Ice sheet surface processes
The Greenland Ice Sheet is a large contributor to global sea-level rise primarily due to enhanced melt at the surface of the ice sheet. We investigate key processes that are responsible for surface melt using satellite and drone remote sensing, climate modeling, and fieldwork. In these studies we have provided insight into accumulation rates, cloud radiative effects, and melt-albedo feedbacks. Papers produced in this research area have been cited in recent IPCC reports and have led to refinements in climate models used to forecast ice sheet contributions to global sea-level rise.
Ryan, J. C., Smith, L. C., van As, D., Cooley, S. W., Cooper, M. G., Pitcher, L., and Hubbard, A. L., (2019), Greenland Ice Sheet surface melt amplified by snowline migration and bare ice exposure, Science Advances, 5(3), doi:10.1126/sciadv.aav3738.
Ryan, J. C., (2024), Contribution of surface and cloud radiative feedbacks to Greenland Ice Sheet meltwater production during 2002–2023. Communications Earth & Environment, doi:10.1038/s43247-024-01714-y.
Ryan, J. C., Cooper, M. G., Cooley, S. W., Rennermalm, A. K., Smith, L.C. (2025), Meltwater ponding has an underestimated radiative effect on the surface of the Greenland Ice Sheet, Nature Communications 16 (8274), https://doi.org/10.1038/s41467-025-62503-5.
Machine learning
We are increasingly using machine learning for scientific discovery. In the past, we have used machine learning to synthesize multiple sources of satellite data to generate new cloud radiative effect products at 1 km spatial resolution (Ryan et al., 2019). Now we are using convolutional networks to classify high-resolution satellite images and feed-forward neural networks to forecast meltwater runoff from the Greenland Ice Sheet. Deep learning is a rapidly growing field and we aim to apply the latest advances in our ice sheet research.
Ryan, J. C., Smith, L. C., Cooley, S. W., Pearson, B., Wever, N., Keenan, E., & Lenaerts, J. T. M., (2023), Decreasing surface albedo signifies a growing importance of clouds for Greenland Ice Sheet meltwater production. Nature Communications, 13(1), 1-8, doi:10.1038/s41467-022-31434-w.
ICESat-2
NASA’s ICESat-2 mission, launched in Oct 2018, is a first-of-its-kind satellite laser altimeter that has provided unique insight into ice sheet elevation change. However, it is well-known that light scatters within ice and snow before returning to the satellite. This process, termed subsurface scattering (or volume scattering), has the potential to bias surface elevation measurements from ICESat-2 since light that scatters in the subsurface takes longer to return to sensor (so the surface appear lower than it actually is). In collaboration with scientists at Portland State and University of North Dakota, we are conducting an intensive study of subsurface scattering that includes fieldwork to the Greenland Ice Sheet in 2026.
- Allgaier, M., Cooper, M. G., Carlson, A. E., Cooley, S. W., Ryan, J. C., & Smith, B. J., (2022), Direct measurement of optical properties of glacier ice using a photon-counting diffuse LiDAR. Journal of Glaciology, 1-1, doi:10.1017/jog.2022.34.
Drones and robotics
We use drones and robots to investigate snow and ice processes. Drones are useful since since they can collect data with higher resolution than satellite remote sensing and can survey beneath tree canopies and under clouds. We have used fixed-wing drones to map albedo and surface hydrology over the surface of Greenland Ice Sheet and multi-rotor drones to quantify snow depths in burned forests in the Pacific Northwest. Our most recent research is developing a multi-modular robot team that can drill into the ice sheet surface to measure important properties such as density and light-absorbing impurity concentrations.
Ryan, J. C., Hubbard, A. L., Irvine-Fynn, T. D., Doyle, S. H., Cook, J. M., Stibal, M., and Box, J. E., (2017), How robust are in situ observations for validating satellite-derived albedo over the dark zone of the Greenland Ice Sheet?, Geophysical Research Letters, 44, doi:10.1002/2017GL073661.
Ryan, J. C., Hubbard, A. L., Box, J. E., Todd, J., Christoffersen, P., Carr, J. R., Holt, T. O., and Snooke, N., (2015), UAV photogrammetry and structure from motion to assess calving dynamics at Store Glacier, a large outlet draining the Greenland ice sheet, The Cryosphere, 9, 1-11, doi:10.5194/tc-9-1-2015.
