THE EFFECTS OF INTERANNUAL CLIMATE VARIABILITY ON THE EXTRACTION OF CLIMATE ESTIMATES FROM GLACIAL MORAINES: A CASE STUDY FROM THE COLORADO FRONT RANGE

In most mountainous regions reconstructed glacial histories are the primary record of past climate and are typically based on unsorted accumulations of debris (moraines) deposited at the terminus of glaciers. Former glacier geometries —preserved as moraines and trim lines— are the primary constraint for extracting paleoclimate estimates using either equilibrium-line altitudes or numerical glacier models.

It is an implicit assumption in the glacial geology community that terminal moraines were formed by glaciers responding to the mean value of summer temperature and winter precipitation at the time of formation. In reality glacier termini oscillate around a mean glacial length even in a steady climate, defined by a constant mean and standard deviation. These length oscillations are driven by the alignment of more negative (positive) periods of mass balance that arise out of random year-to-year climate variability. Because glaciers that override moraines almost always destroy them, the furthest terminal moraines from the headwall during the time period of interest represent the maximum excursion of the glacier from its mean length. This implies that paleoclimate estimates based upon the furthest terminal moraine are actually maximum estimates of climate change.

We use a linearized glacier model developed by Roe and O’Neal (2009) to determine the mean length of eleven Last Glacial Maximum (LGM) glaciers in the northern Front Range, Colorado. Mean glacier lengths during the LGM were ~15% upvalley from the LGM terminal moraines. In the Colorado Front Range estimating LGM paleoclimate from the furthest terminal moraine rather than the mean length adds an extra ~1°C temperature change or an additional 25% increase in precipitation to estimated climate changes. Furthermore, it is possible that ‘recessional’ moraines were formed by length oscillations driven by interannual variability.