This Trugman_readme20180625.txt file was generated on 20180625 by Anna T Trugman Links to Publication Field updated. 2021-12-09, SES ------------------- GENERAL INFORMATION ------------------- 1. Title of Dataset Supporting data for ‘Soil moisture drought as a major driver of carbon cycle uncertainty’ published in Geophysical Research Letters. 2. Author Information Principal Investigator Contact Information Name: Trugman, Anna Institution: University of Utah Address: Department of Biology, University of Utah, Salt Lake City, UT 84112 Email: a.trugman@utah.edu Alternate Contact Information Name: Anderegg, William Institution: University of Utah Address: Department of Biology, University of Utah, Salt Lake City, UT 84112 Email: anderegg@utah.edu 3. Date of data collection (single date, range, approximate date) 4. Geographic location of data collection (where was data collected?): Model output data 5. Information about funding sources that supported the collection of the data: The authors acknowledge support from the USDA National Institute of Food and Agriculture Postdoctoral Research Fellowship Grant No. 2017-07164 to A.T.T, the National Science Foundation grant 1714972, the University of Utah Global Change and Sustainability Center, and the USDA National Institute of Food and Agriculture, Agricultural and Food Research Initiative Competitive Programme, Ecosystem Services and Agro-ecosystem Management, grant no. 2017-05521 to W.R.L.A., National Science Foundation Award 1151102 and US Department of Energy, Office of Science, Office of Biological and Environmental Research, Terrestrial Ecosystem Science (TES) Program award DE-SC0014363 to D.M., and the Earth Institute to J.S.M. -------------------------- SHARING/ACCESS INFORMATION -------------------------- 1. Licenses/restrictions placed on the data: Please acknowledge the use of this data in any publications. This data is provided as is without any express or implied warranties whatsoever. 2. Links to publications that cite or use the data: Trugman, A. T., Medvigy, D., Mankin, J. S., & Anderegg, W. R. (2018). Soil moisture stress as a major driver of carbon cycle uncertainty. Geophysical Research Letters, 45(13), 6495-6503. https://doi.org/10.1029/2018GL078131 3. Links to other publicly accessible locations of the data: 4. Links/relationships to ancillary data sets: 5. Was data derived from another source? If yes, list source(s): All date was derived from CMIP5 multi-model ensemble data, available at the Centre for Environmental Data Archival (https://services.ceda.ac.uk/). 6. Recommended citation for the data: Trugman AT, D Medvigy, JS Mankin, WRL Anderegg. (2018) Soil moisture drought as a major driver of carbon cycle uncertainty. The Hive: University of Utah Research Data Repository. Please acknowledge the use of this data in any publications. --------------------- DATA & FILE OVERVIEW --------------------- 1. File List A. Filename: All *_RCP85.nc files Short description: Future projections for individual models for beta and GPPc corresponding to Figures S2-3. Beta is a factor that ranges from 0 to 1, units of GPPc are in kgC/m2/yr. B. Filename: All *_hist.nc files Short description: Historical maps for individual models for beta and GPPc. Beta is a factor that ranges from 0 to 1, units of GPPc are in kgC/m2/yr. 2. Relationship between files: This folder contains global maps of beta and GPPc for historical and future RCP 8.5 climatologies in nine coupled climate-Earth system models corresponding to the following publication: Trugman AT, D Medvigy, JS Mankin, WRL Anderegg. Soil moisture drought as a major driver of carbon cycle uncertainty. Geophysical Research Letters. 3. Additional related data collected that was not included in the current data package: NA 4. Are there multiple versions of the dataset? no -------------------------- METHODOLOGICAL INFORMATION -------------------------- 1. Description of methods used for collection/generation of data: Soil moisture limitation in CMIP5 simulations: We obtained soil moisture at the monthly time scale from historical runs and RCP 8.5 from the CMIP5 multi-model ensemble archive available at the Centre for Environmental Data Archival (https://services.ceda.ac.uk/) for one realization for each of nine models – BCC-CSM1-1, BNU-ESM, CanESM2, CCSM4, CESM1-BGC, GISS-E2-R-CC, HadGEM2-ES, MIROC-ESM, and NorESM1-ME. Only one realization was used in this analysis because the soil moisture equation used to limit photosynthesis (β) is a product of model structure rather than initial conditions. However, using only one realization neglects possible multi-decadal variability in soil moisture between different model realizations. To avoid confounding short-term variability in soil moisture and GPPm, we computed the average soil moisture for each model for each month over the period from 1981-2000 and from 2080-2099 for the historical and RCP 8.5 simulations, respectively. For ease of comparison, model output was re-gridded to a 1° grid and the soil column was re-gridded to the CCSM4 grid that extends through 4.7 m. Our re-gridding calculations were performed in MATLAB using a nearest neighbor method from the "interp" function to preserve the spatial distribution of soil moisture as best as possible for each individual model. The functional form of β generally requires a metric of soil moisture, soil field capacity, and soil wilting point (Supporting Text S1). Thus we downloaded global maps at 1° resolution of volumetric soil water content at saturation, wilting point, and field capacity as well as soil water potential at saturation, and rooting depth encompassing 50% of root biomass from the GWSP2 database (Dirmeyer et al., 2002), a soil texture database used in a number of Earth system models (Sato et al., 2007). We further calculated soil water potential at field capacity and wilting point using the Clapp-Hornberger equation and downloaded soil parameters from the GWSP2. Additionally, all models except the MIROC-ESM (Sato et al., 2007) require the amount of root biomass in each soil layer. This output was not available for individual models in the CMIP5 archive, so we instead calculated the root biomass fraction in each soil layer by fitting an established rooting depth curve from Jackson et al., 1996 (one of the primary references from which global vegetation root distribution are based in VMs (Zeng, 2001)) to the 50% root biomass fraction from the GWSP2 database. Finally, we masked out all locations covered in ice year-round circa year 2000. After this post-processing, we averaged monthly-level values of β over the year for both the historical and RCP 8.5 simulations to obtain two global maps of β for comparison with each model, one representing average conditions circa 2000 and one circa 2100. Estimated of impacts of soil moisture limitation on simulated GPP: We downloaded average daily minimum temperature and GPP, averaged to the monthly scale, from historical runs and RCP 8.5. For NorESM1-ME only, we used average monthly temperature rather than average daily minimum temperature because daily minimum temperature was not available. We processed and re-gridded these model outputs in the same manner as soil moisture. Because we were interested in examining the water limitation impact of β on GPP, we masked out GPP during months when average minimum temperature decreased below 273.15 K. We then calculated the simulated global GPP (GPPm) that was reduced by β (GPPc) in each model according eqn. 1. For numerical purposes, in our calculation of GPPc, we limited the minimum value of β to 0.1. Though in some models β is used to regulate the maximum rate of photosynthesis and in others it is used to regulate canopy conductance (Supporting Text S1), GPPm scales roughly linearly with β in either case and we treated both identically in our first-order estimates of GPPc. After obtaining estimates of monthly-level GPPc, we summed monthly-level values during the growing season over the year for both the historical and RCP 8.5 simulations to obtain global maps of GPPc (using β functions associated with their own respective CMIP5 model) for average conditions circa 2000 and circa 2100. To quantify the effect that different β functions used across models has on GPPc, we applied all seven different β equations (Supporting Text S1) to a single model’s soil moisture output. We compared the variability in globally integrated β for a given model soil moisture using the seven different β functions to the variability in globally integrated β across models (each with their own model-specific β function) to attribute the first-order variability in β and GPPc associated with the functional form of the β equation alone, thus enabling a better understanding the role of β in carbon cycle uncertainty. All methods are described in Trugman AT, D Medvigy, JS Mankin, WRL Anderegg. Soil moisture drought as a major driver of carbon cycle uncertainty. Geophysical Research Letters. ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: *_RCP85.nc and *_hist.nc ----------------------------------------- 1. Number of variables: 4 (Beta, GPPc, lat, lon) 2. Number of cases/rows: 180x360 (1 degree resolution) 3. Variable List A. Name: GPPc Description: Constrained gross primary productivity (kgC/m2/yr) B. Name: Beta Description: Water limitation factor (ranges from 0 to 1) C. Name: lat Description: Degrees latitude D. Name: lon Description: Degrees longitude 4. Missing data codes: Code/symbol nan (ocean values)