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Latewood ring widths of longleaf pine (Pinus palustrisMill.) are effective recorders of annual variability of tropical cyclone (TC) precipitation (TCP), accounting for approximately half of the explained variance. Based on a regional chronology comprised of data from five sites in coastal North Carolina, we reconstructed TCP during 1750–2015 to examine temporal variability of multidecadal dry and wet TCP regimes, the synoptic controls that contributed to an exceptionally dry phase in 1843–1876, and the effectiveness of using latewood to identify droughts independent of TCP. We found six phases of alternating dry/wet phases occurred during the 250+ years in the reconstruction (duration range = 17–62 years) and the 1843–1876 period of exceptionally narrow latewood widths and low TCP values (i.e., the Great Suppression) was unique during the past quarter millennium. The Great Suppression coincided with a period of anomalously low pressure (relative mean hPa deviation = −60 DAM) over the eastern USA at 500 hPa heights, which strongly affects the steering of TCs. We found that while each dry phase was characterized by a persistence of these steering lows, including the most recent (2006–2016) period absent of major landfalling TCs in the United States, the Great Suppression was unmatched in intensity. Finally, we determined that variability in longleaf pine latewood widths do not reflect overall soil‐moisture conditions, as neither narrow nor wide latewood widths are coincident with variations in non‐TC‐related precipitation. Rather, latewood growth flushes are associated with ephemeral periods of elevated water tables following high‐intensity TC‐related rainfall events.

 

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.