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Data for: Freeze tolerance influenced forest cover and hydrology during the Pennsylvanian


Matthaeus, William et al. (2021), Data for: Freeze tolerance influenced forest cover and hydrology during the Pennsylvanian, Dryad, Dataset,


Global forest cover affects the Earth system by altering surface mass and energy exchange. Physiology determines plant environmental limits and influences geographical vegetation distribution. Ancient plant physiology, therefore, likely affected vegetation-climate feedbacks. We combine climate modeling and ecosystem-process modeling to simulate arboreal vegetation in the late Paleozoic ice age. Using GENESIS V3 GCM simulations, varying pCO2pO2, and ice extent for the Pennsylvanian, and fossil-derived leaf C:N, maximum stomatal conductance, and specific conductivity for several major Carboniferous plant groups, we simulated global ecosystem processes at a 2-degree (longitude, latitude) resolution with Paleo-BGC. Based on leaf water constraints, Pangaea could have supported widespread arboreal plant growth and forest cover. However, these models do not account for the impacts of freezing on plants. According to our interpretation, freezing would have affected plants in 89% of unglaciated land during peak glacial periods, and 65% during the warmer interglacials. Comparing forest cover, minimum temperatures, and paleo-locations of Pennsylvanian-aged plant fossils from the Paleobiology Database supports restriction of global forest extent due to freezing. Many genera were limited to 25% of unglaciated land where temperatures remained above −4°C. Freeze-intolerance of Pennsylvanian arboreal vegetation had the potential to alter surface runoff, silicate weathering, CO2­ levels, and climate forcing. As a bounding case, we assume total plant mortality at −4°C and estimate that contracting forest cover increased net global surface runoff by up to 6.1%. Repeated freezing likely influenced freeze- and drought-tolerance evolution in lineages like the coniferophytes, which became increasingly dominant in the Permian and early Mesozoic.


Forest cover in the Pennsylvanian was simulated for a global grid consisting of 2° × 2° cells using the ecosystem process model Paleo-BGC (1), a modified version of the ecosystem model BIOME-BGC (2, 3), driven by daily data derived from GENESIS V3 (4, 5) Forest cover in the Pennsylvanian was simulated for a global grid consisting of 2° × 2° cells using the ecosystem process model Paleo-BGC (1), which is a modified version of the ecosystem model BIOME-BGC (2, 3) driven by daily-resolution output derived from GENESIS V3 (4, 5)( Global Environmental and Ecological Simulation of Interactive Systems; Thompson and Pollard 1997, Alder et al. 2011).  The Paleo-BGC model (1) includes variable atmospheric pO2, mesophyll conductance important for plants with thickened leaves, and leaf hydraulic conductivity to account for simple and complex vascular pathways. Measurements of leaf carbon to nitrogen ratio (C:N; kg kg-1) were derived from preserved cuticular material. Maximum stomatal conductance (gsmax; mol m-2s-1) was determined from stomatal density and size of leaf fossil impressions. Fossil measurements were collected for Pennsylvanian representative plant-fossil taxa (described in (6) including lycopsids, sphenophytes, pteridosperms, stem-group marattialean tree ferns (tree ferns), and early-diverging coniferophytes (cordaitaleans; (6, 7). Specific leaf area (SLA; m­­2 kg C-1) and related leaf attributes, important for converting carbon allocated to leaves to leaf area, were estimated using measured leaf C:N to SLA relationships from extant relatives (8, 9). Boundary layer conductance (gb; μmol s-1Pa-1m-2) was estimated from the mean leaf or leaflet width (10).  For leaf mesophyll conductance (gm; μmol s-1Pa-1m-2), a single, mean value of 0.273 mol H2O m-2s-1 was used as a basis for measurements of leaf air space, leaf thickness, mean mesophyll cell width, and cell-wall thickness of leaf fossil cross-sections of Pennsylvanian taxa (1).  Finally, leaf hydraulic conductivity values (Kleaf; mmol m-2s-1MPa-1) were calculated from an empirical relationship (11) using minimum and maximum leaf mesophyll pathlength derived from leaf fossil cross-sections (11).    

Data was generated by GENESIS V3 as described in (12–14). Minimum temperatures were averaged over terrestrial locations that were not covered by glacial ice. Landmasses and glacial extents were specified after the description of Ziegler, Hulver, and Rowley (15) for ~290 Ma (earliest Artinskian). Vegetated land area was mapped following a two-part categorization process for each un-glaciated terrestrial grid cell.

 Inputs taken for Paleo-BGC included daily maximum, minimum, and average temperature (Tmin, Tmax, and Tave), precipitation, and shortwave radiation for ten years. Vapor pressure deficit (VPD), a required input for Paleo-BGC, was calculated using Tmin and Tave in the modified Tetens Equation (16) for each grid cell. Finally, daily daylength was derived for each grid cell based on latitude and year day (10).  Two climate scenarios were evaluated, intended to provide a characterization of the glacial and interglacial intervals. One climate scenario with glacial ice and CO2 of 182 ppm is referred to as the glacial, and one with reduced glacial ice and CO2 of 546 ppm is referred to as the interglacial.  For both scenarios, atmospheric oxygen was set to 28%, which is considered a maximum pO2 for this period based on recent modeling (17). 


1.         J. D. White, et al., A Process-Based Ecosystem Model (Paleo-BGC) to Simulate the Dynamic Response of Late Carboniferous Plants to Elevated O2 And Aridification. American Journal of Science In Press (2020).

2.         M. A. White, P. E. Thornton, S. W. Running, R. R. Nemani, Parameterization and sensitivity analysis of the BIOME-BGC terrestrial ecosystem model: net primary production controls. Earth interactions 4, 1–85 (2000).

3.         J. S. Golinkoff, “Estimation and modeling of forest attributes across large spatial scales using BiomeBGC, high-resolution imagery, LiDAR data, and inventory data.,”  University of Montana. (2013).

4.         S. L. Thompson, D. Pollard, Greenland and Antarctic Mass Balances for Present and Doubled Atmospheric CO 2 from the GENESIS Version-2 Global Climate Model. Journal of Climate 10, 871–900 (1997).

5.         J. R. Alder, S. W. Hostetler, D. Pollard, A. Schmittner, Evaluation of a present-day climate simulation with a new coupled atmosphere-ocean model GENMOM. Geoscientific Model Development 4, 69–83 (2011).

6.         I. P. Montañez, et al., Climate, pCO2 and terrestrial carbon cycle linkages during late Palaeozoic glacial–interglacial cycles. Nature Geoscience 9, 824–828 (2016).

7.         J. D. Richey, et al., Influence of temporally varying weatherability on CO2-climate coupling and ecosystem change in the late Paleozoic. Clim. Past 16, 1759–1775 (2020).

8.         M. T. van Wijk, M. Williams, G. R. Shaver, Tight coupling between leaf area index and foliage N content in arctic plant communities. Oecologia 142, 421–427 (2005).

9.         Joseph. D. White, Neal. A. Scott, Specific leaf area and nitrogen distribution in New Zealand forests: Species independently respond to intercepted light. Forest Ecology and Management 226, 319–329 (2006).

10.       G. s. Campbell, J. M. Norman, An introduction to environmental biophysics (Springer, 1998).

11.       T. J. Brodribb, T. S. Feild, G. J. Jordan, Leaf Maximum Photosynthetic Rate and Venation Are Linked by Hydraulics. PLANT PHYSIOLOGY 144, 1890–1898 (2007).

12.       D. E. Horton, C. J. Poulsen, I. P. Montañez, W. A. DiMichele, Eccentricity-paced late Paleozoic climate change. Palaeogeography, Palaeoclimatology, Palaeoecology 331–332, 150–161 (2012).

13.       D. E. Horton, C. J. Poulsen, D. Pollard, Influence of high-latitude vegetation feedbacks on late Palaeozoic glacial cycles. Nature Geoscience 3, 572–577 (2010).

14.       D. E. Horton, C. J. Poulsen, Paradox of late Paleozoic glacioeustasy. Geology 37, 715–718 (2009).

15.       I. P. Martini, Ed., Late glacial and postglacial environmental changes : Quaternary, Carboniferous-Permian, and Proterozoic (Oxford University Press, 1997).

16.       F. W. Murray, On the Computation of Saturation Vapor Pressure. Journal of Applied Meteorology 6, 203–204 (1967).

17.       A. J. Krause, et al., Stepwise oxygenation of the Paleozoic atmosphere. Nature Communications 9 (2018).

Usage Notes

Scripts for porting GENESIS V3 data to Paleo-BGC input can be found at  Paleo-BGC and its associated parameterizations can be found at

GENESIS V3 files are named for their history variable, CO2 and O2 levels, simulation year, and resolution (i.e. Paleo-BGC output is named for the glacial scenario (GLAC/IGLAC), CO2 and O2 levels, longitude and latitude, plant type, and fossil parameter values. Filenames were used to parse Paleo-BGC output into summary data structures in R for analysis. Please note: Paleo-BGC output is in a file structure that associates each model run with its parameterization an initialization files. This output is best processed using unix based command line utilities provided on github above.


Division of Earth Sciences, Award: EAR-1338247

Division of Earth Sciences, Award: EAR-1338281

Division of Earth Sciences, Award: EAR-1338200

Division of Earth Sciences, Award: EAR-1338256

Division of Earth Sciences, Award: EAR-1338256

European Research Council, Award: ERC-2011-StG

European Research Council, Award: 279962-OXYEVOL