Detecting and calibrating large biases in global onshore wind power assessment across temporal scales

Chengzhi Hou, Zhiwei Xu (), Kristopher B. Karnauskas, Danqing Huang and Huayu Lu
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Chengzhi Hou: Nanjing University
Zhiwei Xu: Nanjing University
Kristopher B. Karnauskas: University of Colorado
Danqing Huang: Nanjing University
Huayu Lu: Nanjing University

Nature Communications, 2025, vol. 16, issue 1, 1-10

Abstract: Abstract The global capacity for wind power has grown rapidly in recent years, yet uncertainties in wind power density (WPD) assessments still hinder effective climate change mitigation efforts. One major challenge is the significant underestimation of WPD when using coarser temporal resolutions (∆t) of wind speed data. Here, we show that using daily ∆t results in an average underestimation of 35.6% in global onshore WPD compared to hourly ∆t. This discrepancy arises from the exponential decay of WPD with increasing ∆t, reflecting the intrinsic properties of wind speed distributions, particularly in regions with weaker winds. To address this, we propose a calibration method that introduces a correction coefficient to reduce biases and harmonize WPD estimates across temporal resolutions. Applying this method to future wind energy projections under the Shared Socioeconomic Pathway 585 scenario increases global onshore WPD estimates by 25% by 2100, compared to uncorrected daily data. These findings highlight the effectiveness of calibration in reducing uncertainties, enhancing WPD assessments, and facilitating robust policy action toward carbon neutrality.

Date: 2025
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DOI: 10.1038/s41467-025-59195-2

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