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Gérald Desroziers, Loïk Berre, Vincent Chabot, and Bernard Chapnik

by Fisher (2003a) or Berre et al. (2006) . Such an ensemble of analyses is now operational at Météo-France in order to specify flow-dependent modulations of the background error variances in the operational four-dimensional variational data assimilation (4D-Var) assimilation scheme. An objective of this paper is to show that an ensemble of perturbed analyses also allows the computation of consistency diagnostics and then the determination of more exact observation and background error

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Lihong Zhang, Jiandong Gong, and Ruichun Wang

associated with the DFS. Desroziers et al. (2005) applied the randomization procedure to diagnose the contributions of observations to error reduction in the analysis, and successfully conducted a diagnostic analysis in a 4DVAR system developed in France. Chapnik et al. (2006) found that in a simplified data assimilation system, the randomization procedure can be extended to a slightly nonlinear case. Brousseau et al. (2014) found that the randomization method and DFS had similar results and

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Fengyun Sun, Alfonso Mejia, Sanjib Sharma, Peng Zeng, and Yue Che

indicators are overlapping, it is impossible and unnecessary to include all the indices in a diagnosis. However, some key attributes should be identified and made sure to be comprehensively covered by selected indices in a systematic diagnosis. Currently, only a few diagnostic frameworks have been proposed to evaluate the skill of downscaling methods. In the framework proposed by Hayhoe (2010) , three attributes are identified to evaluate the skill of four downscaling methods in simulating daily

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Richard E. Thomson and Isaac V. Fine

of the model by comparing model estimates of mixed layer depth against those obtained from temperature profiles at Ocean Station P ( Fig. 1 ). Comparisons focus on the Weathership oceanographic observation period 1956–80 and recent profile data collected within several hundred kilometers of Station P by Argo drifters. Section 4 also compares mixed layer depths from the diagnostic model against those from two commonly used bulk mixed layer models. Section 5 examines long-term trends in the

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H. Annamalai

convergence zone (SPCZ; Widlansky et al. 2011 ) toward warmer equatorial SST anomalies associated with El Niño and attendant modulations to the planetary Walker and local Hadley circulations exert considerable control on the seasonal rainfall anomalies over the USAPI ( Annamalai et al. 2014 ). Recognizing that the equatorial Pacific precipitation and the associated diabatic heating anomalies are fundamental to this conceptual framework, the present research applies a suite of process-oriented diagnostics

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Emily Shuckburgh, Helen Jones, John Marshall, and Chris Hill

) or streamfunction variance ( Holloway 1986 ; Keffer and Holloway 1988 ; Stammer 1998 ). In this paper, we will focus on quantifying eddy diffusivities and their influence on tracer distributions using Nakamura’s effective diffusivity diagnostic ( Nakamura 1996 ). This diagnostic was used for the first time in an oceanic context by Marshall et al. (2006) , who applied it to surface velocity fields derived from altimetric observations. Subsequently, Abernathey et al. (2009 hereafter AMMS

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Juan Li, Bin Wang, and Young-Min Yang

capture the essential features of the EASM and EAWM is required for a wide range of applications. Therefore, the aim of this paper is to explore and establish a set of dynamics-oriented diagnostic metrics for objective assessing the fidelity and performance of coupled general circulation models (CGCMs) in simulating the EASM and EAWM. The evaluation is concerned with two types of process-oriented diagnostics. One is the forced response of the climate system to external forcing, such as solar radiation

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Bin Wang, Sun-Seon Lee, Duane E. Waliser, Chidong Zhang, Adam Sobel, Eric Maloney, Tim Li, Xianan Jiang, and Kyung-Ja Ha

MJO and identify their major problems, the U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group designed a suite of diagnostics ( Waliser et al. 2009 ). Because of the strong seasonality of the tropical intraseasonal variability, the diagnostics were applied separately to boreal winter from November to April and boreal summer from May to October. The main diagnostic quantities included (i) seasonal variations of the mean circulation (supplementary metric) and intraseasonal

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Ellen Dyer and Richard Washington

and also at a subseasonal scale. Taking this approach, we will create new subseasonal aggregation periods based on how the season evolves, in the mean and extreme composites, and create a set of atmospheric diagnostics that can be used to guide future model evaluations. The climatology in East Africa is defined by Indian Ocean inflow, accompanied by a strong low-level jet, atmospheric divergence in the midtroposphere, and upper-tropospheric remotely forced descent. The atmosphere in East Africa is

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Daehyun Kim, Prince Xavier, Eric Maloney, Matthew Wheeler, Duane Waliser, Kenneth Sperber, Harry Hendon, Chidong Zhang, Richard Neale, Yen-Ting Hwang, and Haibo Liu

diagnostic that fulfills all of the following conditions: the diagnostic can be constructed from currently available observations; the diagnostic is related to certain characteristics of unresolved-scale processes (i.e., parameterizations) in GCMs; and the diagnostic represents certain features of resolved-scale processes that are important in the MJO dynamics. If a diagnostic satisfies the first and second conditions, it can provide insight into how parameterizations should be improved to make model

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