Abstract. Current changes in the world's climate increasingly impact a wide variety of sectors globally, from agriculture and ecosystems to water and energy supply or human health. Many impacts of climate on these sectors happen at high spatio-temporal resolutions that are not covered by current global climate datasets. Here we present CHELSA-W5E5 (https://doi.org/10.48364/ISIMIP.836809.3, Karger et al., 2022): a climate forcing dataset at daily temporal resolution and 30 arcsec spatial resolution for air temperatures, precipitation 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 Weather Research and Forecasting (WRF) regional climate model, 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 higher-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 and for applications that cover more than one region and benefit from using a consistent dataset across these regions.
CHELSA-W5E5: daily 1 km meteorological forcing data for climate impact studies
D. Karger,Stefan Lange,C. Hari,C. Reyer,O. Conrad,N. Zimmermann,K. Frieler
Published 2023 in Earth System Science Data
ABSTRACT
PUBLICATION RECORD
- Publication year
2023
- Venue
Earth System Science Data
- Publication date
2023-06-12
- Fields of study
Not labeled
- Identifiers
- External record
- Source metadata
Semantic Scholar
CITATION MAP
EXTRACTION MAP
CLAIMS
CONCEPTS
- 30 arcsec spatial resolution
A spatial grid resolution of 30 arcseconds used for the downscaled climate forcing product.
Aliases: 30 arcsec
- chelsa v2 topographic downscaling algorithm
A CHELSA V2 method that uses topographic information to downscale climate forcing data to finer spatial resolution.
Aliases: CHELSA V2, topographic downscaling algorithm
- chelsa-w5e5 dataset
A daily 1 km meteorological forcing dataset produced by spatially downscaling W5E5 with CHELSA V2.
Aliases: CHELSA-W5E5, CHELSA-W5E5 climate forcing dataset
- computational cost
The amount of computing resources required to generate or run a climate data product or model.
Aliases: computing cost
- daily temporal resolution
A time sampling frequency in which values are provided once per day.
Aliases: daily resolution
- meteorological station observations
Measurements from weather stations used here to evaluate the accuracy of the downscaled climate data.
Aliases: station observations, meteorological stations
- topographically complex terrain
Landscape with strong elevation and relief variation that affects climate downscaling performance.
Aliases: complex terrain
- w5e5 dataset
A global climate forcing dataset at 0.5 degree resolution that serves as the input to the downscaling workflow.
Aliases: W5E5
- weather research and forecasting regional climate model
A regional climate modeling system used as a dynamical downscaling reference for comparison.
Aliases: WRF, WRF regional climate model
REFERENCES
Showing 1-57 of 57 references · Page 1 of 1
CITED BY
Showing 1-66 of 66 citing papers · Page 1 of 1