Abstract
The representation of the seasonal mean and interannual variability of the Indian summer monsoon rainfall (ISMR) in nine global ocean-atmosphere coupled models that participated in the North American Multimodal Ensemble (NMME) phase 1 (NMME:1), and in nine global ocean-atmosphere coupled models participating in the NMME phase 2 (NMME:2) from 1982–2009, is evaluated over the Indo-Pacific domain with May initial conditions. The multi-model ensemble (MME) represents the Indian monsoon rainfall with modest skill and systematic biases. There is no significant improvement in the seasonal forecast skill or interannual variability of ISMR in NMME:2 as compared to NMME:1. The NMME skillfully predicts seasonal mean sea surface temperature (SST) and some of the teleconnections with seasonal mean rainfall. However, the SST-rainfall teleconnections are stronger in the NMME than observed. The NMME is not able to capture the extremes of seasonal mean rainfall and the simulated Indian Ocean-monsoon teleconnections are opposite to what are observed.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
1 Introduction
The variations of the seasonal rainfall associated with the south Asian monsoon are enormously important for millions of lives on the Indian subcontinent and beyond. The spatial and temporal variations of rainfall have a significant impact on the agrarian economies of India, Bangladesh and Pakistan. While interannual variations in Indian summer monsoon rainfall (ISMR) are only \(\approx\) 10% of the long term mean, the high and low extremes of the seasonal mean ISMR result in floods and droughts (Shukla and Moolay 1987). Food production in the Indian region is strongly correlated with ISMR (Gadgil et al. 1999), and these floods and droughts can cause devastating human and economic losses. The south Asian monsoon is recognized as a prominent feature of the global circulation (Lau and K.-M. Kim 2006). Continental-scale land-sea contrast has been suggested as primary cause for the monsoon (Webster et al. 1998), while other studies suggest it is driven by the meridional movement of the Intra-Tropical Convergence Zone (ITCZ) (Gadgil et al. 2003). Besides these two basic components the ISMR is also influenced by the topography of Great Himalaya, which introduces an elevated heating source and helps to set the meridional tropospheric temperature gradient. The local reversal of the meridional tropospheric temperature gradient during the summer is thought to be important for the onset of the ISMR. This gradient is maintained in part by the heat fluxes and diabatic heating due to precipitation (Yanai et al. 1992; Wu and Zhang 1998). The topography of Himalaya isolates the Indian monsoon thermal maximum from the dry and cold air in the interior of Asian continent (Chakraborty et al. 2002; Boos and Kuang 2010), and numerical modeling studies have found that by removing the topography the northern extent of the precipitation is greatly reduced (e.g., Hahn and Manabe 1975; Prell and Kutzbach 1992). Another key feature of the monsoon circulation is the climatological low over northwestern India and Pakistan, which is the deepest low in the global tropics during boreal summer (Joshi and Desai 1985; Sikka 1997). It develops in April–May concurrently with the south-westerly wind regime (Ramage 1996). The high winds associated with the monsoon trough not only bring moisture over the land but also natural dust and aerosols. Aerosols can influence the monsoon through direct (interaction with solar radiation) and indirect (interaction with cloud microphysics) effects (Bollasina et al. 2011; Lau and K.-M. Kim 2006). Slowly varying boundary conditions such as SST, snow cover and soil moisture are also key components of the Indian monsoon, particularly in terms of its potential predictability (Charney and Shukla 1981). The teleconnection between southern oscillation and ISMR is among the oldest observed teleconnections (Walker 1925). Observational analysis shows that indian summer monsoon rainfall found below average during El Niño events, while La Niña events lead to above normal rainfall (e.g. Sikka 1980; Pant and Parthasarathy 1981; Rasmusson and Carpenter 1983; Gadgil et al. 2003, 2004). Niño 3.4 index (standardized area average SST average over the region 170\(^{\circ }\)E–120\(^{\circ }\)W, 5\(^{\circ }\)S–5\(^{\circ }\)N) is negatively correlated with ISMR. The observed negative correlation between the ISMR and Niño 3.4 index can be explained to some extent by the modulation of the Walker circulation (Shukla and Paolino 1983; Palmer et al. 1992). Thus, the Indian monsoon includes a complex orographically influenced structure, interaction between convection and large-scale atmospheric circulation, wave propagation in both the zonal and meridional directions, air-sea interaction, and cloud-aerosol interaction. Due to the presence of all the above components and their nonlinear interactions, Indian monsoon rainfall is an extremely challenging phenomenon to simulate (Gadgil et al. 2005).
Uncertainties and model errors in climate prediction can be classified into two groups: (1) uncertainties and errors in model initialization and (2) uncertainties and errors in model parameterizations and model physics (Buizza et al. 2005; Schwierz et al. 2006). The multi-model ensemble (MME) is recognized as one approach to address the above-mentioned uncertainties and errors (Palmer et al. 2004, 2005; Hagedorn et al. 2004). MMEs typically have higher skill for predicting weather and climate as compared to single models, and also provide estimates of model uncertainty. The simulation and prediction of ISMR at both inter-annual and intra-seasonal time scales has been evaluated in several such MMEs (Gadgil and Sajani 1998; Kang et al. 2002; Rajeevan and Nanjundiah 2009; Sperber et al. 2013; Wang et al. 2004, 2004). All MMEs examined previously have been shown to simulate large-scale feature of Indian rainfall with modest skill. Some studies (Wang et al. 2003; Sharmila et al. 2013) have highlighted the importance of air-sea interactions and suggest that coupled ocean-atmospheric models are crucial for monsoon seasonal predictions. Preethi et al. (2010) and Rajeevan et al. (2012) evaluated the seasonal forecast skills of Development of European multi-model ensemble system for seasonal to interannual predictions (DEMETER) (Palmer et al. 2004) and ENSEMBLE (Hewitt and Griggs 2004) projects respectively and found that these multi-model ensembles predict ISMR with positive (modest) skill. The realized skill is still below the limit of potential predictability (Saha et al. 2016).
In this study we investigate the ability of the North-American Multi Model Ensemble (NMME) models to reproduce and predict the seasonal mean and interannual variability of the Indian summer monsoon rainfall. The NMME is a collaborative effort between several modeling centers for seasonal forecasts. The NMME simulations provides us with the opportunitiy to compare the simulations from multiple seasonal models for the same phenomenon. The analysis of the multi-model simulations for identical scenarios will aid us in identifying and understanding the similarities and differences of the various model simulations. The study of Kirtman et al. (2014) have shown that modeling system improvements and data assimilation system improvements led to improved NMME-2 forecast quality. The second objective of this study is to compare the seasonal forecast skill of NMME phase 1 with the currently operational NMME phase 2 to understand whether the improvements in modeling systems and data assimilation systems have contributed to improved seasonal prediction of the Indian summer monsoon.
2 Data and methodology
The NMME is an MME producing both retrospective and real-time intraseasonal to interannual predictions and is comprised of global coupled atmosphere-ocean models from modeling centers in the United States and Canada (Kirtman et al. 2014). The NMME provides retrospective seasonal forecasts for 1982–2010. In this study nine models are selected from the first implementation of the NMME (phase 1; denoted here as NMME:1) and nine models from the current implementation (phase 2; denoted here as NMME:2 as summarized in Table 1. CFSv2, CanCM3 and CanCM4 are the common models in both of the NMME phases (denoted by \(\oplus\) in Table 1). The 15 models have a common re-forecast period of 28 years from 1982–2009. The number of ensemble members for each model ranges from 6 to 24, with 109 total ensemble members from nine models for NMME:1, and 110 ensemble members from nine participating models for NMME:2. Model runs are initialized every month with forecast lengths ranging from 6 to 11 months. In the present study we analyze the June–September (JJAS) seasonal means of precipitation and SST for forecasts starting from May 1 initial conditions. Equal weights are given to each model in calculating the average over all models and ensemble members, denoted the multimodel ensemble mean (MMEM). The choice of reforecasts initialized in May was made in order to avoid inclusion of potential skill from the atmospheric initial conditions. It is assumed that after one month of model integration, the atmospheric initial conditions, which provide much of the skill for numerical weather forecasts at 1–15 days lead-time, have a minimal impact on the forecast skill of the ensuing seasonal mean. It is possible that forecasts initialized in May are subject to the spring predictability barrier (Torrence and Webster 1998), which may mask some of the difference in skill among models. All NMME models are re-gridded to a common 1\(^{\circ } \times 1^{\circ }\) resolution. The Climate Prediction Center Merged Analysis of Precipitation (CMAP) (** intraseasonal prediction. Bull Am Meteorol Soc 95:585–601" href="/article/10.1007/s00382-018-4203-6#ref-CR26" id="ref-link-section-d139343495e3001">2014) indicated that improvement in data assimilation and modeling systems contributed to improved forecast quality in NMME phase 2. However, we find the skill of seasonal prediction of Indian summer monsoon rainfall is nearly the same in NMME:2 (0.46) as compared to NMME:1 (0.40); the NMME is still not able to accurately predict extremes (drought/floods) of rainfall. Therefore seasonal monsoon rainfall forecast is not improved by the improvement in data assimilation system and modeling system in the NMME phase 2. The inability to predict extremes can also be seen in both the DEMETER and ENSEMBLE experiments (Preethi et al. 2010; Rajeevan et al. 2012). Both DEMETER and ENSEMBLE, as well as NMME predicted droughts during the normal monsoon years of 1997 and normal monsoon year during flood year of 1983. This suggests that similar biases found in the DEMETER and ENSEMBLE models exist in the models used in the NMME.
The interannual and intraseasonal time scale variability of ISMR is strongly influenced by SST variability in the Pacific and Indian Oceans. Pointwise correlation of seasonal mean SST from NMME and observations revealed that the skill of interannual predictions is high (0.6–0.9) for most ocean basins, and improved in NMME:2 relative to NMME:1. The most common seasonal mean SST biases in NMME models are cold equatorial Pacific and subtropical Atlantic Ocean and warm biases in northern Pacific Ocean. These biases also remain in the MMEMs, and while the cold bias over the equatorial Pacific is improved in NMME:2, the re-forecasts of the Indian Ocean warm bias worsen. We find that the NMME simulates the observed interannual variability of the NINO3.4 index with correlations greater than 0.8. We also find that predictions of the ENSO anomalies are remain same in both NMME:1 NMME:2.
In this work we also examine teleconnection patterns that affect the monsoon, and find that teleconnections in the MMEMs are stronger than in the observations. The MMEMs capture the ENSO-monsoon, Atlantic-monsoon and west Pacific-monsoon teleconnections correctly, but fail to correctly represent the association with the Indian Ocean. The EQUINO-ISMR relationship in particular is opposite to what is observed. The teleconnection between the ISMR and Indian Ocean SST also was not represented well in the DEMETER and ENSEMBLES models. This again suggests a common systematic error in coupled model forecasts. This error in association may be the reason why the NMME predicted droughts during the normal monsoon years of 1997 and a normal monsoon year during the flood year of 1983, as SST anomalies in the Indian Ocean during 1997 and 1983 played an important role in overcoming the negative impact of El Niño events (Gadgil et al. 2007). The NMME captures the negative correlation between ENSO and the monsoon, but the influence of ENSO on ISMR is stronger in the NMME than is observed. The overly strong ENSO-ISMR relationship suggests that oceanic influence on atmosphere may be too strong in NMME, particularly when comparing the MMEM to observations.
Overall the NNME shows modest skill in predicting Indian summer monsoon rainfall and its interannual variability. However, the NMME models show common biases in rainfall over Indian Ocean, are unable to predict the extremes in seasonal rainfall, and show only modest increases in skill from NMME:1 to NMME:2. The failure to represent the monsoon-EQUINO teleconnection in particular may be a critical limitation of the models comprising the NMME, and the association between this link and the prediction of extremes of seasonal rainfall clearly warrants further investigation.
References
Ashok K, Guan Z, Yamagata T (2001) Impact of the Indian Ocean dipole on the relationship between the Indian monsoon rainfall and enso. Geophys Res Lett 28:4499–4502
Bollasina MA, Ming Y, Ramaswamy V (2011) Anthropogenic aerosols and the weakening of the south asian summer monsoon. Science 334:502–505
Boos WR, Kuang Z (2010) Dominant control of the south Asian monsoon by orographic insulation versus plateau heating. Nature 463:218–222
Buizza R, Houtekamer P, Toth Z, Pellerin G, Wei M, Zhu Y (2005) A comparison of the ecmwf, msc and ncep global ensemble prediction systems. Mon Weather Rev 133:1076–1097
Cash BA, Rodó X, Kinter JL III, Fennessy MJ, Doty B (2008) Differing estimates of observed bangladesh summer rainfall. J Hydrometeorol 9:1106–1114
Cash BA, Barimalala R, Kinter JL, Altshuler EL, Fennessy MJ, Manganello JV, Molteni F, Towers P, Vitart F (2017) Sampling variability and the changing ENSO–monsoon relationship. Clim Dyn 48(11–12):4071–4079
Chakraborty A, Nanjundiah RS, Srinivasan J (2002) Role of Asian and African orography in Indian summer monsoon. Geophys Res Lett 29(20). https://doi.org/10.1029/2002GL015522
Charney JG, Shukla J, Lighthill J, Pearce RP (eds) (1981) Predictability of monsoons monsoon dynamics. Cambridge University Press, Cambridge, pp 99–109
DeWitt DG (2005) Retrospective forecasts of interannual sea surface temperature anomalies from, (1982) to present using a directly coupled atmosphere-ocean general circulation model. Mon Weather Rev 133:2972–2995
Delworth TL, Broccoli AJ, Rosati A, Stouffer RJ, Balaji V, Beesley JA, Cooke WF, Dixon KW, Dunne J, Dunne KA, Durachta JW, Findell KL, Ginoux P, Gnanadesikan A, Gordon CT, Griffies SM, Gudgel R, Harrison MJ, Held IM, Hemler RS, Horowitz LW, Klein SA, Knutson TR, Kushner PJ, Langenhorst AR, Lee H-C, Lin S-J, Lu J, Malyshev SL, Milly PCD, Ramaswamy V, Russell J, Schwarzkopf MD, Shevliakova E, Sirutis JJ, Spelman MJ, Stern WF, Winton M, Wittenberg AT, Wyman B, Zeng F, Zhang R (2006) Gfdl’s cm2 global coupled climate models. part i: Formulation and simulation characteristics. J Clim 19:643–674
Gadgil S, Rajeevan M, Francis P (2007) Monsoon variability: links to major oscillations over the equatorial pacific and Indian oceans. Curr Sci 93:182–194
Gadgil S, Sajani S (1998) Monsoon precipitation in the amip runs. Clim Dyn 14:659–689
Gadgil S, Vinayachandran P, Francis P (2003) Droughts of the Indian summer monsoon: role of clouds over the Indian Ocean. Curr Sci 85:1713–1719
Gadgil S, Abrol YP, Rao SP (1999) On growth and fluctuation of Indian foodgrain production. Curr Sci 76:548–556
Gadgil S, Vinayachandran PN, Francis PA, Gadgil S (2004) Extremes of the Indian summer monsoon rainfall, ENSO and equatorial Indian ocean oscillation. Geophys Res Lett 31(12):L12213. https://doi.org/10.1029/2004GL019733
Gadgil S, Rajeevan M, Nanjundiah R (2005) Monsoon prediction—why yet another failure? Curr Sci 88:1389–1400
Hagedom R, Doblas-Reyes FJ, Palmer TN (2004) The rationale behind the success of multi-model ensembles in seasonal forecasting—I. Basic concept. Tellus A 57:219–233
Hahn D, Manabe S (1975) The role of mountains in the south asian monsoon circulation. J Atmos Sci 33:2461–2463
Heidke P (1926) Berechnung des erfolges und der güte der windstärkevorhersagen im sturmwarnungsdienst. Geografiska Ann:301–349
Hewitt CD, Griggs DJ (2004) Ensembles-based predictions of climate changes and their impacts (ensembles). Eos 85:566
Huffman GJ, Adler RF, Arkin P, Chang A, Ferraro R, Gruber A, Janowiak J, McNab A, Rudolf B, Schneider U (1997) The global precipitation climatology project (gpcp) combined precipitation dataset. Bull Am Meteorol Soc 78:5–20
Infanti JM, Kirtman BP (2016) Prediction and predictability of land and atmosphere initialized CCSM4 climate forecasts over North America. J Geophys Res Atmos 121:12690–12701. https://doi.org/10.1002/2016JD024932
Joshi PC, Desai PS (1985) The satellite-determined thermal structure of heat low during Indian south-west monsoon season. Adv Space Res 5:57–60
Kang IS, ** K, Wang B, Lau K-M, Krishnamurthy JSV, Schubert S, Wailser D, Stern W, Kitoh A, Meehl G, Kanamitsu M, Galin V, Satyan V, Park CK, Liu Y (2002) Intercomparison of the climatological variations of asian summer monsoon precipitation simulated by 10 gcms. Clim Dyn 19:383–395
Kirtman BP, Min D (2009) Multimodel ensemble enso prediction with ccsm and cfs. Mon Weather Rev 137:2908–2930
Kirtman BP, Min D, Infanti JM, Kinter JL III, Paolino DA, Zhang Q, Van Den Dool H, Saha S, Mendez MP, Becker E et al (2014) The north american multimodel ensemble: phase-1 seasonal-to-interannual prediction; phase-2 toward develo** intraseasonal prediction. Bull Am Meteorol Soc 95:585–601
Krishnamurthy V, Kirtman BP (2009) Relation between Indian monsoon variability and sst. J Clim 22:4437–4458
Kumar KK, Rajagopalan B, Cane MA (1999) On the weakening relationship between the Indian monsoon and enso. Science 284:2156–2159
Kumar KK, Rajagopalan B, Hoerling M, Bates G, Cane M (2006) Unraveling the mystery of Indian monsoon failure during el niño. Science 314:115–119
Lau K-M, Kim K-M (2006) Observational relationships between aerosol and Asian monsoon rainfall, and circulation. Geophys Res Lett 33(21):L21810. https://doi.org/10.1029/2006GL027546
Merryfield WJ, Lee W-S, Boer GJ, Kharin VV, Scinocca JF, Flato GM, Ajayamohan R, Fyfe JC, Tang Y, Polavarapu S (2013) The canadian seasonal to interannual prediction system. part i: Models and initialization. Mon Weather Rev 141:2910–2945
Nanjundiah RS, Francis P, Ved M, Gadgil S (2013) Predicting the extremes of Indian summer monsoon rainfall with coupled ocean-atmosphere models. Curr Sci 104:1380–1393
Palmer T, Brankovic Č, Viterbo P, Miller M (1992) Modeling interannual variations of summer monsoons. J Clim 5:399–417
Palmer T, Doblas-Reyes F, Hagedorn R, Alessandri A, Gualdi S, Andersen U, Feddersen H, Cantelaube P, Terres J, Davey M et al (2004) Development of a european multimodel ensemble system for seasonal-to-interannual prediction (demeter). Bull Am Meteorol Soc 85:853–872
Palmer T, Shutts GJ, Hagedorn R, Doblas-Reyes FJ, Jung T, Leutbecher M (2005) Representing model uncertainty in weather and climate predictions. Annu Rev Earth Planet Sci 33:163–193
Palmer TN, Alessandri A, Andersen U, Cantelaube P, Davey M, Delécluse P, Déqué M, Diez E, Doblas-Reyes FJ, Feddersen H, Graham R (2004) Development of a european multi-model ensemble system for seasonal to inter-annual prediction. Bull Am Meteorol Soc 85:853–872
Pant G, Parthasarathy SB (1981) Some aspects of an association between the southern oscillation and Indian summer monsoon. Arch Meteorol Geophys Bioclimatol Ser B 29:245–252
Preethi B, Kripalani R, Kumar KK (2010) Indian summer monsoon rainfall variability in global coupled ocean-atmospheric models. Clim Dyn 35:1521–1539
Prell WL, Kutzbach JE (1992) Sensitivity of the Indian monsoon to forcing parameters and implications for its evolution. Nature 360:647–652
Rajeevan M, Unnikrishnan C, Preethi B (2012) Evaluation of the ensembles multi-model seasonal forecasts of Indian summer monsoon variability. Clim Dyn 38:2257–2274
Rajeevan M, Nanjundiah RS (2009) Coupled model simulations of twentieth century climate of the Indian summer monsoon. Platin Jubilee Spec Vol Ind Academy Sci 537–568
Rajeevan M, Sridhar L (2008) Inter-annual relationship between Atlantic Sea surface temperature anomalies and Indian summer monsoon. Geophys Res Lett 35(21):L21704. https://doi.org/10.1029/2008GL036025
Ramage CS (1996) The summer atmospheric circulation over the Arabian Sea. J Atmos Sci 23:144–150
Rasmusson EM, Carpenter TH (1983) The relationship between eastern equatorial pacific sea surface temperatures and rainfall over india and sri lanka. Mon Weather Rev 111:517–528
Reynolds RW, Rayner NA, Smith TM, Stokes DC, Wang W (2002) An improved in situ and satellite sst analysis for climate. J Clim 15:1609–1625
Saha S, Moorthi S, Wu X, Wang J, Nadiga S, Tripp P, Behringer D, Hou Y-T, Chuang H-Y, Iredell M et al (2014) The ncep climate forecast system version 2. J Clim 27:2185–2208
Saha S, Nadiga S, Thiaw C, Wang J, Wang W, Zhang Q, Van den Dool H, Pan H-L, Moorthi S, Behringer D et al (2006) The ncep climate forecast system. J Clim 19:3483–3517
Saha SK, Pokhrel S, Salunke K, Dhakate A, Chaudhari HS, Rahaman H, Sujith K, Hazra A, Sikka DR (2016) Potential predictability of Indian summer monsoon rainfall in NCEP CFSv2. J Adv Model Earth Syst 8:96–120. https://doi.org/10.1002/2015MS000542
Saji N, Goswami B, Vinayachandran P, Yamagata T (1999) A dipole mode in the tropical Indian ocean. Nature 401:360–363
Schwierz C, Appenzeller C, Davies HC, Liniger MA, Muller W, Stocker TF, Yoshimore M (2006) Challenges posed by and approaches to the study of seasonal-to-decadal climate variability. Clim Change 79:31–63
Sharmila S, Pillai P, Joseph S, Roxy M, Krishna R, Chattopadhyay R, Abhilash S, Sahai A, Goswami B (2013) Role of ocean-atmosphere interaction on northward propagation of Indian summer monsoon intra-seasonal oscillations (MISO). Clim Dyn 41:1651–1669
Shukla J, Moolay DA (1987) Empirical prediction of the summer monsoon rainfall over India. Mon Weather Rev 115:695–703
Shukla J, Paolino DA (1983) The southern oscillation and long-range forecasting of the summer monsoon rainfall over India. Mon Weather Rev 111:1830–1837
Sikka D (1980) Some aspects of the large scale fluctuations of summer monsoon rainfall over India in relation to fluctuations in the planetary and regional scale circulation parameters. Proc Indian Acad Sci Earth Planet Sci 89:179–195
Sikka DR (1997) Desert climate and its dynamics. Curr Sci 72:35–46
Sperber KR, Annamalai H, Kang I-S, Kitoh A, Moise A, Turner A, Wang B, Zhou T (2013) The asian summer monsoon: an intercomparison of cmip5 vs. cmip3 simulations of the late 20th century. Clim Dyn 41:2711–2744
Torrence C, Webster PJ (1998) The annual cycle of persistence in the el nño/southern oscillation. Q J R Meteorol Soc 124:1985–2004
Tribbia J (2015) Ncar contribution to a us national multi-model ensemble (nmme) isi prediction system, Technical report. University Corporation for Atmospheric Research, Boulder
Vecchi GA, Delworth T, Gudgel R, Kapnick S, Rosati A, Wittenberg AT, Zeng F, Anderson W, Balaji V, Dixon K, Jia L, Kim H-S, Krishnamurthy L, Msadek R, Stern WF, Underwood SD, Villarini G, Yang X, Zhang S (2014) On the seasonal forecasting of regional tropical cyclone activity. J Clim 27:7994–8016
Vernieres G, Keppenne C, Rienecker MM, Jacob J, Kovach R (2012) The GEOS-ODAS, description and evaluation. NASA Tech Rep Series Glob Mod Data Ass NASA/TM 30:104606
Walker GT (1925) Correlation in seasonal variations of weather? A further study of world weather. Mon Weather Rev 53:252–254
Wang B, Lee J-Y, Kang I-S, Shukla J, Kug J-S, Kumar A, Schemm J, Luo J-J, Yamagata T, Park C-K (2008) How accurately do coupled climate models predict the leading modes of asian-australian monsoon interannual variability? Clim Dyn 30:605–619
Wang B, Wu R, Li T (2003) Atmosphere-warm ocean interaction and its impacts on Asian–Australian monsoon variation. J Clim 16:1195–1211
Wang B, Kang I, Lee J (2004) Ensemble simulations of Asian–Australian monsoon variability by 11 AGCMS. J Clim 17:803–818
Webster PJ, Magana VO, Palmer T, Shukla J, Tomas R, Yanai M, Yasunari T (1998) Monsoons: processes, predictability, and the prospects for prediction. J Geophys Res Oceans 103:14451–14510
Webster PJ, Moore AM, Loschnigg JP, Leben RR (1999) Coupled ocean-atmosphere dynamics in the Indian ocean during 1997–98. Nature 401:356–360
Wu G, Zhang Y (1998) Tibetan plateau forcing and timing of the monsoon onset over South Asia and South China sea. Mon Weather Rev 126:913–927
**e P, Arkin PA (1997) Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull Am Meteorol Soc 78:2539
Yanai M, Li C, Song Z (1992) Seasonal heating of the Tibetan plateau and its effects on the evolution of the Asian summer monsoon. J Meteorol Soc Jpn 70:319–351
Zhou T, Wu B, Scaife A, Brönnimann S, Cherchi A, Fereday D, Fischer A, Folland C, ** K, Kinter J et al (2009) The clivar c20c project: which components of the asian-australian monsoon circulation variations are forced and reproducible? Clim Dyn 33:1051–1068
Acknowledgements
Funding of COLA for this study is provided by Grants from NSF (AGS-1338427), NOAA (NA09OAR4310058 and NA14OAR4310160), NASA (NNX14AM19G), and the ONR Grant (N00014-15-1-2745). We acknowledge NOAA MAPP, NSF, NASA, and the DOE that support the NMME-Phase II system, and we thank the climate modeling groups (Environment Canada, NASA, NCAR, NOAA/GFDL, NOAA/NCEP, and University of Miami) for producing and making available their model output. NOAA/NCEP, NOAA/CTB, and NOAA/CPO jointly provided coordinating support and led development of the NMME-Phase II system. We thank the anonymous reviewers for their constructive comments which helped improve this paper.
Author information
Authors and Affiliations
Corresponding author
Additional information
This paper is a contribution to the special collection on the North American Multi-Model Ensemble (NMME) seasonal prediction experiment. The special collection focuses on documenting the use of the NMME system database for research ranging from predictability studies, to multi-model prediction evaluation and diagnostics, to emerging applications of climate predictability for subseasonal to seasonal predictions.This special issue is coordinated by Annarita Mariotti (NOAA), Heather Archambault (NOAA), ** Huang (NOAA), Ben Kirtman (University of Miami) and Gabriele Villarini (University of Iowa).
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
About this article
Cite this article
Singh, B., Cash, B. & Kinter III, J.L. Indian summer monsoon variability forecasts in the North American multimodel ensemble. Clim Dyn 53, 7321–7334 (2019). https://doi.org/10.1007/s00382-018-4203-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00382-018-4203-6