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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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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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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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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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Yan Zhu, Tim Li, Ming Zhao, and Tomoe Nasuno

understand the relative role of MJO dynamic and thermodynamic fields on the development of HFW. The model consists of a two-level free atmosphere and a barotropic boundary layer. The model covers the global tropical domain (40°S–40°N). It is an anomalous model with specified basic states derived from interpolated ERA-Interim reanalysis data in winter. The basic state consists of both the climatological mean state and active or suppressed MJO field. The upper-level and lower-level background wind fields

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Wan-Ling Tseng, Huang-Hsiung Hsu, Noel Keenlyside, Chiung-Wen June Chang, Ben-Jei Tsuang, Chia-Ying Tu, and Li-Chiang Jiang

; Miura et al. 2007 ; Wu and Hsu 2009 ; Birch et al. 2016 ) and atmosphere–ocean coupling ( Zhu et al. 2010 ). The present study uses the newly developed ECHAM5-SIT model (described in section 2 ), one of the few GCMs that realistically simulate the MJO ( Tseng et al. 2015 ; Jiang et al. 2015 ), to address this unresolved concern. Three experiments are conducted to delineate the relative effects of land–sea contrast and orography in the MC on the MJO and address the following questions: 1) How

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Lei Song and Renguang Wu

standard deviation, and phase 2 is defined as the day located in the middle between phases 1 and 3. The obtained temporal evolution of composite anomalies based on the phase of cold events is similar to that based on the lead–lag time. As there are more time slices in the composite based on the lead–lag days, we obtain clearer features of continuous evolution. Thus, we only show composite anomalies based on the lead–lag days. 3. Features of the MC and IO convection-related cold events Temperature

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Claire L. Vincent and Todd P. Lane

satellite precipitation estimates show a decrease in hourly rainfall over the highest peaks of New Guinea (seen as a paler stripe through the middle of the island), an effect that is much less pronounced in the WRF Model simulations. This region is problematic, since there is also a possibility of errors in the satellite estimates over the steepest and highest topography, and there are few rain gauges in the area for independent validation [see discussion in Vincent and Lane (2016a) and Hassim et al

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