Algorithms for Estimating Trends in Global Temperature Volatility

TL;DR

This paper introduces new algorithms to estimate trends in global temperature variability using satellite data, providing tools to better understand climate change impacts on species and ecosystems.

Contribution

The paper develops two novel algorithms tailored for dense, gridded climate data to accurately estimate temperature variability trends over space and time.

Findings

Algorithms successfully estimate temperature variability trends.

Methods validated with simulations and real satellite data.

Potential to improve climate change impact assessments.

Abstract

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.