Introduction

Anthropgenic C02 emissions are responsible for the contribution made to climate change by industrialised society.

# system ("wget ftp://data.iac.ethz.ch/CMIP6/input4MIPs/UoM/GHGConc/CMIP/mon/atmos/UoM-CMIP-1-1-0/GHGConc/gr3-GMNHSH/v20160701/mole_fraction_of_carbon_dioxide_in_air_input4MIPs_GHGConcentrations_CMIP_UoM-CMIP-1-1-0_gr3-GMNHSH_000001-201412.csv")

#system("wget https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_mm_mlo.txt")

ml<-read.table("mlo.txt")[,c(1,2,4)]
ml$date<-dmy(paste(1,ml$V2,ml$V1))
ml<-data.frame(date=ml$date,co2=ml$V4)

library(readr)
co2<- read_csv("mole_fraction_of_carbon_dioxide_in_air_input4MIPs_GHGConcentrations_CMIP_UoM-CMIP-1-1-0_gr3-GMNHSH_000001-201412.csv")
co2 %>% separate(datetime,into=c("date","time"), sep=" ") -> co2
co2<-data.frame(date=dmy(co2$date),co2=co2$data_mean_global)

co2 %>% filter(date > dmy("01/01(1700")) -> co2

CO2 records

CMIP6

From ftp://data.iac.ethz.ch/CMIP6

dygraph(xts(co2$co2,co2$date)) %>% dyRoller(rollPeriod = 1)

Mauna loa

Raw data

dygraph(xts(ml$co2,ml$date)) %>% dyRoller(rollPeriod = 1)

Difference between high and low each year

Taking the highest and lwest reading within each calendar year.

library(zoo)
ml$year<-year(ml$date)
ml %>% group_by(year) %>% summarise(mean=mean(co2)) ->mly
mly %>% mutate(dif = mean - lag(mean, order_by = year)) ->mly
ggplot(mly,aes(x=year,y=dif)) +geom_line() + geom_smooth()

Rolling lagged 12 month diference

The calendar year is an arbitrary 12 month period. An alternative approach is to lag the whole data set by twelve months and take the running difference between the reading in any month and the reading 12 months previously.

ml %>% arrange(date) %>% mutate(lag=lag(co2, 12,order_by = date)) %>% mutate(dif=co2-lag) ->mll
ggplot(mll,aes(x=date,y=dif)) +geom_line() + geom_smooth()

Rolling lagged 12 month diference dygraph

dygraph(xts(mll$dif,mll$date)) %>% dyRoller(rollPeriod = 12)

Discussion

The

Emissions data from our world in data

library(readr)
d<- read_csv("annual-co2-emissions-per-country(1).csv")
d %>% filter(d$Entity=="World") -> wd
wd<-data.frame(year=wd$Year,co2=wd$`Annual CO2 emissions`)
dygraph(wd) %>% dyRoller(rollPeriod = 1)

References