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Aaron D. Kennedy, Xiquan Dong, Baike Xi, Shaocheng Xie, Yunyan Zhang, and Junye Chen

, including the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR; Mesinger et al. 2006 ) and the Modern-Era Retrospective analysis for Research and Applications (MERRA) reanalysis ( Rienecker et al. (2011) ). Compared to their predecessors, these new reanalyses have been improved significantly. For example, NARR includes an assimilation of precipitation at a high resolution over North America and has shown improvements over the NCEP/Department of Energy Global

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Siegfried Schubert, Hailan Wang, and Max Suarez

) summarized the teleconnectivity associated with the boreal summer waveguides and preferred propagation patterns toward and away from the waveguides. Newman and Sardeshmukh (1998) examined the seasonality of the Pacific–North American response to remote low-frequency forcing and showed that the changes are tied to the seasonal changes in the shape and location of the Rossby waveguide. They further showed that the amplitude of the forced response over the United States to forcing over the west Pacific

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

estimates. 3. Results a. Precipitation and evaporation Climatological averages of the TRMM, GPCP, and MERRA P in 2004–08 are shown in Fig. 1 for boreal winter [December–January–February (DJF) in Figs. 1a–c ] and summer [June–July–August (JJA) in Figs. 1d–f ]. The geographical distributions of the MERRA P are consistent with those of the TRMM and GPCP P in both seasons. However, the MERRA P is larger over the west Pacific, the Indian, and North American monsoon area. Fig . 1. Geographical

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Kyle F. Itterly and Patrick C. Taylor

errors are the primary cause of NRMSE OLR . The NRMSE OLRCLR reaches 100% in several oceanic convective regions; however, in absolute terms RMSE LWCF is between 3 and 5 times larger than RMSE OLRCLR . The NRMSE LWCF is also the dominant contributor to NRMSE OLR in land convective regions (e.g., central South America and Central Africa). The largest NRMSE OLR and NRMSE LWCF values occur over areas (identified by Yang and Slingo 2001 ) where propagating convection dominates the diurnal cycle (e

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

(1969) also noted a semipermanent trough close to the coast of northwestern Africa. Reed et al. (1977) analyzed eight waves observed during August–September 1974 and found evidence of two circulation centers at the surface, both over land, with the northernmost one located at about 10° north of the monsoonal trough. The Norquist et al. (1977) investigation of wave energetics revealed two centers of energy conversion: one characterized by the predominance of moist convective processes to the

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Rolf H. Reichle, Randal D. Koster, Gabriëlle J. M. De Lannoy, Barton A. Forman, Qing Liu, Sarith P. P. Mahanama, and Ally Touré

observations (see references in Miralles et al. 2010 ). In their model, the largest I values, ranging from I = 0.15 to I = 0.24, are found in the boreal forests of North America, Scandinavia, and Russia. Somewhat smaller values of I = 0.06 to I = 0.15 are found in tropical rain forests (including Indonesia and the Amazon and Congo basins) and midlatitude forested regions (eastern United States, parts of Europe). Globally averaged, Miralles et al. (2010) estimate I = 0.06. For comparison

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

–f ). MERRA generally underestimates T max relative to AMSR-E for most NH areas but overestimates T max for most SH land areas. Relative large (>3°C) differences can be found in mountainous areas such as the Tibetan Plateau and western North America and some desert regions such as the Sahara desert and Middle East. The overestimation in MERRA T max in SH areas, especially in South America, and different signs of differences in northern Amazonia and African rain forest areas are associated with the

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Behnjamin J. Zib, Xiquan Dong, Baike Xi, and Aaron Kennedy

reanalysis. However, in a companion study over middle latitudes, Kennedy et al. (2011) also found that the CF and radiative fluxes derived from MERRA generally agreed better than those derived from the North American Regional Reanalysis (NARR) with ARM Southern Great Plains (SGP) ground-based observations. Furthermore, while utilizing two long-term surface-based observation stations does provide valuable information, these results may not be representative of the entire Arctic region, rather a

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Franklin R. Robertson, Michael G. Bosilovich, Junye Chen, and Timothy L. Miller

. Climatological mean maps of the moisture and heating increments are presented in Fig. 1 . The degree of systematic spatial structure found in these fields is consistent with our understanding that model physics deficiencies can vary as a function of climate regime (e.g., regions of predominantly upward or downward motion). In general, positive moisture increment values maximize over the tropics, most notably in the Northern Hemisphere summer warm pool areas (western Pacific, northern Indian Ocean, and inter-American

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Brian E. Mapes and Julio T. Bacmeister

–longitude sections. In this RMM-based MJO definition, longitude and latitude dependences are retained since RMM1 and RMM2 are simply daily time series. We composited 1979–2005 MERRA data in the WH04 octants of RMM1–RMM2 phase space. For these composites we also rebinned the data from ⅔° × ½° resolution (540 × 360 arrays, discarding the 361st latitude, the North Pole) to 3.3° × 5° (108 × 36 arrays). Data are on 42 pressure levels (25 levels in the troposphere with 25–50-hPa spacing). RMM phases thus range from

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