the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
HighResClimNevada: a high-resolution climatological dataset for a high-altitude region in Southern Spain (Sierra Nevada)
Abstract. High spatiotemporal climate datasets are essential to assess the impacts of climate change in high mountains, where the climate is highly variable. However, these regions are characterized by a lack of climatic information, and if any, it is usually short, sparse, or incomplete. This work presents a new series of very high-resolution (1 km) gridded climate datasets for Sierra Nevada (SN), a mountain range classified as a double climate-change hotspot, as it is a semi-arid mountain range in the Mediterranean area that is particularly vulnerable to climate change. The database, called HighResClimNevada, consists of a set of climate data derived from a climate simulation using the Weather Research and Forecasting (WRF) model for the period from 1991 to 2022 and forced with the ERA5 reanalysis. HighResClimNevada provides hourly/daily primary climate variables (i.e., near-surface temperature, precipitation, near-surface relative humidity, surface pressure, surface net radiation, and wind speed), but also bioclimatic variables, extremes indices from the Expert Team of Climate Change Detection and Indices (ETCCDI), and precipitation-hour indicators, which were postprocessed using aggregated temperature and precipitation values from primary climate variables. To evaluate the database performance, HighResClimNevada temperature and precipitation values were compared with reference datasets from different sources. In general, HighResClimNevada captures reasonably well the spatiotemporal variability of raw temperature but also bioclimatic variables and extreme indices in SN. It displays comparable behavior to other climatic products but with a greater level of detail due to its higher spatial resolution. For precipitation, variable, more uncertain and difficult to characterize, HighResClimNevada exhibits a higher amount of precipitation when compared to station-based, coarse satellite-based, and reanalysis-based products. However, these latter present problems in characterizing precipitation in high mountain regions due to the scarcity of data in areas with high spatiotemporal variability, such as SN. The precipitation from HighResClimNevada is comparable to other climatic products like CHIRPS or CERRA-Land, which captures better the spatiotemporal variability in this region. These findings, therefore, suggest HighResClimNevada as a valuable long-term climate tool for a variety of applications, including land management, hydrometeorological research, flora and fauna phenology, and risk assessment. The reported datasets are freely available for download via Zenodo platform (García-Valdecasas Ojeda et al., 2024, https://doi.org/10.5281/zenodo.14052394).
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Status: open (until 19 Jan 2025)
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RC1: 'Comment on essd-2024-522', Anonymous Referee #1, 07 Jan 2025
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This work provides a 1km-resolution climate dataset from January 1991 to December 2022 for the Sierra Nevada mountain range in Spain. This high-resolution dataset will help to study the impacts of climate change on the botany, ecology and other aspects of mountainous regions.
The dataset was generated using the Weather Research and Forecasting (WRF) model with lateral boundary conditions given by the ERA5 reanalysis. The configuration of the WRF model is clearly described. The quality of the dataset is carefully evaluated. The climate variables of the dataset are well introduced.
General comments:
- Do the authors think that assimilating station data into WRF simulations will improve data quality? There could be some discussion at the end of the paper.
- The authors clearly described how the WRF model is configured and listed relevant references. However, as a non-expert in this model, I would have appreciated a bit more information on the justification of the configuration chosen by the authors and how well the WRF model simulates mountain climates in general.
- There seems to be some data (daily primary climate variables) missing at
- Longitude: -3.296478271484375, Latitude: 37.09039306640625
- Longitude: -3.352783203125, Latitude: 37.099708557128906
- Longitude: -3.465118408203125, Latitude: 37.16318893432617
- Longitude: -3.476409912109375, Latitude: 37.16325378417969
HighResClimNevada.GFAT-UGR.ECMWF-ERA5.Evaluation.WRF433.day.tasmax.1991010100-2022123100.nc (day 1)
Specific comments:
- Line 23-24: “For precipitation, variable, more uncertain and difficult to characterize, HighResClimNevada exhibits a higher amount of precipitation when compared to station-based, coarse satellite-based, and reanalysis-based products.” This relates to the second general comment. How sensitive are the results to the model configuration? Is the chosen configuration optimal?
- Line 176-177: Why were temperature observations from only two stations used?
- Table 2: Is there information on the number of stations in the second column?
- Line 314-315: In addition to the citation, is it possible to describe briefly how to obtain the pseudo-PDFs?
- Line 480: Is it possible to provide a link to the data download page?
- Figure 6: Black is used to define two things.
Technical corrections:
- Line 38 and 40: Missing space at “(Parmesan, 2006).For” and “(Beniston, 2003).This”
- Line 76-79: Inconsistent use of “.” and “;”
- Figure 4: One dataset name in the caption and plot title does not match (GFAT-grid -> UGR-SNGrid)
Citation: https://doi.org/10.5194/essd-2024-522-RC1 -
RC2: 'Comment on essd-2024-522', Anonymous Referee #2, 12 Jan 2025
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Data sets
HighResClimNevada: a high-resolution climatological dataset for a high-altitude region in Southern Spain (Sierra Nevada) M. García-Valdecasas Ojeda, F. Solano-Farias, D. Montaño-Donaire, Y. Castro-Díez, S. R. Gámiz-Fortis, and M. J. Esteban-Parra https://doi.org/10.5281/zenodo.14052394
Model code and software
HighResClimNevada: a high-resolution climatological dataset for a high-altitude region in Southern Spain (Sierra Nevada) M. García-Valdecasas Ojeda, F. Solano-Farias, D. Montaño-Donaire, Y. Castro-Díez, S. R. Gámiz-Fortis, and M. J. Esteban-Parra https://doi.org/10.5281/zenodo.14052394
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