Dynamic downscaling of wind speed over the North Atlantic Ocean using CMIP6 projections: Implications for offshore wind power density

Fernández‐Alvarez, José C.; Costoya, X.; Pérez‐Alarcón, Albenis; Rahimi, Stefan; Nieto, Raquel; Gimeno, Luis

doi:10.1016/j.egyr.2022.12.036

Cited by 14 publications

(3 citation statements)

References 52 publications

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“…We used monthly data for three periods: 1985–2014 for the historical simulation, 2036–2065 for the mid‐term, and 2071–2100 for the long‐term of the 21st century. The RCM output will be called WRF‐CESM2 from now on; for a detailed explanation of the downscaling process, please check Fernández‐Alvarez et al 17 …”

Section: Methodsmentioning

confidence: 99%

“…We used monthly data for three periods: 1985-2014 for the historical simulation, 2036-2065 for the mid-term, and 2071-2100 for the long-term of the 21st century. The RCM output will be called WRF-CESM2 from now on; for a detailed explanation of the downscaling process, please check Fernández-Alvarez et al 17 In addition, the SSP used for the WRF-CESM2 was SSP585, 32,33 as the downscaling has high computational demand. The SSP585 is a scenario (worst-case scenario) that represents emissions high enough to produce a radiative forcing of 8.5 W m −2 in 2100 under extreme conditions.…”

Section: Rcm: Dynamic Downscaling With Wrfmentioning

confidence: 99%

“…However, the current spatial resolution of GCMs is not sufficient to accurately simulate the regional-scale processes that influence SWS, particularly in areas with complex topography, such as the IP. 17 A higher spatial resolution is required to study the SWS changes and multidecadal variability over these regions, which can be achieved through the application of a dynamical downscaling by means of regional climate models (RCMs). 18 As Jung and Schindler 18 proposed in their review of SWS projections, most studies suggest a general decline of SWS over the IP for the 21st century.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty in surface wind speed projections over the Iberian Peninsula: CMIP6 GCMs versus a WRF‐RCM

Andres‐Martin,

Azorin‐Molina,

Shen

et al. 2023

Annals of the New York Academy of Sciences

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This study assessed the projected near‐surface wind speed (SWS) changes and variability over the Iberian Peninsula for the 21st century. Here, we compared Coupled Model Intercomparison Project Phase 6 global climate models (GCMs) with a higher spatial resolution regional climate model (RCM; ∼20 km), known as WRF‐CESM2, which was created by a dynamic downscaling of the Community Earth System Model version 2 (CESM2) using the Weather Research and Forecasting (WRF) model. Our analysis found that the GCMs tended to overestimate observed SWS for 1985–2014, while the higher spatial resolution of the WRF‐CESM2 did not improve the accuracy and underestimated the SWS magnitude. GCMs project a decline of SWS under high shared socioeconomic pathways (SSPs) greenhouse concentrations, such as SSP370 and SSP585, while an interdecadal oscillation appears in SSP126 and SSP245 for the end of the century. The WRF‐CESM2 under SSP585 predicts the opposite increasing SWS. Our results suggest that 21st‐century projections of SWS are uncertain even for regionalized products and should be taken with caution.

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Section: Methodsmentioning

confidence: 99%

Section: Rcm: Dynamic Downscaling With Wrfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Uncertainty in surface wind speed projections over the Iberian Peninsula: CMIP6 GCMs versus a WRF‐RCM

Andres‐Martin,

Azorin‐Molina,

Shen

et al. 2023

Annals of the New York Academy of Sciences

Self Cite

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Evolution of the subtropical surface wind in the north-east Atlantic under climate change

Alves,

Miranda,

Tomé

et al. 2024

Clim Dyn

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The climate of western Europe and northwest Africa strongly depends on the Azores anticyclone strength, location, and shape and, locally, also on the characteristics of the Iberia and Sahara summer thermal lows. EC-EARTH3 global simulations are here used to assess the predicted behaviour of these two relevant surface pressure systems and associated surface wind, by the end of XXI century (2071–2100), considering two climate change scenarios. Additionally, a high-resolution Weather Research and Forecasting (WRF) simulation centred on Madeira Island is used to assess the influence of climate change on the surface wind at smaller scales, in a region well known for its perturbed flows. Results indicate a general mean wind speed decrease over a sector of the North-Atlantic, associated with a flatter Azores anticyclone. However, the predicted intensification of the Iberia and Sahara thermal lows imposes an increasing surface wind speed near west Iberia and northwest Africa, in summer. Southwest of Iberia, Madeira Island is also predicted to experience a summer intensification of its tip-jets. The projected changes in low-level wind speed and variability will impact on different sectors of activity, either directly as are the cases of aeronautical operation and offshore renewable energy, or indirectly through changes in the ocean circulation.

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A deep learning approach for wind downscaling using spatially correlated global wind data

Adytia,

Latifah,

Saepudin

et al. 2024

Int J Data Sci Anal

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Dynamic downscaling of wind speed over the North Atlantic Ocean using CMIP6 projections: Implications for offshore wind power density

Cited by 14 publications

References 52 publications

Uncertainty in surface wind speed projections over the Iberian Peninsula: CMIP6 GCMs versus a WRF‐RCM

Uncertainty in surface wind speed projections over the Iberian Peninsula: CMIP6 GCMs versus a WRF‐RCM

Evolution of the subtropical surface wind in the north-east Atlantic under climate change

A deep learning approach for wind downscaling using spatially correlated global wind data

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