92nd American Meteorological Society Annual Meeting (January 22-26, 2012)

Thursday, 26 January 2012: 4:00 PM
Cloud, Radiation, and Precipitation Diurnal Cycles in the CanAM4: Relationship Between Diurnal Cycle and Mean State Errors
Room 355 (New Orleans Convention Center )
Patrick C. Taylor, LaRC, Hampton, VA; and J. N. S. Cole

The diurnal cycle represents a fundamental mode of system variability, linked to the periodic, daily variability of solar insolation. Many atmospheric variables including precipitation, clouds, and radiation have been shown to exhibit significant variability on the diurnal time scale. This study investigates the representation of cloud, precipitation, and radiation diurnal cycles in a simulation spanning 2000-2009 by the fourth version of the Canadian Centre for Modelling and Analysis Atmospheric GCM (CanAM4) using a suite of satellite observations of top-of-atmosphere (TOA) radiation, clouds and precipitation in the tropics. The CanAM4 simulates the general patterns of observed first diurnal cycle harmonic amplitude in clouds, precipitation, and TOA radiation well. However, large differences in TOA outgoing longwave radiation (OLR) and longwave cloud forcing (LWCF) diurnal cycles are found in convective regions (e.g., central South America and central Africa). Further, significant low cloud amount diurnal cycle amplitude biases are identified over oceans. The results suggest that errors in the TOA OLR mean state may be linked to diurnal cycle biases. In this study, the relationships between diurnal cycle and mean state biases are investigated with diurnal cycle regimes separated by land surface type and the influence of deep convection. In tropical land convective regions (e.g. over the Amazon), a negative diurnal cycle bias in precipitation is significantly related to a negative bias in mean precipitation. This suggests that the importance of the diurnal cycle component of precipitation is underestimated in the model. Further, this misrepresentation of the precipitation mechanism may significantly influence model simulation of the energy and water cycle.

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