First Application of Artificial Neural Networks to Estimate 21st Century Greenland Ice Sheet Surface Melt
Future Greenland ice sheet (GrIS) melt projections are limited by the lack of explicit melt calculations within most global climate models and the high computational cost of dynamical downscaling with regional climate models (RCMs). Here, we train artificial neural networks (ANNs) to obtain relation...
| Published in: | Geophysical Research Letters |
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| Main Authors: | , |
| Format: | Text |
| Language: | English |
| Published: |
John Wiley and Sons Inc.
2021
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| Subjects: | |
| Online Access: | http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9285918/ http://www.ncbi.nlm.nih.gov/pubmed/35866045 https://doi.org/10.1029/2021GL092449 |
| Summary: | Future Greenland ice sheet (GrIS) melt projections are limited by the lack of explicit melt calculations within most global climate models and the high computational cost of dynamical downscaling with regional climate models (RCMs). Here, we train artificial neural networks (ANNs) to obtain relationships between quantities consistently available from global climate model simulations and annually integrated GrIS surface melt. To this end, we train the ANNs with model output from the Community Earth System Model 2.1 (CESM2), which features an interactive surface melt calculation based on a downscaled surface energy balance. We find that ANNs compare well with an independent CESM2 simulation and RCM simulations forced by a CMIP6 subset. The ANNs estimate a melt increase for 2,081–2,100 ranging from 414 [Formula: see text] 275 Gt [Formula: see text] (SSP1‐2.6) to 1,378 [Formula: see text] 555 Gt [Formula: see text] (SSP5‐8.5) for the full CMIP6 suite. The primary source of uncertainty throughout the 21st century is the spread of climate model sensitivity. |
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