The atmospheric hinder for intraseasonal sea-air interaction over the Bay of Bengal during Indian summer monsoon in CMIP6
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Abstract: The surroundings of the Bay of Bengal (BoB) suffer a lot from the extreme rainfall events during Indian summer monsoon (ISM). Previous studies have proved that the sea-air interaction is an important factor for the monsoonal precipitation. Using the 6th Coupled Modol Inter-comparison Project (CMIP6) models, this study examined the biases of surface heat flux, which is the main connection between atmosphere and ocean. Results show that although CMIP6 have a better simulation of intraseasonal sea surface temperature (SST) anomalies over BoB than the previous ones, the “atmospheric blockage” still delays the response of latent heat flux to the oceanic forcing. Specifically, during the increment of positive latent heat flux in CMIP6, the negative contribution from wind effects covers most of the positive contribution from humidity effects, due to the underestimate of humidity effects. Further diagnostic analysis denote that the surface air humidity has a quarter of a phase ahead of warm SST in observation, but gets wet along with the warm SST accordingly in most CMIP6 models. As a result, the simulated transfer of intraseasonal moisture flux is hindered between ocean and atmosphere. Therefore, as a bridge between both sides, the atmospheric boundary layer is essential for a better sea-air coupled simulation, especially when the atmospheric and the oceanic variabilities involved in a climate model becomes increasingly sophisticated. The surface air humidity and boundary layer processes require more attention as well as better simulations.
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Key words:
- Bay of Bengal /
- ocean-atmosphere interaction /
- CMIP6 /
- latent heat flux /
- intraseasonal variability
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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 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 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 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 models Institute/country Atmospheric grids SST grids ACCESS-CM2 Commonwealth Scientific and Industrial Research Organization (CSIRO) / Bureau of Meteorology (BOM), Australia 192×144 360×300 ACCESS-ESM1-5 192×144 360×300 CanESM5 Canadian Centre for Climate Modelling and Analysis (CCCMA), Canada 128×64 360×291 CESM2 National Center for Atmospheric Research (NCAR), USA 288×192 320×384 CESM2-FV2 National Center for Atmospheric Research (NCAR), USA 144×96 320×384 CESM2-WACCM-FV2 National Center for Atmospheric Research (NCAR), USA 144×96 320×384 EC-Earth3-Veg EC-Earth-Consortium, European Community (EC) 512×256 362×292 GFDL-CM4 National Oceanic and Geophysical Fluid Dynamics Laboratory (GFDL), Atmospheric Administration (NOAA), USA 144×90 1440×1080 MIROC6 Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute, Japan 256×128 360×256 MPI-ESM-1-2-HAM Max Planck Institute for Meteorology (MPI-M), Germany 192×96 256×220 MPI-ESM1-2-HR Max Planck Institute for Meteorology (MPI-M), Germany 384×192 802×404 MPI-ESM1-2-LR Max Planck Institute for Meteorology (MPI-M), Germany 192×96 256×220 MRI-ESM2-0 Meteorological Research Institute (MRI), Japan 320×160 360×363 NorESM2-LM Norwegian Climate Centre (NCC) , Norway 144×96 360×385 NorESM2-MM Norwegian Climate Centre (NCC) , Norway 288×192 360×385 SAM0-UNICON Seoul National University (SNU), Korea 288×192 320×384 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. -
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