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The distribution of forest cover alters Earth surface mass and energy exchange and is controlled by physiology, which determines plant environmental limits. 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 global climate model simulations, varyingpCO₂,pO₂, 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° resolution withPaleo-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 59% of unglaciated land during peak glacial periods and 73% during interglacials, when more high-latitude land was unglaciated. Comparing forest cover, minimum temperatures, and paleo-locations of Pennsylvanian-aged plant fossils from the Paleobiology Database supports restriction of forest extent due to freezing. Many genera were limited to unglaciated land where temperatures remained above −4 °C. Freeze-intolerance of Pennsylvanian arboreal vegetation had the potential to alter surface runoff, silicate weathering, CO₂levels, 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.

 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.