The effect of two types of El Niño on the southerly low‐level jets in North America

Yu, Lejiang; Zhong, Shiyuan; Heilman, Warren E.; Bian, Xindi

doi:10.1002/2016ea000164

Cited by 4 publications

(4 citation statements)

References 58 publications

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“…(2017) found a difference between precipitation in Mexico between El Niño and La Niña years which depended on seasonality, considered to be due to a southwards shift in the sub‐tropical jet (STJ; Magaña et al ., 2003). The influence of ENSO on the location of STJ is also important in determining whether weather systems from the North propagate into Mexico (Yu et al ., 2016). Some authors have also noted differences in surface wind patterns with ENSO (e.g., Vega et al ., 1998; Maldonado et al ., 2018).…”

Section: Introductionmentioning

confidence: 99%

Drivers of extreme wind events in Mexico for windpower applications

Thomas

Martínez‐Alvarado

Drew

et al. 2020

Intl Journal of Climatology

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In this study, we use a k-mean clustering approach to investigate the weather patterns responsible for extreme wind speed events throughout Mexico using 40 years of the ERA-5 atmospheric reanalysis. Generally, we find a large geographical split between the weather patterns that generate the strongest winds across the country. The highest wind power production periods therefore occur at different times in different regions across the country. In the South, these are associated with cold surge events, where an anticyclone is present in the Gulf of Mexico resulting in a strong Northerly flow across the Isthmus of Tehuantepec. In the NorthEast , Easterly trade winds are responsible for the strongest wind events, whereas in the NorthWest , it is the proximity of the North Pacific High. However, the weakest winds and lowest power production periods occur at the same times for all stations with the exception of Baja California Sur, meaning that low wind power production may be unavoidable at these times. The El Niño Southern Oscillation is found to influence wind speeds at some locations across Mexico at sub-seasonal timescales. We report that statistically stronger wind speeds are observed during the Summer during El Niño months than during La Niña months for both sites in Chiapas and Oaxaca.

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Section: Introductionmentioning

confidence: 99%

Drivers of extreme wind events in Mexico for windpower applications

Thomas

Martínez‐Alvarado

Drew

et al. 2020

Intl Journal of Climatology

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“…Currently, monthly predictions of mid to late-summer circulation are heavily influenced by SST anomaly conditions. Opposite SST anomalies in the tropical Pacific and North Atlantic often result in low-level circulation patterns that favor Great Plains LLJ strengthening (Hu and Feng 2012;Patricola et al 2015;Weaver et al 2009b;Weaver 2013;Weaver et al 2016;Yu et al 2017). Spring LLJs have been linked to the cool phase of El Niño-Southern Oscillation (ENSO), whereas the summer LLJ has been linked to the warm phase (Danco and Martin 2018;Krishnamurthy et al 2015;Ting and Wang 1997;Trenberth and Guillemot 1996;Yu et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Opposite SST anomalies in the tropical Pacific and North Atlantic often result in low-level circulation patterns that favor Great Plains LLJ strengthening (Hu and Feng 2012;Patricola et al 2015;Weaver et al 2009b;Weaver 2013;Weaver et al 2016;Yu et al 2017). Spring LLJs have been linked to the cool phase of El Niño-Southern Oscillation (ENSO), whereas the summer LLJ has been linked to the warm phase (Danco and Martin 2018;Krishnamurthy et al 2015;Ting and Wang 1997;Trenberth and Guillemot 1996;Yu et al 2016). ENSO state influences a circulation response, but the moisture supply for the extreme events originates in the subtropical Atlantic (Li et al 2016;Li et al 2017;Veres and Hu 2013).…”

Section: Introductionmentioning

confidence: 99%

Predictability of Midsummer Great Plains Low-Level Jet and Associated Precipitation

Malloy

Kirtman

2020

Weather and Forecasting

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Warm-season precipitation in the U.S. “Corn Belt,” the Great Plains, and the Midwest greatly influences agricultural production and is subject to high interannual and intraseasonal variability. Unfortunately, current seasonal and subseasonal forecasts for summer precipitation have relatively low skill. Therefore, there are ongoing efforts to understand hydroclimate variability targeted at improving predictions, particularly through its primary transporter of moisture: the Great Plains low-level jet (LLJ). This study uses the Community Climate System Model, version 4 (CCSM4), July forecasts, made as part of the North American Multi-Model Ensemble (NMME), to assess skill in reproducing the monthly Great Plains LLJ and associated precipitation. Generally, the CCSM4 forecasts capture the climatological jet but have problems representing the observed variability beyond two weeks. In addition, there are predictors associated with the large-scale variability identified through linear regression analysis, shifts in kernel density estimators, and case study analysis that suggest potential for improving confidence in forecasts. In this study, a strengthened Caribbean LLJ, negative Pacific–North American (PNA) teleconnection, El Niño, and a negative Atlantic multidecadal oscillation each have a relatively strong and consistent relationship with a strengthened Great Plains LLJ. The circulation predictors, the Caribbean LLJ and PNA, present the greatest “forecast of opportunity” for considering and assigning confidence in monthly forecasts.

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“…On seasonal time-scales, Bravo-Cabrera et al (2017) found a difference between precipitation in Mexico between El Niño and La Niña years which depended on seasonality, considered to be due to a southwards shift in the sub-tropical jet (STJ; Magaña et al, 2003). The influence of ENSO on the location of STJ is also important in determining whether weather systems from the North propagate into Mexico (Yu et al, 2016). Some authors have also noted differences in surface wind patterns with ENSO (e.g., Vega et al, 1998;Maldonado et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Drivers of Extreme Wind Events in Mexico for Wind Power Applications

Thomas¹,

Martínez‐Alvarado²,

Drew

et al. 2020

Preprint

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<p>In this talk, we investigate the causes of the strongest and weakest winds observed across Mexico and explore the consequences of these to current and future wind energy production in the country. Using 40 years of the ERA-5 atmospheric reanalysis data, we find that the strongest winds in this region are caused by cold surges, where an anticyclone moves South from the Central United States of America resulting in strong Northerly winds across the Gulf of Mexico which channel through the gap in the mountains to the South of Mexico. Other regions have different drivers for high and low wind speed events. The strongest winds across the East coast of Mexico originate from Easterly trade winds propagating across the Gulf of Mexico, whereas those in Baja California Sur are influenced by the proximity of the North Pacific High. These regions in Mexico have&#160;peak (and sustained low) wind speeds at different times of year&#160;which suggests that wind farms in different regions could compliment one another to&#160;optimise wind power generation. However, all stations but&#160;Baja California Sur see the same weather patterns associated with weak wind events, meaning that low wind power production may be unavoidable at these times. The conditions that proceed these sustained periods of strong and weak winds are explored to gain some predictability for wind power applications. The El Nino Southern Oscillation is found to influence wind speeds at some locations across Mexico at sub-seasonal time-scales.</p>

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The effect of two types of El Niño on the southerly low‐level jets in North America

Cited by 4 publications

References 58 publications

Drivers of extreme wind events in Mexico for windpower applications

Drivers of extreme wind events in Mexico for windpower applications

Predictability of Midsummer Great Plains Low-Level Jet and Associated Precipitation

Drivers of Extreme Wind Events in Mexico for Wind Power Applications

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