Global Climate

Dunn, Robert; Aldred, Freya; Gobron, Nadine; Miller, J. B.; Willett, Kate M.; Ades, Melanie; Adler, Robert E.; Allan, Richard P.; Anderson, J.; Anneville, Orlane; Aono, Yasuyuki; Argüez, Anthony; Arosio, Carlo; Augustine, John; Azorín-Molina, César; Barichivich, Jonathan; Basu, Aman; Beck, Hylke E.; Bellouin, Nicolas; Benedetti, Angela; Blagrave, Kevin; Blenkinsop, Stephen; Bock, Olivier; Bodin, Xavier; Bosilovich, Michael G.; Boucher, Oliviér; Bove, Gerald; Buechler, Dennis E.; Buehler, Stefan; Carrea, Laura; Chang, Kai‐Lan; Christiansen, Hanne H.; Christy, John R.; Chung, Eui‐Seok; Ciasto, Laura M.; Coldewey‐Egbers, Melanie; Cooper, O. R.; Cornes, Richard; Covey, Curt; Cropper, Thomas; Crotwell, Molly; Cusicanqui, Diego; Davis, Sean; Jeu, R. A. M. de; Degenstein, D. A.; Delaloye, Reynald; Donat, Markus G.; Dorigo, Wouter; Durre, Imke; Dutton, G. S.; Duveiller, Grégory; Elkins, James W.; Estilow, T. W.; Fedaeff, Nava; Fereday, David; Fioletov, Vitali; Flemming, Johannes; Foster, Michael J.; Frith, S. M.; Froidevaux, L.; Füllekrug, Martin; Garforth, Judith; Garg, Jay; Gentry, Matthew; Goodman, Steven M.; Gou, Qiqi; Granin, N. G.; Guglielmin, Mauro; Hahn, Sebastian; Haimberger, Leopold; Hall, B. D.; Harris, Ian; Hemming, Debbie; Hirschi, Martin; Ho, Shu-pen (Ben); Holzworth, R. H.; Hrbáček, Filip; Hubert, D.; Hulsman, Petra; Hurst, D. F.; Inness, Antje; Isaksen, Ketil; John, Viju O.; Jones, Philip; Junod, Robert; Kääb, Andreas; Kaiser, Johannes; Kaufmann, Viktor; Kellerer‐Pirklbauer, Andreas; Kent, Elizabeth C.; Kidd, Richard; Kim, Hyungiun; Kipling, Zak; Koppa, Akash; L’Abée-Lund, Jan Henning; X, Lan; Lantz, Kathleen; Lavers, David A.; Loeb, Norman G.; Loyola, Diego; Madelon, Remi; Malmquist, Hilmar J.; Marszelewski, Włodzimierz; Mayer, Michael; McCabe, Matthew; McVicar, Tim R.; Mears, C. A.; Menzel, Annette; Merchant, Christopher J.; Miralles, Diego G.; Montzka, S. A.; Morice, Colin; Moesinger, Leander; Mühle, Jens; Nicolas, Julien P.; Noetzli, Jeannette; Nõges, Tiina; Noll, Ben; O’Keefe, John; Osborn, Tim; Park, Taejin; Pellet, Cécile; Pelto, Maury S.; Perkins‐Kirkpatrick, Sarah E.; Phillips, Coda; Po‐Chedley, Stephen; Polvani, Lorenzo M.; Preimesberger, Wolfgang; Price, Colin; Pulkkanen, Merja; Rains, Dominik; Randel, William J.; Rémy, Samuel; Ricciardulli, Lucrezia; Richardson, Andrew D.; Robinson, David A.; Rodell, M.; Rodriguez‐fernandez, Nemesio; Rosenlof, K. H.; Roth, Chris; Rozanov, Alexei; Rutishäuser, This; Sánchez-Lugo, Ahira; Sawaengphokhai, P.; Schenzinger, Verena; Schlegel, Robert W.; Schneider, Udo; Sharma, Sapna; Shi, Lei; Simmons, A. J.; Siso, Carolina; Smith, Sharon L.; Soden, Brian J.; Sofieva, Viktoria; Sparks, Tim H.; Stackhouse, Paul W.; Stauffer, Ryan M.; Steinbrecht, Wolfgang; Steiner, Andrea; Stewart, Kenton M.; Stradiotti, Pietro; Streletskiy, Dimitri A.; Telg, Hagen; Thackeray, Stephen J.; Thibert, Emmanuel; Todt, Michael A.; Tokuda, Daisuke; Tourpali, Kleareti; Tye, Mari R.; A, Ronald van der; Schalie, Robin van der; Schrier, Gerard van der; Vliet, Mendy van der; Werf, Guido R. van der; Vliet, Arnold van; Vernier, Jean‐Paul; Vimont, Isaac; Virts, Katrina S.; Vivero, Sebastián; Vömel, H.; Vose, Russell S.; Wang, Ray; Weber, Markus; Wiese, D. N.; Wild, Jeanette D.; Williams, Earle; Wong, Takmeng; Woolway, R. Iestyn; Yin, Xungang; Yuan, Ye; Zhao, Lin; Zhou, Xinjia; Ziemke, J. R.; Ziese, Markus; Zotta, Ruxandra-Maria

doi:10.1175/bams-d-22-0092.1

Cited by 22 publications

(6 citation statements)

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“…As for the class 9 wind frequency, a consistent decrease is found in nearly all continents (figure 5(i)). This rapid decrease in strong wind concords with a series of earlier regional studies conducted in America (Pryor et al 2007(Pryor et al , 2009, China (Guo et al 2011, Zha et al 2017, Central Asia (Dunn et al 2022b), the United Kingdom (Earl et al 2013) and Spain and Portugal (Azorin-Molina et al 2016).…”

Section: Frequency Changes Of Global and Regional Swssupporting

confidence: 58%

Global assessment of spatiotemporal changes of frequency of terrestrial wind speed

Zhao

Liang

Liu

et al. 2023

Environ. Res. Lett.

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Wind energy, an important component of clean energy, is highly dictated by the disposable wind speed within the working regime of wind turbines (typically between 3 - 25 m s-1 at the hub height). Following a continuous reduction (“stilling”) of global annual mean surface wind speed (SWS) since the 1960s, recently, researchers have reported a “reversal” since 2011. However, little attention has been paid to the evolution of the effective wind speed for wind turbines. Since wind speed at hub height increases with SWS through power law, we focus on the wind speed frequency variations at various ranges of SWS through hourly in-situ observations and quantify their contributions to the average SWS changes over 1981-2021. We found that during the stilling period (here 1981-2010), the strong SWS (>= 5.0 m s-1, the 80th of global SWS) with decreasing frequency contributed 220.37% to the continuous weakening of mean SWS. During the reversal period of SWS (here 2011-2021), slight wind (0 m s-1< SWS <2.9 m s-1) contributed 64.07% to a strengthening of SWS. The strengthened strong wind (>= 5.0 m s-1) contributed 73.38% to the trend change of SWS from decrease to increase in 2010. Based on the synthetic capacity factor series calculated by considering commercial wind turbines (General Electric GE 2.5-120 model with rated power 2.5 MW) at the locations of the meteorological stations, the frequency changes resulted in a reduction of wind power energy (-10.02 TWh yr-1, p<0.001) from 1981 to 2010 and relatively weak recovery (2.67 TWh yr-1, p<0.05) during 2011-2021.

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Section: Frequency Changes Of Global and Regional Swssupporting

confidence: 58%

Global assessment of spatiotemporal changes of frequency of terrestrial wind speed

Zhao

Liang

Liu

et al. 2023

Environ. Res. Lett.

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“…Global sc-PDSI data were downloaded from the Climate Research Unit (CRU), with a spatial resolution of 0.5°, a monthly temporal resolution, and a time span ranging from 1901 to 2020 ( https://crudata.uea.ac.uk/cru/data//drought/#global ) 44 , 45 . We organized the data in annual data by the arithmetic mean, then resampled to 4 km by the nearest neighbor method to match the VCI and TCI data.…”

Section: Methodsmentioning

confidence: 99%

An improved global vegetation health index dataset in detecting vegetation drought

Zeng

Zhou

et al. 2023

Sci Data

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Due to global warming, drought events have become more frequent, which resulted in aggravated crop failures, food shortage, larger and more energetic wildfires, and have seriously affected socio-economic development and agricultural production. In this study, a global long-term (1981–2021), high-resolution (4 km) improved vegetation health index (VHI) dataset integrating climate, vegetation and soil moisture was developed. Based on drought records from the Emergency Event Database, we compared the detection efficiency of the VHI before and after its improvement in the occurrence and scope of observed drought events. The global drought detection efficiency of the improved high-resolution VHI dataset reached values as high as 85%, which is 14% higher than the original VHI dataset. The improved VHI dataset was also more sensitive to mild droughts and more accurate regarding the extent of droughts. This improved dataset can play an important role in long-term drought monitoring but also has the potential to assess the impact of drought on the agricultural, forestry, ecological and environmental sectors.

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“…It should be noted that the 2020-2021 winter was a very unusual La Niña winter, partly due to the development of sudden stratospheric warming that led to a disruption of the stratospheric polar vortex (Dunn et al, 2022; Lu Seven of the 22 historical Atlantic Niño events followed the spring or winter peak of (+) AMM, while 13 followed the spring or winter peak of (−) AMM, and only two followed AMM-neutral spring or winter (Figure S8k in Supporting Information S1). The (+) AMM develops in conjunction with a decrease in the trade winds in the tropical North Atlantic (Figures S8g-S8i in Supporting Information S1, e.g., Chiang & Vimont, 2004).…”

Section: What Caused the Off-equatorial Westerly Wind Anomalies?mentioning

confidence: 99%

On the Genesis of the 2021 Atlantic Niño

Lee,

Lopez,

Tuchen

et al. 2023

Geophysical Research Letters

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An extreme Atlantic Niño developed in the boreal summer of 2021 with peak‐season sea surface temperature anomalies exceeding 1°C in the eastern equatorial region for the first time since global satellite measurements began in the early 1970s. Here, we show that the development of this outlier event was preconditioned by a series of oceanic Rossby waves that reflected at the South American coast into downwelling equatorial Kelvin waves. In early May, an intense week‐long westerly wind burst (WWB) event, driven by the Madden‐Julian Oscillation (MJO), developed in the western and central equatorial Atlantic and greatly amplified one of the reflected Kelvin waves, directly initiating the 2021 Atlantic Niño. MJO‐driven WWBs are fundamental to the development of El Niño in the Pacific but are a previously unidentified driver for Atlantic Niño. Their importance for the 2021 event suggests that they may serve as a useful predictor/precursor for future Atlantic Niño events.

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Global Climate

Cited by 22 publications

References 0 publications

Global assessment of spatiotemporal changes of frequency of terrestrial wind speed

Global assessment of spatiotemporal changes of frequency of terrestrial wind speed

An improved global vegetation health index dataset in detecting vegetation drought

On the Genesis of the 2021 Atlantic Niño

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