Direct current global electric circuit and tropical modes of climate variability

Autor: Slyunyaev, Nikolay, Ilin, Nikolay, Mareev, Evgeny, Sarafanov, Fedor, Kozlov, Alexander, Shatalina, Maria, Frank-Kamenetsky, Alexander, Price, Colin
Jazyk: angličtina
Rok vydání: 2023
Zdroj: XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
Popis: The ionospheric potential (IP), being the sum of contributions from thunderstorms and electrified shower clouds all over the globe, is arguably the most fundamental characteristic of the direct current global electric circuit (GEC) intensity. The IP variation on different timescales reflects global changes in the distribution of electrified clouds, which, in turn, are closely associated with the dynamics of deep convection. This motivates the search for patterns in the GEC variation which would reflect various modes of climate variability (especially those affecting the tropics, where convection is strongest).Here we report our recent findings in this direction, focusing on two important modes of climate variability which affect tropical convection, namely the El Niño—Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO). Using the results of long-term IP simulations involving the Weather Research and Forecasting model (WRF) and the results of long-term surface potential gradient (PG) measurements at the Vostok station in Antarctica, we compare the data for El Niño and La Niña years and for the eight traditionally distinguished phases of the MJO. This reveals clear and statistically significant effects of both the ENSO and MJO on the main parameters of the GEC.Our findings agree with other observations published in the literature, but simulations also allowed us to identify the mechanisms behind the observed effects, clearly demonstrating how changes in global convection patterns eventually result in the patterns observed in the simulated IP and in the PG measured in Antarctica and other locations.
The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)
Databáze: OpenAIRE