Using Satellite Observations of Cloud Vertical Distribution to Improve Global Model Estimates of Cloud Radiative Effect on Key Tropospheric Oxidants

Friday, 19 December 2014: 10:20 AM
Hongyu Liu1, Seung-Hee Ham2, James H Crawford2, Seiji Kato2, Gao Chen2, Apostolos Voulgarakis3 and Bryan N Duncan4, (1)National Institute of Aerospace, Hampton, VA, United States, (2)NASA Langley Research Center, Hampton, VA, United States, (3)Imperial College London, London, United Kingdom, (4)NASA Goddard Space Flight Center, Greenbelt, MD, United States
Clouds directly affect tropospheric photochemistry through modification of solar radiation that determines photolysis frequencies. This effect is an important component of global tropospheric chemistry-climate interaction, and its understanding is thus essential for predicting the feedback of climate change on tropospheric chemistry. Our previous studies showed that cloud vertical distributions and optical depths in the meteorological data sets used to drive tropospheric chemistry simulations often vary from model to model, contributing substantially to intermodel discrepancies in key tropospheric oxidants, especially hydroxyl radical (OH), which largely defines the oxidizing capacity of the atmosphere. While the differing magnitudes of column cloud optical depths explain part of these discrepancies, the differing vertical distribution of clouds plays a more important role. In this study, we evaluate GEOS-Chem/MERRA model clouds and their vertical distributions with CCCM, a unique 3-D cloud data product developed at NASA Langley Research Center merged from multiple A-Train satellite (CERES, CloudSat, CALIPSO, and MODIS) observations. We constrain the model cloud vertical and latitudinal distributions and cloud optical depths with those of CCCM, and quantify the biases in model-estimated direct radiative effect of clouds on photolysis frequencies (J[O1D]and J[NO2]), key tropospheric oxidants (ozone and OH), and related species. This approach can be used in other chemical transport or chemistry-climate models to reduce biases in model-simulated OH.