Max.ice.vol extraction in R

setwd("C:/Users/R°/Desktop/")

ice.vol <- read.csv("ice.vol.csv")

ice.vol <- ice.vol[3:(nrow(ice.vol)-2),] # starting from 2007-04-21

ice.vol$date <- date_decimal(ice.vol$date)

max.ice.vol <- NULL

max.ice.vol <- rbind(max.ice.vol,

max(ice.vol$ice.vol[(1):(100)]))

max.ice.vol <- rbind(max.ice.vol,

max(ice.vol$ice.vol[(nrow(ice.vol)-100):nrow(ice.vol)]))

max.ice.vol <- rbind(max.ice.vol,

max(ice.vol$ice.vol[(-100+i*365):(100+i*365)])

max.ice.vol <- cbind(data.frame(year = c(2007:2019)), max.ice.vol)

Was my previous comment deleted?

why does this post limit data to 2007-2019 when the data goes back to 2003?

Trends since 2003, 2004, 2005, and 2006 are all negative.

and if you take into account seasonal variation, the trend of maximum sea ice since 2007 is negative too.

https://docs.google.com/spreadsheets/d/1SAxunOY7mVuCSQbY2lbAlH1u5tUdwSbLMHfs4l793ho/edit

A more robust robust statistical approach would be something like STL decomposition that incorporates seasonal fluctuations. I ran the stats in R (on the same 12 year period), here are the results:

https://imgur.com/a/sKCyW1v

.no long-term positive or negative trend. short-term time-frame only shown inter-annual variation.

https://imgur.com/a/gpob3LU

Also, data from other sources that support anthropocentric climate change also show no significant trend in ice volume since 2007. This information is not excluded or hidden; it's looked at in the context of trends since 1979.