Publications on temperate deciduous tree refugia in Europe are abundant, but
little is known about the patterns of temperate tree refugia in eastern
Asia, an area where biodiversity survived Quaternary glaciations and which
has the world's most diverse temperate flora. Our goal is to compare climate
model simulations with pollen data in order to establish the location of
glacial refugia during the Last Glacial Maximum (LGM). Limits in
which temperate deciduous trees can survive are taken from the literature.
The model outputs are first tested for the present by comparing climate
models with published modern pollen data. As this method turned out to be
satisfactory for the present, the same approach was used for the LGM.
Climate model simulations (ECHAM5 T106), statistically further downscaled,
are used to infer the temperate deciduous tree distribution during the LGM.
These were compared with available fossil temperate tree pollen occurrences.
The impact of the LGM on the eastern Asian climate was much weaker than on
the European climate. The area of possible tree growth shifts only by about
2∘ to the south between the present and the LGM. This contributes
to explaining the greater biodiversity of forests in eastern Asia compared to
Europe. Climate simulations and the available, although fractional, fossil
pollen data agree. Therefore, climate estimations can safely be used to fill
areas without pollen data by mapping potential refugia distributions. The
results show two important areas with population connectivity: the Yellow
Sea emerged shelf and the southern Himalayas. These two areas were suitable
for temperate deciduous tree growth, providing corridors for population
migration and connectivity (i.e. less population fragmentation) in glacial
periods. Many tree populations live in interglacial refugia, not glacial
ones. The fact that the model simulation for the LGM fits so well with
observed pollen distribution is another indication that the model used is
good enough to also simulate the LGM period.
Introduction
Eastern Asian temperate deciduous forests boast the world's most
diverse temperate deciduous forest flora (Donoghue and Smith, 2004; Qiu et
al., 2011). They also contain the highest numbers of Tertiary relict taxa
that have disappeared from Europe (Milne and Abbott, 2002; Svenning, 2003),
such as Carya and Parrotia (Li and Del Tredici, 2008; Orain et al., 2013). The reason for
this situation should be sought in the history of these forests through
Quaternary glaciations and earlier. The last time these forests had a
considerable reduction of their population or underwent a shift of their
distribution was during the Last Glacial Maximum (LGM), i.e. 21 000 years
ago. On different continents, this happened in different ways due to the
climate of the area, the topography (including the orientation of the main
mountain ranges that may act as geographical corridors or barriers), the
location and extent of ice caps, and the extent of emerged coastal shelves. In
Europe during the LGM, temperate deciduous forests, especially the
warm–temperate tree species, died out in much of northern and central Europe
and survived in refugia in the mountainous areas of the three southern
peninsulas: Iberia, Italy and the Balkans, as well as in some smaller areas
around the Black Sea and the southern Caspian Sea (Leroy and Arpe, 2007;
Arpe et al., 2011).
Various methods have been used to establish the locations of glacial refugia
of temperate deciduous trees during the LGM in eastern Asia. For example,
population distributions have been published based on phylogenetic data in
eastern Asia (Qian and Ricklefs, 2000) and based on biomization using
palaeo-data for the Japanese archipelago (Takahara et al., 2000; Gotanda and
Yasuda, 2008) as well as for China (Harrison et al., 2001). A disagreement
regarding the location of temperate tree refugia in China, especially at its
northern limit, has appeared: Harrison et al. (2001) proposed the northern
limit of the temperate deciduous forest biome to have retreated far south
(south of 35∘ N) versus Qian and Ricklefs (2000), who suggested an
extension of the temperate forest over the emerged continental shelf. Qian
and Ricklefs (2000) highlighted the important role played by physiography
heterogeneity, climatic change and sea-level changes in allopatric
speciation. According to the results of their ecological analysis, a
temperate tree population extended across the emerged shelf and linked
populations in China, Korea and Japan during glacial times. This led to the
concept of interglacial fragmentation and refugia.
Additional information from phylogenetics of temperate deciduous trees
should also be considered for phylogeography purposes. But few trees and bushes
belonging to the deciduous forest have been analysed so far. A temperate
deciduous bush, Ostryopsis davidiana, indicates multiple LGM refugia both south and north of the
Qin Mountains (Tian et al., 2009).
To be complete, it should be mentioned that the distribution of key
temperate tree biomes (discrete points) for the LGM can be found in Ni et al. (2014).
Our aim is to contribute to this debate on the northern limit of temperate
deciduous trees by using another approach to ecology, biomization and
phylogeography, i.e one based on climate model simulations. The results from
this approach are validated by pollen data, whose amount has increased
spectacularly since 2010. Distribution maps are then produced.
Material and methods
The climatic data, model and methods used in this study are described by
Leroy and Arpe (2007) and Arpe et al. (2011) in more detail. Coupled ECHAM5–MPIOM
atmosphere ocean model simulations were carried out, though with a very low
horizontal resolution of T31 (i.e. a spectral representation which resolves
waves down to 31∘ on any great circle on the Earth corresponding to approx. 3.75∘). In such a coupled model, the atmosphere as well as the
ocean and vegetation were simulated to interact with each other and
generate their own sea surface temperature (SST) and vegetation parameters.
These SSTs and vegetation parameters were then used for uncoupled ECHAM5
T106 atmospheric simulations. The ECHAM models, including the coupled ocean
model, were developed at the Max Planck Institute (MPI) for Meteorology in Hamburg.
The models were run with the present-day conditions concerning
orography, solar radiation, ice cover and CO2 as well as
under LGM conditions concerning the same parameters (e.g. atmospheric
CO2 concentration at 185 ppm). Simulations for the present and the
LGM with a T106 resolution (approx. 1.125∘ horizontal resolution)
model with 39 atmospheric vertical levels were carried out with the ECHAM5
atmospheric model (Roeckner et al., 2003). The boundary data, e.g. the SST
and vegetation parameters, were taken from the coupled ECHAM5–MPIOM
atmosphere ocean dynamic vegetation model (Mikolajewicz et al., 2007)
simulations, which have been made for the present and the LGM with a
spectral resolution of T31 and 19 vertical levels. The experimental setup is
largely consistent with the Paleoclimate Modelling Intercomparison Project
phase 2 PMIP2 (Braconnot et al., 2007). These SSTs were corrected for
systematic errors of the coupled run by adding the SST differences between
observed SSTs and simulated ones for the present; the corrections are
generally below 3 ∘C.
In Arpe et al. (2011), comparisons of the model-generated SSTs with other
reconstructions, e.g. from the MARGO project (Kucera et al., 2005), were
performed and good agreement was found. Differences to the CLIMAP (1981)
reconstruction agree with findings by PMIP2 (Braconnot et al., 2007). Also,
other information from the LGM gave further confidence in the performance of
the model. In Arpe et al. (2011), the importance of high resolution is
stressed. Therefore, we again use the T106 model. Intuitively one
assumes that the model that provides good estimations for the present
climate would also be best for simulating a climate with a different
external forcing such as during the LGM. Indeed, Arpe et al. (2011) found
good correspondence between pollen findings for the LGM and the estimation
of possible tree growth for Europe, which increased confidence in that
model. As the climate of eastern Asia is quite different to that of Europe,
we try to find further evidence for the high performance of the model in
eastern Asia.
It is generally assumed that results from model simulations become more
robust when using an ensemble of different model simulations, but we did not
do that. As the ECHAM models have been shown by Reichler and Kim (2008) to
be the best, by including others we would only dilute
our results because of very different results in different simulations (Tian
and Jiang, 2016). Further, most of the available simulations are of much
lower resolution than T106 used here, and which we believe is
essential for a region of diverse topography such as eastern Asia. When
combining the results of different models, an interpolation to a common grid
is inevitable, and that creates some smoothing with a further loss of
resolution.
Nevertheless, even a T106 model resolution might not be sufficient for our
investigation. Kim et al. (2008) demonstrate the importance of high
resolution with their model for the response of the eastern
Asian summer monsoon under LGM conditions. Therefore, we did a downscaling
to a 0.5∘ resolution. For that, the differences between the model
simulations for the LGM and the present are added to a high-resolution
present-day climatology. The climatology that seemed best for our
investigation is that of Cramer and Leemans (Leemans and Cramer, 1991;
Cramer, 1996), below abbreviated as C&L. With this method, the impact
of possible systematic errors of the model is reduced. This method works
only if the simulations are already reasonable; otherwise, it might happen
that e.g. negative precipitation amounts may occur. We could use this method
only for the precipitation and 2 m air temperature (T2m), while the winds had
to be taken directly from the model simulations.
To improve the understanding of limitations in the climate data, estimates
of the present climatology with data from the Global Precipitation Climate
Center (GPCC) (Schneider et al., 2011; Becker et al., 2013; GPCC, 2013) and
with data from the ECMWF Interim Reanalyses (ERA-Interim) (Dee et al., 2011; ECMWF,
2019) are used.
A lower CO2 concentration in the atmosphere during the LGM caused a
decline in pollen production. Therefore, low pollen concentrations or
influxes may already be indicative of the presence of trees (Ziska and
Caulfield, 2000; Leroy, 2007). It should be noted that we are not
working at the level of forests or biomes. Hence, it is considered that
pollen sites will reliably indicate the survival of temperate deciduous
trees (summer-green and broadleaf) if records have a subcontinuous curve
of at least one temperate taxon such as deciduous Quercus, Ulmus, Carpinus or Tilia. The study focuses on
the period of the LGM, hence on an age of 21±2 cal ka BP (Mix et al.,
2001). The geographical areas of China, Japan, SE Russia, Korea and the
Himalayas are explored. The dataset includes terrestrial and marine sites. A
literature review of pollen data was made. It was first based on the large
compilations of Cao et al. (2013), mainly for China, and of Gotanda and Yasuda (2008) for Japan. Then this was enlarged geographically with an update
including more recent publications.
Modern pollen assemblages were used to check the validity of the tree growth
limits chosen. The following databases were used: Zheng et al. (2014) for
China and Gotanda et al. (2002) for Japan. This was complemented by local
studies such as by Park (2011) and Park and Park (2015) for Korea and the
Himalayas (Fuji and Sakai, 2002; Chung et al., 2010; Kotlia et al., 2010; Yi
and Kim, 2010). It was not intended to be exhaustive. From these databases,
occurrences of temperate deciduous trees (mainly deciduous Quercus and Ulmus, but also
others such as Carya, Tilia and Carpinus) of at least 0.5 % were selected.
Climate of eastern Asia
In our earlier investigations on glacial refugia of trees over Europe (Leroy
and Arpe 2007; Arpe et al., 2011), limiting factors for possible tree growth
were precipitation during summer, the mean temperature of the coldest
months and the growing degree days (number of days with temperatures
>5∘C) (GDD5); the latter is related to summer
temperatures. The climate of eastern Asia is different to that of Europe, and
a short review of its climate is therefore needed in order to adapt the
limits.
The climate of eastern Asia is dominated by the monsoon (more information in
Sect. S1 of the Supplement) and its very strong topographic
variability. The latter makes it difficult to create a reliable climatology
on a regular grid. This is demonstrated for air temperature (T2m) during
December to February (DJF) by comparing the C&L climatology with a
long-term mean from the ECMWF Interim Reanalysis (Dee et al., 2011;
ECMWF, 2019) (ERA-Interim), a simulation for the present (CTR) and LGM
simulations (Fig. 1).
Climatological mean distribution of T2m over eastern Asia for
December to February (DJF).
Values by Leemans and Cramer (1991) (C&L), the ECMWF reanalysis (ERA-Interim)
and model simulations (CTR and LGM), as well as some differences between
them.
Much stronger structures in the C&L climatology compared to the other
climatologies can be seen (Fig. 1). Moreover, substantial differences are
observed; e.g. the white band (-5 to 0 ∘C) is positioned about
5∘ further north in eastern Asia in ERA-Interim compared to C&L, with up
to 4 ∘C warmer temperatures over a large part of eastern Asia
(Fig. 1, ERA–C&L). For the Caspian region, Molavi-Arabshahi et al. (2016) showed how biases of several degrees Celsius in ERA-Interim can occur in
mountainous areas when the topographic height in the ECMWF model and the
real topography are different. So it is assumed that the warmer temperatures
in ERA-Interim compared to C&L are due to this analysis system. The climate
simulation for the present (Fig. 1, CTR) agrees similarly well with ERA-Interim and
C&L, a little warmer than C&L and cooler than ERA-Interim (not shown).
A main purpose of different simulation periods (Fig. 1) is the display of
changes from the LGM to the present (Fig. 1 lower right). Over the Yellow
Sea, temperatures differ by up to 16 ∘C, as a large area of the
ocean shelf emerged during the LGM, while the differences are much smaller
for continental China, mainly 4 to 5 ∘C. These changes between
the present and the LGM are overall much weaker than for Europe in winter
(Fig. 2). Typical differences for continental central Europe are 8–15 ∘C, while they are only around 4–5 ∘C for
the eastern Asian continent. One has to take into account that China is
further south than central Europe; the central latitudes on the European map
are 45 to 50∘ N, while for China they are 32 to 37∘ N,
which contributes to explaining the large differences in the temperature
change. Also, the proximity of the Fennoscandian ice sheet is of importance
for the colder temperatures in Europe, as is weakening of the
Gulf Stream, which presently supplies Europe with warmer temperatures. The
strong temperature change over the Yellow Sea is a consequence of the larger
heat capacity of the ocean, which limits the wintertime cooling under
present-day conditions. During the LGM, this area emerged due to the lower
sea level, which leads to much stronger wintertime cooling.
Difference maps between simulated CTR and LGM T2m during winter
(DJF) for Europe and eastern Asia.
The summer temperatures are shown in Fig. 3. ERA-Interim temperatures are often
warmer by around 2 ∘C than the ones in the C&L climatology
(Fig. 3, lower left panel); the arguments for this difference given above for
DJF apply here as well. The differences between the present and the LGM in
the simulations increase from China's east coast of 2–3 ∘C to up
to 6 ∘C over Tibet. This is similar to what Tian and Jiang (2016)
found in PMIP3 simulations; they state that the temperature drop in the LGM
is too low compared to proxy data. The summer temperatures are being used to
calculate the GDD5. For the small changes shown here, we do not expect
GDD5 to impose more limitations for the LGM than for the present for
tree growth.
Climatological mean distribution of temperature (T2m; ∘C) over eastern Asia for June, July and August (JJA).
Values by Leemans and Cramer (1991) (C&L), the ECMWF reanalysis (ERA-Interim)
and model simulations (CTR and LGM), as well as some differences between
them.
The difference maps for CTR–LGM temperatures show values over the ocean
(Figs. 1 to 3). These differences may have an important impact on
continental temperatures. Therefore, it is interesting to compare these data
with other estimates of the SST. For example, Annan and Hargreaves (2013)
show annual means of SST differences of around 2 ∘C for the South
China Sea, while our simulations have slightly larger values of 2.5 to
3 ∘C, though this falls within the uncertainty range given by
Annan and Hargreaves (2013). A main difference is less cooling during the
LGM in our estimates at the Gulf Stream and Kuroshio Current off the US and Japanese
coasts (not shown as they are too far outside the area of interest).
Summer precipitation is an important limiting factor for possible tree
growth (Fig. 4). The sharp gradient of precipitation along the southern
slopes of the Himalayas in the three sets of analyses (the climatology by C&L and the long-term means from ERA-Interim and GPCC) is clearly marked. The
general patterns agree in the three sets, though with some biases. C&L
and GPCC agree best, probably because they are both based on precipitation
observations at gauges. In contrast, ERA-Interim is a model product forced by a
very large range and more evenly distributed observations; moreover, ERA-Interim does
not use observed gauge precipitation. Differences between C&L and GPCC
are mostly below 50 mm, especially in the northern areas where
precipitation is moderate. The differences between C&L and ERA-Interim are also
small in northern areas but can become quite large where the amounts of
precipitation are large, mostly with ERA-Interim having larger precipitation
amounts. The lower precipitation rates in ERA-Interim for Korea and southern Japan
in contrast to C&L and GPCC are remarkable. Here the latter data are
probably more accurate because this area is well-covered by observations
(Fig. S2.3) and the ERA-Interim model may not be able to resolve the strong
topographic structures. Many of the large uncertainties are probably due to
the strong topographic structures over eastern Asia, which makes an analysis
difficult and which is enhanced by a low density of observational sites over
western China (more information on precipitation accuracy is presented in Sect. S2).
The systematic error of the model concerning China is due to the monsoon
front being too far north by 2∘ of latitude (Fig. S1.2) and with a
northward propagation too early in the season (Sect. S1). As we only use the differences between the present and LGM
this systematic error is assumed to have only a minor impact on our results.
Tian and Jiang (2016) found a general weakening of the summer monsoon in
PMIP3 simulations, especially a decrease in precipitation in most of the
simulations, but they do not go into the details shown in Sect. S1, which makes a comparison difficult. However, they noticed a
large variability within the models. For the area used in Fig. S1.2, they
show a decrease of 10 %–20 % in summer precipitation in the LGM compared to
the control, which agrees with our simulation; it is strongest in June south of
32∘ N, though both CTR and LGM are too strong compared to ERA-Interim. In
our simulation, the strengthening of precipitation and 850 hPa wind north of 32∘ N for March to
August in the CTR and LGM simulations is
stronger compared to ERA-Interim. This systematic error is assumed to have only a
minor impact on our results. Indeed, most of the differences turn out to be
less important for further use in this study, except higher
precipitation over western China at 37∘ N on the northern
slope of the Kunlun Shan in the C&L dataset, which is investigated in
more detail in Sect. S2. Also in the area 105∘–110∘ E, 35∘–40∘ N, the drop in
precipitation during the LGM may be important, as discussed below in Sect. 6.
Summer (JJA) precipitation over eastern Asia as analysed by Leemans and Cramer (1991) (C&L), ERA-Interim and GPCC and as simulated for the present
(CTR). Differences between the various fields are shown. Units: millimetres per season.
Below we will concentrate on summer precipitation because that is the time
when plants need water most. Other scientists use the annual mean
precipitation as a limiting factor (e.g. Tian et al., 2016). When comparing
the analyses with the model simulations for the present (CTR), one finds
that the model fits the GPCC best and ERA-Interim least (Fig. 4) away from
high mountain ranges where agreement between the different
precipitation climatologies is very low. The amounts of precipitation in ERA-Interim
are higher on a large scale than the others. For most of China south of
35∘ N, the precipitation in ERA-Interim is much lower than in the other
climatologies. The belt with stronger precipitation at 25 to 35∘ N
in CTR is assigned in Sect. S1 to an earlier northward
propagation of the monsoon front in CTR compared to ERA-Interim that is weakened
from the CTR to LGM, which results in a belt of largest differences between
the present and the LGM of up to 150 mm (Fig. 5). Kim et al. (2008) found
similar differences in their higher-resolution simulation, though spreading
further north. In Sect. S1, it is shown that the
monsoon, as represented by the wind direction, does not change much over the
continent between the present and the LGM, and with the monsoon front
propagating northward already in June the wind speeds increase. This is
somewhat in contrast to results by Jiang and Lang (2010), who showed a reduction of the JJA wind speeds for the
ensemble mean of model simulations (all with a much lower horizontal
resolution than the one used here). The
lower JJA precipitation during the LGM may also result from lower temperatures
during the LGM when the atmosphere can carry only a lower amount of water
vapour.
While Tian and Jiang (2016) found a general decrease in
precipitation in PMIP3 simulations, we find it only for a belt at 29–36∘ N where the
model already shows values that are too large for the present (CTR–C&L in Fig. 4).
Summer (JJA) precipitation simulated for eastern Asia and
differences between CTR and LGM. Units: millimetres per season.
Comparing pollen information with climatic data
In Leroy and Arpe (2007) and Arpe et al. (2011), climatic data were combined
to find the areas where temperate deciduous trees could survive due to
limiting criteria and then compared with palaeo-data for such trees for
Europe. The same method is now applied for eastern Asia. Europe is limited
to the south by steppe and by the Mediterranean Sea. However, in eastern Asia, a
vast subtropical area with deciduous temperate trees mixed with conifers and
broadleaved evergreens (i.e. between biomes TEDE and WTEM of Ni et al.,
2010) lies south of the temperate deciduous forest (Qiu et al., 2011). It
was therefore essential to add a climatic limit to separate these two main
vegetation types. In addition to the limits used for Europe, we add also a
maximal winter temperature (Tmax), which the climatological temperature must
fall below to allow deciduous trees to grow but not evergreen trees,
as suggested by Sitch et al. (2003) and Roche et al. (2007) (Table 1). Sitch et al. (2003) require a less strong limit of -17∘C minimum
temperature and +15.5 ∘C maximum temperature in the coldest
month for temperate deciduous trees, but only for very few sites would such a
relaxation of limits decrease the number of sites that fail the
comparison with the climatological estimate. Roche et al. (2007) used Tmin=-2∘C and Tmax of +5∘C for
temperate broadleaf forest. We regard a Tmin limit of -2∘C as only
valid for warm-weather deciduous trees.
Limiting factors for temperate deciduous tree growth used in this
study. Tmin: minimum temperature of the coldest month, Tmax: maximum
temperature of the coldest month, GDD5: growing degree days for which the
excess over 5 ∘C is accumulated for each day,
JJA precipitation: accumulated summer precipitation.
TminTmax in winterGDD5JJA precipitation-15∘C+5∘C80050 mm per summer
When combining these limits with the climate data we arrive at the
distribution shown in Fig. 6.
Only very few stations with observed pollen are outside (not within a
distance of approximately three grid points, i.e. ∼150 km radius)
the area of possible tree growth according to our criteria (filled markers;
see also Table 2a and b for the LGM). For the present, 13 out of 380
stations with observed deciduous tree pollen do not fit the climate data
for the present, most because of winter temperatures that are too cold (-20 to
-23∘C), one (at 91∘ E, 31∘ N) because of a
short summer (GDD5 < 600), two (both at 109∘ E, 18∘ N) because of winter temperatures that are too warm (>17∘C), and one (77∘ E, 37∘ N) because of a lack
of summer precipitation and winter temperatures that are too cold, though these are
near the given limits. South-eastern Japan is often too warm in winter for
deciduous trees, though there are many observations in that area. These
stations are, however, within three grid points of areas that are marked as
suitable for their growth.
In Fig. 6 for the present, two areas marked by red ovals in western China at
latitude 37∘ N indicate possible tree growth according to
the climatic data, in which the precipitation in the C&L climatology (Fig. 4)
exceeds that in ERA-Interim and GPCC considerably. Also, ERA-Interim and GPCC show
relative maxima at 37∘ N in that area but shifted by 5∘ to the east. We believe that the precipitation from C&L
is deficient here, as explained in Sect. S2.
In the South China Sea around 120∘ E, 28∘ N only one
marker with observed tree pollen for the LGM is shown in Fig. 6, although
around that position four cores are available (see Table 2 for details). All
four observations agree with the possibility of trees according to the
climate estimate. Because of the use of marine sediment, pollen must have
been transported from the land, which is further discussed in the next
section.
In eastern Asia, some species might have evolved which are hardier than
those of the same genus present in Europe. Fang et al. (2009) show Ulmus pumila, a species that can withstand drought and
extremely cold temperatures in winter (Solla et al., 2005), over
large areas of northern China and SE Siberia.
Ulmus has the most failures in our comparison with model data. Fang et al. (2009)
show a wide spread of Tilia amurensis in NE China, SE Siberia and N Korea, which is also
absent from Europe. This tree, like the elm, is extremely frost-hardy
(Piggott, 2012).
Possible tree growth during the LGM
A total of 35 pollen sites for the LGM were used (Table 2). There is a good overall fit
between the climate data and the LGM pollen data. In Fig. 6b, only two filled markers not agreeing with climate data are found on
the continent. The site of Huguangyan in the south has winter temperatures
higher than 10 ∘C, which are too high for deciduous trees. In
north-west China in the Tarim basin is another filled marker. The
observation consists of only 1 % pollen for Ulmus. There, the winter
temperatures are -17∘C, just outside the limit used here (Table 1) but within the limits suggested by Sitch et al. (2003). On Hokkaido a
filled marker indicates disagreement between climate and pollen
observations, but it is only slightly too cold in winter (-15.7∘C).
Four cores in the deep ocean in the South China Sea are marked in Table 2 and
Fig. 6 as not agreeing with our given limits when using the downscaled
climate data, but because of the deep sea the pollen must have been
transported there. From Fig. 7, it can be concluded that the pollen could
only have come with the north-easterly 10 m wind from Taiwan where
Quercus was also found during the LGM (Table 2). As the present blooming period for
Quercus variabilis, a widespread species of the deciduous forest, is January to March in
Taiwan (Liao, 1996), the winds during March are shown in Fig. 7, assuming a
slightly later blooming period during the cooler LGM than at present, though
the wind fields for March and February are hardly different. When taking the
wind at a higher level (850 hPa or around 1500 m), the wind is blowing more
from the east in accordance with the Ekman spiral in the atmospheric
boundary layer. Therefore, pollen must have travelled near the surface when
coming from Taiwan or, if it arrived at higher levels, it may have come from
the Philippines (Luzon); that, however, seems to be too far south for deciduous
oak and, moreover, this area is not suggested in our estimate as having
possible deciduous tree growth (Fig. 6).
Thus, the area boundaries for the present and for the LGM are only slightly
different, with a shift for the LGM by 2 to 3∘ to the south of both
the northern and southern limits, as well as an eastward shift of the western
boundary. In northern China, Korea and north Japan (Hokkaido), differences
result mainly from the winter minimum temperatures, as can be seen from Fig. 1 in which winter temperatures drop by more than 6 ∘C from the
present to the LGM.
Possible tree growth according to our limitations given in Table 1.
Darker colours (green) mean that the climate data suggest possible tree
growth. For easier comparison between the present (a) and the LGM
(b), the limits for the present are copied as a solid line into
the LGM panel. Markers indicate where and which tree pollen of deciduous
trees are found. Markers: circles – Quercus, squares – Tilia, triangles – Ulmus, plus
– Juglans and stars – more than one taxon. For modern-day sites in Japan only
dots are used for clarity of the plot. Open markers indicate that, at least within a
distance of approximately three grid points (∼150 km radius), the
climate data suggest possible tree growth; otherwise, filled (red) markers are used.
Red and blue (dashed) ovals show areas of interest mentioned in the text.
Winds at 10 m (V10m) and at 850 hPa (V850) for March as analysed (ERA-Interim)
and simulated for the present (CTR) and LGM. All panels show the
prevailing north-easterlies. Areas with topography above the 850 hPa level
are shaded in blue. Observational sites for the LGM are indicated
by markers.
Selected sites with observed pollen during the LGM. Quercus includes
deciduous Quercus and Lepidobalanus; Ulmus includes Ulmus–Zelkova; and “others” include Carya, Tilia and Carpinus.
“Agree” means that the observations agree with our estimates of possible
tree growth as shown in Fig. 6 or 8.
Long ELat NSiteRegionAlt, depth (m)QuercusUlmusOtherAgreeAuthor(a) East of 120∘ E 126∘32′33∘14′HN-1, Hanon maarJeju Island53YY4126∘33′33∘15′BH-4BJeju Island53YYYY5126∘52′35∘12′Yeonjaedong TrenchGwangju20?YYY6127∘13′33∘15′UD-2Hanam19YYY7128∘04′35∘10′PyonggeodongJinju30YY8128∘57′38∘33′MD982195N of E. China Sea-746YY9130∘23′31∘49′Imutaike PondSouthern Kyushu330YY10130∘23′33∘36′TenjinTenjin Fukuoka0YYY11city, N Kyushu134∘36′34∘24′OhnumaChugoku Mts610YYY12135∘48′35∘12′HatchodairaKyoto810YYYY13135∘53′35∘32′IwayaFukui20YYY14135∘53′35∘33′Lake MikataC Japan0YYY15138∘53′36∘49′Lake NojiriC Japan250YYYY16140∘10′36∘03′Hanamuro River HS1C Japan5YYYY17139∘40′36∘41′NakazatoC Japan183YYYY18141∘47′36∘04′MD01-2421off Kashima-2224YYYY21c130∘42′35∘56′KCES-1Sea of Japan-1464YYY19142 12.0841 10.64C9001CNE Japan-1180YYY?20136∘0335∘15′BIW 95-4Lake Biwa85YYY21a142∘28′44∘03′KenbuchiHokkaido137YYYN21b(b) West of 120∘ E 80∘08′29∘20′Phulara palaeo-lakeKumaun Himalaya1500?YYYY185∘18′27∘14′JW-3Kathmandu valley1300YYY293∘49′27∘32′Ziro valleyArunachal Pradesh1570YYY391∘03′40∘47′CK2Tarim basin780YN2299∘57′27∘55′06SD, lake ShuduYunnan3630YN23102∘47′24∘20′XY08A, Xingyun LakeC Yunnan1772YYY24102∘57′33∘57′RM RuoergaiZoige basin3400YYY26103∘30′32∘55′WasongNE Tibetan Plateau3490YY27106∘30′38∘17′Shuidonggou locality 2Yinchuan-Ningxia1200YYY28109∘30′34∘24′WeinanLoess Plateau650YYYY29110∘00′31∘29′DJH1, DajiuhuShennongjia Mountains1751YYYY30110∘17′21∘09′Huguangyan maarsouthern China23YYYY31115∘57′39∘45′East partYan Shan150?YYYY32117∘23′20∘07′17940S China Sea-1727YN33117∘25′20∘03′ODP 1144S China Sea-2037YN34117∘21'20∘08'MD05-2906S China Sea-1636YYYN35119∘02′26∘46′SZY peat bogFujian1007YY36120∘53′23∘49′Toushe BasinTaiwan650YYY37127∘16′28∘09′DG9603China Sea-1100YY38127∘22′28∘07′MD982194Okinawa Trough-989YYY39118∘16′20∘20′STD235S China Sea-2630YYYN40
Authors:
1: Kotlia et al. (2010);
2: Fuji and Sakai (2002);
3: Bhattacharyya et al. (2014);
4: Park and Park (2015);
5: Chung (2007);
6: Chung et al. (2010);
7: Yi and Kim (2010);
8: Chung et al. (2006);
9: Kawahata and Ohshima (2004);
10: Shimada et al. (2014);
11: Kuroda and Ota (1978);
12: Miyoshi and Yano (1986);
13: Takahara and Takeoka (1986);
14: Takahara and Takeoka (1992);
15: Nakagawa et al. (2002);
16: Kumon et al. (2003);
17: Momohara et al. (2016);
18: Nishiuchi et al. (2017);
19: Chen et al. (2016);
20: Sugaya et al. (2016);
21a: Hayashi et al. (2010);
21b: Igaraachi and Zarov (2011);
21c Igarachi (2009);
22: Yang et al. (2013);
23: Cook et al. (2011);
24: Chen et al. (2014) and Chen et al. (2015) IPS abstract;
26: Shen et al. (2005);
27: Yan et al. (1999);
28: Liu et al. (2011);
29: Sun et al. (1996);
30: Li et al. (2013);
31: Wang et al. (2012), Lu et al. (2003);
32: Xu et al. (2002);
33: Sun and Li (1999); Sun et al. (2000);
34: Sun et al. (2003);
35: Dai et al. (2015);
36: Yue et al. (2012);
37: Liew et al. (2006);
38: Xu et al. (2010);
39: Zheng et al. (2013);
40: Yu et al. (2017).
The downscaling method used here does not allow us to present values over
the emerged shelf of the Yellow Sea during the LGM, when the mean sea level
was 120 m below the present one (Lambeck et al., 2014). Therefore, in Fig. 8, the possible tree distribution is shown using model data without
downscaling, when the high spatial resolution is lost and more impacts from
systematic errors of the model may be expected. However, fortunately, such
impacts can hardly be seen when comparing Fig. 6 with Fig. 8, except for the
present along the southern slopes of the Himalayas and the southern border
of possible tree growth, where T2m from C&L is lower than that of CTR (also
than that from ERA-Interim), leading to a better fit with pollen data when using T2m
from C&L.
LGM connectivity and distribution mapping
The results show two areas worth discussing in terms of population connectivity:
one is over the Yellow Sea emerged shelf and one along the south of the
Himalayan range.
The northern limit of temperate deciduous trees assumed by previous
research (Harrison et al., 2001, their Fig. 1) is much further south (30–35∘ N) than what is found here. Therefore, population
connectivity over the shelf was rejected by Harrison et al. (2001). It
should be mentioned that the results by Harrison et al. (2001) were based on
the model available at that time, which had a lower resolution and was also
based on observational data available at that time; these have improved
considerably since then. Indeed, 80 % of the sites used in the current
investigation were published post-2001. Moreover, the Harrison et al. (2001)
study is based on biomes, not tree occurrences. Three arguments can be
presented now to support this connectivity.
Firstly, the model results clearly show the connectivity of tree populations
between China, Korea and Japan during the LGM over the emerged shelf. This
connectivity takes place because the limit for possible tree growth in
our investigation (darker areas in Fig. 8 and Fig. 6) still reaches
quite far north (40∘ N), which is in accordance with pollen data.
A second argument is the presence of deciduous trees at sites located around
the shelf in amounts suggesting more than a simple tree presence, perhaps even
woodlands or forests. In several places around the emerged shelf the
percentages of temperate deciduous trees indeed exceed 10%. These include the
Yeonjaedong swamp in Korea, with 20 %–30 % deciduous Quercus and 7 %–20 %
Ulmus–Zelkova (Chung et al., 2010), the two sites at the Jeju Island maar lake (Chung,
2007; Park and Park, 2015), the Tenjin peatland in Japan, with 12 % deciduous
Quercus, 8 % Carpinus and 2.5 % Tilia (Kuroda and Ota, 1978), and the marine cores DG9603 and
MD982194, with 15 % deciduous Quercus (Xu et al., 2010).
Thirdly, information derived from recent phylogenetic investigations is
supportive of the occurrence of deciduous trees on the emerged shelf. For
example, the phylogeography of one of the most widely distributed deciduous
species in eastern Asia, the oak Quercus variabilis, clearly suggests the occurrence of land
bridges over the East China Sea (Chen et al., 2012). Around the East China
Sea, other phylogenetic data indicate both mixing and the absence of mixing
between populations depending on plant type (Qi et al., 2014). The
occurrence of mixing indicates that contacts were possible across the
emerged shelf (e.g. Tian et al., 2016); while the absence of mixing for
other species indicates that not all species mixed, it certainly does not
suggest a total absence of migration for other species. It appears, therefore,
that the East China Sea acted as a filter, letting some through but not others
(Qi et al., 2014).
The eastern Asian case is very different from Europe, where fragmentation is
the rule in the LGM. In Europe (Fig. 2), the temperatures were much lower
than at present (8 to 15 ∘C) compared to eastern Asia (3 to
5 ∘C), and therefore the shift of possible temperate
deciduous tree growth is much smaller in eastern Asia than in Europe.
Phylogenetic results in eastern Asia are indeed in favour of the hypothesis of
species surviving both in the north and the south of China (Qian and
Ricklefs, 2000) and not of species surviving only in the south (Harrison et
al., 2001). The basic expansion–contraction model of vegetation belts in
Europe was much less important in eastern Asia (Qiu et al., 2011) due to
the smaller Asian ice cap and different topography (López-Pujol et
al., 2011). Eastern Asian biodiversity was therefore preserved across the Ice
Ages, owing not only to the more moderate lowering of temperatures but also
to the better connectivity between populations.
One remaining question is whether the pollen found in the emerged shelf of the Yellow
Sea is produced locally or remotely. According to the Harrison et al. (2001)
study, these pollen grains must have come from the southern part of China.
Yu et al. (2004) have tried to calculate such long-distance transports. For
Quercus and Ulmus they found transports of up to 6∘ latitude–longitude in any
direction. This would be too short for transport from China south of 30∘ S. Also, the high pollen percentages at the observed sites speak
against such long-distance transport.
We are not convinced that the Yu et al. (2004) calculations are robust enough
to use their results in our investigation, especially as their Fig. 3
does not agree with plant distributions by Fang et al. (2009). Therefore,
the wind fields for the present as analysed by the ECMWF (ERA-Interim) and as simulated
by our model for the LGM were investigated. In Sect. S1
and in Fig. 7, it is shown that ERA-Interim and the simulation for the
present agree quite well, at least for the wind directions, which makes us
confident that we can use the model simulations for the LGM.
The winds at 10 m and 850 hPa for March, a central month for the blooming of
Quercus variabilis, are shown in Fig. 7 for the present (ERA-Interim), the CTR and the LGM. Over the
emerged shelf of the Yellow Sea, the 10 m winds are very light from the
north-west during the LGM (much stronger in ERA-Interim because of the lower surface
friction over the sea). For the higher level of 850 hPa, all datasets show
very similar distributions, all with north-westerlies. Long-distance
transport of deciduous tree pollen would have come from NE China, an area
that Harrison et al. (2001) assume to be void of deciduous trees, though
some recent studies (including the present one) indicate the opposite (Yu et
al., 2004). Further on in the year, the 850 hPa winds blow from the
south-west, starting in April and fully crossing the 30∘ N
latitude in May (similar to CTR in Fig. S1.1), i.e. transport from
mainland China would have been possible though a little late for the main
blooming of deciduous oak. In Sect. S1, it is shown
for the present that the simulations suffer from a progression of
the monsoon front that is too early, which suggests that the turn of the wind to
south-westerlies may have also occurred later for the LGM, thus leading to
less likely transport from mainland China.
The source for the pollen found in the emerged Yellow Sea is not completely
clear, but May is late for the blooming season in central China (for Taiwan
it is January to March). Therefore, local production or transport from
northern China is more likely, supporting our argument that the emerged
Yellow Sea was occupied by deciduous trees during the LGM, as indicated by Fig. 8.
Same as Fig. 6 using model data without downscaling. The Yellow Sea
is shown as land in the LGM.
Another important population connectivity result is that the Himalayas were
more favourable to temperate deciduous trees in the LGM and provided the
possibility of a quasi-continuous band of temperate forest at its southern
slope, which is beneficial for the spreading and diffusion of genes (e.g. for Chinese
mole shrew, He et al., 2016), more so than in the present (Fig. 6). Three
observational sites that are currently available support this chain of
possible tree growth during the LGM. For the present, this link does not
exist because of winter temperatures that are too warm (warmer than 5 ∘C in the C&L climatology). Along the slopes of the high
Himalayas it is most likely that there is a level at which the
temperature would be below 5 ∘C (an issue which needs
further investigation).
Two significant cases occur in which population connectivity was higher,
indicating less population fragmentation, in glacial than in interglacial
periods. So, it appears that many tree populations currently live in
interglacial refugia.
During the LGM the precipitation and temperature were lower than at
present, but which was more important for tree growth cannot be said
with certainty. Tian et al. (2016) stated that “annual precipitation is
considered as the most important determinant”, and in our study we have
some indication to agree with that. In Figs. 6 and 8, there is a cluster of
pollen findings over central China (105–110∘ E, 35–40∘ N)
for the present but not for the LGM. In this area the temperature does not
change much (Figs. 2 and 3), but the summer precipitation decreases
substantially (Fig. 5). This change is only slightly reflected by the
boundaries of possible tree growth in Fig. 6 (north of 40∘ N). The
lack of observational sites with tree pollen is not proof
because it could be due to many reasons, but the massive change in
occurrence is suggestive that we should have perhaps increased summer
precipitation requirements for tree growth (Table 1). This can, however,
also indicate reduced water use efficiency of the trees during the LGM due to
lower atmospheric CO2.
Finally, this investigation shows that the model simulations suggest
possible tree growth where pollen grains of such trees are found. This leads
to the possibility of using the model data to fill gaps between
observational sites by way of maps. Such gaps especially occur around 30–37∘ N, 105–120∘ E and 25–30∘ N, 110–115∘ E, i.e. the provinces Hupeh to Kiangsu and Hunan (ovals in Fig. 6b).
Same as Fig. 6 for the whole of Eurasia. Pollen data for Europe have
been described by Arpe et al. (2011). Darker colours (green) are areas in
which trees are able to grow according to model data. Lighter green indicates
areas where not all criteria are completely fulfilled.
By extending the view of our investigation to the whole of Eurasia (Fig. 9), a stronger link between China and Europe is shown during the LGM than at
present. Along the foot of the Himalayas, a continuum existed, but
westwards of it, a gap north of Afghanistan (probably going back to
the Tertiary) is still maintained, inhibiting a total link across Eurasia. This
continuum is broken for the present climate by model results because winter
temperatures exceed 7 ∘C, hence being too warm for
temperate deciduous trees.
Conclusions
Generally, the estimates of possible temperate deciduous tree growth in the
LGM in eastern Asia from model simulations agree with fossil pollen
observations. Therefore, the model estimates can fill the areas without
observations. The results in the form of LGM distribution maps are
considered robust enough, as model simulations for the present are within the
range of climate estimates. Nevertheless, we are aware of some uncertainties
in the climate of eastern Asia, and we can safely say they are not a
limitation of this study.
During the LGM, major connectivities between populations are found, which is
in agreement with observations, i.e. less tree population fragmentation. This
is especially visible in two places. Firstly, the link between China, Korea
and Japan is clear. Sufficient new pollen studies around and on the emerged
Yellow Sea shelf are now available, confirming the results of the model.
They suggest the presence of temperate deciduous trees, perhaps even
woodlands, in the area.
Secondly, connectivity during the glacial period occurred at the southern
slope of the Himalayan chain, favouring genetic flow in interglacial refugia.
Currently, this link does not exist because of winter temperatures
there that are too warm. Our simulations cannot be taken as proof of this hypothesis, as one
cannot imagine that along the Himalayan chain there would not be a level at
which winter temperatures do not exceed 5 ∘C, also for
the present day; a higher-resolution dataset would be able to show how wide
and continuous such a corridor of possible tree growth would be in the
present.
Another outcome of this research is the contribution to the conservation
agenda (López-Pujol et al., 2001). The areas of LGM refugia often match
areas of present hotspots of biodiversity. Hence, the distribution of
temperate forest obtained in our investigation can serve as a guide to
establish nature parks for plants and animals. Moreover, the difference
between the LGM and present distribution contributes to the understanding of the rate
of distribution change (as well as genetic flow), which is important to monitor
in light of possible climatic change.
Code availability
The model version is already widely known and available. We have clearly
described what has been done, and the follow-up programmes are written in
Fortran. This can be requested from Klaus Arpe if wanted.
Data availability
Table 2 provides a list of all observational sites and observational tree
pollen data. Most of the other data are referred to by giving the website. It does not seem feasible to provide the model simulation data in a simple
way. They can be obtained from Klaus Arpe in GRIB format.
The supplement related to this article is available online at: https://doi.org/10.5194/cp-16-2039-2020-supplement.
Author contributions
SAGL looked after conceptual issues, collected the data and wrote the paper. KA wrote most of the paper, prepared the figures, and was responsible for
meteorological and climatological issues. UM provided the model simulations. JW provided observational tree pollen data. SAGL was responsible for the overall research and
collected the pollen data with help from JW. KA was responsible for the
meteorology and climatology aspects as well as most of the programming
and writing the paper. UM provided the climate model simulations,
and JW contributed to the data search and Chinese aspects.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
Jing Zheng (Fujian Agriculture and Forestry University) started collecting
the LGM data during a post-doctoral stay with Suzanne Leroy at Brunel
University, London. Uwe Mikolajewicz acknowledges funding from the German
Federal Ministry of Education and Research within the research framework for
sustainable development (FONA3, FKZ 01LP1502A).
Financial support
The article processing charges for this open-access publication were covered by the Max Planck Society.
Review statement
This paper was edited by Helen McGregor and reviewed by two anonymous referees.
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