CHELSA-W5E5: daily 1 km meteorological forcing data for climate impact studies
<p>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...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2023-06-01
|
| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/15/2445/2023/essd-15-2445-2023.pdf |
| Summary: | <p>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 (<a href="https://doi.org/10.48364/ISIMIP.836809.3">https://doi.org/10.48364/ISIMIP.836809.3</a>, 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<span class="inline-formula"><sup>∘</sup></span> 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.</p> |
|---|---|
| ISSN: | 1866-3508 1866-3516 |