ISIMIP3a: Errors in coastal water levels (HCC) dataset #55
issue
Matthias Büchner reported on April 4, 2024.
Two significant issues have been identified in the Hourly Coastal water levels with Counterfactual (HCC) dataset, which require immediate attention and action.
Firstly, we have found an issue with the vertical datum used in the dataset. Specifically, the datum should be aligned with the Mean Dynamic Topography (MDT) dataset provided by the E.U. Copernicus Marine Service Information (CMEMS) https://doi.org/10.48670/moi-00150. Unfortunately, due to an oversight, the current dataset does not adhere to this specification. If you used the HCC dataset in combination to another dataset and aligned them with a common
reference system, this error will affect your analysis.
Additionally, we identified an issue regarding the handling of the crustal response to present-day ice melt. This has resulted in inaccuracies within the annual signal of the final dataset. This error might influence your analysis if it includes inter-annual sea level variability. The faulty signal is below 5cm for most of the stations. In the attached figure we illustrate the magnitude of this signal on a global map.
We sincerely apologize for any inconvenience these issues may have caused. We are actively working to correct these errors and will release an updated version of the dataset as soon as possible. We appreciate your understanding and patience as we work to resolve these matters.
If you have any questions or require further information, please do not hesitate to contact us.
Firstly, we have found an issue with the vertical datum used in the dataset. Specifically, the datum should be aligned with the Mean Dynamic Topography (MDT) dataset provided by the E.U. Copernicus Marine Service Information (CMEMS) https://doi.org/10.48670/moi-00150. Unfortunately, due to an oversight, the current dataset does not adhere to this specification. If you used the HCC dataset in combination to another dataset and aligned them with a common
reference system, this error will affect your analysis.
Additionally, we identified an issue regarding the handling of the crustal response to present-day ice melt. This has resulted in inaccuracies within the annual signal of the final dataset. This error might influence your analysis if it includes inter-annual sea level variability. The faulty signal is below 5cm for most of the stations. In the attached figure we illustrate the magnitude of this signal on a global map.
We sincerely apologize for any inconvenience these issues may have caused. We are actively working to correct these errors and will release an updated version of the dataset as soon as possible. We appreciate your understanding and patience as we work to resolve these matters.
If you have any questions or require further information, please do not hesitate to contact us.
Details
Affected datasets should not be used until this issue is resolved.
Severity high
Status new
- Versions
- before 20240404
- Data product
- Input Data
- Input subcategory
- Sea level
- Simulation round
- ISIMIP3a
Affected datasets
There are 8 datasets affected by this issue. You can download a complete list of all the datasets, or files affected by this issue as JSON. Alternatively, can also download a list the files as a flat file suitable for Wget. You can use the search interface to further restrict your query.
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45.2 GB ISIMIP3a InputData climate 20231206hcc_counterclim_geocentricwaterlevel_global_hourly
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45.2 GB ISIMIP3a InputData climate 20231206hcc_counterclim_waterlevel_global_hourly
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134.0 MB ISIMIP3a InputData climate 20231206hcc_counterclim_geocentricwaterlevel_global_monthly
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134.0 MB ISIMIP3a InputData climate 20231206hcc_counterclim_waterlevel_global_monthly
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45.2 GB ISIMIP3a InputData climate 20231206hcc_obsclim_geocentricwaterlevel_global_hourly
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45.2 GB ISIMIP3a InputData climate 20231206hcc_obsclim_waterlevel_global_hourly
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134.0 MB ISIMIP3a InputData climate 20231206hcc_obsclim_geocentricwaterlevel_global_monthly
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134.0 MB ISIMIP3a InputData climate 20231206hcc_obsclim_waterlevel_global_monthly