Tag Archives: ice shelves

How does the Weddell Polynya affect Antarctic ice shelves?

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The Weddell Polynya is a large hole in the sea ice of the Weddell Sea, near Antarctica. It occurs only very rarely in observations, but is extremely common in ocean models, many of which simulate a near-permanent polynya. My new paper published today in Journal of Climate finds that the Weddell Polynya increases melting beneath the nearby Filchner-Ronne Ice Shelf. This means it’s important to fix the polynya problems in ocean models, if we want to use them to study ice shelves.

The Southern Ocean surrounding Antarctica is cold at the surface – often so cold that it freezes to form sea ice – but warmer below. The deep ocean is about 1°C, which might not sound warm to you, but to Antarctic oceanographers this is positively balmy. If regions of the Southern Ocean start to convect, with strong top-to-bottom mixing, the warm deep water will come to the surface and melt the sea ice.

In observations, this doesn’t happen very often, and it only seems to happen in one region: the Weddell Sea, in the Atlantic sector of the Southern Ocean. Satellites spotted a large polynya (about the size of the UK) for three winters in a row, from 1974-1976. But then the Weddell Polynya disappeared until 2017, when a much smaller polynya (about a tenth of the size) showed up for a few months in the spring. We haven’t seen it since.

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The Weddell Polynya in the winter of 1975. (Holland et al., 2001)

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The Weddell Polynya in the spring of 2017. (NSIDC)

By contrast, models of the Southern Ocean simulate Weddell Polynyas very enthusiastically. In many ocean models, it’s a near-permanent feature of the Weddell Sea, and is often much larger than the observed polynya from the 1970s. This can happen very easily if the model’s surface waters are slightly too salty, which makes them dense enough to sink, triggering top-to-bottom convection. We also think it might have something to do with imperfect vertical mixing schemes.

It’s a rite of passage for Southern Ocean modellers that sooner or later you will work with a model that forms massive polynyas, all the time, and you can’t make them go away. I spent months and months on this during my PhD, and eventually I gave up and did “surface salinity restoring” to prevent the salty bias from forming. Basically, I killed it with freshwater. If you throw enough freshwater at this problem, the problem will go away.

So when the little Weddell Polynya of 2017 showed up, I was paying attention. And when the worldwide oceanography community jumped on the idea and started publishing lots of papers about the Weddell Polynya, I was paying attention. But soon I noticed that there was an important question nobody was trying to answer: what does the Weddell Polynya mean for Antarctic ice shelves?

Ice shelves are the floating edges of the Antarctic Ice Sheet. They’re in direct contact with the ocean, and they slow down the flow of the glaciers behind them. Ice shelves are what stand between us and massive sea level rise, so we should give them our respect. But ocean modellers have largely neglected them until now, because ice shelf cavities – the pockets of ocean between the ice shelf and the seafloor – are quite difficult to model. This is changing as supercomputers improve and high resolution becomes more affordable. More and more ocean models are adding ice shelf cavities to their simulations, and calculating melt rates at the ice-ocean interface. So if it turns out that the Weddell Polynya contaminates these ice shelf cavities, it would be even more important to fix the models’ polynya biases. It would also be interesting from an observational perspective, especially if the polynya shows up again soon.

At the time I started wondering about the Weddell Polynya and ice shelves, I was conveniently already setting up a new model of the Weddell Sea, which includes ice shelves. This model doesn’t produce Weddell Polynyas spontaneously, and for that I am eternally grateful. But I found a way to create “idealised” polynyas in the model, by choosing particular regions and forcing the model to convect there, whether or not it wanted to. This way I had control over where the polynyas occurred, how large they were, and how long they stayed open. I could run simulations with polynyas, compare them to a simulation with no polynyas, and see how the ice shelf cavities were affected.

I found that Weddell Polynyas do increase melt rates beneath nearby ice shelves. This happens because the polynyas cause density changes in the ocean, which allows more warm, salty deep water to flow onto the Antarctic continental shelf. The melt rates increase the larger the polynya gets, and the longer it stays open. This is bad news for Southern Ocean models with massive, permanent polynyas.

First I looked at the Filchner-Ronne Ice Shelf (FRIS), the second-largest ice shelf in Antarctica, and the focus of my Weddell Sea research these days. On the continental shelf in front of FRIS, the sea ice formation is so strong that the warm signal from the Weddell Polynya gets wiped out. The water ends up at the surface freezing point anyway, and the extra heat is lost to the atmosphere. But the salty signal is still there, and these salinity changes cause the ocean currents beneath FRIS to speed up. Stronger circulation means stronger ice shelf melting, in this case by up to 30% for the largest Weddell Polynyas.

For smaller ice shelves in the Eastern Weddell Sea, the nearby sea ice formation is weaker. So both the warm signal and the salty signal from the Weddell Polynya are preserved, and the ice shelf cavities are flooded with warmer, saltier water. Melting beneath these ice shelves increases by up to 80%.

The modelled changes are smaller for Weddell Polynyas which match observations, in terms of size as well as duration. So if the Weddell Polynya of the 1970s affected the FRIS cavity, it probably wasn’t by very much. And the effect of the little 2017 polynya was probably so small that we’ll never detect it.

However, these results should send a message to Southern Ocean modellers: you really need to fix your polynya problem if you want to model ice shelf cavities. I’m sorry.

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Future projections of Antarctic ice shelf melting

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Climate change will increase ice shelf melt rates around Antarctica. That’s the not-very-surprising conclusion of my latest modelling study, done in collaboration with both Australian and German researchers, which was just published in Journal of Climate. Here’s the less intuitive result: much of the projected increase in melt rates is actually linked to a decrease in sea ice formation.

That’s a lot of different kinds of ice, so let’s back up a bit. Sea ice is just frozen seawater. But ice shelves (as well as ice sheets and icebergs) are originally formed of snow. Snow falls on the Antarctic continent, and over many years compacts into a system of interconnected glaciers that we call an ice sheet. These glaciers flow downhill towards the coast. If they hit the coast and keep going, floating on the ocean surface, the floating bits are called ice shelves. Sometimes the edges of ice shelves will break off and form icebergs, but they don’t really come into this story.

Climate models don’t typically include ice sheets, or ice shelves, or icebergs. This is one reason why projections of sea level rise are so uncertain. But some standalone ocean models do include ice shelves. At least, they include the little pockets of ocean beneath the ice shelves – we call them ice shelf cavities – and can simulate the melting and refreezing that happens on the ice shelf base.

We took one of these ocean/ice-shelf models and forced it with the atmospheric output of regular climate models, which periodically make projections of climate change from now until the end of the century. We completed four different simulations, consisting of two different greenhouse gas emissions scenarios (“Representative Concentration Pathways” or RCPs) and two different choices of climate model (“ACCESS 1.0”, or “MMM” for the multi-model mean). Each simulation required 896 processors on the supercomputer in Canberra. By comparison, your laptop or desktop computer probably has about 4 processors. These are pretty sizable models!

In every simulation, and in every region of Antarctica, ice shelf melting increased over the 21st century. The total increase ranged from 41% to 129% depending on the scenario. The largest increases occurred in the Amundsen Sea region, marked with red circles in the maps below, which happens to be the region exhibiting the most severe melting in recent observations. In the most extreme scenario, ice shelf melting in this region nearly quadrupled.

Percent change in ice shelf melting, caused by the ocean, during the four future projections. The values are shown for all of Antarctica (written on the centre of the continent) as well as split up into eight sectors (colour-coded, written inside the circles). Figure 3 of Naughten et al., 2018, © American Meteorological Society.

So what processes were causing this melting? This is where the sea ice comes in. When sea ice forms, it spits out most of the salt from the seawater (brine rejection), leaving the remaining water saltier than before. Salty water is denser than fresh water, so it sinks. This drives a lot of vertical mixing, and the heat from warmer, deeper water is lost to the atmosphere. The ocean surrounding Antarctica is unusual in that the deep water is generally warmer than the surface water. We call this warm, deep water Circumpolar Deep Water, and it’s currently the biggest threat to the Antarctic Ice Sheet. (I say “warm” – it’s only about 1°C, so you wouldn’t want to go swimming in it, but it’s plenty warm enough to melt ice.)

In our simulations, warming winters caused a decrease in sea ice formation. So there was less brine rejection, causing fresher surface waters, causing less vertical mixing, and the warmth of Circumpolar Deep Water was no longer lost to the atmosphere. As a result, ocean temperatures near the bottom of the Amundsen Sea increased. This better-preserved Circumpolar Deep Water found its way into ice shelf cavities, causing large increases in melting.

Slices through the Amundsen Sea – you’re looking at the ocean sideways, like a slice of birthday cake, so you can see the vertical structure. Temperature is shown on the top row (blue is cold, red is warm); salinity is shown on the bottom row (blue is fresh, red is salty). Conditions at the beginning of the simulation are shown in the left 2 panels, and conditions at the end of the simulation are shown in the right 2 panels. At the beginning of the simulation, notice how the warm, salty Circumpolar Deep Water rises onto the continental shelf from the north (right side of each panel), but it gets cooler and fresher as it travels south (towards the left) due to vertical mixing. At the end of the simulation, the surface water has freshened and the vertical mixing has weakened, so the warmth of the Circumpolar Deep Water is preserved. Figure 8 of Naughten et al., 2018, © American Meteorological Society.

This link between weakened sea ice formation and increased ice shelf melting has troubling implications for sea level rise. The next step is to simulate the sea level rise itself, which requires some model development. Ocean models like the one we used for this study have to assume that ice shelf geometry stays constant, so no matter how much ice shelf melting the model simulates, the ice shelves aren’t allowed to thin or collapse. Basically, this design assumes that any ocean-driven melting is exactly compensated by the flow of the upstream glacier such that ice shelf geometry remains constant.

Of course this is not a good assumption, because we’re observing ice shelves thinning all over the place, and a few have even collapsed. But removing this assumption would necessitate coupling with an ice sheet model, which presents major engineering challenges. We’re working on it – at least ten different research groups around the world – and over the next few years, fully coupled ice-sheet/ocean models should be ready to use for the most reliable sea level rise projections yet.

A modified version of this post appeared on the EGU Cryospheric Sciences Blog.

On model development, and sanity

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When I was a brand-new PhD student, full of innocence and optimism, I loved solving bugs. I loved the challenge of it and the rush I felt when I succeeded. I knew that if I threw all of my energy at a bug, I could solve it in two days, three days tops. I was full of confidence and hope. I had absolutely no idea what I was in for.

Now I am in the final days of my PhD, slightly jaded and a bit cynical, and I still love solving bugs. I love slowly untangling the long chain of cause and effect that is making my model do something weird. I love methodically ruling out possible sources of the problem until I eventually have a breakthrough. I am still full of confidence and hope. But it’s been a long road for me to come around full circle like this.

As part of my PhD, I took a long journey into the world of model coupling. This basically consisted of taking an ocean model and a sea ice model and bashing them together until they got along. The coupling code had already been written by the Norwegian Meteorological Institute for Arctic domains, but it was my job to adapt the model for an Antarctic domain with ice shelf cavities, and to help the master development team find and fix any problems in their beta code. The goal was to develop a model configuration that was sufficiently realistic for published simulations, to help us understand processes on the Antarctic continental shelf and in ice shelf cavities. Spoiler alert, I succeeded. (Paper #1! Paper #2!) But this outcome was far from obvious for most of my PhD. I spent about two and a half years gripped by the fear that my model would never be good enough, that I would never have any publishable results, that my entire PhD would be a failure, etc., etc. My wonderful supervisor insisted that she had absolute confidence in my success at every step along the way. I was afraid to believe her.

Model coupling is a shitfight, and anyone who tells you otherwise has never tried it. There is a big difference between a model that compiles and runs with no errors, and a model that produces results in the same galaxy as reality. For quite a while my model output did seem to be from another galaxy. Transport through Drake Passage – how we measure the strongest ocean current in the world – was going backwards. In a few model cells near the Antarctic coast, sea ice grew and grew and grew until it was more than a kilometre thick. Full-depth convection, from the ocean surface to the seafloor, was active through most of the Southern Ocean. Sea ice refused to export from the continental shelf, where it got thicker and thicker and older and older, while completely disappearing offshore.

How did I fix these bugs? Slowly. Carefully. Methodically. And once in a while, frantically trying everything I could think of at the same time, flailing in all directions. (Sometimes this works! But not usually.) My colleagues (who seem to regard me as The Fixer of Bugs) sometimes ask what my strategy is, if there is a fixed framework they can follow to solve bugs of their own. But I don’t really have a strategy. It’s different every time.

It’s very hard to switch off from model development, as the bugs sit in the back of your brain and follow you around day and night. Sometimes this constant, low-level mulling-over is helpful – the solutions to several bugs have come to me while in the shower, or walking to the shops, or sitting in a lecture theatre waiting for a seminar to start. But usually bug-brain just gets in the way and prevents you from fully relaxing. I remember one night when I didn’t sleep a wink because every time I closed my eyes all I could see were contour plots of sea ice concentration. Another day, at the pub with my colleagues to celebrate a friend’s PhD submission, I stirred my mojito with a straw and thought about stratification of Southern Ocean water masses.

***

When you spend all your time working towards a goal, you start to glorify the way you will feel when that goal is reached. The Day When This Bug Is Fixed. Or even better, The Day When All The Bugs Are Fixed. The clouds will part, and the angels will sing, and the happiness you feel will far outweigh all the strife and struggle it took to get there.

I’m going to spoil it for you: that’s not how it feels. That is just a fiction we tell ourselves to get through the difficult days. When my model was finally “good enough”, I didn’t really feel anything. It’s like when your paper is finally accepted after many rounds of peer review and you’re so tired of the whole thing that you’re just happy to see the back of it. Another item checked off the list. Time to move on to the next project. And the nihilism descends.

But here’s the most important thing. I regret nothing. Model development has been painful and difficult and all-consuming, but it’s also one of the most worthwhile and strangely joyful experiences I’ve had in my life. It’s been fantastic for my career, despite the initial dry spell in publications, because it turns out that employers love to hire model developers. And I think I’ve come out of it tough as nails because the stress of assembling a PhD thesis has been downright relaxing in comparison. Most importantly, model development is fun. I can’t say that enough times. Model development is FUN.

***

A few months ago I visited one of our partner labs for the last time. I felt like a celebrity. Now that I had results, everyone wanted to talk to me. “If you would like to arrange a meeting with Kaitlin, please contact her directly,” the group email said, just like if I were a visiting professor.

I had a meeting with a PhD student who was in the second year of a model development project. “How are you doing?” I asked, with a knowing gaze like a war-weary soldier.

“I’m doing okay,” he said bravely. “I’ve started meditating.” So he had reached the meditation stage. That was a bad sign.

“Try not to worry,” I said. “It gets better, and it will all work out somehow in the end. Would you like to hear about the kinds of bugs I was dealing with when I was in my second year?”

I like to think I gave him hope.