The atmospheric hinder for intraseasonal sea-air interaction over the Bay of Bengal during Indian summer monsoon in CMIP6

Ze Meng Lei Zhou Baosheng Li Jianhuang Qin Juncheng Xie

Ze Meng, Lei Zhou, Baosheng Li, Jianhuang Qin, Juncheng Xie. The atmospheric hinder for intraseasonal sea-air interaction over the Bay of Bengal during Indian summer monsoon in CMIP6[J]. Acta Oceanologica Sinica, 2022, 41(10): 119-130. doi: 10.1007/s13131-022-2023-3
Citation: Ze Meng, Lei Zhou, Baosheng Li, Jianhuang Qin, Juncheng Xie. The atmospheric hinder for intraseasonal sea-air interaction over the Bay of Bengal during Indian summer monsoon in CMIP6[J]. Acta Oceanologica Sinica, 2022, 41(10): 119-130. doi: 10.1007/s13131-022-2023-3

doi: 10.1007/s13131-022-2023-3

The atmospheric hinder for intraseasonal sea-air interaction over the Bay of Bengal during Indian summer monsoon in CMIP6

Funds: The National Natural Science Foundation of China under contract Nos 42076001 and 42106003; the Scientific Research Fund of the Second Institute of Oceanography, Ministry of Natural Resources under contract No. JB2206; the China Postdoctoral Science Foundation under contract No. 2022M711010.
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  • Figure  1.  Intraseasonal ocean-atmosphere relationship (colors, correlation between SST and LHF+SHF) and rainfall standard deviation (contours, unit: mm/d) over the Bay of Bengal during June, July, August and September (short for JJAS): a. objectively analyzed ocean-atmosphere fluxes (OAFlux) and Tropical Rainfall Measuring Mission (TRMM), and b–q. outputs of the sixteen CMIP6 models. All signals shown are statistically significant at a 95% confidence level.

    Figure  2.  The STD of intraseasonal SST (unit: °C) anomalies over the Bay of Bengal during boreal summer by OAFlux (a) and the sixteen CMIP6 models (b–q). The gray box in a denotes the region of 15°–23°N and 80°–98°E for reference time series.

    Figure  3.  Intraseasonal SST (black curves, unit:°C) anomalies during 1985–1994 (a) , 1995–2004 (b) and 2005–2014 (c) averaged within 15°–23°N and 80°–98°E from OAFlux. The blue dashed lines and black dashed lines are one-time standard deviation and zero line for SST anomalies. The red circles are the Day 0s of involved cases with positive SST peaks with values larger than its one-time standard deviation.

    Figure  4.  Intraseasonal LHF anomalies (colors, unit: W/m2) and SHF anomalies (solid contours, unit: W/m2) with respect to Day 0s in observation and CMIP6 outputs. The contours for SHF are from 1 W/m2 to 5 W/m2 with an interval of 1 W/m2. The blue dot lines denote the negative OLR anomalies from –5 W/m2 to –30 W/m2 with an interval of –5 W/m2. All signals shown are statistically significant at a 95% confidence level.

    Figure  5.  Intraseasonal LHF anomalies (colors, unit: W/m2) and SST anomalies (contours, unit: °C) with respect to Day 0s in observation and CMIP6 outputs. The contours for SST anomalies are from 0.05°C to 0.5°C with an interval of 0.05°C. The gray dot lines denote the reference of Day 0 in each panel. All signals shown are statistically significant at a 95% confidence level.

    Figure  6.  The time lags for the first peak of positive LHF anomalies (after Day 0) with respect to Day 0 in OAFlux and each CMIP6 output. The red line denotes that the first peak of positive LHF anomalies in OAFlux occurs 5 days later than Day 0.

    Figure  7.  The LHF decomposition averaged within Day –5 to Day 0 from observation and CMIP6 outputs. The unit is W/m2. The dash line denotes the observed H value of 15 W/m2.

    Figure  8.  The effects from humidity anomalies (also referred as H in Eq. (2), colors, unit: W/m2) and the effects from wind anomalies (also reffered as W in Eq. (2), dashed contours, unit: W/m2) of Eq. (2) with respect to Day 0s in observation and CMIP6 outputs. The contours for W are from –5 W/m2 with an interval of –5 W/m2. All signals shown are statistically significant at a 95% confidence level.

    Figure  9.  The background surface wind speed (a) and the humidity difference (b) averaged within Day –5 to Day 0 in observation and CMIP6 outputs. The dash line in a denotes the observed background wind speed of 7.4 m/s and that in b denotes the observed humidity difference of 0.53 g/kg.

    Figure  10.  The correlation between simulated humidity difference (unit: g/kg), surface air humidity (unit: g/kg) and the associated effects from humidity anomalies (also referred as H, unit: W/m2) averaged within Day –5 to Day 0 for each CMIP6 model. Red dots are the humidity difference and the associated H, while the blue dots are the same as red dots but for the surface air humidity and the associated H. The red (blue) line is estimated by the least square fit, and R1 and R2 are the correlation coefficients. ** denotes that the correlation coefficient is statistically significant at a 95% confidence level.

    Figure  11.  The saturated humidity at the condition of SST (blue bars) and the surface air humidity (orange bars) averaged within Day –5 to Day 0 in observation and CMIP6 outputs. The blue dash line denotes the observed humidity from SST effects at the value of 0.4 g/kg and the orange dash line denotes the observed surface air humidity of –0.12 g/kg.

    Figure  12.  Intraseasonal surface air humidity anomalies (colors, unit: g/kg) and saturated humidity at the condition of SST (solid contours, unit: g/kg) with respect to Day 0s in observation and CMIP6 outputs. The solid contours are from 0. 1 g/kg to 0.5 g/kg with an interval of 0.1°C. The dashed contours are the vertical integrated atmospheric moisture within boundary layer (700–1000 hPa) at the intraseasonal time scale (unit: g·Pa/kg). The values for dashed contours are from –20 g·Pa/kg to –200 g·Pa/kg with an interval of –20 g·Pa/kg. All signals shown are statistically significant at a 95% confidence level.

    Table  1.   List of the sixteen CMIP6 models included in this study

    CMIP6 modelsInstitute/countryAtmospheric gridsSST grids
    ACCESS-CM2Commonwealth Scientific and Industrial Research Organization (CSIRO) / Bureau of Meteorology (BOM), Australia192×144360×300
    ACCESS-ESM1-5192×144360×300
    CanESM5Canadian Centre for Climate Modelling and Analysis (CCCMA), Canada128×64360×291
    CESM2National Center for Atmospheric Research (NCAR), USA288×192320×384
    CESM2-FV2 National Center for Atmospheric Research (NCAR), USA144×96320×384
    CESM2-WACCM-FV2 National Center for Atmospheric Research (NCAR), USA144×96320×384
    EC-Earth3-VegEC-Earth-Consortium, European Community (EC)512×256362×292
    GFDL-CM4National Oceanic and Geophysical Fluid Dynamics Laboratory (GFDL), Atmospheric Administration (NOAA), USA144×901440×1080
    MIROC6Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute, Japan256×128360×256
    MPI-ESM-1-2-HAMMax Planck Institute for Meteorology (MPI-M), Germany192×96256×220
    MPI-ESM1-2-HR Max Planck Institute for Meteorology (MPI-M), Germany384×192802×404
    MPI-ESM1-2-LR Max Planck Institute for Meteorology (MPI-M), Germany192×96256×220
    MRI-ESM2-0Meteorological Research Institute (MRI), Japan320×160360×363
    NorESM2-LMNorwegian Climate Centre (NCC) , Norway144×96360×385
    NorESM2-MM Norwegian Climate Centre (NCC) , Norway288×192360×385
    SAM0-UNICONSeoul National University (SNU), Korea288×192320×384
    Note: All the included models provide variables for our analysis: daily SST, LHF, SHF, surface wind speed, surface air humidity and atmospheric moisture. For fair comparisons, the first realization (r1i1p1f1) of the historical simulations is chosen model in each CMIP6 model. The time period is from 1985 to 2014.
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  • Bellenger H, Guilyardi E, Leloup J, et al. 2014. ENSO representation in climate models: from CMIP3 to CMIP5. Climate Dynamics, 42(7): 1999–2018
    Bretherton C S, Peters M E, Back L E. 2004. Relationships between water vapor path and precipitation over the tropical oceans. Journal of Climate, 17(7): 1517–1528. doi: 10.1175/1520-0442(2004)017<1517:RBWVPA>2.0.CO;2
    Carton J A, Chepurin G A, Chen Ligang. 2018. SODA3: a new ocean climate reanalysis. Journal of Climate, 31(17): 6967–6983. doi: 10.1175/JCLI-D-18-0149.1
    Cayan D R. 1992a. Latent and sensible heat flux anomalies over the northern oceans: the connection to monthly atmospheric circulation. Journal of Climate, 5(4): 354–369. doi: 10.1175/1520-0442(1992)005<0354:LASHFA>2.0.CO;2
    Cayan D R. 1992b. Latent and sensible heat flux anomalies over the northern oceans: driving the sea surface temperature. Journal of Physical Oceanography, 22(8): 859–881. doi: 10.1175/1520-0485(1992)022<0859:LASHFA>2.0.CO;2
    Chen Ziming, Zhou Tianjun, Zhang Lixia, et al. 2020. Global land monsoon precipitation changes in CMIP6 projections. Geophysical Research Letters, 47(14): e2019GL086902
    DeMott C A, Benedict J J, Klingaman N P, et al. 2016. Diagnosing ocean feedbacks to the MJO: SST-modulated surface fluxes and the moist static energy budget. Journal of Geophysical Research, 121(14): 8350–8373. doi: 10.1002/2016JD025098
    Eyring V, Bony S, Meehl G A, et al. 2016. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5): 1937–1958. doi: 10.5194/gmd-9-1937-2016
    Flaounas E, Bastin S, Janicot S. 2011. Regional climate modelling of the 2006 West African monsoon: sensitivity to convection and planetary boundary layer parameterisation using WRF. Climate Dynamics, 36(5): 1083–1105
    Flather R A. 1994. A storm surge prediction model for the northern Bay of Bengal with application to the cyclone disaster in April 1991. Journal of Physical Oceanography, 24(1): 172–190. doi: 10.1175/1520-0485(1994)024<0172:ASSPMF>2.0.CO;2
    Gao Yingxia, Klingaman N P, DeMott C A, et al. 2019. Diagnosing ocean feedbacks to the BSISO: SST-modulated surface fluxes and the moist static energy budget. Journal of Geophysical Research, 124(1): 146–170
    Gasparin F, Roemmich D, Gilson J, et al. 2015. Assessment of the upper-ocean observing system in the equatorial pacific: the role of Argo in resolving intraseasonal to interannual variability. Journal of Atmospheric and Oceanic Technology, 32(9): 1668–1688. doi: 10.1175/JTECH-D-14-00218.1
    Goswami B N, Ajayamohan R S, Xavier P K, et al. 2003. Clustering of synoptic activity by Indian summer monsoon intraseasonal oscillations. Geophysical Research Letters, 30(8): 1431
    Goswami B N, Mohan R S A. 2001. Intraseasonal oscillations and interannual variability of the Indian summer monsoon. Journal of Climate, 14(6): 1180–1198. doi: 10.1175/1520-0442(2001)014<1180:IOAIVO>2.0.CO;2
    Hendon H H, Glick J. 1997. Intraseasonal air-sea interaction in the tropical Indian and Pacific Oceans. Journal of Climate, 10(4): 647–661. doi: 10.1175/1520-0442(1997)010<0647:IASIIT>2.0.CO;2
    Jiang Xianan, Li Tim, Wang Bin. 2004. Structures and mechanisms of the northward propagating boreal summer intraseasonal oscillation. Journal of Climate, 17(5): 1022–1039. doi: 10.1175/1520-0442(2004)017<1022:SAMOTN>2.0.CO;2
    Konda G, Vissa N K. 2021. Assessment of ocean-atmosphere interactions for the boreal summer intraseasonal oscillations in CMIP5 models over the Indian monsoon region. Asia-Pacific Journal of Atmospheric Sciences, 57(4): 717–739. doi: 10.1007/s13143-021-00228-3
    Lee H T, Gruber A, Ellingson R G, et al. 2007. Development of the HIRS outgoing longwave radiation climate dataset. Journal of Atmospheric and Oceanic Technology, 24(12): 2029–2047. doi: 10.1175/2007JTECHA989.1
    Li Yuanlong, Han Weiqing, Wang Wanqiu, et al. 2017. Bay of Bengal salinity stratification and Indian summer monsoon intraseasonal oscillation: 2. Impact on SST and convection. Journal of Geophysical Research, 122(5): 4312–4328. doi: 10.1002/2017JC012692
    Li Baosheng, Zhou Lei, Qin Jianhuang, et al. 2021. The role of vorticity tilting in northward-propagating monsoon intraseasonal oscillation. Geophysical Research Letters, 48(3): e2021GL093304
    Li Baosheng, Zhou Lei, Qin Jianhuang, et al. 2022a. Maintenance of cyclonic vortex during monsoon intraseasonal oscillation: a view from kinetic energy budget. Geophysical Research Letters, 49(7): e2022GL097740
    Li Baosheng, Zhou Lei, Qin Jianhuang, et al. 2022b. Key process diagnostics for monsoon intraseasonal oscillation over the Indian Ocean in coupled CMIP6 models. Climate Dynamics,
    Li Baosheng, Zhou Lei, Wang Chunzai, et al. 2020. Modulation of tropical cyclone genesis in the Bay of Bengal by the central Indian ocean mode. Journal of Geophysical Research, 125(12): e2020JD032641
    Maloney E D, Sobel A H. 2004. Surface fluxes and ocean coupling in the tropical intraseasonal oscillation. Journal of Climate, 17(22): 4368–4386. doi: 10.1175/JCLI-3212.1
    McKenna S, Santoso A, Gupta A S, et al. 2020. Indian Ocean dipole in CMIP5 and CMIP6: characteristics, biases, and links to ENSO. Scientific Reports, 10(1): 11500. doi: 10.1038/s41598-020-68268-9
    Menemenlis D, Campin J M, Heimbach P, et al. 2008. ECCO2: High resolution global ocean and sea ice data synthesis. Mercator Ocean Quarterly Newsletter, 31: 13–21
    Meng Ze, Zhou Lei, Murtugudde R, et al. 2022. Tropical oceanic intraseasonal variabilities associated with central Indian Ocean mode. Climate Dynamics, 58(3): 1107–1126
    Meng Ze, Zhou Lei, Qin Jianhuang, et al. 2019. Assessment of intraseasonal variabilities over Indian Ocean based on oceanic reanalysis datasets. Journal of Marine Sciences, 37(4): 1–13
    Menon A, Levermann A, Schewe J, et al. 2013. Indian summer monsoon in CMIP-5 projections: more rain, more erratically. Consistent increase in Indian monsoon rainfall and its variability across CMIP-5 models. Earth System Dynamics, 4(2): 287–300
    Niyogi D, Kishtawal C, Tripathi S, et al. 2010. Observational evidence that agricultural intensification and land use change may be reducing the Indian summer monsoon rainfall. Water Resources Research, 46(3): W03533
    Peña M, Kalnay E, Cai M. 2003. Statistics of locally coupled ocean and atmosphere intraseasonal anomalies in Reanalysis and AMIP data. Nonlinear Processes in Geophysics, 10(2): 245–251
    Pirro A, Wijesekera H W, Jarosz E, et al. 2020. Dynamics of intraseasonal oscillations in the Bay of Bengal during summer monsoons captured by mooring observations. Deep-Sea Research Part II: Topical Studies in Oceanography, 172: 104718. doi: 10.1016/j.dsr2.2019.104718
    Qin Jianhuang, Meng Ze, Xu Wenlong, et al. 2022a. Modulation of the intraseasonal chlorophyll-a concentration in the tropical Indian Ocean by the central Indian ocean Mode. Geophysical Research Letters, 49(7): e2022GL097802
    Qin Jianhuang, Zhou Lei, Ding Ruiqiang, et al. 2022b. Predictability limit of monsoon intraseasonal precipitation: an implication of central Indian Ocean mode. Frontiers in Marine Science, 8: 809798
    Qin Jianhuang, Zhou Lei, Li Baosheng, et al. 2020. Simulation of central Indian Ocean mode in S2S models. Journal of Geophysical Research, 125(21): e2020JD033550
    Qin Jianhuang, Zhou Lei, Meng Ze, et al. 2022c. Barotropic energy conversion during Indian summer monsoon: implication of central Indian Ocean mode simulation in CMIP6. Climate Dynamics,
    Rajeevan M, Gadgil S, Bhate J. 2010. Active and break spells of the Indian summer monsoon. Journal of Earth System Science, 119(3): 229–247. doi: 10.1007/s12040-010-0019-4
    Rochetin N, Couvreux F, Grandpeix J Y, et al. 2014. Deep convection triggering by boundary layer thermals. Part I: LES analysis and stochastic triggering formulation. Journal of the Atmospheric Sciences, 71(2): 496–514. doi: 10.1175/JAS-D-12-0336.1
    Roxy M, Tanimoto Y. 2007. Role of SST over the Indian Ocean in influencing the intraseasonal variability of the Indian summer monsoon. Journal of the Meteorological Society of Japan, 85(3): 349–358
    Roxy M, Tanimoto Y, Preethi B, et al. 2013. Intraseasonal SST-precipitation relationship and its spatial variability over the tropical summer monsoon region. Climate Dynamics, 41(1): 45–61. doi: 10.1007/s00382-012-1547-1
    Sabeerali C T, Ramu Dandi A, Dhakate A, et al. 2013. Simulation of boreal summer intraseasonal oscillations in the latest CMIP5 coupled GCMs. Journal of Geophysical Research, 118(10): 4401–4420. doi: 10.1002/jgrd.50403
    Sanchez-Franks A, Kent E C, Matthews A J, et al. 2018. Intraseasonal variability of air-sea fluxes over the Bay of Bengal during the southwest monsoon. Journal of Climate, 31(17): 7087–7109. doi: 10.1175/JCLI-D-17-0652.1
    Sobel A H, Maloney E D, Bellon G, et al. 2008. The role of surface heat fluxes in tropical intraseasonal oscillations. Nature Geoscience, 1(10): 653–657. doi: 10.1038/ngeo312
    Vinayachandran P N, Neema C P, Mathew S, et al. 2012. Mechanisms of summer intraseasonal sea surface temperature oscillations in the Bay of Bengal. Journal of Geophysical Research, 117(C1): C01005
    Xi Jingyuan, Zhou Lei, Murtugudde R, et al. 2015. Impacts of intraseasonal SST anomalies on precipitation during Indian summer monsoon. Journal of Climate, 28(11): 4561–4575. doi: 10.1175/JCLI-D-14-00096.1
    Yu Lisan. 2019. Global air-sea fluxes of heat, fresh water, and momentum: energy budget closure and unanswered questions. Annual Review of Marine Science, 11: 227–248. doi: 10.1146/annurev-marine-010816-060704
    Yu Lisan, Weller R A. 2007. Objectively analyzed air-sea heat fluxes for the global ice-free oceans (1981–2005). Bulletin of the American Meteorological Society, 88(4): 527–540. doi: 10.1175/BAMS-88-4-527
    Zhang Chidong. 2005. Madden-Julian oscillation. Reviews of Geophysics, 43(2): RG2003
    Zhang Min, Zhou Lei, Fu Hongli, et al. 2016. Assessment of intraseasonal variabilities in China Ocean Reanalysis (CORA). Acta Oceanologica Sinica, 35(3): 90–101. doi: 10.1007/s13131-016-0820-2
    Zhou Lei, Murtugudde R. 2014. Impact of northward-propagating intraseasonal variability on the onset of Indian summer monsoon. Journal of Climate, 27(1): 126–139. doi: 10.1175/JCLI-D-13-00214.1
    Zhou Lei, Murtugudde R, Chen Dake, et al. 2017. A central Indian Ocean mode and heavy precipitation during the Indian summer monsoon. Journal of Climate, 30(6): 2055–2067. doi: 10.1175/JCLI-D-16-0347.1
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  • 收稿日期:  2022-02-28
  • 录用日期:  2022-04-10
  • 网络出版日期:  2022-08-18
  • 刊出日期:  2022-10-27

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