Semidirect dynamical and radiative effect of north african dust transport on lower tropospheric clouds over the subtropical north atlantic in CESM 1.0

• Main Authors: Deflorio, MJ, Ghan, SJ, Singh, B, Miller, AJ, Cayan, DR, Russell, LM, Somerville, RCJ
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: eScholarship, University of California 2014
• Subjects: Meteorology & Atmospheric Sciences; North Atlantic
• Online Access: https://escholarship.org/uc/item/0fh3k2n4

This study uses a century length preindustrial climate simulation by the Community Earth System Model (CESM 1.0) to explore statistical relationships between dust, clouds, and atmospheric circulation and to suggest a semidirect dynamical mechanism linking subtropical North Atlantic lower tropospheric cloud cover with North African dust transport. The length of the run allows us to account for interannual variability of North African dust emissions and transport in the model. CESM’s monthly climatology of both aerosol optical depth and surface dust concentration at Cape Verde and Barbados, respectively, agree well with available observations, as does the aerosol size distribution at Cape Verde. In addition, CESM shows strong seasonal cycles of dust burden and lower tropospheric cloud fraction, with maximum values occurring during boreal summer, when a strong correlation between these two variables exists over the subtropical North Atlantic. Calculations of Estimated Inversion Strength (EIS) and composites of EIS on high and low downstream North African dust months during boreal summer reveal that dust is likely increasing inversion strength over this region due to both solar absorption and reflection. We find no evidence for a microphysical link between dust and lower tropospheric clouds in this region. These results yield new insight over an extensive period of time into the complex relationship between North African dust and North Atlantic lower tropospheric clouds, which has previously been hindered by spatiotemporal constraints of observations. Our findings lay a framework for future analyses using different climate models and submonthly data over regions with different underlying dynamics.