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Title: Forecasting Responses of a Northern Peatland Carbon Cycle to Elevated CO 2 and a Gradient of Experimental Warming

Abstract

The ability to forecast ecological carbon cycling is imperative to land management in a world where past carbon fluxes are no longer a clear guide in the Anthropocene. However, carbon–flux forecasting has not been practiced routinely like numerical weather prediction. This study explored (1) the relative contributions of model forcing data and parameters to uncertainty in forecasting flux– versus pool–based carbon cycle variables and (2) the time points when temperature and CO 2 treatments may cause statistically detectable differences in those variables. We developed an online forecasting workflow (Ecological Platform for Assimilation of Data (EcoPAD)), which facilitates iterative data–model integration. EcoPAD automates data transfer from sensor networks, data assimilation, and ecological forecasting. We used the Spruce and Peatland Responses Under Changing Experiments data collected from 2011 to 2014 to constrain the parameters in the Terrestrial Ecosystem Model, forecast carbon cycle responses to elevated CO 2 and a gradient of warming from 2015 to 2024, and specify uncertainties in the model output. Our results showed that data assimilation substantially reduces forecasting uncertainties. Interestingly, we found that the stochasticity of future external forcing contributed more to the uncertainty of forecasting future dynamics of C flux–related variables than model parameters. However, the parametermore » uncertainty primarily contributes to the uncertainty in forecasting C pool–related response variables. Given the uncertainties in forecasting carbon fluxes and pools, our analysis showed that statistically different responses of fast–turnover pools to various CO 2 and warming treatments were observed sooner than slow–turnover pools. In conclusion, our study has identified the sources of uncertainties in model prediction and thus leads to improve ecological carbon cycling forecasts in the future.« less

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [4]; ORCiD logo [2]; ORCiD logo [5]; ORCiD logo [5]; ORCiD logo [6]
  1. Nanjing Forestry Univ., Nanjing (China)
  2. Univ. of Oklahoma, Norman, OK (United States)
  3. Northern Arizona Univ., Flagstaff, AZ (United States)
  4. Univ. of Oklahoma Information Technology, Norman, OK (United States)
  5. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  6. Northern Arizona Univ., Flagstaff, AZ (United States); Tsinghua Univ., Beijing (China)
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) (SC-23)
OSTI Identifier:
1468262
Alternate Identifier(s):
OSTI ID: 1429538
Grant/Contract Number:  
AC05-00OR22725; 4000144122
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Geophysical Research. Biogeosciences
Additional Journal Information:
Journal Volume: 123; Journal Issue: 3; Journal ID: ISSN 2169-8953
Publisher:
American Geophysical Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; data assimilation; uncertainty; SPRUCE; model‐experiment; model‐data fusion; EcoPAD

Citation Formats

Jiang, Jiang, Huang, Yuanyuan, Ma, Shuang, Stacy, Mark, Shi, Zheng, Ricciuto, Daniel M., Hanson, Paul J., and Luo, Yiqi. Forecasting Responses of a Northern Peatland Carbon Cycle to Elevated CO2 and a Gradient of Experimental Warming. United States: N. p., 2018. Web. doi:10.1002/2017JG004040.
Jiang, Jiang, Huang, Yuanyuan, Ma, Shuang, Stacy, Mark, Shi, Zheng, Ricciuto, Daniel M., Hanson, Paul J., & Luo, Yiqi. Forecasting Responses of a Northern Peatland Carbon Cycle to Elevated CO2 and a Gradient of Experimental Warming. United States. doi:10.1002/2017JG004040.
Jiang, Jiang, Huang, Yuanyuan, Ma, Shuang, Stacy, Mark, Shi, Zheng, Ricciuto, Daniel M., Hanson, Paul J., and Luo, Yiqi. Fri . "Forecasting Responses of a Northern Peatland Carbon Cycle to Elevated CO2 and a Gradient of Experimental Warming". United States. doi:10.1002/2017JG004040. https://www.osti.gov/servlets/purl/1468262.
@article{osti_1468262,
title = {Forecasting Responses of a Northern Peatland Carbon Cycle to Elevated CO2 and a Gradient of Experimental Warming},
author = {Jiang, Jiang and Huang, Yuanyuan and Ma, Shuang and Stacy, Mark and Shi, Zheng and Ricciuto, Daniel M. and Hanson, Paul J. and Luo, Yiqi},
abstractNote = {The ability to forecast ecological carbon cycling is imperative to land management in a world where past carbon fluxes are no longer a clear guide in the Anthropocene. However, carbon–flux forecasting has not been practiced routinely like numerical weather prediction. This study explored (1) the relative contributions of model forcing data and parameters to uncertainty in forecasting flux– versus pool–based carbon cycle variables and (2) the time points when temperature and CO2 treatments may cause statistically detectable differences in those variables. We developed an online forecasting workflow (Ecological Platform for Assimilation of Data (EcoPAD)), which facilitates iterative data–model integration. EcoPAD automates data transfer from sensor networks, data assimilation, and ecological forecasting. We used the Spruce and Peatland Responses Under Changing Experiments data collected from 2011 to 2014 to constrain the parameters in the Terrestrial Ecosystem Model, forecast carbon cycle responses to elevated CO2 and a gradient of warming from 2015 to 2024, and specify uncertainties in the model output. Our results showed that data assimilation substantially reduces forecasting uncertainties. Interestingly, we found that the stochasticity of future external forcing contributed more to the uncertainty of forecasting future dynamics of C flux–related variables than model parameters. However, the parameter uncertainty primarily contributes to the uncertainty in forecasting C pool–related response variables. Given the uncertainties in forecasting carbon fluxes and pools, our analysis showed that statistically different responses of fast–turnover pools to various CO2 and warming treatments were observed sooner than slow–turnover pools. In conclusion, our study has identified the sources of uncertainties in model prediction and thus leads to improve ecological carbon cycling forecasts in the future.},
doi = {10.1002/2017JG004040},
journal = {Journal of Geophysical Research. Biogeosciences},
number = 3,
volume = 123,
place = {United States},
year = {2018},
month = {3}
}

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Figures / Tables:

Figure 1 Figure 1: Historical climate from the USDA MEF site during 1961-2014 and stochastic weather generation for 2015-2024. (a) Daily air temperature and (b) cumulative precipitation along Julian calendar. Curves and shaded areas represent mean and standard deviation (S.D.), respectively (gray is historical data, and black areas represent ensemble of futuremore » data). (c) and (d) are standard deviations of ensembles against means of each day for air temperature and precipitation, respectively.« less

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