Data Analysis on Climate Change PDF

Data analysis Michele Pezzoni 1  Acknowledgements This class is an adaptation of an exercise proposed by core- econ.org https://www.core-econ.org/doing-economics/book/text/0-3-contents.ht ml...

Data analysis Michele Pezzoni 1  Acknowledgements This class is an adaptation of an exercise proposed by core- econ.org https://www.core-econ.org/doing-economics/book/text/0-3-contents.ht ml 2  Research question Climate change is one of the effects of the rapid economic growth that has occurred in most countries since the Industrial Revolution. RQ1: Is climate change really happening? Aside from changes in the average temperature, the government is also worried that climate change will result in more frequent extreme weather events. RQ2: Will weather become more extreme as a result of climate change? To reply to these questions, we look at the temperature variations 3  Extant work on the subject A New York Times article uses temperature data to investigate the distribution of temperatures and temperature variability over time. Read through the article, paying close attention to the descriptions of the temperature distributions. ( https://www.nytimes.com/interactive/2017/07/28/climate/more-freque nt-extreme-summer-heat.html?emc=edit_th_20170729&nl=todayshe adlines&nlid=36015469 ) 4  Retrieve the temperature data Go to NASA’s Goddard Institute for Space Studies website (https://data.giss.nasa.gov/gistemp/) The default name of this file is NH.Ts+dSST.csv. Give it a suitable name (e.g. NHTemp.csv) and save it in an easily accessible location, such as a folder on your Desktop or in your personal folder. 5  How do we import temperature data into R? Commands: setwd("YOURFILEPATH") tempdata = read.csv("NHTemp.csv",skip=1,na.strings = "***") (or use the import data tool in RStudio) tempdata = data.frame(tempdata) head(tempdata) str(tempdata) The columns labelled DJF, MAM, JJA, and SON contain seasonal averages. The column labelled J–D contains the average temperature anomaly for each year. 6  Temperature “anomalies” In this dataset, temperature is measured as ‘anomalies’ rather than as absolute temperature. NASA’s Frequently Asked Questions -> definition of temperature anomalies ( https://data.giss.nasa.gov/gistemp/faq/#q101) – ‘Temperature anomalies indicate how much warmer or colder the temperature is than normal for a particular place and time. – For the GISS analysis, normal always means the average over the 30-year period 1951–1980 for that place and time of year. […] 7  Create a line chart Choose one month (e.g., January) and plot a line chart with average temperature anomaly on the vertical axis and time (1880–2016) on the horizontal axis. Label each axis appropriately and give your chart a suitable title Commands: plot( tempdata$Year, tempdata$Jan ,type="l",col="blue", lwd = 2, ylab="Annual temperature anomalies", xlab="Year") title("Average temperature anomaly in January \n in the northern hemisphere (1880-2016)") abline(h=0,col="darkorange2",lwd = 2) text(2000,-0.1,"1951-1980 average") 8 January average temperature anomalies 9  Annual average temperature anomalies Plot a line chart with annual average temperature anomaly on the vertical axis and time (1880–2016) on the horizontal axis. plot( tempdata$Year, tempdata$J.D ,type="l",col="blue", lwd = 2, ylab="Annual temperature anomalies", xlab="Year") title("Average annual temperature anomaly \n in the northern hemisphere (1880-2016)") abline(h=0,col="darkorange2",lwd = 2) text(2000,-0.1,"1951-1980 average") 10 Annual average temperature anomalies 11  Conclusion (RQ1) You now have charts for two different time intervals: month and year. For each time interval, discuss what we can learn about temperature patterns? Do you think the government should be concerned about climate change? 12 RQ2: Are we at risk of more extreme events? 13  Plot the summer temperature distribution We create three datasets according to the period of observation Commands: temp21_50=subset(tempdata,(Year >= 1921) & (Year = 1951) & (Year = 1981) & (Year p70) mean(t2) 18  Standard deviation The New York Times article discusses whether temperatures have become more variable over time. One way to measure temperature variability is by calculating the standard deviation of the temperature distribution for each season (e.g., summer, JJA). Calculate the mean and standard deviation separately for the following time periods: – 1921–1950 – 1951–1980 – 1981–2010 19  Standard deviation For summer, compare the standard deviations in different periods, and explain whether or not temperature appears to be more variable in later periods. Commands: table_mean_sd=data.frame(mean=mean(temp21_50$JJA),sd=sd(temp21_50$J JA)) table_mean_sd=rbind(table_mean_sd,c(mean(temp51_80$JJA),sd(temp51_80$ JJA))) table_mean_sd=rbind(table_mean_sd,c(mean(temp81_10$JJA),sd(temp81_10$ JJA))) rownames(table_mean_sd)=c("1921-1950","1951-1980","1981-2010") Summe table_mean_sd r mean sd 1921-1950 -0.0596 0.1459 1951-1980 -0.0003 0.1197 1981-2010 0.4020 0.2610 20  Conclusion Using your analyses, would you advise the government to spend more money on mitigating the effects of extreme weather events? 21