Analysis of Carbon Dioxide and Cloud Effects on Temperature in Northeast China
Procedia Computer Science, ISSN: 1877-0509, Vol: 91, Page: 690-699
2016
- 1Citations
- 52Usage
- 18Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations1
- Citation Indexes1
- CrossRef1
- Usage52
- Downloads44
- Abstract Views8
- Captures18
- Readers18
- 18
Article Description
With the observed rise in temperature, many researchers have tried to identify the causes of such climate change to help mitigate its effects. The objective of this study is to determine whether, under the same carbon dioxide (CO 2 ) concentrations, CO 2 with lower cloud coverage would raise the temperature at a greater rate than CO 2 with higher cloud coverage. The hypothesis was tested through data analysis and modeling. The relationships between the temperature and the CO 2 emissions, the temperature and the cloud coverage, and the CO 2 emissions and the cloud coverage were identified using Pearson's correlation test. The data analysis concluded that the relationship between the temperature and the CO 2 emission is positively proportional with a significant correlation. The relationship between the cloud coverage and the temperature and the relationship between the CO 2 emissions and the cloud coverage were determined to be negatively proportional with significant correlations. For modeling, the temperature increased more rapidly as cloud coverage shrank. The results supported the hypothesis that the cloud coverage mitigates warming effects created by carbon dioxide emissions. Further research is anticipated to reduce the uncertainties in the data along with specification of cloud types.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1877050916313655; http://dx.doi.org/10.1016/j.procs.2016.07.172; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84984985801&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S1877050916313655; https://api.elsevier.com/content/article/PII:S1877050916313655?httpAccept=text/xml; https://api.elsevier.com/content/article/PII:S1877050916313655?httpAccept=text/plain; https://scholars.fhsu.edu/geo_facpubs/10; https://scholars.fhsu.edu/cgi/viewcontent.cgi?article=1009&context=geo_facpubs; https://dx.doi.org/10.1016/j.procs.2016.07.172
Elsevier BV
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