Monday, January 10, 2022

Antarctica’s Doomsday glacier: how doomed are we?

Thwaites Glacier, a.k.a the Doomsday Glacier
Jeff Goodell

From RollingStone by Jeff Goodell

Two new papers offer radically different predictions of the glacier’s future — and thus for the future of low-lying cities around the world.
Here’s how to understand the divergent projections 

I came face to face with the Doomsday Glacier (a.k.a. Thwaites glacier) in 2019, on a trip to Antarctica aboard the Nathaniel B. Palmer, a 308-foot-long icebreaker operated by the National Science Foundation.
I had dubbed the Florida-sized slab of ice its nickname in an article I’d written a few years earlier, and the name stuck.
Nevertheless, I was unprepared for how spooky it would be to actually confront the 100-foot-tall wall of ice from the deck of a ship.
Locked up here in the West Antarctic ice sheet was enough water to raise global sea levels nearly 10 feet.
As I wrote in a dispatch from Antarctica on the day we encountered Thwaites, it was both terrifying and thrilling to know that our future is written in this craggy, luminous continent of ice.

Our world is heating up fast.
And as every kid knows, on hot days, ice melts.
The question is how quickly.
At Thwaites, the melting is mostly a result of warm ocean water attacking it from below, which is stressing and fracturing both the ice shelf that protects the glacier and the glacier itself.
Just how fast Thwaites and the other big glaciers that make up the West Antarctic ice sheet will all fall apart is one of the most important scientific questions of our time.
And it is a question upon which the future of virtually every coastal city in the world depends.
“We know there are tipping points in Antarctic ice sheets, and we also know that Antarctica is the biggest wildcard in the future sea level rise projections,” says Andrea Dutton, a professor of geology at the University of Wisconsin-Madison and a 2019 MacArthur Fellow.
“Basically, it all comes down to ‘when will we reach that tipping point?’ ”

Last week, two new papers were published simultaneously in the science journal Nature that offer radically different visions of the Doomsday glacier, as well as radically different visions of how climate models work and what they can tell us about the future.
But they agree on one thing: “Both papers make it very clear that human decisions are important, and that limiting warming can limit sea level rise,” says Richard Alley, a glaciologist at Penn State and one of the most respected ice scientists in the world.
But beyond that, the two papers may as well be describing life on different planets.

Thwaites Glacier in Antarctica is one of the largest in the world It currently contributes to % of global annual sea level rise As oceans get warmer, its ice is moving faster and it is at risk of collapsing Copernicus Sentinel1 image of 29 Dec.

The first paper might be called the Holy Shit vision of Antarctica’s future.
In this scenario, led by Rob DeConto, a climate modeler at the University of Massachusetts-Amherst (Dutton and Alley and 10 other scientists are co-authors), the West Antarctic ice sheet remains fairly stable as long as warming stays below 2 C, which is the temperature threshold identified in the Paris climate agreement.
Beyond 2 C, however, all hell breaks loose.
Thwaites begins to fall into the sea like a line of dominoes pushed off a table and soon takes the rest of the West Antarctic ice sheet with it.
And once the collapse begins, it will be impossible to stop – at least on any human time scale.
In a century or so, global sea levels could rise 10 feet, which would swamp much of South Florida and Bangladesh and many other low-lying regions of the world.

In fact, it could happen even faster than that, says Alley: “We just don’t know what the upper boundary is for how fast this can happen.
We are dealing with an event that no human has ever witnessed before.
We have no analogue for this.” All in all, the paper makes a very strong argument that cutting emissions today may avert a centuries-long climate catastrophe.
DeConto’s paper also warns against betting on a quick techno-fix like CO2 removal.
Unless it is widely deployed by 2070, which, the way things are going, is highly unlikely given the cost and scale-up of the technology that is required, it will be too late.

The second paper might be called the What, Me Worry? vision of Antarctica’s future.
Unlike the DeConto study, which is based on a single model, the second paper, which was led by Tamsin Edwards, a climate scientist at King’s College London, involved 84 people working at 62 institutes in 15 countries.
Edwards and her co-authors use an “emulation” technique to compare the outcome of the different climate models, making the results less dependent on assumptions built into any one scenario, creating what amounts to a statistical average of climate-model outcomes.

In this study, the Doomsday glacier isn’t very doom-y at all.
There’s no collapse, no tipping point, no big jumps in sea level rise.
In fact, although the paper makes clear that the rate of CO2 emissions over the next few decades is clearly important, the difference in global sea level rise from the melting of all land glaciers, not just Thwaites, only differs by 4 ½ inches between a 1.5 C global temperature rise and a 3 C temperature rise (which is a little above where we are headed with current commitments under the Paris agreement).
And much of that comes from increased melt in Greenland and mountain glaciers.

As for Antarctica, the paper says explicitly: “No clear dependence on emissions scenario emerges for Antarctica.” Or as Alley put it to me, a tone of mild astonishment in his voice: “For Antarctica, the Edwards paper basically says, Antarctica doesn’t matter to us and our decisions don’t matter to Antarctica.”

So let me roughly sum up where we are with our scientific understanding of sea level rise risk from Antarctica after more than three decades of serious climate change research: One study tells us that if we don’t cut CO2 emissions fast we will condemn the world to a century of rising seas that will flood every major coastal city and reshape the global map.
The other study tells us that the likely difference between dramatically cutting CO2 emissions and cruising along on the current path is 4 ½ inches of water.
That means more coastal flooding, more erosion, more salt-water intrusion into drinking wells, but it’s a long way from Waterworld.

What to make of all this? Well, for one thing, the discrepancy between the papers demonstrates not only how little scientists really understand about what is going on in Antarctica, but also what a low priority our society has put on funding research to better understand it.
For another, modeling ice sheets is just plain hard, in part because it requires high-resolution models, and in part because a lot of the important events in the story of ice happened 20,000 years ago (or more), for which data is sparse.

Finally, there is a big difference in perspective between the two studies: The Edwards paper only looks at sea level rise out to 2100, whereas the DeConto paper stretches out to 2300.
Even in the DeConto paper, Antarctica doesn’t really start to fall apart until 2120 or so.
As always, what you see depends on the lens you look through.
There’s also the question of how additional snowfall from a warmer atmosphere may offset some or all of the melting from warmer ocean water.
(Warmer air holds more moisture, and thus can result in more snow.) As Edwards tells me via email, “We are not yet sure how much we have control over Antarctica, because snowfall has a counteracting effect that may also increase in future.”

The most important distinction, however, is that the DeConto paper includes a mechanism called Marine Ice Cliff Instability, or MICI (scientists pronounce it “Mickey,” like the mouse) and the Edwards paper doesn’t.

MICI is best understood as a hypothesis about how ice sheets behave in a rapidly warming world.
The gist of it is that, in some conditions, ice sheets don’t simply melt — they collapse.
Warm ocean water can get beneath the glaciers, causing them to fracture and destabilize.
When the ice shelves that keep the glaciers wedged in place break up, the glaciers themselves become vulnerable.
According to the MICI hypothesis, ice cliffs above about 100 meters high or so don’t have the structural integrity to stand on their own, and without ice shelves to buttress them, they will collapse, or calve, into the sea (there’s a more detailed explanation of MICI in my 2017 article on the Doomsday Glacier).

This is more or less what’s happening right now at a few glaciers in Greenland, including Jakobshaven, the fastest flowing glacier in the world.
A few years ago, I flew across the front of Jakobshaven in a helicopter and watched huge chunks of ice calve into the water, creating an army of icebergs that float out into Glacier Bay, where climate-catastrophe tourists take pictures of them and post them to their Instagram accounts.

The calving front at Thwaites is roughly 10 times bigger than Jakobshaven.
If Thwaites’ ice shelf breaks up and starts behaving like Jakobshaven, a whole lotta real estate is gonna get wet real fast.

MICI may be a radical idea, but it is not new.
It has been around since at least the 1960s, when climate scientist John Mercer first traveled to Antarctica and realized that the land beneath the ice in West Antarctica was shaped like a bowl, which means that if warm water got under the ice and began to destabilize the glacier, it could trigger a runaway retreat that could dump a lot of ice into the Southern Ocean very quickly.
Richard Alley took up the idea in the early 2000s, understanding it could be a mechanism to explain why sea levels were so high during the Pliocene era, 3 million years ago, when levels of CO2 in the atmosphere were about the same as they are today.
In 2016, DeConto co-authored a paper with Dave Pollard, a climate modeler at Penn State, that modeled the implications of MICI in Antarctica for the first time.
The paper added more than three feet to sea level rise projections and scared the bejesus out of climate scientists everywhere.

The MICI hypothesis also prompted the formation of the International Thwaites Glacier Collaboration, a five year-long, $50 million joint research effort between the U.S.
and the U.K., which began in 2018 (my trip to Thwaites in 2019 was with scientists participating in this joint research venture).
Among the key questions scientists are asking: How much warm water is getting under Thwaites ice shelf? (Quite a bit, according to a new paper by Swedish oceanographer Anna Wåhlin, which is based on measurements she made while we were in Antarctica together.) How quickly is the glacier losing its grip on the bedrock near the current ice front? How quickly is the ice shelf breaking up?

“In the last few years, we have seen a lot of dynamic change at Thwaites and other glaciers in the region,” says Robert Larter, a geophysicist with the British Antarctic Survey who was the chief scientist on the Palmer on my trip to Antarctica.
According to one recent study, the net ice-mass loss from Thwaites and nearby glaciers is now more than six times what it was 30 years ago, which Larter calls “mind-boggling.”

None of this research is conclusive, and most of it is still too new in include in climate models.
For the moment, MICI remains an outlier idea, one that mainstream climate modelers have yet to fully embrace, despite the risks that civilization faces from it.

“If you want to be generous,” Alley tells me, “You could say that climate modelers really want to make their models carefully, make sure they are calibrated precisely, and they don’t know what to do with MICI.”

An image presented by the researchers shows the ice shelf in 2015 and in 2021.
American Geophysical Union
 
It’s also true that there are still a lot of unanswered questions about exactly how MICI works.
“Yes, ice cliffs can fail,” says Ted Scambos, the lead U.S. glaciologist in the Thwaites research project.
“But is a runaway failure realistic?”
And just because ice cliff collapse is happening at Jakobshaven, it doesn’t mean it will necessarily happen at Thwaites.
“Jakobshaven is not physically the same as all Antarctic glaciers, nor does their model include all possible physics (e.g.
negative feedbacks or other factors that limit the rate and extent of cliff collapse),” Edwards tells me via email.
“It’s far too simple to say, ‘Clearly MICI exists so why don’t you believe in it?’ ”

“The reason nobody is rushing off an unstable marine cliff in search of what DeConto and Pollard have done is because nobody thinks there is any good reason to single out MICI and make it the cause of instability in glaciers,” says Gavin Schmidt, a climate modeler and director of the NASA Goddard Institute for Space Studies in New York.
“Is MICI a large part of why glaciers calve? It’s not inconceivable, but it’s also not inconceivable that it could be other factors.”

Finally, some of the resistance to MICI may simply be a failure of imagination.
No human has ever witnessed the rapid collapse of a glacier in Antarctica like Thwaites; ergo, it can’t happen.
Alley himself thinks about it simply in terms of risk.
“Maybe we’ll get lucky and the ice cliffs won’t disintegrate in Antarctica quite as fast as we predict,” he says.
“But if you are even a little bit worried that scientists might have made mistakes in their calculations about what is going on in Antarctica, then maybe we should pay attention to this.”

He compares Thwaites and other glaciers in West Antarctica with drunk drivers.
“They are out there, they are scary, and they don’t behave as you expect them to,” Alley says.
“That’s why it’s a good idea to have a seatbelt in your cars.”

In the end, climate modelers are a little like sci-fi writers.
They use facts and physics to spin out possible futures.
DeConto’s paper imagines we are moving into a new world that will behave very differently from the world we have lived in so far.
Edwards’ paper imagines that the rest of the 21st century will look pretty much like it does today, only hotter, and with a little less ice.
Both visions are based in science.
Both visions are plausible.
And both visions are fraught with deep uncertainty about where we are going.

I hope we live in Edwards’ world, but I fear we live in DeConto’s.

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Sunday, January 9, 2022

How the nautical charts are made (1951)

Originally filmed as "Kaizu no dekirumade" by Hydrographic and Oceanographic Department (JHOD), Japan Coast Guard, for its celebration of the 80th anniversary in 1951.
This historical movie still well describes processes of hydrographic and oceanographic survey and chart production as well as the importance of those works for all marine related activities.
The movie is provided by JHOD for IHO centenary celebrations.

Saturday, January 8, 2022

Image of the week : Avalanche

Avalanche // New release gallery print featured in my 2022 Ocean Art Calendar

 

Friday, January 7, 2022

The danger of leaving weatherprediction to AI


From Wired by Meghan Herbst

When it comes to forecasting the elements, many seem ready to welcome the machine.
But humans still outperform the algorithms—especially in bad conditions.


HUMANS HAVE TRIED to anticipate the climate’s turns for millennia, using early lore—“red skies at night” is an optimistic sigil for weather-weary sailors that’s actually associated with dry air and high pressure over an area—as well as observations taken from roofs, hand-drawn maps, and local rules of thumb.
These guides to future weather predictions were based off years of observation and experience.

Then, in the 1950s, a group of mathematicians, meteorologists, and computer scientists—led by John von Neumann, a renowned mathematician who had assisted the Manhattan Project years earlier, and Jule Charney, an atmospheric physicist often considered the father of dynamic meteorology—tested the first computerized automatic forecast.

Charney, with a team of five meteorologists, divided the United States into (by today’s standards) fairly large parcels, each more than 700 kilometers in area.
By running a basic algorithm that took the real-time pressure field in each discrete unit and prognosticated it forward over the course of a day, the team created four 24-hour atmospheric forecasts covering the entire country.
It took 33 full days and nights to complete the forecasts.
Though far from perfect, the results were encouraging enough to set off a revolution in weather forecasting, moving the field toward computer-based modeling.

Over the ensuing decades, billions of dollars in investments and the evolution of faster, smaller computers led to a surge in predictive capability.
Models are now capable of interpreting the dynamics of parcels of atmosphere as small as 3 kilometers in area, and since 1960 these models have been able to include ever-more-accurate data sent from weather satellites.

In 2016 and 2018, the GOES-16 and -17 satellites launched into orbit, providing a host of improvements, including higher-resolution images and pinpoint lightning detection.
The most popular numerical models, the US-based Global Forecasting System (GFS) and European Center for Medium-Range Weather Forecasts (ECMWF), released significant upgrades this year, and new products and models are being developed at a faster clip than ever.
At a finger’s touch, we can access an astonishingly precise weather forecast for our exact location on the Earth’s surface.

Today’s lightning-speed predictions, the product of advanced algorithms and global data collection, appear one step away from complete automation.
But they’re not perfect yet.
Despite the expensive models, array of advanced satellites, and mega-computers, human forecasters have a unique set of tools all their own.
Experience—their ability to observe and draw connections where algorithms cannot—gives these forecasters an edge that continues to outperform the glitzy weather machines in the highest-stake situations.

On the left is the new paper’s “Deep Learning Weather Prediction” forecast.
The middle is the actual weather for the 2017–18 year,
and at the right is the average weather for that day. Image via: J. A. Weyn, D. R. Durran, and R. Caruana, “Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere”

THOUGH TREMENDOUSLY USEFUL with big-picture forecasting, models aren’t sensitive to, say, the little updraft in one small land quadrant that suggests a waterspout is forming, according to Andrew Devanas, an operational forecaster at the National Weather Service office in Key West, Florida.
Devanas lives near one of the world’s most active regions for waterspouts, marine-based tornadoes that can damage ships that pass through the Florida Straits# and even come onshore.

The same limitation impedes predictions of thunderstorms, extreme precipitation, and land-based tornadoes, like those that tore through the Midwest in early December, killing more than 60 people.
But when tornadoes occur on land, forecasters can often spot them by looking for their signature on radar; waterspouts are much smaller and often lack this signal.
In a tropical environment like the Florida Keys, the weather doesn’t change much from day to day, so Devanas and his colleagues had to manually look at variations in the atmosphere, like wind speed and available moisture—variations that the algorithms don’t always take into account—to see if there was any correlation between certain factors and a higher risk of waterspouts.
They compared these observations to a modeled probability index that indicates whether waterspouts are likely and found that with the right combination of atmospheric measurements, the human forecast “outperformed” the model in every metric of predicting watersprouts.

Similarly, research published by NOAA Weather Prediction Service director David Novak and his colleagues show that while human forecasters may not be able to “beat” the models on your typical sunny, fair-weather day, they still produce more accurate predictions than the algorithm-crunchers in bad weather.
Over the two decades of information Novak’s team studied, humans were 20 to 40 percent more accurate at forecasting near-future precipitation than the Global Forecast System (GFS) and the North American Mesoscale Forecast System (NAM), the most commonly used national models.
Humans also made statistically significant improvements to temperature forecasting over both model’s guidance.
“Oftentimes, we find that in the bigger events is when the forecasters can make some value-added improvements to the automated guidance,” says Novak.

Particularly in adverse conditions, great improvements to the model’s forecast were usually due to human augmentation, he adds.
This is even more true for local, severe events like thunderstorms and tornadoes, which rely on split-second decision-making in order to save lives.
As forecasters become more familiar with a particular model, they begin to notice its biases and failings, Novak adds.
Just like the model learns from us, we learn from the model.

picture : ECMWF
 
AT EMBRY-RIDDLE AERONAUTICAL College in Arizona, meteorologist Shawn Milrad prepares would-be forecasters to use the glut of tools now at their disposal.

Milrad entered the field in the early 2000s, an era when the dominant forecasting methods were shifting from older techniques to numerical weather models and automated observations.

These technologies were critical to recent advances in atmospheric science, but Milrad cautions his students against complacency and dependence on the automated data models.

“If they’re going to be forecasting precipitation, they should be able to defend it by analyzing the physical processes and mechanisms that they see on the maps,” says Milrad.
He sees utility in the continued use of rules of thumb and pattern recognition techniques, not only as teaching tools, but also to defend against losing the vital experience forecasters bring to bear in severe weather situations or when models are off-base.
“There’s an old adage that ‘all models are wrong, some are useful,’” says Milrad.
“Even if it’s a great forecast it’s going to be slightly wrong.
It's how you can add value to that model.”

Plus, even though computer-generated forecasts are likely to continue improving over time, a number of challenges stand in the way of anything resembling full automation, which requires a significant expansion of computing power, with a multibillion dollar price tag.
The Department of Energy bankrolled the development of three exascale computers—capable of performing 1018 calculations per second—in 2018.
The first of these, the Aurora supercomputer under development at Argonne National Laboratory in Illinois, is slated to go online in 2022 and will be able to perform 1 quintillion calculations per second, but several different scientific fields are vying for access to its immense processing power.
And current infrastructure could also be at risk, since full rollout of 5G threatens to interfere with several key weather satellites.
Radio interference could degrade the quality of satellite observations of water vapor and potentially set forecasting capability back by decades.

In truth, the future of accurate weather forecasting may not necessarily rely on automation, but on a more mundane solution: financial support.
Thanks to these technological advances in weather forecasting and meteorology, human forecasters who once juggled the more tedious aspects of the job now have the bandwidth to focus on severe weather, research, and communicating important information about risks and preparation to agencies and people living in their area.
If such important work is to continue, the National Weather Service, on which so much of our weather infrastructure relies, must remain adequately funded.

Though it’s the private weather companies—like Accuweather and Weather Underground—that can provide more frequent, pinpoint forecasts, their business models rely on advertisement, subscription revenue, and enhanced services offered at a premium, and most employ few meteorologists (Accuweather employs around 100, while the NWS has more than 2,000).
Prior attempts by legislators—with financial backing from Accuweather executives—to limit the NWS from sharing weather information with the public have been met with outrage by the meteorological community.
If we want to continue to receive in-depth weather forecasts and crucial warnings, touched by human hands, we need to preserve agencies and services that value human-augmented forecasts and the public’s right to know.
(The service’s budget fell considerably during the Trump administration but thankfully is now reaching new highs, with a $6.2 billion NOAA funding package proposed for 2022—the largest in the agency’s history.)

Devanas, the NWS forecaster in Key West, agrees that the private sector has a lot to contribute to forecasting but is wary of the amount of unreliable weather information that is circulated as a result.
Even as algorithms and models continue to improve, Devanas believes we can’t lose sight of the science behind everything.
“I'm not here to say ‘Today is going to be 92 degrees, and it’s going to be 80 degrees at night with a 20 percent chance of rain.’ I could in essence get a monkey to do that,” he says.
“Those are things where we need some local expertise.
Those are things where the rules of thumb come in, and that local knowledge becomes invaluable.”

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