Advances in Atmospheric Sciences

, Volume 36, Issue 4, pp 378–396 | Cite as

Interannual Salinity Variability in the Tropical Pacific in CMIP5 Simulations

  • Hai Zhi
  • Rong-Hua ZhangEmail author
  • Pengfei Lin
  • Peng Yu
Original Paper


Salinity variability and its causes in the tropical Pacific are analyzed using observations, reanalysis products and model simulations. The mixed-layer salinity (MLS) budget analyses from observations and reanalysis products indicate that its interannual evolution is closely related to ENSO and is predominantly governed by surface forcing and surface advection in the western-central equatorial Pacific. It is found that the observed MLS tendency leads Ni˜no3.4 by about 12 months due to the effect of negative freshwater flux (evaporation minus precipitation). These observation-based analyses are used to evaluate the corresponding simulation using GFDL-ESM2M. It is evident that the model can simulate the spatiotemporal variations of MLS with some discrepancies compared to observations. In the warm pool of the equatorial Pacific the MLS tendency in the model is sensitive to ocean dynamics, however model biases cause the tendency to be underestimated. In particular, the freshwater flux is overestimated while the ocean surface zonal current and vertical velocity at the base of the mixed layer are underestimated. Due to model biases in representing the related physics, the effects of surface forcing on the simulated MLS budget are overestimated and those of subsurface and surface advection are relatively weak. Due to weaker surface advection and subsurface forcing than observed, the simulated compensations for surface forcing are suppressed, and the simulated MLS tendency that leads Ni˜no3.4 by 8–10 months, which is shorter than the observed lead time. These results are useful for the interpretation of observational analyses and other model simulations in the tropical Pacific.

Key words

mixed-layer salinity salt budget interannual variability tropical Pacific model simulation 

摘 要

利用观测, 再分析数据和模式模拟, 分析了热带太平洋盐度变化及其原因. 从观测和再分析的混合层盐度(MLS)的收支分析表明, 其年际演化与ENSO演变密切相关. 热带太平洋的盐度的变化主要受赤道西太平洋表层强迫和表面平流的控制. 研究发现, 由于负淡水通量(蒸发-降水)的影响, 观测的MLS变化趋势超前Niño3.4提前约12个月. 同时挑选GFDL-ESM2M作为CMIP5的代表, 利用基于观测的分析结果来评估相应的模拟. 很明显, 模式可以模拟MLS的时空变化, 但与观测结果有一定的差异. 在赤道太平洋暖池中, 模式的MLS趋势对海洋动力过程非常敏感, 相关物理场的模拟偏差会导致MLS趋势被低估, 特别GFDL-ESM2M对淡水通量的模拟过高, 而表面纬向流和混合层底部垂直速度的估计过低都会导致盐度的收支的偏差. 该模式高估了表面强迫对模拟MLS收支的影响, 但低估了次表层混合和表层平流对盐度收支的的影响相. 由于模拟的表面平流和次表层混合比观测到的弱, 模拟的和淡水通量相关的表层强迫的补偿被抑制, 模拟的MLS趋势只超前Niño3.4提前8–10个月, 比观测到的提前时间短. 这些结果对于解释热带太平洋的观测分析和评估气候模式模拟很有益处.


混合层盐度 盐度收支 年际变率 热带太平洋 数值模拟 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



The authors wish to thank the anonymous reviewers for their numerous comments that helped improve the original manuscript. This study was supported by the National Natural Science Foundation of China (Grant Nos. 41690122, 41690120 and 41475101), the NSFC–Shandong Joint Fund for Marine Science Research Centers (Grant No. U1406401), the NSFC Innovative Group Grant (Project No. 41421005), and Taishan Scholarship.


  1. Adler, R. F., and Coauthors, 2003: The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). Journal of Hydrometeorology, 4, 1147–1167,<1147:TVGPCP>2.0.CO;2.CrossRefGoogle Scholar
  2. Behringer, D. W., M. Ji, and A. Leetmaa, 1998: An improved coupled model for ENSO prediction and implications for ocean initialization. Part I: The ocean data assimilation system. Mon. Wea. Rev., 126, 1013–1021,<1013:AICMFE>2.0.CO;2.CrossRefGoogle Scholar
  3. Bingham, F. M., G. R. Foltz, and M. J. McPhaden, 2010: Seasonal cycles of surface layer salinity in the Pacific Ocean. Ocean Science, 6(3), 775–787, Scholar
  4. Da-Allada, C. Y., F. Gaillard, and N. Kolodziejczyk, 2015: Mixedlayer salinity budget in the tropical Indian Ocean: Seasonal cycle based only on observations. Ocean Dynamics, 65, 845–857, Scholar
  5. Da-Allada, C. Y., Y. du Penhoat, J. Jouanno, G. Alory, and N. M. Hounkonnou, 2014: Modeled mixed-layer salinity balance in the Gulf of Guinea: Seasonal and Interannual variability. Ocean Dynamics, 64(12), 1783–1802, Scholar
  6. Delcroix, T., and C. Hénin, 1991: Seasonal and interannual variations of sea surface salinity in the tropical Pacific Ocean. J. Geophys. Res., 96(C12), 22 135–22 150, Scholar
  7. Delcroix, T., and J. Picaut, 1998: Zonal displacement of the western equatorial Pacific “fresh pool”. J. Geophys. Res., 103(C1), 1087–1098, Scholar
  8. Delcroix, T., and M. McPhaden, 2002: Interannual sea surface salinity and temperature changes in the western Pacific warm pool during 1992–2000. J. Geophys. Res., 107(C12), 8002, Scholar
  9. Delcroix, T., M. J. McPhaden, A. Dessier, and Y. Gouriou, 2005: Time and space scales for sea surface salinity in the tropical oceans. Deep Sea Research Part I: Oceanographic Research Papers, 52(5), 787–813, Scholar
  10. Delcroix, T., G. Alory, S. Cravatte, T. Corrège, and M. J. McPhaden, 2011: A gridded sea surface salinity data set for the tropical Pacific with sample applications (1950–2008). Deep Sea Research Part I: Oceanographic Research Papers, 58(1), 38–48, Scholar
  11. Dong, S. F., S. L. Garzoli, and M. Baringer, 2009: An assessment of the seasonal mixed layer salinity budget in the Southern Ocean. J. Geophys. Res., 114(C12), C12001, Scholar
  12. Dunne, J. P., and Coauthors, 2012: GFDL’s ESM2 global coupled climate–carbon earth system models. Part I: Physical formulation and baseline simulation characteristics. J. Climate, 25, 6646–6665, Scholar
  13. Dunne, J. P., and Coauthors, 2013: GFDL’s ESM2 global coupled climate–carbon earth system models. Part II: Carbon system formulation and baseline simulation characteristics. J. Climate, 26(7), 2247–2267, Scholar
  14. Durack, P. J., S. E. Wijffels, and R. J. Matear, 2012: Ocean salinities reveal strong global water cycle intensification during 1950 to 2000. Science, 336(4080), 455–458, Scholar
  15. Forget, G., J.-M. Campin, P. Heimbach, C. N. Hill, R. M. Ponte, and C. Wunsch, 2015: ECCO version 4: An integrated framework for non-linear inverse modeling and global ocean state estimation. Geoscientific Model Development, 8, 3071–3104, Scholar
  16. Gao, S., T. D. Qu, and X. W. Nie, 2014: Mixed layer salinity budget in the tropical Pacific Ocean estimated by a global GCM, J. Geophys. Res., 119, 8255–8270, Scholar
  17. Gnanadesikan, A., and Coauthors, 2006: GFDL’s CM2 global coupled climate models. Part II: The baseline ocean simulation. J. Climate, 19, 675–697, Scholar
  18. Good, S. A., M. J. Martin, and N. A. Rayner, 2013: EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates. J. Geophys. Res., 118(12), 6704–6716, Scholar
  19. Gouriou, Y., and T. Delcroix, 2002: Seasonal and ENSO variations of sea surface salinity and temperature in the South Pacific Convergence Zone during 1976–2000. J. Geophys. Res., 107(C12), 8011, Scholar
  20. Griffies, S. M., 2009: Elements of MOM4p1. GFDL Ocean Group Tech. Rep. 6, 377 pp.Google Scholar
  21. Hackert, E., A. J. Busalacchi, and J. Ballabrera-Poy, 2014: Impact of Aquarius sea surface salinity observations on coupled forecasts for the tropical Indo-Pacific Ocean. J. Geophys. Res., 119, 4045–4067, Scholar
  22. Ham, Y.-G., and J.-S. Kug, 2014: Effects of Pacific Intertropical Convergence Zone precipitation bias on ENSO phase transition. Environmental Research Letters, 9(6), 064008, Scholar
  23. Huang, B. Y., and Coauthors, 2015: Further exploring and quantifying uncertainties for Extended Reconstructed Sea Surface Temperature (ERSST) version 4 (v4). J. Climate, 29, 3119–3142, Scholar
  24. Hasson, A. E. A., T. Delcroix, and R. Dussin, 2013: An assessment of the mixed layer salinity budget in the tropical Pacific Ocean: Observations and modelling (1990–2009). Ocean Dynamics, 63, 179–194, Scholar
  25. Katsura, S., E. Oka, B. Qiu, and N. Schneider, 2013: Formation and subduction of North Pacific tropical water and their interannual variability. J. Phys. Oceanogr., 43, 2400–2415, Scholar
  26. Kim, K.-Y., 2002: Investigation of ENSO variability using cyclostationary EOFs of observational data. Meteor. Atmos. Phys., 81(3–4), 149–168, Scholar
  27. Kim, K.-Y., and G. R. North, 1997: EOFs of harmonizable cyclostationary processes. J. Atmos. Sci., 54, 2416–2427,<2416:EOHCP>2.0.CO;2.CrossRefGoogle Scholar
  28. Kim, K.-Y., G. R. North, and J. P. Huang, 1996: EOFs of one-dimensional cyclostationary time series: Computations, examples, and stochastic modeling. J. Atmos. Sci., 53, 1007–1017,<1007:EOODCT>2.0.CO;2.CrossRefGoogle Scholar
  29. Kim, W., S.-W. Yeh, J.-H. Kim, J.-S. Kug, and M. Kwon, 2011: The unique 2009–2010 El Niño event: A fast phase transition of warm pool El Niño to La Niña. Geophys. Res. Lett., 38(15), L15809, Scholar
  30. Kug, J.-S., and I.-S. Kang, 2006: Interactive feedback between ENSO and the Indian Ocean. J. Climate, 19(9), 1784–1801, Scholar
  31. Langford, S., S. Stevenson, and D. Noone, 2014: Analysis of lowfrequency precipitation variability in CMIP5 historical simulations for southwestern North America. J. Climate, 27(7), 2735–2756, Scholar
  32. Li, G., S.-P. Xie, 2012: Origins of tropical-wide SST biases in CMIP multi-model ensembles. Geophys. Res. Lett., 39, L22703, Scholar
  33. Li, G., S.-P. Xie, 2014: Tropical biases in CMIP5 multimodel ensemble: The excessive equatorial Pacific cold tongue and double ITCZ problems. J. Climate, 27, 1765–1780, Scholar
  34. Lin, J.-L., 2007: The double-ITCZ problem in IPCC AR4 Coupled GCMs: Ocean-atmosphere feedback analysis. J. Climate, 20, 4497–4525, Scholar
  35. Lin, S.-J., 2004: A “vertically Lagrangian” finite-volume dynamical core for global models. Mon. Wea. Rev., 132, 2293–2307,<2293:AVLFDC>2.0.CO;2.CrossRefGoogle Scholar
  36. Lukas, R., and E. Lindstrom, 1991: The mixed layer of the western equatorial Pacific Ocean. J. Geophys. Res., 96, 3343–3357, Scholar
  37. Maes, C., K. Ando, T. Delcroix, W. S. Kessler, M. J. McPhaden, and D. Roemmich, 2006: Observed correlation of surface salinity, temperature and barrier layer at the eastern edge of the western Pacific warm pool. Geophys. Res. Lett., 33(6), L06601, Scholar
  38. Mignot, J., C. de Boyer Montégut, A. Lazar, and S. Cravatte, 2007: Control of salinity on the mixed layer depth in the world ocean: 2. Tropical areas. J. Geophys. Res., 112(C10), C10010, Scholar
  39. Picaut, J., M. Ioualalen, C. Menkes, T. Delcroix, and M. J. McPhaden, 1996: Mechanism of the zonal displacements of the Pacific warm pool: Implications for ENSO. Science, 274(5292), 1486–1489, Scholar
  40. Picaut, J., M. Ioualalen, T. Delcroix, F. Masia, R. Murtugudde, and J. Vialard, 2001: The oceanic zone of convergence on the eastern edge of the Pacific warm pool: A synthesis of results and implications for El Niño-Southern Oscillation and biogeochemical phenomena. J. Geophys. Res., 106(C2), 2363–2386, Scholar
  41. Ponte, R. M., and N. T. Vinogradova, 2016: An assessment of basic processes controlling mean surface salinity over the global ocean. Geophys. Res. Lett., 43, 7052–7058, Scholar
  42. Qu, T. D., S. Gao, and R. A. Fine, 2013: Subduction of south pacific tropical water and its equatorward pathways as shown by a simulated passive tracer. J. Phys. Oceanogr., 43, 1551–1565, Scholar
  43. Qu, T. D., Y. T. Song, and C. Maes, 2014: Sea surface salinity and barrier layer variability in the equatorial Pacific as seen from Aquarius and Argo. J. Geophys. Res., 119(1), 15–29, Scholar
  44. Ren, L., and S. C. Riser, 2009: Seasonal salt budget in the northeast Pacific Ocean. J. Geophys. Res., 114, C12004, Scholar
  45. Schmitt, R. W., 1990: On the density ratio balance in the central water. J. Phys. Oceanogr., 20(6), 900–906,<0900:OTDRBI>2.0.CO;2.CrossRefGoogle Scholar
  46. Shevliakova, E., and Coauthors, 2009: Carbon cycling under 300 years of land use change: Importance of the secondary vegetation sink. Global Biogeochemical Cycles, 23, GB2022, Scholar
  47. Singh, A., T. Delcroix, and S. Cravatte, 2011: Contrasting the flavors of El Niño-Southern Oscillation using sea surface salinity observations. J. Geophys. Res., 116(C6), C06016, Scholar
  48. Skliris, N., R. Marsh, S. A. Josey, S. A. Good, C. L. Liu, and R. P. Allan, 2014: Salinity changes in the World Ocean since 1950 in relation to changing surface freshwater fluxes. Climate Dyn., 43, 709–736, Scholar
  49. Sprintall, J., and M. Tomczak, 1992: Evidence of the barrier layer in the surface layer of the tropics. J. Geophys. Res., 97, 7305–7316, Scholar
  50. Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485–498, Scholar
  51. Terray, L., L. Corre, S. Cravatte, T. Delcroix, G. Reverdin, and A. Ribes, 2012: Near-surface salinity as nature’s rain gauge to detect human influence on the tropical water cycle. J. Climate, 25, 958–977, Scholar
  52. Vialard, J., and P. Delecluse, 1998: An OGCM study for the TOGA decade. Part I: Role of salinity in the physics of the western Pacific fresh pool. J. Phys. Oceanogr., 28, 1071–1088,<1071:AOSFTT>2.0.CO;2.CrossRefGoogle Scholar
  53. Vialard, J., P. Delecluse, and C. Menkes, 2002: A modeling study of salinity variability and its effects in the tropical Pacific Ocean during the 1993–1999 period. J. Geophys. Res., 107(C12), 8005, Scholar
  54. Vinogradova, N. T., and R. M. Ponte, 2013: Clarifying the link between surface salinity and freshwater fluxes on monthly to interannual time scales. J. Geophys. Res., 118, 3190–3201, Scholar
  55. Webster, P. J., 1994: The role of hydrological processes in oceanatmosphere interactions. Rev. Geophys., 32, 427–476, Scholar
  56. Winton, M., 2000: A reformulated three-layer sea ice model. J. Atmos. Oceanic Technol., 17, 525–531,<0525:ARTLSI>2.0.CO;2.CrossRefGoogle Scholar
  57. Yan, Y. F., L. Li, and C. Z. Wang, 2017a: The effects of oceanic barrier layer on the upper ocean response to tropical cyclones. J. Geophys. Res., 122, 4829–4844, Scholar
  58. Yan, Y. F., D. Z. Xu, K. Yu, and Y. Q. Qi, 2017b: Propagation of the subsurface freshening water and its major source in the northwestern Pacific. J. Geophys. Res., 122, 6857–6871, Scholar
  59. Yeo, S.-R., and K.-Y. Kim, 2014: Global warming, low-frequency variability, and biennial oscillation: An attempt to understand the physical mechanisms driving major ENSO events. Climate Dyn., 43, 771–786, Scholar
  60. Yim, B. Y., S.-W. Yeh, Y. Noh, B.-K. Moon, and Y.-G. Park, 2008: Sea surface salinity variability and its relation to El Niño in a CGCM. Asia-Pacific Journal of the Atmospheric Sciences, 44, 173–189.Google Scholar
  61. Yoo, S.-H., J. Fasullo, S. Yang, and C.-H. Ho, 2010: On the relationship between Indian Ocean sea surface temperature and the transition from El Niño to La Niña. J. Geophys. Res., 115, D15114, Scholar
  62. Yu, L. S., 2011: A global relationship between the ocean water cycle and near-surface salinity. J. Geophys. Res., 116, C10025, Scholar
  63. Yu, L. S., and R. A. Weller, 2007: Objectively analyzed air-sea heat fluxes for the global ice-free oceans (1981–2005). Bull. Amer. Meteor. Soc., 88, 527–539, Scholar
  64. Yu, Y. Q., L. Chen, and Y. L. Zhang, 2014: ENSO and PDO in two versions of FGOALS. Flexible Global Ocean-Atmosphere-Land System Model, T. Zhou et al., Eds., Springer, Berlin, Heidelberg, 107–113,
  65. Zhang, R.-H., and A. J. Busalacchi, 2009: Freshwater flux (FWF)-induced oceanic feedback in a hybrid coupled model of the tropical Pacific. J. Climate, 22, 853–879, Scholar
  66. Zhang, R.-H., A. J. Busalacchi, R. G. Murtugudde, P. A. Arkin, and J. Ballabrera-Poy, 2006: An empirical parameterization for the salinity of subsurface water entrained into the ocean mixed layer (Se) in the tropical Pacific. Geophys. Res. Lett., 33(2), L02605, Scholar
  67. Zhang, R.-H., G. H. Wang, D. K. Chen, A. J. Busalacchi, and E. C. Hackert, 2010: Interannual biases induced by freshwater flux and coupled feedback in the tropical Pacific. Mon. Wea. Rev., 138, 1715–1737, Scholar
  68. Zhang, R.-H, F. Zheng, J. S. Zhu, Y. H. Pei, Q. A. Zheng, and Z. G. Wang, 2012: Modulation of El Niño-Southern Oscillation by freshwater flux and salinity variability in the tropical Pacific. Adv. Atmos. Sci., 29(4), 647–660, Scholar
  69. Zhang, R.-H., C. Gao, X. B. Kang, H. Zhi, Z. G. Wang, and L. C. Feng, 2015: ENSO modulations due to interannual variability of freshwater forcing and ocean biology-induced heating in the tropical Pacific. Scientific ReportsSci. Rep., 5, 18506; Scholar
  70. Zheng, F., and R.-H. Zhang, 2012: Effects of interannual salinity variability and freshwater flux forcing on the development of the 2007/08 La Niña event diagnosed from Argo and satellite data. Dyn. Atmos. Oceans, 57, 45–57, Scholar
  71. Zheng, F., R.-H. Zhang, and J. Zhu, 2014: Effects of interannual salinity variability on the barrier layer in the western-central equatorial Pacific: A diagnostic analysis from Argo. Adv. Atmos. Sci., 31(3), 532–542, Scholar
  72. Zhi, H., R.-H. Zhang, P. F. Lin, and L. N. Wang, 2015: Quantitative analysis of the feedback induced by the freshwater flux in the tropical Pacific using CMIP5. Adv. Atmos. Sci., 32(10), 1341–1353, Scholar
  73. Zhu, J. S., and Coauthors, 2014: Salinity anomaly as a trigger for ENSO events. Scientific Reports, 4, 6821, Scholar

Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Hai Zhi
    • 1
  • Rong-Hua Zhang
    • 2
    • 3
    • 4
    Email author
  • Pengfei Lin
    • 4
    • 5
  • Peng Yu
    • 6
  1. 1.College of Atmospheric SciencesNanjing University of Information Science and TechnologyNanjingChina
  2. 2.Key Laboratory of Ocean Circulation and Waves, Institute of OceanologyChinese Academy of SciencesQingdaoChina
  3. 3.Laboratory for Ocean and Climate DynamicsQingdao National Laboratory for Marine Science and TechnologyQingdaoChina
  4. 4.University of Chinese Academy of SciencesBeijingChina
  5. 5.State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics (IAP)Chinese Academy of SciencesBeijingChina
  6. 6.The Cooperative Institute for Climate and Satellites (CICS)University of MarylandCollege ParkUSA

Personalised recommendations