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Home » Biology Articles » Bioclimatology » East Asian Monsoon and paleoclimatic data analysis:a vegetation point of view » A inverse modelling technique to reconstruct climatespatial variability in China

A inverse modelling technique to reconstruct climatespatial variability in China
- East Asian Monsoon and paleoclimatic data analysis:a vegetation point of view

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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