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.Comment: Published in AAAI-1
