6 Decades of Snow Water Equivalent

Scientists developed a very high resolution dataset that goes back 6 decades: TerraClimate (Nature link). I am continuing my examination of their data (with model stuffing). Today I will look at their swe series from 1958 to 2021 (inclusive).

swe stands for Snow Water Equivalent.

Snow Water Equivalent – January 2021

Let’s see what the data shows …

I expected an increase given my previous article on global snow trend.

SWE ranges from 46 to 1196 mm. That would be 1150 mm over 64 years, or ~18mm/yr. Amazing!

We got an extra 1.15 meters of snow water equivalent, or 11.5 centimeters of snow over land in 6 decades, and all that extra man-made carbon dioxide failed to melt it. lol

Let’s look at month to month changes …

Difference from Month to Month

That spike is very interesting. What could it be? An error in the data? or maybe this? It is exactly in January 2016. I’m too lazy to investigate this further.

Let’s take a look now at the annualized trend …

Annualized Trend

1981 is the only year when SWE declined.

The trend clearly shows a growth in the accumulation of Snow Water Equivalent. To be more exact:

1959 to 2021 (Inclusive)
Annualized Linear Regression Trend: 1.308 to 1.607: +22.845%

Definite Conclusion: Carbon Dioxide radiative forcing doesn’t work at all in the arctic regions where the bulk of SWE is.

Warmists still have a way out by claiming that warming drives more water vapor to the poles. That’s fine. But please, could you not at the same time tell us that CO2 will melt snow and ice in the arctic directly via the radiative greenhouse effect? Thank you.

Enjoy 🙂 -Zoe


# Zoe Phin, 2022/06/14
# File: swe.sh
# Run: source swe.sh; require; download; extract; plot

require() { sudo apt-get install -y gmt nco gnuplot python3-xarray python3-netcdf4; }
download() {
    for y in {1958..2021}; do
        wget -cO $y.nc "http://thredds.northwestknowledge.net:8080/thredds/fileServer/TERRACLIMATE_ALL/data/TerraClimate_swe_$y.nc"
extract() { echo "import xarray as x; import numpy as n
    a=6378.137; e=1-6356.752**2/a**2; r=n.pi/180
    d = x.open_dataset('1958.nc')['swe']
    for y in range(2021,2022):
        for m in x.open_dataset(str(y)+'.nc')['swe'].weighted(by_lat).mean({'lon','lat'}).values:
    " | sed 's/\t//1' | python3 -u | tee swe.tmp
parse.orig() { awk 'NR==1 { L=$2 } NR>1 { printf "%.3f %.6f\n", 1958+NR/12-1/24, $2 }' swe.tmp; }
parse() { awk 'NR==1 { L=$2 } NR>1 { printf "%.3f %+10.6f\n", 1958+NR/12-1/24, $2-L; L=$2 }' swe.tmp; }
annual() { awk '{S[substr($0,1,4)]+=$2/12} END {for (y in S) printf "%d %.6f\n",y,S[y]}'; }
yoy() { awk '{printf "%s ",$2}' | awk -vp=$1 '{
    for (i=0;i<p/2;i++) print ""
    for (i=p/2;i<=NF-p/2;i++) { s=0
        for (j=i-p/2+1;j<i+p/2;j++) s+=$j/p
            printf "%8.6f\n", s
    } }'
plot() { 
#	parse.orig > swe.csv
#	parse > swe; parse | yoy 240 > swe.yoy
#	paste -d ' ' swe swe.yoy > swe.csv
    echo -n "Annualized Linear Regression Trend: "
    parse | annual | gmt gmtregress | awk 'NR==2{S=$3}END{printf "%.3f to %.3f: %+.3f%\n", S, $3, 100*($3-S)/S}'
    parse | annual | gmt gmtregress > swe.csv
    echo "set term png size 740,540
    set key outside top center horizontal
    set xtics out; set ytics out
    set mxtics 10; set mytics 2
    set grid xtics ytics
    set ytics format '%.1f'
#	set yrange [-21:41]
    set xrange [1959:2021]
    plot 'swe.csv' u 1:2 t 'Snow Water Equivalent (mm)' w lines lw 1 lc rgb 'blue',\\
         'swe.csv' u 1:3 t 'Linear Regression Trend' w lines lw 2 lc rgb '#000044'
    "| gnuplot > swe.png 

Published by Zoe Phin


25 thoughts on “6 Decades of Snow Water Equivalent

  1. Huh. Ain’t it funny how the increased oxidation of hydrocarbons causes increase in oxides of carbon and of hydrogen?


      1. “The trend clearly shows a growth in the accumulation of Snow Water Equivalent”

        Aren’t you saying that there is more SWE?

        “And SWE includes “dry ice”?”
        No, CO2 in all its phases is blamed for enough mischief – I don’t care to pile on.

        Its just I’ve always wondered why such a small quantative increase in such a benign organic trace element as CO2 can have such disasterous result but an even greater increase of another substance (in the form of what is arguably a greater GHG — water vapor — if I was to believe in all I’ve been told) can have no effect whatsoever.

        I’ve always felt that “megadroughts” were half imaginary or hyperbole, and half caused by ~5ky of increasingly competent civil engineering in parallel with ~25ky of increasingly effective freshwater habitat destruction, at least more so than recent CO2 emissions. Increased SWE seems like it might be evidence for the imaginary half.

        Just spitballing, it seems like one (cooling) effect of having a little more available water, as vapor, might conceivably be a little more precipitation, but having garnered little but ridicule for asking the question, I won’t propose any answers.

        I just found your analysis interesting. I hope that there’s more to come.


        1. The atmospheric theory is that CO2’s and H2O’s spectral overlaps make them partners in crime.

          The surface theory is that CO2->Warming Surface->More Evaporation->More water vapor transported to Poles

          The problem is that they want have their cake and eat it too on every front.

          Thanks. I’m still not sure this is a good dataset. I just picked it because of the high resolution (1/24th of a degree!). The fact that it’s land only is concerning.

          We’ll see.


        2. “have their cake and eat it,too”

          Yes. As our friend Willis points out, tropical rain is a net cooling event that occurs regularly. I would go further and say that every sort of precipitation is a cooling event — even in the arctic. Putting twice as much (mole average) water into the hydrologic cycle as CO2 into the atmosphere has a net COOLING effect. Not having the knowledge, intelligence or skill to prove it, however, it is embarassingly more a matter of blind faith belief than fact,

          But you (and Willis) sometimes give me hope.


  2. Zoe, a couple of things.

    First, you say that “Scientists developed a very high resolution dataset that goes back 6 decades.” This is not true. The JMA-55 is just the output of a reanalysis climate model …

    Second, I greatly suspect that you’ve made an error somewhere in your code. Unfortunately, I don’t speak whatever computer language you are using …

    However, there’s a general rule of thumb that snow has about a 10:1 snow to liquid ratio, meaning that 10 inches/cm of snow will yield one inch/cm of water. Given that, your claim of a 1,150 mm increase in snow water equivalent would mean an average increase of 11.5 meters (38 feet) of snow globally … and I don’t believe that for one minute.

    Finally, here’s another reality check. This is the snow water equivalent from another reanalysis model, the ERA5 model. As you can see, over the period of 1960 – present they say that snow water equivalent has increased by about 7 mm. Not 1150 mm as you claim.

    Please check your math.



    1. Willis, there is nothing wrong with my code or math.

      You are free to download my source data yourself and see. You can download first and last files for quick comparison. I take it you know how to process netcdf files? So why didn’t you already do that?

      My first chart is exactly what the data shows.

      The issue is that the data is in some sort of CUMULATIVE form. Because of that, comparing it to ERA5 chart is meaningless.

      I agree that TerraClimate really confused things by making it cumulative. Also, it’s land only. Is ERA5 land only? I doubt it.

      I’m actually using 3 programming languages here: Bash, Awk, Python.

      Python is used for speed. If there was any coding problems it would be in the black-box Python code. I can’t even tell you what it does outside what the API says it does.

      Anyway, just look at the scale of first and last files in a visual netcdf viewer (such as ncview), and you’ll quickly understand.


  3. Thanks, Zoe. You say:

    “The problem that the data is in some sort of CUMULATIVE form. Because of that, comparing it to ERA5 chart is meaningless. That 7mm will be almost 500mm over that time period.”

    I don’t understand this. The graph I gave you shows an increase in SWE of about 7 mm over the entire time period, NOT annually.

    Your graph shows an increase in SWE of about 1,150 mm over the entire time period. As I said, that would be an increase in snow on the order of 11 meters … you sure you want to go with that claim?

    What am I missing?



    1. lol, well, what does your graph show? An additional monthly swe or some sort of annual moving average of additional swe?

      71 × 12 × 0.007 = 5.964 m
      71 × 0.007 = 0.497 m

      Latter makes more sense. Is the Python code doing an average over land only, not the entire globe? We can fix that.

      71 × 0.007 / (0.29/0.71) = ~ 1.22 m

      OK, mine is 1.15 meters. No big deal.

      You confused total swe with an addition.


      1. You’re still not getting it. The ERA5 chart shows the SWE of all of the snow on the ground, month by month. It shows an increase of 7 mm over the ENTIRE PERIOD.

        Not “additional monthly SWE”. Not “annual moving average”. Increase in SWE over the entire period. 7 mm. That converts to an increase of 70 mm (~3″) of snow in six decated.

        In addition, you say “We got an extra 1.15 meters of snow over land in 6 decades”. But your graph is not in snow, it’s in snow water equivalent. As I mentioned, the rule of thumb is 10:1 snow:SWE.

        So that would mean we got an extra 11.5 metres of snow … sorry, not happening.



        1. OK, Willis, I shouldn’t trust my lying eyes. I used to live in NYC. Average annual snowfall ~ 0.75 meters. That’s over 53 meters of snow in 71 years for a city at ~40 latitude. That would be 5325 mm of SWE.


    2. Here’s a quick sanity check:

      We recovered “800,000 years” record from 3.2 km of ice.

      We should expect, over 71 years …

      3200 / 800000 * 71 = 0.284 m = 284 mm

      What’s the water equivalent of ice? Don’t know, but very close to 1:1? No?

      Well, you see, the 7mm figure flies out the door. You were looking at an addition, not an accumulated stack.


        1. Protip. Stop with the snark. It just makes you look uncertain and vindictive.

          The overwhelming majority of snow melts every year. If it didn’t we’d be in an ice age.



        2. Not really. It makes me look baffled, by what I consider to be, a silly comment, and I can’t help but laugh. If you can answer:

          Where did the ice for the ice cores come from?

          If not from snowfall, then I’ll stop laughing right away.


    3. And please don’t make public accusations about my work, unless I ACTUALLY made a mistake.

      If my source has problems, or it’s different from other sources, YOU KNOW that’s not my fault.

      I didn’t go out of my way to find some SWE source that looks different from the rest. I just found very high resolution (1/24th deg.) source of “data” and thought it would be neat to go through it.

      So please check my source first.

      Do you think TerraClimate is no good?

      Thanks -Z


        1. OK. I took a preliminary look at the data. It’s strange. It claims to be “Snow Water Equivalent at End of Month”, but it keeps increasing as you said. So it must be showing, not monthly snowfall, but total global frozen water. Curious.

          So I threw it up on a global map, and the answer became clear. The accumulation is all in Greenland and Antarctica.

          What’s happening must be that they are not accounting for ice loss from Antarctica and Greenland. As a result, the SWE number just keeps increasing. Here’s the global map.

          As you can see, my data agrees basically with yours, an increase of 1085 mm of SWE. However … that’s mostly Greenland and Antarctica. They show Greenland increasing by from 30 up to 80 meters of ice over that time … and meanwhile, the best data that we have shows that Greenland is LOSING ice. So clearly, they’re not accounting for ice loss from the great ice sheets in Antarctica and Greenland.

          I see no easy way to remove that anomaly from the data, although it can be done. As a rough cut, here’s the data from 60°N to 60°S:

          Note that without those two, the SWE increased, not by 1,085 mm, but by a mere 22 mm … much more in agreement with the ERA5 data I show above.

          Best regards,



        2. Willis,
          If you look closely, your ERA5 data is not “swe”, but “snld”.

          Quite frankly none of the data makes sense to me.

          Let’s take a look at your ERA5 data for just New York City:

          It’s never actually zero, even in the summer.

          So what are we looking at here?

          Let’s do Atlanta:

          It’s never zero, even in the summer. Has the same peaks as NYC. Yet more frequent? Total nonsense.

          An ignorant foreigner looking at this data would conclude that Atlanta can be snowier than NYC. Total nonsense.

          Since your data is some kind of crap, I doubt you can judge TerraClimate’s swe.

          What do you think?


        3. Yes, Willis, “The accumulation is all in Greenland and Antarctica.”

          That’s why we take ice cores there.

          “the best data that we have shows that Greenland is LOSING ice.”

          I can’t remember if I knew that or its opposite as a fact. You sure? Are you talking about ice loss at its water edges? or the top as well?


        4. “Note that without those two, the SWE increased, not by 1,085 mm, but by a mere 22 mm”

          Which is funny because you essentially left only the northern hemisphere in place, and snowfall actually declined in NH, as my previous post showed.

          But how can you leave out Antarctica and Greenland?

          The “ice” core record shows we got an average of 4 millimeters of water equivalent every year in one of those places. That would be 284 millimeters if that trend is extended over the last 71 years.

          Please address the “ice” core record. I’d love to hear what you think about this in relation to swe.


        5. Willis, where did you go?

          I didn’t get a chance to show you the best ERA5 data.

          This is Miami.

          Despite the fact that there were only snow flurries in 1977, this shows Miami as a snowy place.

          32 centimeters in September 1960!

          I’d show you Riyadh as well, but I think you got the point.

          How’s the downwelling solar going?

          Best regards, -Z


Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

%d bloggers like this: