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Title: Using Image Processing Techniques to Identify and Quantify Spatiotemporal Carbon Cycle Extremes

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 Earthmore » system model simulation.« less

Authors:
 [1]; ORCiD logo [1];  [2]; ORCiD logo [1]
  1. ORNL
  2. Northeastern University, Boston
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1928908
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: IEEE International Conference on Data Mining Workshops (ICDMW) - Orlando, Florida, United States of America - 11/28/2022 5:00:00 AM-12/1/2022 5:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Sharma, Bharat, Kumar, Jitendra, Ganguly, Auroop R., and Hoffman, Forrest. Using Image Processing Techniques to Identify and Quantify Spatiotemporal Carbon Cycle Extremes. United States: N. p., 2022. Web. doi:10.1109/ICDMW58026.2022.00148.
Sharma, Bharat, Kumar, Jitendra, Ganguly, Auroop R., & Hoffman, Forrest. Using Image Processing Techniques to Identify and Quantify Spatiotemporal Carbon Cycle Extremes. United States. https://doi.org/10.1109/ICDMW58026.2022.00148
Sharma, Bharat, Kumar, Jitendra, Ganguly, Auroop R., and Hoffman, Forrest. 2022. "Using Image Processing Techniques to Identify and Quantify Spatiotemporal Carbon Cycle Extremes". United States. https://doi.org/10.1109/ICDMW58026.2022.00148. https://www.osti.gov/servlets/purl/1928908.
@article{osti_1928908,
title = {Using Image Processing Techniques to Identify and Quantify Spatiotemporal Carbon Cycle Extremes},
author = {Sharma, Bharat and Kumar, Jitendra and Ganguly, Auroop R. and Hoffman, Forrest},
abstractNote = {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.},
doi = {10.1109/ICDMW58026.2022.00148},
url = {https://www.osti.gov/biblio/1928908}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2022},
month = {11}
}

Conference:
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