Elevation dependence of projected hydro-climatic change in eastern Siberia. (1st November 2022)

• Record Type: Journal Article
• Authors: Finnegan, Joseph M; Miller, James R
• Is Part Of: Environmental research letters. Volume 17:Number 11(2022)
• Publication Date: 2022-11-01
• Subjects: elevation-dependent climate change -- eastern Siberia -- Arctic amplification -- snow cover change; Environmental sciences -- Periodicals; Human ecology -- Research -- Periodicals; Environmental health -- Periodicals; 333.7
• DOI: 10.1088/1748-9326/ac97f8
• Languages: English
• ISSNs: 1748-9326

Abstract

Abstract: Over the last several decades, eastern Siberia has experienced some of the largest temperature increases worldwide. We use the RCP8.5 simulation of the Community Climate System Model version 4 to examine how projected monthly changes in temperature and hydro-climatic variables in eastern Siberia depend on latitude and elevation. Temperature increases are largest at the highest latitudes in winter and late fall and are smaller at higher elevations. For precipitation and snowfall, there is a latitudinal dependence in autumn and spring, with precipitation, snowfall, and snow depth mostly increasing between 60 and 70° N. Although snow cover extent (SCE) decreases almost everywhere, the largest changes occur during the transition seasons which we define as spring and autumn, and the timing of the changes depends on latitude, elevation, and the specific month within seasons. The decreases in SCE are larger at lower latitudes and lower elevations in April and November and larger at higher elevations and higher latitudes in June and September. For the highest latitudes, snow depth actually increases, and increases more at higher elevations. These projections are generally consistent with those of four other climate models. For precipitation, all models project increases in non-summer seasons, but they are not consistent with respect to the direction of the elevation dependence of precipitation. We discuss the complex interactions among the projected changes in all the variables.