Past and future trends of diurnal temperature range and their correlation with vegetation assessed by MODIS and CMIP6.

Temperature anomalies and changes in the diurnal temperature range (DTR) are expected to pose physiological challenges to biota; hence, both spatial and temporal variations in DTR provide important insights into temperature-induced stress in humans, animals, and vegetation. Furthermore, vegetation could dampen temperature variability. Here, we use the Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data of Land Surface Temperature (LST) to evaluate the global variation in DTR and its rate of change in spatial and temporal scales for the two decades spanning from 2001 to 2020.

Climate-ecosystem modelling made easy: The Land Sites Platform.

Dynamic Global Vegetation Models (DGVMs) provide a state-of-the-art process-based approach to study the complex interplay between vegetation and its physical environment. For example, they help to predict how terrestrial plants interact with climate, soils, disturbance and competition for resources. We argue that there is untapped potential for the use of DGVMs in ecological and ecophysiological research.

Identifying climate thresholds for dominant natural vegetation types at the global scale using machine learning: Average climate versus extremes.

The global distribution of vegetation is largely determined by climatic conditions and feeds back into the climate system. To predict future vegetation changes in response to climate change, it is crucial to identify and understand key patterns and processes that couple vegetation and climate. Dynamic global vegetation models (DGVMs) have been widely applied to describe the distribution of vegetation types and their future dynamics in response to climate change.

20 century Betula pubescens subsp. czerepanovii tree- and forest lines in Norway.

Background: Georeferenced tree- and forest line data has a wide range of applications and are increasingly used for e.g. monitoring of climate change impacts and range shift modelling.

Location of plant species in Norway gathered as a part of a survey vegetation mapping programme.

Georeferenced species data have a wide range of applications and are increasingly used for e.g. distribution modelling and climate change studies.

Climate warming feedback from mountain birch forest expansion: reduced albedo dominates carbon uptake.

Expanding high-elevation and high-latitude forest has contrasting climate feedbacks through carbon sequestration (cooling) and reduced surface reflectance (warming), which are yet poorly quantified. Here, we present an empirically based projection of mountain birch forest expansion in south-central Norway under climate change and absence of land use. Climate effects of carbon sequestration and albedo change are compared using four emission metrics.