Figure 5. Comparison of the Holocene brGDGTs-based MAAT record from Huguangyan Maar Lake with potential independent internal and external drivers of climate change. (a ) Zonal SST gradient anomaly relative to the late Holocene calculated as the difference between the western and eastern Pacific (Koutavas and Joanides, 2012); (b ) ENSO record from Lake El Junco in the Galápagos Islands (Conroy et al., 2008); (c ) Sedimentary record of231Pa/230Th from the subtropical North Atlantic Ocean (McManus et al., 2004); (d ) brGDGTs-based MAAT record from Huguangyan Maar Lake (the data for 10–20 kyr BP are from Chu et al., 2017); (e ) Ice rafted debris record from North Atlantic sediment cores (Bond et al., 2001); (f ) Greenland δ18O ice core record (GRIP,11-point running average) (Vinther et al., 2006); (g ) Record of total solar irradiance (TSI; Steinhilber et al., 2012); (h ) Record of GHG radiative forcing (CO2, CH4, N2O) (Köhler et al., 2017); (i ) Record of Volcanic forcing from a Greenland ice core (Kobashi et al., 2017).
The MAAT at Huguangyan Maar Lake can potentially help understand the dynamical origin of both gradual and abrupt Holocene temperature changes. Widely accepted potential drivers of climate change include fluctuations of ice volume or ice sheet dynamics, external drivers such as solar and volcanic activity, ocean circulation (e.g., involving North Atlantic Deep Water, Antarctic Bottom Water, and ENSO), polar sea ice, and atmospheric greenhouse gases (GHG).
Our results suggest that ice volume or ice sheet dynamics suppressed any other forcings and controlled the regional temperature evolution from the last glacial maximum to the early Holocene (8.2 ka BP; Figure 4, 5). GHG radiative forcing (CO2, CH4, N2O; Köhler et al., 2017) may be a secondary factor. Several previous studies have highlighted the importance of ice-sheet dynamics as a driver of temperature change (Baker et al., 2017; Liu et al., 2014; Marsicek et al., 2018). For example, the δ18O record from Kinderlinskaya cave in the Ural Mountains, which is interpreted as a winter temperature proxy (Baker et al., 2017), reveals negative δ18O anomalies at 11.0 and 8.2 ka BP, which could be caused by the effects of enhanced meltwater influx from melting Northern Hemisphere ice sheets on North Atlantic Ocean circulation (Baker et al., 2017).
Changes in North Atlantic Deep Water (NADW), Atlantic Meridional Overturning Circulation (AMOC), and Antarctic Bottom Water (ABW) have been suggested as major causes of abrupt climate changes (McManus et al., 2004; Struve et al., 2020). Both the AMOC (McManus et al., 2004) and Antarctic Bottom Water (UCDW and AAIW) show a pronounced shift in the middle Holocene (Figure 5), and the distinct temperature shift at ~5.8 ka BP evident in the MAAT record from Huguangyan Maar Lake could be related to this major change in oceanic circulation.
El Niño–Southern Oscillation (ENSO) variability and the ENSO mode (e.g., EP, CP) may significantly influence both SSTs and temperature changes in some terrestrial regions. Generally, El Niño is significantly positively correlated with winter air temperature variability, especially in the case of the negative temperature anomalies in eastern China during the EP La Niña phase (Ren et al., 2020; Wang et al., 2017). An amplified ENSO in the late Holocene and a damped ENSO during the middle Holocene have also been recognized (Carré et al., 2014; Conroy et al., 2008; Koutavas & Joanides, 2012), although their long-term linkage with climate in China is unclear.
Sea ice extent in the Arctic and Antarctic has been recognized as an important driver of abrupt climatic change on both interannual to annual timescales. The influence of sea ice on abrupt climatic events on decadal to centennial timescales via the amplification of sea-ice-albedo feedbacks, deep-water formation, and shifts of the Intertropical Convergence Zone (e.g., Bond et al., 2001; Moros et al., 2006) are also recognized. Bond et al. (2001) attributed abrupt Holocene climatic events recorded in marine sediment cores and other paleoclimatic records to the effects of changes in solar activity on sea ice extent via amplification and transmission processes involved in ocean-atmosphere circulation. However, the variation of sea-ice extent during the Holocene shows a complex pattern or even opposite trends in the Arctic oceans (Moros et al., 2006). Although it remains difficult to accurately reconstruct spatiotemporal changes in sea ice or ice-rafting (Moros et al., 2006), sea ice variability could be one of several important drivers of abrupt climate changes.
Within the limits of the dating uncertainty, most of the cooling events observed in the brGDGTs-based MAAT record can be linked to oceanic and external forcings; however, the effects of the solar minima at ~2.6, ~6.2 and ~7.4 ka BP are less clearly evident (Figure 5). The large temperature variations observed at tropical Huguangyan Maar Lake could also be caused by winter monsoon amplification. In continental Eurasia, the winter monsoon plays an important role in the amplification and rapid transmission of the temperature signal from the Arctic to the tropics, and in the migration of the intertropical convergence zone(Chu et al., 2017).
3.3 The brGDGTs-based temperature change during the last deglaciation
Previous brGDGTs-based records indicate that the average MAAT was 17.8ºC during the Oldest Dryas, which was followed by a gradual rise during the Bølling-Allerød interstadial, when it reached a maximum of 21.8℃ at 13.0 ka BP. Subsequently, the average MAAT decreased to 19.5℃ during the Younger Dryas (Figure 6b; Chu et al., 2017). This temperature pattern strongly suggests the influence of ice volume or ice sheet and meltwater dynamics on the temperature evolution of the Huguangyan Lake region from the Last Glacial Maximum to the early Holocene (Figure 6).