From the Arctic to Antarctica – how northern expertise is providing new insights in the south

Over several decades, the Nansen Center has built strong expertise in ocean, sea-ice and climate research in the Arctic. Today, researchers are increasingly applying that expertise to challenges in the Southern Hemisphere while also bringing in new Antarctic knowledge and perspectives. Two recent studies highlight how this growing effort is helping improve our understanding of both past and future conditions around Antarctica.

Understanding Antarctica’s past
Antarctica is one of the least observed regions on Earth, yet changes in its ice cover, ocean and atmosphere can have major consequences for the global climate system. To understand future change, scientists need a reliable picture of the past. In Antarctica, that remains a challenge. Compared with most parts of the world, observations are limited, particularly before the satellite era. As a result, significant uncertainties remain in our understanding of historical sea-ice and ocean conditions around this continent.
A recent study by Quentin Dalaiden from the Nansen Center and Ingo Bethke from the University of Bergen investigates how these uncertainties influence our understanding of Antarctic climate change. Their work shows that uncertainties in Southern Ocean conditions can substantially affect estimates of Antarctic temperature and snowfall. The findings underline the importance of improving methods to produce reliable climate reconstructions, which provide a foundation for understanding long-term climate variability and testing climate models.

Improving predictions of Antarctic sea ice development
Looking back is only part of the story. Scientists also want to improve their ability to predict future conditions.
In a separate study, Nicholas Williams, Yiguo Wang, and François Counillon from the Nansen Center examined whether information about sea-ice thickness could improve forecasts of Antarctic sea ice. Using the NorCPM climate prediction system, they found that adding observational data of sea-ice thickness improves both estimates of current sea-ice conditions and forecasts in the Antarctic region. Their results demonstrate how advances in modelling and data assimilation can help strengthen predictions, also in a region where observations remain sparse and environmental conditions change rapidly.

Different questions, shared expertise
Although the two studies focus on different scientific questions, they share a common goal: reducing uncertainty tied to climate changes in Antarctica. One study improves our understanding of the past, while the other strengthens our ability to look ahead. Together, they highlight the value of combining Arctic expertise in climate modelling, sea ice and data assimilation with dedicated Antarctic research.
As Antarctica continues to play an increasingly important role in climate research, this combination of knowledge is helping scientists build a clearer picture of how the region is changing and why it matters for the rest of the world.

 

Key researchers: Quentin Dalaiden, Yiguo Wang, and François Counillon

Why Antarctica matters far beyond the polar regions

Antarctica contains vast amounts of ice that influence global sea level. The Southern Ocean surrounding the continent plays a central role in the Earth’s climate system by transporting heat, carbon, and nutrients around the globe.
Antarctic sea ice also affects the exchange of heat, moisture, and carbon between the ocean and the atmosphere. Changes in the region can influence weather patterns, ocean circulation and ecosystems far beyond the polar regions.

Arctic tools meet Antarctic questions

Researchers are not simply repeating Arctic studies in Antarctica. Instead, they are adapting methods and models originally developed for northern polar environments to a very different setting, helping reveal what makes Antarctica unique.

Examples of expertise being applied in the south:

  • Climate reconstructions
  • Sea-ice modelling
  • Climate prediction systems such as NorCPM
  • Data assimilation
  • Machine learning and forecasting approaches