Algorithms for Estimating Trends in Global Temperature Volatility
This work addresses the need for better tools to monitor temperature variability trends, which is crucial for ecological and climate studies, though it appears incremental as it builds on existing data and methods.
The paper tackles the problem of estimating trends in global temperature volatility, which is more relevant for species viability than mean temperature trends, by developing two novel algorithms tailored for dense, gridded climate data and evaluating them on a simulation and a large global dataset.
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.