Variation of the global electric circuit and Ionospheric potential in a general circulation model

Mareev, E. A.; Volodin, E. M.

doi:10.1002/2014gl062352

Cited by 40 publications

(46 citation statements)

References 38 publications

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“…The results of our simulations presented in Figures and , and Table agree with those of Mareev and Volodin (), whose model also produced a significant contribution of ocean convection to the IP with a nearly flat diurnal variation. At the same time estimates on the basis of aircraft and satellite measurements usually indicate that the contribution of oceans to the GEC is indeed flat but rather small (Blakeslee et al, ; Mach et al, ).…”

Section: Resultssupporting

confidence: 88%

“…To summarize, neither of the existing attempts to simulate the diurnal variation of the GEC using a climate or weather forecasting model has succeeded in obtaining a realistic peak‐to‐peak amplitude (cf. Mareev & Volodin, , Figure 1a; Lucas et al, , Figure 7; Jánský et al, , Figure 3a). Our analysis has shown that when parameterizing the IP, it is important to take into account both convective activity (CAPE) and the area covered by electrified clouds (which can be inferred from the amount of precipitation), but neither of various reasonable alterations of our basic IP parameterization is able to significantly improve the agreement in magnitude between the simulated IP variation and the classical Carnegie curve.…”

Section: Resultsmentioning

confidence: 91%

“…The developed parameterization takes into account the effective area covered by such clouds in each grid cell, estimated on the basis of calculated precipitation, and the altitudes of their lower and upper boundaries, calculated within the model. The resulting diurnal variation of the IP obtained by Mareev and Volodin () with this parameterization turned out to be more or less similar to that of the classical Carnegie curve, but with a smaller peak‐to‐peak variation.…”

Section: Introductionmentioning

confidence: 71%

“…By the introduction of the dimensionless parameter

α_{i}

, specifying the portion of the area of the grid column occupied by GEC generators, we try to take into consideration the fact that relatively large grid columns, generally speaking, are not completely covered by clouds. To parameterize

α_{i}

, we follow the approach suggested by Mareev and Volodin (), who assumed this coefficient to be proportional to the ratio

P_{i} false/ W_{i}

of the precipitation

P_{i}

per model time step (1 hr) and the total amount of precipitable water

W_{i}

(defined as the total mass of water vapor in the air column). In this study we also assume that

α_{i} = k \cdot \frac{P_{i}}{W_{i}}

with some proportionality coefficient

k

, although we will not strictly specify the length of the time interval over which

P_{i}

is totaled and consider several different values.…”

Section: Parameterization Of the Ionospheric Potentialmentioning

confidence: 99%

See 3 more Smart Citations

Toward a Realistic Representation of Global Electric Circuit Generators in Models of Atmospheric Dynamics

2020

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The diurnal variation of the global electric circuit (GEC) is simulated using the Weather Research and Forecasting model by estimating contributions of grid columns to the ionospheric potential (IP). A parameterization based on cutting off the grid columns with shallow convection and estimating the area covered by GEC generators (electrified clouds) in each column from the amount of precipitation yields the IP variation of the same shape as the classical Carnegie curve with a correlation coefficient of 0.97, although with a smaller peak‐to‐peak amplitude (18% against 34% of the mean) and maxima and minima shifted by 1–2 hr. It is shown that omission in the IP parameterization of either convective activity or the area covered by electrified clouds (estimated using the calculated precipitation) would result in poor similarity between the modeled IP variation and the classical Carnegie curve. The results of simulation show the best agreement with the Carnegie data during Northern Hemisphere winters; the shape of the simulated diurnal variation of the IP substantially changes throughout the year owing to the decrease of South America's contribution and the increase of Southern Asia's contribution in summer. The discrepancies between the results of modeling and the results of observations are probably caused by the limited ability of large‐scale models to differentiate between electrified and nonelectrified clouds. Further improvement of the parameterization of the IP in models of atmospheric dynamics requires using finer grids, which should allow computing microphysical characteristics of clouds and estimating source currents more accurately.

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Section: Resultssupporting

confidence: 88%

Section: Resultsmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 71%

“…By the introduction of the dimensionless parameter

α_{i}

α_{i}

, we follow the approach suggested by Mareev and Volodin (), who assumed this coefficient to be proportional to the ratio

P_{i} false/ W_{i}

of the precipitation

P_{i}

per model time step (1 hr) and the total amount of precipitable water

W_{i}

(defined as the total mass of water vapor in the air column). In this study we also assume that

α_{i} = k \cdot \frac{P_{i}}{W_{i}}

with some proportionality coefficient

k

, although we will not strictly specify the length of the time interval over which

P_{i}

is totaled and consider several different values.…”

Section: Parameterization Of the Ionospheric Potentialmentioning

confidence: 99%

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Toward a Realistic Representation of Global Electric Circuit Generators in Models of Atmospheric Dynamics

2020

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“…It is important to know which clouds produce Wilson currents, how the strengths of those currents vary between electrified cloud types, whether the currents contribute to (upward‐directed) or discharge (downward‐directed) the circuit, and how these clouds are distributed across the globe. These questions are still under active discussion [see Williams , ; Williams and Mareev , for a review] including the questions of how much current is contributed by ESCs compared to thunderstorms [ Mareev and Volodin , ], and to what extent slow transients from lightning discharges contribute to this DC branch of the GEC [ Mareev et al , ].…”

Section: Introductionmentioning

confidence: 99%

A TRMM/GPM retrieval of the total mean generator current for the global electric circuit

et al. 2017

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A specialized satellite version of the passive microwave electric field retrieval algorithm (Peterson et al., 2015) is applied to observations from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) satellites to estimate the generator current for the Global Electric Circuit (GEC) and compute its temporal variability. By integrating retrieved Wilson currents from electrified clouds across the globe, we estimate a total mean current of between 1.4 kA (assuming the 7% fraction of electrified clouds producing downward currents measured by the ER‐2 is representative) to 1.6 kA (assuming all electrified clouds contribute to the GEC). These current estimates come from all types of convective weather without preference, including Electrified Shower Clouds (ESCs). The diurnal distribution of the retrieved generator current is in excellent agreement with the Carnegie curve (RMS difference: 1.7%). The temporal variability of the total mean generator current ranges from 110% on semi‐annual timescales (29% on an annual timescale) to 7.5% on decadal timescales with notable responses to the Madden‐Julian Oscillation and El Nino Southern Oscillation. The geographical distribution of current includes significant contributions from oceanic regions in addition to the land‐based tropical chimneys. The relative importance of the Americas and Asia chimneys compared to Africa is consistent with the best modern ground‐based observations and further highlights the importance of ESCs for the GEC.

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A global electric circuit model within a community climate model

2015

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To determine the complex dependencies of currents and electric fields within the Global Electric Circuit (GEC) on the underlying physics of the atmosphere, a new modeling framework of the GEC has been developed for use within global circulation models. Specifically, the Community Earth System Modeling framework has been utilized. A formulation of atmospheric conductivity based on ion production and loss mechanisms (including galactic cosmic rays, radon, clouds, and aerosols), conduction current sources, and ionospheric potential changes due to the influence of external current systems are included. This paper presents a full description of the calculation of the electric fields and currents within the model, which now includes several advancements to GEC modeling as it incorporates many processes calculated individually in previous articles into a consistent modeling framework. This framework uniquely incorporates effects from the troposphere up to the ionosphere within a single GEC model. The incorporation of a magnetospheric potential, which is generated by a separate magnetospheric current system, acts to modulate or enhance the surface level electric fields at high‐latitude locations. This produces a distinct phasing signature with the GEC potential that is shown to depend on the observation location around the globe. Lastly, the model output for Vostok and Concordia, two high‐latitude locations, is shown to agree with the observational data obtained at these sites over the same time period.

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Variation of the global electric circuit and Ionospheric potential in a general circulation model

Cited by 40 publications

References 38 publications

Toward a Realistic Representation of Global Electric Circuit Generators in Models of Atmospheric Dynamics

Toward a Realistic Representation of Global Electric Circuit Generators in Models of Atmospheric Dynamics

A TRMM/GPM retrieval of the total mean generator current for the global electric circuit

A global electric circuit model within a community climate model

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