Mesoscale temperature fluctuations in the stratosphere

Abstract

Abstract. An airborne instrument that measures altitude temperature profiles is ideally suited for the task of characterizing statistical properties of the vertical displacement of isentrope surfaces. Prior measurements of temperature fluctuations during level flight could not be used to infer isentrope altitude variations because lapse rate information was missing. The Microwave Temperature Profiler instrument, which includes lapse rate measurements at flight level as a part of temperature profiles, has been used on hundreds of flights to produce altitude versus ground track crosssections of potential temperature. These cross-sections show isentrope altitude variations with a horizontal resolution of ∼3 km for a >6 km altitude region. An airborne isentropealtitude cross-section (IAC) can be compared with a counterpart IAC generated from synoptic scale data, based on radiosondes and satellite instruments, in order to assess differences between the altitudes of isentrope surfaces sampled at mesoscale versus synoptic scale. It has been found that the synoptic scale isentropes fail to capture a significant component of vertical displacement of isentrope surfaces, especially in the vicinity of jet streams. Under the assumptions that air parcels flow along isentrope surfaces, and change temperature adiabatically while undergoing altitude displacements, it is possible to compute mesoscale temperature fluctuations that are not present in synoptic scale back trajectory parcel temperature histories. It has been found that the magnitude of the mesoscale component of temperature fluctuations varies with altitude, season, latitude and underlying topography. A model for these dependences is presented, which shows, for example, that mesoscale temperature fluctuations increase with altitude in a systematic way, are greatest over mountainous terrain, and are greater at polar latitudes during winter.

Tropical tropopause ice clouds: a dynamic approach to the mystery of low crystal numbers

“…For our approach this restriction was unnecessary, because the nucleation event was determined by the high frequencies (ω ∈ [0.005 s −1 : 0.029 s −1 ]) and not by the amplitude of the waves. Thus, our simulations were more realistic in this sense, because the whole spread of amplitudes in the range up to T ∼ 1 K can be observed from measurements (Gary, 2006). Our results are in qualitative agreement with former studies by Barahona and Nenes (2011) for their investigations with temperature amplitudes δT ≤ 1 K although we had a different approach where we used high-frequency waves.…”

“…These observations turned the "supersaturation" into a "nucleation puzzle", which is supported by earlier measurements of low ice crystal numbers (McFarquhar et al, 2000;Thomas et al, 2002;Lawson et al, 2008). The "nucleation puzzle" is currently being intensely discussed and other nucleation pathways suppressing, modifying or replacing homogeneous freezing have been proposed (Kärcher and Koop, 2005;Gensch et al, 2008;Murray, 2008;Zobrist et al, 2008;Krämer et al, 2009;Spichtinger and Gierens, 2009c;Murray et al, 2010;Jensen et al, 2013). Most of the former approaches explaining the TTL ice nucleation are of a chemical or microphysical nature.…”

“…Thus, we can assume that some of these waves have very high internal frequencies. Typical vertical amplitudes of these waves have been estimated to be in the range z ∼ 100 m or even much smaller (Gary, 2006;Gierens et al, 2007), which translates roughly into temperature changes of T ≤ 1 K. These waves represent the prototype for TTL gravity waves in our investigations. In addition to the boundary conditions from thermal stratification and winds in the TTL, we added more information about a possible restriction of the frequency spectrum in gravity waves (Ruprecht et al, 2010;Ruprecht and Klein, 2011) on the propagation in the gravity waves in a convectively dominated environment, we know that the full spectrum of frequencies, i.e.…”

“…Heterogeneous nucleation was brought into the discussion to explain the low ice crystal number concentrations in the TTL (Khvorostyanov et al, 2006;Gensch et al, 2008;Krämer et al, 2009;Jensen et al, 2008Jensen et al, , 2010Murray et al, 2010). Thus, we extended our simulations by including heterogeneous nucleation in the following way: we added another class of ice representing heterogeneous nucleation on solid particles, although we did not specify the nature of the ice nuclei (IN) but merely the effect of freezing ice crystals on them.…”

Lidar observation and model simulation of a volcanic-ash-induced cirrus cloud during the Eyjafjallajökull eruption

“…We added a Gaussian distribution of temperature noise with various amplitudes and a typical peak-to-peak fluctuation length of 10 min to make the trajectory and the model behavior more realistic. The amplitude depends on altitude, geographic latitude and longitude, and is parametrized in a study of Gary (2006). For our study, the fluctuation amplitude is around 0.3 to 0.5 • C. Nevertheless, this Gaussian temperature distribution is arbitrary and generates a random behavior of ice formation.…”