DOI: 10.5061/dryad.qrfj6q5s5
title: Data and code for: Combining eddy covariance towers, field measurements, and the MEMS 2 ecosystem model improves confidence in the climate impacts of bioenergy with carbon capture and storage

description: Carbon dioxide removal technologies such as bioenergy with carbon capture
 and storage (BECCS) are required if the effects of climate change are to
 be reversed over the next century. However, BECCS demands extensive land
 use change that may create positive or negative radiative forcing impacts
 upstream of the BECCS facility through changes to in situ greenhouse gas
 fluxes and land surface albedo. When quantifying these upstream climate
 impacts, even at a single site, different methods can give different
 estimates. Here we show how three common methods for estimating the net
 ecosystem carbon balance of bioenergy crops established on former
 grassland or former cropland can differ in their central estimates and
 uncertainty. We place these net ecosystem carbon balance forcings in the
 context of associated radiative forcings from changes to soil N2O and CH4
 fluxes, land surface albedo, embedded fossil fuel use, and geologically
 stored carbon. Results from long term eddy covariance measurements, a soil
 and plant carbon inventory, and the MEMS 2 process-based ecosystem model
 all agree that establishing perennials such as switchgrass or mixed
 prairie on former cropland resulted in net negative radiative forcing
 (i.e., global cooling) of -26.5 to -39.6 fW m-2 over 100 years.
 Establishing these perennials on former grassland sites had similar
 climate mitigation impacts of -19.3 to -42.5 fW m-2. However, the largest
 climate mitigation came from establishing corn for BECCS on former
 cropland or grassland, with radiative forcings from -38.4 to -50.5 fW m-2,
 due to its higher plant productivity and therefore more geologically
 stored carbon. Our results highlight the strengths and limitations of each
 method for quantifying the field scale climate impacts of BECCS and show
 that utilizing multiple methods can increase confidence in the final
 radiative forcing estimates.
description: # Combining eddy covariance towers, field measurements, and the MEMS 2
 ecosystem model improves confidence in the climate impacts of bioenergy
 with carbon capture and storage Authors Grant Falvo1,2, Yao Zhang3,4,
 Michael Abraha2,5, Samantha Mosier1,2, Yahn-Jauh Su2,5, Cheyenne Lei6,
 Jiquan Chen2,5, M. Francesca Cotrufo3,4, and G. Philip Robertson1,2
 Affiliations 1 Department of Plant, Soil and Microbial Sciences, Michigan
 State University, East Lansing, Michigan, United States. 2 Great Lakes
 Bioenergy Research Center, East Lansing, Michigan, United States. 3
 Department of Soil and Crop Sciences, Colorado State University, Fort
 Collins, Colorado, United States. 4 Natural Resource Ecology Laboratory,
 Fort Collins, Colorado, United States. 5 Department of Geography,
 Environment and Spatial Sciences, Michigan State University, East Lansing,
 Michigan, United States. 6 School of Environment and Sustainability,
 University of Michigan, Ann Arbor, Michigan, United States. ## Files and
 their organization This Dryad.zip file contains the following directories
 and files ### Observations\_Directory * all_non_soil_obs.csv * merged
 quality assured field and eddy covariance tower observations * NA denotes
 missing or low quality data * metadata_for_all_non_soil_obs.csv *
 accompanying metadata for field and eddy covariance tower observations *
 all_soil_obs.csv * merged quality assured soil observations * NA denotes
 missing or low quality data * metadata_for_all_soil_obs.csv * accompanying
 metadata for soil observations * MEMS_2_model * Common_Input_Files * Model
 input files to the MEMS 2 model that are shared between sites. * L1 - M4 *
 Model input and output files for each of the sites, denoted with
 'in_*' and 'out_*', respectively. * in_AtmCO2.csv *
 Surface air co2 concentration file. Specify the timeseries evolution of
 surface atmospheric co2 concentrations here. * in_Basepars.csv *
 Parameters of the model that can be easily changed. These include things
 like soil organic matter decay rates and microbial carbon use efficiency.
 * in_Control.csv * Definitions of model run protocols. Point to the other
 input files here. And set run start and end years. * in_Crop.csv *
 Parameters of the crop module for each plant type. This includes
 parameters such as specific leaf area and light use efficiency. *
 in_Operations.csv * Agronomic operation definitions. This includes
 specifications for things such as fertilization and planting events. *
 in_OutputOptions.csv * Define which outputs to write daily and yearly. *
 in_Sch.csv * Define the scenario plant and agronomic events. Make a
 timeseries of these management events. * in_Site.csv * Site level
 parameters and initial conditions. This includes parameters like the
 elevation and initial carbon pool stocks. * in_Soil.csv * Initial
 conditions of soil in each layer. This includes parameters such as the
 sand, silt, and clay content of the soil. * in_Tree.csv * Not active in
 this model version. * in_Weather_Spin_Up.csv * Forcing data for the spin
 up period. This includes parameters such as solar radiation and
 precipitation. * in_Weather.csv * Forcing data for the model scenario
 period. This includes parameters such as solar radiation and
 precipitation. * log.txt * Logfile from the last model run. This will
 contain messages from the Java code such as warnings and errors. *
 out_daily_Crop.csv * Daily output of model fields for plants. This
 includes things like leaf and root carbon stocks. Units are g C or N per
 square meter. * out_daily_Harvest.csv * Daily output of model fields for
 agronomic harvests. This includes seeds and residue removals from the
 field. Units are g C or N per square meter. * out_daily_NutrientsFlow.csv
 * Daily output of model fields for nitrogen flows and heterotrophic
 respiration. This includes things like nitrate leaching and heterotrophic
 respiration. Units are g C or N per square meter. *
 out_daily_SoilWaterContent.csv * Daily output of model fields for soil
 water content by layer. This includes the soil water content of each soil
 layer. Units are percent volume. * out_daily_SOM.csv * Daily output of
 model fields for soil carbon stocks by layer. This includes things like
 particulate and mineral-associated organic matter carbon stocks. Units are
 g C or N per square meter. * out_daily_SurfOM.csv * Daily output of model
 fields for surface litter carbon stocks and nitrogen. This includes
 surface litter carbon and nitrogn pools. Units are g C or N per square
 meter. * out_daily_WaterFlow.csv * Daily output of model fields for water
 fluxes. This includes things like runoff and evapotranspiration. Units are
 mm per day. * Rscripts * Files to run and calibrate the model to the
 observations. Some directories are hard coded to be local, will need to
 change these * RF_Timeseries *
 GLBRC_scale_up_ghg_and_albedo_timeseries.csv * final timeseries of
 radiative forcing values and R scripts used in this study *
 metadata_for_GLBRC_scale_up_ghg_and_albedo_timeseries.csv * accompanying
 metadata for radiative forcing timeseries. Some directories are hard coded
 to be local, will need to change these * Tables * Final tables as they
 appear in the manuscript * Table S1. Harvest yields, aboveground net
 primary productivity, and harvest efficiency for each study site over the
 13-year study period. The mean and standard error of all years are
 provided. For the corn sites both seeds and residue are combined when both
 were harvested in the same year (i.e. 2015-2021). Harvest efficiency is
 calculated as harvest yield divided by aboveground net primary
 productivity. Units are Mg biomass per hectare. * Table S2. Net ecosystem
 carbon balance (NECB) for each study site and method over the 13-year
 study period. Central, 2.5 %, and 97.5 % NECB estimates are provided for
 each method. Positive values indicate C sequestration and negative values
 indicate C loss from the ecosystem. Units are Mg carbon per hectare. *
 Table S3. Net ecosystem carbon balance (NECB) components for the C
 inventory and MEMS 2 model methods. Positive values indicate C
 sequestration and negative values indicate C loss from the ecosystem.
 Units are Mg carbon per hectare. * Table S4. Average instantaneous
 radiative forcing for each component and method during the 100-year period
 following the land use change event. Positive values indicate a warming
 impact and negative values indicate a cooling impact, relative to their
 baseline scenario, which is set to zero by definition. Note that only the
 NECB differs by method. Units are fW per square meter of earth, per square
 meter of land transformed. * Table S5. Parameters included in the MEMS 2
 model calibration for each plant type. Values are the lower and upper
 bound of the uniform distribution used in the calibration. ###
 Stats_Output * Output from the statistical models used in this study *
 glbrc_scale_up_bulk_soil_C_lm_output.csv * Linear regression output for
 changes in bulk soil carbon stocks at each of the sites over time. *
 glbrc_scale_up_MAOM_soil_C_lm_output.csv * Linear regression output for
 changes in mineral-associated soil carbon stocks at each of the sites over
 time. * glbrc_scale_up_POM_soil_C_lm_output.csv * Linear regression output
 for changes in particulate soil carbon stocks at each of the sites over
 time. * glbrc_scale_up_delta_toa_sw_out_gam_output.csv * Generalized
 additive model output for changes in top of atmosphere outgoing shortwave
 radiation at each of the study sites. *
 glbrc_scale_up_soil_CH4_gam_output.csv * Generalized additive model output
 for changes in soil methane fluxes at each of the study sites. *
 glbrc_scale_up_soil_N2O_gam_output.csv * Generalized additive model output
 for changes in soil nitrous oxide fluxes at each of the study sites. *
 MEMS_Uncertainty_Output/L1-M4 * Confidence intervals for carbon stock
 component changes at each site from the MEMS model uncertainty runs. These
 intervals characterize the sensitivity of the model output to the
 posterior distribution of the calibrated parameters.