Characteristics of Cloud Size of Deep Convection Simulated by a Global Cloud Resolving Model over the Western Tropical Pacific( The International Workshop on High-Resolution and Cloud Modeling, 2006)

Cloud size distributions of cloud cluster are analyzed for global cloud resolving simulations with the global nonhydrostatic model NICAM whose mesh interval is about 3.5km and 7km. The 3.5km-mesh simulation is performed for 7 days started at 00 UTC 25 Dec 2006 by giving an initial condition of reanalysis data, while the 7km-mesh simulation is run for 32 days from 00 UTC 15 Dec 2006. We used outgoing long-wave radiation (OLR) simulated by NICAM to calculate size distributions of deep convection, and compared with those analyzed using equivalent blackbody temperature (TBB) of the infrared channel of 11 μm of the Japanese geostationary meteorological satellite (MTSAT-1R). We select two threshold temperatures, 208 K and 253 K, to identify deep convective areas including anvil cloud. Specifically, we call clouds defined by the 208 K-threshold “deeper” convective clouds. Over the tropical region covering the maritime continent and the western tropical Pacific ocean (10S-10N, 90E-160W), we examined size of cloud areas defined by the two TBB threshold values and corresponding threshold values of OLR, which were chosen by comparing cumulative histograms of TBB and OLR in this region. Resolution dependency by NICAM shows that the overall cloud size distribution of the 3.5 km-mesh simulation is much closer to that of the MTSAT-1R observation than that of the 7 km-mesh simulation. Size distributions of deep convection in both simulations indicate nearly lognormal as is seen in the satellite observations. The 3.5 km-mesh simulation shows slightly less frequency than the MTSAT-1R observation for smaller size of deeper convection, and it does not reproduce very large clouds. When compared cloud characteristics over land and ocean, simulated cloud size statistics are closer to the satellite observation in the maritime continent region (westward of 160E) than in the open ocean region (eastward of 160E). Comparison of temporal variation of cloud area shows that the 3.5 km-mesh simulation captures clear signals of diurnal cycles both over the maritime continent and the open ocean regions, together with amplification associated with the MJO event. Morning and afternoon difference of convective activity over large island within the maritime continent is also simulated by 3.5km-mesh simulation. When one uses a global cloud resolving model for climate studies, the analysis of cloud size distributions gives another dimension to improve cloud properties of simulations. It is not only relevant to realistic representations of deep convection, but is also useful for improving the energy budget of global cloud resolving simulations.

[1]  Toshiro Inoue,et al.  A cloud type classification with NOAA 7 split‐window measurements , 1987 .

[2]  K. Lau,et al.  Diurnal Variations in Tropical Oceanic Cumulus Convection during TOGA COARE , 1997 .

[3]  Eric J. Fetzer,et al.  Modulation of the diurnal cycle of tropical deep convective clouds by the MJO , 2006 .

[4]  Yukari N. Takayabu,et al.  Spectral representation of rain profiles and diurnal variations observed with TRMM PR over the equatorial area , 2002 .

[5]  Hiroaki Miura,et al.  A Madden-Julian Oscillation Event Realistically Simulated by a Global Cloud-Resolving Model , 2007, Science.

[6]  Masaki Satoh,et al.  Nonhydrostatic icosahedral atmospheric model (NICAM) for global cloud resolving simulations , 2008, J. Comput. Phys..

[7]  Masafumi Hirose,et al.  Spatial and diurnal variation of precipitation systems over Asia observed by the TRMM Precipitation Radar , 2005 .

[8]  Hirofumi Tomita,et al.  A new dynamical framework of nonhydrostatic global model using the icosahedral grid , 2004 .

[9]  Mark Williams,et al.  Satellite-Observed Characteristics of Winter Monsoon Cloud Clusters , 1987 .

[10]  H. Tomita,et al.  A global cloud‐resolving simulation: Preliminary results from an aqua planet experiment , 2005 .

[11]  Y. Tsushima,et al.  Modeling of the radiative process in an atmospheric general circulation model. , 2000, Applied optics.

[12]  Hiroaki Miura,et al.  Precipitation Statistics Comparison Between Global Cloud Resolving Simulation with NICAM and TRMM PR Data , 2008 .

[13]  T. Inoue,et al.  Cloud Properties over the Bay of Bengal Derived from NOAA-9 Split Window Data and the TRMM PR Product , 2006 .

[14]  Robert A. Houze,et al.  Cloud clusters and superclusters over the oceanic warm pool , 1993 .

[15]  H. Tomita,et al.  A short‐duration global cloud‐resolving simulation with a realistic land and sea distribution , 2007 .

[16]  Chih-Pei Chang.  Westward Propagating Cloud Patterns in the Tropical Pacific as seen from Time-Composite Satellite Photographs , 1970 .

[17]  M. Desbois,et al.  Diurnal Variations and Modulation by Easterly Waves of the Size Distribution of Convective Cloud Clusters over West Africa and the Atlantic Ocean , 1993 .

[18]  Tetsuo Nakazawa,et al.  Tropical Super Clusters within Intraseasonal Variations over the Western Pacific , 1988 .

[19]  Hirofumi Tomita,et al.  An optimization of the Icosahedral grid modified by spring dynamics , 2002 .

[20]  W. Grabowski.  Toward Cloud Resolving Modeling of Large-Scale Tropical Circulations: A Simple Cloud Microphysics Parameterization , 1998 .

[21]  Hiroaki Miura,et al.  Development of a global cloud resolving model - a multi-scale structure of tropical convections - , 2005 .

[22]  Hirofumi Tomita,et al.  Shallow water model on a modified icosahedral geodesic grid by using spring dynamics , 2001 .

[23]  T. Yasunari,et al.  Propagating diurnal disturbances embedded in the Madden‐Julian Oscillation , 2007 .

[24]  Ming-Jen Yang,et al.  Estimation of Oceanic Precipitation Efficiency in Cloud Models , 2005 .

[25]  G. Mellor,et al.  A numerical simulation of BOMEX data using a turbulence closure model coupled with ensemble cloud relations , 1979 .

[26]  T. Yasunari,et al.  Time-space characteristics of diurnal rainfall over borneo and surrounding oceans as observed by TRMM-PR , 2006 .

[27]  Hiroaki Miura,et al.  Multiscale Organization of Convection Simulated with Explicit Cloud Processes on an Aquaplanet , 2007 .

[28]  Y. Tsushima,et al.  Climatology of a nonhydrostatic global model with explicit cloud processes , 2007 .

[29]  Steven A. Ackerman,et al.  Radiative Effects of Various Cloud Types as Classified by the Split Window Technique over the Eastern Sub-tropical Pacific Derived from Collocated ERBE and AVHRR Data , 2002 .

[30]  R. Neale,et al.  The Maritime Continent and Its Role in the Global Climate: A GCM Study , 2003 .

[31]  H. Niino,et al.  An Improved Mellor–Yamada Level-3 Model with Condensation Physics: Its Design and Verification , 2004 .