The future of Arctic ocean modelling

Last week, I went to Cape Code in the US to attend this year’s Forum for Arctic Modelling and Observational Synthesis (FAMOS) meeting. Attendants described their respective advances to improve the representation of the Arctic Ocean and sea ice in models of various scales: from the short-term forecast of sea ice conditions in a specific sea for navigation to 2100 projections in climate models. As the number of participants is kept low for logistical reasons, I report here some of the main findings and discussions, for any Arctic modeller, or simply Arctic-modelling-curious!

Why do we need to model the Arctic?

The main motivation for this work meeting is to model the Arctic Ocean. There are two reasons why we want to properly model this region: because it is a complex system, and because it is changing rapidly.

Map of the Arctic Ocean: its seas, bathymetry and main rivers discharging in it. From the Arctic Monitoring and Assessment Programme

Water enters the Arctic Ocean via Fram Strait (north of the Atlantic Ocean) and Bering Strait (Pacific Ocean), and exits it via the complex system of channels of the Canadian archipelago and Fram Strait. The Arctic Ocean is also being fed by large rivers, mostly in Russia and Canada.
With the advent of new technologies, enabling us to study the ocean under the ice, and with reliable satellite measurements to study said ice, we can now better understand the Arctic Ocean and its seasonal variability.

Also, until recently, even in summer the Arctic Ocean was totally covered in sea ice. But over the last decades, this ice cover has dramatically decreased, opening new opportunities. From Europe in particular, navigating over the pole is a faster and much safer shipping route to Asia than via the Suez canal and Indian Ocean. And although I personally think it’s a terrible environmental choice, the retreat of the ice over the shallow regions of the Arctic facilitates oil exploration. To limit the risks, being able to forecast sea ice conditions is key.

Which directions are the modelling efforts taking?

One of the aims of the FAMOS meeting is to channel everyone’s ideas into particular topics of interest. That means that most of the presentations and poster fell in one of the three categories below.
– A less idealised sea ice: if you’ve seen my pictures from this summer, you have noticed that sea ice is not a flat white surface. In particular, it is covered in cracks and melt ponds, which allow sun light to penetrate in the ocean, warm it and trigger phytoplankton (algae) bloom. There is also no such thing as “just” sea ice. There is landfast (attached to land) ice vs drifting ice, first-year vs multi-year ice (less fragile), ice covered in snow, ice floes… They all have different behaviours and responses to stress that need to be taken into account.

– A higher resolution ocean: with more open areas as the ice cover decreases, more wind stress can be transmitted under the ocean surface. Which means that eddies and internal waves become critical in the Arctic Ocean. But these are not properly modelled yet, as they require a relatively high resolution (hence longer running times and higher running costs). Part of the discussions were about how to do high resolution modelling correctly (including different grid systems), while others concentrated on how to parameterise the high resolution processes.

– A changing ocean: as someone (I can’t remember who, sorry) said during the meeting, there is no  “mean state of the Arctic Ocean” any more. The Arctic is changing. The large-scale ocean modelling questions hence concentrate on these changes, notably freshwater changes due mostly to Greenland melt, and heat changes. Particular emphasis is put on the regional impacts of these changes, and the need for a better understanding of various model responses by running specifically designed experiments. My favourite is a tracer release in several global ocean models to understand the paths taken by Greenland meltwaters.

What is the role of observations?

Although the meeting mostly concentrated on modelling, it also acknowledged the importance of observations. First, because models need to be fed some information about the real world before they can run. And then, because models need to be compared with the real world once they have run to see how well they did it (don’t try arguing over that point, or you’d say that my thesis was meaningless… please).

Ice-tethered profilers are basically CTDs that are attached under the ice. They provide oceanic observations from areas otherwise impossible to reach. A few presentations were about this year’s drift experiment by the Norwegian Polar Institute: in winter, they anchored their ship to ice floes and drifted with them, all the while measuring for the first time the interactions between the ice, the atmosphere, the ocean and the biology in the polar winter. Our own expedition to Petermann provide measurements of an unknown region, by one of the fastest melting glaciers. All these new data improve our understanding of the Arctic regions, in particular of their seasonal cycle.

But new is not everything! Climate models in particular are interested on long timescales, up to a century. To study these, long time series are key to distinguish year-to-year anomalies from long time changes. A few of these exist, in particular in Fram Strait an on the Russian shelf, and are used for example to assess the changes and mechanisms of these changes in heat transport through the Arctic.

Overall, the meeting provided a great opportunity for modellers and observationalists to exchange ideas and work together, which most tend to avoid. Personally, it showed me that I am not alone in studying the impact of Greenland meltwaters on the Atlantic circulation, and that plenty of methods and results already exist to draw inspiration from!

Talking of methods, fun fact to finish. Each year, a joke contest is to ask participants to predict the sea ice minimum of the following year, including drawing its extent. The person in charge of deciding who won did not want to have to compare 100+ drawings by eye, hence coded a method, and published a paper about it! (can also be used more seriously to compare sea ice extent in different models)


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