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Derek J. Posselt, Andrew R. Jongeward, Chuan-Yuan Hsu, and Gerald L. Potter

the assumed IWP–ice effective diameter relationship using Heymsfield et al’s (2003) tropics-only functional fit. In both of these tests, condensate mass is evenly distributed among subcolumns, and the results are presented in Fig. 11 . Exponential weighting of cloud mass across the MERRA subcolumns results in a reduction in the number of nondetected objects from 55 to 37 (1.25% of the total number of cloud objects). It appears that this is largely due to an increase in the total number of deep

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Kyle F. Itterly and Patrick C. Taylor

timing and intensity unrealistically force the surface water and energy budget, leading to errors in surface runoff and evaporation ( Del Genio and Wu 2010 ). Decker et al. (2012) found significant errors in the diurnal cycle of surface turbulent fluxes in reanalysis models as well. Slingo et al. (2003) evaluated the diurnal cycle of the Hadley Centre Coupled Model, version 3 (HadCM3) GCM over the tropics and found the largest differences between the GCM and observations occur over the Maritime

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Brian E. Mapes and Julio T. Bacmeister

, suggesting that it is erroneous, and also has a negative stripe at 550 hPa (the 0°C level in the tropics). Fig . 1. Time–height sections of the (a) MERRA total physical heating (K day −1 ) and (b) temperature analysis tendency (K day −1 ) for the grid point at 155.625, −1.875 corresponding to the TOGA COARE intensive flux array area in the equatorial western Pacific, 15–31 Dec 1992. Based on these AT stripes, under interpretation (5) above, we suspected that the model was melting its precipitation at

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Man-Li C. Wu, Oreste Reale, and Siegfried D. Schubert

produced can be conveniently illustrated as a function of time and longitude with a Hovmöller diagram. In Figs. 2 and 3 , mode 2 is shown for the latitude band centered at 12°N in the deep tropics, covering the longitude range from 80° to 20°W (over the ocean) and from 25°W to 35°E (over the African continent). A comparison between these and the same figures computed for a latitude band in the midlatitudes (e.g., 44°N, not shown) covering both longitude ranges from 80° to 20°W (over the ocean) and

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Michael A. Brunke, Zhuo Wang, Xubin Zeng, Michael Bosilovich, and Chung-Lin Shie

, as in Bourras (2006) , which needed a bulk algorithm to derive the “observed” surface turbulent fluxes, we compare the product fluxes to direct observations taken from 12 experimental ship cruises in the tropics and Northern Hemisphere subtropics and mid- and high latitudes. These cruises are described briefly in section 3 . To better understand the product flux biases, we split the total bias into two components, a bulk variable uncertainty and a residual one, and rank the products according

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Yonghong Yi, John S. Kimball, Lucas A. Jones, Rolf H. Reichle, and Kyle C. McDonald

-SRB underestimates SW rad (~1.5 MJ m −2 day −1 ) over the Tibetan Plateau ( Yang et al. 2008 ); the SW rad differences in Fig. 3 are compounded by positive MERRA bias and negative GEWEX-SRB bias over these highland areas. The MERRA SW rad estimates also show a small negative discrepancy in the tropics, mainly over northeastern Amazonia, portions of northern Africa, and tropical western Pacific regions. In contrast, the GEOS-4 product generally underestimates SW rad relative to the GEWEX-SRB, with maximum

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Michael G. Bosilovich, Franklin R. Robertson, and Junye Chen

extratropical regions. In MERRA, the tropics have more cloud effect similar to the inter-America seas (IAS) region (this tends toward boreal summer, not shown). On the other hand, extratropical regions have a weaker cloud effect, generally during the boreal winter (not shown). The center of the ITCZ in the eastern tropical Pacific Ocean is also weaker in MERRA than SRB. The NCEP reanalyses show reasonable comparison to the SRB data except that the CFSR South Pacific convergence zone (SPCZ) has a notably

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Franklin R. Robertson and Jason B. Roberts

to this parameterizability uncertainty is the degree to which improved data assimilation methodologies and new observations, particularly from satellite remote sensing, can overcome model physics deficiencies and provide depictions of ISV useful in advancing our understanding. Finally, the degree to which ISV in convective intensity and organization is important in enabling the quasi-steady-state energy balance of the tropics as a whole remains uncertain. Even though ISV in cloudiness and

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Sun Wong, Eric J. Fetzer, Brian H. Kahn, Baijun Tian, Bjorn H. Lambrigtsen, and Hengchun Ye

cloud-induced sampling biases. Fetzer et al. (2006) showed that the lack of sampling in opaque and precipitating clouds cause the total column water vapor obtained from AIRS retrievals in the tropics to be low by 2%–5% in comparison with the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). These biases can be higher in tropical deep convective regions and the midlatitudes since high filaments of water vapor are disproportionately found in cloudy atmospheric rivers

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Michele M. Rienecker, Max J. Suarez, Ronald Gelaro, Ricardo Todling, Julio Bacmeister, Emily Liu, Michael G. Bosilovich, Siegfried D. Schubert, Lawrence Takacs, Gi-Kong Kim, Stephen Bloom, Junye Chen, Douglas Collins, Austin Conaty, Arlindo da Silva, Wei Gu, Joanna Joiner, Randal D. Koster, Robert Lucchesi, Andrea Molod, Tommy Owens, Steven Pawson, Philip Pegion, Christopher R. Redder, Rolf Reichle, Franklin R. Robertson, Albert G. Ruddick, Meta Sienkiewicz, and Jack Woollen

). Although the RMS differences in the Northern Hemisphere are still diminishing after 4 yr, they appear to reach a predictability limit, especially in the tropics and Southern Hemisphere where the RMS differences also display some seasonality. This seasonality appears to be related to the corresponding seasonality in the RMS differences in precipitation (not shown), which are presumably, in turn, related to the seasonal cycle of precipitation over tropical land. The maps of root-zone soil wetness

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