Assessing the hydroclimate changes in Western Himalayas during the Little Ice Age

Abstract

The Little Ice Age (LIA) was a period of most recent glacial advancement and had pronounced cooling effect in the North Atlantic region. Synchronous hydroclimate changes are also reported from the Himalayas, however owing to the heterogeneity within the proxy reconstructions, their relationship with LIA cooling is unclear. Varied topography, huge glacial mass, and multiple moisture sources (both from the Indian Summer Monsoon (ISM) and the Westerlies) makes understanding of the impact of LIA cooling on this region ambiguous. In this study, we review and assess the existing paleoclimatic proxy records for a comprehensive analysis of the regional response of the Western Himalayas to LIA cooling. Using the existing meteorological reanalysis data for back trajectory analysis for the last 20 years, the Western Himalayan region was classified into three different zones based on the relative percentage of moisture-bearing-wind-source contribution. The upper Western Himalayas receive most of the moisture from the Westerlies, both Middle and Lower Western Himalayas receive majority of rainfall from the ISM, with a relatively higher contribution of Westerlies in the Lower Western Himalayas. Comparison of reconstructions using Principal Component Analysis reveal consistent high moisture conditions during the LIA, with increased winter precipitation and decreased summer precipitation coherently in all the records. Spectral analysis of the available proxy records and various climate forcing for LIA cooling show similar dominant frequency, attesting that the LIA cooling drove the hydroclimate changes in the Western Himalayan region. External forcings such as decreased solar activity and increased volcanic activity caused cooling, influenced the Inter Tropical Convergence Zone, and resulted in weaker summer rainfall during the LIA. Synchronous changes in the North Atlantic Oscillations and El Niño–Southern Oscillation records with precipitation records suggest a link between “monsoon breaks” and enhanced Westerly intensity and an intensified winter precipitation in this region.

Keywords

Indian Summer monsoon Little Ice Age speleothems tree rings Westerlies Western Himalayas

Introduction

Catastrophic natural calamities such as landslides, rock falls, debris flow etc. are frequent in the mountainous regions because of the geological setting, rock mass properties, soil moisture conditions, land cover, prolonged precipitation, natural & anthropogenic erosion, earthquakes and climatic variations (Haque et al., 2016; Stäubli et al., 2018). In the context of the Indian subcontinent, the unique geographical location of Western Himalayas comprising higher mountains, deep valleys and vast glaciers make it a climate sensitive zone that responds rapidly to the temperature and precipitation fluctuations in the region. While large part of the Indian subcontinent receives most of its rainfall from ISM, the Western Himalayas also receives a significant moisture contribution from Western Disturbances (WD) which are eastward moving extratropical cyclones embedded in mid-latitude westerlies causing most of the winter precipitation over the region. The most recent cooling called the Little Ice Age from 1580 to 1880 A.D. (PAGES 2k Consortium, 2013) is well documented as a decrease in ~0.6°C in the Northern hemisphere (Bradley and Jonest, 1993; Mann et al., 1999). However, the impacts of LIA cooling in the Western Himalayas is still unclear because there is a considerable heterogeneity in the existing paleoclimate records.

Varied orography, high glacial mass and multiple sources play a crucial role in modifying the regional climate and therefore there is a lack of consensus on the impacts of LIA cooling across the Western Himalayas. Most of the ISM dominated regions on the Indian subcontinent document dry conditions during the LIA and the impact if LIA cooling in the Western Himalayas is unclear.

Majority of the existing high-resolution climate reconstructions in the Western Himalayas are based in tree ring and speleothem archives, both of which report inconsistent findings. Managave et al. (2020) suggested peak in glacial activity in the Borgaonkar et al., 2011; Cook et al., 2003 Karakoram region during the first half of LIA period popularly known as “Karakoram Anomaly.” Shekhar et al. (2017) suggested that the combined effect of El Niño–Southern Oscillation (ENSO) and Total Solar Irradiance (TSI) caused significant loss of glacier mass in the Himalayas region during the time period 1650–1850 A.D. According to Yadav and Singh (2002) and Anderson et al., (2002), Western Himalayas experienced wetter conditions during these intervals of LIA.

The aim of this study is to understand the hydroclimate conditions in the Western Himalayas synthesizing and assessing the existing paleoclimate records (Figure 1; Table 1) quantitatively. Since this region receives rainfall from both ISM and WD, we first use back trajectory analysis to understand the modern-day moisture source dynamics. Principal Component Analysis (PCA) and spectral analysis are used to develop a better understanding of the regional coherency (or lack thereof), impacts of LIA in this region.

Table 1.

List of Proxy data location, Proxy used, dating technique and time period of the record in the Western Himalayan region.

ID	Study Area	Latitude	Longitude	Type of proxy	Dating technique	Time period (AD)	References
T1	Dhanolti	30.75	78.42	Tree ring growth index	Dendrochronology	1720–1990	Borgaonkar et al. (2004a)
T2	Jageshwar	29.77	79.17	Tree ring growth index	Dendrochronology	1657–1990	Borgaonkar et al. (2004b)
T3	Ghansali	30.62	78.75	Tree ring growth index	Dendrochronology	1796–1989	Borgaonkar et al. (2004c)
T4	Tuni	30.83	77.43	Tree ring growth index	Dendrochronology	1801–1988	Borgaonkar et al. (2004d)
T5	Dokriani Bamak	30.51	78.485	Tree ring indices	Cross-dating technique	1618–1995	Bhattacharya et al. (2001)
T6	Gahan	31.18	77.27	Tree ring growth index	Dendrochronology	1745–1989	Borgaonkar et al. (2007)
T7	Kanasar	30.75	77.8	Tree ring growth index	Dendrochronology	1711–1988	Borgaonkar et al. (2004e)
T8	Keylong	32.75	76.62	d18O (tree rings)	d18O chronology	1146–2006	Managave et al. (2020)
T9	Manali	32.27	77.17	Tree ring standardized growth index	Dendrochronology	1767–2008	Borgaonkar et al. (2004f)
T10	Kufri	31.12	77.17	Tree ring growth index	Dendrochronology	1775–1988	Borgaonkar et al. (2004g)
T11	Narkhanda	31.2	77.23	Tree ring growth index	Dendrochronology	1590–1989	Borgaonkar et al. (2004h)
T12	Gulmarg	35.08	74.3	Tree ring standardized growth index	Dendrochronology	1604–1980	Hughes (2002a)
T13	Khillanmarg	35.08	74.33	Tree ring standardized growth index	Dendrochronology	1608–1980	Hughes (2002b)
T14	Sarbal	34.5	75.75	Tree ring standardized growth index	Dendrochronology	1643–1981	Hughes (2002c)
T15	Sonmarg	34.58	75.55	Tree ring standardized growth index	Dendrochronology	1732–1980	Hughes (2002d)
T16	Thijwas	34.58	75.47	Tree ring standardized growth index	Dendrochronology	1654–1981	Hughes (2002e)
T17	Pahalgam	34.03	75.7	Tree ring standardized growth index	Dendrochronology	1609–1982	Pant (2013)
T18	Chandanwani	34.25	75.92	Tree ring standardized growth index	Dendrochronology	1777–1981	Hughes (2002f)
S1	Sahiya Cave	30.6	77.86	δ¹⁸O	Oxygen Isotope Data	142 BC- 2004	Sinha et al. (2015)
S2	Sainji Cave	30.268	79.304	δ¹⁸O	U-Th	0–1770	Kotlia et al. (2015)
S3	Dharamjali Cave	29.524	80.211	δ¹⁸O	U-Th(StalAge)	210–1950	Sanwal et al. (2013)
S4	Chulerasim cave	29.885	79.352	δ¹⁸O	U-Th(StalAge)	1600–1950	Kotlia et al. (2017)
S5	Panigarh Cave	29.553	80.117	δ¹⁸O	U-Th	1260–2000	Liang et al. (2015)

Methodology

Moisture source analysis

Hybrid single-particle lagrangian integrated trajectory (HYSPLIT) back trajectory model from National Oceanic and Atmospheric Administration (NOAA), (http://www.arl.noaa.gov/ready/hysplit4.html) was used along with the NCEP/NCAR reanalysis dataset to infer the moisture transport history and develop a better understanding of the circulation patterns in the Western Himalaya region on local and regional scales. However, it is important to note that HYSPLIT provides back trajectory of air mass only, we infer the moisture percentage contribution considering that most of the atmospheric moisture was carried by winds at lower heights (~2500 m agl) in the atmosphere.

This back trajectory run was performed for the time period 2000–2020 to understand the modern day moisture source in the region. One hundred sixty-eight hours back trajectory data was used in this study considering that the residence time of water vapor in the atmosphere is about a week. Also, an altitude of 500–2500 m agl was used for our analysis as this is the height where most of the water laden cloud forming air mass generally moves. However, at higher heights the speed of wind is generally higher due to which it would mark a longer back trajectory compared to lower heights with the same time period. Daily back trajectory run was performed for all the locations specified in our study area using the meteorological parameters provided above and the dominant moisture source for each location was obtained using cluster analysis (Supplemental Figure S1 and Supplemental Table S1). For each of the 23 study sites, a total of 1095 daily back trajectories for the 20 year time period were clustered into three primary paths of moisture source directions namely Bay of Bengal, Arabian Sea and Westerlies. Based on the relative contribution of moisture from various sources, the region was divided into three different zones.

Principal component analysis

The Monte Carlo Principal Component Analysis (MC-PCA) technique was used to assess the regional coherency and patterns in the records during the LIA. We conducted analyses on 10 high-resolution hydroclimate records divided into two different zones Upper Western Himalayas (UWH) and Lower Western Himalayas (LWH) (listed in Table 1) from around the Western Himalayan region. Data unavailability restricted our analysis to the period starting from 1600 AD to 1900 AD. Z-Score was calculated in order to bring the δ¹⁸O and Tree ring growth index (trgsi) proxy values on the same scale and for δ¹⁸O the z-score values were inversed. To address the challenge of non-uniform age spacing, we followed Deininger et al. (2017) approach. For consistent temporal resolution, a linearly spaced age range was developed, and 2000 age models with Gaussian-distributed variances within one standard deviation were generated. Linear interpolation was used to generate age models with varied temporal resolutions from proxy values. Using a Gaussian kernel, upscaling of age models was done using 5-year-long bins and the proxy records were normalized. 1000 MC-PCA were run with each simulation choosing 10 age models at random from a pool of 2000. Any flipped Principal Components (PCs) produced by age uncertainty were addressed using a flipping method. At a 95% confidence level, significant main components were chosen using the Kolmogorov-Smirnov (KS) test.

Spectral and wavelet analysis

Spectral analysis was performed on a set of 23 proxy records and different indices of climatic drivers (Sunspot number (SN), TSI, Nino 3 index, North Atlantic Oscillation index). Using the REDFIT software, Lomb-Scargle Fourier transform was applied in conjunction with Welch’s Overlapped Sequence Averaging approach to find periodic components in the spectrum. REDFIT generates first-order autoregressive (AR1) time series with sample and characteristic timescales that are consistent with the input climate data. A variable number of overlapping (50%) detrended segments were employed and the spectra were bias-corrected using 1000 Monte Carlo simulations. The upper confidence interval of the AR1 noise for various significance levels was determined using REDFIT software based on a χ2-distribution to examine the statistical significance of a spectral peak (Supplemental Figure S2 and Table S2). Additionally, a modified harmonic-filtering algorithm was used to account for the temporal changes in the signal components. To preserve the serial dependence of time series and boost accuracy, confidence intervals of correlation coefficients were computed using the pairwise moving-block bootstrap approach. The reconstruction spectra are shown with their 90% and 95% confidence intervals, and the main peaks that touch the 90% significance threshold have been considered for the time series analysis of the paleoclimate record. In addition, wavelet analysis was also performed on both the proxy records and climatic factor indices to obtain wavelet scalograms (Supplemental Figure S3). Band pass filtering was applied on the common significant (90% significance level) frequency bands of proxy records and climatic drivers, based on the results of spectral analysis (Supplemental Figure S4). PC1 score, obtained using the records covering the entire LIA period (1580–1880 AD) such as Sahiya, Keylong, Panigarh, and Dharamjali, was compared with different climatic drivers to obtain the correlation between them during the LIA (Figure 5).

Results and discussion

Moisture source dynamics in Western Himalayas

Based on the relative percentages of moisture contribution from various sources in a specific region, the study area was divided into three sub-zones: the Upper Western Himalayas (UWH) which receives most of its moisture from Westerlies, Middle (MWH) and Lower Western Himalayas (LWH) which receives majority of rainfall from ISM with relatively increasing contribution from westerlies in the LWH. Sites in the UWH (indicated by blue circle in Figure 2) primarily gets moisture from the westerlies and the percentage of ISM rainfall and specifically that from the Arabian sea branch of ISM increases progressively southwards. However, a further increase in contribution of westerlies was recorded toward the south-east direction in the LWH zone (indicated by the green circle in the plot). This increasing contribution of westerlies driven precipitation could be attributed to the topography of the region. Since no significant tectonic activity or topographical changes have occurred in the past 2000 years, we expect a similar moisture source hydrological dynamics of the region during the LIA period also.

Regional comparison of records in the Western Himalayas

The first principal component (PC1) accounts for about 65.44% variance and 35.45% of variance for the UWH zone and LWH zone respectively. For the UWH zone, the first principal component (PC1) shows an abrupt shift to dry conditions beginning at 1600 AD (blue curve in Figure 3a). The drying pattern shown by PC1 is a feature of all original records and is not the consequence of an abrupt regionally asynchronous change. However, for the LWH zone, PC1 shows a wetting trend after 1600 AD. This shift to wet conditions is documented in most of the records in the LWH zone, except Dokriyani Bamak which have a negative PC1 loading. Increase in precipitation in the Western Himalayan region during the LIA period has been reported by several previous studies (Denniston et al., 2000; Kotlia et al., 2015; Liang et al., 2015; Sanwal et al., 2013; Sinha et al., 2015). The increased LIA precipitation in the Himalayan region is synchronous with more frequent monsoon break events (Kotlia et al., 2015; Raghavan, 1973; Rao, 1976; Sinha et al., 2011). A few studies argued that the high moisture conditions can not be solely explained by monsoon break events instead increase in winter monsoon precipitation (through westerlies) played an important role during that time (Kotlia et al., 2015; Liang et al., 2015).

In contrast, a drying pattern is observed in the UWH zone despite being the westerlies dominated zone from 1600 to 1650 in Figure 3. The proxy records from UWH zone (Khillanmarg, Gulmarg, and Pahalgam) are tree ring standardized growth index (trsgi) values obtained from trees located in Kashmir valley. Calibration and verification studies from most of the Kashmir valley records, such as Pahalgam, have revealed a statistically significant and reliable reconstruction of precipitation during the growing season which in this case is the summer season (May-September) (Borgaonkar et al., 1994). These records are reconstructing the summer ISM precipitation and therefore PC1 values shows drying pattern, consistent with the weakening of ISM during the LIA period (Gupta et al., 2003; Kotlia et al., 2012). The negative PC1 loading of Dokriyani Bamak record indicative of dry conditions, is also because of the biases in tree ring (trsgi) based reconstruction of precipitation due to its growing season.

Further, positive PC1 loading values of Keylong (tree ring data) indicates a wet pattern as reported in the original study (Managave et al., 2020). The tree rings δ¹⁸O from Keylong (Lahaul-Spiti region) is reported to preserve the integrated winter and summer hydrological signal strength (Chinthala et al., 2022) suggesting that while the overall annual precipitation is recorded to increase during the LIA period, the summer precipitation decreased, and therefore westerlies driven winter precipitation was recorded in other records in this region.

The second principal component (PC2) of both the zones show a fluctuating dry and wet pattern which may be attributed to monsoon break events. There also is an out-of-phase relationship between the PC scores of LWH and UWH zones indicating the out-of-phase relationship between ISM and westerlies in this region supported by many existing studies (Chen et al., 2009; Kotlia et al., 2017).

Regional hydroclimate changes and climate drivers during LIA

It is now well known through reconstructions and CMIP5-PMIP3 simulations that the LIA period was characterized by cooler global temperatures (Atwood et al., 2016; Díaz and Vera, 2018; Rojas et al., 2016; Shi et al., 2016; Yang et al., 2023; Yang and Jiang, 2017). Atwood et al. (2016) analyzed the global top of atmosphere energy budget over the last millennium using CMIP5-PMIP3 simulations and reported that volcanic forcing (~65%) was the major driver of cooling, while contributions from changes in land use (13%), greenhouse gas concentrations (12%), and solar insolation (10%) were significantly lower. Miller et al. (2012) also concluded that enhanced volcanic activity resulted in increased Sulfur loading in the atmosphere, resulting in cooling of the atmosphere. However, Atwood et al. (2016) argued that the temperature anomalies linked with significant volcanic episodes are typically much larger in models than in reconstructions. Such inconsistencies have been previously attributed to errors in tree-ring-based temperature reconstructions (e.g. anomalous tree growth immediately following large volcanic events; Mann et al. (2012); uncertainties in volcanic reconstructions (Sigl et al., 2014); and/or the models’ tendency to overestimate the impact of large volcanic events (Atwood et al., 2016)). Asian tree ring temperature reconstructions also indicate low temperatures (1815–1850 AD) (PAGES 2k Consortium, 2013), which followed the Tambora volcanic event during 1815 AD (Gao et al., 2017) combined with the effect of Dalton minima. The multi-annual dry period (1840–1850 AD) is also observed in the Western Himalayan region which is also linked to the historical drought interval during this time in India Figure 5 (Kotlia et al., 2012).

Centennial scale variability during the LIA

The CMIP5-PMIP3 models suggest a relatively lower contribution of solar forcing in the LIA cooling, primarily because only one run considered “solar-driven-ozone-changes.” Solar forcing is however, now regarded as one of the primary drivers of LIA cooling (Atwood et al., 2016; Shindell et al., 2006; Sun et al., 2022). The solar activity (TSI and SN) reduced during LIA with extended periods of solar minima like Maunder minimum (1645–1715 AD), and Dalton minimum (1790–1830) (Figure 5). Spectral results of both SN and TSI revealed 100–140 years and 200–250 years periodicities, which has also been documented in previous studies (Agnihotri et al., 2002; Sonett and Suess, 1984; Stuiver and Braziunas, 1989). Common low frequency periodicities like 100–140 years were only found in the Sainji cave record in the Western Himalayan region (Figure 4a). Our spectral analysis using cross comparison of 100–140 year band pass filter results showed matching high relative amplitudes during 1600–1850 AD suggesting that changes in precipitation patterns in the Western Himalayan region could be related to the solar variability at centennial scale that drove the LIA cooling (Figure 4a). According to Attolini et al. (1990), the period of 132 years is one of the sub-harmonics of the Hale cycle (22 years). Therefore, this could be linked to the typical significant period band of 100–140 years, observed in the spectral analysis of the reconstructions conducted in this study.

Spectral analysis also document a co-occurrence of solar activity and precipitation changes in the UWH zone (Figure 3a), which document a decrease in summer monsoon precipitation from 1600 to 1650. The summer monsoon weakening is known to be caused by relatively southward location of Inter Tropical Convergence Zone (ITCZ) (Fleitmann et al., 2007; Wang et al., 2005; Yan et al., 2015). Solar activity has previously been reported to be linked with monsoon precipitation via its direct effect on ITCZ (Fleitmann et al., 2007; Gupta et al., 2005; Kodera, 2004; Wang et al., 2005). During the summer season, a north-south seesaw of convective activity is produced by ITCZ that forms over land and across the equatorial Indian Ocean (Waliser and Gautier, 1993). Reduced solar activity can decrease the north-south seesaw convective activity leading to low precipitation (weakening of monsoon) over the Indian subcontinent (Chandrasekar and Kitoh, 1998; Gupta et al., 2005; Kodera, 2004). Colder NH temperatures due to increased ice cover in high-latitudes and a slowing of the Atlantic meridional overturning circulation (Haug et al., 2001) overall contributed to the southward position of ITCZ (Kaushik et al., 2023) (Figure 5) which explains the low summer precipitation recorded in the UWH zone from 1600 to 1650 (Figure 3a).

Decadal variability during the LIA

The multidecadal period band (40–80 years) band pass filter results of proxy records in LWH zone matches well with the NAO variability (D’Arrigo et al., 2005), with high amplitude during 1500–1800 AD, indicating that LIA cooling significantly impacted the Western Himalayan region on decadal timescale (Figure 4b). The multidecadal period band (40–80 years) has been previously linked with atmosphere-ocean-land system interactions, the NAO (Cook et al., 1995; Cullen et al., 2001; Reddy and Gandhi, 2022). Xu et al. (2018) reported the power spectra of the NAO series derived from CCSM4 and MPI-ESM-P models showing 40 to 80 year significant bands. Numerous sites in central Asia’s mid-latitudes also provide evidence of the association between NAO anomalies and precipitation changes (Aizen et al., 2001; Archer and Fowler, 2004; Chen et al., 2006; Kurths et al., 2019) and reported NAO to be a dominant climatic driver during LIA. Negative NAO index (Trouet et al., 2009) was observed during LIA with dips at ∼1450 year, ∼1600 year, and ∼1750 year (Figure 5). These dips are coincident with peaks (wettest intervals) in PC1 score of Western Himalayan records (Figure 5). The proposed mechanism is associated with negative NAO index indicating low-pressure gradient between Azorean high and Icelandic low, leading to dry winters in European region and wet winters in mid-latitude Asia, including Western Himalayan region caused by changing westerlies intensity and increased vapor transport to the Western Himalayan region (Aizen et al., 2001; Chen et al., 2006; Chinthala et al., 2023). Amitai et al. (2020) reported the southward shifting of NAO during LIA in the Mediterranean region, based on the CMIP5/PMIP3 models, supporting the hypothesis of additional moisture-bearing rains to the Himalayan foothills through westerlies during the LIA.

Comparing the experiments using Last Millennium (LM) simulations with those from the control (CTL) experiments conducted with Community Climate System Model, version 4 (CCSM4) and Max Planck Institute for Meteorology Paleoclimate Model (MPI-ESM-P), Xu et al. (2018) reported that external forcings, such as volcanism and solar activity could have a significant impact on the phases of both ENSO and the NAO. They suggested that these shifts in NAO phases were likely initiated by variations in solar irradiance and volcanic activity, and were also influenced by changes in the Atlantic meridional overturning circulation (AMOC) (Xu et al., 2018). During the LIA, the AMOC is reported to be notably weaker than it is today (Cronin et al., 2003; Trouet et al., 2012) that caused reduced northward heat transport and lower sea surface temperatures in the North Atlantic at high northern latitudes and consequently weaker summer monsoon precipitation as seen in UWH region (Figure 3a).

All the tree ring records showed 2–7 year band periodicities because of the high resolution (annual-scale) (Supplemental Figure S4). Previous studies suggest that atmospheric oscillations such as the quasi-biennial oscillation (QBO), solar activity and/or ENSO could be the drivers at these time scales (D’Arrigo et al., 2005; Reddy and Gandhi, 2022; Trenberth, 1976; Yadav, 2011; Henke et al., 2017). The Nino 3 index (D’Arrigo et al., 2005) showed a dominant band of 2–7 years and the band pass filtering results showed high relative amplitude during 1550–1650, 1800–1850, 1900–1950, which matches with the pattern in band pass filtering results of tree ring records (Figure 4c). Tejavath et al. (2019) used PMIP3 simulations to show a predominantly drier ISM season during the LIA in comparison to the Last Millennium mean conditions. They reported a statistically significant ENSO-Monsoon association in the LM, such that percentage of strong El Niños led to increased droughts during LIA. Previous modeling studies indicate that the solar variations and volcanic aerosols modulate the impact of ENSO on this region (Dimri et al., 2016; Kotlia et al., 2015; Sanwal et al., 2013).

Additionally, a positive phase of Pacific Decadal Oscillation (PDO) is also observed during LIA (Man and Zhou, 2011). ISM reconstructions suggest that negative NAO anomaly along with positive PDO and Nino 3 index coincide with drought conditions in the core monsoon zone of India that possibly triggered more “monsoon breaks,” and strengthening of the westerlies leading to increased precipitation in the Himalayan foothills (Dixit and Tandon, 2016; Kotlia et al., 2015; Raghavan, 1973; Rao, 1976; Sinha et al., 2011; Figure 5). However, spectral and wavelet analysis of PDO in this study did not reveal any common significant spectral band.

Conclusion

In this study, we used the modern moisture source dynamics, to divide the Western Himalayan region into three subdivisions based on the relative contribution of diverse moisture sources, the Bay of Bengal, the Arabian Sea, and Westerlies. The influence of ISM increases southwards progressively, excluding the southwest part where the contribution of westerlies is higher. Our aback-trajectory analysis did not show a clear linear trend, which is usually considered in the reconstructions. This is because of the complex topography of the region. Continuous monitoring of isotopic composition of precipitation can help understand the hydroclimate and moisture source dynamics better.

Regional comparison of proxy records reveal coherent high moisture conditions during LIA with enhanced winter precipitation and decreased summer precipitation (weaker ISM). Out-of-phase relationship between ISM and westerlies in this region was also observed. Common period bands identified in proxy records using spectral and wavelet analysis, 100–140, 40–80, and 2–7 years, are coherent with climate drivers such as solar cycle changes (SN and TSI), NAO, and ENSO respectively. Spectral analysis shows clear signature of LIA in this region as the climate drivers responsible for LIA globally have similar dominant frequency. External forcings such as decreased solar activity and increased volcanic activity caused cooling in the atmosphere, which influenced the ITCZ and resulted in decreased monsoon strength during the LIA.

Despite lack of the number of continuous high-resolution records from Western Himalayan region, this study uses the existing records to provide a comprehensive regional picture of the hydroclimatic variability in the region. This review demonstrate that the Western Himalayas responded spatially heterogeneously to LIA cooling and demonstrate the need of climate reconstructions for a better understanding of the climatic patterns and associated mechanisms. The Western Himalayas cannot be considered as a region responding homogeneously to global change and therefore careful considerations have to be made while assessing the regional impacts of recent and future climate changes.

Supplemental Material

sj-docx-1-hol-10.1177_09596836231225727 – Supplemental material for Assessing the hydroclimate changes in Western Himalayas during the Little Ice Age

Supplemental material, sj-docx-1-hol-10.1177_09596836231225727 for Assessing the hydroclimate changes in Western Himalayas during the Little Ice Age by Anubhav Singh, Aakanksha Kumari, Bhavuk Sharma, Rajalakshmi Senthilnathan and Yama Dixit in The Holocene

Footnotes

Acknowledgements

Ankit Yadav, Department of Physics, Khalifa University (UAE) is thanked for his inputs in the Principal Component analysis part of this work.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was made possible through the financial support provided by the Ministry of Human Resources Development (MHRD) and the UQ – IIT Delhi Academy of Research (UQIDAR).

ORCID iDs

Anubhav Singh

Yama Dixit

Supplemental material

Supplemental material for this article is available online.

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