Downscaling climate models

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This page is part of the Climate Change AI Wiki, which aims provide resources at the intersection of climate change and machine learning.

Climate models often are run on a coarser grid (for computational speed). Downscaling climate projections for smaller grids or specific regions is an important source of information for local impact assessments. ML and deep learning can be useful for interpolation and approximating the fine-scale regional responses based on such coarser climate model output[1][2].

Background Readings[edit | edit source]

Conferences, Journals, and Professional Organizations[edit | edit source]

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Data[edit | edit source]

Future Directions[edit | edit source]

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References[edit | edit source]

  1. Amato, Federico; Guignard, Fabian; Robert, Sylvain; Kanevski, Mikhail (2020-12-17). "A novel framework for spatio-temporal prediction of environmental data using deep learning". Scientific Reports. 10 (1): 22243. doi:10.1038/s41598-020-79148-7. ISSN 2045-2322.
  2. Heinze-Deml, Christina; Sippel, Sebastian; Pendergrass, Angeline G.; Lehner, Flavio; Meinshausen, Nicolai (2020-10-28). "Latent Linear Adjustment Autoencoders v1.0: A novel method for estimating and emulating dynamic precipitation at high resolution". Geoscientific Model Development Discussions: 1–39. doi:10.5194/gmd-2020-275. ISSN 1991-959X.