
Antarctica losing six times more ice mass annually now than 40 years ago - mturmon
https://news.uci.edu/2019/01/14/uci-jpl-study-antarctica-losing-six-times-more-ice-mass-annually-now-than-40-years-ago/
======
macinjosh
I find headlines like this frustrating and potentially misleading. It provides
zero context. Do we know that this is abnormal? Forty years is a short period
of time in the grand scheme of things. Six times more than what?

~~~
credit_guy
"The team was able to discern that between 1979 and 1990, Antarctica shed an
average of 40 gigatons of ice mass annually. (A gigaton is 1 billion tons.)
From 2009 to 2017, about 252 gigatons per year were lost."

I'm a climate skeptic. This type of research is why I'm a climate skeptic. 252
gigatons per year means a bit less than 0.001% of the mass of the ice sheet of
the Antarctic continent. Or, to put it differently, about 1.8cm decrease in
average ice height, when the current average is about 1.9km. They are telling
me they have a method to estimate an average decrease of 1.8cm? Really? How
can anyone claim such a thing with a straight face? Civilian GPS doesn't have
this type of vertical resolution. You can achieve better resolution by the law
of large numbers, however that is at the rate of the square root of the number
of observations, and I doubt there are a tremendous number of observation
points in Antarctica.

~~~
mturmon
You know less than you think you do.

GPS is not part of the measurement suite in OP, but better than 1.8 cm
vertical resolution from GPS has been possible since the late 1990s, using
after the fact orbit determination. It is now routine to get sub-cm vertical
accuracy in GPS measurements [1, fig. 3, magenta line]. The horizontal
displacements are known to about a mm.

These displacements are known so accurately that you can readily measure
groundwater withdrawals, the extra mass related to of high atmospheric
pressure over the GPS sensor (daily cadence), and post-glacial rebound
(decadal cadence).

Your remarks below about the accuracy of the GRACE gravimetric measurement are
also misguided. You can’t just choose a baseline mass and say it’s impossible
to measure a change in that mass to some percent accuracy. The GRACE
measurement depends on how well you can measure a distance between two
spacecraft, and how well you can eliminate systematics from that difference.
It does not depend on a relative mass.

Anyway, the paper uses a different technique to get ice fluxes. If you don’t
know how they did the measurement, why are you so sure it is wrong?

[1] [https://www.unavco.org/data/gps-gnss/derived-
products/docs/H...](https://www.unavco.org/data/gps-gnss/derived-
products/docs/Herring_et_al_2016_RevGeophys.pdf)

~~~
credit_guy
> You know less than you think you do.

Not clear how this ad-hominem enhances your argument.

>If you don’t know how they did the measurement, why are you so sure it is
wrong?

I'm not "so sure", I'm just skeptical. I find it hard to believe that one can
estimate the net ice loss over a huge and dynamic body of ice with an accuracy
better than 0.001%, using any type of measurements conceivable.

You are saying that somehow the whole mass of Antarctica is irrelevant as a
base mass to calculate accuracy? Why so? The ice naturally compresses and
flows and this happens at continental scale. A large iceberg calving on the
western coast of the continent may be offset by a relatively modest ice
accumulation on the much larger eastern side. However, that accumulation on
the eastern side will be conflated by the general compression that will lead
to some ice being pushed towards the edges, etc, etc. There's no natural way
to take a part of the continent out of the equation and say "well, we are
interested only in this subset".

~~~
mturmon
> You are saying that somehow the whole mass of Antarctica is irrelevant as a
> base mass to calculate accuracy...

Yes, that is correct. The mass of Antartica, whatever that might mean, is not
required in itself. Percent error in estimating that is not relevant. Remember
that we are interested in mass changes only.

The GRACE measurement is based on explaining the relative position of a pair
of orbiting spacecraft by mass changes in 4500 "mascons" \-- equal-area
spherical caps that blanket the globe (see figs 2 and 3 of [1]). The
fundamental observation is the range and relative velocity of the pair of
spacecraft, which can be very accurately determined. The mass within each
mascon has an analytically-known relationship to these observations (eqs. 8, 9
of [1] - relating mass sigma to acceleration a).

It's a monthly measurement -- you accumulate a month's worth of orbits, and
solve a least-squares problem to fit the range-rate data with the masses. This
can also be viewed as maximum a posteriori estimation in the conventional way.
See equation 13 of [1].

I hope this clarifies why it does not matter what the ice underneath is doing.
The ice has mass, and therefore it affects the gravitational potential that
the satellites operate in.

You are correct to be surprised. The measurement is surprising, and it has
been revolutionary. Hundreds of papers have been published using it, and it
has received scrutiny and undergone improvements for more than a decade. Three
independent groups (JPL, UTexas, DLR) have worked on the full retrieval over
this time. Lots of what we know about groundwater withdrawals, ice sheets, and
more recently deep ocean currents, is based on this measurement.

There _is_ an attribution issue at ice/water boundaries (sec. 6 of [1]). It is
due to mixed pixels -- a mascon that is part water, part ice. The mass change
observed at the mascon should not be spread evenly over the whole pixel. It is
split in constrained way based on errors and priors. But as [1] explains, this
is not an "order of magnitude" type error, it's just a correction.

[1]
[https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/201...](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2014JB011547)

But to repeat -- the fundamental result of the OP does not rely on GRACE (nor
does it rely on GPS). It relies on ice sheet velocity models.

~~~
credit_guy
Thanks for taking your time to explain all this. It's quite cool. I spent some
time with your link and also with the wikipedia page about GRACE. This is
quite an ingenious experiment. First I thought the two satellites were at
different altitudes, one in LEO and one in, let's say GSO. The high altitude
satellite perceives the Earth more or less like a mass point, while the low
altitude satellite is much more sensitive to the distribution of mass,
especially at the Earth's surface. You could in principle use several
satellites at different altitudes, and do some sort of "triangulations". This
way, you could potentially learn about the mass distribution deep inside the
Earth. Anyway, the actual way it's done is also cool, a lump of mass, like a
mountain pulls faster the leading satellite, Jerry, and the second satellite,
Tom, lags a little behind. Later on, Tom goes over the same mass and catches
on.

That being said, the complexity of this model sounds absolutely humongous.
Complex models are exciting for mathy guys to work with, but are inevitably
subject to more model risk. As coders say, the number of bugs is roughly
proportional with the number of lines of code.

You're going to say that this numerical calculation is bug free. A numerical
calculation with 4 thousand mass element (mascons) and distance measurements
probably in the billions, and lots and lots of approximations and
contributions from the sun and the moon and the planets. Of course you are not
going to say it is bug free. Should we trust ice loss calculations coming from
GRACE? To be honest, after I read your explanation and all other things about
GRACE, I'm less skeptical. I don't fully trust it, but I don't fully distrust
it either.

But as you said, this is not that relevant, since the actual result published
was not based on GRACE, but rather on ice flow models. Now these models I take
with quite a large grain of salt. Why? Models produce estimates, and the
estimates generally include an uncertainty level. The uncertainty reflects
only how the uncertainty in inputs propagates to the uncertainty in the final
result, not the model uncertainty per se.

Take a look at the figure 1 in [1]. It shows the precipitation in Antarctica
over 2.5 decades based on several models. The average number (labeled "multi-
model mean") shows very little variability from year to year, and you could
infer that very little uncertainty as well. However, this is just an artifact
of taking the average of several not very highly correlated time series. The
model uncertainty is better reflected by the wide range of the numbers
produced by the different models. If you look at this range, it's about
200mm/y, which is 10 times the net ice loss we are talking about here.

Do you see why I'm a bit skeptical?

But anyway, thanks again for taking your time with me. I learned something
cool today.

[1]
[https://www.sciencedirect.com/science/article/pii/S187396521...](https://www.sciencedirect.com/science/article/pii/S1873965217300981#fig1)

~~~
mturmon
I appreciate the reply. I happen to know some of the GRACE team, so I'm
personally predisposed to respect their results. But, it's also significant
evidence that the retrieval was re-implemented by 2 other teams, one US, one
German, with similar results. All 3 datasets are available to the community
for comparison.

About fig. 1 of [1]: Of the spectrum of important Earth system models,
precipitation is the least well-described. For good reasons: highly non-
gaussian, very local in nature, highly dependent on nonlinear
condensation/temperature properties. Even in the continental US, precipitation
is not well-described. Your remark about deciding on a bogus "uncertainty" of
the mean precipitation in fig. 1 is absolutely correct.

But it's of no use to be "skeptical about models" based on poor results of one
class of models. The whole reason that paper was published is to draw
attention to that fact! They literally say this in their conclusion. ("Most
CMIP5 and reanalysis models are unable to simulate a consistent spatial and
temporal precipitation pattern for the Antarctic.")

I have come to believe that skepticism is somewhat empty as a value. _That 's
why you have experts who know what the state of the art is, and what to be
skeptical about._

~~~
credit_guy
> But it's of no use to be "skeptical about models" based on poor results of
> one class of models

This is not just "one class of models". This is the gain side of the gain-loss
equation needed for the estimation of ice balance in Antarctica. If that side
has an uncertainty 10 times higher than the stated result, then how can I not
be skeptical?

>That's why you have experts who know what the state of the art is, and what
to be skeptical about.

"Trust the experts" lead to the 2008 financial crisis. "Trust the experts" was
what string theorists were telling us until Lee Smolin exposed them. "Trust
the experts" is ultimately what religion is.

>skepticism is somewhat empty as a value

That's a surprising (for me at least) point of view for a scientist.

I'm quite sure you wanted so say something else. Something like in your
experience, those who describe themselves as climate skeptics (I mean in what
other context do people declare themselves skeptics? I'm a skeptic about the
dark matter as well, by the way) are simply obstructionists, or ignorants, or
have a political agenda or something else. Since we are all more efficient
forming Bayesian priors to work in life with, your Bayesian prior in this case
is that "skepticism is an empty value". And that's quite fair. I happen to
agree that a large part of "climate skeptics" are quite toxic. The way the
Trump administration treats the EPA, or science in general seems quite
deranged. But that doesn't mean you can't be genuinely skeptic. In this case
skepticism has nothing to do with values.

I simply approach this debate and I use my own heuristics. I don't know any
climate scientist to use heuristics such as human character, you seem you do.
For me, I apply other heuristics, based on my day to day work with models. I
reached other conclusions so far than those that you reached.

