Varying climate response across the tundra, forest-tundra and boreal forest biomes in northern West Siberia

• Published in: Environmental Research Letters
• Main Authors: Martin W Miles, Victoria V Miles, Igor Esau
• Format: Article in Journal/Newspaper
• Language: English
• Published: IOP Publishing 2019
• Subjects: Arctic tundra; boreal forest; normalized difference vegetation index; Environmental technology. Sanitary engineering; TD1-1066; Environmental sciences; GE1-350; Science; Q; Physics; QC1-999; Arctic; Browning; taiga; Tundra; Siberia
• Online Access: https://doi.org/10.1088/1748-9326/ab2364; https://doaj.org/article/66b4d1daff1445338fa00cde35e6ac4a

Satellite studies using the normalized difference vegetation index (NDVI) have revealed changes in northern Eurasian vegetation productivity in recent decades, including greening in tundra and browning in the boreal forests. However, apparent NDVI changes and relationships to climate depend on the temporal and spatial sampling and the biome and forest-land cover type studied. Here we perform a consistent analysis of NDVI and climate across four bioclimatic zones (tundra, forest-tundra, northern and middle taiga) in northern West Siberia (NWS), further stratified into eight forest-land cover types. We utilize NDVI data from the Moderate Resolution Imaging Spectroradiometer and climate reanalysis data from 2000 to 2016, a period including the record warm anomaly in 2016 (+2 °C–5 °C June–July surface air temperature (SAT) across NWS). Statistically significant ( α = 0.05) correlations were found for two bivariate relationships at the biome level: between NDVImax and June–July surface air temperature (SAT)( r ∼ +0.79), and between middle taiga NDVImax and July precipitation ( r ∼ +0.48). No significant statistical relationships were found for the northern taiga and forest-tundra biomes. However, within these biomes we found that deciduous needle-leaf (larch) NDVImax is significantly correlated with July temperature ( r ∼ +0.48). Qualitatively, spatial composites of NDVI and climate variables were effective for revealing insights and patterns of these relationships at the sub-regional scale. The spatial heterogeneity of NDVI patterns indicates divergent reactions of specific types of vegetation, as well as local effects that are clearly important on the background of a regional climate response.