Accurate Global Greening

In a previous post, Fortunate Global Greening, I used low resolution NASA data to determine changes to Vegetation Index. I did this because I didn’t want to spend 5 hours downloading 23 gigabytes of data for the highest resolution. I didn’t think this would matter that much, but unfortunately for me, it does. Here’s the new analysis:

0.3746 --> 0.3937 is +5.08%

I made two other changes. I now use proportion of land, rather than whole globe. That’s why these numbers are much larger than previously. I also use a linear regression so I don’t remove a whole year for a moving average.

The actual global greening this century, using highest resolution data, is a little over 5%, not nearly 10% as I previously found.

Semper veritas. -Zoe

Code

# Zoe Phin, 2021/03/07
# File: veg.sh  (Version 2.0)
# Run: source veg.sh; sets; download; index; plot

sets() {
    for y in {2000..2021}; do
        wget -qO- "https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MOD_NDVI_16&year=$y" 
    done | awk -F\' '/"viewDataset/{print $4" "$2}' > sets.csv 
}

download() {
    rm -f wget.log [0-9]*.csv
    awk '{print "wget -a wget.log -O "$1".csv -c \"https://neo.sci.gsfc.nasa.gov/servlet/RenderData?si="$2"&cs=rgb&format=CSV&width=3600&height=1800\""}' sets.csv > sets.sh
    bash sets.sh
    rm -f 201[789]-12-31.csv
}

index() {
    for f in $(ls -1 2*.csv); do
        echo -n "${f/.csv/} "
        awk -F, '{
            a=6378.137; e=1-6356.752^2/a^2; r=atan2(0,-1)/180;
            l=NR*180/1800-90.05
            A=(0.1*a*r)^2*(1-e)*cos(r*l)/(1-e*sin(r*l)^2)^2
            for (i=1;i<=NF;i++) { 
                if ($i==99999) continue
                SA+=A; S+=$i*A
            }
        } END {
            printf "%.6f\n", S/SA
        }' $f
    done > .csv
}

linear() {
    cat .csv | 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() { 
    linear; paste -d ' ' .csv .lin > plot.csv
    echo "
        set term png size 740,620
        set key outside top center horizontal
        set timefmt '%Y-%m-%d'
        set xdata time
        set xtics format '%Y'
        set ytics format '%4.2f'
        set ytics 0.01
        set mxtics 2
        set mytics 5
        set xrange ['2000-01-01':'2021-02-28']
        set grid xtics mxtics ytics
        plot 'plot.csv' u 1:2 t 'Vegetation Index ' w lines lw 2 lc rgb '#00CC00',\
                     '' u 1:3 t 'Linear Regression' w lines lw 3 lc rgb '#005500'		
    " | gnuplot > veg.png 
}

Published by Zoe Phin

https://phzoe.com

19 thoughts on “Accurate Global Greening

  1. Some observations:

    Low index in January in the height of Northern hemisphere winter.
    Greater inter-annual variability in January than in July
    Index in January is depressed at the end of La Nina cooling events.

    Conclusion, Planet is cooler than desirable. Winter temperature in Northern Hemisphere is sub optimal.

    Liked by 2 people

  2. Hi. Zoe. Thanks for this work of yours. But could you provide a direct link to the NASA data you analyzed? I find a little strange that NASA doesn’t provide a plot of their data — actually this is not the only case, but I can’t figure exactly why. Maybe you have an interpretation? Semper Veritas!

    Like

  3. Hi Zoe,
    I have a question. I read a few comments you made about the Exxon tripe.I was curious do you have anything on that to add to this claim:

    “83% of ExxonMobil’s peer-reviewed research acknowledges that climate change is real and human-caused.
    Source: Supran et al. (2017), “Assessing ExxonMobil’s climate change communications”

    Link:https://www.researchgate.net/publication/319248858_Assessing_ExxonMobil%27s_climate_change_communications_1977-2014

    Thank you for your inputs, brilliant as they usually are.

    Liked by 1 person

    1. Research from avowed communists like Oreskes is suspect. Their conclusions magically match their publicly stated assumptions before research is done. Expect cherrypicking. 83% of what preselected subset? You can’t trust people with a clear agenda, but you know that.

      Either way Exxon shifts the burden to consumers via national policy. They risk very little by going along with the mob. They are also owned by banking entities that invest in renewables. Big oil conspiracy theorists are stuck in 19th century thinking about financial organization.

      Liked by 1 person

  4. Thanks for your work here, and for providing the code, not many people do that. I’m wondering though, if your analysis has the old familiar sensitivity to endpoints graphing problem – i.e. if the trend changes based on the choice of endpoints, it is not a true trend. Might be worth a test, especially since both endpoints are well below the mean value range of .37-.39.

    Because the NVDI has a strong yearly cycle, it creates large Y variance in the data, which lends itself to endpoint sensitivity. This is why in temperature time series, an anomaly baseline is used, and the trend of the anomaly is plotted.

    Liked by 1 person

        1. The problem was that the first time around I plain-averaged grid cells within a latitude, and then I area-weighted the latitudes. That was incorrect, as there is missing data. Then I did it correctly (area-weight each cell), without noticing I did it incorrectly the first time. Oopsie. That’s why the drop from 10 to 5 %.

          Like

    1. No account of albedo change. What is the history of this imbalance? If it’s the same for all time … maybe their GHG hypothesis is in error? Maybe their method is deceptive? Look at my last post. But maybe I’m wrong. Sorry, been very busy lately.

      Like

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