The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants. However, contemporary Earth System Models (ESM) are run at spatial resolutions too coarse for assessing effects this localized. Local scale projections can be obtained using statistical downscaling, a technique which uses historical climate observations to learn a low-resolution to high-resolution mapping. The spatio-temporal nature of the climate system motivates the adaptation of super-resolution image processing techniques to statistical downscaling. In our work, we present DeepSD, a generalized stacked super resolution convolutional neural network (SRCNN) framework with multi-scale input channels for statistical downscaling of climate variables. A comparison of DeepSD to four state-of-the-art methods downscaling daily precipitation from 1 degree (~100km) to 1/8 degrees (~12.5km) over the Continental United States. Furthermore, a framework using the NASA Earth Exchange (NEX) platform is discussed for downscaling more than 20 ESM models with multiple emission scenarios.
To paraphrase former Speaker of the House Tip O'Neill, “All climate change is local”—that is, society reacts most immediately to changes in local weather such as regional heat waves and heavy rainstorms. Such phenomena are not well resolved by the current generation of coupled climate models. Here it is shown that dynamical downscaling of climate reanalyses using a high‐resolution regional model can reproduce both the means and extremes of temperature and precipitation as observed in the well‐measured northeastern United States. Given this result, the downscaling is applied to climate projections for the middle and end of the 21st century under Representative Concentration Pathway (RCP) 8.5 as well as for the historical time period to help assess regional climate impacts in the northeastern United States. The resulting high‐resolution projections are intended to support regional sustainability studies for the northeastern United States and are made publicly available.
more » « less- Award ID(s):
- 1101245
- NSF-PAR ID:
- 10460387
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Earth and Space Science
- Volume:
- 5
- Issue:
- 11
- ISSN:
- 2333-5084
- Page Range / eLocation ID:
- p. 801-826
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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