Image Processing Techniques to Identify and Quantify Spatiotemporal Carbon Cycle Extremes

TL;DR

This paper introduces a novel image processing methodology to detect and analyze large spatiotemporal carbon cycle extremes, revealing their significant contribution to negative carbon anomalies and their association with climate drivers.

Contribution

It presents a new approach using image processing tools and neighborhood structures to identify and characterize spatiotemporal carbon cycle extremes in observational and model data.

Findings

100 carbon cycle STEs account for over 75% of negative extremes

Negative extremes are consistent with continuous neighborhood structures

The methodology links STEs to climate drivers

Abstract

Rising atmospheric carbon dioxide due to human activities through fossil fuel emissions and land use changes have increased climate extremes such as heat waves and droughts that have led to and are expected to increase the occurrence of carbon cycle extremes. Carbon cycle extremes represent large anomalies in the carbon cycle that are associated with gains or losses in carbon uptake. Carbon cycle extremes could be continuous in space and time and cross political boundaries. Here, we present a methodology to identify large spatiotemporal extremes (STEs) in the terrestrial carbon cycle using image processing tools for feature detection. We characterized the STE events based on neighborhood structures that are three-dimensional adjacency matrices for the detection of spatiotemporal manifolds of carbon cycle extremes. We found that the area affected and carbon loss during negative carbon cycle extremes were consistent with continuous neighborhood structures. In the gross primary production data we used, 100 carbon cycle STEs accounted for more than 75\% of all the negative carbon cycle extremes. This paper presents a comparative analysis of the magnitude of carbon cycle STEs and attribution of those STEs to climate drivers as a function of neighborhood structures for two observational datasets and an Earth system model simulation.