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Title: Evaluating Carbon Extremes in a Coupled Climate-Carbon Cycle Simulation

Abstract

Gross primary production (GPP) measures the photosynthetic update of carbon by terrestrial ecosystems. Accurately quantifying and simulating GPP and its extremes remains a challenge in global carbon cycle sciences. Here, we evaluate GPP extremes in a coupled biogeochemistry (BGC) simulation by the Department of Energy's Energy Exascale Earth System Model (E3SMv1.1) using the Generalized Extreme Value (GEV) distribution statistical model. The simulation is evaluated against the Global Bio-Atmosphere Flux (GBAF) data. Temporal trends and ENSO dependence are also investigated by using GEV models where time and the Niño3.4 index are introduced as linear covariates. The E3SMv1.1 model simulation generally predicts stronger negative and positive GPP extremes as compared to GBAF data. It also tends to simulate stronger temporal trends of GPP extremes than GBAF data. While negative GPP extreme trends are not significant in either E3SM or GBAF, positive GPP trends are statistically significant over several regions only for the E3SMv1.1 model simulation. ENSO dependence is generally stronger in the E3SMv1.1 model simulation, but ENSO dependence is found not to be significant for the time period analyzed (1980-2006) to match GBAF data. For the longer simulation period of 1900-2006, ENSO dependence is found to be statistically significant over Amazon, themore » maritime continent and Northern Australia for both negative and positive extremes.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1648890
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: 2019 International Conference on Data Mining Workshops (ICDMW) - Beijing, , China - 11/8/2019 5:00:00 AM-11/11/2019 5:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Xu, Min, Mahajan, Salil, Hoffman, Forrest, and Shi, Xiaoying. Evaluating Carbon Extremes in a Coupled Climate-Carbon Cycle Simulation. United States: N. p., 2019. Web. doi:10.1109/ICDMW.2019.00052.
Xu, Min, Mahajan, Salil, Hoffman, Forrest, & Shi, Xiaoying. Evaluating Carbon Extremes in a Coupled Climate-Carbon Cycle Simulation. United States. https://doi.org/10.1109/ICDMW.2019.00052
Xu, Min, Mahajan, Salil, Hoffman, Forrest, and Shi, Xiaoying. 2019. "Evaluating Carbon Extremes in a Coupled Climate-Carbon Cycle Simulation". United States. https://doi.org/10.1109/ICDMW.2019.00052. https://www.osti.gov/servlets/purl/1648890.
@article{osti_1648890,
title = {Evaluating Carbon Extremes in a Coupled Climate-Carbon Cycle Simulation},
author = {Xu, Min and Mahajan, Salil and Hoffman, Forrest and Shi, Xiaoying},
abstractNote = {Gross primary production (GPP) measures the photosynthetic update of carbon by terrestrial ecosystems. Accurately quantifying and simulating GPP and its extremes remains a challenge in global carbon cycle sciences. Here, we evaluate GPP extremes in a coupled biogeochemistry (BGC) simulation by the Department of Energy's Energy Exascale Earth System Model (E3SMv1.1) using the Generalized Extreme Value (GEV) distribution statistical model. The simulation is evaluated against the Global Bio-Atmosphere Flux (GBAF) data. Temporal trends and ENSO dependence are also investigated by using GEV models where time and the Niño3.4 index are introduced as linear covariates. The E3SMv1.1 model simulation generally predicts stronger negative and positive GPP extremes as compared to GBAF data. It also tends to simulate stronger temporal trends of GPP extremes than GBAF data. While negative GPP extreme trends are not significant in either E3SM or GBAF, positive GPP trends are statistically significant over several regions only for the E3SMv1.1 model simulation. ENSO dependence is generally stronger in the E3SMv1.1 model simulation, but ENSO dependence is found not to be significant for the time period analyzed (1980-2006) to match GBAF data. For the longer simulation period of 1900-2006, ENSO dependence is found to be statistically significant over Amazon, the maritime continent and Northern Australia for both negative and positive extremes.},
doi = {10.1109/ICDMW.2019.00052},
url = {https://www.osti.gov/biblio/1648890}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {11}
}

Conference:
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