Algorithms for Estimating Trends in Global Temperature Volatility

  • Arash Khodadadi Indiana University
  • Daniel J. McDonald Indiana University


Trends in terrestrial temperature variability are perhaps more relevant for species viability than trends in mean temperature. In this paper, we develop methodology for estimating such trends using multi-resolution climate data from polar orbiting weather satellites. We derive two novel algorithms for computation that are tailored for dense, gridded observations over both space and time. We evaluate our methods with a simulation that mimics these data’s features and on a large, publicly available, global temperature dataset with the eventual goal of tracking trends in cloud reflectance temperature variability.

AAAI Special Technical Track: AI for Social Impact