Open AccessTrends of Sampling Error in Surface Air Temperature on the Tibetan Plateau during Recent Decades
Author(s) -
Wei Hua,
Tianci Huang,
Zouxing Lin,
Lihua Zhu,
Xin Wang,
Guangzhou Fan
Publication year - 2019
Publication title -
advances in meteorology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.482
H-Index - 32
eISSN - 1687-9317
pISSN - 1687-9309
DOI - 10.1155/2019/3160327
Subject(s) - sampling (signal processing) , plateau (mathematics) , sampling error , climatology , environmental science , climate change , geography , physical geography , statistics , observational error , mathematics , geology , oceanography , computer science , mathematical analysis , filter (signal processing) , computer vision
Understanding errors in surface air temperature (SAT) data and related uncertainties is crucial for climate studies because of their impact on the accuracy of statistical inferences in scientific conclusions. In recent decades, considerable research has focused on the trends and evolution of SAT on the Tibetan Plateau (TP). However, assessment of the uncertainties in SAT change on the TP has not been done adequately, which is of considerable importance for climate research. Using station-observed SAT data from the TP, this study estimates long-term variations and trends of sampling error variances in gridded monthly SAT data over recent decades. Results revealed large sampling error variances in northern and western parts of the TP but small variances in eastern, southern, and central areas. The sampling error variances also exhibited strong monthly variations with maximum errors in winter and minimum values in summer. Furthermore, spatial distributions of the trends of seasonal and annual mean sampling error variances were found distributed unevenly with decreasing trends found mainly in central and southern parts of the TP and increasing trends in northeastern, southeastern, and northwestern areas. Additionally, differences were also found in the trends of seasonal and annual mean sampling error variances on various timescales.