Year: 2022
Type: article
Abstract: Climate change forecasting demands models that capture complex nonlinear interactions across atmospheric, oceanic,and terrestrial systems at spatial and temporal scales ranging from regional weather patterns to century-scale globaltrends. Physics-based general circulation models (GCMs) remain the gold standard for climate projection but arecomputationally expensive and struggle with sub-grid-scale processes that must be parameterised rather than resolved.This study evaluates six AI-based approaches -- convolutional LSTMs, graph neural networks on climate grids, Fourierneural operators (FNO), vision transformers applied to reanalysis fields, hybrid physics-ML models (ML-correctedGCMs), and ensemble gradient-boosted models for regional downscaling -- across four climate forecasting tasks: globaltemperature anomaly prediction (ERA5, 1979-2021, 1-12 month horizons), regional precipitation forecasting (E-OBSEuropean gridded data, seasonal), extreme event detection (tropical cyclone genesis from ERA5 fields), and statisticaldownscaling (CMIP6 coarse-resolution to 0.1-degree regional fields). A total of 2,160 experiments were conducted usingstandardised train-validation-test splits respecting temporal ordering. The hybrid physics-ML model achieved the lowesttemperature RMSE at 6-month horizon (0.24 +- 0.03 K vs. 0.38 +- 0.04 K for the persistence baseline and 0.32 +- 0.04 Kfor the pure ML ConvLSTM), confirming that physics-informed architectures outperform pure data-driven approaches onclimate time scales. The FNO achieved the best spatial pattern reconstruction (SSIM = 0.92 +- 0.02 for globaltemperature fields) at 1,000x lower computational cost than equivalent GCM resolution. For extreme events, the visiontransformer achieved the highest tropical cyclone genesis detection AUC (0.924 +- 0.008) by capturing large-scaleatmospheric patterns across hemispheric fields. Gradient-boosted downscaling achieved the smallest regionalprecipitation bias (2.4 +- 0.8 mm/month) at 50x lower cost than dynamical downscaling. A practical model selectionframework mapping forecast horizon, spatial resolution requirements, and computational budget to recommended AIapproaches is proposed.
Source: Bio-QI Journal
Authors: Noah Ivanov, Daniel Dubois, Erik Ivanov
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: diamond
OpenAlex ID: W7203708720
FWCI:
Cites:
Cited by:
Related to: