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- East Asian Monsoon and paleoclimatic data analysis:a vegetation point of view

The past 21 000 years are a very interesting time period pe- riod as it contains two extreme states of the climate. The Last Glacial maximum (LGM, 21 000 years BP) is a cold and generally dry period driven by enlarged ice sheets and low at- mospheric CO 2 . The mid-Holocene period (6000 years BP), generally warmer and wetter than the present one, is consid- ered as orbital forced period with perihelion in northern sum- mer/autumn and greater-than-present axial tilt (Berger, 1978) but free of major ice-sheet and relatively high CO 2 (taking as reference the pre-industrial present time). These two peri- ods have been chosen as key time periods by the Palaeocli- mate Modelling Intercompraison Project, PMIP (Joussaume and Taylor, 1995). The mid-Holocene, with its high summer insolation, is a period of high land-sea contrast and conse- quently enhanced monsoon (Braconnot et al., 2002). It is of particular interest for climate modellers to test their simula- tions through palaeodata from the monsoonal regions. The East Asian monsoon (EAM) is one of the most ac- tive components of the global climate system, influencing a large area of China and its surrounding countries. In China and surrounding countries, a megathermal period was recon- structed from 9500 to 4000 yr ago (Shi et al., 1993). How- ever, many recent studies have shown that Holocene climatic changes were asynchronous across China (An, 2000; An et al., 2006; He et al., 2004). The Holocene optimum was de- fined as EAM precipitation maximum, occurring ca. 11 000­ 9000 yr ago in northeastern China, 11 000­8000 yr ago in north-central and northern east-central China, 8000­6000 yr ago in central China, and ca. 3500 yr ago in southern China (An, 2000). The reason for debate on Holocene climatic variations is that complexity of the EAM, and different re- sponses of environmental proxies to climatic changes (Wei and Gasse, 1999; Wang et al., 2003). Therefore, more precisely dated palaeo-records and improved quantitative re- construction are required to provide quantitative insights into the processes of climatic changes, and their links to the EAM.

The goal of this paper is threefold. First we explore the temporal variability of a record located in a sensitive re- gion at the northern edge of the EAM using a multiproxy approach. Second we explore the spatial variability of the Chinese climate at 6 ka BP, when EAM is assumed to be the strongest. Third we illustrate a new methodology of climate reconstruction based on vegetation model inversion. The temporal study is based on a core sampled in Lake Bayanchagan (Inner Mongolia) (Jiang et al., 2006) (Fig. 1). This region is particularly sensitive to climate variations as it is located at the edge of the present EAM. Their results suggest that this region was dominated by steppe vegeta- tion throughout the Holocene, except for the period 9200 to 6700 yr BP, when forest patches were relatively common. This period can then be correlated to enhanced EAM. But these findings need to be confirmed by a multiproxy analysis. We will synthesise in the first part of this paper an statistical approach based, in addition to pollen, on isotopic data and concentration of a green algae species (Jiang et al., 2008)

This study will focus on the timing of this enhanced EAM period. This approach based on detailed time series in a sensitive region will be completed by a spatial analysis based also on pollen data but done with the newest tools involving a pro- cess model able to relate vegetation and climatic variations (Luo et al., 2008 2 ). The strong feature of this approach is to be able to take into account the large differences existing between present and mid-Holocene conditions as (i) climate seasonality, possibly resulting in lack of modern analogues, or (ii) atmospheric CO 2 close to pre-industrial concentration but significantly lower than the present one. This spatial anal- ysis will be first replaced in the context of previously pub- lished data syntheses at the sub-continental scale.

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