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).