Multi-proxy approach is a way to produce robust paleoclimatic
information but, as it is based on modern data using a
statistical approach, it does not solve all the problems. The
reconstruction methods are built upon the assumption that
plant-climate interactions remain the same through time, and
implicitly assume that these interactions are independent of
changes in atmospheric CO2. This assumption may lead to
a considerable bias, as polar ice core records show that the
atmospheric CO2 concentration has fluctuated significantly
over the past (EPICA, 2004). At the same time, a number of
physiological and palaeoecological studies (Farquhar, 1997;
Jolly and Haxeltine, 1997; Cowling and Sykes, 1999) have
shown that plant-climate interactions are sensitive to atmospheric
CO2 concentration. Therefore, the use of mechanistic
vegetation models has been proposed to deal with these
problems (Guiot et al., 2000). Wu et al. (2007) have improved
the approach based on the BIOME4 model to provide
better spatial and quantitative climate estimates from pollen
records and correct for CO2 bias to pollen-based climate reconstructions
in Eurasia and Africa. The same method is
quickly presented here for Eastern Asian data.
4.1 Data and method
The pollen data used have been compiled by the BIOME6000
project (Prentice and Jolly, 2000) for three key periods: 0 k,
6 k and 21 ka BP to classify pollen assemblages into a set of
vegetation types. For the study described here, a subset containing
601 sample sites for 0 ka BP and 116 sites for 6 ka BP
from China and Mongolia were used (MCPD, 2000, 2001;
Tarasov et al., 1998). The selection of the 6 ka BP samples is
based following the BIOME6000 convention. Among them,
84 sites have a good age control, i.e. either with at least two
dates encompassing 6 ka BP at less than than 2000 years distance.
BIOME4 is a physiological-process global vegetation
model, with a photosynthesis scheme that simulates the response
of plants to changed atmospheric CO2 and by accounting
for the effects of CO2 on net assimilation, stomatal
conductance, leaf area index and ecosystem water balance. It
is driven by monthly temperature, precipitation, sunshine, by
absolute minimum temperature, CO2 concentration and soil
texture. The principle of the model inversion is to estimate
the input to BIOME4, the monthly climate, given that we
know some information related to the output of the model,
biome scores derived from pollen in our case (Prentice et al.,
1996). This inversion, which uses a Monte-Carlo-Markov-
Chain algorithm to explore possible combinations of climate
parameters, allows an assessment of the probability of different
anomalies, and therefore the investigation of different
scenarios which may result in similar vegetation pattern.
The procedure is described in Wu et al. (2007). As Guiot
et al. (2000), they showed that several solutions were possible
for the LGM climate in Western Europe where a mixture
of steppes and tundra existed. As these biomes have no clear
analogues today, reconstructions based on statistical methods
will tend to choose the least poor match or fail to find a real
match. With the inverse modelling, Wu et al. (2007) showed
that a climate significantly warmer than inferred with modern
CO2 levels was the most probable. The overestimation
of MTCO anomalies was about 10C. Moreover uncertainties
were also underestimated with the statistical methods.
4.2 Validation
We present here an analysis of Chinese mid-Holocene data
(Luo et al., 20082). In a first step, the ability of this inversion
scheme to reproduce the modern climate of China is
evaluated, using the 601 modern spectra available. The statistical
squared correlations (R2) between actual and reconstructed
climate variables at the sample sites are presented in
Fig. 3. These R2 are very large, generally above 0.67, except
for MTWA which then does not appear to be a key factor
to explain the modern vegetation distribution in China. The
straight line between estimates and observations is expected
to have an intercept of 0 and a slope of 1. The slope is slightly
biased for MTWA, GDD and MAP. The intercepts are biased
for MTCO, MTWA and MAT, showing a tendency to overestimate
the cold climates. There is also large error in estimating
MAP and in cold desert sites of the Tibet Plateau,
where below 60% are frequently estimated below 20%, i.e.
values typical of warmer deserts.
4.3 The 6000 yr BP climate
For the 6 ka BP period, the atmopsheric CO2 concentration is
set to 270 ppmv (EPICA, 2004). The results (MAP, MAT, )
are presented as maps of anomalies versus present climate
(Fig. 4). Large circles indicate significant differences from
the modern values. The results show that, in most of the sites
at 6 ka BP, the changes in precipitation and were significantly
different from modern values, while most of temperature
changes are not. This is due to the larger uncertainty
on the reconstructed temperature, which indicates a larger tolerance range of the vegetation to thermal variables while
hydrological variables were more limiting factors. Annual
temperature were generally lower than present one in southern
China, but a significant warming was found over Mongolia,
and a slight warming in northeast China.
Hydrological variables have a much more coherent distribution.
MAP was generally higher than today in southern,
northeast China, and northern Mongolia, but lower or similar
to today in northwest China and north China. was considerably
higher than today in north China, and slightly higher
than present in northeast China. In contrast, drier conditions
are shown in northwest China and Mongolia.
Lake Bayanchagan is situated in a zone where most of the
sites had a positive anomaly of MAP whereas a few ones had
a negative one. This is broadly consistent with the reconstruction
of Fig. 2e where MAP was found 200 mm higher
than at present. The anomaly of for this zone is significantly
positive, between +15 and 30% in agreement with
Fig. 2f where was found 30% higher than at present. For
these two variables, Lake Bayanchagan reconstruction provide
values at the upper limit of the inverse modelling. MAT
appears also higher than at present, in good agreement with
the reconstruction of Fig. 2c–d. The reconstructions based on
the inverse modelling are then approximately consistent with
the Lake Bayanchagan, at least for the majority of surrounding
sites, but the multiproxy statistical approach infers values
at the wetter limit of the inverse modelling. When compared
to Tarasov et al. (1999), Fig. 4 shows also wetter and warmer
conditions on northern Mongolia and warmer and drier conditions
In the central part of the country.
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