Scholarlink

<p>Large-scale meteorological patterns (LSMPs) and regional mechanisms that promote/inhibit summer rainfall and extremes in Eastern Mexico (EMex) are investigated using an unsupervised neural network, the self-organizing maps (SOMs). Daily sea level pressure, relative humidity at 850&#xa0;hPa, and vorticity at 925&#xa0;hPa from ERA5 were used to drive the SOMs during June-October of 1981–2020. SOMs were classified into nine patterns and the composites of days of each SOM were used to calculate the mean LSMPs linked to extreme rainfall and the possible role of regional climatic drivers (Caribbean low-level jet (CLLJ), Texas-Tamaulipas low-level jet (TTLLJ) defined here for the first time, North Atlantic subtropical high (NASH), and trade winds from the Intertropical Convergence Zone (ITCZ)). Distinct transition to summer and fall patterns were identified. Also, three “dry” SOMs occurred during the mid-summer drought (July–August) characterized by intense NASH, CLLJ, and TTLLJ. In contrast, these mechanisms weakened in the three “wet” SOMs promoting cyclonic circulation in the Gulf of Mexico (GoM) and southwesterly winds and humidity from the ITCZ into EMex. These factors, combined with a large warm pool and negative outgoing long wave radiation, increased convective precipitation and extreme rainfall in EMex during tropical cyclone (TC) and non-TC days. Neutral and La Niña conditions during the positive phase of the Atlantic Multidecadal Oscillation (+ AMO) tend to favor more TCs and extreme rainfall than the -AMO, such as in the last two decades. Non-TC extreme rainfall events contributed more to summer rainfall than TC-derived extremes and occurred independently of AMO phase.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Identifying patterns of summer precipitation and extremes in eastern Mexico with self-organizing maps: large scale and regional drivers

  • 期刊论文

  • 发表日期：

    2025年5月8日

查看全文
  • Gabriela Colorado-Ruiz,
  • Tereza Cavazos

摘要

Large-scale meteorological patterns (LSMPs) and regional mechanisms that promote/inhibit summer rainfall and extremes in Eastern Mexico (EMex) are investigated using an unsupervised neural network, the self-organizing maps (SOMs). Daily sea level pressure, relative humidity at 850 hPa, and vorticity at 925 hPa from ERA5 were used to drive the SOMs during June-October of 1981–2020. SOMs were classified into nine patterns and the composites of days of each SOM were used to calculate the mean LSMPs linked to extreme rainfall and the possible role of regional climatic drivers (Caribbean low-level jet (CLLJ), Texas-Tamaulipas low-level jet (TTLLJ) defined here for the first time, North Atlantic subtropical high (NASH), and trade winds from the Intertropical Convergence Zone (ITCZ)). Distinct transition to summer and fall patterns were identified. Also, three “dry” SOMs occurred during the mid-summer drought (July–August) characterized by intense NASH, CLLJ, and TTLLJ. In contrast, these mechanisms weakened in the three “wet” SOMs promoting cyclonic circulation in the Gulf of Mexico (GoM) and southwesterly winds and humidity from the ITCZ into EMex. These factors, combined with a large warm pool and negative outgoing long wave radiation, increased convective precipitation and extreme rainfall in EMex during tropical cyclone (TC) and non-TC days. Neutral and La Niña conditions during the positive phase of the Atlantic Multidecadal Oscillation (+ AMO) tend to favor more TCs and extreme rainfall than the -AMO, such as in the last two decades. Non-TC extreme rainfall events contributed more to summer rainfall than TC-derived extremes and occurred independently of AMO phase.