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The Southwest Indian Ocean Thermocline Dome in CMIP5 Models: Historical Simulation and Future Projection


doi: 10.1007/s00376-015-5076-9

  • Using 20 models of the Coupled Model Intercomparison Project Phase 5 (CMIP5), the simulation of the Southwest Indian Ocean (SWIO) thermocline dome is evaluated and its role in shaping the Indian Ocean Basin (IOB) mode following El Niño investigated. In most of the CMIP5 models, due to an easterly wind bias along the equator, the simulated SWIO thermocline is too deep, which could further influence the amplitude of the interannual IOB mode. A model with a shallow (deep) thermocline dome tends to simulate a strong (weak) IOB mode, including key attributes such as the SWIO SST warming, antisymmetric pattern during boreal spring, and second North Indian Ocean warming during boreal summer. Under global warming, the thermocline dome deepens with the easterly wind trend along the equator in most of the models. However, the IOB amplitude does not follow such a change of the SWIO thermocline among the models; rather, it follows future changes in both ENSO forcing and local convection feedback, suggesting a decreasing effect of the deepening SWIO thermocline dome on the change in the IOB mode in the future.
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  • Annamalai H., P. Liu and S.-P. Xie, 2005: Southwest Indian Ocean SST variability: Its local effect and remote influence on Asian monsoons. J. Climate, 18, 4150- 4167.950c31a1-834d-48e6-9f93-5a3fe4fa8c8e61e3aca8ab3ef28d091912ba5e930ef2http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2005JCli...18.4150Arefpaperuri:(d017b2226bc0d494aa3154273e38faf8)/s?wd=paperuri%3A%28d017b2226bc0d494aa3154273e38faf8%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2005JCli...18.4150A&ie=utf-8
    Annamalai H., H. Okajima, and M. Watanabe, 2007: Possible impact of the Indian Ocean SST on the Northern Hemisphere during El Niño. J. Climate, 20, 3164- 3189.
    Cai W. J., T. Cowan, 2013: Why is the amplitude of the Indian Ocean Dipole overly large in CMIP3 and CMIP5 climate models? Geophys. Res. Lett., 40, 1200- 1205.10.1002/grl.5020860182579ec4aae6ae32390a6f1f42af2http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fgrl.50208%2Fabstracthttp://onlinelibrary.wiley.com/doi/10.1002/grl.50208/abstract[1] The Indian Ocean Dipole (IOD) affects weather and climate in many parts of the world, but a realistic simulation of the IOD in state-of-the-art climate models remains a challenge. In most models, IOD peak-season amplitudes are systematically larger than that of the observed, a bias that deterministically affects climate projections in IOD-affected regions. Understanding the cause of this bias is therefore essential for alleviating model errors and reducing uncertainty in climate projections. Here it is shown that most Coupled Model Intercomparison Project Phase Three (CMIP3) and CMIP5 models produce too strong a Bjerknes feedback in the equatorial Indian Ocean, leading to the IOD bias. The thermocline-sea surface temperature (SST) feedback exerts the strongest influence on the simulated IOD amplitude; models simulating a stronger thermocline-SST feedback systematically generate a greater IOD amplitude. The strength of the thermocline-SST feedback in most models is predominantly controlled by the climatological west-east slope of the equatorial thermocline, which features an unrealistic mean slope tilting upward toward the eastern Indian Ocean. The unrealistic thermocline structure is accompanied by too strong a mean easterly wind and an overly strong west-minus-east SST gradient. The linkage of the mean climatic conditions, feedback strength, and projected climate highlights the fundamental importance of realistically simulating these components of the climate system for reducing uncertainty in climate change projections in IOD-affected regions.
    Cai W. J., X.-T. Zheng, E. Weller, M. Collins, T. Cowan, M. Lengaigne, W. D. Yu, and T. Yamagata, 2013: Projected response of the Indian Ocean Dipole to greenhouse warming. Nature Geoscience, 6, 999- 1007.10.1038/ngeo200917ecaa5e9a5fd74d76f64f696346adb8http%3A%2F%2Fwww.nature.com%2Fngeo%2Fjournal%2Fv6%2Fn12%2Fabs%2Fngeo2009.htmlhttp://www.nature.com/ngeo/journal/v6/n12/abs/ngeo2009.htmlNatural modes of variability centred in the tropics, such as the El Nino/Southern Oscillation and the Indian Ocean Dipole, are a significant source of interannual climate variability across the globe. Future climate warming could alter these modes of variability. For example, with the warming projected for the end of the twenty-first century, the mean climate of the tropical Indian Ocean is expected to change considerably. These changes have the potential to affect the Indian Ocean Dipole, currently characterized by an alternation of anomalous cooling in the eastern tropical Indian Ocean and warming in the west in a positive dipole event, and the reverse pattern for negative events. The amplitude of positive events is generally greater than that of negative events. Mean climate warming in austral spring is expected to lead to stronger easterly winds just south of the Equator, faster warming of sea surface temperatures in the western Indian Ocean compared with the eastern basin, and a shoaling equatorial thermocline. The mean climate conditions that result from these changes more closely resemble a positive dipole state. However, defined relative to the mean state at any given time, the overall frequency of events is not projected to change but we expect a reduction in the difference in amplitude between positive and negative dipole events.
    Carton, J. A. and B. S. Giese, 2008: A reanalysis of ocean climate using simple ocean data assimilation (SODA). Mon. Wea. Rev., 136, 2999- 3017.10.1175/2007MWR1978.1454a78caf21a0c20ffebed73838f4b6ahttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2008MWRv..136.2999Chttp://adsabs.harvard.edu/abs/2008MWRv..136.2999CAbstract This paper describes the Simple Ocean Data Assimilation (SODA) reanalysis of ocean climate variability. In the assimilation, a model forecast produced by an ocean general circulation model with an average resolution of 0.25° × 0.4° × 40 levels is continuously corrected by contemporaneous observations with corrections estimated every 10 days. The basic reanalysis, SODA 1.4.2, spans the 44-yr period from 1958 to 2001, which complements the span of the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA-40). The observation set for this experiment includes the historical archive of hydrographic profiles supplemented by ship intake measurements, moored hydrographic observations, and remotely sensed SST. A parallel run, SODA 1.4.0, is forced with identical surface boundary conditions, but without data assimilation. The new reanalysis represents a significant improvement over a previously published version of the SODA algorithm. In particular, eddy kinetic energy and sea level variability are much larger than in previous versions and are more similar to estimates from independent observations. One issue addressed in this paper is the relative importance of the model forecast versus the observations for the analysis. The results show that at near-annual frequencies the forecast model has a strong influence, whereas at decadal frequencies the observations become increasingly dominant in the analysis. As a consequence, interannual variability in SODA 1.4.2 closely resembles interannual variability in SODA 1.4.0. However, decadal anomalies of the 0–700-m heat content from SODA 1.4.2 more closely resemble heat content anomalies based on observations.
    Deser C., A. S. Phillips, and J. W. Hurrell, 2004: Pacific interdecadal climate variability: Linkages between the tropics and the North Pacific during boreal winter since 1900. J. Climate, 17, 3109- 3124.10.1175/1520-0442(2004)017<3109:PICVLB>2.0.CO;2acbf687dc71acec4d1306c3313e75c65http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2004JCli...17.3109Dhttp://adsabs.harvard.edu/abs/2004JCli...17.3109DAbstract This study examines the tropical linkages to interdecadal climate fluctuations over the North Pacific during boreal winter through a comprehensive and physically based analysis of a wide variety of observational datasets spanning the twentieth century. Simple difference maps between epochs of high sea level pressure over the North Pacific (1900–24 and 1947–76) and epochs of low pressure (1925–46 and 1977–97) are presented for numerous climate variables throughout the tropical Indo-Pacific region, including rainfall, cloudiness, sea surface temperature (SST), and sea level pressure. The results support the notion that the Tropics play a key role in North Pacific interdecadal climate variability. In particular, SST anomalies in the tropical Indian Ocean and southeast Pacific Ocean, rainfall and cloudiness anomalies in the vicinity of the South Pacifi
    Du Y., S.-P. Xie, G. Huang, and K. M. Hu, 2009: Role of air-sea interaction in the long persistence of El Niño-induced North Indian Ocean warming. J. Climate, 22, 2023- 2038.10.1175/2008JCLI2590.15b604ebb-7857-4034-819f-57fa212b29a540802b84a0a5f3b47307c832159b6903http%3A%2F%2Fwww.cabdirect.org%2Fabstracts%2F20093162922.htmlrefpaperuri:(d0f4caf821cc80b0c1e4264c7ff06176)http://www.cabdirect.org/abstracts/20093162922.htmlAbstract El Ni09o induces a basin-wide increase in tropical Indian Ocean (TIO) sea surface temperature (SST) with a lag of one season. The north IO (NIO), in particular, displays a peculiar double-peak warming with the second peak larger in magnitude and persisting well through the summer. Motivated by recent studies suggesting the importance of the TIO warming for the Northwest Pacific and East Asian summer monsoons, the present study investigates the mechanisms for the second peak of the NIO warming using observations and general circulation models. This analysis reveals that internal air–sea interaction within the TIO is key to sustaining the TIO warming through summer. During El Ni09o, anticyclonic wind curl anomalies force a downwelling Rossby wave in the south TIO through Walker circulation adjustments, causing a sustained SST warming in the tropical southwest IO (SWIO) where the mean thermocline is shallow. During the spring and early summer following El Ni09o, this SWIO warming sustains an antisymmet...
    Du Y., L. Yang, and S.-P. Xie, 2011: Tropical Indian Ocean influence on Northwest Pacific tropical cyclones in summer following strong El Niño. J. Climate, 24, 315- 322.
    Du Y., S.-P. Xie, Y.-L. Yang, X.-T. Zheng, L. Liu, and G. Huang, 2013: Indian Ocean variability in the CMIP5 multi-model ensemble: The basin mode. J. Climate, 26, 7240- 7266.10.1175/JCLI-D-12-00678.12d34cc44-d00e-4c6d-95ee-6af6cd988798085ac17ba27ef1ea750fe5deab445728http%3A%2F%2Fconnection.ebscohost.com%2Fc%2Farticles%2F90147405%2Findian-ocean-variability-cmip5-multimodel-ensemble-basin-moderefpaperuri:(c40ef9c314af8f89908e6f4fa0b18290)http://connection.ebscohost.com/c/articles/90147405/indian-ocean-variability-cmip5-multimodel-ensemble-basin-modeAbstract This study evaluates the simulation of the Indian Ocean Basin (IOB) mode and relevant physical processes in models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Historical runs from 20 CMIP5 models are available for the analysis. They reproduce the IOB mode and its close relationship to El Ni09o–Southern Oscillation (ENSO). Half of the models capture key IOB processes: a downwelling oceanic Rossby wave in the southern tropical Indian Ocean (TIO) precedes the IOB development in boreal fall and triggers an antisymmetric wind anomaly pattern across the equator in the following spring. The anomalous wind pattern induces a second warming in the north Indian Ocean (NIO) through summer and sustains anticyclonic wind anomalies in the northwest Pacific by radiating a warm tropospheric Kelvin wave. The second warming in the NIO is indicative of ocean–atmosphere interaction in the interior TIO. More than half of the models display a double peak in NIO warming, as observed following El Ni09o, while the rest show only one winter peak. The intermodel diversity in the characteristics of the IOB mode seems related to the thermocline adjustment in the south TIO to ENSO-induced wind variations. Almost all the models show multidecadal variations in IOB variance, possibly modulated by ENSO.
    Du Y., J. J. Xiao, and K. F. Yu, 2014: Tropical Indian Ocean Basin Mode recorded in coral oxygen isotope data from the Seychelles over the past 148 years. Science China Earth Sciences,57, 2597-2605, doi: 10.1007/s11430-014-4956-7.10.1007/s11430-014-4956-7f191eb5a1048985caa821d0aee790db2http%3A%2F%2Fwww.cnki.com.cn%2FArticle%2FCJFDTotal-JDXG201411003.htmhttp://www.cnki.com.cn/Article/CJFDTotal-JDXG201411003.htmThe tropical Indian Ocean(TIO) displays a uniform basin-wide warming or cooling in sea surface temperature(SST) during the decay year of El Niδo-Southern Oscillation(ENSO) events. This warming or cooling is called the tropical Indian Ocean Basin Mode(IOBM). Recent studies showed that the IOBM dominates the interannual variability of the TIO SST and has impacts on the tropical climate from the TIO to the western Pacific. Analyses on a 148-year-long monthly coral δ 18 O record from the Seychelles Islands demonstrate that the Seychelles coral δ 18 O not only is associated with the local SST but also indicates the interannul variability of the basin-wide SST in the TIO. Moreover, the Seychelles coral δ 18 O shows a dominant period of 3–7 years that well represents the variability of the IOBM, which in return is modulated by the inter-decadal climate variability. The correlation between the Seychelles coral δ 18 O and the SST reveals that the coral δ 18 O lags the SST in the eastern equatorial Pacific by five months and reaches its peak in the spring following the mature phase of ENSO. The spatial pattern of the first EOF mode indicates that the Seychelles Islands are located at the crucial place of the IOBM. Thus, the Seychelles coral δ 18 O could be used as a proxy of the IOBM to investigate the ENSO teleconnection on the TIO in terms of long-time climate variability.
    Guo F. Y., Q. Y. Liu, S. Sun, and J. L. Yang, 2015: Three types of Indian Ocean dipoles. J. Climate, 28, 3073- 3092.10.1175/JCLI-D-14-00507.1ea03049b522699d2f0bd0fd6981bae49http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2015JCli...28.3073Ghttp://adsabs.harvard.edu/abs/2015JCli...28.3073GAbstract Using observational data and phase 5 of the Coupled Model Intercomparison Project (CMIP5) model outputs [the preindustrial (PI) control run of the Community Climate System Model, version 4 (CCSM4) and historical simulations of 17 CMIP5 models], Indian Ocean dipoles (IODs) with a peak in fall are categorized into three types. The first type is closely related to the development phase of El Ni09o/La Ni09a. The second type evolves from the basinwide warming (cooling) in the tropical Indian Ocean (IO), usually occurring in the year following El Ni09o (La Ni09a). The third type is independent of El Ni09o and La Ni09a. The dominant trigger condition for the first (third) type of IOD is the anomalous Walker circulation (anomalous cross-equatorial flow); the anomalous zonal sea surface temperature (SST) gradient in the tropical IO is the trigger condition for the second type. The occurrence of anomalous ocean Rossby waves during the forming stage of IO basinwide mode and their effect on SST in the southwestern IO during winter and spring are critical for early development of the second type of IOD. Although most models simulate a stronger El Ni09o–Southern Oscillation and IOD compared to the observations, this does not influence the phase-locking and classification of the IOD peaking in the fall.
    Hu K. M., G. Huang, X.-T. Zheng, S.-P. Xie, X. Qu, Y. Du, and L. Liu, 2014: Interdecadal variations in ENSO influences on Northwest Pacific-East Asian summertime climate simulated in CMIP5 models. J. Climate, 27, 5982- 5998.
    Huang B. H., J. L. Kinter III, 2002: Interannual variability in the tropical Indian Ocean. J. Geophys. Res., 107,3319, doi: 10.1029/2001JC001278.10.1029/2001JC00127888a8541448c02e6441ceffd70218f192http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2001JC001278%2Fpdfhttp://onlinelibrary.wiley.com/doi/10.1029/2001JC001278/pdf[1] The interannual variability in the tropical Indian Ocean is examined using 41-year (1958–1998) seasonal anomalies of the upper-ocean heat content (HCA), sea surface temperature (SSTA), and surface wind stress. Precipitation anomalies from a shorter period (1979–1998) have also been analyzed. This analysis demonstrates that a coupled ocean–atmosphere interannual oscillation with a period ranging from 2 to 5 years is the major variability in the tropical Indian Ocean. At the peak phase, anomalous equatorial zonal winds over the central and the eastern ocean and anomalous trade winds to the south induce zonal SSTA and HCA gradients near the equator and an east–west shift of the convection. This interannual oscillation is the dominant signal from the boreal autumn to the next spring. The westward propagating HCA causes a phase delay between the peaks of the surface cooling near the eastern coast and the warming near the western coast near the equator. During its propagation, the southern HCA branch is strengthened by the anomalous wind curl of the equatorial and southeast trade wind anomalies over the southern ocean. As a result, the southern HCA is maintained near the western coast for a much longer period. This Indian Ocean oscillation is significantly correlated with the El Ni09o/Southern Oscillation (ENSO) variability in the Pacific Ocean.
    Kalnay E., Coauthors , 1996: The NCEP/NCAR 40-Year reanalysis project. Bull. Amer. Meteor. Soc., 77, 437- 471.3f2f81b7-8518-4899-877c-2adbadd1a28f4f641748c1fe7c7d954de7018f8e59a5http%3A%2F%2Fwww.bioone.org%2Fservlet%2Flinkout%3Fsuffix%3Di1536-1098-69-2-93-Kalnay1%26dbid%3D16%26doi%3D10.3959%252F1536-1098-69.2.93%26key%3D10.1175%252F1520-0477%281996%29077%3C0437%253ATNYRP%3E2.0.CO%253B2refpaperuri:(e5daf4d24d7e762c979b7333999e990d)/s?wd=paperuri%3A%28e5daf4d24d7e762c979b7333999e990d%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fwww.bioone.org%2Fservlet%2Flinkout%3Fsuffix%3Di1536-1098-69-2-93-Kalnay1%26dbid%3D16%26doi%3D10.3959%252F1536-1098-69.2.93%26key%3D10.1175%252F1520-0477%281996%29077%253C0437%253ATNYRP%253E2.0.CO%253B2&ie=utf-8
    Kawamura R., T. Matsuura, and S. Iizuka, 2001: Role of equatorially asymmetric sea surface temperature anomalies in the Indian Ocean in the Asian summer monsoon and El Niño-Southern Oscillation coupling. J. Geophys. Res., 106, 4681- 4693.
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    Li G., S.-P. Xie, 2012: Origins of tropical-wide SST biases in CMIP multi-model ensembles. Geophys. Res. Lett., 39,L22703, doi: 10.1029/2012GL053777.10.1029/2012GL053777280febdcca368b60fb64d5f918b24696http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2012GL053777%2Fcitedbyhttp://onlinelibrary.wiley.com/doi/10.1029/2012GL053777/citedbyABSTRACT Long-standing simulation errors limit the utility of climate models. Overlooked are tropical-wide errors, with sea surface temperature (SST) biasing high or low across all the tropical ocean basins. Our analysis based on Coupled Model Intercomparison Project (CMIP) multi-model ensembles shows that such SST biases can be classified into two types: one with a broad meridional structure and of the same sign across all basins that is highly correlated with the tropical mean; and one with large inter-model variability in the cold tongues of the equatorial Pacific and Atlantic. The first type can be traced back to biases in atmospheric simulations of cloud cover, with cloudy models biasing low in tropical-wide SST. The second type originates from the diversity among models in representing the thermocline depth; models with a deep thermocline feature a warm cold tongue on the equator. Implications for inter-model variability in precipitation climatology and SST threshold for convection are discussed.
    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- 1780.10.1175/JCLI-D-13-00337.1e5394ffcc15468747f6408a03a9d234dhttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2014JCli...27.1765Lhttp://adsabs.harvard.edu/abs/2014JCli...27.1765LAbstract Errors of coupled general circulation models (CGCMs) limit their utility for climate prediction and projection. Origins of and feedback for tropical biases are investigated in the historical climate simulations of 18 CGCMs from phase 5 of the Coupled Model Intercomparison Project (CMIP5), together with the available Atmospheric Model Intercomparison Project (AMIP) simulations. Based on an intermodel empirical orthogonal function (EOF) analysis of tropical Pacific precipitation, the excessive equatorial Pacific cold tongue and double intertropical convergence zone (ITCZ) stand out as the most prominent errors of the current generation of CGCMs. The comparison of CMIP–AMIP pairs enables us to identify whether a given type of errors originates from atmospheric models. The equatorial Pacific cold tongue bias is associated with deficient precipitation and surface easterly wind biases in the western half of the basin in CGCMs, but these errors are absent in atmosphere-only models, indicating that the errors arise from the interaction with the ocean via Bjerknes feedback. For the double ITCZ problem, excessive precipitation south of the equator correlates well with excessive downward solar radiation in the Southern Hemisphere (SH) midlatitudes, an error traced back to atmospheric model simulations of cloud during austral spring and summer. This extratropical forcing of the ITCZ displacements is mediated by tropical ocean–atmosphere interaction and is consistent with recent studies of ocean–atmospheric energy transport balance.
    Li G., S.-P. Xie, and Y. Du, 2015a: Monsoon-induced biases of climate models over the tropical Indian Ocean with implications for regional climate projection. J. Climate, 28, 3058- 3072.10.1175/JCLI-D-14-00740.12b127713-fb59-4dac-bd09-fdef768432182dc92e85b23c15b8ccc7d1823fb0193fhttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2015JCli...28.3058Lrefpaperuri:(a4ca937a1bb0021056f79721ed92350e)http://adsabs.harvard.edu/abs/2015JCli...28.3058LAbstractLong-standing biases of climate models limit the skills of climate prediction and projection. Overlooked are tropical Indian Ocean (IO) errors. Based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensemble, the present study identifies a common error pattern in climate models that resembles the IO Dipole (IOD) mode of interannual variability in nature, with a strong equatorial easterly wind bias during boreal autumn accompanied by physically consistent biases in precipitation, sea surface temperature (SST), and subsurface ocean temperature. The analyses show that such IOD-like biases can be traced back to errors in the South Asian summer monsoon. Too weak a southwest summer monsoon over the Arabian Sea generates a warm SST bias over the western equatorial IO. In boreal autumn, Bjerknes feedback helps amplify the error into an IOD-like bias pattern in wind, precipitation, SST, and subsurface ocean temperature. Such mean state biases result in too strong interannual IOD var...
    Li G., S.-P. Xie, and Y. Du, 2015b: Climate model errors over the South Indian Ocean thermocline dome and their effect on the basin mode of interannual variability. J. Climate, 28, 3093- 3098.5a4c384a-25d6-4f1a-8ef4-ccf9ee01a0c2
    Masumoto Y., G. Meyers, 1998: Forced Rossby waves in the southern tropical Indian Ocean. J. Geophys. Res., 103( C12), 27 589- 27 602.
    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. Progress in Oceanography, 31, 181- 244.10.1016/0079-6611(93)90002-U1a54fc834408e623879d4f9b59149e52http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2F007966119390002Uhttp://www.sciencedirect.com/science/article/pii/007966119390002UThe annual-mean circulation has two meridional circulation cells. In the Tropical Cell, water subducts in the southern ocean, flows equatorward in the lower layer of the western-boundary current, and is entrained back into the upper layer in the open-ocean upwelling regions in the southern ocean. In the Cross-Equatorial Cell, the subducted water crosses the equator near the western boundary, where it is entrained in the regions of intense coastal upwelling in the northern ocean. The strength of the cells is directly related to the assumed magnitude of the subduction rate w d , but their structure is not sensitive to the particular parameterization of w d used.
    Nagura M., W. Sasaki, T. Tozuka, J.-J. Luo, S. K. Behera, and T. Yamagata, 2013: Longitudinal biases in the Seychelles Dome simulated by 35 ocean-atmosphere coupled general circulation models. J. Geophys. Res.,118, 831-846, doi: 10.1029/ 2012JC008352.10.1029/2012JC0083521fb220fae704066203fbb0f9ff120a43http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2012JC008352%2Fcitedbyhttp://onlinelibrary.wiley.com/doi/10.1029/2012JC008352/citedby[1] The Seychelles Dome refers to the shallow climatological thermocline in the southwestern Indian Ocean, where ocean wave dynamics efficiently affect sea surface temperature, allowing sea surface temperature anomalies to be predicted up to 12years in advance. Accurate reproduction of the dome by ocean-atmosphere coupled general circulation models (CGCMs) is essential for successful seasonal predictions in the Indian Ocean. This study examines the Seychelles Dome as simulated by 35 CGCMs, including models used in phase five of the Coupled Model Intercomparison Project (CMIP5). Among the 35 CGCMs, 14 models erroneously produce an upwelling dome in the eastern half of the basin whereas the observed Seychelles Dome is located in the southwestern tropical Indian Ocean. The annual mean Ekman pumping velocity in these models is found to be almost zero in the southern off-equatorial region. This result is inconsistent with observations, in which Ekman upwelling acts as the main cause of the Seychelles Dome. In the models reproducing an eastward-displaced dome, easterly biases are prominent along the equator in boreal summer and fall, which result in shallow thermocline biases along the Java and Sumatra coasts via Kelvin wave dynamics and a spurious upwelling dome in the region. Compared to the CMIP3 models, the CMIP5 models are even worse in simulating the dome longitudes.
    Reverdin G., M. Fieux, 1987: Sections in the western Indian Ocean-ariability in the temperature structure. Deep-Sea Res., 34, 601- 626.
    Saji N. H., S.-P. Xie, and T. Yamagata, 2006: Tropical Indian Ocean variability in the IPCC Twentieth-century climate simulations. J. Climate, 19, 4397- 4417.10.1175/JCLI3847.1766d3f5609005aa0e773f8d1cc5a7054http%3A%2F%2Fconnection.ebscohost.com%2Fc%2Farticles%2F22569437%2Ftropical-indian-ocean-variability-ipcc-twentieth-century-climate-simulationshttp://connection.ebscohost.com/c/articles/22569437/tropical-indian-ocean-variability-ipcc-twentieth-century-climate-simulationsThe twentieth-century simulations using by 17 coupled ocean17atmosphere general circulation models (CGCMs) submitted to the Intergovernmental Panel on Climate Change17s Fourth Assessment Report (IPCC AR4) are evaluated for their skill in reproducing the observed modes of Indian Ocean (IO) climate variability. Most models successfully capture the IO17s delayed, basinwide warming response a few months after El Ni17o17Southern Oscillation (ENSO) peaks in the Pacific. ENSO17s oceanic teleconnection into the IO, by coastal waves through the Indonesian archipelago, is poorly simulated in these models, with significant shifts in the turning latitude of radiating Rossby waves. In observations, ENSO forces, by the atmospheric bridge mechanism, strong ocean Rossby waves that induce anomalies of SST, atmospheric convection, and tropical cyclones in a thermocline dome over the southwestern tropical IO. While the southwestern IO thermocline dome is simulated in nearly all of the models, this ocean Rossby wave response to ENSO is present only in a few of the models examined, suggesting difficulties in simulating ENSO17s teleconnection in surface wind. A majority of the models display an equatorial zonal mode of the Bjerknes feedback with spatial structures and seasonality similar to the Indian Ocean dipole (IOD) in observations. This success appears to be due to their skills in simulating the mean state of the equatorial IO. Corroborating the role of the Bjerknes feedback in the IOD, the thermocline depth, SST, precipitation, and zonal wind are mutually positively correlated in these models, as in observations. The IOD17ENSO correlation during boreal fall ranges from -0.43 to 0.74 in the different models, suggesting that ENSO is one, but not the only, trigger for the IOD.
    Schott F. A., S.-P. Xie, and J. P. McCreary Jr., 2009: Indian Ocean circulation and climate variability. Reviews of Geophysics, 47,RG1002, doi: 10.1029/2007RG000245.10.1029/2007RG0002459b5951b5e7107f4f0ee658faff0ad36chttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2007RG000245%2Fpdfhttp://onlinelibrary.wiley.com/doi/10.1029/2007RG000245/pdfABSTRACT In recent years, the Indian Ocean (IO) has been discovered to have a much larger impact on climate variability than previously thought. This paper reviews climate phenomena and processes in which the IO is, or appears to be, actively involved. We begin with an update of the IO mean circulation and monsoon system. It is followed by reviews of ocean/atmosphere phenomenon at intraseasonal, interannual, and longer time scales. Much of our review addresses the two important types of interannual variability in the IO, El Ni&ntilde;o-Southern Oscillation (ENSO) and the recently identified Indian Ocean Dipole (IOD). IOD events are often triggered by ENSO but can also occur independently, subject to eastern tropical preconditioning. Over the past decades, IO sea surface temperatures and heat content have been increasing, and model studies suggest significant roles of decadal trends in both the Walker circulation and the Southern Annular Mode. Prediction of IO climate variability is still at the experimental stage, with varied success. Essential requirements for better predictions are improved models and enhanced observations.
    Smith T. M., R. W. Reynolds, T. C. Peterson, and J. Lawrimore, 2008: Improvements to NOAA's historical merged land-ocean surface temperature analysis (1880-2006). J. Climate, 21, 2283- 2296.
    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- 498.10.1175/BAMS-D-11-00094.10a93ff62-7ac1-4eaa-951b-da834bb5d6acd378bae55de68ca8b37ba4ba57a3c0b9http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2012BAMS...93..485Trefpaperuri:(102c64f576f0dc49ca552e6df691421b)http://adsabs.harvard.edu/abs/2012BAMS...93..485TThe fifth phase of the Coupled Model Intercomparison Project (CMIP5) will produce a state-of-the- art multimodel dataset designed to advance our knowledge of climate variability and climate change. Researchers worldwide are analyzing the model output and will produce results likely to underlie the forthcoming Fifth Assessment Report by the Intergovernmental Panel on Climate Change. Unprecedented in scale and attracting interest from all major climate modeling groups, CMIP5 includes “long term” simulations of twentieth-century climate and projections for the twenty-first century and beyond. Conventional atmosphere–ocean global climate models and Earth system models of intermediate complexity are for the first time being joined by more recently developed Earth system models under an experiment design that allows both types of models to be compared to observations on an equal footing. Besides the longterm experiments, CMIP5 calls for an entirely new suite of “near term” simulations focusing on recent decades and the future to year 2035. These “decadal predictions” are initialized based on observations and will be used to explore the predictability of climate and to assess the forecast system's predictive skill. The CMIP5 experiment design also allows for participation of stand-alone atmospheric models and includes a variety of idealized experiments that will improve understanding of the range of model responses found in the more complex and realistic simulations. An exceptionally comprehensive set of model output is being collected and made freely available to researchers through an integrated but distributed data archive. For researchers unfamiliar with climate models, the limitations of the models and experiment design are described.
    Vecchi G. A., B. J. Soden, 2007: Global warming and the weakening of the tropical circulation. J. Climate, 20, 4316- 4340.10.1175/JCLI4258.1a806368e-bebb-4d5b-ad72-296f452686b473c4454bd5c87d67e3bd17752bb3e9d7http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2006AGUFMOS11A1467Wrefpaperuri:(b30ccd0a9e8c71855c1ae5c8c7674dc2)http://adsabs.harvard.edu/abs/2006AGUFMOS11A1467WAbstract This study examines the response of the tropical atmospheric and oceanic circulation to increasing greenhouse gases using a coordinated set of twenty-first-century climate model experiments performed for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). The strength of the atmospheric overturning circulation decreases as the climate warms in all IPCC AR4 models, in a manner consistent with the thermodynamic scaling arguments of Held and Soden. The weakening occurs preferentially in the zonally asymmetric (i.e., Walker) rather than zonal-mean (i.e., Hadley) component of the tropical circulation and is shown to induce substantial changes to the thermal structure and circulation of the tropical oceans. Evidence suggests that the overall circulation weakens by decreasing the frequency of strong updrafts and increasing the frequency of weak updrafts, although the robustness of this behavior across all models cannot be confirmed because of the lack of data. As the climate warms, changes in both the atmospheric and ocean circulation over the tropical Pacific Ocean resemble “El Ni09o–like” conditions; however, the mechanisms are shown to be distinct from those of El Ni09o and are reproduced in both mixed layer and full ocean dynamics coupled climate models. The character of the Indian Ocean response to global warming resembles that of Indian Ocean dipole mode events. The consensus of model results presented here is also consistent with recently detected changes in sea level pressure since the mid–nineteenth century.
    Wittenberg A. T., A. Rosati, N.-C. Lau, and J. J. Ploshay, 2006: GFDL's CM2 global coupled climate models. Part III: Tropical Pacific climate and ENSO. J. Climate, 19, 698- 722.10.1175/JCLI3631.1d413ed12e268c301bb90e09d0d4697b2http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2006JCli...19..698Whttp://adsabs.harvard.edu/abs/2006JCli...19..698WAbstract Multicentury integrations from two global coupled ocean–atmosphere–land–ice models [Climate Model versions 2.0 (CM2.0) and 2.1 (CM2.1), developed at the Geophysical Fluid Dynamics Laboratory] are described in terms of their tropical Pacific climate and El Ni09o–Southern Oscillation (ENSO). The integrations are run without flux adjustments and provide generally realistic simulations of tropical Pacific climate. The observed annual-mean trade winds and precipitation, sea surface temperature, surface heat fluxes, surface currents, Equatorial Undercurrent, and subsurface thermal structure are well captured by the models. Some biases are evident, including a cold SST bias along the equator, a warm bias along the coast of South America, and a westward extension of the trade winds relative to observations. Along the equator, the models exhibit a robust, westward-propagating annual cycle of SST and zonal winds. During boreal spring, excessive rainfall south of the equator is linked to an unrealistic reversal of the simulated meridional winds in the east, and a stronger-than-observed semiannual signal is evident in the zonal winds and Equatorial Undercurrent. Both CM2.0 and CM2.1 have a robust ENSO with multidecadal fluctuations in amplitude, an irregular period between 2 and 5 yr, and a distribution of SST anomalies that is skewed toward warm events as observed. The evolution of subsurface temperature and current anomalies is also quite realistic. However, the simulated SST anomalies are too strong, too weakly damped by surface heat fluxes, and not as clearly phase locked to the end of the calendar year as in observations. The simulated patterns of tropical Pacific SST, wind stress, and precipitation variability are displaced 20°–30° west of the observed patterns, as are the simulated ENSO teleconnections to wintertime 200-hPa heights over Canada and the northeastern Pacific Ocean. Despite this, the impacts of ENSO on summertime and wintertime precipitation outside the tropical Pacific appear to be well simulated. Impacts of the annual-mean biases on the simulated variability are discussed.
    Woodberry K. E., M. E. Luther, and J. J. O'Brien, 1989: The wind-driven seasonal circulation in the southern tropical Indian Ocean. J. Geophys. Res., 94( C12), 17985- 18002.10.1029/JC094iC12p1798519c72af1da1aa0fa5c084a26524061d0http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2FJC094iC12p17985%2Fpdfhttp://onlinelibrary.wiley.com/doi/10.1029/JC094iC12p17985/pdfA numerical model of the Indian Ocean, driven by climatological monthly mean winds, realistically simulates the major features of the large scale upper ocean circulation observed in the southern hemisphere and equatorial regions. The principal feature in the tropical Indian Ocean is a basin-wide clockwise southern hemisphere (cyclonic) gyre comprised of the South Equatorial Current to the south, the South Equatorial Countercurrent to the north, and the East African Coastal Current in the west. Rossby waves propagate westward in the shear zone between the South Equatorial Current and the South Equatorial Countercurrent, and are obstructed and partially reflected by the banks along the Seychelles-Mauritius Ridge (60掳E). A region of high eddy activity northwest of Madagascar is an extension of the tropical gyre and is a tropical analog to the Gulf Stream recirculation region. Oscillations in meridional transport at the equator have westward phase speed and eastward group velocity and are the result of mixed Rossby-gravity (Yanai) waves forced by oscillations in the highly nonlinear western boundary current region. Oscillations with 40- to 50-day periods are seen in most currents. These oscillations cannot be atmospherically forced, as the shortest period in the mean monthly wind forcing is 60 days. Mean transports in the western basin agree with observations. Small (2 Sv) mean throughflow from the Pacific to the Indian Ocean at the eastern open boundary is due to wind-forced Indian Ocean dynamics alone and is within the range of observations of throughflow from the Pacific.
    Wu R. G., B. P. Kirtman, and V. Krishnamurthy, 2008: An asymmetric mode of tropical Indian Ocean rainfall variability in boreal spring. J. Geophys. Res., 113,D05104, doi: 10.1029/ 2007JD009316.10.1029/2007JD009316437062dd777d1a02c38a10e27ef3c19fhttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2007JD009316%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1029/2007JD009316/full[1] An analysis of observational estimates has revealed an asymmetric mode of boreal spring rainfall and wind variability over the tropical Indian Ocean. The asymmetric mode is characterized by opposite rainfall and surface wind anomalies north and south of the equator. This mode is associated with a cross-equatorial gradient in sea surface temperature anomalies. The evolution of this mode is related to air-sea interactions in the tropical Indian Ocean. In particular, surface heat fluxes play an important role in the initiation, development, and decay of this mode. The occurrence of this mode in observations is closely linked to both El Nino-Southern Oscillation (ENSO) and the Indian Ocean Dipole mode. Coupled general circulation model experiments indicate that this mode can occur in the absence of ENSO. The variability associated with this mode enhances ENSO's teleconnection to the Indian Ocean and affects the seasonal transition in the tropical Indian Ocean.
    Xiang B. Q., B. Wang, Q. H. Ding, F.-F. Jin, X. H. Fu, and H.-J. Kim, 2012: Reduction of the thermocline feedback associated with mean SST bias in ENSO simulation. Climate Dyn.,39, 1413-1430, doi: 10.1007/s00382-011-1164-4.10.1007/s00382-011-1164-469934a4228728e0ce938c19197c94073http%3A%2F%2Flink.springer.com%2F10.1007%2Fs00382-011-1164-4http://link.springer.com/10.1007/s00382-011-1164-4Associated with the double Inter-tropical convergence zone problem, a dipole SST bias pattern (cold in the equatorial central Pacific and warm in the southeast tropical Pacific) remains a common problem inherent in many contemporary coupled models. Based on a newly-developed coupled model, we performed a control run and two sensitivity runs, one is a coupled run with annual mean SST correction and the other is an ocean forced run. By comparison of these three runs, we demonstrated that a serious consequence of this SST bias is to severely suppress the thermocline feedback in a realistic simulation of the El Ni脙卤o/Southern Oscillation. Firstly, the excessive cold tongue extension pushes the anomalous convection far westward from the equatorial central Pacific, prominently diminishing the convection-low level wind feedback and thus the air-sea coupling strength. Secondly, the equatorial surface wind anomaly exhibits a relatively uniform meridional structure with weak gradient, contributing to a weakened wind-thermocline feedback. Thirdly, the equatorial cold SST bias induces a weakened upper-ocean stratification and thus yields the underestimation of the thermocline-subsurface temperature feedback. Finally, the dipole SST bias underestimates the mean upwelling through (a) undermining equatorial mean easterly wind stress, and (b) enhancing convective mixing and thus reducing the upper ocean stratification, which weakens vertical shear of meridional currents and near-surface Ekman-divergence.
    Xie P. P., P. A. Arkin, 1996: Analyses of global monthly precipitation using gauge observations, satellite estimates, and numerical model predictions. J. Climate, 9, 840- 858.10.1175/1520-0442(1996)0092.0.CO;20884667dc836a12c9ff6c5141426b65dhttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1996JCli....9..840Xhttp://adsabs.harvard.edu/abs/1996JCli....9..840XAn algorithm is developed to construct global gridded fields of monthly precipitation by merging estimates from five sources of information with different characteristics, including guage-based monthly analyses from the Global Precipitation Climatology Centre, three types of satellite estimates [the infrared-based GOES Precipitation Index, the microwave (MW) scattering-based Grody, and the MW emission-based Change estimates], and predictions produced by the operational forecast model of the European Centre for Medium-Range Weather Forecasts. A two-step strategy is used to (1) reduce the random error found in the individual sources and (2) reduce the bias of the combined analysis. First, the three satellite-based estimates and the model predictions are combined linearly based on a maximum likelihood estimate, in which the weighting coefficients are inversely proportional to the squares of the individual random errors determined by comparison with gauge observations and subjective assumptions. This combined analysis is then blended with an analysis based on gauge observations using a method that presumes that the bias of the gauge-based field is small where sufficient gauges are available and that the gradient of the precipitation field is best represented by the combination of satellite estimates and model predictions elsewhere. The algorithm is applied to produce monthly precipitation analyses more&raquo; for an 18-month period from July 1987 to December 1988. Results showed substantial improvements of the merged analysis relative to the individual sources in describing the global precipitation field. The large-scale spatial patterns, both in the Tropics and the extratropics, are well represented with reasonable amplitudes. Both the random error and the bias have been reduced compared to the individual data sources, and the merged analysis appears to be of reasonable quality everywhere. 46 refs., 14 figs., 4 tabs. 芦less
    Xie S.-P., S. G. H. Philander, 1994: A coupled ocean-atmosphere model of relevance to the ITCZ in the eastern Pacific. Tellus, 46, 340- 350.10.1034/j.1600-0870.1994.t01-1-00001.x568a38fd-2012-4e72-96f2-fa225f9e874e75c2bd44e340380ddf28319154969252http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1034%2Fj.1600-0870.1994.t01-1-00001.x%2Fcitedbyrefpaperuri:(41fa295687dddc17dd448e78313360fc)http://onlinelibrary.wiley.com/doi/10.1034/j.1600-0870.1994.t01-1-00001.x/citedbyABSTRACT The intertropical convergence zone (ITCZ) stays in the northern hemisphere over the Atlantic and eastern Pacific, even though the annual mean position of the sun is on the equator. To study some processes that contribute to this asymmetry about the equator, we use a two-dimensional model which neglects zonal variations and consists of an ocean model with a mixed layer coupled to a simple atmospheric model. In this coupled model, the atmosphere not only transports momentum into the ocean, but also directly affects sea surface temperature by means of wind stirring and surface latent heat flux. Under equatorially symmetric conditions, the model has, in addition to an equatorially symmetric solution, two asymmetric solutions with a single ITCZ that forms in only one hemisphere. Strong equatorial upwelling is essential for the asymmetry. Local oceanic turbulent processes involving vertical mixing and surface latent heat flux, which are dependent on wind speed, also contribute to the asymmetry.
    Xie S.-P., H. Annamalai, F. A. Schott, and J. P. McCreary Jr., 2002: Structure and mechanisms of South Indian Ocean climate variability. J. Climate, 15, 864- 878.c5d883d3-b4ed-47ad-9a9b-10a314f425e197708127d9485413b97b2a055b35a12bhttp%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2002JCli...15..864X%26db_key%3DPHY%26link_type%3DABSTRACT%26high%3D05992refpaperuri:(53c25a40975bd09875d947c03a67aadf)/s?wd=paperuri%3A%2853c25a40975bd09875d947c03a67aadf%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2002JCli...15..864X%26db_key%3DPHY%26link_type%3DABSTRACT%26high%3D05992&ie=utf-8
    Xie S.-P., K. M. Hu, J. Hafner, H. Tokinaga, Y. Du, G. Huang, and T. Sampe, 2009: Indian Ocean capacitor effect on Indo-Western Pacific climate during the summer following El Niño. J. Climate, 22, 730- 747.
    Xie S.-P., C. Deser, G. A. Vecchi, J. Ma, H. Y. Teng, and A. T. Wittenberg, 2010a: Global warming pattern formation: Sea surface temperature and rainfall. J. Climate, 23, 966- 986.10.1175/2009JCLI3329.13eff0181-4d63-488f-92b9-71a15a93bf303eefc59c87e4f4ca7ffcbe050a36a436http%3A%2F%2Fwww.cabdirect.org%2Fabstracts%2F20103118162.htmlrefpaperuri:(b0ebeb07b4f54809d624dfe9936fb36c)http://www.cabdirect.org/abstracts/20103118162.htmlAbstract Spatial variations in sea surface temperature (SST) and rainfall changes over the tropics are investigated based on ensemble simulations for the first half of the twenty-first century under the greenhouse gas (GHG) emission scenario A1B with coupled ocean揳tmosphere general circulation models of the Geophysical Fluid Dynamics Laboratory (GFDL) and National Center for Atmospheric Research (NCAR). Despite a GHG increase that is nearly uniform in space, pronounced patterns emerge in both SST and precipitation. Regional differences in SST warming can be as large as the tropical-mean warming. Specifically, the tropical Pacific warming features a conspicuous maximum along the equator and a minimum in the southeast subtropics. The former is associated with westerly wind anomalies whereas the latter is linked to intensified southeast trade winds, suggestive of wind揺vaporation揝ST feedback. There is a tendency for a greater warming in the northern subtropics than in the southern subtropics in accordance with asymmetries in trade wind changes. Over the equatorial Indian Ocean, surface wind anomalies are easterly, the thermocline shoals, and the warming is reduced in the east, indicative of Bjerknes feedback. In the midlatitudes, ocean circulation changes generate narrow banded structures in SST warming. The warming is negatively correlated with wind speed change over the tropics and positively correlated with ocean heat transport change in the northern extratropics. A diagnostic method based on the ocean mixed layer heat budget is developed to investigate mechanisms for SST pattern formation. Tropical precipitation changes are positively correlated with spatial deviations of SST warming from the tropical mean. In particular, the equatorial maximum in SST warming over the Pacific anchors a band of pronounced rainfall increase. The gross moist instability follows closely relative SST change as equatorial wave adjustments flatten upper-tropospheric warming. The comparison with atmospheric simulations in response to a spatially uniform SST warming illustrates the importance of SST patterns for rainfall change, an effect overlooked in current discussion of precipitation response to global warming. Implications for the global and regional response of tropical cyclones are discussed.
    Xie S.-P., Y. Du, G. Huang, X.-T. Zheng, H. Tokinaga, K. M. Hu, and Q. Y. Liu, 2010b: Decadal shift in El Niño influences on Indo-Western Pacific and East Asian climate in the 1970s. J. Climate, 23( 12), 3352- 3368.10.1175/2010JCLI3429.17144a7ad-53df-4263-8783-93a96a101e72fb0df0e59d0789c0c8b6b9c8ee24a24dhttp%3A%2F%2Fwww.cabdirect.org%2Fabstracts%2F20103246481.htmlrefpaperuri:(17cb573e4368e9bf71b9cba548c29941)http://www.cabdirect.org/abstracts/20103246481.htmlAbstract El Ni09o’s influence on the subtropical northwest (NW) Pacific climate increased after the climate regime shift of the 1970s. This is manifested in well-organized atmospheric anomalies of suppressed convection and a surface anticyclone during the summer (June–August) of the El Ni09o decay year [JJA(1)], a season when equatorial Pacific sea surface temperature (SST) anomalies have dissipated. In situ observations and ocean–atmospheric reanalyses are used to investigate mechanisms for the interdecadal change. During JJA(1), the influence of the El Ni09o–Southern Oscillation (ENSO) on the NW Pacific is indirect, being mediated by SST conditions over the tropical Indian Ocean (TIO). The results here show that interdecadal change in this influence is due to changes in the TIO response to ENSO. During the postregime shift epoch, the El Ni09o teleconnection excites downwelling Rossby waves in the south TIO by anticyclonic wind curls. These Rossby waves propagate slowly westward, causing persistent SST warming over the thermocline ridge in the southwest TIO. The ocean warming induces an antisymmetric wind pattern across the equator, and the anomalous northeasterlies cause the north Indian Ocean to warm through JJA(1) by reducing the southwesterly monsoon winds. The TIO warming excites a warm Kelvin wave in tropospheric temperature, resulting in robust atmospheric anomalies over the NW Pacific that include the surface anticyclone. During the preregime shift epoch, ENSO is significantly weaker in variance and decays earlier than during the recent epoch. Compared to the epoch after the mid-1970s, SST and wind anomalies over the TIO are similar during the developing and mature phases of ENSO but are very weak during the decay phase. Specifically, the southern TIO Rossby waves are weaker, so are the antisymmetric wind pattern and the North Indian Ocean warming during JJA(1). Without the anchor in the TIO warming, atmospheric anomalies over the NW Pacific fail to develop during JJA(1) prior to the mid-1970s. The relationship of the interdecadal change to global warming and implications for the East Asian summer monsoon are discussed.
    Yang J. L., Q. Y. Liu, S.-P. Xie, Z. Y. Liu, and L. X. Wu, 2007: Impact of the Indian Ocean SST basin mode on the Asian summer monsoon. Geophys. Res. Lett., 34,L02708, doi: 10.1029/2006GL028571.10.1029/2006GL028571d74ba7f8-6522-4eab-b3f5-ffd4862334af353dab2ba8ff639e582352e283adfb3ehttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2006GL028571%2Ffullrefpaperuri:(e898257b777bbefaeee7232eab34598f)http://onlinelibrary.wiley.com/doi/10.1029/2006GL028571/fullABSTRACT 1] Following an El Nino event, a basin-wide warming takes place over the tropical Indian Ocean, peaks in late boreal winter and early spring, and persists through boreal summer. Our observational analysis suggests that this Indian Ocean warming induces robust climatic anomalies in the summer Indo-West Pacific region, prolonging the El Nino's influence after tropical East Pacific sea surface temperature has returned to normal. In response to the Indian Ocean warming, precipitation increases over most of the basin, forcing a Matsuno-Gill pattern in the upper troposphere with a strengthened South Asian high. Near the ground, the southwest monsoon intensifies over the Arabian Sea and weakens over the South China and Philippine Seas. An anomalous anticyclonic circulation forms over the subtropical Northwest Pacific, collocated with negative precipitation anomalies. All these anomaly patterns are reproduced in a coupled model simulation initialized with a warming in the tropical Indian Ocean mixed layer, indicating that the Indian Ocean warming is not just a passive response to El Nino but important for summer climate variability in the Indo-West Pacific region. The implications for seasonal prediction are discussed.
    Yokoi T., T. Tozuka, and T. Yamagata, 2008: Seasonal variation of the Seychelles Dome. J. Climate, 21, 3740- 3754.10.1175/2008JCLI1957.13434883f-ed1b-4df8-99bf-4fee13233c1da0733399c14f131f53b3eae35f4cbfc3http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2008JCli...21.3740Yrefpaperuri:(4925f6237cdd09adde70afe287dda113)http://adsabs.harvard.edu/abs/2008JCli...21.3740YAbstract Using an ocean general circulation model (OGCM), seasonal variation of the Seychelles Dome (SD) is investigated for the first time. The SD is an oceanic thermal dome located in the southwestern Indian Ocean, and its influence on sea surface temperature is known to play an important role in the Indian monsoon system. Its seasonal variation is dominated by a remarkable semiannual cycle resulting from local Ekman upwelling. This semiannual nature is explained by different contributions of the following two components of the Ekman pumping: one term that is proportional to the planetary beta and the zonal wind stress and the other term that is proportional to the wind stress curl. The former is determined by the seasonal change in the zonal component of the wind stress vector above the SD; it is associated with the Indian monsoon and causes downwelling (upwelling) during boreal summer (boreal winter). The latter, whose major contribution comes from the meridional gradient of the zonal wind stress, als...
    Yokoi T., T. Tozuka, and T. Yamagata, 2009: Seasonal variations of the Seychelles Dome simulated in the CMIP3 models. J. Phys. Oceanogr., 39, 449- 457.10.1175/2008JPO3914.1516c36d5bd12909cba55bc5499a7ddcahttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2009JPO....39..449Yhttp://adsabs.harvard.edu/abs/2009JPO....39..449YAbstract Using outputs from the “twentieth-century climate in coupled models” (20c3m) control run of the Coupled Model Intercomparison Project, phase 3 (CMIP3), coupled GCMs, the authors have examined how seasonal variations of the Seychelles Dome (SD) are simulated in the southwestern Indian Ocean. The observed SD shows a dominant semiannual signal due to the semiannual variation in the local Ekman upwelling resulting from a combination of two terms related to the wind stress curl and the zonal wind stress. However, all models fail to reproduce this important mechanism. In particular, the latter contribution—that determined by the seasonal variation of the zonal wind stress associated with the Indian monsoon—is not well simulated. Successful models need to reproduce the asymmetric nature of the monsoon: a shorter and stronger summer monsoon and a longer and weaker winter monsoon. Possible remedies for the model bias are also discussed.
    Yokoi T., T. Tozuka, and T. Yamagata, 2012: Seasonal and interannual variations of the SST above the Seychelles Dome. J. Climate, 25, 800- 814.10.1175/JCLI-D-10-05001.17cbd32157071bad0d09cb39283b9ea35http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2012JCli...25..800Yhttp://adsabs.harvard.edu/abs/2012JCli...25..800YAbstract The seasonal and interannual variations of the sea surface temperature (SST) above the Seychelles Dome (SD) are investigated using outputs from an OGCM. The SST warms from August to April and cools from May to July. The surface heat flux plays the most important role in the seasonal variation, and it is mostly due to shortwave radiation. The horizontal advection tends to warm the SST in austral winter owing to the southward Ekman heat transport associated with the Indian summer monsoon. The cooling by the vertical turbulent diffusion becomes most effective in austral summer owing to the thin mixed layer during that time. On the interannual time scale, the SST becomes anomalously warm (cool) when the SD is weak (strong). In contrast to the seasonal variation, the vertical diffusion plays the most important role and causes anomalous warming (cooling). This warming (cooling) is due to the anomalously warm (cold) water below the mixed layer as a result of the deeper (shallower) thermocline in response to ocean dynamics. Also, the cooling by the vertical diffusion becomes less (more) efficient, because the mixed layer is anomalously thick (thin). The horizontal advection contributes to the anomalous warming (cooling) due to the anomalous southward (northward) Ekman heat transport. On the other hand, the anomalous surface heat flux tends to cool (warm) the mixed layer, because the warming of the mixed layer by the shortwave radiation becomes less (more) efficient due to the anomalously thick (thin) mixed layer.
    Yu W. D., B. Q. Xiang, L. Liu, and N. Liu, 2005: Understanding the origins of interannual thermocline variations in the tropical Indian Ocean. Geophys. Res. Lett., 32,L24706, doi: 10.1029/2005GL024327.10.1029/2005GL024327ef22564f9e7d8e712c606f023ec71971http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2005GL024327%2Fcitedbyhttp://onlinelibrary.wiley.com/doi/10.1029/2005GL024327/citedbyABSTRACT Based on the data analysis of the 1000 hPa wind, SST and SSH anomalies, it is revealed that the atmospheric variations associated with Indian Ocean Dipole (IOD, or referred as Indian Ocean Zonal Dipole Mode, IOZDM) consist of a pair of anticyclones closely north and south of the equator with accompanying intense equatorial easterly anomalies, while the atmospheric variations related to El Ni&ntilde;o/Southern Oscillation (ENSO) include a strong anticyclone in the southeastern tropical Indian Ocean (TIO) at higher latitudes with strong along-shore wind anomalies near Java-Sumatra coast. The different atmospheric forcing patterns lead to the fact that oceanic thermocline variations associated with IOD/IOZDM are more closely confined to the region north of 10掳S, while ENSO-induced thermocline variations are dominant south of 10掳S.
    Zheng X.-T., S.-P. Xie, G. A. Vecchi, Q. Y. Liu, and J. Hafner, 2010: Indian Ocean dipole response to global warming: Analysis of ocean-atmospheric feedbacks in a coupled model. J. Climate, 23, 1240- 1253.10.1175/2009JCLI3326.186a5b86d97610a3eded00fbc6b4194dehttp%3A%2F%2Fwww.cabdirect.org%2Fabstracts%2F20103118386.htmlhttp://www.cabdirect.org/abstracts/20103118386.htmlLow-frequency modulation and change under global warming of the Indian Ocean dipole (IOD) mode are investigated with a pair of multicentury integrations of a coupled ocean09 tmosphere general circulation model: one under constant climate forcing and one forced by increasing greenhouse gas concentrations. In the unforced simulation, there is significant decadal and multidecadal modulation of the IOD variance. The mean thermocline depth in the eastern equatorial Indian Ocean (EEIO) is important for the slow modulation, skewness, and ENSO correlation of the IOD. With a shoaling (deepening) of the EEIO thermocline, the thermocline feedback strengthens, and this leads to an increase in IOD variance, a reduction of the negative skewness of the IOD, and a weakening of the IOD09-NSO correlation. In response to increasing greenhouse gases, a weakening of the Walker circulation leads to easterly wind anomalies in the equatorial Indian Ocean; the oceanic response to weakened circulation is a thermocline shoaling in the EEIO. Under greenhouse forcing, the thermocline feedback intensifies, but surprisingly IOD variance does not. The zonal wind anomalies associated with IOD are found to weaken, likely due to increased static stability of the troposphere from global warming. Linear model experiments confirm this stability effect to reduce circulation response to a sea surface temperature dipole. The opposing changes in thermocline and atmospheric feedbacks result in little change in IOD variance, but the shoaling thermocline weakens IOD skewness. Little change under global warming in IOD variance in the model suggests that the apparent intensification of IOD activity during recent decades is likely part of natural, chaotic modulation of the ocean tmosphere system or the response to nongreenhouse gas radiative changes.
    Zheng X.-T., S.-P. Xie, and Q. Y. Liu, 2011: Response of the Indian Ocean basin mode and its capacitor effect to global warming. J. Climate, 24, 6146- 6164.10.1175/2011JCLI4169.116785415-9401-4ad6-aad2-eec96b94b78b25d97c70371cbb6deb840fdc6eb1cc25http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2011JCli...24.6146Zrefpaperuri:(ef6e5441e0faa47e822bdcb6ba852b3f)http://adsabs.harvard.edu/abs/2011JCli...24.6146ZAbstract The development of the Indian Ocean basin (IOB) mode and its change under global warming are investigated using a pair of integrations with the Geophysical Fluid Dynamics Laboratory Climate Model version 2.1 (CM2.1). In the simulation under constant climate forcing, the El Ni09o–induced warming over the tropical Indian Ocean (TIO) and its capacitor effect on summer northwest Pacific climate are reproduced realistically. In the simulation forced by increased greenhouse gas concentrations, the IOB mode and its summer capacitor effect are enhanced in persistence following El Ni09o, even though the ENSO itself weakens in response to global warming. In the prior spring, an antisymmetric pattern of rainfall–wind anomalies and the meridional SST gradient across the equator strengthen via increased wind–evaporation–sea surface temperature (WES) feedback. ENSO decays slightly faster in global warming. During the summer following El Ni09o decay, the resultant decrease in equatorial Pacific SST strengthens the SST contrast with the enhanced TIO warming, increasing the sea level pressure gradient and intensifying the anomalous anticyclone over the northwest Pacific. The easterly wind anomalies associated with the northwest Pacific anticyclone in turn sustain the SST warming over the north Indian Ocean and South China Sea. Thus, the increased TIO capacitor effect is due to enhanced air–sea interaction over the TIO and with the western Pacific. The implications for the observed intensification of the IOB mode and its capacitor effect after the 1970s are discussed.
    Zheng X.-T., S.-P. Xie, Y. Du, L. Liu, G. Huang, and Q. Y. Liu, 2013: Indian Ocean Dipole response to global warming in the CMIP5 multi-model ensemble. J. Climate, 26, 6067- 6080.10.1175/JCLI-D-12-00638.1217b6d62-44c0-4b96-b573-4e2e97f60adf041735ee362041f5e1f92012ac1b199ahttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2013JCli...26.6067Zrefpaperuri:(e607ff29921db99b0496d07bdde9439f)http://adsabs.harvard.edu/abs/2013JCli...26.6067ZAbstract The response of the Indian Ocean dipole (IOD) mode to global warming is investigated based on simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5). In response to increased greenhouse gases, an IOD-like warming pattern appears in the equatorial Indian Ocean, with reduced (enhanced) warming in the east (west), an easterly wind trend, and thermocline shoaling in the east. Despite a shoaling thermocline and strengthened thermocline feedback in the eastern equatorial Indian Ocean, the interannual variance of the IOD mode remains largely unchanged in sea surface temperature (SST) as atmospheric feedback and zonal wind variance weaken under global warming. The negative skewness in eastern Indian Ocean SST is reduced as a result of the shoaling thermocline. The change in interannual IOD variance exhibits some variability among models, and this intermodel variability is correlated with the change in thermocline feedback. The results herein illustrate that mean state changes modulate interannual modes, and suggest that recent changes in the IOD mode are likely due to natural variations.
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Manuscript received: 19 March 2015
Manuscript revised: 27 May 2015
Manuscript accepted: 04 June 2015
通讯作者: 陈斌, bchen63@163.com
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The Southwest Indian Ocean Thermocline Dome in CMIP5 Models: Historical Simulation and Future Projection

  • 1. Key Laboratory of Physical Oceanography, Ministry of Education, and Key Laboratory of Ocean-Atmosphere Interaction and Climate in Universities of Shandong, Ocean University of China, Qingdao 266100
  • 2. Qingdao Collaborative Innovation Center of Marine Science and Technology, Ocean University of China, Qingdao 266003
  • 3. State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301

Abstract: Using 20 models of the Coupled Model Intercomparison Project Phase 5 (CMIP5), the simulation of the Southwest Indian Ocean (SWIO) thermocline dome is evaluated and its role in shaping the Indian Ocean Basin (IOB) mode following El Niño investigated. In most of the CMIP5 models, due to an easterly wind bias along the equator, the simulated SWIO thermocline is too deep, which could further influence the amplitude of the interannual IOB mode. A model with a shallow (deep) thermocline dome tends to simulate a strong (weak) IOB mode, including key attributes such as the SWIO SST warming, antisymmetric pattern during boreal spring, and second North Indian Ocean warming during boreal summer. Under global warming, the thermocline dome deepens with the easterly wind trend along the equator in most of the models. However, the IOB amplitude does not follow such a change of the SWIO thermocline among the models; rather, it follows future changes in both ENSO forcing and local convection feedback, suggesting a decreasing effect of the deepening SWIO thermocline dome on the change in the IOB mode in the future.

1. Introduction
  • The tropical Indian Ocean (TIO) is a crucial region for global climate on intraseasonal, interannual and decadal timescales (Schott et al., 2009). In particular, a thermocline dome, located in the Southwest Indian Ocean (SWIO), is important to the local and remote climate. The variation of SST over this region affects local biological production, the activity of tropical cyclones (Xie et al., 2002, 2009), the South Asian monsoon onset (Annamalai et al., 2005; Du et al., 2009), and even remote climate by atmospheric teleconnections (Annamalai et al., 2005, 2007; Xie et al., 2009, 2010b; Du et al., 2011). Besides the several coastal upwelling regions, the SST variation in the SWIO is relatively large in the TIO due to the shallow thermocline. The interannual SST variability cannot be explained by local heat flux, indicating the ocean dynamics are crucial in this region (Klein et al., 1999; Xie et al., 2002; Li et al., 2015b).

    Previous studies have investigated this thermocline dome using observations and model simulations (Reverdin and Fieux, 1987; Woodberry et al., 1989; McCreary et al., 1993; Yokoi et al., 2008, 2009, 2012; Du et al., 2014). The local winds in the tropical South Indian Ocean (SIO) have been suggested as being responsible for generating Ekman upwelling and shoaling of the SWIO thermocline with westward propagating Rossby waves (Masumoto and Meyers, 1998, Yokoi et al., 2008). This shallow thermocline induces vertical entrainment that influences SST more effectively, leading to larger interannual variance. On the interannual timescale, the variation in the SWIO is affected by oceanic dynamics (Huang and Kinter, 2002; Xie et al., 2002). During an El Niño event, as the deep convection center moves, equatorial Indian Ocean (EIO) easterlies and related anticyclonic wind anomalies appear during boreal autumn to winter in developing years and excite downwelling Rossby wave in the tropical SIO region (Yu et al., 2005). In the boreal spring following El Niño, this downwelling Rossby wave propagates to the SWIO where the mean thermocline is shallow, deepens the local thermocline, and warms SST (Xie et al., 2002).

    The ENSO-induced SWIO warming leads to a series of local air-sea interactions that shape the spatiotemporal structures of the Indian Ocean Basin (IOB) mode. In boreal spring, an atmospheric antisymmetric pattern is induced by the SWIO warming: more (less) rainfall with northwesterly (northeasterly) wind anomalies south (north) of the equator (Wu et al., 2008). The wind-evaporation-SST feedback (Xie and Philander, 1994) helps sustain this antisymmetric pattern through early summer, operating on the easterly climatological winds (Kawamura et al., 2001; Wu et al., 2008). When the summer monsoon breaks out in May over the North Indian Ocean (NIO), the anomalous northeasterlies warm the SST there, inducing a second warming over the NIO and maintenance of the IOB warming through boreal summer following El Niño (Du et al., 2009). The IOB further affects the climate over the subtropical northwestern Pacific and East Asia via the so-called capacitor effect (Yang et al., 2007; Xie et al., 2009, 2010b).

    Due to the importance of the SWIO thermocline to local and remote climate, there have been numerous studies that have evaluated simulations of the thermocline dome and its interannual variation in coupled general circulation models (CGCMs, Saji et al., 2006; Yokoi et al., 2009; Du et al., 2013; Nagura et al., 2013). (Yokoi et al., 2009) found that most models capture the dome structure of the thermocline in the tropical SIO and its annual cycle, but the simulated dome is deeper and more eastward than observed. (Nagura et al., 2013) pointed out these biases are related to the easterly wind bias over the EIO. Recently, (Li et al., 2015a) reported a common equatorial easterly wind bias along the EIO in CGCMs, which is related to errors in the South Asian summer monsoon. This simulated wind bias leads to a SWIO thermocline that is too deep, influencing the IOB amplitude further (Li et al., 2015b).

    The interannual SST variability in the SWIO and its effect on the TIO climate in CMIP5 models have been evaluated (Du et al., 2013). However, the role of the SWIO thermocline dome in shaping the interannual TIO variability simulation, especially the local air-sea interactions, needs to be examined in detail. Furthermore, the mean states in the TIO, especially that of equatorial wind, change significantly under global warming (Zheng et al., 2010, 2013). The responses of the thermocline dome to global warming, as well as its climate effects, should also be examined.

    In this paper, we use coupled models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to evaluate the simulation of the thermocline dome and its response to global warming. Compared with observations, the simulated SWIO thermocline is too deep and shifted eastward in most models. The erroneous simulation is related to the easterly wind bias in the EIO, which affects the local Ekman upwelling effect. In addition, the diversity of the SWIO thermocline depth among models further influences the interannual IOB mode and its key attributes. Under global warming, simulation of the thermocline dome deepens in the SWIO due to the weakened Walker circulation and easterly wind trend along the equator in most of the models. However, the changes of the IOB mode do not follow the SWIO thermocline change among the models, due to the changes in ENSO forcing and local convection feedback in the SWIO.

    The rest of the paper is organized as follows. Section 2 briefly describes the model simulations and observations used in this study. Section 3 reports the simulation of the thermocline dome and related TIO interannual variability in the CMIP5 models. The responses of the SWIO thermocline to global warming are presented in section 4. Section 5 is a summary.

2. Model simulations and observations
  • To examine the capability of simulating the thermocline dome and its interannual variability, we use the 20 model outputs from the World Climate Research Program CMIP5 multi-model ensemble organized by the Program for Climate Model Diagnosis and Intercomparison for the Intergovernmental Panel on Climate Change Fifth Assessment Report (Table 1). In this study, two sets of simulations from the 20 CMIP5 models are analyzed (Taylor et al., 2012). We use historical climate experiments (historical run) to examine the simulation of the thermocline dome in the models, and compare them with the +8.5 W m-2 Representative Concentration Pathway (RCP8.5) experiments to investigate how the SWIO thermocline and its interannual variability change under global warming. The historical experiments are forced by historical greenhouses gases, aerosols, and other radiative forcing; and the RCP8.5 experiments are run under the radiative forcing reaching 8.5 W m-2 near 2100 (equivalent to >1370 ppm CO2 in concentration). Here, we choose 50 years separately in the 20th (1950-99) and 21st (2045-94) centuries to represent present-day and future climate for our investigation.

    To assess the skills of the CMIP5 models in SWIO thermocline dome simulation, we use the observed SST from the National Oceanic and Atmospheric Administration Extended Reconstructed SST version 3b dataset (Smith et al., 2008). The surface wind and precipitation are from the National Centers for Environmental Prediction-National Center for Atmospheric Research atmospheric reanalysis (Kalnay et al., 1996) and the Center for Climate Prediction Merged Analysis of Precipitation (CMAP) (Xie and Arkin, 1996), respectively. We also use the ocean temperature from the Simple Ocean Data Assimilation (SODA) product (Carton and Giese, 2008) from 1979 to 2010 (limited by CMAP and SODA).

    In this study, we use the variables averaged from 5° to 10°S and 50° to 80°E, referred to as the indices in the SWIO dome region. To illustrate the interannual variability, we perform a three-month running average to reduced intraseasonal variability and calculate a 9-year running mean to remove decadal and longer variations, which are also significant over the TIO (Deser et al., 2004).

3. Simulations of the thermocline dome and its effect on TIO interannual variability
  • This section examines the simulations of the thermocline dome in the CMIP5 models and its roles in the persistence of IOB warming. We start with the simulation of the thermocline dome in the models and then follow with an investigation of each related attribution of the IOB mode, including local SST variability in the SWIO, the antisymmetric atmospheric pattern associated with the SWIO warming, and the second warming in the NIO during boreal summer.

  • In observations, the thermocline dome is located in the SWIO at approximately (5°-10°S, 50°-80°E) (Xie et al., 2002). Figure 1 shows the climatology (1950-99) of the thermocline depth in the 20th century, represented by the 20°C isotherm (Z20), in the historical runs of the 20 CMIP5 models and SODA outputs. Most of the CMIP5 models capture the features of the thermocline dome seen in SODA, such as its location and depth. However, as reported in previous studies, there is an eastward displacement bias in the mutli-model ensemble (MME; Fig. 1b). This bias mainly appears in six of the models; namely, bcc-csm1-1, CNRM-CM5, GFDL-CM3, HadGEM2-CC, HadGEM2-ES and MRI-CGCM3. In addition, compared with observations, the thermocline is too deep in several of the models, such as FGOALS-s2 and MRI-CGCM3, but too shallow in others, such as CSIRO-Mk3-6-0 and GFDL-ESM2G. In general, simulation of the thermocline depth is too deep in the MME (Fig. 2c). The annual mean of Z20 in the SWIO reaches about 94 m, while it is only 83 m in observations.

    Figure 1.  Annual mean Z20 (unit: m; gray shading $<$100) in (a) observations, (b) the MME simulation and (c-v) the 20 CMIP5 model historical runs. Red boxes show the SWIO.

    Figure 1.  (Continued.)

    Figure 2.  Annual mean Z20 (unit: m; contours), surface wind velocity (units: m s$^-1$; vectors) and Ekman pumping velocity (units: m s$^-1$; color scale) in (a) observations and (b) the MME simulation for the historical run. Dashed contours in (a, b) represent 90 m. The observed (blue line) and MME simulation (red dashed line) of annual mean Z20 in the SIO (averaged in 5$^\circ$-10$^\circ$S), equatorial zonal wind (averaged in 3$^\circ$S-3$^\circ$N) and Ekman pumping velocity (averaged in 6$^\circ$-9$^\circ$S) are shown in (c-e), respectively. The shading in (c-e) shows one standard deviation of inter-model variability.

    Figure 3.  Scatterplots of the annual mean CEIO [averaged in (3$^\circ$S-3$^\circ$N, 70$^\circ$-90$^\circ$E)] zonal wind (units: m s$^-1)$ with (a) the Ekman pumping velocity (units: m s$^-1)$ over the SIO and (b) the SWIO Z20 (unit: m) among observations and 20 CMIP5 models. (c, d) As in (a, b) but for the annual mean SWIO Z20 (unit: m) with the standard deviation of TIO SST anomalies ($^\circ$C) in MAM and JJA, respectively. The solid line denotes the linear regression. The inter-model correlation and $p$ value are shown in each panel.

    Figure 4.  Scatterplots of annual mean SWIO Z20 (unit: m) with the (a) standard deviation of SWIO SST anomalies (unit: $^\circ$C) in MAM, (b) regression of the MAM(1) SWIO SST upon the NDJ(0) ENSO index, (c) spatial correlation of precipitation EOF1 in observations with each model, (d) explained variance of precipitation EOF1 (%), (e) standard deviation of NIO SST anomalies (unit: $^\circ$C) in JJA and (f) regression of the JJA(1) NIO SST upon the NDJ(0) ENSO index among observations and 20 CMIP5 models. The solid line denotes the linear regression. The inter-model correlation and $p$ value are shown in each panel. MRI-CGCM3 is excluded from the correlation calculation.

    Figure 5.  Regressions of precipitation (units: mm d$^-1$; contours) and surface wind anomalies (units: m s$^-1$; vectors) upon the antisymmetric pattern principal component in (a) observations and (b-u) each model. The values in the top right are explained variances of EOF1.

    Figure 5.  (Continued.)

    Figure 6.  Annual mean states of SST (unit: $^\circ$C; contours), $Z_\max$ (unit: m; color scale) and surface wind (units: m s$^-1$; vectors) in the (a) historical run, (b) RCP8.5 run, and (c) their differences.

    Figure 7.  (a) Scatterplot of $Z_\max$ (unit: m) between 1950-99 and 2045-94. (b) Scatterplot of $Z_\max$ (unit: m) and CEIO zonal wind (units: m s$^-1$) differences between 1950-99 and 2045-94. The solid line denotes the linear regression. The inter-model correlation and $p$ value are shown in each panel.

    Figure 8.  Scatterplots of the $Z_\max$ differences (unit: m) between 1950-99 and 2045-94 with that of (a) SWIO SST amplitude (unit: $^\circ$C) in MAM, (b) $R$ ($T_SWIO$, Niño3.4), (c) IOB amplitude (unit: $^\circ$C) in MAM and (d) NIO SST amplitude (unit: $^\circ$C) in JJA. The solid line denotes the linear regression. The inter-model correlation and $p$ value are shown in each panel.

    Figure 9.  Scatterplots of (a) TIO SST amplitude (unit: $^\circ$C) and (b) SWIO SST amplitude (unit: $^\circ$C) in MAM with that of ENSO (unit: $^\circ$C) in NDJ. (c, d) As in (a, b) but for the differences between 1950-99 and 2045-94. The solid line denotes the linear regression. The inter-model correlation and $p$ value are shown in each panel.

    Figure 10.  Scatterplot of the SWIO SST warming magnitude (unit: $^\circ$C) with (a) the trend of CEIO zonal wind (units: m s$^-1$) and (b) SWIO percentage precipitation change $\Delta P/P$ (unit: %) between 1950-99 and 2045-94. (c) Scatterplot of the change in the SWIO convection feedback parameter (units: mm d$^-1$ $^\circ$C$^-1$) with that of NIO SST amplitude (unit: $^\circ$C) in JJA. The solid line denotes the linear regression. The inter-model correlation and $p$ value are shown in each panel.

    The thermocline bias in the models is related to that of surface wind, which is crucial for the formation of the dome. Previous studies have suggested that the shallow thermocline in the SWIO is related to the cyclonic wind stress curls over the southern TIO (Xie et al., 2002). Here, we examine the relationship between the surface wind along the EIO and the thermocline depth in the SWIO. Both in the observation and MME, there are upwelling Ekman pumping velocities over the entire tropical SIO region with the thermocline dome in the SWIO (Figs. 2a and b). However, there is a pronounced easterly wind bias in the MME over the central EIO (CEIO) region (Fig. 2d), which is consistent with previous studies (Cai and Cowan, 2013; Li et al., 2015a, b). This corresponds to an Ekman pumping velocity in the SIO that is too weak (Fig. 2e), deepening the thermocline in the SWIO (Fig. 2c).

    The agreement among the simulations of the thermocline depth, zonal wind and Ekman pumping velocity is pronounced in the inter-model analysis. The inter-model scatterplot between zonal wind in the EIO and Ekman pumping velocity over the SIO shows a high correlation at r=0.72 (Fig. 3a). Furthermore, the inter-model zonal wind along the equator is also highly correlated with the thermocline depth along the equator at r=-0.59 (Fig. 3b). These results support the hypothesis of the origin of the deep SWIO thermocline in coupled models (Li et al., 2015b); that is, when the easterly wind bias appears in a model, the simulated thermocline dome tends to be deepened with weakened cyclonic wind curls over the tropical SIO region.

  • The interannual variability of SWIO SST is largely induced by an oceanic downwelling Rossby wave, which is forced by El Niño (Xie et al., 2002; Du et al., 2009). When propagating to the SWIO during MAM(1), the downwelling Rossby wave suppresses the local entrainment and increases the SST [MAM: March-April-May; numerals in parentheses denote ENSO developing (0) and decay (1) years]. This is why the surface heat flux cannot explain the SST warming in this region (Klein et al., 1999; Yokoi et al., 2012). Here, we examine the importance of the SWIO thermocline depth to the local SST variability. Since the SWIO thermocline is too deep in MRI-CGCM3 compared with observations and other models, we exclude MRI-CGCM3 from the following inter-model analyses.

    Because the SWIO interannual warming is mainly forced by El Niño, the inter-model amplitude of SWIO SST is highly correlated with ENSO amplitude (r=0.76), which is represented by the standard deviation of Niño3.4 SST during November-December-January (NDJ). In addition, the local thermocline depth does indeed influence the SWIO SST variability. As shown in Fig. 4a, the correlation of inter-model variability in SWIO SST amplitude with Z20 is -0.39, exceeding the 90% confidence level based on the t-test. Furthermore, the regression of SWIO SST upon Niño3.4 anomalies, R(T SWIO, Niño3.4), is also significantly correlated with thermocline depth (r=-0.48; Fig. 4b). This relationship indicates the importance of the SWIO thermocline to local SST variability: when the thermocline is shallow (deep) in the SWIO, ENSO influences the SWIO SST more (less) effectively.

  • Following an El Niño event, an antisymmetric atmospheric pattern always appears over the TIO region during boreal spring, with more (less) rainfall and northwesterly (northeasterly) wind anomalies in the southern (northern) TIO. Previous studies have suggested that this antisymmetric pattern is maintained by wind-evaporation-SST feedback (Xie and Philander, 1994), with prevailing southeasterly wind in the southern TIO (Kawamura et al., 2001; Wu et al., 2008; Du et al., 2009). The SWIO warming, which is related to the ENSO-induced oceanic downwelling Rossby wave, is important to the antisymmetric wind pattern (Du et al., 2009). This SST warming intensifies local convection and induces a cross-equatorial SST gradient, leading to the antisymmetric precipitation/surface wind pattern due to the Coriolis force acting on the northerly wind crossing the equator induced by SWIO warming.

    Here, we perform an EOF analysis of precipitation anomalies over the TIO in MAM(1) for observations and each model (Fig. 5). As shown in a previous study (Wu et al., 2008), the antisymmetric pattern emerges as the first EOF mode (Fig. 5a). About half of the CMIP5 models can reproduce the antisymmetric pattern as the first EOF mode in MAM (Figs. 5b-u), consistent with a previous multi-model analysis (Du et al., 2013). The spatial correlation of the first EOF mode in observations with that simulated exceeds 0.6 in 10 of the 20 models (Fig. 4c).

    Since the antisymmetric pattern is related to SWIO warming, its simulation should also be influenced by the SWIO thermocline in the models. Indeed, we find that the inter-model diversity of spatial correlation is highly correlated with thermocline depth (r=-0.46), illustrating the importance of the dome on the antisymmetric pattern (Fig. 4c). This inter-model relationship indicates that models with a shallower thermocline in the SWIO tend to reproduce a more realistic antisymmetric pattern. Furthermore, the inter-model diversity of the explained variance of the first EOF mode is also significantly correlated with SWIO thermocline depth (r=-0.47), indicating that the models with a deep dome explain fewer of the ENSO-induced precipitation anomalies (Fig. 4d). This confirms the role of the dome in the interannual variability of TIO SST, especially in terms of the local air-sea interactions.

    When the Indian summer monsoon breaks out in late spring, the antisymmetric pattern, especially the northeasterly anomalies over the NIO (0°-20°N, 40°-100°E), are opposite to the prevailing southwesterly wind and act to warm the ocean, inducing a second warming over the NIO and extending the IOB mode through June-July-August (JJA) following El Niño (Du et al., 2009). But is the dome simulation also related to the NIO SST variability in JJA(1)? Comparing the inter-model variability of NIO SST interannual variance in JJA(1) with SWIO thermocline depth, we find a negative correlation of r=-0.41 (Fig. 4e). There is also a negative correlation of r=-0.45 between the inter-model regression of NIO SST anomalies upon Niño3.4 index, R(T NIO, Niño3.4), and thermocline depth (Fig. 4f), indicative of the SWIO thermocline influencing the second NIO warming in the IOB mode.

    According to the above analyses, we find that the dome simulation in the CMIP5 models is important to the IOB warming following El Niño——especially in terms of the local air-sea interactions, including the local interannual SST variability, antisymmetric pattern and the second NIO warming——maintaining the IOB mode to boreal summer. As a result, the diversity of the SWIO thermocline in the models, which is related to the easterly wind bias, truly affects the interannual variability in the TIO (20°S-20°N, 40°-100°E). As shown in Figs. 3c and d, the models with a shallower thermocline dome have a stronger interannual variance of TIO SST in MAM and JJA, with -0.47 and -0.40 inter-model correlations, respectively. These results are consistent with (Li et al., 2015b), indicating the important role of the dome simulation in TIO interannual variability.

4. Changes of the thermocline dome under global warming
  • In most models, an easterly wind bias leads to a deep thermocline dome. The diversity of the thermocline depth among the models further influences the IOB amplitude. Coincidentally, the zonal wind in the EIO shows an easterly trend in CMIP5 future projections with a pronounced IOD-like SST warming pattern (Zheng et al., 2010, 2013; Cai and Cowan, 2013) (IOD: Indian Ocean Dipole). This change indicates a weakening Walker circulation and a robust response to greenhouse gas warming in CGCM projections (Vecchi and Soden, 2007).The trend of zonal wind in the EIO influences the subsurface thermal structure by dynamic adjustment. Under global warming, the thermocline shoals significantly in the eastern EIO, whereas it deepens slightly in the western EIO in spite of thermodynamic shoaling effects on the thermocline due to surface warming intensification (Zheng et al., 2013).

    Since the Z20 deepens and cannot represent the thermocline depth under global warming, here, we use the depth of maximum temperature gradient (Zmax) to represent the dynamical thermocline, following previous studies (Vecchi and Soden, 2007; Zheng et al., 2010, 2013). Figure 6a shows the MME mean states of Zmax, SST and surface wind in the TIO for the historical simulation (1950-99). The pattern of the thermocline represented by Zmax is similar to that of Z20 (Fig. 2a), even though the thermocline dome in the SWIO shifts eastward slightly. By contrast, the thermocline dome moves more eastward in RCP8.5 simulations (Fig. 6b), showing the importance of zonal wind to the location of the thermocline dome (Nagura et al., 2013). Figure 6c shows the MME mean state changes between the 21st century and 20th century. Consistent with previous studies (Zheng et al., 2010, 2013), the SST warming pattern displays an IOD-like pattern: more warming in the Northwest Indian Ocean and less warming along the Sumatran coast, with an easterly wind trend along the equator. The change of the thermocline is coupled with SST and surface wind, shoaling in the eastern EIO and deepening in the western TIO.

    Similar to the effect of equatorial zonal wind on the SWIO thermocline simulation in the models, the SWIO thermocline is influenced by the changes of zonal wind in the CEIO too. We find that, due to the easterly wind trend, the SWIO thermocline deepens slightly in the MME simulation (from 69 to 72 m) and most (13 of 20) of the models, despite a thermodynamic shoaling effect on the thermocline under global warming (Fig. 7a). Figure 7b shows the scatterplot of inter-model variability in changes of SWIO Zmax and zonal wind in the EIO. The changes of Zmax and zonal wind are highly correlated at r=-0.69, indicating that if there is an easterly wind trend along the equator in a particular model, more (less) than the MME, the SWIO thermocline deepens (shoals) in this model.

    But does the change of the SWIO thermocline influence the amplitude of the IOB mode under global warming? We find that the SWIO SST amplitude and R(T SWIO, Niño3.4) decrease under global warming with the deepening thermocline in the MME (Figs. 8a and b), indicating a weakened interannual variability in the SWIO. However, the inter-model change in amplitude of SWIO SST, as well as the IOB, does not follow the change in the Zmax under global warming among the models. The correlations of the changes in SWIO SST amplitude and R(T SWIO, Niño3.4) with the change in SWIO Zmax are both insignificant (Figs. 8a and b). The changes in amplitude of TIO SST during MAM and NIO SST during JJA are also not correlated with the change in Zmax in the SWIO among the models (Figs. 8c and d). This indicates a decreasing effect of the deepening SWIO thermocline on the change in IOB amplitude under global warming.

    But why is it that the change in the SWIO thermocline cannot influence the IOB mode in future projections, given the relationship between IOB amplitude and the SWIO thermocline depth in historical simulations? Since the ENSO simulation is closely related with the IOB mode (Du et al., 2013), we first suppose that the change in IOB amplitude is mainly induced by changes in ENSO instead of the SWIO thermocline. Indeed, the ENSO amplitude is highly correlated with TIO and SWIO amplitude in the historical simulation among the models, at r=0.75 and 0.76, respectively (Figs. 9a and b).Furthermore, the changes in amplitude of IOB and SWIO SST are also highly correlated with change in ENSO amplitude, at r=0.78 and 0.76, respectively (Figs. 9c and d), indicating that the ENSO response to global warming is an effective indicator of the IOB in future projections. Previous studies have suggested that the simulation of ENSO is related to the mean SST bias in the tropical Pacific in coupled models (Wittenberg et al., 2006; Xiang et al., 2012). On the other hand, the mean SST bias in the tropical Pacific identified in previous studies (Li and Xie, 2012, 2014) could also influence the zonal wind in the EIO via the Walker circulation, further influencing the simulations of the SWIO thermocline dome, as well as the IOB mode. So, the SST bias in the tropical Pacific could affect the IOB mode through two ways: by modulating the ENSO variance, and by changing the zonal wind along the equator and the SWIO thermocline depth. The potential inter-basin relationship between the mean state and interannual variability in the Indo-Pacific region needs further investigation.

    We also suppose the enhanced air-sea interaction reported in previous studies (Zheng et al., 2011; Hu et al., 2014) is an additional possible explanation for the inconsistency between changes in the SWIO thermocline and IOB amplitude. In addition to influencing the change in the SWIO thermocline, the trend of CEIO zonal wind is also associated with a dipole-like pattern of SST, including an enhanced warming in the SWIO. The inter-model variability in SWIO SST warming also shows negative correlation with the trend of CEIO zonal wind, at r=-0.48 (Fig. 10a). This enhanced SST warming increases local precipitation following the "warmer-get-wetter" mechanism of (Xie et al., 2010a), with a high inter-model correlation between SST warming and the percentage precipitation change in the SWIO, at r=0.62 (Fig. 10b). The increased precipitation can further intensify the local air-sea interaction. Indeed, the change in SWIO convection feedback represented by the regression of precipitation upon SST, R(P SWIO, T SWIO), is correlated with changes in amplitude of NIO SST (Fig. 10c). Hence, the second NIO warming during boreal summer strengthens in the MME and in most (13 of 20) of the models, even though the SWIO thermocline deepens (Fig. 8d). This possible strengthening of the atmospheric response counteracts the effect of the deepening thermocline. The total effect of the mean state changes in the SWIO on IOB amplitude under global warming needs further investigation.

5. Summary
  • In this study, we have investigated the SWIO thermocline dome simulation and its response to global warming based on historical simulations and future climate projections by 20 CMIP5 models. Compared with observations, an easterly equatorial zonal wind bias exists in the MME and most of the models. As a result, the simulated dome in the MME is too deep and east-displaced with a weaker surface wind stress curl over the southern TIO region. This relationship between the simulated zonal wind and SWIO thermocline depth is also clear in the multi-model variability: a model with an easterly wind bias in the EIO tends to simulate a SWIO thermocline that is too deep, indicating the importance of equatorial wind simulation to the dome simulation.

    Similar to the results of (Li et al., 2015b), our inter-model analysis suggests that the dome simulation is important for the interannual amplitude of SST in the TIO during boreal spring and summer. In addition, compared with the Li et al. (2015b) study, our further examination found that the simulated SWIO thermocline depth modulates the key attributes of the IOB mode. Firstly, the SWIO thermocline depth is correlated with local SST amplitude and R(T SWIO, Niño3.4) during boreal spring. Secondly, the thermocline depth is related to the simulations of the antisymmetric atmospheric pattern in the models. In a model with a shallow (deep) thermocline dome, the first EOF mode of precipitation during MAM(1) explains more (less) interannual variance, and is more (less) similar to that in observations, which shows an antisymmetric pattern across the equator. Thirdly, we also found a close inter-model relationship between the thermocline depth and the second warming in the NIO during JJA(1). These close inter-model relationships suggest that the dome simulation is important to the formation and persistence of the IOB mode following El Niño. Recently, (Guo et al., 2015) reported a new type of IOD following El Nino, which is related with the SWIO warming and east-west SST contrast. So, the dome simulation could also influence the IOD simulation in coupled models, which is an idea we plan to investigate in the future.

    We also explored the responses of the SWIO thermocline to global warming based on CMIP5 RCP8.5 projections. Because of the weakened Walker circulation and easterly wind trend along the equator under global warming, the dome displaces eastward and the SWIO thermocline deepens slightly in the MME, in spite of a thermodynamic shoaling effect. A close relationship between the changes of the SWIO thermocline depth and equatorial zonal wind among the models confirms the importance of zonal wind to the SWIO thermocline. However, the inter-model variability of thermocline change in the SWIO shows no correlation with changes in amplitude of SWIO SST and the IOB mode, inconsistent with the thermocline depth-IOB amplitude relationship in historical runs. The inter-model diversity of future changes in both ENSO forcing and SWIO convection feedback could be responsible for that in the IOB mode, suggesting a decreasing role of the SWIO thermocline dome in maintaining the IOB mode in the future.

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