Benchmarking Uninitialized CMIP6 Simulations for Inter-Annual Surface Wind Predictions

Atmosphere, Journal Year: 2025, Volume and Issue: 16(3), P. 254 - 254

Published: Feb. 23, 2025

This study investigates the potential of uninitialized global climate projections for providing 12-month (inter-annual) wind forecasts in Europe light increasing demand long-term predictions. is important a context where models based on past may not fully account implications variability current warming trends, and initialized are still widely available (i.e., seasonal forecasts) and/or consolidated decadal predictions). To this aim, we use two types simulations: from CMIP6 (Coupled Model Intercomparison Project Phase 6) 6-month (ECMWF’s SEAS5), using latter as benchmark. All predictions bias-corrected with five distinct approaches (quantile delta mapping, empirical quantile scaling bias-adjustment proprietary mapping) verified against weather observations ECA&D E-OBS project (684 stations across Europe). It observed that quantile-mapping techniques outperform other bias-correction algorithm adjusting cumulative distribution function (CDF) to reference and, also, reducing mean bias error closer zero. However, simple -correction by improves time-series predictive accuracy (root square error, anomaly correlation coefficient absolute scaled error) simulations over corrections. Thus, results suggest provide valuable preliminary framework comprehending variations ensuing period. Finally, while baseline methods like climatology could presented terms root error), our approach highlights key advantage: static, whereas offers dynamic, evolving view climatology. The combination dynamism correction makes starting point understanding next 12 months. Furthermore, workload schedulers within high-performance computing frameworks essential effectively handling these complex ever-evolving datasets, highlighting critical role advanced computational realizing analysis.

Language: Английский

Impact of Climate Change on River Flow, Using a Hybrid Model of LARS_WG and LSTM: A Case Study in the Kashkan Basin

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104956 - 104956

Published: April 1, 2025

Language: Английский