Distributions of Relative Humidity, Vertical Velocity, and Chemical Tracers in the Tropical Tropopause Layer from ATTREX and CONTRAST Campaigns

Ice clouds play an important role in energy balance at the Earth's surface. These clouds can reflect incoming solar radiation and trap outgoing terrestrial radiation, which causes the net effects to be largely variable depending on the cloud microphysical properties. That is a main reason why ice clouds remain an uncertainty for Earth's changing climate. In-situ observations from two flight campaigns, the NSF Convective Transport of Active Species in the Tropics (CONTRAST) and the NASA Airborne Tropical Tropopause Experiment (ATTREX), will be used to analyze ice cloud formation in the tropical tropopause layer (TTL). WRF-Chem outputs that correspond with the flight campaigns will be used to examine if the microphysical properties of ice cloud formation, identified in the observational data, are represented in the model. Distributions of chemical compositions, vertical velocity, and relative humidity (RH) in the TTL will be examined in this study. Our preliminary results from the CONTRAST campaign show an anti-correlation between ozone concentration and RH values in clear sky conditions for certain temperature ranges (i.e., > 0°C and 0°C to -40°C). At temperatures below -40°C, relatively higher average vertical velocity ( 0.25 m/s) and higher frequency of updrafts ( 70%) were observed at RH with respect to ice (RHi) values above 100%, compared to those at RHi < 100% (i.e., -0.3 m/s and 30%, respectively), indicating correlations between RHi and updrafts in the TTL region. Higher potential temperatures ( 360K) were found to be associated with lower RHi values (< 20%), while lower potential temperature ( 353K) were found in higher RHi (≥ 20%). These results show that the dynamics of the TTL could potentially play an important role in ice cloud formation. Correlations between carbon monoxide and ozone, as well as RHi and vertical velocity, will be compared between WRF-Chem simulations and those from in-situ observations. This work will help with the scientific understanding of ice cloud formations, leading to a more accurate representation in numerical models.