Assessment of CMIP6 Regional Climate Model Performance for Wind Speed in Ireland. 

Abstract

This study focuses on the validation of wind speed simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) against observed station data in Ireland.The aim is to assess the performance of 10 CMIP6 regional climate model (RCM) ensembles in capturing the spatiotemporal variability of wind speeds, a crucial parameter for various applicationssuch as renewable energy assessments and climate impact studies. Station observation data for wind speed are obtained from 7 Met Éireann weather stations across Ireland, providing a comprehensive and high-quality dataset for model evaluation from 1981 to 2010.The 10 CMIP6 model ensembles are selected based on their representation of historical climate conditions, and a detailed comparison is conducted for various temporal scales. Key metrics, such as bias, root mean square error, and correlation coefficients, percentiles are employed to quantify the agreement between CMIP6 model outputs and observed wind speed data.Additionally, annual, and monthly climatological patterns of wind speed across different regions of Ireland are examined to identify potential biases or deficiencies in model performance. The COSMO-CLM regional models overestimate similar patterns, while the WRF simulates underestimated wind speeds for the stations. Despite these notable differences, all models accurately predicted the windiest months, which are January and February, and the least windy months, which are July and August. The windiest location in Ireland is also well represented by the models, which are Malin Head in County Donegal, where winds peak in January while the lowest wind speed is recorded at Valentia Observatory in July. The findings of this validation study contribute to our understanding of the reliability and accuracy of CMIP6 model simulations in reproducing wind speed characteristics specific to Ireland. The outcomes have implications for climate model improvement and can enhance the credibility of future climate projections for the region. Improved confidence in wind speed simulations is crucial for supporting informed decision-making in areas such as renewable energy planning, infrastructure design, and climate change adaptation strategies.