NASA has a data product that tracks the amount of chlorophyll in water across the globe.
I downloaded all available 2003-2020 (inclusive) monthly data in 1440 by 720 pixel format to see how chlorophyll in water changes over time.
This task is actually not easy because there’s a lot of missing data (black pixels). I decided to use only non-missing pixels that are persistent across all 216 months. There are 27998 of them. That’s 2.7% of the globe.
This is a map of these persistent locations:
Here’s the trend for these locations:
Change (2003,2020): 2.942 %
Linear Regression Change (2003-2020): 2.736 %That’s a nice positive change.
I then thought about it some more, and decided to do it on a monthly basis. Here’s a map of all persistent January pixels for all 18 years:
And here is All July:
Here is the linear regression trend for every month:
01 -0.046 %
02 +4.834 %
03 +8.112 %
04 +1.008 %
05 -7.842 %
06 -1.898 %
07 +1.130 %
08 +5.267 %
09 +6.818 %
10 +1.999 %
11 +5.372 %
12 +3.786 %
Avg: +2.378 %Enjoy 🙂 -Zoe
Update
Some more data:
Note the rise in 1997 to 1998. That was a very warm season. Look at the rise! How can anyone be against global warming, except the cult of death?
Code
# Zoe Phin, 2021/03/24
# File: chloro.sh
# Run: source chloro.sh; require; download; convert; chloro; cmap; plot; change; annual; months
require() { sudo apt-get install -y gmt gnuplot; }
download() {
rm -f *.csv
for y in {2003..2020}; do
wget -qO- "https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MY1DMM_CHLORA&year=$y"
done | awk -F\' '/"viewDataset/{print $4" "$2}' > sets.csv
awk '{print "wget -O "$1".csv \"https://neo.sci.gsfc.nasa.gov/servlet/RenderData?si="$2"&cs=rgb&format=CSV&width=1440&height=720\""}' sets.csv > sets.sh
bash sets.sh
}
convert() {
for f in $(ls -1 2*.csv); do
cat $f | tr ',' '\n' > ${f/.csv/.dat}
done
ls -1 *.dat | xargs paste -d ' ' | nl > .db
grep -v '99999.0' .db > chloro.db
rm -f *.dat .db
}
chloro() {
awk 'BEGIN {
a=6378.137; e=1-6356.752^2/a^2; r=atan2(0,-1)/180
} {
l = sprintf("%d", ($1-1)/1440)/4-89.875
A = (a*r)^2*(1-e)*cos(r*l)/(1-e*sin(r*l)^2)^2
SA += A
for (i=2; i<=NF; i++)
S[i] += $i*A
} END {
for (m in S)
printf("%.5f\n", S[m]/SA)
}' chloro.db | awk '{
printf "%.2f %.5f\n", 2003+(NR-1)/12+1/24, $1
}' > chloro.csv
}
cmap() {
awk '{
lat = sprintf("%d", ($1-1)/1440)/4-89.875
lon = ($1-1)%1440*360/1440-180
printf "%.3f %.2f\n", lat, lon
}' chloro.db > cmap.csv
echo "set term png size 560,360
set margin 4,2,2,1
set nokey
set yrange [-90:90]
set xrange [-180:180]
set ytics 30; set xtics 30
set grid ytics xtics
plot 'cmap.csv' u 2:(-\$1) t 'Chlorophyll' pt 5 ps 0.1 lc rgb '#00CC00'
"| gnuplot > cmap.png
}
plot() {
echo -n "Linear Regression Slope: "
cat chloro.csv | gmt gmtregress -Fp -o5 | awk '{printf "%.5f /year\n", $1}'
cat chloro.csv | gmt gmtregress | sed 1d | awk '{printf "%.2f %.5f %.5f\n", $1, $2, $3}' > plot.csv
echo "set term png size 740,620
set key outside top center horizontal
set mxtics 2; set mytics 2
set format y '%.2f'
set xrange [2003:2021]
set grid xtics mxtics ytics mytics
plot 'plot.csv' u 1:2 t 'Chlorophyll (mg/m³)' w lines lw 2 lc rgb '#00CC66',\
'' u 1:3 t 'Linear Regression' w lines lw 3 lc rgb '#006666'
"| gnuplot > chloro.png
}
change() {
echo -n "Change (2003,2020): "
awk '{Y[substr($1,1,4)]+=$2/12} END { printf "%.3f %\n", (Y[2020]/Y[2003]-1)*100 }' plot.csv
echo -n "Linear Regression Change (2003-2020): "
awk '{Y[substr($1,1,4)]+=$3/12} END { printf "%.3f %\n", (Y[2020]/Y[2003]-1)*100 }' plot.csv
}
annual() {
awk '{Y[substr($1,1,4)]+=$2/12} END { for (y in Y) printf "%s %.5f\n", y, Y[y]}' chloro.csv
}
months() {
for m in {01..12}; do
echo -n "$m "
for f in $(ls -1 2*-$m-*.csv); do
cat $f | tr ',' '\n' > ${f/.csv/.dat}
done
ls -1 2*-$m-*.dat | xargs paste -d ' ' | nl > .db
grep -v '99999.0' .db | tee chloro.db | awk 'BEGIN {
a=6378.137; e=1-6356.752^2/a^2; r=atan2(0,-1)/180
} {
l = sprintf("%d", ($1-1)/1440)/4-89.875
A = (a*r)^2*(1-e)*cos(r*l)/(1-e*sin(r*l)^2)^2
SA += A
for (i=2; i<=NF; i++)
S[i] += $i*A
} END {
for (m in S)
printf("%.7f\n", S[m]/SA)
}' | awk '{
printf "%.2f %.5f\n", 2003+(NR-1), $1
}' | gmt gmtregress | sed 1d | awk '{Y[substr($1,1,4)]+=$3/12} END { printf "%+.3f %\n", (Y[2020]/Y[2003]-1)*100 }'
done | tee .seas
awk '{S+=$2} END { printf "\nAvg: %+.3f %\n", S/NR }' .seas
}
