surface mass budget – Sterna Paradisaea

Half a century of ice loss: the new IMBIE dataset

More publication news! This month I’ve actually had 3 co-authored publications come out and hot on the heels of the mélange paper, a rather different piece of work I’ve contributed to has just come out: the latest assessment from the Ice Sheet Mass Balance Inter-comparison Exercise known as IMBIE to its friends, has just been published in Scientific Data. The paper is open access here: Otosaka et al., 2026. It’s the third (at least) in a series of articles monitoring the health of the ice sheets.

Figure 2 from the IMBIE paper is the money shot, as it were. The changes in ice sheet mass budget since the 1970s…

This one is a bit different from my usual, as it’s a data paper — the whole point is the dataset itself, which is freely available for anyone to download and use. But the numbers in it are rather striking and here is already a bit of misinformation spreading, so I thought it worth a short post.

What is IMBIE?

For readers who haven’t come across it before: IMBIE is a big international collaboration, supported by ESA and NASA, that tries to answer a seemingly simple question: how much ice are Greenland and Antarctica actually losing?

The problem is that measuring the mass of an ice sheet from space is really hard, and there are several different ways to do it — satellites that measure changes in ice sheet height (altimetry), satellites that measure changes in Earth’s gravity field (gravimetry), and the input-output method, where you compare how much snow falls on the ice sheet with how much ice flows out to sea at the glacier fronts. Each method has its strengths and weaknesses, and no single one gives the whole picture, specific choices in analysing satellite datasets can also give quite different results, not to mention model estimates of SMB can have a wide divergence.

IMBIE’s remit is to bring all the different groups together, get everyone to compute their estimates in a common framework, and then combine them. When many independent measurements agree, we can be much more confident in the answer, and that answer feeds directly into things like the IPCC reports and projections of future sea level rise. It’s community science at its best, even if it probably feels like herding cats to the fantastic coordinator Ones Otosaka, and I’ve been proud to contribute estimates from our HIRHAM5 surface mass balance modelling for both ice sheets to several of the IMBIE assessments over the years.

What’s new this time?

This latest assessment is the most comprehensive yet. The team combined 42 independent satellite surveys from 27 satellite missions — and extended the record further back than ever before, all the way to 1972 for Greenland and 1979 for Antarctica, using the early Landsat archive. That gives us a half-century view of how the ice sheets have changed. Though admittedly the recent years are much better covered than the earlier ones.

So what does half a century of satellite data tell us? (Note: just me who finds it hard to understand the 1970s as half a century ago?)

The two ice sheets have lost 11,300 billion tonnes of ice since 1979, raising global sea level by 31.4 mm — about three centimetres. Greenland accounts for the larger share, with Antarctica contributing 13.3 mm. The ice sheets are now responsible for roughly a quarter of all global sea level rise. The remainder is mostly due to thermal expansion as the oceans warms..

Three centimetres may not sound like much, but as Andrew Shepherd put it in the press release, that puts another six to nine million people at risk of coastal flooding and erosion. And the trend is very much in the wrong direction. In low-lying Denmark this gives us more extreme storm surges and coastal flooding as even a few centimetres

A headline and a nuance: it’s the ice dynamics, not the surface melt, or is it?

There’s an important part of the paper which partitions the mass budget between dynamical losses and SMB changes. This is highlighted and the reasoning seemed obvious to me but in conversation with others is perhaps less obvious, so I want to discuss it here.

Of all that ice loss, 84% came from ice dynamics, that is outlet glaciers speeding up and discharging more ice into the ocean, from both calving icebergs and submarine melt, but “only” 16% from enhanced surface melting.

Now, I have spent a large part of my career working on surface mass balance, how much snow falls on the ice sheet and how much melts off it. We track it in near real-time on the Polar Portal, I write annual updates about it, and it’s genuinely important as it is the only way an ice sheet can maintain itself, no snowfall, no ice sheet.

Melt is also an important driver of ice dynamics, especially for Greenland. So it maybe should be with some professional humility that I report that the long-term mass loss story is apparently mostly a dynamics story: the ice sheets are responding to a warming ocean by flowing faster into the sea. Greenland’s rate of loss went from around 60 billion tonnes per year in the 1980s to 264 billion tonnes per year in the 2010s; Antarctica’s went from 48 to 202 billion tonnes per year over the same period, driven overwhelmingly by ocean melting at the outlet glaciers — with West Antarctica’s Pine Island and Thwaites glaciers leading the charge.

But there’s an important nuance in that 84/16 split, and it’s worth being clear about what it does and doesn’t mean. Surface mass balance is a two-way term: it’s positive when snow accumulates and negative when ice melts and runs off. And a warming climate pushes on both sides of the equation, more melt, certainly, but also more precipitation, at first as snow and increasingly we can measure over Greenland at least, as rain. Dynamic mass loss, by contrast, can only ever be negative: glaciers can only discharge ice into the ocean, they can’t drag it back up again. So we honestly wouldn’t expect SMB to be the biggest term in the loss budget, both sides of the SMB equation are increasing, it is in a race with itself. The genuinely worrying scenario is the one where melt and runoff together becomes bigger than snowfall. We are a very long way from that scenario fortunately, particularly in Antarctica but we’ll be in big trouble if, or perhaps when, that happens.

(Calving glaciology colleagues will note this whole discussion connects rather nicely to the question of what controls calving rates, which was the subject of my last post on melange…)

And now for a note of caution on the recent slowdown

You might have seen headlines suggesting ice loss has slowed down recently. It’s true that the most recent years in the record (2020–2023) show a temporary slowdown. A run of milder Greenland summers roughly halved its surface melting, and record snowfall over East Antarctica has offset some of the glacier losses there.

In fact we as a community have been on it as that East Antarctic snowfall is a story in itself. In a paper led by Marlen Kolbe earlier this year (Kolbe et al., 2026), (that I didn’t quite get around to talking about then, but I will rectify that), we showed that the extra snow is being delivered by atmospheric rivers — those great corridors of moisture streaming from a much warmer ocean towards the continent. Since 2020 they’ve become more frequent and more intense, dumping enough snow over East Antarctica to tip the whole ice sheet’s mass budget briefly into positive territory.

Is that reversal in mass budget temporary or permanent? Honestly, we don’t know yet but it matters because atmospheric rivers are double-edged. They bring massive snowfall, but they’re also often accompanied by a lot of melt — warm, wet air is rather good at melting ice as well as adding it. So the balance between accumulated snowfall and melt becomes more critical to understand here too, and the existing balance may well tip in the future.

The long-term picture remains one of accelerating loss, and as the climate continues to warm, we expect the losses to pick up again, especially in Greenland which is much further south than Antarctica is north, if that makes sense?

Refrozen melt layers in an Antarctic shallow ice core. We found a lot more of these than we expected based on satellites. Our all-seeing eyes in the sky don’t always see everything..

The response of the ice sheets to a warming climate is the single largest source of uncertainty in projections of future sea level rise. High-end estimates of global sea level rise by 2150 increase by a factor of 2.6 once the risk of ice sheet instability is accounted for. A continuous, half-century record of what the ice sheets are actually doing is exactly what we need to test the models and narrow those uncertainties, which is precisely why this dataset is such a valuable community resource, and why sustaining the satellite missions (CryoSat-2 and the Sentinels among them) that make it possible matters so much.

The full dataset is freely available from the UK Polar Data Centre, and you can read more about the IMBIE project at imbie.org. My thanks to Inès, Andrew, Tyler and the whole IMBIE team for pulling it all together — these assessments are an enormous amount of work, and the credit for this one belongs firmly with them.

As always, comments and questions welcome, here or on mastodon or blue sky.

Otosaka, I. N., Shepherd, A., Amory, C., et al. (2026). Mass balance of the Greenland and Antarctic ice sheets from the 1970s to 2023. Scientific Data, 13, 1301. https://doi.org/10.1038/s41597-026-08088-0

Small differences that make a really big difference.

I’m a co-author on a new paper that has just come out in GRL. It’s based on simulations we did with our collaborators in the PROTECT project on sea level contributions from the cryosphere. What Glaude et al shows is that, to quote the first of the 3 key points:

“With identical forcing, Greenland Ice Sheet surface mass balance from 3 regional climate models shows a two-fold difference by 2100”

In perhaps more familiar terms, if you run 3 regional climate models (that is a climate model run only over a small part of the world, in this case Greenland) with identical data feeding in from the same global climate model around the edges, you will get 3 quite different futures. Below you can see how the 3 different models think the ice sheet will look on average between 2080 and 2100. The model on the right, HIRHAM5 is our old and now retired RCM. It has a much smaller accumulation area left by the end of the century than the other two, which have much more intense melt going on in the margins.

Greenland Ice Sheet annual surface mass balance (a, b, c, 2080–2099 average) and annual surface mass balance anomaly (d, e, f, 2080–2099 average relative to 1980–1999) [mm WE/yr]. From left to right, RACMO (a and d), MAR (b and e), and HIRHAM (c and f). The equilibrium line (SMB = 0) is displayed as a solid black line in (d-f). Glaude et al., 2024, GRL.

In fact, by the end of the century, although the maps above seem to show HIRHAM having much more melt, there is in fact more runoff from the MAR model, because of this intense melt.

Spatially aggregated annual GrIS SMB anomalies (a), total precipitation (PP, b), and runoff (RU, c) [Gt/yr]. The solid lines represent the anomalies using a 5-year moving average, while the transparent lines display the unfiltered model output.

The surface mass balance (SMB) at the present day is in fact positive. This often surprises people, but SMB as the name suggests, only describes surface processes. Ice sheets can (and do) also lose a lot of ice by calving and subglacial and submarine melt. As SMB should balance everything if a glacier is to remain stable or even grow, present day SMB is usually 300 to 400 GT positive at the end of each year, and even so the Greenland ice sheet loses, net around 270Gt per year.

Our work here shows that, at least under this pathway, not only does SMB become net negative in itself by the middle of this century, there are significant differences in SMB projections between the estimates of how negative it will be, between the three RCMs. The global model we used, CESM2 under the high-end SSP5-8.5 scenario, is famously a warm scenario, but our estimated end of the century SMBs are extraordinary : (−964, −1735, and −1698 Gt per year, respectively, for 2080–2099). As I’ve discussed previously, one gigatonne is a cubic kilometre of water, 360Gt is roughly 1mm global mean sea level rise. (Though note your local sea level rise is *definitely* not the same as global average!) Even the lowest estimate here the is giving around 3 mm of global average sea level rise from surface melt and runoff *alone* by the end of this century each year. That’s pretty close to the modern day observed sea level rise from all sources.

And this is in spite of the fact that at the present day, the 3 models are rather similar in their estimates of SMB. The Devil is as usual in the details.

We attribute these startling divergences in the end of the century results to small differences in 1) the way melt water is generated, due to the albedo scheme (that is how the ice sheet surface reflects incoming energy); 2) but also due to the cloud parameters that control long-wave radiation at the surface, which again can promote or suppress melting. (We really need to know how much liquid water or ice there are in clouds, as this paper also emphasises in Antarctica); and 3) mainly down to the way liquid water that percolates down from the surface is handled in the snow pack. That is, how much air there is in the snowpack, how warm the snow is and how much refreezing can occur to buffer that melt.

The problem is that all of these processes happen at very small scales, from the mm (snow grains and air content), to the micron scale (cloud microphysics). That means that even in high (~5km) resolution regional models, we need to use parameterisations (approximations that generalise small scale processes over larger spatial and/or time scales). Small differences between these parameterisations add up over many decades. Essentially, much like the famous butterfly flapping its wings in Panama and causing a hurricane in Florida, the way mixed phase clouds produce a mix of water vapour and ice over an ice surface might ultimately determine how fast Miami will sink beneath the waves.

More data would certainly help to refine these parameterisations. The main scheme to work out how much liquid can percolate into snow was originally based on work by the US Army engineers in the 1970s. More field data with different types of snow would surely help refine these. Satellite data will be massively helpful, if we can smoothe out some wrinkles in how clouds (there they are again) affect surface reflectivity.

These 3 different types of processes also interact with each other in quite complex ways and ultimately affect how much runoff is generated as well as the size of the runoff zone in each model. So integration of many different types of observations is crucial.

“Different runoff projections stem from substantial discrepancies in projected ablation zone expansion, and reciprocally” as we put it in Glaude et al., 2024.

The timing and magnitude of the expansion of the runoff zone is quite different between the models, but all of them show a very consistent increase in melt and runoff over the next 80 years.

It’s probably also important to understand a couple of key points:

Firstly we ran a very high emissions pathway: SSP5-85 is probably not representative of the path we will follow in emissions (at least I hope not), but in this study we wanted to address the spread on different model estimates. And this is a way to get a good check on the sensitivity.

Secondly, the ice sheet mask and topography in these runs is kept fixed all the way through the century. This means we do not account for any elevation feedbacks (as the ice sheet gets lower because of melt, a larger area becomes vulnerable to melt because it’s lower and thus warmer), but we also don’t account for ice that has basically melted away no longer contributing to calculated runoff later in the century. Ice sheet dynamics are also not factored in.

Finally, we ran different resolution models, and that can have an impact particularly on precipitation and is one of the reasons why the new models we developed and have run in PolarRES (and which are now being analysed), have used a much more consistent set-up.

The 3 models we used, MAR, RACMO and HIRHAM have all been used in many different studies over both Greenland and Antarctica, but we haven’t really done a systematic comparison of future projections before. I think this work shows we need to get better at doing this to capture the uncertainty in the spread, especially when you consider that we’re now looking at using these models as training datasets for AI applications: training on each one of these models would give quite different results long-term. We need to think about how to both improve numerical models and capture that spread better. But ultimately, it’s how fast we can reduce greenhouse gas emissions and bend the carbon dioxide curve down that will determine how much of Greenland we will lose, and how quickly.

All data and model output from these simulations is available to download on our servers (we’re transitioning to a new one download.dmi.dk, not everything has been moved there yet). We also of course have data over land points and the surrounding seas, and we’ve run many more global climate models through the regional system to get high resolution (5km!) climate data also looking at different emissions pathways, if you’re interested in looking at, analysing or using any of this data – get in touch!

My warmest thanks to Quentin Glaude who led this analysis and special thanks to our colleagues in the Netherlands, France and Belgium for running these models and contributing to the paper analysis. Clearly, we have much work to do to get better at this ahead of CMIP7.