Northern PMC brightness zonal variability and its correlation with temperature and water vapor1*Rong, P. P., 1Russell, J.M., 2Randall, C.E., 3S. M. Bailey, and 4A. Lambert

1. CAS, Hampton University, Hampton, VA. 2. LASP, University of Colorado, Boulder, CO. 3. Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, Virginia 4. JPL/Caltech, Pasadena, California

19th Annual School of Science Research Symposium, Hampton University

Introduction In this study we examine the correlations between the Earth polar mesospheric clouds (PMCs) and their environment temperature (T) and water vapor (H2O) on planetary scales. This topic is not extensively studied in the past owing to the poor data coverage in either time or space, or the poor time overlap between the cloud data and the environment T and H2O. Two recent satellite missions that both cover years 2007-current time, i.e., the Aeronomy of Ice in the Mesosphere (AIM) that measures PMCs and Aura that measures T and H2O, made this investigation more approachable. Both data analysis and model simulations are used. A 0-dimensional (0-D) PMC model [Hervig et al., 2009] is adopted to interpret the observed correlations and to assess the relative roles of T and H2O.

References:Hervig, M. E., M. H. Stevens, L. L. Gordley, L. E. Deaver, J. M. Russell, and S. Bailey, Relationships between PMCs, temperature and water vaporfrom SOFIE observations (2009), J. Geophys. Res., 114, D20203, doi:10.1029/2009JD012302.

Datasets and 0-D model Daily global cloud albedo (“daisy”) measured by the Cloud Imaging and Particle Size instrument (CIPS) on the AIM satellite (55⁰N-85⁰N) is used. T and H2O measured by Microwave Limb Sounder (MLS) on the Aura satellite are used to match with the CIPS daily global albedo. 0-D model: mice=F.(PH2O-PSAT)/T/R·109 · MH2O

where mice is the cloud ice mass density, PH2O and PSAT are vapor pressure and saturation vapor pressure, MH2O the molecular weight of H2O, R the gas constant, and F the fraction of H2O that is turn into ice.

Observations

Brighter and colder regions are correlated on large scales but the correlation is poorer in the core of the season Cloud albedo and H2O are poorly correlated throughout the season, which is caused by the vapor depletion when ice is produced, therefore leading to shift between the cloud maxima and the post-ice/measured H2O maxima.

DFS: days from summer solstice0-D Model Results

Original 0-D model results (left two columns) reproduced the albedo and T correlation very well but failed to reproduce the albedo and H2O correlation. Adjusting the fraction of water vapor (in excess of the saturation pressure, i.e., (PH2O-PSAT)) that enters the ice phase improves the agreement between the observation and the model results. This is a reasonable approach because (PH2O-PSAT)is the upper limit of the ice production efficiency.

Daily correlation coefficients16-day smoothed (black is observation and red is model) Daily correlation coefficients of albedo and post-ice H2O

post-ice H2O: H2O condition before after the model calculation

0-D Model Results (continue)

0-D Model diagram When clouds are weaker, or the environment is warmer and drier, temperature plays an increasingly important role in determining the mice variation. Water vapor takes a strong role in determining the mice variation in the core of the season when the clouds are stronger and environment is colder and wetter.

Conclusions Temperature and albedo daily zonal variations are anti-correlated in the season start and end, whereas in the core of the season the correlation is relatively poor. The albedo and H2O correlation in the zonal direction is poor throughout the season. 0-D model diagram explains why the anti-correlations of temperature and albedo are stronger at the start and end of the season. The H2O depletion associated with the ice production will lead to significant shift of the ice maxima and post-ice H2O maxima in the zonal direction, which leads to the poor correlation between the observed H2O and albedo.

albedo T H2O

Acknowledgements:Funding for this work was provided by NASA’s Small Explorers Program under the AIM mission Contract NAS5-03132. We thank the AIM CIPS team in the Laboratory for Atmospheric and Space Physics, Boulder, Colorado ,and the MLS team in Jet Propulsion Laboratory/California Institute of Technology, Pasadena, California, for providing us with data and advice in data screening.