Snow in the Era of Global Warming

Is anyone curious to know what the global snowfall trend was in this era of “extreme” global warming?

I was. Luckily NASA covertly provides us with all the necessary data to figure this out.

March 2021

I downloaded all available monthly images from 1980 to 2020 (inclusive), such as the one shown above, then I converted the pixel colors back to data using the provided scale.

The error margin is small and time-persistent and so this is a clever way to extract a rich dataset which I haven’t been able to find anywhere else.

As far as I know, you will not see this anywhere else. All other snowfall or snow-cover datasets are limited by region or date and so researchers reach the wrong conclusion!

Here is the result of my quest:

Global Snowfall
2.773 -> 2.854 is +2.90%

Snowfall has increased by nearly 3 percent over the last four decades!

Units are milligrams per square meter per second.

Let’s also see how this breaks down by North and South hemisphere:

North Hemisphere Snowfall
2.722 -> 2.468 is -9.35%
South Hemisphere Snowfall
2.824 -> 3.239 is +14.71%

SH increase in snow more than compensates NH decrease in snow. This led to an overall increase in snow during our great era of global warming!

Chart data is archived @ https://pastebin.com/raw/XpsVwdjj

That’s it. Enjoy 🙂 -Zoe

Code:

# Zoe Phin, v2 - 2021/05/07
# File: snow.sh
# Run: source snow.sh; download; index; plots
# Output: snow.png

require() { sudo apt-get install -y gmt gnuplot netpbm; }

download() {
    for y in {1980..2020}; do
        for m in {01..12}; do
            d="$y-$m-01"
            echo "wget -O $d.png 'https://gibs.earthdata.nasa.gov/wms/epsg4326/all/wms.cgi?REQUEST=GetMap&SERVICE=WMS&FORMAT=image/png&VERSION=1.1.1&SRS=epsg:4326&BBOX=-180,-90,180,90&TRANSPARENT=TRUE&WIDTH=360&HEIGHT=180&LAYERS=MERRA2_Snowfall_Monthly&TIME=$d'"
        done
    done > sets.sh
    bash sets.sh
}

scale() {
    for m in {01..12}; do
        pngtopnm 2020-$m-01.png | pnmtoplainpnm | sed '1,3d;s/  /\n/g' | awk '{
            printf "%03d %03d %03d\n", $1, $3, $2}' 
    done | sort -r | uniq | awk '{
        printf "s/%s %s %s/%0.2f/\n", $1, $3, $2, (NR-1)/140*7 }' > replace.sed
}

all() {
    pngtopnm $1 | pnmtoplainpnm | sed '1,3d;s/  /\n/g' | awk '{
    printf "%03d %03d %03d\n", $1, $2, $3}' | sed -f replace.sed | awk '{
        l=sprintf("%d",(NR-1)/360)-89.5; a=6378.137; e=1-6356.752^2/a^2; r=atan2(0,-1)/180; 
        A=(a*r)^2*(1-e)*cos(r*l)/(1-e*sin(r*l)^2)^2; SA+=A; S+=$1*A
    } END { printf "%.6f\n", S/SA }'
}

nhs() {
    pngtopnm $1 | pnmtoplainpnm | sed '1,3d;s/  /\n/g' | sed -n 1,32400p | awk '{
    printf "%03d %03d %03d\n", $1, $2, $3}' | sed -f replace.sed | awk '{
        l=sprintf("%d",(NR-1)/360)-89.5; a=6378.137; e=1-6356.752^2/a^2; r=atan2(0,-1)/180; 
        A=(a*r)^2*(1-e)*cos(r*l)/(1-e*sin(r*l)^2)^2; SA+=A; S+=$1*A
    } END { printf "%.6f\n", S/SA }'
}

shs() {
    pngtopnm $1 | pnmtoplainpnm | sed '1,3d;s/  /\n/g' | sed -n 32401,64800p | awk '{
    printf "%03d %03d %03d\n", $1, $2, $3}' | sed -f replace.sed | awk '{
        l=sprintf("%d",(NR-1)/360)+0.5; a=6378.137; e=1-6356.752^2/a^2; r=atan2(0,-1)/180; 
        A=(a*r)^2*(1-e)*cos(r*l)/(1-e*sin(r*l)^2)^2; SA+=A; S+=$1*A
    } END { printf "%.6f\n", S/SA }'
}

index() {
    scale
    for f in $(ls -1 [12]*.png); do echo -n "${f/.png/} "; all $f; done | tee all.csv
    for f in $(ls -1 [12]*.png); do echo -n "${f/.png/} "; nhs $f; done | tee nhs.csv
    for f in $(ls -1 [12]*.png); do echo -n "${f/.png/} "; shs $f; done | tee shs.csv
}

linear() {
    cat $1.csv | sed \$d | awk '{ "date +%Y\\ %j -d "$1 | getline t; print t" "$2 }' | awk '
        {printf "%.4f %s\n", $1+$2/365, $3}' | gmt gmtregress | awk '
        NR>1 { printf "%.6f\n", $3 }' | tee .lin | sed -n '1p;$p' | tr '\n' ' ' | awk '{
        printf "%.4f -> %.4f is %+0.2f%\n", $1, $2, ($2/$1-1)*100 }'
}

plot() { 
    echo -n "$1: "
    linear $1; paste -d ' ' $1.csv .lin > plot.csv
    echo "set term png size 740,470
        set key outside top center horizontal
        set timefmt '%Y-%m-%d'
        set xdata time
        set xtics format '%Y'
        set ytics format '%.1f'
        set xtics 157788864
        set ytics 0.2; set mxtics 5; set mytics 2
        set xrange ['1979-11-01':'2021-03-01']
        set grid xtics mxtics ytics
        plot 'plot.csv' u 1:(10*\$2) t 'Snowfall (mg/m²/s)' w lines lw 2 lc rgb '#0000CC',\
                     '' u 1:(10*\$3) t 'Linear Regression'  w lines lw 3 lc rgb '#000055'		
    " | gnuplot > snow-$1.png 
}

plots() { plot all; plot nhs; plot shs; }

archive() {
    ( echo "Date,Global,NH,SH"; 
    paste all.csv nhs.csv shs.csv | awk '{print $1","$2","$4","$6}' ) > data.csv
}

Published by Zoe Phin

https://phzoe.com

17 thoughts on “Snow in the Era of Global Warming

  1. Could be that the strengthening of the Antarctic sink preferentially pulls more air and moisture southwards as time goes on. Hydrological cycle has probably strengthened in response to warming waters and faster air flows driving evaporation.

    Liked by 1 person

  2. Zoe,
    looks to me like the area south of the 60th parallel south….

    https://en.wikipedia.org/wiki/60th_parallel_south

    … makes up at least 10% of the image you referenced.

    Now suppose this is an area where an increase in snowfall has been observed –

    then you have a serious problem with weighting because, in reality, the area south of 60 degrees south only makes up 6.7% of Earth’s total.

    IOW,
    it appears your analysis is based on a distorted world map that would give any trend near the South Pole too much weight.

    Like

  3. Ok, so to be clear –

    roughly 10% of the images pixels are south of 60 degrees south latitude, but those pixels are only given 6.7% of the global trend’s weight, correct?

    Like

  4. Is anyone interested that the world goes through cycles, goes through natural changes, and long before human beings ever entered the scene, the Earth went through warming and ice-ages. Perhaps the dinosaurs were smoking cigarettes.

    Like

  5. Hi There Zoe,

    Long time reader who greatly enjoys your data and fact driven approach. Thank you. I realize that this work takes time and offers no remuneration so it is simply an act of selflessness that is rare these days.

    Anyways, my reason for posting is to ask whether you might be interested in updating some math that was done by another data driven climate change skeptic. His name is Greg Goodman and he did an analysis of Arctic Sea Ice minima showing the downward trend had reversed.

    The math is above my pay grade, but it looks to be right up your alley.

    Either way – thanks again for this great site!

    Joe in NY

    https://judithcurry.com/2016/09/18/is-the-arctic-sea-ice-spiral-of-death-dead/
    https://climategrog.wordpress.com/

    Like

  6. Newscasters say we have had an unusual amount of snow in southern Brazil. A respectable hail fell in Paraná a couple of days back. The week before, a sliver of ice thinner than a windowpane actually made the news.

    Like

  7. I believe that the “natural ingredients” that enter the equation of climate are so numerous, the components currently impossible for “experts” to quantify/qualify, and the results of interactions and periodic changes, are so impossible for them to enter into computer models that whenever they do come up with a computer model, entering things like C02, smog, and such, that it can never return results that match what happens in real life. Remember, even those climate scientists can’t accurately predict day to day, but they stare and go with the most “accurate” model, which always ends up being filled with innacuracies, whethe they inform or not.

    Liked by 1 person

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