New insight into pattern divergence of the Indian summer monsoon during the Holocene

Abstract

As one of the paramount components of Asian monsoon and global atmospheric circulation system, the ISM (Indian summer monsoon) plays a vitally important role in the natural environment, ecological balance, and cultural development. Moreover, understanding the ISM could provide valuable insights into global hydrological, atmospheric, and carbon cycles. In recent decades, many ISM records based on different archives have been established within the Holocene, however, issues still remain: the TP (Tibetan Plateau) and the Indian subcontinent are often analyzed separately, despite the both are strongly influenced by the ISM, impeding comprehensive understanding of the monsoon and its object relationship with the East Asian summer monsoon (EASM). Here, covering the both regions, 65 published paleoclimate records within the Holocene have been collected, and conclusions are drawn as follow: (1) There are two evolution patterns of the ISM during the Holocene. The first pattern with the Holocene Optimum (HO) at ~10–6 ka BP, mainly distributing in the Arabian Sea and its surrounding areas, the Indian subcontinent, western central TP, northern TP, and southern China. The second pattern with the HO at ~8–3 ka BP, mainly distributing in the Bay of Bengal and its surrounding areas and southern TP. (2) The delay between the optimum of the second pattern and insolation maximum may be caused by the variation of high-latitude ice cover, although the underlying mechanism concerning the spatial distribution of the two patterns is still unclear. (3) The ISM and the EASM evolve synchronously, and share similar pattern divergence. Arguments over this issue mainly owe to the selected records originating from different patterns. This study makes up the gap in the pattern divergence of monsoon evolution, deepening the understanding of the ISM, even entire Asian monsoon system, and therefore is of great significance for future climate prediction.

Keywords

EASM Holocene optimum ISM pattern divergence

Introduction

In the boreal summer, a low-pressure cell forms in the Indian subcontinent due to rapidly heating up of the landmass, trapping moisture-loaded monsoon from the Indian ocean, causing abundant precipitation, and forming the ISM (Indian summer monsoon) (Ali et al., 2019; Huguet et al., 2018; Li et al., 2016). As one of the paramount components of Asian monsoon and global atmospheric circulation system, the Indian summer monsoon (ISM) plays an extremely important role in shaping natural environment, maintaining ecological balance and blooming cultural development (Chen et al., 2014; Cook et al., 2013; Govil and Divakar Naidu, 2011; Wang et al., 2005; Webster et al., 1998). In the context of global warming and increasing climate uncertainty, a further exploration to the ISM could offer a more comprehensive insight into the global hydrological and carbon cycle process, especially during the Holocene, which shares astronomical and geological setting similar to the present (Gupta et al., 2005), for the purposes of accurate prediction, natural disasters avoidance, and better guiding national production (Chen et al., 2014).

In recent years, impeded by the short time span of the instrumental data with limited variation (Ali et al., 2019; Misra et al., 2019), many ISM paleoclimate records based on different archives have been successively established, including ice core records (Thompson et al., 1997, 2000, 2003, 2006), lacustrine sediment records (Günther et al., 2015; Kasper et al., 2012; Wei and Gasse, 1999; Zhu et al., 2009), peat records (Yu et al., 2006; Zhao et al., 2011), stalagmite records (Cai et al., 2012; Kathayat et al., 2017; Sinha et al., 2011) etc. Chen et al. (2020) summarized the ISM records over the TP, and suggested that the warm period coincided with the solar insolation maximum occurring in the early to middle Holocene, while the humidity records showed different distribution patterns: humidity optimum on the southwestern TP occurred at the early Holocene, while the northeastern in the middle to late-Holocene (Chen et al., 2020). Misra et al. (2019) aggregated 76 lake records from the Indian subcontinent, and found that corresponding to the global HO (Holocene Optimum), the monsoon intensified at 9–5 ka, and gradually weakened after 4 ka (Misra et al., 2019). In addition, the two prominent cold- dry climate events, 8.2 ka and 4.2 ka events, both are preserved in the ISM records. Specifically, most 8.2 ka signals are documented in the marine records (Gupta et al., 2020), while 4.2 ka signals are widely distributed in the ISM region except the central India (Prasad et al., 2014). However, both the TP and the Indian subcontinent with its surrounding area, are deeply affected by the ISM. Consequently, it may hinder the understanding process by separating them when exploring the ISM evolution history and underlying mechanism. To this end, it is urgent to conduct a review combining records from the both areas.

For another, there is a long-standing debate on whether the evolution of the ISM and the EASM is synchronized. Some studies suggested that they evolved asynchronously: An et al. (2000) found that the HO in different regions of China occurred at different time, among which the HO on the southwestern China appeared at ~10 ka BP, which was believed to be influenced by the ISM (An et al., 2000); Hong et al. (2005) found that the ISM and the EASM intensity, indicated by Hongyuan peat and Hani peat respectively, displayed antiphase variation during the Holocene (Hong et al., 2005); After summarizing more than 90 paleoclimate records in the East Asian core monsoon region, Wang et al. (2010) found that PC1 with early-Holocene optimum mainly distributes in the ISM region, while the PC2 with mid-Holocene optimum mainly distributes in the ESM region, hence supporting the asynchronous evolution viewpoint (Wang et al., 2010). Meanwhile, opposite conclusion could be draw when comparing stalagmite records from the two monsoon regions respectively (Cheng et al., 2012; Jinguo et al., 2010; Zhang et al., 2013). Based on the comparison results of stalagmite records and the numerical simulation results, Li et al. (2014b) found that the two monsoon systems have different responses to the Holocene climate at different scales, and the relationship between them could not be simply summarized as whether they were synchronized or not. In order to clarify the relationship between them, accurately grasp the ISM is a priority. Consequently, a comprehensive review and exploration regarding it is urgently needed.

In this study, 65 published ISM records are collected, covering the Tibetan Plateau, the Indian subcontinent with its adjacent oceans, in order to: (1) Understand the distribution and evolution of the ISM during the Holocene, (2) Explore mechanisms underlying the forementioned spatiotemporal distribution pattern, and (3) Explore whether the ISM and the EASM evolve synchronously during the Holocene, and the reasons regarding to the debate on this issue.

Study area

As “the roof of the world” and “the third pole,” the Tibetan Plateau covers an area of ~2.5 × 10⁶ km², with an average altitude of more than 4000 m. Surrounded by Kunlun, Qilian, Hengduan, Himalayas, Pamirs, and Hindu Kush mountains, it is the highest and largest plateau on the earth, and its glacier reserves reach ~7 × 10³ km³(Farinotti et al., 2019). From 1980 to 2018, the multi-year average temperature of the Tibetan Plateau was 4.1°C, and the annual average precipitation was 482 mm (Zhang et al., 2020). Lakes on the plateau distribute widely and densely, with a total area of 5 × 10⁴ km² (Zhang et al., 2019), accounting for 1.9% of the global lake area and 57.2% of the national lake area (Zhang et al., 2020). Among them, 1424 lakes are larger than 1 km², and mainly concentrated on the middle of the Plateau (Zhang et al., 2019). The Tibetan Plateau is under the combined action of the westerly, Indian monsoon and East Asian monsoon: the southern part of the Plateau is mainly affected by the Indian monsoon (Wu et al., 2012), the northern part is mainly controlled by the westerly (Yao et al., 2013), and the northern boundary of the modern summer monsoon is about 34°–35°N (Tian et al., 2001). Many types of plants are distributed on the plateau, from tropical rain forest, boreal forest to tundra. Among them, the coverage of tropical rain forest and seasonal rain forest extends to ~29°N, which is the northernmost distribution of such forest in the world (Luo et al., 2002). The southern part of the plateau is mainly covered by alpine forests and meadows, which gradually transit to alpine meadows and deserts northwestern toward (Chen et al., 2020).

The Indian subcontinent is located in southern Asia, encircled by the Arabian Sea and Bay of Bengal, with the Himalayan Mountains and their branches locating on the north (Figure 1). The Sulaiman and Kitar ranges extend to the Hindu Kush in the northwest, and the Naga and Araganyoma constitute the Indo-Burman mountains in the northeast. The entire Indian subcontinent can be divided into three topographic units: the Indian peninsula, the alluvial plain of the Indian Ganges, and the Himalayas (Roy and Purohit, 2018). The four seasons on the subcontinent are: pre-monsoon season (March–May), monsoon season (June–September), post-monsoon season (October–December), and winter (January–February) (Oza et al., 2020). The typical Indian climate is characterized by warm and wet summer with about 70% of the annual precipitation concentrated from June to September, and mild and dry winter (Dixit and Tandon, 2016). Occupying only ~7% of the Asian continent landmass, India contains ~30% of the Asian population.

Figure 1.

The 65 collected ISM records in this study. The hollow cross, hollow circle, solid triangle, solid circle, and black cross indicate marine, peat, lake, cave sediments, ice cores respectively.

Data and method

A total of 65 published ISM records based on many proxies were collected in this study, involving hydrogen and oxygen isotopes, and pollen etc. (Table 1, at the end of the paper). Due to the complexity of data sources, discrepancy exits in the resolution and responding amplitude. To ensure data quality, the selected records should meet the following requirements: (1) Proxies have clear indication meaning, and reflecting monsoon intensity variation. Precipitation records have the priority when both temperature and precipitation records are available in the same location. If multiple temperature or precipitation indicators are available at the same location, quantitative data such as pollen and GDGT are preferred, followed by hydrogen and oxygen isotopes, and other indicators come last. (2) reliable dating, including explicit age-depth data, with at least five dating points in the Holocene time scale; (3) a continuous sequence, covering at least about 7000 years of the Holocene with a break of no more than 1500 years, especially between 10,000 BP and 6000 BP; (4) the data resolution is less than 200 years.

Table 1.

The Indian summer records from the Tibetan plateau and the Indian subcontinent with its surrounding areas.

Number	Site	Latitude (°N)	Longitude (°E)	Altitude (m a.s.l)	Sediment type	Proxy	Resolution(a)	Dating method	Dating number	Time span (yr BP)	Reference
1	Dandak cave	19	82	400	Cave sediment	Stalagmite δ¹⁸O	0.5	U-series	13	390–1330	Sinha et al. (2007)
2	Tianmen cave	30.91667	90.06667	4800	Cave sediment	Stalagmite δ¹⁸O	3-7	U-series	9	4300–8700	Cai et al. (2012)
3	Sahiya cave	30.6	77.86667	1190	Cave sediment	Stalagmite δ¹⁸O	1.8	U-series	32	−60 to 5700	Kathayat et al. (2017)
4	Baratang cave	12.08333	92.75	20	Cave sediment	Stalagmite δ¹⁸O	23	U-series	15	−3.74 to 3920	Laskar et al. (2013)
5	Jhumar cave	18.8667	81.8667	600	Cave sediment	Stalagmite δ¹⁸O	1.45	U-series	4	−58 to 875	Sinha et al. (2011)
6	Wah Shikar cave	25.25	91.8667	1290	Cave sediment	Stalagmite δ¹⁸O	0.97	U-series	17	−57 to 551	Sinha et al. (2011)
7	Dongge cave*	25.28	108.08	680	Cave sediment	Stalagmite δ¹⁸O	17.45	U-series	45	−16.5 to 15810	Dykoski et al. (2005)
8	Qunf cave*	17.17	54.3	650	Cave sediment	Stalagmite δ¹⁸O	7.21	U-series	78	400–1400, 2700–10,300	Fleitmann et al. (2003)
9	Mawmluh cave*	25.2622	91.8167	1290	Cave sediment	Stalagmite δ¹⁸O	6.01	U-series	11	3650–12,400	Berkelhammer et al. (2012)
10	Hoti cave	23.08	57.35	800	Cave sediment	Stalagmite δ¹⁸O	4.31	U-series	12	6100–9600	Neff et al. (2001)
11	Huangye cave	33.583	105	1650	Cave sediment	Stalagmite δ¹⁸O	4.305	U-series	28	−1864	Liangcheng et al. (2011)
12	Wanxiang cave	33.3167	105	1200	Cave sediment	Stalagmite δ¹⁸O	2.519	U-series	20	−53 to 1758	Zhang et al. (2008)
13	Dayu cave	33.133	106.3	870	Cave sediment	Stalagmite δ¹⁸O	1.332	U-series	15	−32 to 684	Tan et al. (2015)
14	Baratang cave	12.0833	92.75	20	Cave sediment	Stalagmite δ¹⁸O	22.95	U-series; AMS¹⁴C	2;15	−4 to 3920	Laskar et al. (2013)
15	Dasuopu ice core	28.38	85.72	7200	Ice core	Stalagmite δ¹⁸O	1	–	–	−46 to 500	Thompson et al. (2000)
16	Thimmannanayakanakere	14.20217	76.39938	792	Lake sediment	Carbonate content	113	AMS¹⁴C	2	40–3660	Warrier et al. (2014)
17	paleolake Kotla Dahar	28	76.95	187	Lake sediment	Carbonate δ¹⁸O	93.44	AMS¹⁴C	10	510–6490	Dixit et al. (2014)
18	Selin Co	31.75764	89.04792	4530	Lake sediment	Carbonate δ¹⁸O	285.8	AMS¹⁴C	4	−12 to 14,563	Wei and Gasse (1999)
19	Lake Qinghai	36.75495	100.34546	3194	Lake sediment	Carbonate δ¹⁸O	232.1	AMS¹⁴C	3	204–12,970	Lister et al. (1991)
20	Lake Qinghai*	36.9267	100.24463	3194	Lake sediment	Carbonate δ¹⁸O	191	AMS¹⁴C	10	0–17,400	Liu et al. (2002)
21	Kuhai lake*	35.30534	99.16515	4132	Lake sediment	Carbonate δ¹⁸O	20	AMS¹⁴C	32	0–14,000	Wünnemann et al. (2018)
22	Lonar lake*	19.976	76.508	481	Lake sediment	Carbonate δ¹³C	19.15	AMS¹⁴C	19	–58 to 11,413	Prasad et al. (2014)
23	Bednikund lake	30.21	79.67	~3350-3800	Lake sediment	δ¹³C	62.35	AMS¹⁴C	7	−32 to 5891	Rawat et al. (2021)
24	Nal Sarovar	22.8	72	5	Lake sediment	δ¹³C	180.2	AMS¹⁴C	8	−31 to 6635	Prasad et al. (1997)
25	Nirpa Co	29.73357	92.38668	4775	Lake sediment	δ¹³C	74.47	AMS¹⁴C	4	−34 to 2945	Bird et al. (2017)
26	Benital lake*	30.16056	79.24583	2084	Lake sediment	Geochemistry proxies	75.39	AMS¹⁴C	7	1275–9760	Bhushan et al. (2018)
27	Lake Qinghai	36.97484	100.21808	3194	Lake sediment	Redness percentage	36.53	AMS¹⁴C	9	31–17,454	Ji et al. (2005)
28	Koucha lake	34.00916	97.2343	4540	Lake sediment	Pollen	227	AMS¹⁴C	5	0–14,900	Herzschuh et al. (2009)
29	Lake Zigetang*	32	90.9	4560	Lake sediment	Pollen	157	AMS¹⁴C	5	0–10,500	Herzschuh et al. (2006)
30	Yidun Lake	30.29833	99.55167	4470	Lake sediment	Pollen	382	AMS¹⁴C	3	0–13,300	Shen et al. (2006)
31	Lake Hidden*	29.81283	92.37283	4980	Lake sediment	Pollen	135.7	AMS¹⁴C	7	0–13,000	Tang et al. (1999)
32	Ren Co	30.73283	96.68283	4450	Lake sediment	Pollen	476	AMS¹⁴C	7	200–18,800	Tang et al. (1999)
33	Qongjiamong Co*	29.81283	92.37283	4980	Lake sediment	Pollen	193	AMS¹⁴C	11	300–14,400	Shen (2003)
34	Taro Co*	31.12677	84.07902	4566	Lake sediment	Pollen	179.1	AMS¹⁴C	12	0–10,211	Ma et al. (2014)
35	Nam Co	30.73742	90.79033	4718	Lake sediment	Pollen	200	AMS¹⁴C	24	0–24,000	Zhu et al. (2015)
36	Lake Kuhai	35.3	99.2	4150	Lake sediment	Pollen	333	AMS¹⁴C	17	200–20,000	Wischnewski et al. (2011)
37	Lake Naleng*	31.10017	99.75188	4200	Lake sediment	Pollen	93	AMS¹⁴C	11	100–17,700	Opitz et al. (2015)
38	Lake Ximencuo*	33.3792	101.1035	4020	Lake sediment	Pollen	208	AMS¹⁴C	24	0–19,000	Herzschuh et al. (2014)
39	Muge Co*	30.13333	101.83333	3780	Lake sediment	Pollen	65	AMS¹⁴C	8	0–12,200	Ni et al. (2019)
40	Wuxu Lake*	29.15319	101.406	3709	Lake sediment	Pollen	89.68	AMS¹⁴C	18	−41 to 12,245	Zhang et al. (2016)
41	Dagze Co	31.8801	87.53277	4450	Lake sediment	Leaf wax δD	16.84	AMS¹⁴C	7	−51 to 1920	Li et al. (2015)
42	Nam Co	30.73742	90.79033	4722	Lake sediment	Leaf wax δD	241	AMS¹⁴C	24	7486–23,630	Günther et al. (2015)
43	Tangra Yum Co	31.15486	86.68669	4545	Lake sediment	Leaf wax δD	126	AMS¹⁴C	28	0–17,400	Günther et al. (2016)
44	Lake Pumoyum Co	28.55	90.43	5030	Lake sediment	Leaf wax δD	483	AMS¹⁴C	25	0–18,800	Wang et al. (2016)
45	Paru Co	29.79535	92.35185	4845	Lake sediment	Leaf wax δD	780.2	AMS¹⁴C	8	11–10,934	Bird et al. (2014)
46	TangraYum Co*	31.2526	86.7228	4545	Lake sediment	Ostracoda δ¹⁸O	110	AMS¹⁴C	28	0–17,500	Alivernini et al. (2018)
47	Nam Co	30.73742	90.64089	4718	Lake sediment	Elements	6.78	AMS¹⁴C	11	−57 to 3829	Kasper et al. (2013)
48	Tiancai Lake*	26.63439	99.71667	3898	Lake sediment	Chironomid	50	AMS¹⁴C	18	0–12,200	Zhang et al. (2017)
49	Kukkal Lake	10.2667	77.3667	1887	Lake sediment	Grain size	193.3	AMS¹⁴C	4	18–9105	Rajmanickam et al. (2017)
50	Hongyuan peat*	32.7783	102.5167	3505	Peat sediment	Greyscale	17.7	AMS¹⁴C	32	70–11,540	Yu et al. (2006)
51	Zoige peat*	33.16667	102.75	3467	Peat sediment	TC	17.98	AMS¹⁴C	9	0–9996	Zhao et al. (2011)
52	Chopta Valley*	27.9045	88.5254	4000	Peat sediment	δ¹³C	84.96	AMS¹⁴C	5	119–12,693	Ali et al. (2018)
53	SK 157/4	2.66667	78	−3500	Marine sediment	Mg/Ca	1007	AMS¹⁴C	4	54349–137,333	Saraswat et al. (2005)
54	Core SK218/1	14.035	82.00333	−3307	Marine sediment	Mg/Ca	506.5	AMS¹⁴C	8	300–36,884	Govil and Divakar Naidu (2011)
55	RC12-344	12.77	96.07	−2140	Marine sediment	δ¹⁸O	496.8	AMS¹⁴C	9	1630–21,500	Rashid et al. (2007)
56	SO93-126KL*	19.02922	90.03383	−1253	Marine sediment	δ¹⁸O	114.7	δ¹⁸O GISP and Toba Ash	−	0–100,263	Kudrass et al. (2001)
57	SO126_39KL	20.2066	89.7076	−318	Marine sediment	δ¹⁸O	74.04	AMS¹⁴C	8	9219–21,732.5	Weldeab et al. (2019)
58	SN-6	12.48667	74.13306	−589	Marine sediment	δ¹⁸O	131.9	AMS¹⁴C	5	154–4772	Tiwari et al. (2015)
59	63KA*	24.36	65.55	−692	Marine sediment	δ¹⁸O	15.26	AMS¹⁴C	9	–40 to 12,754	Staubwasser et al. (2002)
60	NGHP-01-19B	18.97761	85.65867	−1422	Marine sediment	TOC	1297	AMS ¹⁴C; δ¹⁸O	–	1500–22,250	Phillips et al. (2014)
61	SO90-111KL*	23.1	66.48333	−775	Marine sediment	TOC	104.6	AMS¹⁴C	10	2000–62,270	Schulz et al. (1998)
62	RC27-14	18.2533	57.655	−596	Marine sediment	δ¹⁵N	68.45	δ¹⁸O GISP;¹⁴C	10	4110–63,323	Altabet et al. (2002)
63	RC 27-23	18	57.59	−820	Marine sediment	δ¹⁵N	86.68	AMS¹⁴C	16	130–63,578	Overpeck et al. (1996)
64	SO90-56KA	24.83333	65.91667	−695	Marine sediment	³⁷UK	20.67	AMS¹⁴C	19	5.8–4883	Doose-Rolinski et al. (2001)
65	Core BoB-88	17.68	89.61	−2225	Marine sediment	Fe₂O₃	286.1	AMS¹⁴C	9	0–24,888	Li et al. (2020)
66	723A	18	58	−807.8	Marine sediment	G.bull%	104.2	AMS¹⁴C	11	560–10,877	Gupta et al. (2003)
67	NGHP-01-16A	16.59331	82.68345	−1268	Marine sediment	δ¹³C	503.3	AMS¹⁴C	11	0–16,171.6	Ponton et al. (2012)
68	3104G	12.5	74.2	−1680	Marine sediment	C_org	887.7	AMS¹⁴C	10	1300–39,470	Agnihotri et al. (2003)

The records satisfying the criteria.

According to the above requirements, a total of 23 records were selected finally, including 9 pollen records and 14 non-pollen records. In order to facilitate the later analysis, data were standardized and Z-score processed, so as to convert into a unified measurement for comparison. Linear interpolation was then used to adjust the resolution of these records uniformly to 100 years, covering 0–10,000 BP. In order to reduce the error, the part that needs to be extrapolated is replaced by the average of each sequence. Given that different geographical locations, indicators, and archives have different sensitivity to climate, causing different variation amplitude (Wang et al., 2010), data were uniformly converted into monsoon intensity index (−2, −1, 0, 1, and 2, representing the weakest, weak, middle, strong, and strongest respectively). To determine the consistency of pollen and non-pollen data, the PCA results were analyzed by Procrustes analysis. The sum of square of deviation (M²) was 0.6287, and p value was less than 0.001 obtained through 999 displacement tests, indicating that the pollen and non-pollen data had a good consistency (see Supplemental Figure S1, available online) and could be analyzed uniformly.

Results and discussion

Evolution of the Indian summer monsoon in the Holocene

In this study, a total of 65 published ISM records were summarized, covering the Tibetan Plateau and the Indian subcontinent with its surrounding area (Table 1). After conducting principal component analysis on the selected records (with * marks in Table 1), the results showed that the first and second principal components (PC1 and PC2) account for 30.8% and 16.9% of the variability respectively (Figure 2), representing the two main evolution patterns of the ISM. The HO of PC1 occurs at ~10 ka BP–6 ka BP, negatively relating to monsoon intensity, followed by declining trend until present (Figure 3a). The HO of PC2 occurs at ~8 ka BP–3 ka BP, positively relating to monsoon intensity, followed by declining trend toward present with fluctuation (Figure 3j).

Figure 2.

The PCA results of the selected ISM records following the criteria (see section3 “Data and method”). PC1 and PC2 account for 30.8% and 16.9% of the variability respectively.

Figure 3.

Two-pattern divergence also exhibits in records excluded for PCA, which is similar with the PCA results based on 23 selected records. (a and j) The PC1 and PC2 variation of selected twenty-three records; (b) The Globigerina bulloides percentage of the 723A (Gupta et al., 2003); (c) The δ¹⁵N variation of RC27-23 (Altabet et al., 2002); (d) The δ¹⁵N variation of RC27-24 (Altabet et al., 2002); (e) The C_org wet weight variation of 3104G (Agnihotri et al., 2003); (f) The δ¹³C variation of NGHP-16A (Ponton et al., 2012); (g) The δD variation of Pumoyum Co (Wang et al., 2016); (h) The carbonate δ¹⁸O variation of Selin Co (Wei and Gasse, 1999); (i) The precipitation reconstructed by pollen records of Lake Donggi Cona (Wang et al., 2014); (k) The sand percentage variation of Kukkal Lake (Rajmanickam et al., 2017); (l) The δ¹⁸O variation of SO93-126 (Kudrass et al., 2001); (m) The Fe₂O₃ variation of Core BoB-88 (Li et al., 2020); (n) The precipitation reconstructed by pollen records of Ren Co (Tang et al., 1999). The temporal distributions of the HO of the two patterns are indicated by light and dark gray boxes respectively.

In order to get insight into the spatial distribution characteristics of these two evolution patterns, correlation was represented by circle with different radius (Figure 4a and b, the larger radius indicating the stronger correlation). Results shows that the records in the Arabian Sea and its surrounding areas, northwestern TP, Yunnan and Sichuan province closely relate to PC1 (Figure 4a). Records in the Bay of Bengal and its surrounding areas, northeastern India, central Tibetan plateau, and the surrounding areas of Qinghai Province closely relate to PC2 (Figure 4b).

Figure 4.

Spatial distribution of the two evolution patterns. (a) Spatial distribution of the correlation between selected records and PC1; (b) Spatial distribution of the correlation between selected records and PC2; (c) Spatial distribution of typical ISM records with evident two-pattern divergency. The red indicates the PC1 pattern with HO occurring at ~6–10 ka, the blue indicates the PC2 pattern with HO occurring at ~3–8 ka. The physical (grain size and greyscale etc.), chemical (δ¹⁸O, δD, δ¹³C, and other geochemical elements), and biological indicators (pollen, chironomid, and foraminifer content etc.) are marked as the triangle, the cross, and the rectangle respectively.

Interestingly, other ISM records (excluded for PCA) also display the two-pattern divergence: the optimum of the PC1-parttern appears at ~10 ka–6 ka BP, and the optimum of the PC2-parttern appears at ~8 ka–3 ka BP (Figure 3). By summarizing all collected ISM records, it can be noted that the PC1 pattern mainly distributes in the Arabian Sea and its surrounding areas, the Indian subcontinent, the western central part of TP, the northern TP, and the southern China; PC2 pattern mainly distributes in the Bay of Bengal and its surrounding areas and southern TP (Figure 4c). In order to exclude the potential influence of indicator types on the evolution pattern divergence, records based on different proxies were grouped into physical indicators (grain size and greyscale etc.), chemical indicators (δ¹⁸O, δD, δ¹³C, and other geochemical elements) and biological indicators (pollen, chironomid, and foraminifer content etc.). Figure 4c shows that the three indicator- types are uniformly distributed in the two evolution patterns. Therefore, it could be believed that the pattern divergence is independent of the indicator types.

Underlying mechanism exploration

Over the Holocene scale, the mechanisms driving monsoon system include solar insolation (involving ITCZ), hydrosphere factors (global ice volume, sea level change, fresh water flux, sea temperature, ENSO, AMOC), atmospheric factors (greenhouse gas concentration, Hadler Circulation). Among which, the ISM is mainly forced by the solar insolation (Bird et al., 2017; Fleitmann et al., 2003; Gupta et al., 2005; Overpeck et al., 1996), which could be underpinned by the consistence between the pattern 1 and the solar insolation variation in our results (Figure 5). However, the optimum of pattern 2 is ~3000 years later than the solar maximum, suggesting that there are other factors contributing to the delay and the divergence of the two patterns (Figure 5a1, a2, e, and f). Given the divergence occurs in the early and middle Holocene, when characterized by the Laurentian Ice Sheet melting except the solar insolation enhancement (Kaplan and Wolfe, 2006), hence the influence of the Laurentian ice sheet is considered secondly. Further, the Laurentian Ice Sheet ablation process (Dyke et al., 2003) is also the process of freshwater input decline (Liu et al., 2014) and gradual sea level increase (Bard et al., 1996). At ~7 ka BP, the Laurentian Ice Sheet completely melted, the fresh water input gradually stabilized at 1 m/yr, and the sea level gradually reached the present level (Figure 5g–i). At around this time, the pattern 2 HO has emerged and the trends of the two patterns begin to converge. Therefore, the melting process of the Laurentian Ice Sheet may be one of the main factors leading to the delay, and also the ISM pattern divergence in the Holocene (Figure 5). Most interestingly, Kaplan and Wolfe (2006) compared the spatiotemporal variations of temperature records in the North Atlantic, and noted that a similar pattern divergence also exists in the region during the Holocene: The first pattern is roughly consistent with the solar insolation variation, while the other pattern is 1000–3000 years later than the solar insolation; When exploring further, it was found that the delay duration was positively correlated with the distance from the Laurentian Ice Sheet (Figure 5d1 and d2). It adds further support to the high latitude ice sheets driving hypothesis (Kaplan and Wolfe, 2006). Then the pattern divergence may be transmitted to the Asian monsoon region through complicated atmosphere-ocean-land circulation processes (such as the AMOC), and profoundly affects the ISM variation.

Figure 5.

The ISM patterns and driving forces in the Holocene. (a1 and a2) The PCA result of the ISM records in this study; (b1 and b2) The PCA result of paleoclimate records in the core Asian monsoon area(Wang et al., 2010); (c) Humidity Index in the monsoon region of China (Zhao et al., 2009); (d1 and d2) The PCA result paleoclimate records around the North Atlantic (Kaplan and Wolfe, 2006); (e) The solar insolation at 30°N (Berger, 1978); (f) The Ti concentration of ODP1002, indicating the ITCZ movement (Haug et al., 2001); (g) The freshwater flux in the NH (Liu et al., 2014); (h) The area variation of Laurentide ice cap, indicated by the dotted line (Dyke et al., 2003); (i) The evaluated variation of global sea level (Bard et al., 1996); (j) The variation of CO₂ concentration, marked as the dotted line (Lüthi et al., 2008); (k) The variation of ENSO intensity, indicated by its frequency in 100-year window (Moy et al., 2002); (l) The variation of the AMOC intensity, indicated by ²³¹Pa/²³⁰Th (McManus et al., 2004), marked as the dotted line; (m) Synthetic humidity index in arid and semiarid regions of China (Li et al., 2014a). The temporal distributions of the two HO (Holocene Optimum) are indicated by light and dark gray boxes respectively.

It has been supposed that the AMOC (Atlantic Meridional Overturning Circulation) exerts deeply influence on the monsoon system through acting on the interhemispheric temperature gradient and the latitudinal position of the ITCZ (intertropical convergence zone)(Wassenburg et al., 2021). Figure 5l shows that the intensity of the AMOC, indicated by ²³¹Pa/²³⁰Th (McManus et al., 2004), has been declining since the early Holocene, following the melting of the Laurentian Ice Sheet and the increase of freshwater input into the North Atlantic (Broecker et al., 1999). Until ~6 ka BP, the ice sheet disappeared and the intensity of the AMOC picked up, coinciding with the occurrence of the optimum of the pattern 2. Consequently, given its significant effect on the ISM variation and indispensable bond-like role in linking the Atlantic climate change to the Asian monsoon system, together with the forementioned temporal coincidence, the AMOC might be another main contributor associated with the high latitude ice cover leading to pattern divergence.

Many studies have demonstrated that the ENSO tele-connects with the evolution of ISM (Bird et al., 2014; Krishnamurthy and Kirtman, 2009; Srivastava et al., 2017). Especially since the late-Holocene, in the setting of weakening solar insolation, the ENSO variability has continued to increase, and its influence on the monsoon has gradually became prominent (Moy et al., 2002) (Figure 5k). However, the pattern divergence occurs in the early to middle Holocene, being inconsistent with the late-Holocene when the ENSO playing a major role. Thereby although it profoundly affects the ISM by acting on SST and atmospheric processes, evidence is still limited to link the ENSO to the pattern divergence.

In addition, since the Last Glacial Age, the CO₂ concentration has increased by ~100 ppmv, playing a vitally important role in affecting the ISM (Schneider et al., 2014) by promoting high latitude warming of northern hemisphere (Liu et al., 2009) and thus the northward migration of ITCZ. And lines of evidence reveal that the ISM tends to enhance in the modern greenhouse warming scenario (Li et al., 2010). On the Holocene scale, however, its variation is small (~20 ppmv) (Lu et al., 2019). And the possible linkage between such magnitude change and the ISM evolution has not been explored in detail. Moreover, Figure 5j shows that CO₂ concentration maintains at a low level in the early Holocene and gradually increase since 8 ka BP, the trend of which is notably differ with our two ISM evolution patterns, indicating CO₂ might not largely contribute to the ISM evolution and pattern divergence.

Overall, the high-latitude ice caps with its related processes (the change of the fresh water input flux and sea level) and the AMOC might be the two most important contributor to the ISM pattern divergence. Specifically, the melting of the high-latitude ice caps possibly leads to the different pattern divergence, while the AMOC transports the divergence into the downstream monsoon system.

ISM and EASM varied synchronously during the Holocene

There is a long-standing debate on whether the evolution of ISM and EASM is synchronized. Some studies demonstrated that they evolved asynchronously: An et al. (2000) found that the HO of the southwest China affected by the ISM appears at ~11 ka BP, which is different from the regions influenced by the EASM (An et al., 2000); After summarized paleoclimate records across China, He et al. (2004) suggested that the start time, duration, and end time of the HO differ among regions affected by different circulation system (He et al., 2004); Indicated by Hongyuan Peat and Hani Peat respectively, the ISM and the EASM display an anti-phase variation pattern during the Holocene (Hong et al., 2005). Based on the spatial distribution characteristics of principal components, Wang et al. (2010) proposed that the PC1 with HO occurring at ~10–7 ka BP indicates the ISM variation, and the PC2 with HO occurring at the ~8–4.5 ka BP indicates the East Asian summer monsoon, and the two monsoon evolved asynchronously (Wang et al., 2010). However, other studies suggested the opposite viewpoints: Many stalagmite records from the ISM or EASM region demonstrated that they share the similar evolution pattern during the Holocene, in addition to the similar responding pattern to the Younger Dryas and 8.2 ka events (Cheng et al., 2012; Jinguo et al., 2010; Li et al., 2014b; Zhang et al., 2013). Zhang et al. (2011) emphasized that the two sub-systems of the Asian monsoon were varying with similar patterns on millennial timescales during the Holocene (Zhang et al., 2011); After comparing the paleoclimate records and the numerical simulation results of the two monsoon regions, Li et al. (2014) noted that the relationship between them is the result of the interaction between various systems of atmosphere, land, ocean and vegetation, which could not be simply summarized by synchronization or not, and is closely related to the time scale (Li et al., 2014b).

As mentioned in 4.1, there are mainly two evolution patterns of the ISM in the Holocene. The HO of pattern 1 and pattern 2 occurs at ~6– 10 ka BP (Figure 5a1) and ~3–8 ka BP respectively (Figure 5a2). Interestingly, the similar pattern-divergence phenomena also exist in the EASM region. Using 31 high-resolution pollen records, Zhao et al. (2009) synthesized the humidity index of the Chinese monsoon region, which showed that humidity increases since the beginning of the Holocene and reached the maximum at ~9 ka BP, then decreases toward the present (Figure 5c) (Zhao et al., 2009). After principal component analysis of 92 Holocene humidity and temperature records, covering China, Mongolia, northern India and Kyrgyzstan, Wang et al. (2010) found that the high value of PC1 occurs at ~10–7 ka BP, mainly distributing in northern India, the Tibetan Plateau and southwestern China (Figure 5b1), while the high value of PC2 occurs at ~8–4.5 ka BP, mainly distributing in central and eastern China (Figure 5b2) (Wang et al., 2010). Based on 115 Aeolian records and 31 vegetation transition records, Li et al. (2014a) established the effective humidity record in the desert area of northern China since the last Great Glacial Age, which demonstrated that it reaches maximum at ~8–4 ka (Figure 5m) (Li et al., 2014a). By comparing multiple records, Lu et al. (2019) found that there are different precipitation patterns in the north and south of China during the Holocene: the high value of precipitation occurs at ~10–6 ka BP in the south, while it occurs at ~8–3 ka BP in the north of China (Lu et al., 2019). These evidences indicates that the two-pattern divergence also exist in the EASM region, which is similar with the ISM realm. That is to say, such pattern divergence is a common feature of the entire Asian monsoon. Most importantly, it also provides valuable insight into the long-standing controversy concerning whether the two Asian monsoon subsystems evolved synchronically within the Holocene. The viewpoint centering on this proposition may very likely or in part be affected by the records chosen to represent the ISM and EASM variation. Specifically, when they belong to the same pattern, with similar temporal pattern of the optimum and trend, conclusion would come to that they evolve synchronously, and vice versa.

From the perspective of the entire Asian monsoon affected area within the Holocene, records displaying pattern 1 distribute in the Arabian Sea and its surrounding areas, western Tibet, central Tibet, northeast China and southern China, while records displaying pattern 2 distribute in the Bay of Bengal and its surrounding areas, the southern Tibetan Plateau and the northern China. Although it has been discussed in 4.2 that the “delay” of pattern 2 may be largely induced by the high-latitude ice sheet melting together with the AMOC, the spatial distribution mechanism of these two patterns is still unclear.

It is noteworthy that the two sub-systems of the Asian monsoon are independent to but also interacting with each other tightly. Many studies demonstrate that the speleothem δ¹⁸O records from southern China, which spatially belong to the EASM domain, may principally document the ISM variation rather than the EASM (Chen et al., 2014; Li et al., 2019; Yang et al., 2014). Combined with modern observation data, Cheng et al. (2012) found that the ISM penetrates deep into the EASM region and profoundly affected its precipitation status (Cheng et al., 2012). Cao et al. (2012) defined the “IIE” index to quantify the interaction between the ISM and the EASM, and found that it has a notable interannual sensitivity to measure the seesaw variation between the ISM and EASM, impacted deeply by the western North Pacific subtropical high (Cao et al., 2012). Undoubtedly, the complex interaction provides a clue for explaining the similar pattern divergence of both the EASM and the ISM within the Holocene. Nevertheless, the specific physical mechanism still remains ambiguous. Evidently, further exploration is urgently needed: based on the extensive collection of paleoclimate records in the Asian monsoon region, combined with the results of numerical simulation analysis, we should also pay more attention to the irreplaceable role in mechanism interpretation of modern observation data.

Conclusion

During past decades, a great deal of effort is made to reveal the evolution history and underlying mechanism of the ISM. Hitherto, fruitful achievements have been made, and the insight into the monsoon have become increasingly comprehensive and profound. A total of 65 published ISM records during the Holocene, covering the Tibetan Plateau and the Indian subcontinent with surrounding area, were collected in this study. The conclusions are as follows: (a) There are two evolution patterns of the Indian summer monsoon during the Holocene. The first pattern with the Holocene Optimum (HO) appearing at ~10,000–6000 BP, mainly distributes in the Arabian Sea and its surrounding areas, the Indian subcontinent, western central TP, northern TP, and southern China. The second pattern with the Holocene Optimum (HO) appearing at ~8000–3000 BP, mainly distributes in the Bay of Bengal and its surrounding areas together with southern TP. (b) The delay between pattern 2 optimum and insolation maximum may be caused by the variation of high-latitude ice cover. However, the underlying mechanism concerning the spatial distribution of the two patterns is still unclear. AMOC may play an important role in this process, but the specific dynamic is still unclear. And relevant modern observational data are still needed. (3) The Indian summer monsoon (ISM) and the East Asian summer monsoon (EASM) evolve synchronously, and sharing similar pattern differentiation. Arguments centering on this issue mainly owe to records selected for comparison originating from different pattern. In fact, the two-pattern divergence exhibits throughout the entire Asian monsoon region. Although modern observations have demonstrated that the ISM can penetrate deep into the ESM region and profoundly affect the precipitation in the latter region, the specific mechanism of the two-pattern spatial distribution is still unclear. More efforts are needed to unravel abovementioned puzzles, including extensive collection of paleoclimate records, combined with the results of numerical simulation analysis, in addition to the modern observation data for dynamic interpretation. This study sheds light on the pattern divergence of the ISM evolution, therefore deepening the understanding of the ISM, even the entire Asian monsoon, which is of great significance for future climate prediction.

Supplemental Material

sj-docx-1-hol-10.1177_09596836221080757 – Supplemental material for New insight into pattern divergence of the Indian summer monsoon during the Holocene

Supplemental material, sj-docx-1-hol-10.1177_09596836221080757 for New insight into pattern divergence of the Indian summer monsoon during the Holocene by Yuwei Zhang, Baiqing Xu, Wanlong Xu and Mei Hou in The Holocene

Footnotes

Funding

The author(s) disclosed receipt of the following financial support for the research, anthorship and/or publication of this article: This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA20070102), the Basic Science Center for Tibetan Plateau Earth System (41988101-03), and the Second Tibetan Plateau Scientific Expedition and Research Program of the state ministry of science and technology (2019QZKK0101).

ORCID iD

Yuwei Zhang

Supplemental material

Supplemental material for this article is available online.

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