再分析资料中土壤温度记忆的对比及观测数据的差异  

Differences in soil temperature memory between reanalysis datasets and observational data

作　　者：宋耀明[1] 赵勇[2] SONG Yaoming;ZHAO Yong(Key Laboratory of Meteorological Disaster,Ministry of Education(KLME)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD),Nanjing University of Information Science and Technology,Nanjing 210044,China;School of Atmospheric Sciences,Chengdu University of Information Technology,Chengdu 610225,China)

机构地区：[1]南京信息工程大学气象灾害教育部重点实验室/气象灾害预报预警与评估协同创新中心,江苏南京210044 [2]成都信息工程大学大气科学学院,四川成都610225

出　　处：《大气科学学报》2025年第2期300-311,共12页Transactions of Atmospheric Sciences

基　　金：国家自然科学基金项目(41005047,42130609,41875102);成都信息工程大学科技项目(KYTD202333)。

摘　　要：土壤温度异常的记忆建立了前期土壤温度异常与后续土壤温度及大气异常的联系,因而土壤温度再分析数据的大量使用使得土壤温度异常的记忆在再分析数据中的评估尤其重要。本研究利用1979—2019年ERA-Interim、ERA5和GLDAS土壤温度再分析数据及中国区域土壤温度观测数据,采用统计分析的方法对比了我国土壤温度异常在月尺度上的记忆特征。结果显示,ERA-Interim、ERA5和GLDAS数据对浅层土壤温度气候态的空间分布均有很好的再现能力,对表层土壤温度在表层的持续特征也有很好的再现。在土壤温度记忆的空间分布上,ERA5和ERA-Interim在整个土壤层土壤温度的记忆高值区主要位于400~800 mm的多年平均降水区,约为8~10 mon;GLDAS的空间分布同ERA5、ERA-Interim明显不同,西部显著高于东部。在月际变化上,再分析数据土壤温度记忆在不同月份间的空间分布均呈现出显著的相似性。此外,土壤温度异常存在明显的随时间向土壤深层传播的特征。ERA-Interim和ERA5的前期整个土壤层的土壤温度异常信号在浅层土壤持续较长的区域主要位于山西、陕西及河南,而GLDAS主要在西部地区。同观测数据的对比显示,GLDAS、ERA5能较好地表达出观测数据中表层土壤温度异常在土壤表层持续的特征,但3种再分析数据对整层土壤温度异常在整个土壤层的持续特征不能很好地体现。再分析数据对土壤温度异常持续时间的表达能力具有很大的月份及地区差异,因此在统计分析及数值模拟中使用土壤温度再分析数据研究土壤温度异常对后续气候的影响时,应对再分析数据中土壤温度异常的持续性进行评估以保证研究结论的可靠性。Soil temperature(ST)anomaly memory describes the persistence of ST anomalies and their influence on subsequent ST and atmospheric anomalies.Due to the limited availability of long-term ST observational data,reanalysis datasets are widely used in numerical simulations and statistical diagnostic analyses.However,the extent to which reanalysis data affect the persistence of ST anomalies remains uncertain,directly impacting the reliability of conclusions drawn from numerical and statistical studies on the influence of antecedent ST anomalies on atmospheric processes.Therefore,evaluating the memory of ST anomalies in reanalysis datasets is essential.This study evaluates the memory of ST anomalies at a monthly timescale using ERA-Interim,ERA5,and GLDAS reanalysis datasets from 1979 to 2019,alongside observational data from China.The ST anomaly memory is quantified using the autocorrelation method while considering the vertical propagation of ST anomaly signals across different soil layers.Results indicate that all three reanalysis datasets effectively reproduce the spatial distribution of multi-year average shallow ST.However,notable differences exist in ST memory.High-memory regions in ERA5 and ERA-Interim are primarily located in areas with an average annual precipitation of 400 to 800 mm,where ST anomalies persist for approximately 8-10 months.In contrast,GLDAS exhibits a distinctly different spatial pattern,with ST memory values significantly higher in the western part of China than in the eastern part of China.The intermonthly variations of ST memory show strong spatial consistency across different months in all datasets.Additionally,ST anomalies tend to propagate into deeper soil layers over time.ERA-Interim and ERA5 indicate longer ST anomaly persistence in the first soil layer in Shanxi,Shaanxi,and Henan Provinces,while in GLDAS,ST anomalies persist longer in western China.Compared with observational data,GLDAS and ERA5 better represent the persistence characteristics of ST anomalies in the first soil layer but

关 键 词：土壤温度 记忆 再分析数据 持续性 陆面过程 

分 类 号：P423[天文地球—大气科学及气象学]