Unsupervised Downscaling of Climate Simulations – CliMA

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Climate simulations play a crucial role in understanding and predicting climate change scenarios. However, the spatial resolution that simulations can be carried out with is often limited by computational resources to around ~50-250 km in the horizontal. This leads to a lack of high-resolution detail; moreover, since small-scale dynamical processes can influence behavior on larger scales, coarse resolution simulations can additionally be biased compared to a high-resolution “truth”. For example, simulations run at coarse resolutions fail to accurately capture important phenomena such as convective precipitation, tropical cyclone dynamics, and local effects from topography and land cover. Consequently, these simulations have a limited ability in making predictions on regional and sub-regional scales, especially for extreme temperatures and precipitation rates. To overcome these challenges, downscaling techniques have been developed to enhance resolution and correct biases in fluid and c

by Tobias Bischoff and Katherine Deck Climate simulations play a crucial role in understanding and predicting climate change scenarios. However, the spatial resolution that simulations can be carried out with is often limited by computational resources to around ~50-250 km in the horizontal. This leads to a lack of high-resolution detail; moreover, since small-scale dynamical processes can influence behavior on larger scales, coarse resolution simulations can additionally be biased compared to a high-resolution “truth”. For example, simulations run at coarse resolutions fail to accurately…

Unsupervised Downscaling of Climate Simulations – CliMA

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