An assessment of global ocean tide simulation by a coupled climate model FGOALS-g3
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摘要: 潮汐在海洋能量的传递和混合过程中起着重要的作用，为维持全球热盐环流提供了主要的能量，影响着全球的海洋环流。此前已有工作在低分辨率的、单独的海洋模式中研究潮汐作用对海洋环流状态的敏感性，为进一步研究潮汐作用对环流和气候状态的敏感性，有必要将潮强迫引入到气候耦合模式中。本文成功地将8个主要平衡分潮显式地加入到耦合模式FGOALS-g3中，并评估了其对全球海洋潮汐的模拟能力，对于进一步研究潮汐对大尺度环流及气候状态的影响有重要意义。本文通过对模拟的海表面高度数据进行潮汐调和分析，得到各个分潮的调和常数，并将其与全球潮汐模型TPXO9和FES2014，以及开放海洋潮汐数据集st102进行对比。结果表明，FGOALS-g3耦合模式可以合理地模拟全球海洋中的正压潮，模拟结果与潮汐模型和实测数据集相比均比较接近。与这两套全球潮汐模型相比，均方误差均相对较小，且误差大多分布在振幅较大的区域。与st102数据集相比，FGOALS-g3模拟的8个主要分潮的平均振幅相对误差均在10%以内，且总均方误差均小于10 cm。Abstract: Tides act an important role in the transfer of ocean energy and mixing, and provide the main energy to maintain the global thermohaline circulation and influence the global ocean circulation. Previous work has explored the sensitivity of ocean circulation states to tidal forcing within an individual ocean model at a low resolution. To further investigate the influence of tidal forcing on ocean circulation and climate states, it is imperative to incorporate the tidal forcing into a coupled climate model. In this paper, the eight major equilibrium constituents are included into the coupled climate model FGOALS-g3 explicitly, and we evaluate its ability to simulate global ocean tides, which lays the basic for the further research on the influence of tidal forcing on large-scale circulation and climate states.We apply tidal harmonic analysis on the sea surface height data to obtain the harmonic constants of each constituent, and compare the model results with the global tidal models TPXO9 and FES2014, and the open ocean tide dataset from st102. The results show that the coupled model FGOALS-g3 can effectively simulate the barotropic tides in the global ocean, with relatively small errors compared to the global tidal models and the observation dataset. Compared with these two global tidal models, the mean square error is relatively small, and the errors are mostly distributed in the region of larger amplitudes. And compared with st102 dataset, the average amplitude relative errors of the eight major equilibrium constituents simulated by FGOALS-g3 are all less than 10%, and the total mean square errors are all less than 10 cm.
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Key words:
- barotropic tide /
- coupled climate model /
- harmonic analysis
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图 1 TPXO9、FES2014和 FGOALS-g3试验M2分潮和K1分潮的振幅和迟角的空间分布
图中M2分潮 (a−c) 和K1分潮 (d−f) 的等值线间隔分别为30°和60°
Fig. 1 Spatial patterns of the amplitude and phase of the M2 and K1 constituents for TPXO9, FES2014 and FGOALS-g3
The lines of the constant phase are plotted every 30° for M2 (a−c) and every 60°for K1 (d−f)
图 2 FGOALS-g3相比于TPXO9的M2分潮和K1分潮的均方误差
a, d. 振幅误差；b, e. 迟角误差；c, f. 总均方误差
Fig. 2 Errors of the simulated M2 constituent (a–c) and K1 constituent (d–f) relative to TPXO9 in FGOALS-g3
a, d. Amplitude error；b, e. phase error；c, f. total error
图 3 FGOALS-g3的潮汐模式模拟的大潮和小潮的空间分布
每列中相邻两幅图的时间间隔为6 h
Fig. 3 Spatial patterns of the spring tides and neap tides for the FGOALS-g3 tidal module
The interval between the rows is 6 h
表 1 TPXO9 和 FGOALS-g3模拟的八大分潮的全球平均振幅，以及FGOALS-g3 相比于 TPXO9 的振幅误差、迟角误差和总均方误差
Tab. 1 Global mean values of the amplitudes of the eight tidal constituents for TPXO9 and FGOALS-g3, and the amplitude, phase, and total errors of the eight tidal constituents for FGOALS-g3 compared with TPXO9
M2 S2 N2 K2 K1 O1 P1 Q1 平均振幅(TPXO9)/cm 34.84 13.55 7.41 3.80 11.73 8.12 3.66 1.69 平均振幅(FGOALS-g3)/cm 31.66 13.07 7.28 3.50 13.78 13.00 4.28 2.74 振幅误差/cm 7.40 3.25 1.39 0.97 2.83 3.86 0.87 0.89 迟角误差/(°) 8.79 3.52 1.97 0.99 3.27 2.30 1.01 0.43 总均方误差/cm 12.79 5.31 2.67 1.53 4.83 4.89 1.49 1.08 
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表 2 FES2014和 FGOALS-g3模拟的八大分潮的全球平均振幅，以及FGOALS-g3 相比于FES2014的振幅误差、迟角误差和总均方误差
Tab. 2 Global mean values of the amplitudes of the eight tidal constituents for FES2014 and FGOALS-g3, and the amplitude, phase, and total errors of the eight tidal constituents for FGOALS-g3 compared with FES2014
M2 S2 N2 K2 K1 O1 P1 Q1 平均振幅(FES2014)/cm 35.88 14.24 7.49 3.97 14.20 10.43 4.15 1.96 平均振幅(FGOALS-g3)/cm 31.66 13.07 7.28 3.50 13.78 13.00 4.28 2.74 振幅误差/cm 11.17 4.15 1.69 1.02 5.11 2.97 0.85 0.43 迟角误差/cm 8.94 3.92 1.62 1.23 5.32 3.06 0.95 0.40 总均方误差/cm 14.31 5.71 2.34 1.60 7.38 4.26 1.27 0.59
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表 3 st102 和 FGOALS-g3 模拟的八大分潮的全球平均振幅和振幅相对误差，以及 FGOALS-g3 相比于st102 的振幅误差、迟角误差和总均方误差
Tab. 3 Global mean values and the relative errors of the amplitudes of the eight tidal constituents for st102 and FGOALS-g3, and the amplitude, phase, and total errors of the eight tidal constituents for FGOALS-g3 compared with st102
M2 S2 N2 K2 K1 O1 P1 Q1 平均振幅(st102)/cm 40.40 15.15 8.11 3.97 12.57 8.73 3.84 1.77 平均振幅(FGOALS-g3)/cm 36.79 14.17 8.48 3.75 13.14 9.07 4.02 1.91 振幅相对误差/% 8.94 6.49 4.50 5.58 4.53 3.89 4.69 7.91 振幅误差/cm 5.43 2.27 1.12 0.77 2.01 3.14 0.72 0.84 迟角误差/cm 7.34 3.22 1.68 0.84 3.05 2.19 0.93 0.45 总均方误差/cm 9.35 4.12 2.09 1.22 3.25 3.84 1.14 1.03
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[1] Huang Ruixin. Mixing and energetics of the oceanic thermohaline circulation[J]. Journal of Physical Oceanography, 1999, 29(4): 727−746. doi: 10.1175/1520-0485(1999)029<0727:MAEOTO>2.0.CO;2 [2] MacKinnon J. Mountain waves in the deep ocean[J]. Nature, 2013, 501(7467): 321−322. doi: 10.1038/501321a [3] Munk W, Wunsch C. Abyssal recipes II: energetics of tidal and wind mixing[J]. Deep Sea Research Part I: Oceanographic Research Papers, 1998, 45(12): 1977−2010. doi: 10.1016/S0967-0637(98)00070-3 [4] Wang Xiaowei, Liu Zhiyu, Peng Shiqiu. Impact of tidal mixing on water mass transformation and circulation in the South China Sea[J]. Journal of Physical Oceanography, 2017, 47(2): 419−432. doi: 10.1175/JPO-D-16-0171.1 [5] Wunsch C, Ferrari R. Vertical mixing, energy, and the general circulation of the oceans[J]. Annual Review of Fluid Mechanics, 2004, 36: 281−314. doi: 10.1146/annurev.fluid.36.050802.122121 [6] EgbertG D, Gary R D. Semi-diurnal and diurnal tidal dissipation from TOPEX/Poseidon altimetry[J]. Geophysical Research Letters, 2003, 30(17): 1907. [7] Jayne S R, St. Laurent L C. Parameterizing tidal dissipation over rough topography[J]. Geophysical Research Letters, 2001, 28(5): 811−814. doi: 10.1029/2000GL012044 [8] Bryan K. A numerical method for the study of the circulation of the world ocean[J]. Journal of Computational Physics, 1997, 135(2): 154−169. doi: 10.1006/jcph.1997.5699 [9] Cox M D. A primitive equation 3-dimensional model of the ocean[R]. Princeton: Princeton University, 1984. [10] Killworth P D, Webb D J, Stainforth D, et al. The development of a free-surface bryan-cox-semtner ocean model[J]. Journal of Physical Oceanography, 1991, 21(9): 1333−1348. doi: 10.1175/1520-0485(1991)021<1333:TDOAFS>2.0.CO;2 [11] St. Laurent L C, Simmons H L, Jayne S R. Estimating tidally driven mixing in the deep ocean[J]. Geophysical Research Letters, 2002, 29(23): 2106. [12] Simmons H L, Jayne S R, St. Laurent L C, et al. Tidally driven mixing in a numerical model of the ocean general circulation[J]. Ocean Modelling, 2004, 6(3/4): 245−263. [13] Yu Yi, Liu Hailong, Lan Jian. The influence of explicit tidal forcing in a climate ocean circulation model[J]. Acta Oceanologica Sinica, 2016, 35(9): 42−50. doi: 10.1007/s13131-016-0931-9 [14] Saenko O A, Merryfield W J. On the effect of topographically enhanced mixing on the global ocean circulation[J]. Journal of Physical Oceanography, 2005, 35(5): 826−834. doi: 10.1175/JPO2722.1 [15] Jayne S R. The impact of abyssal mixing parameterizations in an ocean general circulation model[J]. Journal of Physical Oceanography, 2009, 39(7): 1756−1775. doi: 10.1175/2009JPO4085.1 [16] Melet A, Hallberg R, Legg S, et al. Sensitivity of the ocean state to the vertical distribution of internal-tide-driven mixing[J]. Journal of Physical Oceanography, 2013, 43(3): 602−615. doi: 10.1175/JPO-D-12-055.1 [17] Song Pengyang, Sidorenko D, Scholz P, et al. The tidal effects in the Finite-volumE Sea ice–Ocean Model (FESOM2.1): a comparison between parameterised tidal mixing and explicit tidal forcing[J]. Geoscientific Model Development, 2023, 16(1): 383−405. doi: 10.5194/gmd-16-383-2023 [18] Thomas M, Sündermann J, Maier-Reimer E. Consideration of ocean tides in an OGCM and impacts on subseasonal to decadal polar motion excitation[J]. Geophysical Research Letters, 2001, 28(12): 2457−2460. doi: 10.1029/2000GL012234 [19] Schiller A. Effects of explicit tidal forcing in an OGCM on the water-mass structure and circulation in the Indonesian through flow region[J]. Ocean Modelling, 2004, 6(1): 31−49. doi: 10.1016/S1463-5003(02)00057-4 [20] Schiller A, Fiedler R. Explicit tidal forcing in an ocean general circulation model[J]. Geophysical Research Letters, 2007, 34(3): L03611. [21] Müller M, Haak H, Jungclaus J H, et al. The effect of ocean tides on a climate model simulation[J]. Ocean Modelling, 2010, 35(4): 304−313. doi: 10.1016/j.ocemod.2010.09.001 [22] Jin Jiangbo, Guo Run, Zhang Minghua, et al. Formulation of a new explicit tidal scheme in revised LICOM2.0[J]. Geoscientific Model Development, 2022, 15(10): 4259−4273. doi: 10.5194/gmd-15-4259-2022 [23] Arbic B K, Wallcraft A J, Metzger E J. Concurrent simulation of the eddying general circulation and tides in a global ocean model[J]. Ocean Modelling, 2010, 32(3/4): 175−187. [24] Shriver J F, Arbic B K, Richman J G, et al. An evaluation of the barotropic and internal tides in a high-resolution global ocean circulation model[J]. Journal of Geophysical Research: Oceans, 2012, 117(C10): C10024. [25] Müller M, Cherniawsky J Y, Foreman M G G, et al. Global M2 internal tide and its seasonal variability from high resolution ocean circulation and tide modeling[J]. Geophysical Research Letters, 2012, 39(19): L19607. [26] Müller M. On the space-and time-dependence of barotropic-to-baroclinic tidal energy conversion[J]. Ocean Modelling, 2013, 72: 242−252. doi: 10.1016/j.ocemod.2013.09.007 [27] Li Lijuan, Yu Yongqiang, Tang Yanli, et al. The flexible global ocean-atmosphere-land system model grid-point version 3 (FGOALS-g3): description and evaluation[J]. Journal of Advances in Modeling Earth Systems, 2020, 12(9): e2019MS002012. doi: 10.1029/2019MS002012 [28] Li Lijuan, Dong Li, Xie Jinbo, et al. The GAMIL3: model description and evaluation[J]. Journal of Geophysical Research: Atmospheres, 2020, 125(15): e2020JD032574. doi: 10.1029/2020JD032574 [29] Xie Zhenghui, Wang Longhuan, Wang Yan, et al. Land surface model CAS-LSM: model description and evaluation[J]. Journal of Advances in Modeling Earth Systems, 2020, 12(12): e2020MS002339. doi: 10.1029/2020MS002339 [30] Lin Pengfei, Yu Zhipeng, Liu Hailong, et al. LICOM model datasets for the CMIP6 ocean model intercomparison project[J]. Advances in Atmospheric Sciences, 2020, 37(3): 239−249. doi: 10.1007/s00376-019-9208-5 [31] Craig A P, Vertenstein M, Jacob R. A new flexible coupler for earth system modeling developed for CCSM4 and CESM1[J]. The International Journal of High Performance Computing Applications, 2012, 26(1): 31−42. doi: 10.1177/1094342011428141 [32] Wang Yaqi, Yu Zipeng, Lin Pengfei, et al. FGOALS-g3 model datasets for CMIP6 flux-anomaly-forced model intercomparison project[J]. Advances in Atmospheric Sciences, 2020, 37(10): 1093−1101. doi: 10.1007/s00376-020-2045-8 [33] Lin Pengfei, Zhao Bowen, Wei Jilin, et al. The super-large ensemble experiments of CAS FGOALS-g3[J]. Advances in Atmospheric Sciences, 2022, 39(10): 1746−1765. doi: 10.1007/s00376-022-1439-1 [34] Zheng Weipeng, Yu Yongqiang, Luan Yihua, et al. CAS-FGOALS datasets for the two interglacial epochs of the holocene and the last interglacial in PMIP4[J]. Advances in Atmospheric Sciences, 2020, 37(10): 1034−1044. doi: 10.1007/s00376-020-9290-8 [35] Wei Jilin, Liu Hailong, Zhao Yan, et al. Simulation of the climate and ocean circulations in the Middle Miocene Climate Optimum by a coupled model FGOALS-g3[J]. Palaeogeography, Palaeoclimatology, Palaeoecology, 2023, 617: 111509. doi: 10.1016/j.palaeo.2023.111509 [36] Pu Ye, Liu Hongbo, Yan Ruojing, et al. CAS FGOALS-g3 model datasets for the CMIP6 scenario model intercomparison project (ScenarioMIP)[J]. Advances in Atmospheric Sciences, 2020, 37(10): 1081−1092. doi: 10.1007/s00376-020-2032-0 [37] Griffies S M, Biastoch A, Böning C, et al. Coordinated ocean-ice reference experiments (COREs)[J]. Ocean Modelling, 2009, 26(1/2): 1−46. [38] Hendershott M C. The effects of solid earth deformation on global ocean tides[J]. Geophysical Journal International, 1972, 29(4): 389−402. doi: 10.1111/j.1365-246X.1972.tb06167.x [39] Eyring V, Bony S, Meehl G A, et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization[J]. Geoscientific Model Development, 2016, 9(5): 1937−1958. doi: 10.5194/gmd-9-1937-2016 [40] Egbert G D, Erofeeva S Y. Efficient inverse modeling of barotropic ocean tides[J]. Journal of Atmospheric and Oceanic Technology, 2002, 19(2): 183−204. doi: 10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2 [41] Lyard F H, Allain D J, Cancet M, et al. FES2014 global ocean tide atlas: design and performance[J]. Ocean Science, 2021, 17(3): 615−649. doi: 10.5194/os-17-615-2021 [42] Shum C K, Woodworth P L, Andersen O B, et al. Accuracy assessment of recent ocean tide models[J]. Journal of Geophysical Research: Oceans, 1997, 102(C11): 25173−25194. doi: 10.1029/97JC00445 [43] von Storch JS, Hertwig E, Lüschow V, et al. Open-ocean tides simulated by ICON-O, version icon-2.6.6[J]. Geoscientific Model Development, 2023, 16(17): 5179−5196. doi: 10.5194/gmd-16-5179-2023 [44] Arbic B K. Incorporating tides and internal gravity waves within global ocean general circulation models: a review[J]. Progress in Oceanography, 2022, 206: 102824. doi: 10.1016/j.pocean.2022.102824 -
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