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Title: Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments

Abstract

Predicting how forest carbon cycling will change in response to climate change and management depends on the collective knowledge from measurements across environmental gradients, ecosystem manipulations of global change factors, and mathematical models. Formally integrating these sources of knowledge through data assimilation, or model–data fusion, allows the use of past observations to constrain model parameters and estimate prediction uncertainty. Data assimilation (DA) focused on the regional scale has the opportunity to integrate data from both environmental gradients and experimental studies to constrain model parameters. Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation to Predict Productivity for Ecosystems and Regions, DAPPER) that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the southeastern US to constrain parameters in a modified version of the Physiological Principles Predicting Growth (3-PG) forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO 2) concentration, water, and nutrients, along with nonexperimental surveys that spanned environmental gradients across an 8.6 × 10 5 km 2 region. We optimized regionally representative posterior distributions for model parameters, which dependably predicted data from plots withheld from the data assimilation. While the mean bias in predictions ofmore » nutrient fertilization experiments, irrigation experiments, and CO 2 enrichment experiments was low, future work needs to focus modifications to model structures that decrease the bias in predictions of drought experiments. Predictions of how growth responded to elevated CO 2 strongly depended on whether ecosystem experiments were assimilated and whether the assimilated field plots in the CO 2 study were allowed to have different mortality parameters than the other field plots in the region. We present predictions of stem biomass productivity under elevated CO 2, decreased precipitation, and increased nutrient availability that include estimates of uncertainty for the southeastern US. Overall, we (1) demonstrated how three decades of research in southeastern US planted pine forests can be used to develop DA techniques that use multiple locations, multiple data streams, and multiple ecosystem experiment types to optimize parameters and (2) developed a tool for the development of future predictions of forest productivity for natural resource managers that leverage a rich dataset of integrated ecosystem observations across a region.« less

Authors:
ORCiD logo [1];  [1];  [1];  [2]; ORCiD logo [1];  [1];  [3];  [1];  [4];  [1];  [5]; ORCiD logo [6];  [7];  [8];  [9]
  1. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States). Dept. of Forest Resources and Environmental Conservation
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Environmental Sciences Division. Climate Change Science Inst.
  3. North Carolina State Univ., Raleigh, NC (United States). State Climate Office of North Carolina
  4. Bordeaux Sciences Agro, Gradignan (France); Duke Univ., Durham, NC (United States). Nicholas School of the Environment
  5. Oregon State Univ., Corvallis, OR (United States). Dept. of Forest Engineering, Resources and Management
  6. Univ. of Florida, Gainesville, FL (United States). School of Forest Resources and Conservation
  7. North Carolina State Univ., Raleigh, NC (United States). Dept. of Forestry and Environmental Resources
  8. Arizona State Univ., Tempe, AZ (United States). Decision Center for a Desert City
  9. Univ. of Georgia, Athens, GA (United States). Warnell School of Forestry and Natural Resources
Publication Date:
Research Org.:
Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Bordeaux Sciences Agro, Gradignan (France)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23); USDA National Inst. of Food and Agriculture (NIFA) (United States); French Research Agency (ANR) (France)
Contributing Org.:
North Carolina State Univ., Raleigh, NC (United States); Duke Univ., Durham, NC (United States); Oregon State Univ., Corvallis, OR (United States); Univ. of Florida, Gainesville, FL (United States); Arizona State Univ., Tempe, AZ (United States); Univ. of Georgia, Athens, GA (United States)
OSTI Identifier:
1376417
Grant/Contract Number:
AC05-00OR22725; 2015-67003-23485; 2011-68002-30185; MACACC ANR-13-AGRO-0005; MARIS ANR-14-CE03-0007
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Biogeosciences (Online)
Additional Journal Information:
Journal Name: Biogeosciences (Online); Journal Volume: 14; Journal ID: ISSN 1726-4189
Publisher:
European Geosciences Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Thomas, R. Quinn, Brooks, Evan B., Jersild, Annika L., Ward, Eric J., Wynne, Randolph H., Albaugh, Timothy J., Dinon-Aldridge, Heather, Burkhart, Harold E., Domec, Jean-Christophe, Fox, Thomas R., Gonzalez-Benecke, Carlos A., Martin, Timothy A., Noormets, Asko, Sampson, David A., and Teskey, Robert O. Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments. United States: N. p., 2017. Web. doi:10.5194/bg-14-3525-2017.
Thomas, R. Quinn, Brooks, Evan B., Jersild, Annika L., Ward, Eric J., Wynne, Randolph H., Albaugh, Timothy J., Dinon-Aldridge, Heather, Burkhart, Harold E., Domec, Jean-Christophe, Fox, Thomas R., Gonzalez-Benecke, Carlos A., Martin, Timothy A., Noormets, Asko, Sampson, David A., & Teskey, Robert O. Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments. United States. doi:10.5194/bg-14-3525-2017.
Thomas, R. Quinn, Brooks, Evan B., Jersild, Annika L., Ward, Eric J., Wynne, Randolph H., Albaugh, Timothy J., Dinon-Aldridge, Heather, Burkhart, Harold E., Domec, Jean-Christophe, Fox, Thomas R., Gonzalez-Benecke, Carlos A., Martin, Timothy A., Noormets, Asko, Sampson, David A., and Teskey, Robert O. Wed . "Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments". United States. doi:10.5194/bg-14-3525-2017. https://www.osti.gov/servlets/purl/1376417.
@article{osti_1376417,
title = {Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments},
author = {Thomas, R. Quinn and Brooks, Evan B. and Jersild, Annika L. and Ward, Eric J. and Wynne, Randolph H. and Albaugh, Timothy J. and Dinon-Aldridge, Heather and Burkhart, Harold E. and Domec, Jean-Christophe and Fox, Thomas R. and Gonzalez-Benecke, Carlos A. and Martin, Timothy A. and Noormets, Asko and Sampson, David A. and Teskey, Robert O.},
abstractNote = {Predicting how forest carbon cycling will change in response to climate change and management depends on the collective knowledge from measurements across environmental gradients, ecosystem manipulations of global change factors, and mathematical models. Formally integrating these sources of knowledge through data assimilation, or model–data fusion, allows the use of past observations to constrain model parameters and estimate prediction uncertainty. Data assimilation (DA) focused on the regional scale has the opportunity to integrate data from both environmental gradients and experimental studies to constrain model parameters. Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation to Predict Productivity for Ecosystems and Regions, DAPPER) that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the southeastern US to constrain parameters in a modified version of the Physiological Principles Predicting Growth (3-PG) forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO2) concentration, water, and nutrients, along with nonexperimental surveys that spanned environmental gradients across an 8.6 × 105 km2 region. We optimized regionally representative posterior distributions for model parameters, which dependably predicted data from plots withheld from the data assimilation. While the mean bias in predictions of nutrient fertilization experiments, irrigation experiments, and CO2 enrichment experiments was low, future work needs to focus modifications to model structures that decrease the bias in predictions of drought experiments. Predictions of how growth responded to elevated CO2 strongly depended on whether ecosystem experiments were assimilated and whether the assimilated field plots in the CO2 study were allowed to have different mortality parameters than the other field plots in the region. We present predictions of stem biomass productivity under elevated CO2, decreased precipitation, and increased nutrient availability that include estimates of uncertainty for the southeastern US. Overall, we (1) demonstrated how three decades of research in southeastern US planted pine forests can be used to develop DA techniques that use multiple locations, multiple data streams, and multiple ecosystem experiment types to optimize parameters and (2) developed a tool for the development of future predictions of forest productivity for natural resource managers that leverage a rich dataset of integrated ecosystem observations across a region.},
doi = {10.5194/bg-14-3525-2017},
journal = {Biogeosciences (Online)},
number = ,
volume = 14,
place = {United States},
year = {Wed Jul 26 00:00:00 EDT 2017},
month = {Wed Jul 26 00:00:00 EDT 2017}
}

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  • Here, large uncertainties in land surface models (LSMs) simulations still arise from inaccurate forcing, poor description of land surface heterogeneity (soil and vegetation properties), incorrect model parameter values and incomplete representation of biogeochemical processes. The recent increase in the number and type of carbon cycle-related observations, including both in situ and remote sensing measurements, has opened a new road to optimize model parameters via robust statistical model–data integration techniques, in order to reduce the uncertainties of simulated carbon fluxes and stocks. In this study we present a carbon cycle data assimilation system that assimilates three major data streams, namely themore » Moderate Resolution Imaging Spectroradiometer (MODIS)-Normalized Difference Vegetation Index (NDVI) observations of vegetation activity, net ecosystem exchange (NEE) and latent heat (LE) flux measurements at more than 70 sites (FLUXNET), as well as atmospheric CO 2 concentrations at 53 surface stations, in order to optimize the main parameters (around 180 parameters in total) of the Organizing Carbon and Hydrology in Dynamics Ecosystems (ORCHIDEE) LSM (version 1.9.5 used for the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations). The system relies on a stepwise approach that assimilates each data stream in turn, propagating the information gained on the parameters from one step to the next. Overall, the ORCHIDEE model is able to achieve a consistent fit to all three data streams, which suggests that current LSMs have reached the level of development to assimilate these observations. The assimilation of MODIS-NDVI (step 1) reduced the growing season length in ORCHIDEE for temperate and boreal ecosystems, thus decreasing the global mean annual gross primary production (GPP). Using FLUXNET data (step 2) led to large improvements in the seasonal cycle of the NEE and LE fluxes for all ecosystems (i.e., increased amplitude for temperate ecosystems). The assimilation of atmospheric CO 2, using the general circulation model (GCM) of the Laboratoire de Météorologie Dynamique (LMDz; step 3), provides an overall constraint (i.e., constraint on large-scale net CO 2 fluxes), resulting in an improvement of the fit to the observed atmospheric CO 2 growth rate. Thus, the optimized model predicts a land C (carbon) sink of around 2.2 PgC yr -1 (for the 2000–2009 period), which is more compatible with current estimates from the Global Carbon Project (GCP) than the prior value. The consistency of the stepwise approach is evaluated with back-compatibility checks. The final optimized model (after step 3) does not significantly degrade the fit to MODIS-NDVI and FLUXNET data that were assimilated in the first two steps, suggesting that a stepwise approach can be used instead of the more “challenging” implementation of a simultaneous optimization in which all data streams are assimilated together. Most parameters, including the scalar of the initial soil carbon pool size, changed during the optimization with a large error reduction. This work opens new perspectives for better predictions of the land carbon budgets.« less
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  • Patterns of carbon allocation and mycorrhizal colonization were examined in loblolly pine seedlings from two half-sib families exposed to three ozone treatments (charcoal-filtered air, ambient air + 80 ppb O{sub 3}, and ambient air + 160 ppb O{sub 3}) and three rain pH levels (5.2, 4.5, and 3.3) for 12 weeks in open-topped chambers in a field setting. No statistically significant effects of ozone or rain pH were detected on biomass, root:shoot ratios, or carbon allocation; some consistent patterns were observed, however. Coarse root starch concentrations and mycorrhizal infection varied significantly with ozone levels. No significant interactions of ozone, rainmore » pH, or genotype were detected.« less