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Geographic range of plants drives long-term climate change

Earth Sciences

Geographic range of plants drives long-term climate change

K. Gurung, K. J. Field, et al.

This groundbreaking research by Khushboo Gurung, Katie J. Field, Sarah A. Batterman, Simon W. Poulton, and Benjamin J. W. Mills explores how plant geographic range significantly influences long-term climate change. Discover how limited ranges on the arid Pangaea supercontinent led to higher atmospheric CO2 levels, and how increased continental dispersion enhanced carbon fixation. A fascinating look at the often overlooked role of vegetation in climate dynamics!

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~3 min • Beginner • English
Introduction
Atmospheric CO₂ is a key driver of Earth’s long-term temperature. Over the Phanerozoic Eon, atmospheric CO₂ has fluctuated due to tectonic and biotic events, while stabilizing processes such as temperature-dependent silicate weathering prevented runaway climates. The relative contributions of biogeological and tectonic processes to climate remain debated, and existing Earth system models have struggled to reproduce Phanerozoic climate change from first principles. Vegetation links the atmospheric boundary layer and land surface via exchanges of carbon, energy, and water, acting as both a sink (photosynthetic fixation and weathering enhancement) and a source (respiration, pyrogenic carbon). These interactions depend on local climate (temperature, moisture, light), and changes in plant community structure and function modulate carbon exchange. Classic long-term biogeochemical box models (e.g., GEOCARB, COPSE/SCUUM) include simple global feedbacks between plants and climate, sometimes incorporating fossil plant taxa and highlighting potential CO₂ drawdown during Paleozoic land colonization, though with uncertainty. However, most long-term models apply globally averaged vegetation effects, neglecting spatial heterogeneity driven by local climate. This simplification reduces reliability of carbon cycle predictions. The study addresses this gap by incorporating detailed plant geographical range dynamics into a spatially resolved long-term climate–biogeochemical framework.
Literature Review
Prior work on Phanerozoic carbon–climate dynamics includes GEOCARB-family models and COPSE, which parameterize plant influences on silicate weathering and organic carbon burial using global scalars. Fossil-based evidence has been used to infer vegetation–climate feedbacks, such as CO₂ drawdown during Paleozoic land plant expansion, but uncertainties persist, especially regarding sensitivity of proxies and the temporal applicability of certain proxies (e.g., boron isotopes versus paleosols, stomatal indices). Spatially explicit treatments of land surface processes in earlier models still typically used single global vegetation factors, limiting their ability to capture heterogeneous climate–biosphere interactions. The study builds on these efforts by adding spatially dynamic vegetation (FLORA) to a spatially resolved long-term carbon–climate model (SCION), enabling assessment of how plant biogeography shapes weathering and carbon burial.
Methodology
The study couples the FLORA (Fast Land Occupancy and Reaction Algorithm) vegetation model with the spatially resolved climate–biogeochemical model SCION (Spatial Continuity Integration). SCION is driven by pre-run, steady-state FOAM GCM climate simulations and computes continental weathering on a 2D grid using local temperature, erosion, and runoff. Previously, vegetation in SCION was a single global biomass factor affecting weathering and organic carbon burial. Here, FLORA supplies spatially explicit, dynamic vegetation fields each SCION timestep. For every SCION timestep, FLORA iterates to near-steady-state biomass (<1% change between successive biomass calculations), producing 2D maps of NPP and biomass. These maps feed back to SCION by: (1) enhancing silicate weathering locally via a biotic factor linked to FLORA NPP; and (2) setting global terrestrial organic carbon burial from total CO₂ burial, which also influences terrestrial phosphorus cycling and ocean P fluxes. Initialization and forcing: At the start of each FLORA run, the terrestrial biosphere is seeded at 2.5×10⁴ gC m⁻² (average present-day biomass) to represent re-establishment after each timestep. FLORA is forced by time, temperature, runoff, continental configuration, and atmospheric CO₂ from SCION. Hydrology limitations arise because the FOAM emulator lacks precipitation; water stress is approximated via runoff/temperature-related constraints. Biotic enhancement of weathering: The enhancement factor f_biota is modeled as a linear function of FLORA NPP with an abiotic baseline, f_biota = 0.0005·NPP + f_minbiota·R_CO2^ε, adopting a GEOCARB-style R_CO2 formulation with ε = 0.25 (conservative), consistent overall with 4–7× enhancement factors used in global models. Biomass turnover and fire: Biomass death includes oxygen-dependent fire turnover: B_burnout = min(max(0.002·O₂ − 0.08, 0), 0.2), implying increasing turnover from 17% O₂ up to a maximum of 20% turnover at high O₂. Biomass is not carried over between SCION steps and is reseeded each time. Leaf respiration/photosynthesis scaling: Leaf respiration uses daylight hours h to scale photosynthesis, R = (24/h). With no seasonality at long timescales, h = 12 h day⁻¹ globally; insolation is adjusted for atmospheric transmission and an earlier Phanerozoic solar constant about 5% lower than present. Model coupling workflow: FLORA computes biome distributions and reaches steady biomass; NPP informs weathering enhancement; total CO₂ burial informs C_org burial; SCION advances climate, weathering, and carbon cycle state to the next timestep. Sensitivity analysis: An ensemble of 1000 SCION-FLORA runs was performed; 852 completed successfully. Failures were mainly due to unstable initial parameterizations leading to runaway states. Model–data comparison: Outputs were compared with proxy records for atmospheric CO₂ (paleosols, stomatal ratios, alkenones, phytane, liverworts, boron isotopes) and with paleotemperature reconstructions (climate-sensitive lithologies and δ¹⁸O), focusing on Mesozoic warm climates where vegetation–climate coupling is robust and FOAM climate states are more consistent with high-temperature conditions.
Key Findings
- Incorporating spatially dynamic vegetation substantially alters long-term carbon–climate projections compared with models using a single global vegetation factor. The model shows lower global carbon fixation and up to double previously predicted atmospheric CO₂ over arid Pangaea due to restricted plant geographical range. - Continental configuration strongly controls plant habitable area and hence weathering. During the Triassic–Jurassic on Pangaea, aridity and subsidence in western continental interiors restricted biomass largely to equatorial regions, reducing the area of biotically enhanced weathering and CO₂ sequestration. - As continents fragmented through the Mesozoic, modeled plant geographical range increased from about 65% to over 90%, expanding habitable area and amplifying global CO₂ removal, consistent with geological proxy data. - Modeled habitable area rose by ~20% from the Triassic to early Cretaceous; during the Jurassic–Cretaceous, relative habitable land increased from roughly 79% to 85% as Pangaea broke apart, improving water accessibility and spreading vegetation. - The spatial biosphere representation improves model fit to proxy records of atmospheric CO₂ and global average surface temperature relative to the original SCION without spatial vegetation. - Spatially dynamic vegetation raised modeled CO₂ and temperatures during the Triassic and Jurassic (due to limited vegetated area and weaker weathering), while the Cretaceous breakup increased vegetated area and strengthened weathering, lowering global temperature by about 2–3 °C (hundreds of ppm CO₂ lower) compared to a default run without spatial vegetation feedbacks. - Late Cenozoic model behavior includes an increase in atmospheric CO₂ between ~30 and 15 Ma associated with a decline in habitable area and shifts of land to higher latitudes; regional effects from continental flooding and weathering enhancement are evident in Australia, South America, and Africa. - Overall, although Mesozoic climates supported high potential plant productivity, the climatic and biogeochemical impacts were limited by the area where plants could grow; the breakup of Pangaea increased habitable area and helped suppress otherwise warm Cretaceous climates driven by high volcanic degassing.
Discussion
The study demonstrates that plant geographical range exerts a first-order control on long-term climate by modulating the spatial extent of biotically enhanced silicate weathering and terrestrial organic carbon burial. When continents are configured as a supercontinent (Pangaea), aridity and circulation patterns constrain vegetation to limited regions, diminishing the global efficacy of plant-driven CO₂ drawdown and leading to higher atmospheric CO₂ and temperatures. As continents fragment, hydrological regimes diversify and water accessibility improves, expanding vegetated land area, increasing the spatial footprint of weathering enhancement, and thereby lowering CO₂ and temperatures. This spatially explicit vegetation–climate coupling explains why prior models with global vegetation scalars overpredicted weathering efficacy during times of limited habitable area and misfit certain proxy constraints (e.g., warm Cretaceous). By explicitly resolving biomes and linking NPP to weathering, SCION-FLORA better reproduces proxy trends in CO₂ and global temperature. The results underscore that vegetation’s impact on the long-term carbon cycle depends not only on total biomass or productivity but critically on the distribution of vegetation across climates and continents, highlighting plant biogeography as a key, previously underexplored regulator of Phanerozoic climate.
Conclusion
Coupling the fast, spatially explicit vegetation model FLORA to the SCION long-term climate–biogeochemical framework reveals that plant geographical range is a major control on Earth’s long-term carbon cycle and climate. Restricted habitable area under Pangaea limited plant-enhanced weathering and CO₂ sequestration, yielding higher CO₂ and warmer conditions. Continental breakup in the Mesozoic expanded plant habitable area from roughly two-thirds to over 90% of land, amplifying weathering and cooling climate, improving agreement with geological proxy data. The work advances long-term Earth system modeling by integrating dynamic, spatial vegetation feedbacks and shows that biogeography must be considered when interpreting past and projecting future climate states. Future research should include evolutionary innovations (e.g., angiosperm radiation) that could alter weathering efficiency, implement dynamic two-way vegetation–climate–hydrology feedbacks (eco-hydrological coupling), incorporate additional hydrological forcings (precipitation) in the climate emulator, and apply the coupled framework to transient perturbations (e.g., CAMP, mass extinctions) and shorter-term biosphere processes (colonization, turnover).
Limitations
- Evolutionary dynamics not included: The model omits key evolutionary events (e.g., angiosperm radiation) that may have increased weathering efficiency and modified vegetation–climate feedbacks. - Limited climate–biosphere coupling: The FOAM-based emulator does not include precipitation or allow dynamic feedbacks from vegetation (e.g., transpiration effects on local hydrology). Water stress is approximated without full hydrological representation. - Temporal resetting of biomass: Biomass is reseeded each timestep and run to steady state, not representing short-term processes such as colonization, disturbance, and turnover. - External perturbations excluded: Major carbon cycle events (e.g., Central Atlantic Magmatic Province, end-Triassic mass extinction) are not simulated, though they likely caused substantial biomass and climate changes. - Model stability and ensembles: 148 of 1000 ensemble runs failed due to unstable initial parameterizations, indicating sensitivity to parameter choices. - Proxy and forcing uncertainties: CO₂ proxies have large uncertainties and differing sensitivities; FOAM climate states may deviate from present (e.g., 0 Ma resembling glacial conditions), affecting modern benchmark comparisons. - Hydrological data limitations: Lack of precipitation data in the emulator constrains realism of water stress; coarse-resolution GCM climate versus high-resolution datasets causes discrepancies in present-day biomass patterns.
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