Assessing earth system model predictions of C ₄ grass cover in North America: From the glacial era to the end of this century

Aim: C ₄ grasses are distinct from C ₃ grasses, because C ₄ grasses respond in a different manner to light, temperature, CO ₂ and nitrogen and often have higher resource-use efficiencies. C ₃ and C ₄ grasses are typically represented in earth system models (ESMs) by different plant functional types (PFTs). The ability of ESMs to capture C ₄ grass biogeography and ecology across differing time periods is important to assess, given the crucial role they play in ecosystems and their divergent responses to global change. Location: North America. Time periods: Last Glacial Maximum (LGM), historical modern period (ca. 1850) and end of this century. Major taxa studied: C ₄ grasses. Methods: Proxy data representing relative cover and productivity of C ₄ grasses were collated, including carbon isotope ratios of soil carbon and animal grazer tissue, and vegetation plot data in undisturbed grasslands. We selected available model predictions of C ₄ PFT percentage cover. Models were compared against one another and assessed against proxy data at key time points: the LGM, the historical modern period before widespread grassland conversion to agriculture, and the end of this century. Results: We highlight large differences among model predictions of percentage C ₄ grass cover across North America: all pairwise combinations have correlations <.5, and most are <.2. Models also do not capture spatial patterns of the percentage C ₄ grass cover from proxy data, during either the LGM or the historical modern period. Models generally under-predict percentage C ₄ grass cover, particularly during the historical modern period. Main conclusions: Earth system models do not accurately represent the biogeography of C ₄ grasses across a range of time-scales, and their outputs do not agree with one another. We suggest model improvements to represent this crucial functional type better, including more collection and greater integration of C ₃ and C ₄ grass trait data, explicit representations of tree–grass competition for water, and a greater focus on disturbance ecology.