Global temperatures

Introduction

Data from https://data.giss.nasa.gov/gistemp and https://gml.noaa.gov/webdata/ccgg/trends/co2

d1<-read_csv("https://data.giss.nasa.gov/gistemp/tabledata_v4/GLB.Ts+dSST.csv",skip = 1)[,1:13]
d2<-read_csv("https://data.giss.nasa.gov/gistemp/tabledata_v4/NH.Ts+dSST.csv",skip = 1)[,1:13]
d3<-read_csv("https://data.giss.nasa.gov/gistemp/tabledata_v4/SH.Ts+dSST.csv",skip = 1)[,1:13]

d1[,2:13] <- lapply(d1[,2:13], function(x) as.numeric(as.character(x)))
d1<-pivot_longer(d1,cols=2:13)
d2[,2:13] <- lapply(d2[,2:13], function(x) as.numeric(as.character(x)))
d2<-pivot_longer(d2,cols=2:13)
d3[,2:13] <- lapply(d3[,2:13], function(x) as.numeric(as.character(x)))
d3<-pivot_longer(d3,cols=2:13)
d1$hem<-"Global"
d2$hem<-"North"
d3$hem<-"South"
library(lubridate)
d<-rbind(d1,d2,d3)
d$date<-ymd(paste(d$Year,d$name,1))
d<-data.frame(hem=d$hem,date=d$date,temp=d$value)
d %>% pivot_wider(names_from = "hem",values_from = "temp")->d
d<-na.omit(d)

#d$year<-year(d$date)
#d %>% group_by(year) %>% summarise(temp=mean(Global)) %>% aqm::dt()

Temperatures

don <- xts(x = d[,-1], order.by = d$date)
dygraph(don) %>%  dyRoller(rollPeriod = 12)

Northen hemisphere winters and summers

d$year<- year(d$date)
d$month<- month(d$date)
d$month<-ifelse(d$month %in% c(11,12,1,2,3),"winter", "summer")
d[,-c(1,2,4)] %>% pivot_wider(names_from = "month", values_from = "North", values_fn=mean) %>% dygraph()

Carbon dioxide concentrations measured at Mauna Loa

d3<-read_table("https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_mm_mlo.txt", skip=54,col_name=FALSE) 
d3$date<-ymd(paste(d3$X1,d3$X2,1))
dd<-data.frame(date=ymd(paste(d3$X1,d3$X2,1)),co2=d3$X4)

library(xts)
don <- xts(x = dd$co2, order.by = dd$date)
dygraph(don) %>%  dyRoller(rollPeriod = 12)

dd$year<-year(dd$date)
dd %>% group_by(year) %>% summarise(max=max(co2),min=min(co2),dif=max-min) %>% filter(year<2021) -> don

dygraph(don[,c(1,4)]) %>%  dyRoller(rollPeriod = 12)

don %>% ggplot(aes(x=year,y=dif)) + geom_line() +geom_smooth()

Emissions

library(readr)
dd %>% filter(year<2021) %>% group_by(year) %>% summarise(co2=mean(co2)) %>% mutate(dif = co2 - lag(co2, default = first(co2))) ->c2

em<- read_csv("annual-co-emissions-by-region.csv") 
em$`Annual CO2 emissions`<-em$`Annual CO2 emissions`/1000000000
em %>% filter(Entity=="World")->em
dygraph(em[,-c(1:2)])

em<-data.frame(year=em$Year,emission=em$`Annual CO2 emissions`)
merge(c2, em) ->em
em %>% filter(year>1970) %>% ggplot(aes(x=emission/7.8, y=dif, label=year)) +geom_text(size=2) + geom_smooth() + xlab("Emiissions ppm") + ylab("Addition to atmosphere ppm")

em %>% filter(year>1970) %>% mutate(emission=emission/7.8/2) ->em
dygraph(em[,-2])

# library(giscourse)
# con<-sconnect11("clima2")
# query<-"select avg(airtemp) t, yrmon from global_monthly_airtemp group by yrmon"
# d<-try(dbGetQuery(con,query))
# library(lubridate)
# d$year<-year(d$yrmon)
# d %>% group_by(year) %>% summarise(temp=mean(t)) %>% dygraph()