the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 18 Oct 2024
Dheed: an ERA5 based global database of dry and hot extreme events from 1950 to 2022
Abstract. The intensification of climate extremes is one of the most immediate effects of global climate change. Heatwaves and droughts have uneven impacts on ecosystems that can be exacerbated in case of compound events. To comprehensively study these events, e.g. with local high-resolution remote sensing or in-situ data, a global catalogue of such events is essential. Here, we propose a workflow to build a database of large-scale dry and hot extreme events based on data from ERA5 reanalysis. Drought indicators are constructed based on evapotranspiration and precipitation data averaged over 30, 90 and 180 days. Extreme events are detected with the peak-over-threshold approach for the 1950–2022 period. Extremes and non-extremes are defined for daily maximum temperature at 2 m in combination with three drought indicators. In the last step, the spatiotemporal extent of the events is computed by a connected component analysis. The identified events are validated against extreme events reported in the literature.
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Status: final response (author comments only)
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RC1: 'Comment on essd-2024-396', Anonymous Referee #1, 16 Nov 2024
The manuscript essd-2024-396 presents a dataset identifying historical compound dry and hot extreme events derived from ERA5 global meteorological data. The study's focus on compound events, which are more damaging than univariate extremes, is timely and valuable for the community. However, several issues require attention to enhance the dataset's reliability and utility.
Major Concerns:
- Global Trend of Heat Extremes:
The data analysis indicates a non-significant trend in heat extreme day numbers globally from 1970 to 2022. This finding contradicts numerous studies showing that heatwaves have become more frequent and severe over time. This discrepancy should be thoroughly investigated, as it undermines the reliability of the dataset. - Data & Methodology:
a. The dataset relies exclusively on ERA5 data. The authors should include a literature review demonstrating that ERA5 is widely accepted for historical drought and heat stress analyses.
b. The use of the 1% threshold for defining "extreme" events requires justification through references to relevant literature.
c. The parameterization G=0.5Rn for ground heat flux is uncommon as this ratio is typically associated with vegetation cover. Now the impact from the surface cover is missing. This choice should be explained or supported with references. - Seasonal Detrending:
The proposed methodology lacks seasonal detrending, a standard preprocessing step in drought and heat analyses. Without removing seasonal cycles, anomalies are compared to absolute values rather than seasonal baselines. For example, warmer days in spring might qualify as heat stress even if their absolute temperatures are lower than those in summer. Similarly, seasonal cycles in PEI could influence water stress results. This methodological issue weakens the robustness of the findings and should be addressed. - Omission and Commission Errors:
The authors conducted a qualitative literature survey of extreme events captured or missed by the dataset. This evaluation is commendable but could be improved by quantitatively summarizing omission and commission errors. These statistics should also be briefly mentioned in the abstract for clarity. - Global Trend Mapping:
Figure 6 presents trend analyses across continents. A pixelwise global trend map would provide a more detailed spatial representation and is strongly recommended. - Intercomparison with Other Indices:
The dataset should be compared with drought and heat indices from other sources over a long time span to validate its reliability.
Additional Comments:
- Data Citation: ESSD requires the data URL and citation to be explicitly mentioned in the abstract.
- Figure 1: The terms "ET0" and "ETref" should be unified for consistency.
- Hourly ETref Calculation: Clarify why θ (daily mean temperature) is used instead of hourly temperature.
- Missing References: Two papers by Ima are cited but not included in the reference list.
- Figure 2: Clarify the difference between "no extreme" and "10th–90th percentile."
- Figure 3: Add a legend explaining the meaning of the additional colors.
- Table 2: Specify the units for area and volume.
- Data Accessibility: URLs for the data cubes are not accessible. Please ensure they are active and functional.
Citation: https://doi.org/10.5194/essd-2024-396-RC1 - Global Trend of Heat Extremes:
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RC2: 'Comment on essd-2024-396', Anonymous Referee #2, 18 Dec 2024
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2024-396/essd-2024-396-RC2-supplement.pdf
Data sets
Dheed : a global database of dry and hot extreme events (Version 3.0.0) Mélanie Weynants et al. https://doi.org/10.5281/zenodo.11546130
Model code and software
DeepExtremes/ExtremeEvents: v3.0.0 Mélanie Weynants et al. https://doi.org/10.5281/zenodo.13711289
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