes in the world’s climate increasingly impact a wide variety of sectors globally, from agricul-ture, ecosystems, to water and energy supply or human health. Many impacts of climate on these sectors hap-pen at high spatio-temporal resolutions that are not covered by current global climate datasets. Here we pre-sent Climatologies at high resolution for the Earth’s land surface areas - WFDE5 over land merged with ERA5 over the ocean data (CHELSA-W5E5, https://doi.org/10.48364/ISIMIP.836809.3, Karger et al., 2022): a cli-mate forcing dataset at daily temporal resolution and 30 arcsec spatial resolution for air-temperatures, precipi-tation rates, and downwelling shortwave solar radiation. This dataset is a spatially downscaled version of the 0.5° W5E5 dataset using the CHELSA V2 topographic downscaling algorithm. We show that the downscaling generally increases the accuracy of climate data by decreasing the bias, and increasing the correlation with measurements from meteorological stations. Bias reductions are largest in topographically complex terrain. Limitations arise for minimum near surface air temperatures in regions that are prone to cold air pooling, or at the upper extreme end of surface downwelling shortwave radiation. We further show that our topographically downscaled climate data compare well with the results of dynamical downscaling using the regional climate model Weather Research and Forecasting Model (WRF), as time series from both sources are similarly well correlated to station observations. This is remarkable given the lower computational cost of the CHELSA V2 algorithm compared to WRF and similar models. Overall, we conclude that the downscaling can provide high-er resolution climate data with increased accuracy. Hence, the dataset will be of value for a wide range of climate change impact studies both at global level but also as for applications that cover more than one region and benefit from using a consistent dataset across these regions.
Dirk Nikolaus Karger et al.
Status: closed
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CC1: 'Comment on essd-2022-367', ali jaan, 02 Dec 2022
Publisher’s note: the content of this comment was removed on 12 December 2022 since the comment function was misused for promotional purposes.
Citation: https://doi.org/10.5194/essd-2022-367-CC1 -
AC3: 'Reply on CC1', Dirk N. Karger, 17 Feb 2023
no comment
Citation: https://doi.org/10.5194/essd-2022-367-AC3
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AC3: 'Reply on CC1', Dirk N. Karger, 17 Feb 2023
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RC1: 'Comment on essd-2022-367', Anonymous Referee #1, 08 Dec 2022
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AC1: 'Reply on RC1', Dirk N. Karger, 17 Feb 2023
This manuscript published the daily 1 km (30 arcsec) meteorological forcing data set. It obviously improves the resolution of existing data products. It is very meaningful for climate impact studies and lays a foundation for the related topics. However, some other problems in the manuscript are still concerned in the following:
RESPONSE: Thank you for taking time to review the manuscript. We have taken your comments into account and will adjust the manuscript accordingly.
- In the results (Table 5), the proposed CHELSA-W5E5 was only compared with WRF. Could the authors compare their data sets with more data sets for a better validation?
RESPONSE: The comparison here is not really a validation in the strict sense, as we do compare a model with a model. We used WRF mainly because of its high spatio-temporal (daily close to 1km) resolution in this case, and there are only a very limited set of data available with at these resolutions. We also performed a quite extensive comparison to other datasets in recent publications that use parts of the algorithm as well (https://doi.org/10.5194/essd-14-5573-2022 , https://doi.org/10.1038/s41597-021-01084-6 ), so we would like to refrain from repeating this exercise if possible.
- The correlation with observations at meteorological stations is evaluated in North America. How are other regions?
RESPONSE: The initial idea of only using North America was that the quality of the stations in this region is high compared to other regions. We however see the point of that it might be interesting to also show other regions with the remark that the error in the stations might be different across the regions.
We therefore opted for the following approach:
- We keep the focus on North America for the already existing figures due to the reliable observations and dense station network in these regions.
- We add a figure showing the temporal performance of the dataset (see also the comment of reviewer 2).
- We add a global overview of the downscaling performance.
- The organization of this manuscript should be added to the end of the introduction.
RESPONSE: Will be changed as suggested:
- A flow chart of the data set production is suggested to be shown. It is very important for the coming readers.
RESPONSE: We will add a flowchart highlighting the different steps of the algorithm:
- A section of “conclusions” is suggested
RESPONSE: Will be changed as suggested:
Citation: https://doi.org/10.5194/essd-2022-367-AC1
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AC1: 'Reply on RC1', Dirk N. Karger, 17 Feb 2023
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RC2: 'Comment on essd-2022-367', Anonymous Referee #2, 24 Dec 2022
This paper developed a new long-term daily 1 km meteorological forcing dataset for air-temperature, precipitant and shortwave solar radiation, using the CHELSA topographic downscaling algorithm. This work is useful for climate impact studies. However, there are several things that need to be addressed before the paper can be accepted. The authors should go over the comments that I listed below and carefully address them.
- As shown in Table 2 and Figure 2, the performance of the CHELSA-W5E5 generally outperform the W5E5 for air temperature, precipitation, and downward shortwave radiation. Is the performance of the CHELSA-W5E5 varied with different regions of the world? I suggest more description and justification of the spatial-temporal accuracy of this dataset.
- In Figure 3, for example in the sub-graph “Absolute Bias Reduction”, it is hardly to identify the negative and positive values in the eastern North American. The color bar should be changes.
- As shown in Figure 3 and 4, the obvious improvement of the CHELSA-W5E5 is observed in the western North American with complex terrain. For the eastern North American, does the CHELSA-W5E5 show the worse performance compared to W5E5? I suggest that the authors also give this information.
Minor comments
- Page 14 line 15 “Shortwave downwelling radiation” can be changed to “downwelling shortwave solar radiation”.
- Page 19 line 14 “Then” should be changed to “than”.
- Page 20 Line 5 The color represented WRF data should be added in the title of Figure 5.
Citation: https://doi.org/10.5194/essd-2022-367-RC2 -
AC2: 'Reply on RC2', Dirk N. Karger, 17 Feb 2023
This paper developed a new long-term daily 1 km meteorological forcing dataset for air-temperature, precipitant and shortwave solar radiation, using the CHELSA topographic downscaling algorithm. This work is useful for climate impact studies. However, there are several things that need to be addressed before the paper can be accepted. The authors should go over the comments that I listed below and carefully address them.
RESPONSE: Thank you for taking time to review the manuscript. We have taken your comments into account and adjusted the manuscript accordingly.
1. As shown in Table 2 and Figure 2, the performance of the CHELSA-W5E5 generally outperform the W5E5 for air temperature, precipitation, and downward shortwave radiation. Is the performance of the CHELSA-W5E5 varied with different regions of the world? I suggest more description and justification of the spatial-temporal accuracy of this dataset.
RESPONSE: The initial idea of only using North America was that the quality of the stations in this region is high compared to other regions. We however see the point of that it might be interesting to also show other regions with the remark that the error in the stations might be different across the regions.
We therefore opted for the following approach:
- We keep the focus on North America for the already existing figures due to the reliable observations and dense station network in these regions.
- We add a figure showing the temporal performance of the dataset (see also the comment of reviewer 2).
- We add a global overview of the downscaling performance.
2. In Figure 3, for example in the sub-graph “Absolute Bias Reduction”, it is hardly to identify the negative and positive values in the eastern North American. The color bar should be changes.
RESPONSE: Will be see to change this.
3. As shown in Figure 3 and 4, the obvious improvement of the CHELSA-W5E5 is observed in the western North American with complex terrain. For the eastern North American, does the CHELSA-W5E5 show the worse performance compared to W5E5? I suggest that the authors also give this information.
RESPONSE: We will add this in the discussion
Minor comments
- Page 14 line 15 “Shortwave downwelling radiation” can be changed to “downwelling shortwave solar radiation”.
- Page 19 line 14 “Then” should be changed to “than”.
- Page 20 Line 5 The color represented WRF data should be added in the title of Figure 5.
RESPONSE: Will be changed as suggested:
Citation: https://doi.org/10.5194/essd-2022-367-AC2
Status: closed
-
CC1: 'Comment on essd-2022-367', ali jaan, 02 Dec 2022
Publisher’s note: the content of this comment was removed on 12 December 2022 since the comment function was misused for promotional purposes.
Citation: https://doi.org/10.5194/essd-2022-367-CC1 -
AC3: 'Reply on CC1', Dirk N. Karger, 17 Feb 2023
no comment
Citation: https://doi.org/10.5194/essd-2022-367-AC3
-
AC3: 'Reply on CC1', Dirk N. Karger, 17 Feb 2023
-
RC1: 'Comment on essd-2022-367', Anonymous Referee #1, 08 Dec 2022
-
AC1: 'Reply on RC1', Dirk N. Karger, 17 Feb 2023
This manuscript published the daily 1 km (30 arcsec) meteorological forcing data set. It obviously improves the resolution of existing data products. It is very meaningful for climate impact studies and lays a foundation for the related topics. However, some other problems in the manuscript are still concerned in the following:
RESPONSE: Thank you for taking time to review the manuscript. We have taken your comments into account and will adjust the manuscript accordingly.
- In the results (Table 5), the proposed CHELSA-W5E5 was only compared with WRF. Could the authors compare their data sets with more data sets for a better validation?
RESPONSE: The comparison here is not really a validation in the strict sense, as we do compare a model with a model. We used WRF mainly because of its high spatio-temporal (daily close to 1km) resolution in this case, and there are only a very limited set of data available with at these resolutions. We also performed a quite extensive comparison to other datasets in recent publications that use parts of the algorithm as well (https://doi.org/10.5194/essd-14-5573-2022 , https://doi.org/10.1038/s41597-021-01084-6 ), so we would like to refrain from repeating this exercise if possible.
- The correlation with observations at meteorological stations is evaluated in North America. How are other regions?
RESPONSE: The initial idea of only using North America was that the quality of the stations in this region is high compared to other regions. We however see the point of that it might be interesting to also show other regions with the remark that the error in the stations might be different across the regions.
We therefore opted for the following approach:
- We keep the focus on North America for the already existing figures due to the reliable observations and dense station network in these regions.
- We add a figure showing the temporal performance of the dataset (see also the comment of reviewer 2).
- We add a global overview of the downscaling performance.
- The organization of this manuscript should be added to the end of the introduction.
RESPONSE: Will be changed as suggested:
- A flow chart of the data set production is suggested to be shown. It is very important for the coming readers.
RESPONSE: We will add a flowchart highlighting the different steps of the algorithm:
- A section of “conclusions” is suggested
RESPONSE: Will be changed as suggested:
Citation: https://doi.org/10.5194/essd-2022-367-AC1
-
AC1: 'Reply on RC1', Dirk N. Karger, 17 Feb 2023
-
RC2: 'Comment on essd-2022-367', Anonymous Referee #2, 24 Dec 2022
This paper developed a new long-term daily 1 km meteorological forcing dataset for air-temperature, precipitant and shortwave solar radiation, using the CHELSA topographic downscaling algorithm. This work is useful for climate impact studies. However, there are several things that need to be addressed before the paper can be accepted. The authors should go over the comments that I listed below and carefully address them.
- As shown in Table 2 and Figure 2, the performance of the CHELSA-W5E5 generally outperform the W5E5 for air temperature, precipitation, and downward shortwave radiation. Is the performance of the CHELSA-W5E5 varied with different regions of the world? I suggest more description and justification of the spatial-temporal accuracy of this dataset.
- In Figure 3, for example in the sub-graph “Absolute Bias Reduction”, it is hardly to identify the negative and positive values in the eastern North American. The color bar should be changes.
- As shown in Figure 3 and 4, the obvious improvement of the CHELSA-W5E5 is observed in the western North American with complex terrain. For the eastern North American, does the CHELSA-W5E5 show the worse performance compared to W5E5? I suggest that the authors also give this information.
Minor comments
- Page 14 line 15 “Shortwave downwelling radiation” can be changed to “downwelling shortwave solar radiation”.
- Page 19 line 14 “Then” should be changed to “than”.
- Page 20 Line 5 The color represented WRF data should be added in the title of Figure 5.
Citation: https://doi.org/10.5194/essd-2022-367-RC2 -
AC2: 'Reply on RC2', Dirk N. Karger, 17 Feb 2023
This paper developed a new long-term daily 1 km meteorological forcing dataset for air-temperature, precipitant and shortwave solar radiation, using the CHELSA topographic downscaling algorithm. This work is useful for climate impact studies. However, there are several things that need to be addressed before the paper can be accepted. The authors should go over the comments that I listed below and carefully address them.
RESPONSE: Thank you for taking time to review the manuscript. We have taken your comments into account and adjusted the manuscript accordingly.
1. As shown in Table 2 and Figure 2, the performance of the CHELSA-W5E5 generally outperform the W5E5 for air temperature, precipitation, and downward shortwave radiation. Is the performance of the CHELSA-W5E5 varied with different regions of the world? I suggest more description and justification of the spatial-temporal accuracy of this dataset.
RESPONSE: The initial idea of only using North America was that the quality of the stations in this region is high compared to other regions. We however see the point of that it might be interesting to also show other regions with the remark that the error in the stations might be different across the regions.
We therefore opted for the following approach:
- We keep the focus on North America for the already existing figures due to the reliable observations and dense station network in these regions.
- We add a figure showing the temporal performance of the dataset (see also the comment of reviewer 2).
- We add a global overview of the downscaling performance.
2. In Figure 3, for example in the sub-graph “Absolute Bias Reduction”, it is hardly to identify the negative and positive values in the eastern North American. The color bar should be changes.
RESPONSE: Will be see to change this.
3. As shown in Figure 3 and 4, the obvious improvement of the CHELSA-W5E5 is observed in the western North American with complex terrain. For the eastern North American, does the CHELSA-W5E5 show the worse performance compared to W5E5? I suggest that the authors also give this information.
RESPONSE: We will add this in the discussion
Minor comments
- Page 14 line 15 “Shortwave downwelling radiation” can be changed to “downwelling shortwave solar radiation”.
- Page 19 line 14 “Then” should be changed to “than”.
- Page 20 Line 5 The color represented WRF data should be added in the title of Figure 5.
RESPONSE: Will be changed as suggested:
Citation: https://doi.org/10.5194/essd-2022-367-AC2
Dirk Nikolaus Karger et al.
Data sets
CHELSA-W5E5 v1.0: W5E5 v1.0 downscaled with CHELSA v2.0 Dirk N. Karger, Stefan Lange, Chantal Hari, Christopher P. O. Reyer, Niklaus E. Zimmermann https://doi.org/10.48364/ISIMIP.836809.3
Dirk Nikolaus Karger et al.
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