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Arun Kumar, Jieshun Zhu, and Wanqiu Wang

-explained variance in Fig. 3 . For the sake of clarity, the time series plots are shown only over 2007–10 whereas the scatterplots cover the entire analysis period. Fig . 4. (a),(b),(d),(e) Time series of original (black curves) and reconstructed (red curves) anomalous fields for (top) 2007–08 and (middle) 2009–10 and (c),(f) scatterplots of original ( x axis) vs reconstructed ( y axis) anomalous fields during winter 1988–2017 for (left) OLR (W m −2 ) averaged over (10°S–10°N, 70°–100°E) and (right) U200 (m s

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Kevin E. Trenberth and Yongxin Zhang

lag to estimate the degrees of freedom in each series and thus the standard error of the mean. Fig . 11. (top) Time series of MHT as 12-month running means across the equator for the oceans: global, Atlantic, Pacific, and Indian; and (middle) meridional energy transports at TOA and for the atmosphere in PW. (bottom) Mean values along with the standard error of the mean, and the standard deviation in PW. Fig . 12. Schematic of the ocean heat flows across the equator for 2000 to 2016 as blue arrows

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Chu-Chun Chen, Min-Hui Lo, Eun-Soon Im, Jin-Yi Yu, Yu-Chiao Liang, Wei-Ting Chen, Iping Tang, Chia-Wei Lan, Ren-Jie Wu, and Rong-You Chien

explored in the theoretical framework of Neelin and Held (1987) , in which they showed that an increase in the MSE in the lower to middle troposphere has a tendency to increase the precipitation. In the deforestation simulations, the land surface forcing is prescribed, which leads to higher surface temperatures and provides a thermodynamic source to trigger the instability in the atmosphere. The convection also leads to vertical mixing of the MSE. Thus, we examine the vertical profile of the

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Ching-Shu Hung and Chung-Hsiung Sui

, and 56 for the MC ( Fig. 4d ). The vertical structure of important thermodynamic and dynamic fields during the four stages over the CIO (solid line) and MC (dashed line) are shown in Figs. 5a–d . In the suppressed stage, intraseasonal downward motion causes a strong dryness in the middle troposphere and associated anomalous easterlies in the lower troposphere. In the cloud developing stage, as low-level moisture has been built up, the atmosphere becomes relatively unstable, which provides

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Satoru Yokoi, Shuichi Mori, Masaki Katsumata, Biao Geng, Kazuaki Yasunaga, Fadli Syamsudin, Nurhayati, and Kunio Yoneyama

convection during nighttime. Warner et al. (2003) and Mapes et al. (2003b) proposed that an ascent motion in the lower troposphere, which was due to gravity waves emanating from the nighttime radiative cooling of the elevated terrain of the Andes, destabilized the offshore atmosphere west of the Pacific coast of Panama and Columbia. Love et al. (2011) and Hassim et al. (2016) suggested the role of gravity waves emanating from convective systems over land. While diabatic heating within convective

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D. Argüeso, R. Romero, and V. Homar

structure of the atmosphere between the various 4-km runs with the purpose of putting forward a physical interpretation of the impacts from the convection representation. a. Precipitation The domain-average mean precipitation is a first-order measure of the model water balance physical realism. Figure 2a shows the domain-averaged precipitation mean from the various observations and all model simulations for the 2015–16 austral summer. According to these results, the model is overall well calibrated

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Satoru Yokoi, Shuichi Mori, Fadli Syamsudin, Urip Haryoko, and Biao Geng

physical processes of the offshore migration and proposed possible mechanisms based on analyses of available observational data such as those from satellites, weather radars, and radiosondes launched from land sites, as well as numerical experiments. Warner et al. (2003) and Mapes et al. (2003b) proposed that gravity waves excited by nighttime radiative cooling of the elevated terrain of the Andes propagated offshore in the lower troposphere and destabilized the offshore atmosphere, contributing to

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Jian Ling, Yuqing Zhao, and Guiwan Chen

compared to the observation. Fig . 2. Scatter diagrams of MJO simulation skill scores in terms of occurrence frequencies (yr −1 ) vs averaged (left) propagation ranges (longitude), (middle) strength (mm day −1 ), and (right) ending longitude (°E) of tracked MJO events in the observations and GCMs for the (top) boreal winter and (bottom) boreal summer. Their correlation coefficients are given on the top right corners. The black dots represent the observations and the color dots represent the model

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Benjamin A. Toms, Susan C. van den Heever, Emily M. Riley Dellaripa, Stephen M. Saleeby, and Eric D. Maloney

-scale interactions within the MJO. By permitting the direct representation of cloud formation, the environments within which clouds form can be analyzed according to the governing physics of the atmosphere, rather than via convective parameterizations as is typically the case in global circulation models ( Zhang and Mu 2005 ; Jiang et al. 2015 ; Moncrieff et al. 2012 ). We therefore simulate a boreal summer MJO event propagating over the Maritime Continent using a CRM to investigate whether any relationships

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See Yee Lim, Charline Marzin, Prince Xavier, Chih-Pei Chang, and Bertrand Timbal

of CS have been defined since the winter monsoon experiment (e.g., Chang et al. 1979 ; Chu and Park 1984 ), most of which are based on a low-level meridional wind component averaged over a specific area in the northern or middle SCS. However, a strong low-level northerly wind could be induced by local tropical circulations, rather than cold surges forced by the southward extension of the Siberian high ( Ding 1990 ). Therefore, we have combined both a wind index and a pressure index in this

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