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

10 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

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