The Influence of Dust Levels on Atmospheric Carbon Dioxide and Global Temperature

The purpose of this paper is to examine the causality between DUST, CO2 and temperature for the Vostok ice core data series [Vostok Data Series], dating from 420 000 years ago, and the EPICA C Dome data going back 800 000 years. In addition, the time-varying volatility and coefficient of variation i...

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Main Authors: Allen, David, Sandakchiev, Danail, Hooper, Vincent, Ivanov, Ivan
Format: Report
Language:unknown
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Online Access:https://mpra.ub.uni-muenchen.de/103862/1/MPRA_paper_103862.pdf
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spelling ftrepec:oai:RePEc:pra:mprapa:103862 2023-05-15T16:06:18+02:00 The Influence of Dust Levels on Atmospheric Carbon Dioxide and Global Temperature Allen, David Sandakchiev, Danail Hooper, Vincent Ivanov, Ivan https://mpra.ub.uni-muenchen.de/103862/1/MPRA_paper_103862.pdf unknown https://mpra.ub.uni-muenchen.de/103862/1/MPRA_paper_103862.pdf preprint ftrepec 2020-12-04T13:43:40Z The purpose of this paper is to examine the causality between DUST, CO2 and temperature for the Vostok ice core data series [Vostok Data Series], dating from 420 000 years ago, and the EPICA C Dome data going back 800 000 years. In addition, the time-varying volatility and coefficient of variation in the CO2, dust and temperature is examined, as well as their dynamic correlations and interactions. We find a clear link between atmospheric C02 levels, dust and temperature, together with a bi-directional causality effects when applying both Granger Causality Tests (1969) and multi-directional Non-Linear analogues, i.e. Generalized Correlation. We apply both parametric and non-parametric statistical measures and testing. Linear interpolation with 100 years and 1000 years is applied to the three variables, in order to solve the problem of data points mismatch among them. The visualizations and descriptive statistics of the interpolated variables (using the two periods) show robustness in the results. The data analysis points out that variables are volatile, but their respective rolling mean and standard deviation remain stable. Additionally, 1000 years interpolated data suggests positive correlation between temperature and CO2, while dust is negatively correlated with both temperature and CO2. The application of the non-parametric Generalized Measure of Correlation to our data sets, in a pairwise fashion suggested that CO2 better explains temperature than temperature does CO2, that temperature better explains dust than dust does temperature, and finally that CO2 better explains dust than vice -versa. The latter two pairs of relationships are negative. The summary of the paper presents some avenues for further research, as well as some policy relevant suggestions. CO2, Temperature, Dust, Causality, Ice Core, Generalized Correlation Report EPICA ice core RePEc (Research Papers in Economics)
institution Open Polar
collection RePEc (Research Papers in Economics)
op_collection_id ftrepec
language unknown
description The purpose of this paper is to examine the causality between DUST, CO2 and temperature for the Vostok ice core data series [Vostok Data Series], dating from 420 000 years ago, and the EPICA C Dome data going back 800 000 years. In addition, the time-varying volatility and coefficient of variation in the CO2, dust and temperature is examined, as well as their dynamic correlations and interactions. We find a clear link between atmospheric C02 levels, dust and temperature, together with a bi-directional causality effects when applying both Granger Causality Tests (1969) and multi-directional Non-Linear analogues, i.e. Generalized Correlation. We apply both parametric and non-parametric statistical measures and testing. Linear interpolation with 100 years and 1000 years is applied to the three variables, in order to solve the problem of data points mismatch among them. The visualizations and descriptive statistics of the interpolated variables (using the two periods) show robustness in the results. The data analysis points out that variables are volatile, but their respective rolling mean and standard deviation remain stable. Additionally, 1000 years interpolated data suggests positive correlation between temperature and CO2, while dust is negatively correlated with both temperature and CO2. The application of the non-parametric Generalized Measure of Correlation to our data sets, in a pairwise fashion suggested that CO2 better explains temperature than temperature does CO2, that temperature better explains dust than dust does temperature, and finally that CO2 better explains dust than vice -versa. The latter two pairs of relationships are negative. The summary of the paper presents some avenues for further research, as well as some policy relevant suggestions. CO2, Temperature, Dust, Causality, Ice Core, Generalized Correlation
format Report
author Allen, David
Sandakchiev, Danail
Hooper, Vincent
Ivanov, Ivan
spellingShingle Allen, David
Sandakchiev, Danail
Hooper, Vincent
Ivanov, Ivan
The Influence of Dust Levels on Atmospheric Carbon Dioxide and Global Temperature
author_facet Allen, David
Sandakchiev, Danail
Hooper, Vincent
Ivanov, Ivan
author_sort Allen, David
title The Influence of Dust Levels on Atmospheric Carbon Dioxide and Global Temperature
title_short The Influence of Dust Levels on Atmospheric Carbon Dioxide and Global Temperature
title_full The Influence of Dust Levels on Atmospheric Carbon Dioxide and Global Temperature
title_fullStr The Influence of Dust Levels on Atmospheric Carbon Dioxide and Global Temperature
title_full_unstemmed The Influence of Dust Levels on Atmospheric Carbon Dioxide and Global Temperature
title_sort influence of dust levels on atmospheric carbon dioxide and global temperature
url https://mpra.ub.uni-muenchen.de/103862/1/MPRA_paper_103862.pdf
genre EPICA
ice core
genre_facet EPICA
ice core
op_relation https://mpra.ub.uni-muenchen.de/103862/1/MPRA_paper_103862.pdf
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