# introduction
– Example from
– Raw data from
“`{r message=FALSE}
#install.packages(“tidyverse”)
library(tidyverse)
library(lubridate)
#install.packages(“fpp2”)
library(fpp2)
library(zoo)
“`
“`{r message=FALSE}
if(!file.exists(“co2_mm_mlo.txt”))
download.file(“ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_mlo.txt”,
“co2_mm_mlo.txt”)
co2 <- read_table("co2_mm_mlo.txt",
comment="#",
col_names = c("year", "month", "decimal_date", "average",
"interpolated", "trend", "days"),
na = c("-1", "-99.99"))
co2
```
```{r}
ggplot(co2, aes(x = decimal_date, y = average)) + geom_line()
```
```{r}
co2 %>%
gather(series, co2_conc, average, interpolated, trend) %>%
ggplot(aes(x = decimal_date, y = co2_conc, col = series)) +
geom_line()
“`
“`{r}
co2 %>%
ggplot(aes(x = decimal_date)) +
geom_line(aes(y = trend, color = “trend”)) +
geom_line(aes(y = average))
“`
————————
# Exercise I: Temperature Data
Each of the last years has consecutively set new records on global climate. In this section we will analyze global mean temperature data.
Data from:
The data described the level Global Land-Ocean Temperature, and the measurements are made by year which from 1800 to 2018.The uncertainty measurement are Lowess. The temperate measurements the globle surface average.
The data type is double which shows at the bottom. Most of the temperature list are showing double data which is all number, so that it is eaiser to compare with each other.
“`{r}
typeof(c(315.71, 317.45, 317.50))
“`
The data list
“`{r message=FALSE}
co2 <- read_table(file = "http://climate.nasa.gov/system/internal_resources/details/original/647_Global_Temperature_Data_File.txt",
skip = 6,
col_names = c("year", "No_Smoothing", "Lowess(5)" ),
col_types=cols(
year = col_integer(),
No_Smoothing = col_double(),
`Lowess(5)` = col_double()
))
View(co2)
```
However, the data are missing the control group, which is the original smoothing value and the lowess value. The data that showed on the list are only showed the trend of it. To the result that the data is meaningless after 1940 which is the one that have first time positive data of smoothing.
The data trend graph
```{r}
co2 %>% reshape2::melt(‘year’)%>%
ggplot(aes(x = year, y=value)) +
geom_line(aes(color = variable))
“`
From the graph and data we can see the significant upward trend is after 1976 and between 1940 to 1944, the No Smoothing and the lowess(5) are showing the close trend. Nevertheless the “No Smoothing” are more more fluctuations up and down.
According to Karl Mathiesen and the trend from top side, there is no true “pause” in warming. The global warming continues because of we are keep burning fossil fuels and warming the big environment such as driving.
## Question 5: Rolling averages
– What is the meaning of “5 year average” vs “annual average”?
– Construct 5 year averages from the annual data. Construct 10 & 20-year averages.
– Plot the different averages and describe what differences you see and why.
# Exercise II: Melting Ice Sheets?
– Data description:
– Raw data file:
## Question 1:
– Describe the data set: what are the columns and units? Where do the numbers come from?
– What is the uncertainty in measurment? Resolution of the data? Interpretation of missing values?
## Question 2:
Construct the necessary R code to import this data set as a tidy `Table` object.
## Question 3:
Plot the data and describe the trends you observe.
# Exercise III: Rising Sea Levels?
– Data description:
– Raw data file:
## Question 1:
– Describe the data set: what are the columns and units?
– Where do these data come from?
– What is the uncertainty in measurment? Resolution of the data? Interpretation of missing values?
## Question 2:
Construct the necessary R code to import this data set as a tidy `Table` object.
## Question 3:
Plot the data and describe the trends you observe.
# Exercise IV: Arctic Sea Ice?
–
–
## Question 1:
– Describe the data set: what are the columns and units?
– Where do these data come from?
– What is the uncertainty in measurement? Resolution of the data? Interpretation of missing values?
## Question 2:
Construct the necessary R code to import this data set as a tidy `Table` object.
## Question 3:
Plot the data and describe the trends you observe.
# Exercise V: Longer term trends in CO2 Records
The data we analyzed in the unit introduction included CO2 records dating back only as far as the measurements at the Manua Loa observatory. To put these values into geological perspective requires looking back much farther than humans have been monitoring atmosopheric CO2 levels. To do this, we need another approach.
[Ice core data](http://cdiac.ornl.gov/trends/co2/ice_core_co2.html):
Vostok Core, back to 400,000 yrs before present day
– Description of data set:
– Data source: