the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Comparison of high-resolution climate reanalysis datasets for hydro-climatic impact studies
Abstract. Continuous high-quality meteorological information is needed to describe and understand extreme hydro-climatic events, such as droughts and floods. Information of highest quality relying on observations is often only available on a national level and for few meteorological variables. As an alternative, large-scale climate reanalysis datasets blending model simulations with observations are often used. However, their performance can be biased due to coarse spatial resolution, model uncertainty, and data assimilation biases. Previous studies on the performance of reanalysis datasets either focused on the global scale, on single variables, or on few aspects of the hydro-climate. Therefore, we here conduct a comprehensive spatio-temporal evaluation of different precipitation, temperature and snowfall metrics for four state-of-the-art reanalysis datasets (ERA5, ERA5-Land, CERRA-Land, and CHELSA-v2.1) over complex terrain. We consider climatologies of mean and extreme climate metrics, daily to inter-annual variability, as well as the consistency in long term trends. Further, we compare the representation of extreme events, namely the intensity and severity of the 2003 and 2018 droughts, as well as the 1999 and 2005 floods in Switzerland. The datasets generally show a satisfactory performance for most of these characteristics, exceptions being the representation of snowfall (solid precipitation) and the number of wet days in ERA5 and ERA5-Land. Our results show clear differences in the representation of precipitation among datasets and a substantial improvement of the representation of precipitation in CERRA-Land compared to the other datasets. In contrast to precipitation, temperature is more comparable among datasets, with CERRA-Land and CHELSA showing smaller biases yet a clear increase of bias with elevation. All datasets are able to identify the 2003 and 2018 drought events, however, ERA5, ERA5-Land, and CHELSA overestimate their intensity and severity, while CERRA-Land underestimates it. The 1999 and 2005 floods are overall well represented by all datasets, with CERRA-Land showing the best agreement with observations and the other datasets overestimating the spatial extent of the events. We conclude that overall, CERRA-Land is the most reliable dataset and suitable for a broad range of analyses, particularly for regions where snow processes are relevant and for applications where the representation of daily to inter-annual precipitation variability is important.
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Status: open (until 02 Dec 2024)
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RC1: 'Comment on egusphere-2024-2905', Laurent Strohmenger, 01 Nov 2024
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Please see the attached pdf file for comments.
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RC2: 'Comment on egusphere-2024-2905', Anonymous Referee #2, 04 Nov 2024
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This manuscript evaluates the performance of four high-resolution climate reanalysis datasets (ERA5, ERA5-Land, CERRA, and CHELSA v2.1) over complex terrain, focusing on catchments in Switzerland. Specifically, the precipitation and temperature from reanalysis datasets are compared with grid observations regarding mean and extreme climatology, temporal variability and trends, and extreme events. The results suggest that CERRA generally performs better than other datasets except for drought events, therefore, it seems to be the best choice for hydrological impact studies.
The manuscript is generally well written with a clear and comprehensive analysis. Selecting the most reliable dataset is important for hydrological impact studies therefore it fits well within the scope of the HESS journal. However, some parts of the manuscript could be further improved to make it more consistent before publication. Here, I list my comments below:
In the abstract, I suggest replacing the CERRA-Land with CERRA since you use both CERRA temperature and CERRA-Land precipitation. The 'drought events' should be specific as 'meteorological droughts', and 'floods' could be replaced by 'extreme precipitation events' or 'heavy precipitation events' as you used in the main text since they are both based on precipitation only.
Lines 192-196: Please clarify which way is used to calculate the results presented in the manuscript. I didn't find any comparison between these two methods in the following sections. If both of them are used in different sections to calculate different metrics, they should be mentioned in the corresponding parts.
Lines 202-208: In this part, you introduced the climate metrics considered in the study. However, I was unable to find results related to cwd, cdd, Rx2d, and R95pTot in the subsequent sections or in the supplementary material. Additionally, I am curious why the annual number of hot days was not considered, while cold days were included. Furthermore, I recommend ensuring consistency in the terminology used throughout the manuscript—specifically for the metrics listed in Table 2. For instance, I noticed both 'Rx1d' and 'rx1day' being used. Finally, it might be better to present the results in a more consistent manner. Instead of presenting different metrics across separate figures, consider retaining only a selected set of metrics and using them consistently across all the figures. For example, in Figure 3, you presented rx1day, r99ptot, and wetdays, while rx5day and drydays were omitted. In Figure 5, Rx1day and Rx5day were included but not discussed in the text. In Figure 7, results for rx1day and drydays were shown this time. I think presenting consistent metrics throughout the figures and results, could enhance the clarity and coherence of the manuscript.
Lines 256-259: Could you explain in more detail how you calculate the cumulative precipitation deficits and SPI6 here? Which reference period is used to calculate the mean precipitation for precipitation deficit? Did the SPI6 calculate based on the moving 6-month window for the whole series and then you choose the one relevant to the event? In my opinion, the SPI already indicates the cumulative precipitation deficits.
Line 264: I think it should be August 2005 here. Similar to drought, why do you use both 2-day sums and 2-day SPI? From the parts of the result, I also did not see significant differences between these two ways.
Lines 284-285: For CHELSA, I agree, however, it seems that the majority of the basins are underestimated by CERRA at low elevations. Similar problems are in Line 303, and Line 305. I suggest adding a threshold to derive the statements here, for example, the percentage of stations that are over- or underestimated compared to all the stations under a specific elevation.
Lines 294-296: I am not sure if the number of wet days will influence the annual maximum 1-day accumulated precipitation.
Lines 299-301: It is better to add reference figures from supplementary material here.
Lines 307-311: I suggest deleting this part since it is discussed in detail in the later part.
Lines 366-370: I suggest deleting this part since it is discussed in detail in the later part.
Line 381: I think it should be Figure 7b.
Line 396: I think it should be Figure 3g.
Lines 466-467, 471-472, 473-474, 477-478, 486-487: I was unable to find results related to the spatial extent for all four reanalysis datasets.
Citation: https://doi.org/10.5194/egusphere-2024-2905-RC2
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