A Sample Form of a Manuscript for the APJAS

A Sample Form of a Manuscript for the APJAS

Jimy Kuo1, Robert Doe2, and Kwon-Lee Hong3

1Climate Research Division, National Meteorological Center, Boulder, Colorado, U.S.A.

2Numerical Weather Prediciton Center, Korea Meteorological Administrartion, Seoul, Korea

3Departmet of Atmospheric Sciences, Han-Kug University, Kongju, Chungnam, Korea

(Division, Institute, City, (Province), Country)

8 September 2016

Corresponding Author: Kwon-Lee Hong, Department of Atmospheric Sciences, College of Science, Han-Kug University, Room 206, Kongju, Chungnam 12345, Korea (complete mailing address)

E-mail:

Abstract (new page)

The large-scale nudging effects on the East Asian summer monsoon (EASM) are examined using the National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM).

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(Abstract should not exceed 250 words. Citation references should not appear)

Key words: Regional climate, Asian monsoon, Aerosol, climate change (equal or less than 5 words)

1. Introduction (primary heading, 1st page of main text, new page)

Regional climate models (RCMs) are one-way nested, limited-area models that are used to downscale low-resolution atmospheric information, usually reanalysis or data simulated by the general circulation model (e.g., Giorgi, 1990; Christenses, 2007) without an input of direct regional-scale observation.

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2. Model description (primary heading)

a. Experimental design and data (secondary heading)

The NCEP RSM was used in this study. The RSM is a primitive equation model using the sigma-vertical coordinates. The model includes parameterizations of surface, boundary layer (BL), and moist processes that account for physical exchanges between the land surface, the boundary layer, and the free atmosphere.

(1) NCEP RSM (tertiary heading)

A description of the NCEP RSM generally follows the study of Juang et al. (1997), and its application to an East-Asian monsoon is available in Yhang and Hong (2008).

(a) Cumulus parameterization scheme (quaternary heading)

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3. Results (primary heading)

Figure 2 shows the interannual variations in the RMSEs (root mean square errors) of the seasonal mean precipitation averaged over the whole domain. To examine the effect of the SSBC on different cases, the years 2000 and 2004 are chosen.

The seasonal mean precipitation of 2000 is shown in Fig. 3. The global mean feature from the model closely follows the pattern from what was observed. …………………………. .………………………………………………………………………

4. Concluding remarks (primary heading)

We have examined the effect of global warming on the Asian monsoon. The major finding of this study lies in the fact that global warming modules the Asian monsoon significantly.

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Acknowledgements: This work was funded by the National Science Foundation, Grant number 2011-123. We acknowledge the critical comments from anonymous reviewers and editor.

APPENDIX (optional)

Microphysics in the WDM6 Scheme

The detailed description of the WDM6 scheme is shown here. Any figures and equations with their explanations, which are not directly related to the major scientific content of the manuscript, can be placed here.

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References (new page)

Alexandru, A., R. de Elia, R. Laprise, L. Separovic, and S. Biner, 2009: Sensitivity study of regional climate model simulations to large-scale nudging parameters. Mon. Wea. Rev., 137, 1666-1686, doi:http://dx.doi.org/10.1175/2008MWR2620.1.

Kanamitsu, M., K. Yoshimura, Y.-B. Yhang, and S.-Y. Hong, 2010: Errors of interannual variability and trend in dynamical downscaling of reanalysis. J. Geophy. Res., 115, D17115, doi:10.1029/2009JD013511.

______, W. Ebisuzaki, J. Woollen, S.-K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEP-DOE AMIP-Ⅱ reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 1631-1643, doi:10.1175/BAMS-83-11-1631.

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〮 For more than 10 authors, use “First author, and Coauthors”.

Dai, Y., and Coauthors, 2003: The Common Land Model. Bull. Amer. Meteor. Soc., 84, 1013-1023, doi:10.1175/BAMS-84-8-1013.

Table captions (new page)

Table 1. Statistics of the bias, root mean square error (RMSE), and pattern correlation (PC) for (a) 2000 and (b) 2004 over land and the oceans.

Table 2. Seasonal mean latent heat flux (W m-2) averaged over the oceans.

List of Figures (new page)

Fig. 1. Model domain and topography.

Fig. 2. Time-series of root mean square error (RMSE) for simulated precipitation with and without SSBC from 1979 to 2004.

Table 1. Statistics of the bias, root mean square error (RMSE), and pattern correlation (PC) for (a) 2000 and (b) 2004 over land and the oceans. (each table at new page)

(a)

2000 / land / ocean
bias / RMSE / PC / bias / RMSE / PC
NoSSBC / 1.08 / 2.23 / 0.83 / -1.08 / 1.84 / 0.91
SSBC / 1.29 / 2.65 / 0.81 / -1.59 / 2.39 / 0.92

(b)

2004 / land / ocean
bias / RMSE / PC / bias / RMSE / PC
NoSSBC / 1.58 / 3.08 / 0.81 / -0.16 / 3.05 / 0.73
SSBC / 1.75 / 2.83 / 0.88 / -1.61 / 2.28 / 0.77

Table 2. Seasonal mean latent heat flux (W m-2) averaged over the oceans. (each table at new page)

2000 / 2004
NoSSBC / 72.3 / 80.3
SSBC / 62.6 / 75.1

Fig. 1. Model domain and topography. (each figure at new page, 8 cm is good look)

a) c)

b) d)

Fig. 2. Seasonal mean of the 850-hPa wind vector, 200-hPa wind speed (m s-1, contour and shading), and 500-hPa geopotential height (m, contour) from the (a) reanalysis data, (b) NoSSBC, and (c) SSBC experiment in the summer of 2000, and the (d) reanalysis in 2004.

(each figure at new page, 16 cm is good look, Figure caption here can be single spaced)