Estimation of Surface Air Temperature Trends within Different regions of Russia Using the Quantile Regression Method
Abstract
The results of the assessment of surface air temperature variations in different climatically homogeneous regions of Russia are presented. Quantile Regression method was applied to daily observation records for surface air temperature. Over 500 geographically distributed weather stations were included in source dataset. Quantile trends and other resulting parameters were generalized within nine pre-defined regions of Russia. Seasonal and regional features of daily minimum, mean, and maximum air temperature trends are considered in a wide range of quantile values. The results reveal generally uneven distribution of quantile trends and several particularly interesting features of changes in temperature variability, e.g. certain parts of distribution responsible for long-term positive trends. Two measures of uncertainty were introduced to characterize the quality generalization of quantile trends. Significant selection of the regions demonstrate high quality of generalization of the results. Although, some regions exhibit high variance within their borders, which prevents from considering the resulting trend as a valid generalization. Changing number and shape of the regions should be considered in order to address this issue. Cluster analysis is considered as a viable approach to this problem.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2016
- Bibcode:
- 2016AGUFM.A21B0022T
- Keywords:
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- 3305 Climate change and variability;
- ATMOSPHERIC PROCESSESDE: 3339 Ocean/atmosphere interactions;
- ATMOSPHERIC PROCESSESDE: 1616 Climate variability;
- GLOBAL CHANGEDE: 1620 Climate dynamics;
- GLOBAL CHANGE