The NASA Ames Mars Global Climate Model (MGCM) software has been in
steady use at NASA for decades and was recently released to the public.
This model simulates the complex interactions of various weather cycles
that exist on Mars, namely the Dust Cycle, the CO2 Cycle, and the Water
cycle. Utilized by NASA, the MGCM is used to help understand their
empirically observed data through the use of sensitivity studies.
However, these sensitivity studies are computationally taxing, requiring
weeks to run. To address this issue, we have developed a surrogate model
using Gaussian processes (GP) that can emulate the output of this model
with relatively small amounts of data in a reduced amount of time (on
the order of minutes). We demonstrate the effectiveness of our emulator
using backward error analysis.