2017 Ablation Zone Photos
In July 2017 I spent 1 week at a field location in the ablation zone in the Issunguata Sermia catchment. This field site was where scientists from the University of Montana and University of Wyoming installed inclinometers and temperature sensors throughout the ice column in 9 boreholes. I helped to dismantle the field experiments and map the bed using ground penetrating radar.
2017 Ablation Zone Photos
In July 2017 I spent 1 week at a field location in the ablation zone in the Issunguata Sermia catchment. This field site was where scientists from the University of Montana and University of Wyoming installed inclinometers and temperature sensors throughout the ice column in 9 boreholes. I helped to dismantle the field experiments and map the bed using ground penetrating radar.
2017 Ablation Zone Photos
About this simulator
Glaciers gain mass through accumulation — snowfall and ice that builds up over time — and lose mass through ablation, which includes surface melting, evaporation, and calving. The difference between these two processes in any given year determines whether a glacier grows, shrinks, or stays roughly the same. In reality, the amount of snow a glacier receives in a given year and the amount it loses to melting are both influenced by dozens of interacting atmospheric and environmental factors, making the net annual balance the result of many compounding sources of variability — analogous to the outcome of a dice roll. This is not just a simplification for teaching purposes: treating year-to-year glacier mass changes as random draws from a distribution is a foundational concept in climate science, where distinguishing genuine long-term trends from the background noise of natural variability is one of the central challenges. This simulator models that process using dice: a blue die represents annual accumulation and a red die represents annual ablation, with each pip equal to one gigaton of ice. Rather than rolling physical dice, the simulator generates each roll virtually using the same probability as a standard six-sided die, so you can run through decades of glacier history in seconds. The key insight the game is designed to reveal is the difference between natural random variability and a forced climate signal. When a climate modifier is applied to the red die during the nonstationary phase, it shifts the odds systematically and over enough years, that shift overwhelms the noise of random variation, producing a trend that becomes visible in the data even though no single year looks dramatically different from the last.
How to use this simulator
Start by setting your glacier's initial mass, the total number of years to simulate, and the length of the stationary phase. During the stationary phase, roll one year at a time or use "Roll all" to run through it quickly, watching how the glacier's mass fluctuates without any directional trend. When the nonstationary phase begins, a climate modifier is added to the red die each year — enter a positive value to simulate a warming climate driving increased ablation, or a negative value to simulate conditions where ablation decreases (for example, increased snowfall outpacing melt). Roll through the nonstationary phase and compare the shape of the two halves of the graph. The star marker on the x-axis shows exactly where the climate shift occurred. Try running the simulation multiple times with the same settings to see how random variability can mask or amplify the underlying trend — and try adjusting the modifier and phase lengths to explore how the strength and duration of a climate signal affects how clearly it shows up in the record.