We are delighted to announce that Professor Daniel Feltham (University of Reading), CPOM Principal Investigator: Sea Ice Modelling, has been awarded the Seligman Crystal by the International Glaciological Society (IGS).

The Seligman Crystal is awarded to a single person or a collaborative group/team that has made exceptional scientific contributions to glaciology, defined as any snow and/or ice studies.

The award recognises Professor Feltham’s pioneering contributions to sea ice physics, from the mechanics of how ice breaks, drifts, and melts, to the fluid dynamics of melt ponds and their role in accelerating ice loss. His work enhancing the development of the CICE model (the world’s most widely used sea ice model) has ensured the most recent physical discoveries have been incorporated into the code used by the IPCC and national weather services.

Petra Heil, the Chair of the IGS Awards Committee, stated in the citation.

“Professor Feltham’s extensive body of work and his dedication to scientific excellence have made him a towering figure in cryospheric physics. His innovation in applying mathematical rigour to the complex, multi-scale problems of sea ice has transformed how we understand and predict the future of the polar regions.“

You can read the full citation on the IGS website.

Dr Joe Phillips (Lancaster University) will present this science as part of Session CR6.5 on Friday, 08 May, 14:45–14:55 (CEST) in Room L2.

Radar altimetry satellites can measure the elevation of ice sheets by firing radio waves at the surface and timing how long the echo takes to return. However, with only a single antenna, these systems cannot tell exactly where on the surface each echo originated from. Current approaches work around this by making simplifying assumptions that reduce each echo to a single elevation estimate, discarding most of the information the waveform contains.

This work takes a fundamentally different approach. Rather than throwing away that ambiguity, a probabilistic deep learning framework was trained to extract the full range of plausible surface elevations encoded within each echo. An ensemble of 16 deep learning models was trained on 600,000 radar echoes collected by CryoSat-2 over Antarctica between 2012 and 2021, using the Reference Elevation Model of Antarctica (REMA) as ground truth.

The framework was tested over Pine Island Glacier – a region kept entirely separate from training – where it successfully reproduced well-established patterns of ice thinning of 2–3 metres per year. Encouragingly, results closely matched those from CryoSat-2’s interferometric products, which rely on additional information from a second antenna that many satellites do not carry.

This matters because elevation change underpins almost everything we calculate about ice sheets: how much ice is being lost, how much seas are rising, and how reliable our future projections are. Extracting more information from each satellite echo – including from historical missions and future satellites that lack a second antenna – could meaningfully improve all of these estimates.

Find out more by reading the abstract and attending his presentation online or in-person at EGU26.

Feature image credit: ESA

Header image credit: Professor Alison Banwell

Dr Karla Boxall (Lancaster University) will present this science as part of Session CR6.5 on Friday, 08 May, 14:35–14:45 (CEST) in Room L2.

Satellite missions such as CryoSat-2, ICESat-2 and Sentinel-3 provide invaluable data for measuring and monitoring ice sheet elevation change and any associated contributions to sea level. To capitalise fully on the immense value of satellite altimetry, the uncertainty associated with its measurements must be considered. Despite this, there is currently no standardised approach towards estimating uncertainty nor is there a method to assess how well existing uncertainties perform.

Karla, and colleagues from Lancaster University, University College London and Earthwave Ltd., have produced the first framework for evaluating methods of uncertainty generation to find that uncertainties based on the complexity of the landscape as well as the quality of the waveform itself are most robust.

The production of reliable uncertainties in this way is important because failing to incorporate uncertainties into downstream applications of satellite altimetry, such as in ice sheet models, can result in unconstrained estimates of ice mass balance, and ultimately, inaccurate predictions of global sea level change.

Satellite altimetry provides us with crucial data on the Cryosphere. Continuing to refine and improve the way we process that data, including identifying and formalising how we deal with uncertainties, is integral to ensuring the effective use of satellite altimetry data. As the Earth warms, and ice melts, this data will help us plan for, and adapt to, the impacts of a changing climate.

Find out more by reading the abstract and attending her presentation online or in-person at EGU26.

This work is also available as a preprint in The Cryosphere.

Feature image credit: ESA

Header image credit: Professor Alison Banwell

The thicknesses of sea ice and the snow that rests upon it are recognised as Essential Climate Variables by the WMO, critical for understanding, monitoring, and predictions of Earth’s climate. However, snow depth on sea ice is difficult to measure by satellite due to how radar penetrates and scatters off the ice, presenting challenges for assessing sea ice thickness.

The challenges surrounding quantifying this variable can have knock on considerations for monitoring ice mass balance, understanding of polar climate feedbacks, the operational safety of shipping routes, and ecosystems. As snow depth on sea ice is dynamic and precipitation and snow pack properties can evolve through seasons as well as varying over longer timescales due to warming, the gap in understanding could become increasingly consequential over time.

A discovery with the KuKa radar was developed into a novel snow depth retrieval approach. KuKa can be operated looking straight down (using Altimeter mode); and looking at an incidence angle (using Scatterometer Mode); both provide can both waveforms and normalised radar cross section values. Scientists have found that polarisation can help to determine snow depth on Arctic and Antarctic sea ice, which would also support estimations of sea ice thickness.

On Friday 8 May, Dr Rosemary (Rosie) Willatt (UCL) will present at EGU26 on the progress of the Polarimetric Synthetic Aperture Radar Altimeter concept (PoSARA) — a novel satellite instrument concept using polarimetric capability to estimate snow depth on sea ice, land ice and land from space. She will share with the audience how well the technique works over Arctic and Antarctic sea ice, lake ice and Arctic tundra, derived from multiple field campaigns.

“Making the unexpected discovery that using polarisation of waveforms provided accurate estimates of snow depth on sea ice in the initial MOSAiC dataset was exciting – nobody had predicted it but it made sense given existing theory on depolarisation in snow and we found one example of another instrument which observed something similar over a glacier. We then found our approach also worked with KuKa data from the Southern Ocean, and over tundra and lake ice in Churchill and Resolute.” – Said Rosie, commenting on the project for CPOM.

The team has developed the concept through to Scientific Readiness Level (SRL) 3 as part of ESA’s NEOMI initiative (New Earth Observation Mission Ideas). NEOMI aims to scientifically advance new Earth Observation mission ideas, empowering emerging scientists as lead investigators for potential future satellite missions and bold new EO research, and to formulate and develop a new scientific idea for an Earth Observation mission up to SRL 3.

Rosie, who is also CPOM’s Principal Investigator (PI) for Sea Ice Earth Observation, was awarded the ESA inaugural Konrad Steffen Award for a presentation on the early stages of this work, and is PI of the project, developing the concept for space application.

The science behind it has been actively tested in the field more recently too. In April 2025, an all-female field team of polar scientists from CPOM and UCL, including Rosemary Willatt, Julienne Stroeve, Carmen Nab and Alicia Fallows, visited Resolute Bay to investigate the use of this frequency radar and different polarisations on ice and snow.

You can watch a video the team made about the fieldwork here: https://cpom.org.uk/testing-kuka-in-the-arctic-new-video/

Satellites observing Earth’s polar regions give scientists the information they need to monitor how ice sheets and sea ice are changing, quantify their contribution to rising seas, and better understanding the complex ways that melting ice reshapes global weather systems.

Read the abstract and find out more about this ‘Highlight’ presentation on the EGU26 website.

Header image credit: Professor Alison Banwell

Feature image credit: Dr Amy Swiggs

CPOM PhD Researcher Ben Graves (KCL) will present this science as part of Session HS2.1.4 on Tuesday, 05 May, 11:55–12:05 (CEST) in Room 3.29/30.

Meltwater from glaciers is an important source of water for downstream communities, so monitoring and projecting the impacts of glacier retreat on these water sources is important when planning for future changes.

But in regions where there are monsoons this can be particularly challenging, as heavy rainfall can obfuscate contributions from glaciers.

This study, led by Ben, used measurements of the isotopes of oxygen and hydrogen found in water samples to estimate the origin of water flowing in the Dudh Koshi river, Nepal.

Isotopes are different versions of the same chemical element that have the same number of protons but a different number of neutrons in their nucleus. This means they have the same chemical behaviour but slightly different masses.

The research integrated new and previous observations from river and snow samples showing different isotopes, in glacio-hydrological modelling, to trace the water sources.

Preliminary results from samples taken post-monsoon revealed the highest contribution of meltwater ever seen in this region.

Find out more by reading the abstract and attending their presentation online or in-person at EGU26.

Feature image credit: Professor Andrew Shepherd

Header image credit: Professor Alison Banwell

CPOM Doctoral Researcher Yiliang Ma (SCENARIO DTP, University of Reading) will present this science as part of Session CR2.2 on Monday, 04 May, 17:00–17:10 (CEST) in Room L2.

Ice–climate feedbacks, the ways in which ice and climate mutually influence and compound each other, are a crucial element in the mechanisms of ice loss in ice sheets. However, many Earth System Models, designed to project climate change, fail to treat ice sheets as interactive components.

This study, led by Yiliang Ma, uses state-of-the-art UK Earth System Model (UKESM) to run two multi-century climate simulations under high-emissions forcing (SSP5–8.5) to compare and quantify the difference between projects of an evolving Greenland ice sheet against a static one.

Yiliang and the team explored the impacts of incorporating ice-climate feedbacks which showed that a reduction in surface albedo as ice is replaced by darker rock, combined with reduced ice sheet elevation, causes more solar energy to be absorbed and accelerates warming, increasing mass loss.

This research emphasises the importance of Earth System Models incorporating a dynamic Greenland ice sheet. As the ice sheet could contribute 7m to global sea levels it’s crucial feedback loops like those identified in these simulations are reflected in future projections of ice melt, particularly when predicting sea level rise.

Find out more by reading the abstract and attending their presentation online or in-person at EGU26.

Feature image credit: Professor Mal McMillan

Header mage credit: Professor Ali Banwell

This study, led by Dr Lauren Gregoire, uses state-of-the-art coupled model FAMOUS–BISICLES (General Circulation Atmosphere–ice-sheet model) to examine ice sheet changes in the Northern Hemisphere during the two most recent deglaciations (which took place around 21,000 to 7,000 years ago and 140,000 to 128,000 years ago).

The FAMOUS model (Fast Met Office—UK Universities Simulator) produces simulations for atmosphere–ocean, while BISICLES, which was developed in partnership with CPOM scientists, simulates ice sheet dynamics using adaptive mesh refinement for higher resolution in critical areas such as the grounding line of the ice sheet. By bringing these two models together FAMOUS-BISICLES can simulate climate-ice sheet interactions over thousands of years.

Following PMIP4 (Palaeoclimate Model Intercomparison Project 4) protocols, the team used ongoing climate model outputs for sea surface temperatures and sea ice to drive the simulations. They assessed the impact of concentrations of greenhouse gases, variations in the Earth’s orbit and orientation relative to the Sun, and uncertainties in model parameters and sea surface temperature, on the model projections of the patterns of ice retreat.

The study projected that there was an acceleration of ice retreat during the final stages of the last glacial person (referred to as Bølling warming), but results varied dependent on the type of terrain and the abruptness of sea surface temperatures. The study also identified marine-based sections of the ice sheet as particularly sensitive to ocean changes.

Understanding how and why ice sheets retreated during previous deglaciations gives us a longer-term perspective on ice sheet behaviour, extending far beyond the few decades of satellite observations. Coupled models like FAMOUS-BISICLES can be tested against these past events, helping us refine and validate the tools we use to project current and future ice loss.

Find out more by reading the abstract and attending their presentation online or in-person at EGU26.

Feature image credit: Dr Amy Swiggs

Header mage credit: Professor Ali Banwell