Global Sea Ice Area 2

I found two more sources of sea ice concentration data. One is the Hadley Centre, in the UK (Description and Data), and the other is NOAA (Description and Data [Used Monthly]).

Here’s what they look like:

Linear Regression Trend: From 0.03789 To 0.03738 is -1.336%
Linear Regression Trend: From 0.03891 To 0.04328 is +11.239%

What to make of such a large difference? I’m not sure. I’ll have to skip this question for now.

Some may say that the Hadley data contradicts my last post, but I’m not too concerned about this because we all know carbon dioxide is not causing sea ice to melt. How do we know this?

Well here’s the same Hadley data with the last 3 years removed:

Linear Regression Trend: From 0.03752 To 0.03815 is +1.664%

Will any climate alarmist seriously argue that carbon dioxide can only melt ice in the last few years but not from 1980 to 2017?

That’s all. Enjoy 🙂 -Zoe

Code hice.sh

# Zoe Phin, 2021/12/04
# File: hice.sh
# Run: . hice.sh; require; download; index; plot
require() { sudo apt-get install -y gmt gnuplot python3-matplotlib python3-xarray; }
download() { wget -O- -c 'https://www.metoffice.gov.uk/hadobs/hadisst/data/HadISST_ice.nc.gz' | zcat > hice.nc; }
index() { echo "import xarray as x; import xarray.ufuncs as u; import matplotlib.pyplot as p
    ice = x.open_dataset('hice.nc').sic
    for m in (ice*u.cos(u.deg2rad(ice.latitude))).mean(['latitude','longitude']).values:
        print(m)" | sed 's/^\t//' | python | awk '{printf "%.2f %7.4f\n", 1870+NR/12-1/24, $1}' > hice.csv
}
plot() { 
    awk '$1>=1980 && $1<=2017 {print}' hice.csv | gmt gmtregress | awk 'NR>1{print $1" "$2" "$3}' > hice.dat
    sed -n '1p;$p' hice.dat | awk 'NR==1{S=$3} NR==2{E=$3} END { printf "\
    Trend: From %.5f To %.5f is %+.3f%\n", S, E, (E/S-1)*100 }'
    echo "set term png size 740,470; unset key; set title 'Global Sea Ice (Hadley)'
    set grid ytics xtics; set mxtics 5; set mytics 5; set ytics format '%.3f'
    set xrange [1979:2021]; plot 'hice.dat' u 1:2 w l lw 1 lc rgb '#0000EE',\
         'hice.dat' u 1:3 w l lw 2 lc rgb '#000088" | gnuplot > hice.png
}

Code icec.sh

# Zoe Phin, 2021/12/04
# File: icec.sh
# Run: . icec.sh; require; download; index; plot
require() { sudo apt-get install -y gmt gnuplot nco; }
download() { wget -O icec.nc -c 'ftp://ftp2.psl.noaa.gov/Datasets/noaa.oisst.v2/icec.mnmean.nc'; }
index() {
    for t in {0..479}; do
        ncks -HC --trd -d time,$t,$t -v icec icec.nc | awk -vt=$t -F '[= ]' '{ 
        if ($8 == 32767) V=0; else V=$8/100+327.65
        a=6378.137; e=1-6356.752^2/a^2; r=atan2(0,-1)/180;
        A=(a*r)^2*(1-e)*cos(r*$4)/(1-e*sin(r*$4)^2)^2
        SA+=A; S+=V*A; } END { printf "%.2f %.5f\n", 1982-1/24+t/12, S/SA/100 }'
    done | tee icec.csv
}
plot() { 
    awk '$1>=1982 && $1<=2020 {print}' icec.csv | gmt gmtregress | awk 'NR>1{print $1" "$2" "$3}' > icec.dat
    sed -n '1p;$p' icec.dat | awk 'NR==1{S=$3} NR==2{E=$3} END { printf "\
    Trend: From %.5f To %.5f is %+.3f%\n", S, E, (E/S-1)*100 }'
    echo "set term png size 740,470; unset key; set title 'Global Sea Ice (NOAA)'
    set grid ytics xtics; set mxtics 5; set mytics 5; set ytics format '%.3f'
    set xrange [1979:2021]; plot 'icec.dat' u 1:2 w l lw 1 lc rgb '#0000EE',\
         'icec.dat' u 1:3 w l lw 2 lc rgb '#000088" | gnuplot > icec.png
}

Published by Zoe Phin

https://phzoe.com

9 thoughts on “Global Sea Ice Area 2

  1. Hi Zoe –
    Great to see you posting more again! I’d missed your last few, just saw this one, then caught up on the others. LOVE your work! +10! 🙂

    Vis a vis the trend, it looks like it’s not even a matter of the last 3 years being the anomaly, it’s actually just the time from the 2017 to the 2018 minima that pulls the trend down, although the 2016 peak was kinda wimpy also. Things were back to normal as the ice was recovering in 2018. So it’s really only a period of ~2 years that are anomalously low, and it seems to have recovered already before we even get to the 2019 tic mark.

    Also cool to see that your pixel-color based method aligned so closely with the actual data!

    Liked by 1 person

  2. “Some differences in seasonal sea ice extent between the Arctic and Antarctic are due to basic geography and its influence on atmospheric and oceanic circulation. The Arctic is an ocean basin surrounded largely by mountainous continental land masses, and Antarctica is a continent surrounded by ocean. In the Arctic, sea ice extent is limited by the surrounding land masses. In the Southern Ocean winter, sea ice can expand freely into the surrounding ocean, with its southern boundary set by the coastline of Antarctica.
    Because Antarctic sea ice forms at latitudes further from the South Pole (and closer to the equator), less ice survives the summer. Sea ice extent in both poles changes seasonally; however, longer-term variability in summer and winter ice extent is different in each hemisphere, due in part to these basic geographical differences.” -quoted from the Royal Society BUT quite accurately describes the Apples and Oranges that is this comparison – fun to do and without functional purpose in understanding anything at all.

    Thanks for keeping on with the great code examples and excellent examples of broken data sets making things harder and harder to comprehend. Geothermal is where warmth begins and ends. Earth atmosphere acts to refrigerate the planet, (chemistry 101) -> the sun warms it in many many ways, some of which are totally absent from consideration in any meaningful way.

    Antartica is covered by land whereas the Arctic is covered by water, ( (salty H2O freezes at ~7C) ) and so one per cent in the north (of total ice mass f loting on water) equals how much in the south?

    Like

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