Comparison to other high resolution climate products

CHELSA, as most other climate products is a ‘model’ that aims at representing reality as precise as possible. It is nevertheless just a model, and it has strength and weaknesses. Here we want to provide a simple comparison to some other products such as PRISM or WorldClim to illustrate some of the differences between these models.

Differences in annual precipitation between CHELSA and WorldClim.

Difference between CHELSA V2.1 (left) and WorldClim V2.1 (right) over the western United States. This area is especially dense in climate stations used by WorldClim, has step precipitation gradients in the west, and a large variety of topography. Precipitation is generally lower in WorldClim compared to CHELSA.

Differences in annual precipitation between CHELSA and Prism.

Difference between CHELSA V2.1 (left) and PRISM (right) over the western United States. PRISM uses a large amount of quality controlled climate stations and for that reason can be considered one of the most precise product available. In the Colorado Mountains precipitation amounts are quite similar between both products. In this regions a large amount of precipitation falls as snow, which PRISM incorporates using snow gauges, and CHELSA incorporates using a gauge undercatch correction.

Differences between CHELSA and WorldClim at a small scale

Difference between CHELSA V2.1 (left) and WorldClim V2.1 (right) in western Canada. In this region the differences between both models are stricking. The amount of precipitation estimated in WorldClim is much lower compared to CHELSA. Additionally, precipitation gradients are reversed in WorldClim with some regions showing higher amounts of precipitation in the lowlands than in the mountains.

Differences between CHELSA and PRISM at a small scale

Difference between CHELSA V2.1 (left) and PRISM (right) in western Canada. Both models are in relative good aggrement with each other and are able to reproduce the complex precipitation gradients in this area.

Comparison to cloud cover in data sparse regions

Comparison between CHELSA V2.1 (left) and MODIS annual cloud cover (right) along the Himalayas. CHELSA predicts precipitation in areas where clouds are more frequent and less precipitation in areas where clouds are rare. While clouds can either mean rain, or no rain, no clouds usually mean no rain. In data sparse regions such as the tropics, the non occurrence of clouds can therefore be used as an indicator how well a model captures orographic rainfall.
Comparison between WorldClim V2.1 (left) and MODIS annual cloud cover (right) along the Himalayas. Notably, certain valleys which have no clouds during the year have high precipitation.