Milankovitch’s Parasol: Quantifying Atmospheric Mediation of Orbital Forcing on ice sheet mass balance
The Milankovitch Theory posits that the pacing of Earth’s ice ages is dictated by cyclical changes in our orbit, eccentricity, obliquity, and precession. These cycles determine the intensity of summer insolation in the Northern Hemisphere, which is widely considered the primary driver of ice sheet growth and decay. However, there is a fundamental disconnect in this theory: the ice sheet never "sees" the Top of the Atmosphere (TOA) solar signal that Milankovitch calculated. Instead, the signal must pass through a dynamic atmospheric filter that can fundamentally reshape the energy budget before it reaches the surface. This filter is not stationary. How much solar radiation reaches the surface largely depends on the amount of atmospheric water vapor and clouds, which in turn depend on temperature. In addition, clouds impact the surface energy balance not through one but two competing mechanisms. In the shortwave spectrum, they act as a cooling shield, reflecting incoming solar energy back to space. In the longwave spectrum, they act as an insulating blanket, absorbing heat radiated from the Earth and sending it back toward the surface (greenhouse effect). How do these competing effects impact ice sheet mass balance? We will use the Eemian Interglacial (~125,000 years before present) and other key intervals as benchmarks.
Project description
The Scientific Challenge
The core challenge of this research is to estimate the effect of clouds on the radiation budget and to understand their net impact on ice. While orbital geometry might suggest a melting event, atmospheric feedbacks can either amplify the signal through increasing the longwave spectrum or dampen it through enhanced cloud albedo. By conducting sensitivity simulations on cloud properties, you will quantify these competing effects.
What will you do?
- Utilize climlab (Python) to generate time-latitude maps of TOA and Bottom of the Atmosphere (BOA) radiation budgets for the Eemian and other benchmark periods.
- Manipulate atmospheric transmissivity and albedo to simulate different cloud feedback strengths.
- Run the BESSI snow model to translate these radiative scenarios into physical melt rates and ice loss.
- Analyze the competing effects of SW cooling vs. LW warming across the different climate regimes (e.g., the high accumulation south vs. the dry interior) of Greenland.
Why Choose This Project?
Physical Insight: Bridge the gap between abstract orbital geometry and the concrete physics of snow melt.
Toolset: Gain high-level proficiency in both Python-based climate modeling (climlab) and professional snow-physics
codes (BESSI).
Fundamental Science: Tackle one of the foundational questions in paleoclimatology: What caused the ice ages (and
what caused them the stop)?