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Vegetation around the Daihai Lake, northern China, is very sensitive to climate changes. In this paper, pollen-based quantitative climate reconstructions using three methods [weighted averaging partial least squares method (WAPLS), modern analog technique (MAT), and pollen response surface method (PRS)] were conducted to obtain robust reconstructions of Holocene climate changes in the Daihai Lake area. The result obtained by the three methods all consistently show the annual precipitation to have been 50–100 mm lower in the early Holocene, 100–200 mm higher in the Mid-Holocene, and 50–100 mm lower again in the late Holocene than at present. The WAPLS and the MAT methods also show quasi-synchronous oscillations of the mean annual temperature (Ta); 1°–2°C lower in the Early Holocene and 1°–3°C higher in the Mid-Holocene than today. The time period from 6200 to 5100 cal yr BP was the wettest and the warmest interval, with an annual precipitation (Pa) greater than 550 mm and mean annual temperature Ta higher than 6.5°C. Several cold and dry events can be identified to occur about 8200, 6000, and 4400 cal yr BP, with an annual precipitation less than 400 mm and a mean annual temperature colder than 4.5°C, respectively. The mean temperature of the warmest month (Tw) as reconstructed using both WAPLS and MAT methods was relatively stable during the Holocene, fluctuating about ±2°C relative to the present level, but the PRS method suggests more varied Tw values in both amplitude and frequency. After 1500 cal yr BP, no consistent pattern can be observed from these three different analyses, probably because of the impact of intensified human disturbances on the natural vegetation. The fluctuations of annual precipitation (Pa) correspond to that observed in Dongge Cave in southern China. The differences might be linked to Indian monsoon and East Asia monsoon climates or caused by the different degree of dating precision, different temporal resolution, and different sensitive response of climate proxies to the climate variations. [ABSTRACT FROM AUTHOR]

The time-frequency characteristics of the variations of temperature and precipitation over the city of Changchun in northeast China and their associations with large-scale atmospheric and oceanic conditions are analyzed. It is found that the variations of the regional climate are characterized by strong semiannual signals. For precipitation, the amplitude of semiannual signal is about half of that of the annual cycle. The relationships of the Changchun temperature and precipitation with local winds and large-scale patterns of atmospheric circulation and sea surface temperature are also strongest on annual and semiannual time scales. These strong semiannual signals are potentially helpful for improving the prediction of the regional climate. On the annual time scale, the northeast China climate is affected by both the thermal contrast between the Asian continent and the tropical Indo-Pacific Oceans and that between the continent and the extratropical North Pacific. These effects are manifested by the cyclonic (anticyclonic) pattern over the Asian continent (North Pacific) and the strong southerly flow over East Asia and northwestern Pacific associated with increases in temperature and precipitation. On the semiannual time scale, the northeast China climate is mainly related to the large-scale circulation pattern centered over the North Pacific, with its western portion over northeast China, North and South Korea, and Japan. While temperature signals are related to extratropical atmospheric process more apparently, both extratropical and tropical influences are seen in the semiannual variation of precipitation. There exist strong relationships between Changchun temperature and precipitation and the North Pacific Oscillation (NPO) in the frequency band up to 7 months. Temperature increases and precipitation decreases when NPO is positive. The relationships were weak before 1980 but became stronger afterward, associated with the strengthening of the East Asian trough. [ABSTRACT FROM AUTHOR]