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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ftmpra:oai::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 2020-10-29 application/pdf https://mpra.ub.uni-muenchen.de/103862/ https://mpra.ub.uni-muenchen.de/103862/1/MPRA_paper_103862.pdf en eng https://mpra.ub.uni-muenchen.de/103862/1/MPRA_paper_103862.pdf Allen, David and Sandakchiev, Danail and Hooper, Vincent and Ivanov, Ivan (2020): The Influence of Dust Levels on Atmospheric Carbon Dioxide and Global Temperature. Q54 - Climate Natural Disasters and Their Management Global Warming MPRA Paper NonPeerReviewed 2020 ftmpra 2023-04-09T05:01:21Z 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. Report EPICA ice core Munich Personal RePEc Archive (MPRA - Ludwig-Maximilians-University Munich) |
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Munich Personal RePEc Archive (MPRA - Ludwig-Maximilians-University Munich) |
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language |
English |
topic |
Q54 - Climate Natural Disasters and Their Management Global Warming |
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Q54 - Climate Natural Disasters and Their Management Global Warming Allen, David Sandakchiev, Danail Hooper, Vincent Ivanov, Ivan The Influence of Dust Levels on Atmospheric Carbon Dioxide and Global Temperature |
topic_facet |
Q54 - Climate Natural Disasters and Their Management Global Warming |
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. |
format |
Report |
author |
Allen, David Sandakchiev, Danail Hooper, Vincent Ivanov, Ivan |
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 |
publishDate |
2020 |
url |
https://mpra.ub.uni-muenchen.de/103862/ 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 Allen, David and Sandakchiev, Danail and Hooper, Vincent and Ivanov, Ivan (2020): The Influence of Dust Levels on Atmospheric Carbon Dioxide and Global Temperature. |
_version_ |
1766402199056809984 |