@InProceedings{Xu_DMESS2019_20191108,
 author		= {Min Xu and Salil Mahajan and Forrest M. Hoffman and Xiaoying Shi},
 title		= {Evaluating Carbon Extremes in a Coupled Climate-Carbon Cycle Simulation},
 booktitle	= {Proceedings of the 2019 {IEEE} International Conference on Data Mining Workshops ({ICDMW} 2019)},
 organization	= {Institute of Electrical and Electronics Engineers (IEEE)},
 publisher	= {Conference Publishing Services (CPS)},
 pages		= {303--310},
 doi		= {10.1109/ICDMW.2019.00052},
 day		= 8,
 month		= nov,
 year		= 2019,
 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.}
}