Abstract
Regional coupled modeling is one of the frontiers of regional climate modeling, but intercomparison has not been well coordinated. In this study, a community regional climate model, WRF4, with a resolution of 15 km, was coupled with a high-resolution (0.1°) North Pacific Ocean model (LICOM_np). The performance of the regional coupled model, WRF4_LICOM, was compared to that of another regional coupled model, RegCM4_LICOM, which was a coupling of version 4 of the Regional Climate Model (RegCM4) with LICOM_np. The analysis focused on the 2005 western North Pacific summer monsoon rainfall. The results showed that the regional coupled models with either RegCM4 or WRF4 as their atmospheric model component simulated the broad features over the WNP reasonably well. Quantitative intercomparison of the regional coupled simulations exhibited different biases for different climate variables. RegCM4_LICOM exhibited smaller biases in its simulation of the averaged June–July–August SST and rainfall, while WRF4_LICOM better captured the tropical cyclone (TC) intensity, the percentage contributions of rainfall induced by TCs to the total rainfall, and the diurnal cycle of rainfall and stratiform percentages, especially over land areas. The different behaviors in rainfall simulated by the two models were partly ascribed to the behaviors in the simulated western North Pacific subtropical high (WNPSH). The stronger (weaker) WNPSH in WRF4_LICOM (RegCM4_LICOM) was driven by overestimated (underestimated) diabatic heating, which peaked at approximately 450 hPa over the region around the Philippines in association with different condensation-radiation processes. Coupling of WRF4 with LIOCM is a crucial step towards the development of the next generation of regional earth system models at the Chinese Academy of Sciences.
摘要
区域耦合模拟是区域气候模拟的前沿方向之一, 但区域耦合模式的模拟比较尚未得到很好的协调. 本研究首先利用耦合器OASIS, 将通用区域气候模式WRF4 (水平分辨率为15km) 与高分辨率 (0.1°) 的北太平洋海洋模式 (LICOM_np)耦合, 发展了一个新的区域海气耦合模式WRF4_LIOCM. 针对 2005 年西北太平洋夏季降水, 我们对比了WRF4_LIOCM与另一个区域耦合模式RegCM4_LICOM的模拟性能. 两个区域耦合模式的分辨率、 对流参数化方案、 试验设计都是一致的. 对比结果显示, 不同区域大气模式耦合LICOM后, 对2005年东亚-西北太平洋区域气候均具有较好的模拟性能. 定量比较显示两个模式在不同变量上表现出不同的模拟差异. 从统计指标上看, RegCM4_LICOM对平均气候的模拟性能 (平均环流、 模拟海温等变量) 较好, 而WRF4_LICOM对极端气候 (强降水、 台风及其影响) 的模拟性能更优. 深入分析表明, 不同区域海气耦合模式的模拟差异与模式模拟的副热带高压差异有关. WRF4_LICOM (RegCM4_LICOM) 高估 (低估) 了菲律宾海附近的非绝热加热, 导致其模拟的西北太平洋副高偏强 (弱). 两个模式对菲律宾海附近非绝热加热的模拟差异则与不同的云-辐射-对流相互作用过程有关.
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References
Antonov, J. I., R. A. Locarnini, T. P. Boyer, A. V. Mishonov, and H. E. Garcia, 2006: World Ocean Atlas 2005, Volume 2: Salinity. S. Levitus, Ed. NOAA Atlas NESDIS 62, U.S. Government Printing Office, Washington, D. C., 182 pp.
Cha, D.-H., C. S. Jin, J. H. Moon, and D. K. Lee, 2016: Improvement of regional climate simulation of East Asian summer monsoon by coupled air-sea interaction and large-scale nudging. International Journal of Climatology, 36, 334–345, https://doi.org/10.1002/joc.4349.
Christensen, J. H., and Coauthors, 2007: Regional Climate Projections. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, 847–940.
Dai, A. G., and K. E. Trenberth, 2004: The diurnal cycle and its depiction in the community climate system model. J. Climate, 17(5), 930–951, https://doi.org/10.1175/1520-0442(2004)017<0930:TDCAID>2.0.CO;2.
Dai, Y. J., H. Q. Li, and L. Q. Sun, 2018: The simulation of East Asian summer monsoon precipitation with a regional ocean-atmosphere coupled model. J. Geophys. Res., 123(20), 11362–11376, https://doi.org/10.1029/2018JD028541.
Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteorol. Soc., 137, 553–597, https://doi.org/10.1002/qj.828.
Drobinski, P., and Coauthors, 2012: Model of the Regional Coupled Earth system (MORCE): Application to process and climate studies in vulnerable regions. Environmental Modelling & Software, 35, 1–18, https://doi.org/10.1016/j.envsoft.2012.01.017.
Fairall, C. W., E. F. Bradley, J. E. Hare, A. A. Grachev, and J. B. Edson, 2003: Bulk parameterization of Air-Sea Fluxes: Updates and verification for the COARE algorithm. J. Climate, 16, 571–591, https://doi.org/10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2.
Fang, Y., Y. Zhang, J. Tang, and X. Ren, 2010: A regional air — sea coupled model and its application over East Asia in the summer of 2000. Adv. Atmos. Sci., 27, 583–593, https://doi.org/10.1007/s00376-009-8203-7.
Fu, C. B., and Coauthors, 2005: Regional climate model intercom-parison project for Asia. Bull. Amer. Meteorol. Soc., 86, 257–266, https://doi.org/10.1175/BAMS-86-2-257.
Giorgi, F., 2019: Thirty years of regional climate modeling: Where are we and where are we going next? J. Geophys. Res., 124, 5696–5723, https://doi.org/10.1029/2018JD030094.
Giorgi, F., and L. O. Mearns, 1999: Introduction to special section: Regional climate modeling revisited. J. Geophys. Res., 104, 6335–6352, https://doi.org/10.1029/98JD02072.
Giorgi, F., and X. Q. Bi, 2000: A study of internal variability of a regional climate model. J. Geophys. Res., 105, 29503–29521, https://doi.org/10.1029/2000JD900269.
Giorgi, F., and X. J. Gao, 2018: Regional earth system modeling: Review and future directions. Atmos. Ocean. Sci. Lett., 11, 189–197, https://doi.org/10.1080/16742834.2018.1452520.
Giorgi, F., C. Jones, and G. R. Asrar, 2009: Addressing climate information needs at the regional level: The CORDEX framework. WMO Bulletin, 58, 175–183.
Giorgi, F., and Coauthors, 2012: RegCM4: Model description and preliminary tests over multiple CORDEX domains. Climate Research, 52, 7–29, https://doi.org/10.3354/cr01018.
Giorgi, F., C. Torma, E. Coppola, N. Ban, C. Schär, and S. Somot, 2016: Enhanced summer convective rainfall at Alpine high elevations in response to climate warming. Nature Geoscience, 9, 584–589, https://doi.org/10.1388/ngeo2761.
Griffies, S. M., and Coauthors, 2012: Datasets and protocol for the CLIVAR WGOMD Coordinated Ocean sea-ice Reference Experiments (COREs). WCRP, Report No. 21/2012. 23 pp
Gutowski Jr, W. J., and Coauthors, 2016: WCRP COordinated Regional Downscaling EXperiment (CORDEX): A diagnostic MIP for CMIP6. Geoscientific Model Development, 9, 4087–4095, https://doi.org/10.5194/gmd-9-4087-2016.
Huffman, G. J., and Coauthors, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi — global, multiyear, combined — sensor precipitation estimates at fine scales. J. Hydrometeorol., 8, 38–55, https://doi.org/10.1175/JHM560.1.
Holtslag, A. A. M., E. I. F. de Bruijn, and H.-L. Pan, 1990: A high resolution air mass transformation model for short-range weather forecasting. Mon. Wea. Rev., 118, 1561–1575, https://doi.org/10.1175/1520-0493(1990)118<1561:AHRAMT>2.0.CO;2.
Hong, S. Y., and J. O. J. Lim, 2006: The WRF single-moment 6-Class microphysics scheme (WSM6). Journal of the Korean Meteorological Society, 42, 129–151.
Hong, S. Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 2318–2341, https://doi.org/10.1175/MWR3199.1.
Huang, R. H., and F. Y. Sun, 1992: Impacts of the tropical western Pacific on the East Asian summer monsoon. J. Meteorol. Soc. Japan, 70, 243–256, https://doi.org/10.2151/jmsj1965.70.1B_243.
Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944.
Jones, C., F. Giorgi, and G. Asrar, 2011: The coordinated regional downscaling experiment: CORDEX, an international downscaling link to CMIP5. Clivar Exchanges, 16, 34–39.
Kiehl, J. T., J. J. Hack, G. B. Bonan, B. A. Boville, B. P. Briegleb, D. L. Williamson, and P. J. Rasch, 1996: Description of the NCAR community climate model (CCM3). Tech. Rep. NCAR/TN-420+STR, National Center for Atmospheric Research. 152 pp.
Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann, 2010: The international best track archive for climate stewardship (IBTrACS): Unifying tropical cyclone data. Bull. Amer. Meteorol. Soc., 91(3), 363–376, https://doi.org/10.1175/2009BAMS2755.1.
Koo, M.-S., S. Y. Hong, and J. Kim, 2009: An evaluation of the tropical rainfall measuring mission (TRMM) multi-satellite precipitation analysis (TMPA) data over South Korea. Asia-Pacific Journal of the Atmospheric Sciences, 45(3), 265–282.
Kummerow, C., and Coauthors, 2001: The evolution of the God-dard Profiling Algorithm (GPROF) for rainfall estimation from passive microwave sensors. J. Appl. Meteorol., 40, 1801–1820, https://doi.org/10.1175/1504-0420(2001)040<1801:TEOTGP>2.0.CO;2.
Leung, L. R., L. O. Mearns, F. Giorgi, and R. L. Wilby, 2003: Regional climate research: Needs and opportunities. Bull. Amer. Meteorol. Soc., 84, 89–95, https://doi.org/10.1175/BAMS-84-1-89.
Li, T., and G. Q. Zhou, 2010: Preliminary results of a regional air-sea coupled model over East Asia. Chinese Science Bulletin, 55, 2295–2305, https://doi.org/10.1007/s11434-010-3071-1.
Liang, X.-Z., and Coauthors, 2019: CWRF performance at down-scaling China climate characteristics. Climate Dyn., 52, 2159–2184, https://doi.org/10.1007/s00382-018-4257-5.
Liu, H. L., P. F. Lin, Y. Q. Yu, and X. H. Zhang, 2012: The baseline evaluation of LASG/IAP Climate system Ocean Model (LICOM) version 2. Acta Meteorologica Sinica, 26, 318–329, https://doi.org/10.1007/s13351-012-0305-y.
Liu, H. L., Y. Q. Yu, P. F. Lin, and F. C. Wang, 2014: High-resolution LICOM. Flexible Global Ocean-Atmosphere-Land System Model, T. J. Zhou et al., Eds., Springer, https://doi.org/10.1007/978-3-642-41801-3_38.
Locarnini, R. A., A. V. Mishonov, J. I. Antonov, T. P. Boyer, and H. E. Garcia, 2006: World ocean atlas 2005, volume 1: Temperature. NOAA Atlas NESDIS 61, S. Levitus, Ed., U.S. Government Printing Office, 182 pp.
Ma, X. H., and Coauthors, 2016: Western boundary currents regulated by interaction between ocean eddies and the atmosphere. Nature, 535, 533–537, https://doi.org/10.1038/nature18640.
Ma, Z. H., J. F. Fei, X. G. Huang, and X. P. Cheng, 2012: Sensitivity of tropical cyclone intensity and structure to vertical resolution in WRF. Asia-Pacific Journal of Atmospheric Sciences, 48(1), 67–81, https://doi.org/10.1007/s13143-012-0007-5.
Murakami, H., 2014: Tropical cyclones in reanalysis data sets. Geophys. Res. Lett., 41(6), 2133–2141, https://doi.org/10.1002/2014GL059519.
Murakami, H., and M. Sugi, 2010: Effect of model resolution on tropical cyclone climate projections. SOLA, 6, 73–76, https://doi.org/10.2151/sola.2010-019.
Murakami, H., and Coauthors, 2012: Future changes in tropical cyclone activity projected by the new high-resolution MRI-AGCM. J. Climate, 25(9), 3237–3260, https://doi.org/10.11751/JCLI-D-11-00415.1.
Nitta, T., 1987: Convective activities in the tropical western Pacific and their impact on the Northern Hemisphere summer circulation. J. Meteorol. Soc. Japan, 65, 373–390, https://doi.org/10.2151/jmsj1965.65.3_373.
Niu, G. Y., and Coauthors, 2011: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139.
Nordeng, T.-E., 1994: Extended versions of the convective parametrization scheme at ECMWF and their impact on the mean and transient activity of the model in the Tropics. ECMWF. TechMemo 206, 41 pp.
Oleson, K. W., and Coauthors, 2008: Improvements to the Community Land Model and their impact on the hydrological cycle. J. Geophys. Res., 113, G01021, https://doi.org/10.1029/2007JG000563.
Pal, J. S., E. E. Small, and E. A. B Eltahir, 2000: Simulation of regional-scale water and energy budgets: Representation of subgrid cloud and precipitation processes within RegCM. J. Geophys. Res., 105(D24), 29579–29594, https://doi.org/10.1029/2000JD900415.
Reynolds, R. W., T. M. Smith, C. Y. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007: Daily high-resolution-blended analyses for sea surface temperature. J. Climate, 20, 5473–5496, https://doi.org/10.1175/2007JCLI1824.1.
Sampe, T., and S. P. Xie, 2010: Large-scale dynamics of the Meiyu-Baiu rainband: Environmental forcing by the westerly jet. J. Climate, 22, 113–134, https://doi.org/10.1175/2009JCLI3128.1.
Sitz, L. E., and Coauthors, 2017: Description and evaluation of the Earth System Regional Climate Model (Reg CM-ES). Journal of Advances in Modeling Earth Systems, 9, 1863–1886, https://doi.org/10.1002/2017MS000933.
Skamarock, W. C., and Coauthors, 2008: A description of the advanced research WRF version 3. NCAR/TN-475+STR
Tiedtke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Wea. Rev., 117, 1779–1800, https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2.
Torma, C., F. Giorgi, and E. Coppola, 2015: Added value of regional climate modeling over areas characterized by complex terrain—Precipitation over the Alps. J. Geophys. Res., 120, 3957–3972, https://doi.org/10.1002/2014JD022781.
Valcke, S., 2006: OASIS3 user guide (prism 2–5). PRISM Support Initiative Report No. 3, CERFACS, Toulouse, France.
Wang, B., Q. H. Ding, X. H. Fu, I. S. Kang, K. Jin, J. Shukla, and F. Doblas-Reyes, 2005: Fundamental challenge in simulation and prediction of summer monsoon rainfall. Geophys. Res. Lett., 32, L15711, https://doi.org/10.1029/2005GL022734.
Wang, Y. Q., L. R. Leung, J. L. Mcgregor, D. K. Lee, W. C. Wang, Y. H. Ding, and F. Kimura, 2004: Regional climate modeling: Progress, challenges, and prospects. J. Meteorol. Soc. Japan, 82, 1599–1628, https://doi.org/10.2151/jmsj.82.1599.
Wu, R. G., 2019: Summer precipitation-SST relationship on different time scales in the northern tropical Indian Ocean and western Pacific. Climate Dyn., 52(9–10), 5911–5926, https://doi.org/10.1007/s00382-018-4487-6.
Xu, K. M., and D. A. Randall, 1996: A semiempirical cloudiness parameterization for use in climate models. J. Atmos. Sci., 52, 3084–3102, https://doi.org/10.1175/1520-0469(1996)053<3084:ASCPFU>2.0.CO;2.
Yao, S. X., and Y. C. Zhang, 2010: Simulation of China summer precipitation using a regional air-sea coupled model. Acta Meteorologica Sinica, 24, 203–214.
Yatagai, A., K. Kamiguchi, O. Arakawa, A. Hamada, N. Yasutomi, and A. Kitoh, 2012: APHRODITE: Constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bull. Amer. Meteor. Soc., 93(9), 1401–1415, https://doi.org/10.1175/BAMS-D-11-00122.1.
Yu, Y. Q., H. L. Liu, and P. F. Lin, 2012: A quasi-global 1/10° eddy-resolving ocean general circulation model and its preliminary results. Chinese Science Bulletin, 57, 3908–3916, https://doi.org/10.1007/s11434-012-5234-8.
Yuan, W. H., 2013: Diurnal cycles of precipitation over subtropical China in IPCC AR5 AMIP simulations. Adv. Atmos. Sci., 30(6), 1679–1694, https://doi.org/10.1007/s00376-013-2250-9.
Zeng, X. B., M. Zhao, and R. E. Dickinson, 1998: Intercomparison of bulk aerodynamic algorithms for the computation of sea surface fluxes using TOGA COARE and TAO data. J. Climate, 11, 2628–2644, https://doi.org/10.1175/1520-0442(1998)011<2628:IOBAAF>2.0.CO;2.
Zhou, T., Y. Yu, H. Liu, W. Li, X. You and G. Zhou, 2007: Progress in the Development and Application of Climate Ocean Models and Ocean-atmosphere Coupled Models in China. Adv. Atmos. Sci., 24(6), 729–738, https://doi.org/10.1007/s00376-007-1109-3.
Zhou, T., B. Wu, and B. Wang, 2009: How well do Atmospheric General Circulation Models capture the leading modes of the interannual variability of the Asian-Australian Monsoon? J. Climate, 22, 1159–1173, https://doi.org/10.1175/2008JCLI2245.1.
Zhou, T. J., R. C. Yu, H. M. Chen, A. G. Dai, and Y. Pan, 2008: Summer precipitation frequency, intensity, and diurnal cycle over China: A comparison of satellite data with rain gauge observations. J. Climate, 21, 3997–4010, https://doi.org/10.1175/2008JCLI2028.1.
Zhou, T. J., and Coauthors, 2018: The FGOALS climate system model as a modeling tool for supporting climate sciences: An overview. Earth and Planetary Physics, 2(4), 276–291, https://doi.org/10.26464/epp2018026.
Zhou, T. J., and Coauthors, 2020: Development of climate and earth system models in China: Past achievements and new CMIP6 results. Journal of Meteorological Research, 34(1), 1–19, https://doi.org/10.1007/s13351-020-9164-0.
Zou, L., T. Zhou, H. Liu, 2019: Performance of a high resolution regional ocean-atmosphere coupled model over western North Pacific region: Sensitivity to cumulus parameterizations. Climate Dyn., 53, 4611–4627, https://doi.org/10.1007/s00382-019-04812-2.
Zou, L. W., and T. J. Zhou, 2011: Sensitivity of a regional ocean-atmosphere coupled model to convection parameterization over western North Pacific. J. Geophys. Res., 116, D18106, https://doi.org/10.1029/2011JD015844.
Zou, L. W., and T. J. Zhou, 2013: Can a regional ocean-atmosphere coupled model improve the simulation of the interannual variability of the western North Pacific summer monsoon? J. Climate, 26, 2353–2367, https://doi.org/10.1175/JCLI-D-11-00722.1.
Zou, L. W., and T. J. Zhou, 2016: Future summer precipitation changes over CORDEX-East Asia domain downscaled by a regional ocean-atmosphere coupled model: A comparison to the stand — alone RCM. J. Geophys. Res., 121, 2691–2704, https://doi.org/10.1002/2015JD024519.
Zou, L. W., and T. J. Zhou, 2017: Dynamical downscaling of East Asian winter monsoon changes with a regional ocean-atmosphere coupled model. Quart. J. Roy. Meteorol. Soc., 143(706), 2245–2259, https://doi.org/10.1002/qj.3082.
Zou, L. W., T. J. Zhou, and D. D. Peng, 2016: Dynamical down-scaling of historical climate over CORDEX East Asia domain: A comparison of regional ocean-atmosphere coupled model to stand-alone RCM simulations. J. Geophys. Res., 121, 1442–1458, https://doi.org/10.1002/2015JD023912.
Zou, L. W., Y. Qian, T. J. Zhou, and B. Yang, 2014: Parameter tuning and calibration of RegCM3 with MIT-Emanuel cumulus parameterization scheme over CORDEX East Asia domain. J. Climate, 27, 7687–7701, https://doi.org/10.1175/JCLI-D-14-00229.1.
Acknowledgements
The comments from the reviewers, which significantly improved the manuscript, are greatly appreciated. All the observational datasets used in this study are listed in the references, and the model results used have been archived in figshare (https://doi.org/10.6084/m9.figshare.9917459.v1). This work was jointly supported by the National Key Research and Development Program of China (Grant No. 2018YFA0606003), the National Natural Science Foundation of China (Grant Nos. 41875132 and 41575105), and the Jiangsu Collaborative Innovation Center for Climate Change.
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Article Highlights
• A regional climate model coupled with a high-resolution (0.1°) North Pacific Ocean model was assessed and intercompared over the western North Pacific.
• A quantitative model intercomparison of the simulated rainfall showed that the two models performed reasonably well, but differences existed.
• The differences in simulated rainfall were associated with the simulated subtropical high associated with diabatic heating around the Philippines.
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Zou, L., Zhou, T., Tang, J. et al. Introduction to the Regional Coupled Model WRF4-LICOM: Performance and Model Intercomparison over the Western North Pacific. Adv. Atmos. Sci. 37, 800–816 (2020). https://doi.org/10.1007/s00376-020-9268-6
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DOI: https://doi.org/10.1007/s00376-020-9268-6