L2-S1 Data Analysis - CO2 PDF

L2-S1 Data Analysis - CO2 Empiric Paper Global Warming Cam Tu BUI, [email protected] Nguyen Huong Giang TRUONG, [email protected] Groupe 3 Abstract: Ever since the Industrial Revolution, th...

L2-S1 Data Analysis - CO2 Empiric Paper Global Warming Cam Tu BUI, [email protected] Nguyen Huong Giang TRUONG, [email protected] Groupe 3 Abstract: Ever since the Industrial Revolution, the global annual temperature has been increasing non-stop. Modifiez vos documents à l'aide de Living in an environment that is being seriously polluted, the rate of increase of the temperature l'application has more than doubled in Docs the last 40 years. The results? A planet that has never been hotter. As young people, we arevos Peaufinez thediapositives, key to a sustainable and environmentally oriented future. We must publiez des commentaires prioritize change, et solutions innovate partagez votre présentation and lead the way towards a more sustainable and harmonious pour la modifier en collaboration avec relationship with our planet. Together, we can build d'autres a modern civilization that respects and personnes. nurtures the environment. Therefore, we are here to analyze the impact of CO2 on the changes of the global temperature from 1959 to 2017 by using R programming NON, MERCI 1. Introduction TÉLÉCHARGER L'APPLICATION Climate change is one of the most environmental challenges facing our planet today, driven :  TÉLÉCHARGER L'APPLICATION Climate change is one of the most environmental challenges facing our planet today, driven by the increase in carbon dioxide (CO2) emissions from human activities. Known as a primary greenhouse gas, CO2 emissions heat up the atmosphere, causing a gradual but considerable raise in global temperature. Understanding that the way carbon dioxide emissions affect global warming is crucial for sustainable development and for formulating meaningful policies to mitigate climate change, the central question we want to focus on is How CO2 emissions contribute to global warming and the correlation between the gas emissions and their effect on the atmosphere. The first thing that comes to mind when we consider these problems is understanding what the carbon dioxide actually is. Carbon dioxide is known as heat-trapping gas that comes from the extraction and burning of fossil fuel (like coal and oil, natural gas); from wildfires and from natural processes such as volcanic eruptions. Since the onset of the industrial revolution in the 18th century, human activities have raised atmospheric CO2 by a half. The middle of the 20th century demonstrated an overwhelming increase of CO2 emissions from burning fossil fuel, from 11 billion tons of carbon dioxide per year in 1960s to 36,6 billion tons last year according Global Carbon Budget In the period of 2011-2020, many experts had shown that natural “sinks” processes that remove carbon dioxide from the atmosphere (on land and also in the ocean) could absorb about a half of the quantity of CO2 emitted by human each year. However, the overloading of natural” sinks” due to the excessive greenhouse gas led to an yearly increase in the total amount of carbon dioxide in the atmosphere. Using data concerning the fluctuation of global temperature and carbon dioxide emissions over the world, we try to find the way CO2 emissions contribute to global warming. 2. Methods and data To see if CO2 really affects the temperature, we looked at the temperature data and CO2 data from the year 1959 to 2017 that we got from Moodle in the Data Analysis course. : from the year 1959 to 2017 that we got from Moodle in the Data Analysis course. The « Temperature “anomalies” » dataset: - The first value: The year that we studied include is from 1959 to 2017 - The second value to the fourth value: The average temperature anomaly for each season. DJF (December, January and February) for Winter, MAM (Mars, April and May) for Spring, JJA (June, July and August) for Summer and SON (September, October, November) for Autumn The CO2 dataset: - The first value: - The second value: - The third value: We can use the mean(name_of_the_table$name_of_variable) to find the average of the value, sd(name_of_the_table$name_of_variable) to find the standard deviation of each, min(name_of_the_table$name_of_variable) and max(name_of_the_table$name_of_variable) to find min and max. After that, we use plot(name_of_the_table$name_of_variable) to create a scatter plot Variable Mean SD Min Max DJF 0.3769492 0.435218 -0.41 1.68 MAM 0.3861017 0.4082934 -0.24 1.49 JJA 0.3023729 0.3530426 -0.22 1.1 SON 0.3276271 0.4136271 -0.32 1.28 Monthly average 352.8254 26.77248 315.62 406.13 Trend 352.7717 26.64048 315.7 405.91 3. Results bjn 5. References https://www.nrdc.org/stories/global-warming-101#warming https://www.climate.gov/news-features/understanding-climate/climate-change-atmospheric- carbon-dioxide https://climate.nasa.gov/vital-signs/carbon-dioxide/?intent=121 https://globalcarbonbudget.org/fossil-co2-emissions-at-record-high-in-2023 R script > mean(temp_co2$DJF) > mean(temp_co2$MAM) > mean(temp_co2$JJA) > mean(temp_co2$SON) > mean(temp_co2$`Monthly average`) > mean(temp_co2$Interpolated) : > mean(temp_co2$Interpolated) > mean(temp_co2$Trend) > sd(temp_co2$DJF) > sd(temp_co2$MAM) > sd(temp_co2$JJA) > sd(temp_co2$SON) > sd(temp_co2$`Monthly average`) > sd(temp_co2$Interpolated) > sd(temp_co2$Trend) > min(temp_co2$DJF) > min(temp_co2$MAM) > min(temp_co2$JJA) > min(temp_co2$SON) > min(temp_co2$`Monthly average`) > min(temp_co2$Interpolated) > min(temp_co2$Trend) > max(temp_co2$DJF) > max(temp_co2$MAM) > max(temp_co2$JJA) > max(temp_co2$SON) > max(temp_co2$`Monthly average`) > max(temp_co2$Interpolated) > max(temp_co2$Trend) :