东亚中高纬土壤温度资料评估与分析
发布时间：2018-11-03 20:41
【摘要】：本文以一套俄罗斯土壤温度历史观测资料RHSTD为基础,分析了四套土壤温度产品[ERA-Interim再分析资料、两套陆面模式离线运行产品ERA-Interim/Land(简称ERA-Land)和MERRA-Land、以及一套二十世纪再分析资料NOAA-CIRES 20CR]在东亚中高纬的可靠性,并重点关注春夏季,主要结论如下:观测地温在0~2 m波动较大,随季节在0°C上下摆动,而2 m以下地温稳定少变,并且在60°N以北地区出现永久冻结。四套地温产品较好地反映了这些特征。无论春夏,还是年平均,四套地温产品气候态都呈"南暖北冷"的特征,但ERA-Land的空间分布与观测最接近。就季节循环而言,ERA-Land最能反映该地区土壤的冻融过程和土壤温度的季节演变。四套地温产品年际变率(标准差)与观测的差异随季节和土壤层变化大,情况比较复杂。就年际变化趋势而言,四套地温产品与观测的相关性,夏季好于春季,表层好于深层,并且ERA-Land土壤温度拥有四套地温产品最多的共性,最能反映观测地温的年际变化。
[Abstract]:Based on a set of Russian soil temperature historical observation data (RHSTD), four sets of soil temperature products [ERA-Interim reanalysis data], two sets of land surface model off-line operation products ERA-Interim/Land (ERA-Land) and MERRA-Land, were analyzed in this paper. The reliability of a set of reanalysis data (NOAA-CIRES 20CR) in the middle and high latitudes of East Asia in the 20th century is also discussed. The main conclusions are as follows: the observed ground temperature fluctuates greatly at 0 ~ 2 m and wobbles up and down with the season at 0 掳C. However, the ground temperature below 2 m has little change, and there is a permanent freezing in the area north of 60 掳N. Four sets of geothermal products better reflect these characteristics. No matter in spring and summer or in average year, the climate state of the four sets of geothermal products is "warm and cold in the south and north", but the spatial distribution of ERA-Land is the closest to the observation. In terms of seasonal cycle, ERA-Land can best reflect the freezing and thawing process of soil and the seasonal evolution of soil temperature in this area. The differences between interannual variability (standard deviation) and observation of four sets of geothermal products vary greatly with seasons and soil layers. In terms of interannual variation trend, the correlation between the four sets of geothermal products and observations is better in summer than in spring, and in surface layer is better than in deep layer, and ERA-Land soil temperature has the most common features of four sets of geothermal products, which can best reflect the interannual variation of observed ground temperature.
【作者单位】： 成都信息工程大学大气科学学院/高原大气与环境四川省重点实验室;中国科学院大气物理研究所;
【基金】：国家重点研发计划项目高性能计算项目2016YFB0200800~~
【分类号】：S152.8
[Abstract]:Based on a set of Russian soil temperature historical observation data (RHSTD), four sets of soil temperature products [ERA-Interim reanalysis data], two sets of land surface model off-line operation products ERA-Interim/Land (ERA-Land) and MERRA-Land, were analyzed in this paper. The reliability of a set of reanalysis data (NOAA-CIRES 20CR) in the middle and high latitudes of East Asia in the 20th century is also discussed. The main conclusions are as follows: the observed ground temperature fluctuates greatly at 0 ~ 2 m and wobbles up and down with the season at 0 掳C. However, the ground temperature below 2 m has little change, and there is a permanent freezing in the area north of 60 掳N. Four sets of geothermal products better reflect these characteristics. No matter in spring and summer or in average year, the climate state of the four sets of geothermal products is "warm and cold in the south and north", but the spatial distribution of ERA-Land is the closest to the observation. In terms of seasonal cycle, ERA-Land can best reflect the freezing and thawing process of soil and the seasonal evolution of soil temperature in this area. The differences between interannual variability (standard deviation) and observation of four sets of geothermal products vary greatly with seasons and soil layers. In terms of interannual variation trend, the correlation between the four sets of geothermal products and observations is better in summer than in spring, and in surface layer is better than in deep layer, and ERA-Land soil temperature has the most common features of four sets of geothermal products, which can best reflect the interannual variation of observed ground temperature.
【作者单位】： 成都信息工程大学大气科学学院/高原大气与环境四川省重点实验室;中国科学院大气物理研究所;
【基金】：国家重点研发计划项目高性能计算项目2016YFB0200800~~
【分类号】：S152.8
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