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. We show that North America, Africa, and Antarctica, as well as the global mean, experienced statistically significant DTR rates of change over the last 20 years in either summer, winter, or the annual mean. The rates were all negative, indicating the day-night temperature differences are decreasing in those regions because night temperatures are increasing at a faster rate than day temperatures. MODIS data of the Normalized Difference Vegetation Index (NDVI) revealed a strongly negative correlation with DTR, with a spatial correlation coefficient of -0.61. This correlation demonstrates a prominent dampening effect of vegetation on diurnal temperature oscillations. For future DTR projections, we used 19 models in the Coupled Model Intercomparison Project 6 (CMIP6) to predict global DTR trends from 2021 to 2050 with low and high CO2 concentration scenarios. The high CO2 emission scenario projects significant decreases in DTR in circumpolar regions, central Africa, and India compared to the low CO2 scenario. This difference in the two scenarios underscores the substantial influence of increased global temperatures and elevated CO2 concentration on DTR and, consequently, on the ecosystems in certain regions.

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. One fundamental barrier to realize this potential is that many researchers with relevant expertize (ecology, plant physiology, soil science, etc.) lack access to the technical resources or awareness of the research potential of DGVMs. Here we present the Land Sites Platform (LSP): new software that facilitates single-site simulations with the Functionally Assembled Terrestrial Ecosystem Simulator, an advanced DGVM coupled with the Community Land Model. The LSP includes a Graphical User Interface and an Application Programming Interface, which improve the user experience and lower the technical thresholds for installing these model architectures and setting up model experiments. The software is distributed via version-controlled containers; researchers and students can run simulations directly on their personal computers or servers, with relatively low hardware requirements, and on different operating systems. Version 1.0 of the LSP supports site-level simulations. We provide input data for 20 established geo-ecological observation sites in Norway and workflows to add generic sites from public global datasets. The LSP makes standard model experiments with default data easily achievable (e.g., for educational or introductory purposes) while retaining flexibility for more advanced scientific uses. We further provide tools to visualize the model input and output, including simple examples to relate predictions to local observations. The LSP improves access to land surface and DGVM modelling as a building block of community cyberinfrastructure that may inspire new avenues for mechanistic ecosystem research across disciplines.

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. As a process-based approach, it partly relies on hard-coded climate thresholds to constrain the distribution of vegetation. What thresholds to implement in DGVMs and how to replace them with more process-based descriptions remain among the major challenges. In this study, we employ machine learning using decision trees to extract large-scale relationships between the global distribution of vegetation and climatic characteristics from remotely sensed vegetation and climate data. We analyse how the dominant vegetation types are linked to climate extremes as compared to seasonally or annually averaged climatic conditions. The results show that climate extremes allow us to describe the distribution and eco-climatological space of the vegetation types more accurately than the averaged climate variables, especially those types which occupy small territories in a relatively homogeneous ecological space. Future predicted vegetation changes using both climate extremes and averaged climate variables are less prominent than that predicted by averaged climate variables and are in better agreement with those of DGVMs, further indicating the importance of climate extremes in determining geographic distributions of different vegetation types. We found that the temperature thresholds for vegetation types (e.g. grass and open shrubland) in cold environments vary with moisture conditions. The coldest daily maximum temperature (extreme cold day) is particularly important for separating many different vegetation types. These findings highlight the need for a more explicit representation of the impacts of climate extremes on vegetation in DGVMs.

Fungal community dynamics across a forest-alpine ecotone.

Climate change is causing upward shift of forest lines worldwide, with consequences for soil biota and carbon (C) sequestration. We here analyse compositional changes in the soil biota across the forest line ecotone, an important transition zone between different ecosystems. We collected soil samples along transects stretching from subalpine mountain birch forests to alpine heath. Soil fungi and micro-eukaryotes were surveyed using DNA metabarcoding of the ITS2 and 18S markers, while ergosterol was used to quantify fungal biomass. We observed a strong shift in the soil biota across the forest line ecotone: Below the forest line, there were higher proportions of basidiomycetes and mucoromycetes, including ectomycorrhizal and saprotrophic fungi. Above it, we observed relatively more root-associated ascomycetes, including Archaeorhizomycetes, ericoid mycorrhizal fungi and dark septate endophytes. Ergosterol and percentage C content in soil correlated strongly and positively with the abundance of root-associated ascomycetes. The predominance of ectomycorrhizal and saprotrophic fungi below the forest line probably promote high C turnover, while root-associated ascomycetes above the forest line may enhance C sequestration. With further rise in forest lines, there will be a corresponding shift in the below-ground biota, probably leading to enhanced release of soil C.

20th 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. As part of a research project, registrations of previously re-mapped tree- and forest lines have been georeferenced. The data described in this paper contains 100 re-mapped registrations of Betula pubescens subsp. czerepanovii throughout Norway. All of the re-mapped tree- and forest line localities are georeferenced, elevation and aspect are given, elevational and spatial uncertainty are provided, and the re-mapping methods are explained. The published data weremapped for the first time between 1819 and 1963. The same sites were re-mapped between 1928 and 1996, but have until now been missing spatial coordinates. The entries contain 40 x 2 tree lines and 60 x 2 forest lines, most likely presenting the regionally highest registered tree- and forest lines at the given time. The entire material is stored and available for download through the GBIF server. NEW INFORMATION: Previously, the entries have been published in journals or reports, partly in Norwegian or German only. Without the provision of the spatial coordinates, the specific locations have been unknown. The material is now available for modelling and monitoring of tree- and forest line range shifts: The recordings are useful for interpretation of climate change impacts on tree- and forest lines, and the locations of re-mapped tree- and forest lines can be implemented in future monitoring projects. Since the recordings most likely provide the highest registered Betula pubescens subsp. czerepanovii locations within their specific regions, they are probably representing the contemporary physiognomic range limits.

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. As an integrated part of an on-going survey programme for vegetation mapping, plant species have been recorded. The data described in this paper contains 18.521 registrations of plants from 1190 different circular plots throughout Norway. All species localities are georeferenced, the spatial uncertainty is provided, and additional ecological information is reported. The published data has been gathered from 1991 until 2015. The entries contain all higher vascular plants and pteridophytes, and some cryptogams. Other ecological information is also provided for the species locations, such as the vegetation type, the cover of the species and slope. The entire material is stored and available for download through the GBIF server.

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. Forest expansion was modeled for a projected 2.6 °C increase in summer temperature in 2100, with associated reduced snow cover. We find that the current (year 2000) forest line of the region is circa 100 m lower than its climatic potential due to land-use history. In the future scenarios, forest cover increased from 12% to 27% between 2000 and 2100, resulting in a 59% increase in biomass carbon storage and an albedo change from 0.46 to 0.30. Forest expansion in 2100 was behind its climatic potential, forest migration rates being the primary limiting factor. In 2100, the warming caused by lower albedo from expanding forest was 10 to 17 times stronger than the cooling effect from carbon sequestration for all emission metrics considered. Reduced snow cover further exacerbated the net warming feedback. The warming effect is considerably stronger than previously reported for boreal forest cover, because of the typically low biomass density in mountain forests and the large changes in albedo of snow-covered tundra areas. The positive climate feedback of high-latitude and high-elevation expanding forests with seasonal snow cover exceeds those of afforestation at lower elevation, and calls for further attention of both modelers and empiricists. The inclusion and upscaling of these climate feedbacks from mountain forests into global models is warranted to assess the potential global impacts.