黄河上游地区近千年气候变化的模拟重建

Reconstruction of climate change over upper reaches of Yellow River Basin during Last Millennium

运用BP人工神经网络较好地建立了全球气候模式模拟数据与区域气候之间的关系,拟合了黄河上游沙漠河谷地区的近千年温度、降水序列。在气候信号年代际和百年际变化特征上,拟合结果较为理想,但对极值的拟合能力较差,尤其是冬季温度和夏季降水的拟合极值偏差较大。拟合结果表明该地区近千年存在中世纪暖期、小冰期和现代暖期,且小冰期降温在冬季更为明显,冬季平均气温小冰期比中世纪暖期低2℃。降水的千年变化趋势较温度略微平缓,尤其冬季降水无明显趋势变化。空间分布显示20世纪暖期在近千年是最暖的,但降水较中世纪暖期偏少。

At present, sources of information on the climate of the last millennium include paleoclimatic proxies and models. Proxies of the last climate are natural archives that have, in some way, incorporated a strong climatic signal into their structure. But they exit several problems, such as spatial representativeness, uncertainty. What＇ s more, they are far from achieving season-specific histories for different climate variables at the regional scale. At mosphere-ocean coupled global circulation models could provide relatively high temporal resolution data. However, they also could not provide the regional scale information for their low spatial resolution. The regional climate re sponse to global change has been an urgent issue to resolve. To understand regional climate information, this article tries to build the relationship between the global climate model outputs and the regional climate by using back prop agation （BP） artificial neural network, a widely recognized statistical downscaling method. The global climate data is the output of ERIK simulation （covering the period 1000-1990） with the coupled atmosphere-ocean global climate model ECHO-G, which is forced by three external forcing factors： solar variability, greenhouse gas concentrations in the atmosphere including C02 and CH4, and the effective radiative effects from stratospheric volcanic aerosols. The meteorological observation data are used for regional climate information during the past 50 years. The desert valley of the upper reaches of the Yellow River, located in arid and semi-arid areas, is much more sensitive to global change. Therefore, the temperature and precipitation series in this area were reconstructed during the last millenni- um. The results were well proved by tree-ring data and historical documents, with correlation coefficient being 0.58and 0.57. Compared with model data, fitting series reproduced the regional characteristics of the interannual and in terdecadal climatic variations. Whereas the extreme values were not well reconstructed, especially the extreme val ues of the winter temperature and the summer precipitation. The reconstructed series showed that there were three typical periods over this region during the last millennium, i.e. the Medieval Warm Period （MWP）, the Little Ice Age （LIA） and the Present Warm Period （PWP）. The difference of winter temperature between MWP and LIA was 2. The amplitude of precipitation variation was smaller than that of the temperature, especially in winter. The spatial pattern showed that PWP was the warmest period during the last millennium, but the precipitation in PWP was less than that in MWP. Back propagation （BP） artificial neural network is a helpful tool to remedy the shortage of proxies and models. It provides useful information about regional response to climate change, such as global warming. It also anism. Consequently, new downscaling techniques by combining statistical and dynamical downscaling techniques would be developed as the main next plan.