Evaluating carbon extremes in a coupled climate-carbon cycle simulation

Min Xu; Salil Mahajan; Forrest M. Hoffman; Xiaoying Shi

doi:10.1109/ICDMW.2019.00052

Evaluating carbon extremes in a coupled climate-carbon cycle simulation

Min Xu, Salil Mahajan, Forrest M. Hoffman, Xiaoying Shi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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, the maritime continent and Northern Australia for both negative and positive extremes.

Original language	English
Title of host publication	Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
Editors	Panagiotis Papapetrou, Xueqi Cheng, Qing He
Publisher	IEEE Computer Society
Pages	303-310
Number of pages	8
ISBN (Electronic)	9781728146034
DOIs	https://doi.org/10.1109/ICDMW.2019.00052
State	Published - Nov 2019
Event	19th IEEE International Conference on Data Mining Workshops, ICDMW 2019 - Beijing, China Duration: Nov 8 2019 → Nov 11 2019

Publication series

Name	IEEE International Conference on Data Mining Workshops, ICDMW
Volume	2019-November
ISSN (Print)	2375-9232
ISSN (Electronic)	2375-9259

Conference

Conference	19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
Country/Territory	China
City	Beijing
Period	11/8/19 → 11/11/19

Funding

We thank DOE E3SM CBGC simulation group for coordinating experiments and conducting simulations. This research was supported through the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation Scientific Focus Area (RUBISCO SFA), which are sponsored by the Climate and Environmental Sciences Division (CESD) of the Biological and Environmental Research (BER) Program in the U.S. Department of Energy Office of Science. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility and the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which are supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 and DE-AC05-00OR22725 respectively.

Funders	Funder number
Climate and Environmental Sciences Division
U.S. Department of Energy	DE-AC05-00OR22725, DE-AC02-05CH11231
Office of Science
Biological and Environmental Research

Keywords

Carbon cycle
Carbon extremes
GEV statistical model
Terrestrial ecosystem

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10.1109/ICDMW.2019.00052

Cite this

Xu, M., Mahajan, S., Hoffman, F. M., & Shi, X. (2019). Evaluating carbon extremes in a coupled climate-carbon cycle simulation. In P. Papapetrou, X. Cheng, & Q. He (Eds.), Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019 (pp. 303-310). Article 8955642 (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2019-November). IEEE Computer Society. https://doi.org/10.1109/ICDMW.2019.00052

Xu, Min ; Mahajan, Salil ; Hoffman, Forrest M. et al. / Evaluating carbon extremes in a coupled climate-carbon cycle simulation. Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019. editor / Panagiotis Papapetrou ; Xueqi Cheng ; Qing He. IEEE Computer Society, 2019. pp. 303-310 (IEEE International Conference on Data Mining Workshops, ICDMW).

@inproceedings{fe11e64d546240deb72860cc47f11ecb,

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 Ni{\~n}o3.4 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.",

keywords = "Carbon cycle, Carbon extremes, GEV statistical model, Terrestrial ecosystem",

author = "Min Xu and Salil Mahajan and Hoffman, {Forrest M.} and Xiaoying Shi",

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Xu, M, Mahajan, S , Hoffman, FM & Shi, X 2019, Evaluating carbon extremes in a coupled climate-carbon cycle simulation. in P Papapetrou, X Cheng & Q He (eds), Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019., 8955642, IEEE International Conference on Data Mining Workshops, ICDMW, vol. 2019-November, IEEE Computer Society, pp. 303-310, 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019, Beijing, China, 11/8/19. https://doi.org/10.1109/ICDMW.2019.00052

Evaluating carbon extremes in a coupled climate-carbon cycle simulation. / Xu, Min; Mahajan, Salil ; Hoffman, Forrest M. et al.
Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019. ed. / Panagiotis Papapetrou; Xueqi Cheng; Qing He. IEEE Computer Society, 2019. p. 303-310 8955642 (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2019-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Xu M, Mahajan S , Hoffman FM , Shi X. Evaluating carbon extremes in a coupled climate-carbon cycle simulation. In Papapetrou P, Cheng X, He Q, editors, Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019. IEEE Computer Society. 2019. p. 303-310. 8955642. (IEEE International Conference on Data Mining Workshops, ICDMW). doi: 10.1109/ICDMW.2019.00052

Evaluating carbon extremes in a coupled climate-carbon cycle simulation

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