Indian Monsoon Rainfall Research Articles

Three-ocean interactions and climate variability: a review and perspective

Interactions among the Pacific, Atlantic and Indian Oceans through ocean–atmosphere coupling can initiate and/or modulate climate variability. The Pacific Ocean is home to ENSO which affects other oceans through atmospheric bridges and the oceanic Indonesian throughflow (ITF). A warm Indian Ocean can produce atmospheric Kelvin waves that propagate eastward and increase equatorial easterly wind anomalies in the western Pacific and thus cool eastern Pacific sea surface temperature (SST). A positive Indian Ocean dipole establishes a southwestward pressure gradient force in the ITF region which increases the ITF transport and decreases ocean heat content in the western Pacific and may cool eastern Pacific SST. The Indian Ocean can also influence the Atlantic by atmospheric bridge and the oceanic Agulhas leakage south of Africa. Midlatitude North Atlantic SSTs may affect Pacific climate variability: (1) The Atlantic multidecadal oscillation (AMO) influences North Pacific variability; (2) The warm AMO phase increases the occurrence of central Pacific (CP)-type El Niño; (3) The warm AMO phase helps induce anomalous cyclonic circulation in the tropical western North Pacific; and (4) A cold midlatitude North Atlantic Ocean in the summer may initiate an El Niño in subsequent year via the East Atlantic/West Russia teleconnection. A warm tropical North Atlantic in the spring can induce a CP-type La Niña in the subsequent winter, via two pathways of the tropical eastern North and South Pacific. Finally, the Atlantic Niño (Niña) in the summer, through the Walker circulation and ocean dynamics, helps induce an eastern Pacific-type La Niña (El Niño) in the subsequent winter. The Atlantic Niño can also warm the tropical western Indian Ocean and weaken Indian monsoon rainfall.

The relationship between Indian monsoon rainfall and low-pressure systems

Indian summer monsoon precipitation is significantly modulated by synoptic-scale tropical low pressure areas (LPAs), the strongest of which are known as monsoon depressions (MDs). Despite their apparent importance, previous studies attempting to constrain the fraction of monsoon precipitation for which such systems are responsible have yielded an unsatisfyingly wide range of estimates. Here, a variant of the DBSCAN algorithm is implemented to identify nontrivial, coherent rainfall structures in TRMM-3B42 precipitation data. Using theoretical considerations and an idealised model, an effective capture radius is computed to be 200 km, providing upper-bound attribution fractions of 57% (17%) for LPAs (MDs) over the monsoon core zone and 44% (12%) over all India. These results are also placed in the context of simpler attribution techniques. A climatology of these clusters suggests that the central Bay of Bengal (BoB) is the region of strongest synoptic organisation. A k-means clustering technique is used to identify four distinct partitions of LPA (and two of MD) track, and their regional contributions to monsoon precipitation are assessed. Most synoptic rainfall over India is attributable to short-lived LPAs originating at the head of the BoB, though longer-lived systems are required to bring rain to west India and east Pakistan. Secondary contributions from systems originating in the Arabian Sea and south BoB are shown to be important for west Pakistan and Sri Lanka respectively. Finally, a database of precipitating-event types is used to show that small-scale deep convection happens independently of MDs, whereas the density of larger-scale convective and stratiform events are sensitive to their presence—justifying the use of a noise-rejecting algorithm.

Dynamical prediction of Indian monsoon: Past and present skill

Ongoing development of dynamical atmosphere–ocean general circulation models keep expectations high regarding seasonal predictions of Indian monsoon rainfall. This study compares past and present skill of four currently operating forecasting systems, CFSv2 from NCEP, ENSEMBLES, System 4 and the newest SEAS5 from ECMWF, by analysing correlations of respective hindcasts with observed all‐India summer rainfall. For the common time period 1982–2005, only ENSEMBLES and CFSv2 give significantly skilful forecasts. It is shown that skill is highly dependent on the chosen time period. Especially the intense El Niño of 1997 seems to degrade the predictions, most notably for SEAS4 and SEAS5 which seem to be linked to El Niño too strongly. We show that by discarding that year, a regime shift in the 1990s is no longer visible. Overall, we observe a convergence of skill towards the present with correlations of about 0.4 for CFSv2 and of 0.6 for System 4 and SEAS5.

The Dynamics of the Southwest Monsoon Current in 2016 from High-Resolution In Situ Observations and Models

AbstractThe strong stratification of the Bay of Bengal (BoB) causes rapid variations in sea surface temperature (SST) that influence the development of monsoon rainfall systems. This stratification is driven by the salinity difference between the fresh surface waters of the northern bay and the supply of warm, salty water by the Southwest Monsoon Current (SMC). Despite the influence of the SMC on monsoon dynamics, observations of this current during the monsoon are sparse. Using data from high-resolution in situ measurements along an east–west section at 8°N in the southern BoB, we calculate that the northward transport during July 2016 was between 16.7 and 24.5 Sv (1 Sv ≡ 106m3s−1), although up to ⅔ of this transport is associated with persistent recirculating eddies, including the Sri Lanka Dome. Comparison with climatology suggests the SMC in early July was close to the average annual maximum strength. The NEMO 1/12° ocean model with data assimilation is found to faithfully represent the variability of the SMC and associated water masses. We show how the variability in SMC strength and position is driven by the complex interplay between local forcing (wind stress curl over the Sri Lanka Dome) and remote forcing (Kelvin and Rossby wave propagation). Thus, various modes of climatic variability will influence SMC strength and location on time scales from weeks to years. Idealized one-dimensional ocean model experiments show that subsurface water masses advected by the SMC significantly alter the evolution of SST and salinity, potentially impacting Indian monsoon rainfall.

Predictability assessment of northeast monsoon rainfall in India using sea surface temperature anomaly through statistical and machine learning techniques

AbstractThe socio‐economic growth of India is adversely affected by the abnormal meteorological phenomena of floods and droughts. Thus, rainfall prediction is highly desirable for livelihood and sustainability. In this study, northeast monsoon rainfall (NEMR) is predicted over the Indian peninsular region for the months of October, November, and December using a global sea surface temperature (SST) anomaly as a predictor by linear regression (LR), artificial neural network (ANN), and extreme learning machine (ELM) techniques. The predictions are made by LR, ANN, and ELM models for the period 1990–2016 using the training input (1871–1989) of different time series samples of global SST anomaly data collected from Hadley Centre SST data set (HadSST3). Principal component analysis was used for dimensionality reduction of the data sets, and its application substantially improved the predictive ability of the machine learning techniques. The performance of the PC‐ELM technique with the ensemble method (ESM 8–9) training window is found to be more accurate than the LR and ANN techniques and provides minimal error scores as per the statistical analysis. This study concludes that the global SST anomaly has the potential to be used as a predictor for northeast monsoon rainfall and useful for long‐range climatic projections.

Probabilistic predictions of the Indian monsoon rainfall in below-normal category using NMME global model products

Seasonal predictions of rainfall during the Indian monsoon from the North American Multi-Model Ensemble (NMME) have been used to compute the prediction skill. Observed and model-predicted rainfall have been put in three categories (namely below-normal, above-normal, and near-normal) based on the value of interannual standard deviation. In this study, focus is on the rainfall in below-normal rainfall category over central India. Forecast products were evaluated against the observed rainfall data for the period from 1982 to 2009. Simple model averaging and singular value decomposition (SVD) methods have been used to prepare the multi-model ensemble (MME) predictions. The root mean square error over central India is very less in the MME predictions compared to the member models. The weighted MME scheme using SVD method only marginally improves the skill over the simple MME scheme. The deterministic forecast skill is low (the correlation coefficient is non-significant) over central India even when the SVD scheme is employed. Both parametric and non-parametric methods have been applied to prepare probabilistic forecasts using multi-model ensemble for seasonal prediction. The probabilistic forecasts are reliable and usable as the hit rate is more than the false alarm rate for below-normal rainfall category.

Different Asian Monsoon Rainfall Responses to Idealized Orography Sensitivity Experiments in the HadGEM3-GA6 and FGOALS-FAMIL Global Climate Models

Recent work has shown the dominance of the Himalaya in supporting the Indian summer monsoon (ISM), perhaps by surface sensible heating along its southern slope and by mechanical blocking acting to separate moist tropical flow from drier midlatitude air. Previous studies have also shown that Indian summer rainfall is largely unaffected in sensitivity experiments that remove only the Tibetan Plateau. However, given the large biases in simulating the monsoon in CMIP5 models, such results may be model dependent. This study investigates the impact of orographic forcing from the Tibetan Plateau, Himalaya and Iranian Plateau on the ISM and East Asian summer monsoon (EASM) in the UK Met Office’s HadGEM3-GA6 and China’s Institute of Atmospheric Physics FGOALS-FAMIL global climate models. The models chosen feature oppositesigned biases in their simulation of the ISM rainfall and circulation climatology.The changes to ISM and EASM circulation across the sensitivity experiments are similar in both models and consistent with previous studies. However, considerable differences exist in the rainfall responses over India and China, and in the detailed aspects such as onset and retreat dates. In particular, the models show opposing changes in Indian monsoon rainfall when the Himalaya and Tibetan Plateau orography are removed. Our results show that a multi-model approach, as suggested in the forthcoming Global Monsoon Model Intercomparison Project (GMMIP) associated with CMIP6, is needed to clarify the impact of orographic forcing on the Asian monsoon and to fully understand the implications of model systematic error.

A minimalistic seasonal prediction model for Indian monsoon based on spatial patterns of rainfall anomalies

Seasonal prediction of Indian Summer Monsoon Rainfall (ISMR, rainfall during June to September over India) has remained an important scientific challenge for decades, due to its complex multi-scale nature. Statistical and dynamical seasonal ISMR predictions have traditionally relied on the relatively small variability of the spatially averaged monsoon rainfall over India, known as All India Monsoon Rainfall (AIMR). While this has served to mitigate socioeconomic impacts to some extent, overall prediction skill has remained relatively low (Wang et al. in Nat Commun 6:7154. https://doi.org/10.1038/ncomms8154 , 2015) while the spatial variability is anything but small. Here we find that the classification of deficit/ dry or surplus/ wet monsoon years based on AIMR does not add value at a regional scale due to the very high heterogeneity of monsoon rainfall, even in the extreme years. To demonstrate the need and the potential to predict this important spatial heterogeneity, we improve the classification of monsoon years by focusing on the spatial patterns of rainfall anomalies within different meteorological subdivisions of India. We apply the k-means clustering methodology and also offer cluster validation. Cluster validation reveals the existence of nine clusters of monsoon years with distinct spatial patterns of monsoon rainfall anomalies. The composite anomalies of sea surface temperature (SST) and winds during March to May (MAM) and June to September (JJAS) show distinct hydroclimatic teleconnections indicating potential predictability of regional monsoon rainfall at seasonal scale. To demonstrate the potential prediction pathways for spatial patterns of ISMR, we develop a statistical seasonal prediction model based on Classification and Regression Tree (CART) between SST over different oceanic regions as predictors and monsoon classes as predictands for the period 1901–2010. Search for the potential regressors reveals the importance of new predictors such as Atlantic Nino and SST over North Pacific region along with conventional predictors such as El Nino Southern Oscillation (ENSO), Indian Ocean Dipole/Zonal Mode (IODZM), etc. Validation of the method is performed for 2011–2015 and the model is able to predict the regional pattern of monsoon rainfall for 4 out of the 5 years. The purpose of this prediction exercise is to demonstrate the need to focus on the process and predictive understanding of these clusters and their predictability.

The Asymmetric Influence of the South China Sea Biweekly SST on the Abnormal Indian Monsoon Rainfall of 2002

An examination of the contrasting behaviour of the Indian summer monsoon during peak monsoon months of July and August 2002 in relation to the South China Sea (SCS) SST variations on intra-seasonal time scale have been carried out in this study. Monsoon rainfall over India was a deficit during July whereas enhanced rainfall activity was evident during August. Interestingly, SST over the SCS also exhibited similar contrasting behaviour with less variation during July and strong biweekly intra-seasonal (BWI) oscillation with 10–20 day periodicity during August. During August, the BWI oscillation in the SCS SST could induce anomalous cyclonic circulation and enhancement in rainfall over the SCS, suggestive of a strong air–sea interaction process. Moreover, a strong relationship between the Indian monsoon and SCS SST is evident during August, at 3-day lead time of SST. The SCS SST variations could enhance westward propagation of moisture flux from the SCS to the Indian subcontinent and thereby influences the Indian monsoon. The SST variations and air–sea interaction processes over the SCS, also westward propagation of BWI moisture flux were weak during July. Our analysis suggests that SST variations over the SCS could modulate the monsoon circulation as well as the moisture flux from the SCS to the Indian subcontinent and thereby influence the Indian monsoon, particularly on biweekly time scale at least 3 days in advance. The study indicates a potential role of the SCS SST in foreshadowing the biweekly intra-seasonal oscillation during the Indian summer monsoon period.

Indian summer monsoon variability forecasts in the North American multimodel ensemble

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.

New spatial and temporal indices of Indian summer monsoon rainfall

The overall yearly seasonal performance of Indian southwest monsoon rainfall (ISMR) for the whole Indian land mass is presently expressed by the India Meteorological Department (IMD) by a single number, the total quantum of rainfall. Any particular year is declared as excess/deficit or normal monsoon rainfall year on the basis of this single number. It is well known that monsoon rainfall also has high interannual variability in spatial and temporal scales. To account for these aspects in ISMR, we propose two new spatial and temporal indices. These indices have been calculated using the 115 years of IMD daily 0.25° × 0.25° gridded rainfall data. Both indices seem to go in tandem with the in vogue seasonal quantum index. The anomaly analysis indicates that the indices during excess monsoon years behave randomly, while for deficit monsoon years the phase of all the three indices is the same. Evaluation of these indices is also studied with respect to the existing dynamical indices based on large-scale circulation. It is found that the new temporal indices have better link with circulation indices as compared to the new spatial indices. El Nino and Southern Oscillation (ENSO) especially over the equatorial Pacific Ocean still have the largest influence in both the new indices. However, temporal indices have much better remote influence as compared to that of spatial indices. Linkages over the Indian Ocean regions are very different in both the spatial and temporal indices. Continuous wavelet transform (CWT) analysis indicates that the complete spectrum of oscillation of the QI is shared in the lower oscillation band by the spatial index and in the higher oscillation band by the temporal index. These new indices may give some extra dimension to study Indian summer monsoon variability.

ENSO Transition from La Niña to El Niño Drives Prolonged Spring–Summer Drought over North China

Persistent drought is a major meteorological disaster causing vast agricultural damage and long-term regional water crises. Over north China, this type of drought tends to begin in spring and to persist until summer with severe societal impacts. This paper aims to identify the large-scale dynamic drivers of the prolonged spring–summer drought (PSSD) over north China. This analysis has shown that the north China PSSD is associated with a persistent anomalous low-level cyclonic circulation spanning the midlatitude North Pacific from spring to summer with reduced moisture transport to north China, in combination with a tropospheric cooling along the East Asian upper-level westerly jet with dynamically forced anomalous descent above. Seven of the selected eight north China PSSD events occurred when La Niña transitioned to El Niño with a negative North Pacific Oscillation (NPO) phase in the preceding winter. The two key circulation anomalies in spring are set directly by a negative NPO phase generated in the preceding winter. The NPO, in turn, forces an El Niño onset in summer through the so-called seasonal footprinting mechanism. Consequently, sea surface temperature anomalies of El Niño in summer suppress Indian monsoon rainfall, triggering the tropospheric temperature cooling over East Asia through a circumglobal teleconnection along the East Asian upper-level westerly jet. Modeling analysis of the long preindustrial control simulation confirms the above findings. This study demonstrates that ENSO phase transition from La Niña to El Niño is one of the important precursors of north China PSSD.

Solar Activity Changes During Prolonged Sunspot Minima as Inferred from Indian Monsoon Rainfall Variations

AbstractWe could find a new 5 year periodicity in the occurrences of peaks in sunspot activity and inferred deviations of annual Indian monsoon rainfall variations from the normal during the Maunder minimum (MM) period. This result is explained in terms of solar dynamo functioning in a different mode from normal during the MM where quadrupole field (first harmonic, 5-5.5 years) dominate over dipole field (fundamental, 11 years) causing extreme north south asymmetry in sunspot activity.

Climate Change and Agriculture in India: Studying Long-Term Patterns in Temperature, Rainfall, and Agricultural Output

This paper provides an estimate of the impact of climate change on agricultural gross domestic product in India. Climate change is now an established reality, and the unusual weather patterns being observed in various parts of the world in the last 30 years is unequivocally due to variations in temperature and rainfall. The long-term trend pattern of the temperature and rainfall in India is studied, which clearly shows a distinct rise in mean temperature and declining trend rainfall after 1980. ARIMA analysis is used to generate the predictive values for temperature and rainfall, which are then used as explanatory variables along with nonclimatic variables to estimate the impact on agricultural output using an augmented Cobb-Douglas production function. The paper clearly establishes a clear and positive correlation between climate change and loss of agricultural output. The trend pattern of long-term productivity growth factor in agriculture is also showing a declining trend, which is due to unfavorable climatic and nonclimatic factors. Climatic parameters like El Niño and sea surface temperature have emerged as key determinants of monsoon rainfall in India. The agriculture sector in India has been adversely affected by rise in mean annual minimum temperature and shown a positive correlation with the changes in monsoon rainfall and mean annual temperature.

Interannual and long term variability of low level jetstream of the Asian summer monsoon

Interannual and long-term variability of the low level jetstream (LLJ) of the Asian summer monsoon during June to September was studied using reanalysis wind data. In order to study the variability, the domain of the LLJ covering the Indian and west Pacific Oceans was divided into six regions (boxes) based on their physical characteristics. The long term variability of LLJ and its relation with Indian monsoon rainfall and El Nino index were analysed. The low level circulation over Indian Ocean becomes weak during the positive phase of El Nino Southern Oscillation leads to warmer sea surface temperature over Indian Ocean. The strength of the low level jet over India depends on the convective heating of the atmosphere and not on the strength of the south Indian Ocean trade wind (ITz) which remains a passive component of the LLJ. The LLJ flow through peninsular India is weaker and the flow south of peninsular India is stronger during the recent decades in association with increasing number of break monsoon days. The cross equatorial flow over west Pacific Ocean has statistically significant increasing trend related to the increasing convection in the west Pacific Ocean.

Aerosols cause intraseasonal short-term suppression of Indian monsoon rainfall

Aerosol abundance over South Asia during the summer monsoon season, includes dust and sea-salt, as well as, anthropogenic pollution particles. Using observations during 2000–2009, here we uncover repeated short-term rainfall suppression caused by coincident aerosols, acting through atmospheric stabilization, reduction in convection and increased moisture divergence, leading to the aggravation of monsoon break conditions. In high aerosol-low rainfall regions extending across India, both in deficient and normal monsoon years, enhancements in aerosols levels, estimated as aerosol optical depth and absorbing aerosol index, acted to suppress daily rainfall anomaly, several times in a season, with lags of a few days. A higher frequency of prolonged rainfall breaks, longer than seven days, occurred in these regions. Previous studies point to monsoon rainfall weakening linked to an asymmetric inter-hemispheric energy balance change attributed to aerosols, and short-term rainfall enhancement from radiative effects of aerosols. In contrast, this study uncovers intraseasonal short-term rainfall suppression, from coincident aerosol forcing over the monsoon region, leading to aggravation of monsoon break spells. Prolonged and intense breaks in the monsoon in India are associated with rainfall deficits, which have been linked to reduced food grain production in the latter half of the twentieth century.

Monsoon climate response in Indian teak (Tectona grandis L.f.) along a transect from coast to inland

Indian monsoon (June–September) and post monsoon (October–November) rainfall show a distinct trend from coast to inland primarily due to moisture availability. However, the response of this synoptic-scale variation of rainfall amount to annual ring growth of Indian teak has not been studied systematically yet. The study is important as (1) ring width of Indian teak is considered as a reliable proxy for studying monsoon climate variability in multi-centennial time scale and (2) observed meteorological data show systematic changes in rainfall variation from coast to inland since last three decades. Towards this, we present here tree-ring width data from two locations—Thatibanda (1747–1979) and Nagzira (1728–2000) and use similar published data from two other locations—Allapalli (1866–1897) and Edugurapalli (1827–2000). The locations fall along a southeast northwest transect from south east Indian coast to inland. Monthly mean data from nearest observatories show an increasing trend in monsoon rainfall and a pronounced decreasing trend in post monsoon rainfall towards inland. Ring width data show moderately positive response to monsoon rainfall and negative response to summer (March–May) temperature for all stations suggesting moisture deficit in hot summer and intense precipitation in monsoon affect ring growth pattern in different ways. Ring width indices also exhibit significantly positive response with post monsoon rainfall at coastal location. The response gradually reduces towards inland. This preliminary study, thus, suggests that Indian teak has a potential to capture signals of the synoptic variation of post monsoon rainfall from coast to inland.

Oceanic and atmospheric conditions associated with the pentad rainfall over the southeastern peninsular India during the North-East Indian Monsoon season

The association of North-East Indian Monsoon rainfall (NEIMR) over the southeastern peninsular India with the oceanic and atmospheric conditions over the adjacent ocean regions at pentad time step (five days period) was investigated during the months of October to December for the period 1985–2014. The non-parametric correlation and composite analyses were carried out for the simultaneous and lagged time steps (up to four lags) of oceanic and atmospheric variables with pentad NEIMR. The results indicated that NEIMR was significantly correlated: 1) positively with both sea surface temperature (SST) led by 1–4 pentads (lag 1–4 time steps) and latent heat flux (LHF) during the simultaneous, lag 1 and 2 time steps over the equatorial western Indian Ocean, 2) positively with SST but negatively with LHF (less heat flux from ocean to atmosphere) during the same and all the lagged time steps over the Bay of Bengal. Consistently, during the wet NEIMR pentads over the southeastern peninsular India, SST significantly increased over the Bay of Bengal during all the time steps and the equatorial western Indian Ocean during the lag 2–4 time steps, while the LHF decreased over the Bay of Bengal (all time steps) and increased over the Indian Ocean (same, lag 1 and 2). The investigation on ocean-atmospheric interaction revealed that the enhanced LHF over the equatorial western Indian Ocean was related to increased atmospheric moisture demand and increased wind speed, whereas the reduced LHF over the Bay of Bengal was associated with decreased atmospheric moisture demand and decreased wind speed. The vertically integrated moisture flux and moisture transport vectors from 1000 to 850hPa exhibited that the moisture was carried away from the equatorial western Indian Ocean to the strong moisture convergence regions of the Bay of Bengal during the same and lag 1 time steps of wet NEIMR pentads. Further, the moisture over the Bay of Bengal was transported to the southeastern peninsular India through stronger cyclonic circulations, which were confirmed by the moisture transport vectors and positive vorticity. The identified ocean and atmosphere processes, associated with the wet NEIMR conditions, could be a valuable scientific input for enhancing the rainfall predictability, which has a huge socioeconomic value to agriculture and water resource management sectors in the southeastern peninsular India.

Link between Indian monsoon rainfall and physical erosion in the Himalayan system during the Holocene

AbstractMineralogical and geochemical analyses conducted on cores located on the active channel‐levee system of the northern Bengal Fan are used to establish changes in the weathering pattern and the sediment transport of the Himalayan system, and evaluate the effect of Indian summer monsoon rainfall during the Holocene. Our data indicate that during the Holocene, sediments from the northern Bengal Fan originate mainly from the G‐B river system without any significant changes in the relative contribution of these rivers. From 9.8 to around 6 ka, relatively low smectite/(illite+chlorite) ratios and relatively high K/Si* ratios indicate high physical denudation rates of the Himalayan highlands together with a rapid transfer of the detrital material to the Bengal Fan. The period between 9.2 and 7 ka is associated to lower values of K/Si* and corresponds to the maximum of Indian monsoon rainfall which indicates a more important chemical weathering material that rapidly transits by the G‐B river system without a long storage in the Indo‐Gangetic plain. From 6.0 ka to present day, higher smectite/(illite+chlorite) ratio and lower K/Si* ratio document a gradual increase of sediments originated from the Indo‐Gangetic plain, characterized by higher degree of chemical weathering. During the last 2.5 ka, the drastic increase in the smectite/(illite+chlorite) ratio could be associated to enhanced alteration of the plain soils due to anthropogenic activity. The comparison of mineralogical and geochemical data with previous reconstructions of the Indian monsoon dynamic indicates a rapid response of erosion and sediment transfer of the G‐B river system to changes of monsoon rainfall intensity.

Evaluation of NMME temperature and precipitation bias and forecast skill for South Asia

Systematic error and forecast skill for temperature and precipitation in two regions of Southern Asia are investigated using hindcasts initialized May 1 from the North American Multi-Model Ensemble. We focus on two contiguous but geographically and dynamically diverse regions: the Extended Indian Monsoon Rainfall (70–100E, 10–30 N) and the nearby mountainous area of Pakistan and Afghanistan (60–75E, 23–39 N). Forecast skill is assessed using the Sign test framework, a rigorous statistical method that can be applied to non-Gaussian variables such as precipitation and to different ensemble sizes without introducing bias. We find that models show significant systematic error in both precipitation and temperature for both regions. The multi-model ensemble mean (MMEM) consistently yields the lowest systematic error and the highest forecast skill for both regions and variables. However, we also find that the MMEM consistently provides a statistically significant increase in skill over climatology only in the first month of the forecast. While the MMEM tends to provide higher overall skill than climatology later in the forecast, the differences are not significant at the 95% level. We also find that MMEMs constructed with a relatively small number of ensemble members per model can equal or outperform MMEMs constructed with more members in skill. This suggests some ensemble members either provide no contribution to overall skill or even detract from it.