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Suzana J. Camargo, Claudia F. Giulivi, Adam H. Sobel, Allison A. Wing, Daehyun Kim, Yumin Moon, Jeffrey D. O. Strong, Anthony D. Del Genio, Maxwell Kelley, Hiroyuki Murakami, Kevin A. Reed, Enrico Scoccimarro, Gabriel A. Vecchi, Michael F. Wehner, Colin Zarzycki, and Ming Zhao

Force (MDTF) ( Maloney et al. 2019 ). Similarly to what was done in the analysis of the TCs in the HWG project ( Shaevitz et al. 2014 ; Daloz et al. 2015 ; Nakamura et al. 2017 ; Ramsay et al. 2018 ), we are considering the tracking provided by each modeling group as part of the model package. This is an ensemble of opportunity; that is, we use the model simulations and TC tracks that are available to us, as they are. These model simulations were not produced for this purpose. Therefore, there

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Samson M. Hagos, L. Ruby Leung, Oluwayemi A. Garuba, Charlotte Demott, Bryce Harrop, Jian Lu, and Min-Seop Ahn

similar manner ( Fig. 4d ). Defining the inverse of the slope of the regression line in Fig. 4c as the precipitable water limit pw lim and the intercept as E 0 /pw lim , (5) NMFC = P − E 0 p w lim . Before moving the analysis further, a brief discussion of NMFC in the context of previous work is warranted. The normalization introduced in Eq. (3) is also relevant to the concept of gross moist stability (GMS) put forward by Neelin and Held (1987) and further developed by Raymond et al. (2009

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Ángel F. Adames and Yi Ming

use of the 1.5° × 1.5° horizontal resolution, daily geopotential height and wind data from ERA-Interim ( Dee et al. 2011 ) for the 33-yr time period of 1979–2011. Rainfall data from the Tropical Rainfall Measuring Mission product 3B42 (TRMM-3B42; Huffman et al. 2007 ) from 1998 to 2011 are also used in this study. b. Methods Many of the results shown in the following sections are obtained through linear regression analysis, following the method described in Adames and Wallace (2014) . We create

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Xianan Jiang, Ángel F. Adames, Ming Zhao, Duane Waliser, and Eric Maloney

[vectors; see a scale at the top right of (a)] based on ERA-Interim from 1998 to 2012. While moisture mode theory has been mainly employed for the winter MJO, a recent diagnostic study on MJO moisture budget by Adames et al. (2016) suggests that distinct MJO propagation between winter and summer is associated with seasonal variations in the mean moisture pattern ( Fig. 1 ). In this study, by conducting a detailed moisture entropy budget analysis for the northward propagation of the summer MJO over

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Grey S. Nearing, Benjamin L. Ruddell, Martyn P. Clark, Bart Nijssen, and Christa Peters-Lidard

1. Introduction The Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) benchmarking experiments by Best et al. (2015) showed that some of the world’s most sophisticated operational land models (CABLE, CH-TESSEL, COLA-SSiB, ISBA-SURFEX, JULES, Mosaic, Noah, ORCHIDEE) were outperformed in their ability to simulate short-term surface energy fluxes by simple regressions. Specifically, the PLUMBER experiments used piecewise

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Alexis Berg and Justin Sheffield

slope of that relationship differs between models. We chose to focus here on the share of explained variance rather than the sensitivity, arguing that it better represents the strength of the relationship between SM and ET. In any case, Fig. 10c also shows that the value of the regression slope and the correlation actually remain strongly correlated over much of the land surface, including over regions of larger spread in SM–ET correlation where our analysis has been focused. Fig . 10. (a

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Maik Renner, Axel Kleidon, Martyn Clark, Bart Nijssen, Marvin Heidkamp, Martin Best, and Gab Abramowitz

slightly imbalanced dataset. Second, we restricted the analysis to days with high potential solar radiation of more than 300 W m −2 . Further days were excluded that have fewer than 30 observations per day, show nonsignificant slopes in determining EF [Eq. (4) ] or nonsignificant slopes of sensible and latent heat fluxes to R sd [Eq. (2) ]. We also exclude days where the Bernardi–Camuffo regression explains less than 50% of the diurnal variation in H and λE . These filter criteria help to

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James F. Booth, Young-Oh Kwon, Stanley Ko, R. Justin Small, and Rym Msadek

2) for the North Atlantic, the across-model covariability of the spatial regressions of T DIFF and/or σ BI and the surface storm tracks is also strong. Thus, in the North Atlantic we find indicators suggesting that the biases in the SST create dominant biases in the surface storm track. An analysis of the multimodel mean using only the CMIP model without the large bias in the surface storm track also shows forcing from the SST biases impact the surface storm track in the North Atlantic

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Allison A. Wing, Suzana J. Camargo, Adam H. Sobel, Daehyun Kim, Yumin Moon, Hiroyuki Murakami, Kevin A. Reed, Gabriel A. Vecchi, Michael F. Wehner, Colin Zarzycki, and Ming Zhao

section 2 and describe our diagnostics and analysis methodology in section 3 . The application of these diagnostics to the six models will be described in section 4 , with a discussion of their implications in section 5 . We provide a summary of the results and conclusions in section 6 . 2. Model simulations a. Models We explore TC intensification processes in six high-resolution climate model long-term (>20 year) historical simulations ( Table 1 ). Several of these simulations were also examined

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Eric D. Maloney, Andrew Gettelman, Yi Ming, J. David Neelin, Daniel Barrie, Annarita Mariotti, C.-C. Chen, Danielle R. B. Coleman, Yi-Hung Kuo, Bohar Singh, H. Annamalai, Alexis Berg, James F. Booth, Suzana J. Camargo, Aiguo Dai, Alex Gonzalez, Jan Hafner, Xianan Jiang, Xianwen Jing, Daehyun Kim, Arun Kumar, Yumin Moon, Catherine M. Naud, Adam H. Sobel, Kentaroh Suzuki, Fuchang Wang, Junhong Wang, Allison A. Wing, Xiaobiao Xu, and Ming Zhao

Atmospheric Administration (NOAA) Modeling, Analysis, Prediction, and Projections program (MAPP) Model Diagnostics Task Force (MDTF) to promote development of PODs and their application to climate and weather prediction models. A product of the first phase of the MDTF (2015–18) is the creation of select demonstrative PODs and a modeler-oriented open-source software framework that is portable, extensible, and open for contribution of PODs from the community. The framework is conceived to be compatible with

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