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Earth system models are our best tools for predicting future climate: that includes temperature, water resources, and extreme events. However, long model runs (1850-2100) take weeks to run on DOE supercomputers. Here,we show examples of how machine learning models can be integrated into existing physics-based models to produce better climate predictions at lower computational costs.

Snow bedforms cover around 8% of the world, mainly on Greenland and Antarctica. Despite being more widespread than sand dunes but have not been the subject of as much research. In the recent years there have been increasing interests in snow bedforms due to their impact on climate. To incorporate snow bedforms in climate models we need to quantitatively understand how they move and grow. We analyzed types of bedforms and their progression through time, using time-lapse imagery which will be presented in the ‘Bedforms and Blizzards on Niwot Ridge’ talk at this symposium. We created the first scaling laws for snow dune and ripple growth and movement. We compared them to the scaling laws for sand dunes and ripples. The end goal is to create a dynamic model for snow bedform growth over a function of time using velocities and wind speeds that takes the temperature and weather into consideration. These results will be helpful for climate scientist, geologists, and geomorphologists. Presented by Chelsea Herbertson at the CU Hydrologic Sciences Symposium 2018.

Presented at the AGU Fall Meeting, 2015.

Wind-swept snow forms organized textures called bedforms. These bedforms ornament about 8% of the surface of the Earth. They alter the thermodynamic properties of the surface, and affect local and global energy fluxes. Their effects have not yet been incorporated into Earth system models because the conditions governing their development are not yet well understood.We present the first rules predicting the formation, or not, of snow bedforms in a range of weather conditions. These rules are generated from three years of observations of snow bedforms in the Colorado Front Range. We identified the weather conditions leading to each observation, and used this data to construct classifiers, validated by bootstrapping, that predict bedform growth as a function of weather. Finally, we identified the variables which best predict bedform presence by constructing and comparing 460 classifiers built from different combinations of weather variables. We find that bedform presence is well predicted by wind speed and time since snowfall. The presence of sastrugi, the most widespread and studied bedform, is best predicted by the time and the highest wind speeds since snowfall.Our results make it possible to forecast bedform extents from widely-available weather data. They represent a first step towards understanding a process that shapes much of the polar world.

Wind-swept snow forms organized textures called bedforms. These bedforms ornament about 8% of the surface of the Earth. They alter the thermodynamic properties of the surface, and affect local and global energy fluxes. Their effects have not yet been incorporated into Earth system models because the conditions governing their development are not yet well understood.We present the first rules predicting the formation, or not, of snow bedforms in a range of weather conditions. These rules are generated from three years of observations of snow bedforms in the Colorado Front Range. We identified the weather conditions leading to each observation, and used this data to construct classifiers, validated by bootstrapping, that predict bedform growth as a function of weather. Finally, we identified the variables which best predict bedform presence by constructing and comparing 460 classifiers built from different combinations of weather variables. We find that bedform presence is well predicted by wind speed and time since snowfall. The presence of sastrugi, the most widespread and studied bedform, is best predicted by the time and the highest wind speeds since snowfall.Our results make it possible to forecast bedform extents from widely-available weather data. They represent a first step towards understanding a process that shapes much of the polar world.

We present a geologic map of the Piute Mountains of the Mojave Desert Region (Fig. IV) and anaccompanying structural interpretation focused on events since the deposition of Paleozoic sedimentsin the region. The region is part of a Precambrian metamorphic core complex. Our investigationis focused on a deformed sequence of Paleoizoic sediments with this complex. These sedimentsare stratigraphically lower than the older Precambrian units, but have a distinctive stratigraphywhich reveals a series of folds which took place during the deformation and overturning of theregion. We found four distinct generations of ductile events, as well as recent extensional faulting.Missing units within several of the first-generation folds are taken as evidence for thrust faultingor extreme shearing during deformation. The timing of deformation events are constrained by twoigneous units: the intrusive Lazy Daisy pluton (74Ma±3) and the late Tertiary Peach Springs Tuff(18.5Ma). Three ductile events date between the arrival of the pluton and the fall of the tuff, butat least one fold is younger than the tuff, implying that ductile deformation continued in the regionfor longer than was previously thought.