Editorial Type:
Article
Article Type:
Research Article

Response of the Madden–Julian Oscillation and Tropical Atmosphere to Changes in Oceanic Mixed Layer Depth over the Indian Ocean

Teresa Cicerone aDepartment of Atmospheric, Oceanic, and Earth Sciences, George Mason University, Fairfax, Virginia

Search for other papers by Teresa Cicerone in
Current site
Google Scholar
PubMed Close
,
Timothy DelSole aDepartment of Atmospheric, Oceanic, and Earth Sciences, George Mason University, Fairfax, Virginia

Search for other papers by Timothy DelSole in
Current site
Google Scholar
PubMed Close
,
Laurie Trenary aDepartment of Atmospheric, Oceanic, and Earth Sciences, George Mason University, Fairfax, Virginia

Search for other papers by Laurie Trenary in
Current site
Google Scholar
PubMed Close
, and
Ben Kirtman bDepartment of Atmospheric Sciences, Rosenstiel School for Marine and Atmospheric Science, University of Miami, Miami, Florida

Search for other papers by Ben Kirtman in
Current site
Google Scholar
PubMed Close
Online Publication:
19 May 2023
Print Publication:
15 Jun 2023
DOI:
https://doi.org/10.1175/JCLI-D-21-0788.1
Page(s):
4071–4086
Restricted access

Abstract

The Madden–Julian oscillation (MJO) is a component of tropical variability that influences high-impact events such as hurricane activity and Asian monsoons on intraseasonal (2–8 weeks) time scales. However, the atmosphere–ocean dynamics responsible for the MJO are highly debated. To gain insight into MJO–Indian Ocean dynamics, we conduct climate model experiments where the ocean is replaced by a motionless slab whose thickness, called the mixed layer depth (MLD), varies in space but not in time. Changes in the MLD and ocean heat convergence over the Indian Ocean have no discernible impact on MJO propagation, predictability, or variability within the Community Earth System Model (CESM) version 1.2.1. This suggests that ocean dynamics may not be critical to the MJO over the Indian Ocean in this dynamical model (CAM5 coupled to motionless slab). To diagnose changes in intraseasonal variability beyond the MJO, a discriminant analysis technique is used to optimize differences in variability between experiments. This analysis reveals that differences caused by changing Indian Ocean MLD were restricted mostly to local surface fluxes and could be explained by simple energy balance physics. Despite modeling adjustments intended to preserve the climate, the control slab has a warmer climate than the fully coupled model. The resulting changes in the mean climate are consistent with changes theoretically expected from global warming, particularly the “wet-gets-wetter” mechanism.

Significance Statement

The Madden–Julian oscillation (MJO) is a tropical eastward-moving pulse of convection that can influence a wide variety of global phenomena. Currently, models do not capture many key aspects of the MJO such as speed and spatial structure. The uppermost ocean layer that communicates with the atmosphere is widely believed to play a large role in the evolution of the MJO. To test the importance of ocean dynamics on simulated MJO, a slab-model configuration is used to allow the atmosphere and ocean to communicate through surface fluxes of heat and moisture while suppressing interactive ocean currents. Changes to the upper ocean did not impact the MJO in our model setup.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Teresa Cicerone, tciceron@gmu.edu

Abstract

The Madden–Julian oscillation (MJO) is a component of tropical variability that influences high-impact events such as hurricane activity and Asian monsoons on intraseasonal (2–8 weeks) time scales. However, the atmosphere–ocean dynamics responsible for the MJO are highly debated. To gain insight into MJO–Indian Ocean dynamics, we conduct climate model experiments where the ocean is replaced by a motionless slab whose thickness, called the mixed layer depth (MLD), varies in space but not in time. Changes in the MLD and ocean heat convergence over the Indian Ocean have no discernible impact on MJO propagation, predictability, or variability within the Community Earth System Model (CESM) version 1.2.1. This suggests that ocean dynamics may not be critical to the MJO over the Indian Ocean in this dynamical model (CAM5 coupled to motionless slab). To diagnose changes in intraseasonal variability beyond the MJO, a discriminant analysis technique is used to optimize differences in variability between experiments. This analysis reveals that differences caused by changing Indian Ocean MLD were restricted mostly to local surface fluxes and could be explained by simple energy balance physics. Despite modeling adjustments intended to preserve the climate, the control slab has a warmer climate than the fully coupled model. The resulting changes in the mean climate are consistent with changes theoretically expected from global warming, particularly the “wet-gets-wetter” mechanism.

Significance Statement

The Madden–Julian oscillation (MJO) is a tropical eastward-moving pulse of convection that can influence a wide variety of global phenomena. Currently, models do not capture many key aspects of the MJO such as speed and spatial structure. The uppermost ocean layer that communicates with the atmosphere is widely believed to play a large role in the evolution of the MJO. To test the importance of ocean dynamics on simulated MJO, a slab-model configuration is used to allow the atmosphere and ocean to communicate through surface fluxes of heat and moisture while suppressing interactive ocean currents. Changes to the upper ocean did not impact the MJO in our model setup.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Teresa Cicerone, tciceron@gmu.edu
Save
Cover Journal of Climate
Journal of Climate

  • Ahn, M.-S., D. Kim, K. Sperber, I.-S. Kang, E. Maloney, D. Waliser, and H. Hendon, 2017: MJO simulation in CMIP5 climate models: MJO skill metrics and process-oriented diagnosis. Climate Dyn., 49, 4023–4045, https://doi.org/10.1007/s00382-017-3558-4.

    • Search Google Scholar
    • Export Citation

  • Ahn, M.-S., and Coauthors, 2020: MJO propagation across the maritime continent: Are CMIP6 models better than CMIP5 models? Geophys. Res. Lett., 47, e2020GL087250, https://doi.org/10.1029/2020GL087250.

    • Search Google Scholar
    • Export Citation

  • Anber, U., S. Wang, and A. Sobel, 2017: Coupling with ocean mixed layer leads to intraseasonal variability in tropical deep convection: Evidence from cloud-resolving simulations. J. Adv. Model. Earth Syst., 9, 616–626, https://doi.org/10.1002/2016MS000803.

    • Search Google Scholar
    • Export Citation

  • Bitz, C. M., K. Shell, P. Gent, D. Bailey, G. Danabasoglu, K. Armour, M. Holland, and J. Kiehl, 2012: Climate sensitivity of the Community Climate System Model, version 4. J. Climate, 25, 3053–3070, https://doi.org/10.1175/JCLI-D-11-00290.1.

    • Search Google Scholar
    • Export Citation

  • Boyle, J. S., S. A. Klein, D. D. Lucas, H.-Y. Ma, J. Tannahill, and S. Xie, 2015: The parametric sensitivity of CAM5’s MJO. J. Geophys. Res. Atmos., 120, 1424–1444, https://doi.org/10.1002/2014JD022507.

    • Search Google Scholar
    • Export Citation

  • Bretherton, C. S., M. E. Peters, and L. E. Back, 2004: Relationships between water vapor path and precipitation over the tropical oceans. J. Climate, 17, 1517–1528, https://doi.org/10.1175/1520-0442(2004)017<1517:RBWVPA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Bui, H. X., and E. D. Maloney, 2019: Transient response of MJO precipitation and circulation to greenhouse gas forcing. Geophys. Res. Lett., 46, 13 546–13   555, https://doi.org/10.1029/2019GL085328.

    • Search Google Scholar
    • Export Citation

  • Bulusu, S., 2016: Poster: The role of ocean dynamics in the MJO initiation over the Indian Ocean. Department of Atmospheric and Oceanic Sciences Colloquium Series, University of South Carolina, https://www.aos.wisc.edu/posters/2016-17/Subra_Bulusu.jpg.

  • Cavanaugh, N. R., A. Gershunov, A. K. Panorska, and T. J. Kozubowski, 2015: The probability distribution of intense daily precipitation. Geophys. Res. Lett., 42, 1560–1567, https://doi.org/10.1002/2015GL063238.

    • Search Google Scholar
    • Export Citation

  • Conley, A. J., 2012: Description of the NCAR Community Atmosphere Model (CAM 5.0). NCAR Tech. Note NCAR/TN-486+STR, 289 pp., https://www2.cesm.ucar.edu/models/cesm1.0/cam/docs/description/cam5_desc.pdf.

  • Dasgupta, P., A. Metya, C. V. Naidu, M. Singh, and M. K. Roxy, 2020: Exploring the long-term changes in the Madden Julian Oscillation using machine learning. Sci. Rep., 10, 18567, https://doi.org/10.1038/s41598-020-75508-5.

    • Search Google Scholar
    • Export Citation

  • de Boyer Montégut, C., G. Madec, A. S. Fischer, A. Lazar, and D. Iudicone, 2004: Mixed layer depth over the global ocean: An examination of profile data and a profile-based climatology. J. Geophys. Res., 109, C12003, https://doi.org/10.1029/2004JC002378.

    • Search Google Scholar
    • Export Citation

  • DelSole, T., and M. Tippett, 2022: Covariance discriminant analysis. Statistical Methods for Climate Scientists, Cambridge University Press, 366–398, https://doi.org/10.1017/9781108659055.017.

  • DeMott, C. A., N. P. Klingaman, and S. J. Woolnough, 2015: Atmosphere-ocean coupled processes in the Madden-Julian oscillation. Rev. Geophys., 53, 1099–1154, https://doi.org/10.1002/2014RG000478.

    • Search Google Scholar
    • Export Citation

  • DeMott, C. A., N. P. Klingaman, W.-L. Tseng, M. A. Burt, Y. Gao, and D. A. Randall, 2019: The convection connection: How ocean feedbacks affect tropical mean moisture and MJO propagation. J. Geophys. Res. Atmos., 124, 11 910–11 931, https://doi.org/10.1029/2019JD031015.

    • Search Google Scholar
    • Export Citation

  • Drushka, K., J. Sprintall, S. Gille, and S. Wijffels, 2012: In situ observations of Madden–Julian oscillation mixed layer dynamics in the Indian and western Pacific Oceans. J. Climate, 25, 2306–2328, https://doi.org/10.1175/JCLI-D-11-00203.1.

    • Search Google Scholar
    • Export Citation

  • Duvel, J. P., R. Roca, and J. Vialard, 2004: Ocean mixed layer temperature variations induced by intraseasonal convective perturbations over the Indian Ocean. J. Atmos. Sci., 61, 1004–1023, https://doi.org/10.1175/1520-0469(2004)061<1004:OMLTVI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Flatau, M., P. J. Flatau, P. Phoebus, and P. P. Niiler, 1997: The feedback between equatorial convection and local radiative and evaporative processes: The implications for intraseasonal oscillations. J. Atmos. Sci., 54, 2373–2386, https://doi.org/10.1175/1520-0469(1997)054<2373:TFBECA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Goswami, B. N., R. S. Ajayamohan, P. K. Xavier, and D. Sengupta, 2003: Clustering of synoptic activity by Indian summer monsoon intraseasonal oscillations. Geophys. Res. Lett., 30, 1431, https://doi.org/10.1029/2002GL016734.

    • Search Google Scholar
    • Export Citation

  • Gottschalck, J., and Coauthors, 2010: A framework for assessing operational Madden–Julian oscillation forecasts: A CLIVAR MJO working group project. Bull. Amer. Meteor. Soc., 91, 1247–1258, https://doi.org/10.1175/2010BAMS2816.1.

    • Search Google Scholar
    • Export Citation

  • Grabowski, W. W., 2006: Impact of explicit atmosphere–ocean coupling on MJO-like coherent structures in idealized aquaplanet simulations. J. Atmos. Sci., 63, 2289–2306, https://doi.org/10.1175/JAS3740.1.

    • Search Google Scholar
    • Export Citation

  • Hagos, S., and Coauthors, 2020: Atmospheric convection and air–sea interactions over the tropical oceans: Scientific progress, challenges, and opportunities. Bull. Amer. Meteor. Soc., 101, E253–E258, https://doi.org/10.1175/BAMS-D-19-0261.1.

    • Search Google Scholar
    • Export Citation

  • Held, I. M., and B. J. Soden, 2006: Robust responses of the hydrological cycle to global warming. J. Climate, 19, 5686–5699, https://doi.org/10.1175/JCLI3990.1.

    • Search Google Scholar
    • Export Citation

  • Hermes, J. C., and C. Reason, 2008: Annual cycle of the South Indian Ocean (Seychelles-Chagos) thermocline ridge in a regional ocean model. J. Geophys. Res., 113, C04035, https://doi.org/10.1029/2007JC004363.

    • Search Google Scholar
    • Export Citation

  • Hermes, J. C., and C. J. C. Reason, 2009: The sensitivity of the Seychelles–Chagos thermocline ridge to large-scale wind anomalies. ICES J. Mar. Sci., 66, 1455–1466, https://doi.org/10.1093/icesjms/fsp074.

    • Search Google Scholar
    • Export Citation

  • Huang, C. J., F. Qiao, and D. Dai, 2014: Evaluating CMIP5 simulations of mixed layer depth during summer. J. Geophys. Res. Oceans, 119, 2568–2582, https://doi.org/10.1002/2013JC009535.

    • Search Google Scholar
    • Export Citation

  • Hung, M.-P., J.-L. Lin, W. Wang, D. Kim, T. Shinoda, and S. Weaver, 2013: MJO and convectively coupled equatorial waves simulated by CMIP5 climate models. J. Climate, 26, 6185–6214, https://doi.org/10.1175/JCLI-D-12-00541.1.

    • Search Google Scholar
    • Export Citation

  • Hurrell, J. W., and Coauthors, 2013: The Community Earth System Model: A framework for collaborative research. Bull. Amer. Meteor. Soc., 94, 1339–1360, https://doi.org/10.1175/BAMS-D-12-00121.1.

    • Search Google Scholar
    • Export Citation

  • Hwang, Y. T., and D. M. Frierson, 2013: Link between the double-Intertropical Convergence Zone problem and cloud biases over the Southern Ocean. Proc. Natl. Acad. Sci. USA, 110, 4935–4940, https://doi.org/10.1073/pnas.1213302110.

    • Search Google Scholar
    • Export Citation

  • Inness, P. M., and J. M. Slingo, 2003: Simulation of the Madden–Julian oscillation in a coupled general circulation model. Part I: Comparison with observations and an atmosphere-only GCM. J. Climate, 16, 345–364, https://doi.org/10.1175/1520-0442(2003)016<0345:SOTMJO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Jiang, X., and Coauthors, 2015: Vertical structure and physical processes of the Madden-Julian oscillation: Exploring key model physics in climate simulations. J. Geophys. Res. Atmos., 120, 4718–4748, https://doi.org/10.1002/2014JD022375.

    • Search Google Scholar
    • Export Citation

  • Jiang, X., A. F. Adames, M. Zhao, D. Waliser, and E. Maloney, 2018: A unified moisture mode framework for seasonality of the Madden–Julian oscillation. J. Climate, 31, 4215–4224, https://doi.org/10.1175/JCLI-D-17-0671.1.

    • Search Google Scholar
    • Export Citation

  • Jiang, X., and Coauthors, 2020: Fifty years of research on the Madden-Julian oscillation: Recent progress, challenges, and perspectives. J. Geophys. Res. Atmos., 125, e2019JD030911, https://doi.org/10.1029/2019JD030911.

    • Search Google Scholar
    • Export Citation

  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437–471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Kay, J. E., and Coauthors, 2015: The Community Earth System Model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal climate variability. Bull. Amer. Meteor. Soc., 96, 1333–1349, https://doi.org/10.1175/BAMS-D-13-00255.1.

    • Search Google Scholar
    • Export Citation

  • Keerthi, M., M. Lengaigne, K. Drushka, J. Vialard, C. de Boyer Montegut, S. Pous, M. Levy, and P. M. Muraleedharan, 2016: Intraseasonal variability of mixed layer depth in the tropical Indian Ocean. Climate Dyn., 46, 2633–2655, https://doi.org/10.1007/s00382-015-2721-z.

    • Search Google Scholar
    • Export Citation

  • Kim, D., and Coauthors, 2009: Application of MJO simulation diagnostics to climate models. J. Climate, 22, 6413–6436, https://doi.org/10.1175/2009JCLI3063.1.

    • Search Google Scholar
    • Export Citation

  • Kirtman, B., and Coauthors, 2014: The North American Multimodel Ensemble: Phase-1 seasonal-to-interannual prediction; Phase-2 toward developing intraseasonal prediction. Bull. Amer. Meteor. Soc., 95, 585–601, https://doi.org/10.1175/BAMS-D-12-00050.1.

    • Search Google Scholar
    • Export Citation

  • Klotzbach, P. J., 2010: On the Madden–Julian oscillation–Atlantic hurricane relationship. J. Climate, 23, 282–293, https://doi.org/10.1175/2009JCLI2978.1.

    • Search Google Scholar
    • Export Citation

  • Lan, Y.-Y., H.-H. Hsu, W.-L. Tseng, and L.-C. Jiang, 2022: Embedding a one-column ocean model in the Community Atmosphere Model 5.3 to improve Madden–Julian Oscillation simulation in boreal winter. Geosci. Model Dev., 15, 5689–5712, https://doi.org/10.5194/gmd-15-5689-2022.

    • Search Google Scholar
    • Export Citation

  • Lau, W. K.-M., and D. E. Waliser, 2012: Intraseasonal Variability in the Atmosphere-Ocean Climate System. 2nd ed. Springer, 613 pp.

  • Liebmann, B., and C. A. Smith, 1996: Description of a complete (interpolated) outgoing longwave radiation datase. Bull. Amer. Meteor. Soc., 77, 1275–1277, https://doi.org/10.1175/1520-0477-77.6.1274.

    • Search Google Scholar
    • Export Citation

  • Lin, J.-L., and Coauthors, 2006: Tropical intraseasonal variability in 14 IPCC AR4 climate models. Part I: Convective signals. J. Climate, 19, 2665–2690, https://doi.org/10.1175/JCLI3735.1.

    • Search Google Scholar
    • Export Citation

  • Ling, J., and Coauthors, 2017: Challenges and opportunities in MJO studies. Bull. Amer. Meteor. Soc., 98, ES53–ES56, https://doi.org/10.1175/BAMS-D-16-0283.1.

    • Search Google Scholar
    • Export Citation

  • Liu, P., Q. Zhang, C. Zhang, Y. Zhu, M. Khairoutdinov, H. Kim, C. Schumacher, and M. Zhang, 2016: A revised real-time multivariate MJO index. Mon. Wea. Rev., 144, 627–642, https://doi.org/10.1175/MWR-D-15-0237.1.

    • Search Google Scholar
    • Export Citation

  • Madden, R. A., and P. R. Julian, 1971: Detection of a 40–50 day oscillation in the zonal wind in the tropical Pacific. J. Atmos. Sci., 28, 702–708, https://doi.org/10.1175/1520-0469(1971)028<0702:DOADOI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Madden, R. A., and P. R. Julian, 1972: Description of global-scale circulation cells in the tropics with a 40–50 day period. J. Atmos. Sci., 29, 1109–1123, https://doi.org/10.1175/1520-0469(1972)029<1109:DOGSCC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Maloney, E. D., and J. T. Kiehl, 2002: Intraseasonal eastern Pacific precipitation and SST variations in a GCM coupled to a slab ocean model. J. Climate, 15, 2989–3007, https://doi.org/10.1175/1520-0442(2002)015<2989:IEPPAS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Maloney, E. D., and A. Sobel, 2004: Surface fluxes and ocean coupling in the tropical intraseasonal oscillation. J. Climate, 17, 4368–4386, https://doi.org/10.1175/JCLI-3212.1.

    • Search Google Scholar
    • Export Citation

  • Marshall, A. G., O. Alves, and H. H. Hendon, 2008: An enhanced moisture convergence–evaporation feedback mechanism for MJO air–sea interaction. J. Atmos. Sci., 65, 970–986, https://doi.org/10.1175/2007JAS2313.1.

    • Search Google Scholar
    • Export Citation

  • Marshall, J., and R. A. Plumb, 2008: Atmosphere, Ocean, and Climate Dynamics. An Introductory Text. International Geophysics Series, Vol. 93, Academic Press, 319 pp.

  • Martin, Z., F. Vitart, S. Wang, and A. Sobel, 2020: The impact of the stratosphere on the MJO in a forecast model. J. Geophys. Res. Atmos., 125, e2019JD032106, https://doi.org/10.1029/2019JD032106.

    • Search Google Scholar
    • Export Citation

  • Masumoto, Y., and G. Meyers, 1998: Forced Rossby waves in the southern tropical Indian Ocean. J. Geophys. Res., 103, 27 589–27 602, https://doi.org/10.1029/98JC02546.

    • Search Google Scholar
    • Export Citation

  • McCreary, J. P., P. K. Kundu, and R. L. Molinari, 1993: A numerical investigation of dynamics, thermodynamics, and mixed-layer processes in the Indian Ocean. Prog. Oceanogr., 31, 181–244, https://doi.org/10.1016/0079-6611(93)90002-U.

    • Search Google Scholar
    • Export Citation

  • NCEP, NWS, NOAA, and U. DoC, 1994: NCEP/NCAR global reanalysis products, 1948-continuing, updated monthly. NOAA/PSL Research Data Archive, accessed 16 March 2020, https://doi.org/10.5065/KHX2-FJ42.

  • Olsson, J., and P. Burlando, 2002: Reproduction of temporal scaling by a rectangular pulses rainfall model. Hydrol. Processes, 16, 611–630, https://doi.org/10.1002/hyp.307.

    • Search Google Scholar
    • Export Citation

  • Phillips, H., and Coauthors, 2021: Progress in understanding of Indian Ocean circulation, variability, air-sea exchange and impacts on biogeochemistry. Ocean Sci., 17, 1677–1751, https://doi.org/10.5194/os-17-1677-2021.

    • Search Google Scholar
    • Export Citation

  • Ray, P., and C. Zhang, 2010: A case study of the mechanics of extratropical influence on the initiation of the Madden-Julian oscillation. J. Atmos. Sci., 67, 515–528, https://doi.org/10.1175/2009JAS3059.1.

    • Search Google Scholar
    • Export Citation

  • Serber, G., 2008: A Matrix Handbook for Statisticians. John Wiley and Sons, Ltd., 592 pp.

  • Shin, S. S., S. D. Park, and B. K. Choi, 2016: Universal power law for relationship between rainfall kinetic energy and rainfall intensity. Adv. Meteor., 2016, 2494681, https://doi.org/10.1155/2016/2494681.

    • Search Google Scholar
    • Export Citation

  • Shinoda, T., W. Han, L. Zamudio, R.-C. Lien, and M. Katsumata, 2017: Remote ocean response to the Madden–Julian oscillation during the DYNAMO field campaign: Impact on Somali current system and the Seychelles–Chagos thermocline ridge. Atmosphere, 8, 171, https://doi.org/10.3390/atmos8090171.

    • Search Google Scholar
    • Export Citation

  • Shoup, C. G., H. L. Roman-Stork, and B. Subrahmanyam, 2020: Analysis of coupled oceanic and atmospheric preconditioning for primary Madden-Julian oscillation events across ENSO phases. J. Geophys. Res. Oceans, 125, e2020JC016358, https://doi.org/10.1029/2020JC016358.

    • Search Google Scholar
    • Export Citation

  • Song, X., and G. J. Zhang, 2018: The roles of convection parameterization in the formation of double ITCZ syndrome in the NCAR CESM: I. Atmospheric processes. J. Adv. Model. Earth Syst., 10, 842–866, https://doi.org/10.1002/2017MS001191.

    • Search Google Scholar
    • Export Citation

  • Taraphdar, S., F. Zhang, L. R. Leung, X. Chen, and O. M. Pauluis, 2018: MJO affects the monsoon onset timing over the Indian region. Geophys. Res. Lett., 45, 10 011–10 018, https://doi.org/10.1029/2018GL078804.

    • Search Google Scholar
    • Export Citation

  • Trenary, L. L., and W. Han, 2012: Intraseasonal-to-interannual variability of South Indian Ocean sea level and thermocline: Remote versus local forcing. J. Phys. Oceanogr., 42, 602–627, https://doi.org/10.1175/JPO-D-11-084.1.

    • Search Google Scholar
    • Export Citation

  • Vialard, J., G. R. Foltz, M. J. McPhaden, J. P. Duvel, and C. de Boyer Montégut, 2008: Strong Indian Ocean sea surface temperature signals associated with the Madden–Julian oscillation in late 2007 and early 2008. Geophys. Res. Lett., 35, L19608, https://doi.org/10.1029/2008GL035238.

    • Search Google Scholar
    • Export Citation

  • Vialard, J., and Coauthors, 2009: Supplement to cirene: Air—Sea interactions in the Seychelles—Chagos thermocline ridge region. Bull. Amer. Meteor. Soc., 90, ES1–ES4, https://doi.org/10.1175/2008BAMS2499.2.

    • Search Google Scholar
    • Export Citation

  • Waliser, D., and Coauthors, 2009: MJO simulation diagnostics. J. Climate, 22, 3006–3030, https://doi.org/10.1175/2008JCLI2731.1.

  • Wang, B., 2006: The Asian Monsoon. Springer and Praxis Publishing, 787 pp.

  • Wang, B., and X. Xie, 1998: Coupled modes of the warm pool climate system. Part I: The role of air-sea interaction in maintaining Madden–Julian oscillation. J. Climate, 11, 2116–2135, https://doi.org/10.1175/1520-0442-11.8.2116.

    • Search Google Scholar
    • Export Citation

  • Wang, L., K. Kodera, and W. Chen, 2012: Observed triggering of tropical convection by a cold surge: Implications for MJO initiation. Quart. J. Roy. Meteor. Soc., 138, 1740–1750, https://doi.org/10.1002/qj.1905.

    • Search Google Scholar
    • Export Citation

  • Wang, W., A. Kumar, J. Xiouhua Fu, and M.-P. Hung, 2015: What is the role of the sea surface temperature uncertainty in the prediction of tropical convection associated with the MJO? Mon. Wea. Rev., 143, 3156–3175, https://doi.org/10.1175/MWR-D-14-00385.1.

    • Search Google Scholar
    • Export Citation

  • Wheeler, M. C., and H. H. Hendon, 2004: An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Mon. Wea. Rev., 132, 1917–1932, https://doi.org/10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Woodberry, K. E., M. Luther, and J. J. O’Brien, 1989: The wind-driven seasonal circulation in the southern tropical Indian Ocean. J. Geophys. Res., 94, 17 985–18 002, https://doi.org/10.1029/JC094iC12p17985.

    • Search Google Scholar
    • Export Citation

  • Woolnough, S. J., J. M. Slingo, and B. J. Hoskins, 2000: The relationship between convection and sea surface temperature on intraseasonal timescales. J. Climate, 13, 2086–2104, https://doi.org/10.1175/1520-0442(2000)013<2086:TRBCAS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Xie, S.-P., H. Annamalai, F. A. Schott, and J. P. McCreary, 2002: Structure and mechanisms of south Indian Ocean climate variability. J. Climate, 15, 864–878, https://doi.org/10.1175/1520-0442(2002)015<0864:SAMOSI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Zhang, C., 2013: Madden–Julian oscillation: Bridging weather and climate. Bull. Amer. Meteor. Soc., 94, 1849–1870, https://doi.org/10.1175/BAMS-D-12-00026.1.

    • Search Google Scholar
    • Export Citation

  • Zhang, G., and N. A. McFarlane, 1995: Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model. Atmos.–Ocean, 33, 407–446, https://doi.org/10.1080/07055900.1995.9649539.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 408 408 8
Full Text Views 134 134 4
PDF Downloads 168 168 7