Global Coupled 모델 2와 3.1의 MJO 모의성능 평가
Assessment of MJO Simulation with Global Coupled Model 2 and 3.1
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초록
A large number of MJO skill metrics and process-oriented MJO simulation metrics have been developed by previous studies including the MJO Working Group and Task Force. To assess models’ successes and shortcomings in the MJO simulation, a standardized set of diagnostics with the additional set of dynamics-oriented diagnostics are applied. The Global Coupled (GC) model developed for the operation of the climate prediction system is used with the comparison between the GC2 and GC3.1. Two GC models successfully capture three-dimensional dynamic and thermodynamic structure as well as coherent eastward propagation from the reference regions of the Indian Ocean and the western Pacific. The low-level moisture convergence (LLMC) ahead of the MJO deep convection, the low-level westerly and easterly associated with the coupled Rossby-Kelvin wave and the upper-level divergence are simulated successfully. The GC3.1 model simulates a better three-dimensional structure of MJO and thus reproduces more realistic eastward propagation. In GC2, the MJO convection following the LLMC near and east of the Maritime Continent is much weaker than observation and has an asymmetric distribution of both low and upper-level circulation anomalies. The common shortcomings of GC2 and GC3.1 are revealed in the shorter MJO periods and relatively weak LLMC as well as convective activity over the western Indian Ocean.
Abstract
A large number of MJO skill metrics and process-oriented MJO simulation metrics have been developed by previous studies including the MJO Working Group and Task Force. To assess models’ successes and shortcomings in the MJO simulation, a standardized set of diagnostics with the additional set of dynamics-oriented diagnostics are applied. The Global Coupled (GC) model developed for the operation of the climate prediction system is used with the comparison between the GC2 and GC3.1. Two GC models successfully capture three-dimensional dynamic and thermodynamic structure as well as coherent eastward propagation from the reference regions of the Indian Ocean and the western Pacific. The low-level moisture convergence (LLMC) ahead of the MJO deep convection, the low-level westerly and easterly associated with the coupled Rossby-Kelvin wave and the upper-level divergence are simulated successfully. The GC3.1 model simulates a better three-dimensional structure of MJO and thus reproduces more realistic eastward propagation. In GC2, the MJO convection following the LLMC near and east of the Maritime Continent is much weaker than observation and has an asymmetric distribution of both low and upper-level circulation anomalies. The common shortcomings of GC2 and GC3.1 are revealed in the shorter MJO periods and relatively weak LLMC as well as convective activity over the western Indian Ocean.
참고문헌 (33)
- Min-Seop Ahn, "MJO simulation in CMIP5 climate models: MJO skill metrics and process-oriented diagnosis", Climate Dynamics, 49(11-12), pp.4023~4045 (2017), Springer Science and Business Media LLC[DOI]
- Min‐Seop Ahn, "MJO Propagation Across the Maritime Continent: Are CMIP6 Models Better Than CMIP5 Models?", Geophysical Research Letters, 47(11) (2020), American Geophysical Union (AGU)[DOI]
- "ERA5 hourly data on pressure levels from 1950 to 1978 (preliminary version). Copernicus Climate Change Service (C3S) Climate Data Store (CDS)" (2020)[URL]
- I. A. Boutle, "Spatial variability of liquid cloud and rain: observations and microphysical effects", Quarterly Journal of the Royal Meteorological Society, 140(679), pp.583~594 (2013), Wiley[DOI]
- Charlotte A. DeMott, "Atmospheric Mechanisms for MJO Decay Over the Maritime Continent", Journal of Geophysical Research: Atmospheres, 123(10), pp.5188~5204 (2018), American Geophysical Union (AGU)[DOI]
- Duchon, C. E., "Lanczos filtering in one and two dimensions", J. Appl. Meteor. Climatol, 18, pp.1016~1022 (1979)
- 현유경, "기상청 기후예측시스템(GloSea6) - Part 2: 기후모의 평균 오차 특성 분석", 대기, 32(2), pp.87~101 (2022), 한국기상학회[DOI]
- Xianan Jiang, "Key processes for the eastward propagation of the Madden-Julian Oscillation based on multimodel simulations", Journal of Geophysical Research: Atmospheres, 122(2), pp.755~770 (2017), American Geophysical Union (AGU)[DOI]
- Daehyun Kim, "Process-Oriented MJO Simulation Diagnostic: Moisture Sensitivity of Simulated Convection", Journal of Climate, 27(14), pp.5379~5395 (2014), American Meteorological Society[DOI]
- 김혜리, "기상청 기후예측시스템(GloSea6) - Part 1: 운영 체계 및 개선 사항", 대기, 31(3), pp.341~359 (2021), 한국기상학회[DOI]
- 김지영, "Global Coupled (GC) 모델 개선에 따른 동아시아 여름 몬순 모의성능 평가", 대기, 31(5), pp.563~576 (2021), 한국기상학회[DOI]
- 김상욱, "GloSea5 모형의 계절내-계절(S2S) 예측성 검정: Part 1. 북반구 중위도 지위고도", 대기, 28(3), pp.233~245 (2018), 한국기상학회[DOI]
- Knutson, T. R., "30-60 day atmospheric oscillation : Composite life cycles of convection and circulation anomalies", Mon. Wea. Rev, 115, pp.1407~1436 (1987)
- Lau, K.-M., "Coherent fluctuations of extratropical geopotential height and tropical convection in intraseasonal time scales", J. Atmos. Sci, 43, pp.1164~1181 (1986)
- Liebmann, B., "Description of a complete(interpolated)outgoing longwave radiation dataset", Bull. Amer. Meteor. Soc, 77, pp.1275~1277 (1996)
- Madden, R. A., "Detection of a 40-50day oscillation in the zonal wind in the tropical Pacific", J. Atmos. Sci, 28, pp.702~708 (1971)
- Madden, R. A., "Description of a global-scale circulation cell in the tropics with a 40-50 day period", J. Atmos. Sci, 29, pp.1109~1123 (1972)
- A. Megann, "GO5.0: the joint NERC–Met Office NEMO global ocean model for use in coupled and forced applications", Geoscientific Model Development, 7(3), pp.1069~1092 (2014), Copernicus GmbH[DOI]
- MJOWG, "MJO simulation diagnostics", J. Clim, 22, pp.3006~3030 (2009)
- Rae, J. G. L., "Development of the Global Sea Ice 6.0 CICE configuration for the Met Office Global Coupled model. Geosci", Model Dev, 8, pp.2221~2230 (2015)[DOI]
- Jeff K. Ridley, "The sea ice model component of HadGEM3-GC3.1", Geoscientific Model Development, 11(2), pp.713~723 (2018), Copernicus GmbH[DOI]
- David Storkey, "UK Global Ocean GO6 and GO7: a traceable hierarchy of model resolutions", Geoscientific Model Development, 11(8), pp.3187~3213 (2018), Copernicus GmbH[DOI]
- Waliser, D. E., "Potential predictability of the Madden-Julian oscillation", Bull. Amer. Meteor. Soc, 84, pp.33~50 (2003)
- David Walters, "The Met Office Unified Model Global Atmosphere 6.0", 6.1 and JULES Global Land 6.0, Geoscientific Model Development, pp.2017, 10(4)[DOI]
- David Walters, "The Met Office Unified Model Global Atmosphere 7.0", 7.1 and JULES Global Land 7.0 configurations, 2019, pp.12(5), 1909~1963[DOI]
- Bin Wang, "A trio-interaction theory for Madden–Julian oscillation", Geoscience Letters, 3(1) (2016), Springer Science and Business Media LLC[DOI]
- Wang, B., "Dynamics-oriented diagnostics for the Madden-Julian Oscillation", J. Clim, 31, pp.3117~3135 (2018)[DOI]
- Jiabao Wang, "MJO Teleconnections over the PNA Region in Climate Models. Part II: Impacts of the MJO and Basic State", Journal of Climate, 33(12), pp.5081~5101 (2020), American Meteorological Society[DOI]
- Weickmann, K. M., "Intraseasonal(30-60 day)fluctuations of outgoing longwave radiation and 250mb streamfunction during northern winter", Mon. Wea. Rev, 113, pp.941~961 (1985)
- Wheeler, M. C., "An all-season real-time multivariate MJO index : Development of an index for monitoring and prediction", Mon. Wea. Rev, 132, pp.1917~1932 (2004)
- K. D. Williams, "The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 and GC3.1) Configurations", Journal of Advances in Modeling Earth Systems, 10(2), pp.357~380 (2018), American Geophysical Union (AGU)[DOI]
- Young-Min Yang, "Diagnosing ISO Forecast from GloSea5 Using Dynamic-Oriented ISO Theory", Atmosphere, 12(1), pp.114~ (2021), MDPI AG[DOI]
- Chidong Zhang, "Madden–Julian Oscillation: Bridging Weather and Climate", Bulletin of the American Meteorological Society, 94(12), pp.1849~1870 (2013), American Meteorological Society[DOI]