Evaluation of Northern Hemisphere surface wind speed and wind power density in multiple reanalysis datasets

Miao, Haozeyu; Dong, Danian; Huang, Gang; Hu, Kaiming; Tian, Qun; Gong, Yuanfa

doi:10.1016/j.energy.2020.117382

Cited by 55 publications

(34 citation statements)

References 60 publications

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“…As shown in supporting information Figure S1, similar spatial patterns of surface wind speed can be found in the three datasets, including high values in northern China and low values over the Sichuan Basin, south edge of Yunnan Province, and southeastern China. This spatial pattern is consistent with that in Miao et al (2020). Similar interannual variation and approximate values are also observed in the temporal evolution of these datasets, with the correlation coefficients between CN05.1 and HadISD/GSOD are 0.97/0.96, respectively, with p < 0.01 (Figure S2).…”

Section: Methodssupporting

confidence: 93%

“…However, few comprehensive studies on the surface wind speed over China have been conducted. In addition, compared to other climate variables, such as temperature and precipitation, wind speed has received less attention, partly due to the relatively poor representation of wind speed in the reanalysis data sets (Miao et al, 2020; Torralba et al, 2017; Yu et al, 2019; Zeng et al, 2019) and the scarcity of high‐quality observation data sets.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation and Projection of Surface Wind Speed Over China Based on CMIP6 GCMs

Shi

2020

JGR Atmospheres

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Surface wind speed has great impacts on the economy, environment, and society around the world. Based on 24 global climate models (GCMs) from Coupled Model Intercomparison Project Phase 6 (CMIP6), this paper assesses the historical surface wind speed over China, and quantifies the advancements of CMIP6 over CMIP5. In addition, future changes of surface wind speed under the Shared Socioeconomic Pathway scenarios of 1-2.6, 2-4.5, and 5-8.5 (SSP1-2.6, SSP2-4.5, and SSP5-8.5) are also provided, by using 18 out of the 24 models with all three scenarios. Results show that multimodel ensemble mean of CMIP6 can well capture the spatial distributions of the annual, summer, and winter surface wind speed and generally performs better than both the median of the individual CMIP6 GCMs and multimodel ensemble mean of CMIP5 in monthly, seasonal, and annual wind climatology. However, CMIP6 GCMs fail to reproduce the observed decreasing trends, in terms of magnitude and/or sign. Meanwhile, the annual cycle of the surface wind speed simulated by CMIP6 GCMs falsely demonstrates the maximum in winter instead of spring. In the context of global warming, surface wind speed is projected to decrease in most parts of China in the middle and late 21st century under the three scenarios. Moreover, the trends of wind speed averaged over China will decrease significantly in both annual and winter under all three scenarios, but it will increase significantly in summer under SSP5-8.5.

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Section: Methodssupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Evaluation and Projection of Surface Wind Speed Over China Based on CMIP6 GCMs

Shi

2020

JGR Atmospheres

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“…Reanalysis datasets, by combining observations and models, generate consistent wind time series at a complete spatial coverage and for this reason can potentially be used for such research (Dee et al ., 2011). Before using a reanalysis product for assessing wind changes, its capability to realistically represent surface winds must be evaluated against in‐situ observations since reanalysis dataset performance is strongly dependent on the selected region and the considered time period (Ramon et al ., 2019; Wohland et al ., 2019; Yu et al ., 2019; Miao et al ., 2020). Among the various reanalyses available nowadays, ERA5 (Hersbach et al ., 2018) is the new promising reanalysis dataset produced by the European Center for Medium‐Range Weather Forecasts (ECMWF).…”

Section: Introductionmentioning

confidence: 99%

Wind stilling‐reversal across Sweden: The impact of land‐use and large‐scale atmospheric circulation changes

Minola

Reese

Lai

et al. 2021

Intl Journal of Climatology

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This study analyses for the first time the break in the stilling detected by previous research around 2010, with focus in Sweden using homogenized nearsurface mean and gust wind speed observations for 1997-2019. During the recent past two decades, both mean and gust wind magnitude and frequency (exceeding the 90th percentile) underwent nonlinear changes, driven by the dominant winter variability. In particular, consistent with previous studies, the significant (p < .05) stilling ceased in 2003, followed by no clear trend afterwards. The detected stilling-reversal is linked to large-scale atmospheric circulation changes, in particular to the North Atlantic Oscillation for both mean and gust wind changes, and the intensity changes of extratropical cyclones passing across Sweden especially for wind gusts. Furthermore, in different wind change phases, the observed wind distribution did not vary uniformly for the various wind speed ranges; instead, strong winds drove most of the changes. In the same way, increases in gust winds are greater compared to changes in mean wind speed conditions. The stilling-reversal is also identified by the ERA5 reanalysis, where large-scale atmospheric circulation changes are captured. But the background slowdown detected in most stations does not appear in the ERA5 data as the observed increase in forest cover is not considered in the reanalysis. This study reveals that, in addition to the large-scale interannual variability, changes in surface roughness (e.g., changes in forest cover) contribute to the observed wind variability across Sweden.

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“…Miao et al. (2020) evaluated the biases of near‐surface wind speed in ERA‐Interim (the Interim European Center for Medium‐Range Weather Forecasts Reanalysis), JRA‐55, Climate Forecast System, and MERRA‐2. Among these reanalysis datasets, they found that JRA‐55 has the best performance in reproducing the spatial pattern of surface wind speed over the Northern Hemisphere.…”

Section: Methodsmentioning

confidence: 99%

Decreasing Dust Over the Middle East Partly Caused by Irrigation Expansion

Xia

Wang

2022

Earth's Future

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The importance of the effects of anthropogenic activities on modulating the global dust cycle has been increasingly recognized. Over the Middle East, we find in observations that there has been a significant decrease in dust optical depth from 2007 to 2019 during which global irrigated areas especially in the Middle East and South Asia have rapidly expanded. Whether irrigation expansion contributes to the decrease of dust in the Middle East is investigated based on observations/reanalyses and global climate model simulations with and without irrigation. Results show that irrigation over the northeast Middle East and Pakistan supplements water to the soil. By increasing local evaporation and moisture advection, irrigation enhances precipitation over the whole Middle East and the northwest Indian subcontinent. As a result, dust wet deposition by precipitation is elevated. Owing to irrigation‐induced land surface cooling, surface wind speed decreases as the planetary boundary layer becomes stable. Along with increased soil moisture, reduced surface wind speed suppresses local dust emissions. Enhanced dust wet deposition and suppressed dust emissions cause atmospheric dust reduction over the Middle East. Vegetation greening in the Middle East as the climate warms has no contribution because there is no obvious trend found in observations.

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Evaluation of Northern Hemisphere surface wind speed and wind power density in multiple reanalysis datasets

Cited by 55 publications

References 60 publications

Evaluation and Projection of Surface Wind Speed Over China Based on CMIP6 GCMs

Evaluation and Projection of Surface Wind Speed Over China Based on CMIP6 GCMs

Wind stilling‐reversal across Sweden: The impact of land‐use and large‐scale atmospheric circulation changes

Decreasing Dust Over the Middle East Partly Caused by Irrigation Expansion

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