Sublimation vapor pressures of nine pure perfluoroalkyl substances, including ammonium perfluoro­(2-methyl-3-oxahexanoate) (GenX), 1H,1H,2H,2H-perfluoro-1-decanol (8:2 FTOH), 1H,1H,2H,2H-perfluoro-1-dodecanol (10:2 FTOH), and C6 to C11 perfluorocarboxylic acids (PFCAs), were measured using the Knudsen technique at near-ambient temperatures. Melting temperatures and fusion enthalpies of these compounds were also measured by differential scanning calorimetry. The vapor pressure of GenX ammonium salt is comparable to that of the much higher molecular weight perfluoroundecanoic acid. GenX ammonium salt also did not show actual melting behavior but instead decomposed at around 470 K. The measured near-ambient temperature sublimation vapor pressures of the PFCAs and FTOHs were compared with some earlier reported liquid phase vapor pressures obtained at higher temperatures, and reasonable agreement exists between the data obtained in the different studies. The sublimation enthalpies of the PFCAs indicate that the contribution to the sublimation enthalpy of the CF2 group in the alkyl chain is comparable to that of the CH2 group in the corresponding nonfluorinated analogues, even though the PFCAs show consistently higher vapor pressures than do the corresponding carbon number alkanoic acids.

Abstract. Lagrangian trajectories are frequently used to trace air parcels from the troposphere to the stratosphere through the tropical tropopause layer (TTL), and the coldest temperatures of these trajectories have been used to reconstruct water vapor variability in the lower stratosphere, where water vapor's radiative impact on Earth's surface is strongest. As such, the ability of these trajectories to accurately capture temperatures encountered by parcels in the TTL is crucial to water vapor reconstructions and calculations of water vapor's radiative forcing. A potential source of error for trajectory calculations is the resolution of the input data. Here, we explore how improving the spatial and temporal resolution of model input data impacts the temperatures measured by Lagrangian trajectories that cross the TTL during boreal winter using ERA5 reanalysis data. We do so by comparing the temperature distribution of trajectories computed with data downsampled in either space or time to those computed with ERA5's maximum resolution. We find that improvements in temporal resolution from 6 to 3 and 1 h lower the cold point temperature distribution, with the mean cold point temperature decreasing from 185.9 to 185.0 and 184.5 K for reverse trajectories initialized at the end of February for each year from 2010 to 2019, while improvements to vertical resolution from that of MERRA2 data (the GEOS5 model grid) to full ERA5 resolution also lower the distribution but are of secondary importance, and improvements in horizontal resolution from 1∘ × 1∘ to 0.5∘ × 0.5∘ or 0.25∘ × 0.25∘ have negligible impacts to trajectory cold points. We suggest that this is caused by excess vertical dispersion near the tropopause when temporal resolution is degraded, which allows trajectories to cross the TTL without passing through the coldest regions, and by undersampling of the four-dimensional temperature field when either temporal or vertical resolution is reduced.

Studying temperature probability distributions and the physical processes that shape them is important for understanding extreme temperature events. Previous work has used a conditional mean temperature framework to reveal whether horizontal temperature advection drives temperature to extreme or median values at a specific location as a method to dynamically interpret temperature probability distributions. In this paper, we generalize this method to study how other processes shape temperature probability distributions and explore the diverse effects of horizontal temperature advection on temperature probability distributions at different locations and different temperature percentiles. We apply this generalized method to several representative regions to demonstrate its use. We find that temperature advection drives temperatures towards more extreme values over most land in the midlatitudes (i.e. cold air advection occurs during cold anomalies and warm air advection occurs during warm anomalies). In contrast, we find that horizontal temperature advection dampens temperature anomalies in some coastal summer monsoon regions, where extreme temperatures result from other processes, such as horizontal humidity advection and vertical temperature advection. By calculating the mean of processes conditioned on the temperature percentile, this method enables composite analysis of processes that contribute to events for all percentiles and a range of processes. We show examples of composites at different percentiles for certain processes and regions to illustrate the conditional mean analysis. This general approach may benefit future studies related to temperature probability distributions and extreme events.