First Application of Artificial Neural Networks to Estimate 21st Century Greenland Ice Sheet Surface Melt
In one sentence. Neural networks trained on CESM2 output emulate 21st-century Greenland ice sheet surface melt across CMIP6 models, avoiding costly regional climate model runs.
Abstract
Abstract 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 414 275 Gt (SSP1‐2.6) to 1,378 555 Gt (SSP5‐8.5) for the full CMIP6 suite. The primary source of uncertainty throughout the 21st century is the spread of climate model sensitivity.
Labels
- Topic
- Climate emulation, Hybrid physics-ML & parameterization
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Climate projections / centennial
- Domain
- Regional
- Data
- CMIP / climate model output
- Status
- Research
- Identifiers
- DOI:10.1029/2021gl092449 OpenAlex:W3187337830
- Added
- 2026-10-22
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