青藏高原工程走廊3个监测点多年冻土温度序列重建

Reconstruction of past permafrost temperature sequences at three monitoring sites along the Qinghai-Tibet Engineering Corridor

连续、长时间序列的多年冻土温度数据在开展气候变化对多年冻土的影响及其生态、水文效应研究中有着重要的科学意义.本文利用西大滩、五道梁、唐古拉3个观测点的实测地温数据首先对多年冻土模型及其参数化方案进行了验证、优化和标校,以实测数据和经过校正的CMIP6逐月5 cm地表土壤温度数据作为模型的驱动数据,模拟了3个观测点1900~2019年多年冻土地温变化,并对1920年以后的模拟温度变化序列进行分析.结果表明:(1)多年冻土模型对处于地温年变化深度以下地温的模拟误差低于0.1℃,表明模型对于多年冻土的热状态具有较好的模拟能力;(2)1920~2019年西大滩、五道梁、唐古拉各模拟深度的年平均温度均呈现升温趋势,年地温变化(15 m)处的平均升温速率为0.07℃/10 a(0.05~0.09),不同深度岩土层的热状态对气候变化具有不同的响应时间,深层地温对气候变化的响应相较于浅层有20年左右的滞后;(3)多年冻土上限下降速率相差不大,平均为0.6 cm/a;多年冻土下限的上升速率分别为13.4、4.0和4.0 cm/a;多年冻土厚度分别减少13.9、4.6、4.7 m;(4)3个观测点多年冻土对气候变化的反应存在差异,处于多年冻土北部下界附近西大滩的多年冻土相对不稳定,对气候变化反应剧烈,而处于连续多年冻土区的五道梁和唐古拉多年冻土相对稳定.本研究重建的不同深度100年月尺度地温变化序列可以较好地反映青藏高原多年冻土温度的变化历史,与近40年来观测到的多年冻土变化的实际情况较为一致.地温序列是对目前青藏高原多年冻土地温数据时间短、连续性差的补充和完善,可被用作研究青藏高原多年冻土热状态变化的重要参考数据.

The long-term and continuous permafrost temperature data is of great significance to the study of permafrost,climate,ecology,hydrology and engineering on the Qinghai-Tibet Plateau(QTP),but the available observed data sequence is no longer than 25 years.To address the gap,we first attempt to reconstruct the sequences of permafrost temperature at three monitoring sites including Xidatan,Wudaoliang and Tanggula along the QTP engineering corridor from 1920 to 2019,based on one of the most used permafrost models worldwide(i.e.,Geophysical Institute Permafrost Lab version 2(GIPL2)).The GIPL2 model and its parameterized schemes were first evaluated and calibrated using the ground temperature observations at the three sites.The monthly near surface ground temperature at the depth of 5 cm after calibration and correction based on monitoring data was used as forcing dataset to simulate the temperature change of the permafrost from 1900 to 2019.The temperature change sequences since 1920 were selected to discuss the changes of permafrost on the QTP,and its responses to climate change.Results showed that(i)the GIPL2 model can well simulate the thermal state of permafrost on the QTP with low simulation errors(below 0.1℃)at the depth of zero annual amplitude;(ii)the annual average ground temperature at different depths for all three sites experienced warming trends from 1920 to 2019,in which the average warming rate was 0.07℃/10 a(0.05–0.09)at the depth of zero annual amplitude(15 m).Besides,the site with the largest warming rate at the shallow layer(3 m)was found in Wudaoliang,while the deep layer(30 m)was in Xidatan;(iii)the permafrost temperature at the shallow layer increased rapidly since 1980.Nevertheless,the response times of the thermal conditions to climate change varied with soil layers,among which the deep layer lagged by about 20 years compared to the shallow layer;(iv)permafrost thicknesses for the Xidatan,Wudaoliang and Tanggula sites were decreased by 13.9 m,4.6 m and 4.7 m respectively.The average deepening rate of the permafrost table and rising rates of permafrost base for the three sites were 0.6 cm/a and 10.27 cm/a,respectively.More specifically,the deepening rate of the permafrost table was 0.5 cm/a for Xidatan,0.6 cm/a for Wudaoliang and 0.7 cm/a for Tanggula,and the rising rate of the permafrost base was 13.4 cm/a for Xidatan and 4.0 cm/a for both Wudaoliang and Tanggula.Compared with that in Wudaoliang and Tanggula,the permafrost in Xidatan was relatively unstable and its response to climate change was more sensitive.Although the simulations of the GIPL2 model could be impacted by the accuracy of the forcing data(e.g.,5 cm ground temperature),the reconstructed permafrost temperature changes from 1920 to 2019 were consistent with the observations over the past 40 years.Besides,our results also confirmed the continuous warming phenomenon of permafrost on the QTP since 1920.These findings can well fill the narrow gap relating to the short sequence and discontinuity of the permafrost temperature dataset on the QTP,and provide a baseline of permafrost changes to the scientific community for a better understanding of the changes in the cryosphere,ecosystem,water resources,and even climate.Nevertheless,some limitations in temperature reconstruction and model processing were noted.In the future,multiple aspects including accurate forcing data and complex factors(e.g.,heat convection and lateral heat flow exchange)should be considered comprehensively in the model to reduce the uncertainties of ground temperature simulations.