Using idealized snow forcing to test teleconnections with the Indian summer monsoon in the Hadley Centre GCM
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- Turner, A.G. & Slingo, J.M. Clim Dyn (2011) 36: 1717. doi:10.1007/s00382-010-0805-3
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Anomalous heavy snow during winter or spring has long been regarded as a possible precursor of deficient Indian monsoon rainfall during the subsequent summer. However previous work in this field is inconclusive, in terms of the mechanism that communicates snow anomalies to the monsoon summer, and even the region from which snow has the most impact. In this study we explore these issues in coupled and atmosphere-only versions of the Hadley Centre model. A 1050-year control integration of the HadCM3 coupled model, which well represents the seasonal cycle of snow cover over the Eurasian continent, is analysed and shows evidence for weakened monsoons being preceded by strong snow forcing (in the absence of ENSO) over either the Himalaya/Tibetan Plateau or north/west Eurasia regions. However, empirical orthogonal function (EOF) analysis of springtime interannual variability in snow depth shows the leading mode to have opposite signs between these two regions, suggesting that competing mechanisms may be possible. To determine the dominant region, ensemble integrations are carried out using HadAM3, the atmospheric component of HadCM3, and a variety of anomalous snow forcing initial conditions obtained from the control integration of the coupled model. Forcings are applied during spring in separate experiments over the Himalaya/Tibetan Plateau and north/west Eurasia regions, in conjunction with climatological SSTs in order to avoid the direct effects of ENSO. With the aid of idealized forcing conditions in sensitivity tests, we demonstrate that forcing from the Himalaya region is dominant in this model via a Blanford-type mechanism involving reduced surface sensible heat and longwave fluxes, reduced heating of the troposphere over the Tibetan Plateau and consequently a reduced meridional tropospheric temperature gradient which weakens the monsoon during early summer. Snow albedo is shown to be key to the mechanism, explaining around 50% of the perturbation in sensible heating over the Tibetan Plateau, and accounting for the majority of cooling through the troposphere.
KeywordsSnowIndian monsoonSeasonal forecastTibetan PlateauEurasiaHimalaya
The Asian summer monsoon is relied on by more than a third of the world’s population for the majority of their water resources, for agriculture and, increasingly, industrial uses. While statistical seasonal forecasts are routinely made of the summer monsoon rainfall, consistently incorporating parameters based on ENSO (Krishna Kumar et al. 1995; Rajeevan et al. 2004, 2007), other global predictors are also used, reflecting the potential predictability of tropical rainfall variability implied by slowly varying surface boundary conditions (Charney and Shukla 1981). Historically, Himalayan snow cover during winter was used to infer details of monsoon rainfall in the subsequent summer. Blanford (1884) suggested an inverse relationship between such snow cover and the subsequent monsoon rains, especially in northern India. Subsequently Eurasian snow cover averaged over the much larger 0–180°E, 20–90°N region was included in statistical forecasts following correlation analysis by Parthasarathy and Yang (1995). However direct measures of snow have been removed from the latest India Meteorological Department power regression model (Rajeevan et al. 2007), perhaps related to decadal variability in the strength of the snow–monsoon teleconnection (e.g. Liu and Yanai 2002; Robock et al. 2003) or decadal changes in snow depth over the Tibetan Plateau (Zhang et al. 2004). Despite several observational and modelling studies the mechanisms involved are poorly understood, relating to several complicating factors, namely the region involved and the presence or absence of ENSO. Any mechanism may also depend on how snow is measured, whether weight, depth, or fraction of area covered.
Several studies use often limited observations to find correlations between regional snow cover and subsequent Asian summer monsoon rainfall, and to infer details of the mechanism involved. For the Himalaya/Tibetan Plateau region, this mechanism essentially fits the Blanford hypothesis (as named in Fasullo 2004), that persisting heavy snow reduces the upward surface sensible heating in spring and hence reduces the deep heating of the troposphere above the Tibetan Plateau. In consequence, the reversal of the meridional tropospheric temperature gradient essential for the monsoon onset (Li and Yanai 1996) is reduced, weakening the Asian monsoon, particularly in north India. Fasullo (2004) made a composite analysis of 1967–2001 NSIDC NH-EASE (National Snow and Ice Data Center Northern Hemisphere Equal-Area Scalable Earth grid) snow cover data for spring (MAM) covering the whole of Eurasia and its effect on Indian monsoon rainfall. Regions neighbouring northern India were noted to feature robust negative correlations, in support of the Blanford hypothesis. Negative correlations with snow were also found further north, although Fasullo (2004) suggested no mechanism.
Earlier observational studies were limited by short data records. Hahn and Shukla (1976) estimated fractional snow cover from satellite photographs over 9 years and saw negative correlations between DJFM Eurasian snow (south of 52°N only) and subsequent monsoon rainfall. Bamzai and Shukla (1999) later analysed 22 (9) years of satellite-derived snow cover (depth) data to show that inverse correlations between snow cover and monsoon rainfall anomalies existed only for western Eurasia, although their composites show consistent anomalies to extend east of this region. Such correlations are particularly significant when snow anomalies persist from winter (DJFM) into spring (AM). Contrary to other work, they found no significant relationship between Himalayan snow cover and the monsoon, despite this region showing amongst the largest interannual variations in snow.
Some studies based on the second release of the Historical Soviet Daily Snow Depth (HSDSD-II) station measurements also noted negative correlations between winter/spring snow cover and Indian monsoon rainfall. During the period 1957–1994, Dash et al. (2005) found strong negative correlations emanating from west Eurasia (35–65°N, 25–70°E) and positive correlations from east Eurasia (70–140°E at the same latitude). Highlighting the non-linearity of the snow–monsoon relationship, they found 57% of heavy snow events in the west were followed by deficient Indian summer monsoon rainfall, against only 24% of light snow events followed by heavy rain. They describe a mid-latitude circulation mechanism affecting the upper level monsoon easterlies in late spring. A similar result was also obtained by Kripalani and Kulkarni (1999). Singh and Oh (2005) also noted the west–east (negative–positive) dipole in correlations with monsoon rainfall using HSDSD-II, describing a mechanism in which the anomalous persistence of snow cover into spring delays heating of the Eurasian continent, leading to a weakened thermal low and consequent weak monsoon westerlies over India, and perturbations to the low-level jet over East Asia. Instead, Ye and Bao (2005) use mean JFM snow depth from 1950 to 1995 HSDSD-II data to show reasonable positive correlations between Eurasian snow and Indian rainfall measured using the Global Gridded Monthly Precipitation data. The HSDSD-II data have no coverage of the Himalaya/Tibetan Plateau region however.
The El Niño-Southern Oscillation (ENSO) has strong negative correlations with the Asian summer monsoon (e.g. Webster and Yang 1992, among many others) however it can also influence snow distribution over the Eurasian landmass via its effect on the zonal flow (Ferranti and Molteni 1999). Therefore any snow–monsoon interaction may be an indirect effect of ENSO, and some works are careful to take ENSO into account. In his stratified analysis, Fasullo (2004) considers anomalous monsoon years under ENSO and ENSO-neutral conditions, to demonstrate that the Blanford hypothesis is much more robust when ENSO is absent. Moreover, during ENSO years the sign of the correlation reverses, reflecting the dominance of ENSO forcing over land surface characteristics in perturbing the monsoon. Other authors make no allowance for ENSO (e.g. Kripalani and Kulkarni 1999; Bamzai and Shukla 1999, likely due to their short data record). In some cases, a significant reduction in sample size is found when removing ENSO effects (Dash et al. 2005). Singh and Oh (2005) show clear evidence of the classic El Niño-horseshoe pattern in a composite difference of Indo-Pacific SSTs between high and low snow years (their Fig. 4). Robock et al. (2003) used the NOAA (National Oceanic and Atmospheric Administration) northern hemisphere snow cover data to show a negative (positive) relationship between Eurasian (Tibetan) snow cover and All-India monsoon rainfall, however virtually all of their composited years were known ENSO events. In their observed study of the impact of Eurasian snow on the East Asian monsoon, Wu and Kirtman (2007) note that whilst ENSO and Tibetan Plateau snow cover compete to affect Indian monsoon rainfall, they act together to increase precipitation over southern China.
Models allow a more targeted and cleaner analysis of snow–monsoon connections and Fasullo (2004) provided a good review of early modelling studies, noting with only one exception that the monsoon response to elevated snow in central or southern Eurasia is negative. Using the UGAMP (UK Universities Global Atmospheric Modelling Programme) GCM, Dong and Valdes (1998) showed robust negative correlations between south Eurasian snow mass and Indian monsoon rainfall, delaying the onset through soil moisture and evaporation processes. Several modelling studies also assess the role of ENSO carefully. Corti et al. (2000) found that their leading mode of snow variability, consisting of a dipole between Himalaya/Tibetan Plateau and north/west Eurasia (their Fig. 2a, and similar to Fig. 5 here), was perturbed strongly by ENSO activity during the immediately preceding winter. This confirmed their hypothesis that the mode was of dynamical origin and caused by the effect of tropical SST anomalies on the large-scale circulation. Corti et al. (2000) showed the effects of nearby snow (i.e., the Himalaya) to dominate unless a strong El Niño is present, in which case the large-scale circulation anomaly prevails, leading to negative correlations with more remote Eurasian snow. By comparing atmosphere-only GCM ensembles forced by observed and average climatologically varying SSTs, Becker et al. (2001) reach much the same conclusion, that Indian rainfall is detrimentally influenced by local snow forcing through hydrology and thermodynamic mechanisms in the absence of ENSO. Meanwhile, monsoon dynamics undergo a negative influence from west Eurasian snow. In ensemble AGCM experiments comparing monsoons following the 1982/83 (El Niño) and 1983/1984 (La Niña) winters, Ferranti and Molteni (1999) show clear evidence for enhanced (reduced) snow over north/west Eurasia following El Niño (La Niña) related to changes in the circulation. Subsequently, weakened westerlies over south Asia and a southward shift of the subtropical jet in the South China Sea highlight a dynamically weakened monsoon.
Potentially local and remote regions of snow forcing can influence the monsoon, although as yet it is unclear which region should dominate in both GCMs and the real world. Teleconnections from the more remote north Eurasia region are also ill-understood. This study aims to assess the impact of snow forcing from local and remote regions separately on the subsequent Indian summer monsoon, determine which region dominates, and under what mechanism. The Hadley Centre models used, their validation and the experimental method are outlined in Sect. 2. In Sect. 3, we perform a composite analysis from a long integration of HadCM3, the coupled version of the Hadley Centre model, and also consider the dominant modes of snow variability in some other coupled models. Results from atmosphere-only experiments in HadAM3 and further sensitivity tests are described in Sect. 4. Discussion and conclusions are listed in Sect. 5. To avoid ambiguity, snow forcing regions are referred to as HimTP meaning Himalaya/Tibetan Plateau and WNEur for the west northern mid-latitudes of Eurasia in the remainder of this manuscript.
2.1 The model and its validation
Monthly mean atmospheric and surface data from a 1050-year pre-industrial control integration of the UK Met Office HadCM3 coupled model were obtained from the NERC British Atmospheric Data Centre (BADC) for this study. HadCM3 (Pope et al. 2000; Gordon et al. 2000) was one of the first coupled ocean-atmosphere models capable of integrating for several hundred years without requiring artificial ocean surface heat-flux corrections to counteract climate drift (Johns et al. 2003) and has been widely used as a global climate model in both the Third and Fourth Assessment Reports of the Intergovernmental Panel for Climate Change (IPCC). The ocean model is solved on a 1.25° grid on 20 vertical levels while the atmosphere has a horizontal resolution of 3.75° × 2.5° in longitude and latitude respectively, on 19 vertical levels. The atmospheric component of this model (HadAM3) was used for the ensemble experiments described in the next section, with a vertical resolution of 30 levels offering a more realistic response of the atmospheric circulation to SST forcing in the tropics (Spencer and Slingo 2003). When referring to both the coupled (HadCM3) and atmosphere-only (HadAM3) models in general, the term Hadley Centre model will be used to avoid confusion.
Both HadCM3 and HadAM3 feature the Met Office Surface Exchange Scheme (MOSES), an interactive land surface model (Cox et al. 1999) featuring a Penman-Monteith boundary layer and hydrology and a four-layer model for soil moisture and temperature. Soil moisture is capable of melting and freezing, allowing for a more realistic simulation of surface temperatures, and in the interactive vegetation canopy, evaporation is dependent on stomatal resistance, itself related to temperature and CO2 (Pope et al. 2000). As Cox et al. (1999) remark, snow acts to make the land surface brighter and smoother, impacting on the radiative, turbulent and ground heat fluxes, as well as insulating the soil beneath. Gridbox surface albedo varies between the snow-free value and a deep-snow value itself dependent on temperature, thus providing a simple representation of snow metamorphosis. A fixed snow density is assumed (ρsnow = 250 kg m−3).
2.1.1 Model mean state
In a comparison of the climatological snow depth (expressed as soil water equivalent, SWE) with ECMWF ERA-40 reanalysis, Putt (2008) shows HadCM3 to possess a reasonable seasonal evolution in the northern hemisphere. Over Eurasia, the main biases are a weakened maximum in snow depth over central Russia during spring, perhaps as much as −30%, and deficient snow over east Siberia during winter and spring.
2.1.2 Forcings of the snow distribution
Authors such as Shaman and Tziperman (2005) have demonstrated relationships between ENSO and enhanced snowfall over the Tibetan Plateau via a stationary wave mechanism, leading to persistent snow depth anomalies even during boreal summer (their Fig. 1; albeit using a short satellite record). Clifford et al. (2009) have also demonstrated strong positive correlations between a DJF ENSO index and concurrent SWE over the HimTP region in a different 545-year control run of HadCM3. This correlation is reproduced in this 1050-year control run (Fig. 3b). There is some observational evidence to suggest that the Indian Ocean dipole may play an even more important role than ENSO in generating winter/spring snow anomalies over the Tibetan Plateau region (Yuan et al. 2009), although we note also that widespread SST warming in the Indian Ocean in the late 1970s has been implicated in increasing snow depth over the Tibetan Plateau on decadal timescales (Zhang et al. 2004). Spencer et al. (2005) have previously shown HadCM3 to be capable of simulating the seasonality of the Indian Ocean dipole (including the timing of its onset, peak and decay) and associated equatorial wind and thermocline behaviour. Correlations between winter/spring snow amount in HadCM3 and the IOD peak during the previous autumn (SON, using the index defined by Saji et al. 1999) also show positive results over the Tibetan Plateau (Fig. 3c), consistent with observations.
This range of evidence suggests the Hadley Centre model is capable of simulating some of the key observed forcings of snow variation over the Eurasian continent.
2.1.3 The Indian monsoon
HadCM3 offers a good representation of the seasonal cycle of monsoon rainfall (see, e.g. Turner and Slingo 2009), which is among the best in state-of-the art coupled GCMs (Annamalai et al. 2007), although the south-westerly winds of the Somali Jet are too strong (Turner et al. 2005). Although interannual variability is reasonable in this model, the teleconnection with ENSO is slightly weak and phased incorrectly, such that equatorial Pacific SSTs during spring feature the largest inverse correlations with the monsoon (Turner et al. 2005; Fig. 15) rather than during boreal summer. Indeed there is some influence of ENSO during the previous winter due to deficiencies in the spring-predictability barrier, which Peings and Douville (2010) note relates to the unusually strong influence of ENSO on climate variability in this and several other coupled models.
2.2 AGCM experimental design
To test the impact of springtime snow in different regions on the subsequent Asian summer monsoon, ensemble integrations of the land-atmosphere component of the Hadley Centre model (HadAM3) have been performed. The model is run using a seasonal cycle of climatological SST and sea-ice forcing in order to completely remove the effects of ENSO on both the monsoon itself and preceding snow cover, and any deficiencies in these links. The snow forcing conditions are described below.
2.2.1 Snow forcing initial conditions
HimTP ± 2σ (named HimTPpos, HimTPneg),
WNEur ± 2σ (WNEurpos, WNEurneg).
2.2.2 Spin-up procedure and ensemble set-up
Previous studies have used a range of methods for initialising snow forcing experiments. Some authors applied their snow forcing initial conditions during winter or spring, in combination with other (climatological) inputs, and allowed the atmosphere or land surface to freely evolve subsequently (e.g. Dash et al. 2006). This method may be disadvantageous as the response to suddenly imposed snow forcing may not be entirely realistic, and part of the signal seen in the subsequent monsoon may involve some spin-up component. Others (e.g. Douville and Royer 1996) ensure a lengthy spin-up procedure is used such that all components of the imposed snow forcing, other land surface parameters and the atmosphere are in balance before the spring to summer evolution is considered. Becker et al. (2001) took initial atmosphere and land/ocean surface conditions from observed data (in pairs of ENSO years with strong snow signals) and started ensemble integrations from early November of year 0. Their snow-forcing experiments then began in early April, using start conditions obtained from their spin-up integrations.
In these experiments, we utilise the spin-up period suggested by Becker et al. (2001). Atmospheric and land initial conditions from 1 November in an existing control integration of HadAM3 are used, and the model is integrated for six months. However, to ensure that the desired snow forcings are present from early spring so that their influence on the subsequent Asian summer monsoon can be examined, snow depth is updated to the desired forcing condition each hour during the spin-up, replacing any existing snow depth. From this initial integration, daily land-atmosphere restart data are taken from 15 March to 16 April inclusive (in the 30-day month common to many GCMs, this gives us an ensemble size of 32 members). These are re-designated as 1 April and the 32-member ensemble is integrated for a further 8 months to 1 December, during which snow is no longer constrained and is free to melt. Such an ensemble size will provide robust statistics with which to test any signal resulting from the lower boundary forcing. As explained above, climatological SST forcings are used throughout the experiment.
3 Results of coupled model analysis
These composites thus demonstrate that the Hadley Centre model is capable of simulating weak (strong) monsoon events following heavy (light) snow over both forcing regions, using a simple method to remove the effects of ENSO. That variance in springtime snow cover is slightly weak over HimTP in HadCM3 suggests that the response of the monsoon and other atmospheric features to snow over this region may be underestimated. Hence we now pursue experiments with the model’s land-atmosphere component, HadAM3, in order to determine the dominant region of forcing in the absence of ENSO and further elaborate on the mechanism involved.
4 HadAM3 AGCM ensemble experiments
Results from the ensemble experiments with the HadAM3 AGCM are analysed in turn, depicted as composite evolutions of differences between the strong and weak forcings outlined in Sect. 2.2.1.
4.1 HimTP results
Thus we have shown here that the early Indian summer monsoon in HadAM3 suffers a detrimental response to strong snow forcing over HimTP. That the HadCM3 coupled model, from which the snow forcing conditions were derived, shows too little variance in snow cover over HimTP suggests this result may be an underestimate of the effects of heavy HimTP snow forcing on Indian monsoon rainfall.
4.2 WNEur results
HadAM3 thus responds in an unusual manner to strong spring snow forcing over the WNEur region, whereby an induced anomaly of opposite sign over HimTP persists longer than the initial forcing, and subsequently influences the Indian monsoon during early summer as outlined in Sect. 4.1. Thus even in the absence of ENSO and its influence on the zonal flow (Corti et al. 2000), snow depth EOF-1 (Fig. 5) dominates in the forced AGCM framework. The bias toward low snow climatological conditions over central and eastern Siberia, and too rapid snow-melt outlined in Putt (2008) may contribute to this, however Qu and Hall (2007) found the land surface model used here to have a mid-range snow-albedo feedback in the seasonal cycle when compared against other GCMs, suggesting that the land surface components of other current models may not necessarily perform better than this one.
It is worth noting that the pattern of June rainfall anomalies in Fig. 11, and indeed in Fig. 9 (though of opposite sign), bears a striking resemblance to the regional pattern demonstrated in observations by Fasullo (2004) (his Fig. 7b) following strong spring snow cover over southwest Asia (the southwest Himalaya and Pakistan). These consist of significant negative correlations over central to eastern north India and positive correlations over the southern tip of peninsular India and the detached northeastern states. Even these latter features are represented in the HadAM3 composites here.
4.3 Sensitivity tests and mechanism
The results here suggest that even when strong highly idealized initial conditions are used, the snow-forced perturbations to the Indian summer monsoon occur only during the early season. Effects on the East Asian and Western North Pacific summer monsoons are potentially longer lasting and thus of greater significance to society.
In order to test the Blanford mechanism relating HimTP snow and reduced heating above it in more detail, we repeat the HimTP1000 sensitivity experiment by replacing the albedo value for deep snow with snow-free albedo over the HimTP forcing region only. HadAM3 snow-free albedo (sfa) is dependent on the vegetation and soil types present in the grid box but is time-invariant. In HimTP1000sfa, the average albedo over the HimTP region is much reduced at 0.20 compared with 0.67 in HimTP1000. Thus the same snow mass is present at 1 April at the commencement of the ensemble integration, but with much lower albedo. The HimTP1000sfa curves in Fig. 13 reveal much more rapid snow-melt, and additional heat flux melting the snow initially. Strongly increased surface net radiation relative to the other experiments is a consequence of much reduced reflected shortwave radiation, and sensible heating anomalies over HimTP reach only half as much as in HimTP1000. These findings confirm that albedo plays a significant role in the impact of HimTP snow forcing on the Asian summer monsoon. However, slight tropospheric cooling still occurs in HimTP1000sfa (but around 1°C relative to HimTPzero, or 3.5°C warmer than HimTP1000, and lasting only until mid-June, not shown) suggesting that reduced upward longwave radiation caused by the insulating effect of snow also plays a minor role. Weak land-atmosphere coupling in HadAM3 (between soil-moisture and any atmospheric response as in Lawrence and Slingo 2005) suggests that soil moisture-evaporation feedbacks may not play such an important role in this model.
In this study we have examined the effects of spring snow forcing on the Asian summer monsoon in the Met Office Hadley Centre model. In an extended control integration of the coupled version of the model (HadCM3) we have shown the strongest mode of variability in spring snow amount to be a north-south mode with opposite signs over west/north mid-latitude Eurasia (WNEur) and the Himalaya/Tibetan Plateau (HimTP; although dominated by this latter region), similar to other coupled models which simulate the monsoon and ENSO well. Composite analysis has shown that the HadCM3 coupled model can simulate weak monsoon conditions following strong spring snow forcing over either the WNEur or HimTP regions in the absence of ENSO, in common with observational studies in the literature. In order to test the dominant forcing region on the monsoon in the Hadley Centre model, the HadAM3 land-atmosphere component forced by climatological SST and sea-ice at the lower boundary was used to run large ensemble integrations for 8 months, avoiding the effects of ENSO. When forced by snow anomalies over the HimTP region, HadAM3 acts as suggested by the Blanford hypothesis in that increased spring snow depth leads to protracted snow melt, reducing the surface sensible heat and upward longwave fluxes over the Tibetan Plateau, and hence heating the troposphere to a lesser degree. This reduces or delays the reversed meridional temperature gradient during late spring and consequently delays the onset of the monsoon (Li and Yanai 1996), thus reducing Indian rainfall during June. In southern China a zonal band of increased precipitation is noted persisting into July, particularly under idealized forcing conditions. This is consistent with Duan and Wu (2005) who noted weakened rainfall in southern China in association with increased sensible heating over the Tibetan Plateau. While the bias towards low variance in spring snow cover over HimTP in the coupled version of the Hadley Centre model may suggest underestimated variability of Indian monsoon rainfall in response to HimTP snow in the coupled framework, the idealized prescribed snow anomalies in the atmosphere-only ensemble experiments suggest a robust response unaffected by this bias. The effect of snow cover on surface albedo over HimTP is found to be very important to the Blanford mechanism, contributing around 75% of the tropospheric cooling at Indian longitudes in sensitivity tests.
These findings have underlined the intensification of the Asian monsoon climate induced by Tibetan Plateau heating (Wu et al. 2007) and the importance of even small heating anomalies for determining the Asian monsoon flow (Liu et al. 2007).
Results are less clear when forced by snow anomalies over the WNEur region, however. Strong anomalies of opposite sign over HimTP outlast the WNEur forcing and ultimately perturb the monsoon in the same way as outlined above. The dominance of the HimTP region in this model may relate to systematic errors in the snow distribution over central-east Siberia, where the model underestimates snow depth in spring and undergoes too rapid melting (Putt 2008). This rapid melting is despite a very reasonable seasonal snow-albedo feedback (Qu and Hall 2007) and reasonable simulation of the snow edge as seen in our assessment of the snow cover. Alternatively, the distinct seasonality (south to north migration during spring) in the variance of snow cover across Eurasia shown in Fig. 2 makes WNEur rather more complex than the HimTP region, where the position of maximum variance changes little through the season. A more complex experimental design, where the idealized snow forcing is varied spatially through the spin-up period may be better in this case.
Previous studies linking remote (not HimTP) snow with the Asian monsoon have done so in the presence of ENSO (e.g. Becker et al. 2001). Such a mechanism has yet to be elucidated under neutral ENSO conditions here or in other studies. For this reason, and the possible weak bias in Siberian snow in this model, a co-ordinated multi-model study would be advantageous to solving this issue. This could compare the impact of idealized spring snow forcing in a variety of different regions in Eurasia, in order to test the impact of individual and common model biases on any teleconnection that is found. Such experiments would be run in the AGCM framework, as in this study, to avoid contamination by ENSO.
The results here have shown that heavy snow forcing over the Himalaya and Tibetan Plateau region can cause significant detriment to the early Indian summer monsoon. Assuming such results could be repeated in other state-of-the-art GCMs, these findings would potentially be of use in a revised statistical forecast model for the Indian monsoon and its onset. A potential difficulty may lie in the availability of reliable data for a large enough portion of the domain in question. The role of ENSO must also be considered carefully owing to its dominance in many models (Peings and Douville 2010). Fasullo (2004) have suggested a conceptual model in which ENSO alters the snow–monsoon relationship. The experiments here could be expanded upon, perhaps by introducing idealized ENSO forcing in the AGCM framework in conjunction with snow forcing. Alternatively, the effects of coupling with the ocean surface could be examined.
A. G. Turner was funded by the EU-ENSEMBLES project and NCAS-Climate, a NERC collaborative centre. Computing resources for running the Hadley Centre model were provided by HPCx and subsequently HECTOR. The 1050-year HadCM3 control run was obtained from the NERC British Atmospheric Data Centre (BADC). We acknowledge the modelling groups, the PCMDI and the WCRP’s Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, U.S. Department of Energy. The authors wish to thank Susanna Corti (the Editor), J. Fasullo, and a further anonymous reviewer for their constructive comments that greatly improved this manuscript. A. G. Turner wishes to thank Jon Vincent for computational support in running the ensemble experiments on HECTOR.