EMERALD – Terrestrial ecosystem–climate interactions of our EMERALD planet

• Erlandsson, Rasmus; Arneberg, Marit Klemetsen; Tømmervik, Hans; Finne, Eirik Aasmo; Nilsen, Lennart & Bjerke, Jarle W. (2023). Feasibility of active handheld NDVI sensors for monitoring of lichen ground cover. Fungal ecology. ISSN 1754-5048. 63. doi: 10.1016/j.funeco.2023.101233. Fulltekst i vitenarkiv Vis sammendrag

  Vegetation indices are corner stones in vegetation monitoring. However, previous field studies on lichens and NDVI have been based on passive sensors. Active handheld sensors, with their own light sources, enables high-precision monitoring under variable ambient conditions. We investigated the use of handheld sensor NDVI for monitoring pale lichen cover across three study sites from boreal heathlands to High Arctic tundra (62–79 °N), and compared it with Sentinel-2 satellite NDVI. NDVI decreased with increasing cover of pale lichens but the correlation between active and satellite NDVI varied between areas. NDVI values declined with lichen cover and ranged from 0.4–0.18 when lichen cover was above 40%. Active ground measurements of NDVI explained 81% of the variation in the satellite NDVI values in Svalbard (High Arctic), while the relationships were lower (∼30% explained variation) in boreal regions (Troms-Finnmark and Røros). We show that active sensors are feasible for extracting information from lichen-dominated vegetation. Lichen, Pale lichens, Cladonia, NDVI, Active sensor, Remote sensing, Monitoring

• Jiao, Yi; Davie-Martin, Cleo L.; Kramshøj, Magnus; Christiansen, Casper Tai; Lee, Hanna & Althuizen, Inge [Vis alle 7 forfattere av denne artikkelen] (2023). Volatile organic compound release across a permafrost-affected peatland. Geoderma. ISSN 0016-7061. 430. doi: 10.1016/j.geoderma.2023.116355. Fulltekst i vitenarkiv
• Naas, Adam Eindride; Halvorsen, Rune; Horvath, Peter; Wollan, Anders Kvalvåg; Bratli, Harald & Brynildsrud, Katrine Marie [Vis alle 16 forfattere av denne artikkelen] (2023). What explains inconsistencies in field-based ecosystem mapping? Applied Vegetation Science. ISSN 1402-2001. 26(1). doi: 10.1111/avsc.12715. Fulltekst i vitenarkiv Vis sammendrag

  Questions Field-based ecosystem mapping is prone to observer bias, typically resulting in a mismatch between maps made by different mappers, that is, inconsistency. Experimental studies testing the influence of site, mapping scale, and differences in experience level on inconsistency in field-based ecosystem mapping are lacking. Here, we study how inconsistencies in field-based ecosystem maps depend on these factors. Location Iškoras and Guollemuorsuolu, northeastern Norway, and Landsvik and Lygra, western Norway. Methods In a balanced experiment, four sites were field-mapped wall-to-wall to scales 1:5000 and 1:20,000 by 12 mappers, representing three experience levels. Thematic inconsistency was calculated by overlay analysis of map pairs from the same site, mapped to the same scale. We tested for significant differences between sites, scales, and experience-level groups. Principal components analysis was used in an analysis of additional map inconsistencies and their relationships with site, scale and differences in experience level and time consumption were analysed with redundancy analysis. Results On average, thematic inconsistency was 51%. The most important predictor for thematic inconsistency, and for all map inconsistencies, was site. Scale and its interaction with site predicted map inconsistencies, but only the latter were important for thematic inconsistency. The only experience-level group that differed significantly from the mean thematic inconsistency was that of the most experienced mappers, with nine percentage points. Experience had no significant effect on map inconsistency as a whole. Conclusion Thematic inconsistency was high for all but the dominant thematic units, with potentially adverse consequences for mapping ecosystems that are fragmented or have low coverage. Interactions between site and mapping system properties are considered the main reasons why no relationships between scale and thematic inconsistency were observed. More controlled experiments are needed to quantify the effect of other factors on inconsistency in field-based mapping.

• Keetz, Lasse Torben; Lieungh, Eva; Karimi-Asli, Kaveh; Geange, Sonya Rita; Gelati, Emiliano & Tang, Hui [Vis alle 24 forfattere av denne artikkelen] (2023). Climate–ecosystem modelling made easy: The Land Sites Platform. Global Change Biology. ISSN 1354-1013. 29(15), s. 4440–4452. doi: 10.1111/gcb.16808. Fulltekst i vitenarkiv Vis sammendrag

  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.

• Wang, You-Ren; Samset, Bjørn Hallvard; Stordal, Frode; Bryn, Anders & Hessen, Dag Olav (2023). Past and future trends of diurnal temperature range and their correlation with vegetation assessed by MODIS and CMIP6. Science of the Total Environment. ISSN 0048-9697. 904. doi: 10.1016/j.scitotenv.2023.166727.
• Pirk, Norbert; Aalstad, Kristoffer; Yilmaz, Yeliz A.; Vatne, Astrid; Popp, Andrea & Horvath, Peter [Vis alle 12 forfattere av denne artikkelen] (2023). Snow-vegetation-atmosphere interactions in alpine tundra. Biogeosciences. ISSN 1726-4170. 20(11), s. 2031–2047. doi: 10.5194/bg-20-2031-2023. Fulltekst i vitenarkiv Vis sammendrag

  The interannual variability of snow cover in alpine areas is increasing, which may affect the tightly coupled cycles of carbon and water through snow–vegetation–atmosphere interactions across a range of spatio-temporal scales. To explore the role of snow cover for the land–atmosphere exchange of CO2 and water vapor in alpine tundra ecosystems, we combined 3 years (2019–2021) of continuous eddy covariance flux measurements of the net ecosystem exchange of CO2 (NEE) and evapotranspiration (ET) from the Finse site in alpine Norway (1210 m a.s.l.) with a ground-based ecosystem-type classification and satellite imagery from Sentinel-2, Landsat 8, and MODIS. While the snow conditions in 2019 and 2021 can be described as site typical, 2020 features an extreme snow accumulation associated with a strong negative phase of the Scandinavian pattern of the synoptic atmospheric circulation during spring. This extreme snow accumulation caused a 1-month delay in melt-out date, which falls in the 92nd percentile in the distribution of yearly melt-out dates in the period 2001–2021. The melt-out dates follow a consistent fine-scale spatial relationship with ecosystem types across years. Mountain and lichen heathlands melt out more heterogeneously than fens and flood plains, while late snowbeds melt out up to 1 month later than the other ecosystem types. While the summertime average normalized difference vegetation index (NDVI) was reduced considerably during the extreme-snow year 2020, it reached the same maximum as in the other years for all but one of the ecosystem types (late snowbeds), indicating that the delayed onset of vegetation growth is compensated to the same maximum productivity. Eddy covariance estimates of NEE and ET are gap-filled separately for two wind sectors using a random forest regression model to account for complex and nonlinear ecohydrological interactions. While the two wind sectors differ markedly in vegetation composition and flux magnitudes, their flux response is controlled by the same drivers as estimated by the predictor importance of the random forest model, as well as by the high correlation of flux magnitudes (correlation coefficient r=0.92 for NEE and r=0.89 for ET) between both areas. The 1-month delay of the start of the snow-free season in 2020 reduced the total annual ET by 50 % compared to 2019 and 2021 and reduced the growing-season carbon assimilation to turn the ecosystem from a moderate annual carbon sink (−31 to −6 gC m−2 yr−1) to a source (34 to 20 gC m−2 yr−1). These results underpin the strong dependence of ecosystem structure and functioning on snow dynamics, whose anomalies can result in important ecological extreme events for alpine ecosystems.

• Jaroszynska, Francesca Orinda Holl; Althuizen, Inge; Halbritter Rechsteiner, Aud Helen; Klanderud, Kari; Lee, Hanna & Telford, Richard James [Vis alle 7 forfattere av denne artikkelen] (2023). Bryophytes dominate plant regulation of soil microclimate in alpine grasslands. Oikos. ISSN 0030-1299. 2023(12). doi: 10.1111/oik.10091. Fulltekst i vitenarkiv Vis sammendrag

  Soil temperature and moisture are important regulators of a broad range of biotic and abiotic processes in terrestrial ecosystems. Vegetation can, in turn, play a role in regulating soil microclimate, which creates potential for powerful and interactive feedbacks from soil and vegetation on the atmosphere. Although the regulatory effect of vegetation on soil microclimatic conditions has been quite extensively and empirically assessed, most studies have determined the net effect of intact woody vegetation versus bare ground. However, for other plant functional groups we lack a clear understanding of their role and any climate-context dependency in controlling microclimatic conditions. We investigated the role of three major plant functional groups – graminoids, forbs and bryophytes – in regulating soil microclimate in semi-natural alpine grasslands. Using a fully factorial above-ground biomass removal experiment, we assessed the role of these plant functional groups in regulating soil temperature amplitude, soil moisture, and number of freezing days. The experiment was replicated across orthogonal temperature and precipitation gradients in Norway to assess whether the effects of functional group abundance varied with climate. The effect of plant biomass on soil microclimate varied among functional groups across the climatic gradients. Bryophytes reduced growing season soil temperature, whereas graminoids and forbs did not (0.5ºC compared to 0ºC), and with a stronger effect in colder climates at higher elevations and on days with high solar radiation. Bryophyte biomass further reduced the number of soil freezing days at boreal and sub-alpine sites. Finally, graminoid biomass partly explained variation in soil moisture: soils dried more under graminoids at drier sites. Our findings highlight that functional group identity plays a key role in regulating soil microclimate in alpine grasslands across seasons. The strong effect of bryophytes on soil temperature points to their importance in the plant community for a variety of ecosystem functions, some of which may be indirectly vulnerable to future warming via biomass reductions of bryophytes.

• Lambert, Marius; Tang, Hui; Aas, Kjetil Schanke; Stordal, Frode; Fisher, Rosie & Bjerke, Jarle Werner [Vis alle 8 forfattere av denne artikkelen] (2023). Integration of a Frost Mortality Scheme Into the Demographic Vegetation Model FATES. Journal of Advances in Modeling Earth Systems. ISSN 1942-2466. 15(7). doi: 10.1029/2022MS003333. Fulltekst i vitenarkiv
• Finne, Eirik Aasmo; Bjerke, Jarle W.; Erlandsson, Rasmus; Tømmervik, Hans; Stordal, Frode & Tallaksen, Lena M. (2023). Variation in albedo and other vegetation characteristics in non-forested northern ecosystems: the role of lichens and mosses. Environmental Research Letters. ISSN 1748-9326. 18(7). doi: 10.1088/1748-9326/ace06d. Fulltekst i vitenarkiv
• Wang, You-Ren; Buchmann, Nina; Hessen, Dag Olav; Stordal, Frode; Erisman, Jan Willem & Vollsnes, Ane Victoria [Vis alle 8 forfattere av denne artikkelen] (2022). Disentangling effects of natural and anthropogenic drivers on forest net ecosystem production. Science of the Total Environment. ISSN 0048-9697. 839. doi: 10.1016/j.scitotenv.2022.156326. Fulltekst i vitenarkiv
• Pirk, Norbert; Aalstad, Kristoffer; Westermann, Sebastian; Vatne, Astrid; van Hove, Alouette & Tallaksen, Lena Merete [Vis alle 8 forfattere av denne artikkelen] (2022). Inferring surface energy fluxes using drone data assimilation in large eddy simulations. Atmospheric Measurement Techniques. ISSN 1867-1381. 15(24), s. 7293–7314. doi: 10.5194/amt-15-7293-2022. Fulltekst i vitenarkiv Vis sammendrag

  Spatially representative estimates of surface energy exchange from field measurements are required for improving and validating Earth system models and satellite remote sensing algorithms. The scarcity of flux measurements can limit understanding of ecohydrological responses to climate warming, especially in remote regions with limited infrastructure. Direct field measurements often apply the eddy covariance method on stationary towers, but recently, drone-based measurements of temperature, humidity, and wind speed have been suggested as a viable alternative to quantify the turbulent fluxes of sensible (H) and latent heat (LE). A data assimilation framework to infer uncertainty-aware surface flux estimates from sparse and noisy drone-based observations is developed and tested using a turbulence-resolving large eddy simulation (LES) as a forward model to connect surface fluxes to drone observations. The proposed framework explicitly represents the sequential collection of drone data, accounts for sensor noise, includes uncertainty in boundary and initial conditions, and jointly estimates the posterior distribution of a multivariate parameter space. Assuming typical flight times and observational errors of light-weight, multi-rotor drone systems, we first evaluate the information gain and performance of different ensemble-based data assimilation schemes in experiments with synthetically generated observations. It is shown that an iterative ensemble smoother outperforms both the non-iterative ensemble smoother and the particle batch smoother in the given problem, yielding well-calibrated posterior uncertainty with continuous ranked probability scores of 12 W m−2 for both H and LE, with standard deviations of 37 W m−2 (H) and 46 W m−2 (LE) for a 12 min vertical step profile by a single drone. Increasing flight times, using observations from multiple drones, and further narrowing the prior distributions of the initial conditions are viable for reducing the posterior spread. Sampling strategies prioritizing space–time exploration without temporal averaging, instead of hovering at fixed locations while averaging, enhance the non-linearities in the forward model and can lead to biased flux results with ensemble-based assimilation schemes. In a set of 18 real-world field experiments at two wetland sites in Norway, drone data assimilation estimates agree with independent eddy covariance estimates, with root mean square error values of 37 W m−2 (H), 52 W m−2 (LE), and 58 W m−2 (H+LE) and correlation coefficients of 0.90 (H), 0.40 (LE), and 0.83 (H+LE). While this comparison uses the simplifying assumptions of flux homogeneity, stationarity, and flat terrain, it is emphasized that the drone data assimilation framework is not confined to these assumptions and can thus readily be extended to more complex cases and other scalar fluxes, such as for trace gases in future studies.

• Jonsdottir, Ingibjørg; Halbritter Rechsteiner, Aud Helen; Christiansen, Casper Tai; Althuizen, Inge; Haugum, Siri Vatsø & Henn, Jonathan J. [Vis alle 15 forfattere av denne artikkelen] (2022). Intraspecific trait variability is a key feature underlying high Arctic plant community resistance to climate warming. Ecological Monographs. ISSN 0012-9615. 93(1). doi: 10.1002/ecm.1555. Fulltekst i vitenarkiv Vis sammendrag

  In the high Arctic, plant community species composition generally responds slowly to climate warming, whereas less is known about the community functional trait responses and consequences for ecosystem functioning. The slow species turnover and large distribution ranges of many Arctic plant species suggest a significant role of intraspecific trait variability in functional responses to climate change. Here we compare taxonomic and functional community compositional responses to a long-term (17-year) warming experiment in Svalbard, Norway, replicated across three major high Arctic habitats shaped by topography and contrasting snow regimes. We observed taxonomic compositional changes in all plant communities over time. Still, responses to experimental warming were minor and most pronounced in the drier habitats with relatively early snowmelt timing and long growing seasons (Cassiope and Dryas heaths). The habitats were clearly separated in functional trait space, defined by 12 size- and leaf economics-related traits, primarily due to interspecific trait variation. Functional traits also responded to experimental warming, most prominently in the Dryas heath and mostly due to intraspecific trait variation. Leaf area and mass increased and leaf δ15N decreased in response to the warming treatment. Intraspecific trait variability ranged between 30% and 71% of the total trait variation, reflecting the functional resilience of those communities, dominated by long-lived plants, due to either phenotypic plasticity or genotypic variation, which most likely underlies the observed resistance of high Arctic vegetation to climate warming. We further explored the consequences of trait variability for ecosystem functioning by measuring peak season CO2 fluxes. Together, environmental, taxonomic, and functional trait variables explained a large proportion of the variation in net ecosystem exchange (NEE), which increased when intraspecific trait variation was accounted for. In contrast, even though ecosystem respiration and gross ecosystem production both increased in response to warming across habitats, they were mainly driven by the direct kinetic impacts of temperature on plant physiology and biochemical processes. Our study shows that long-term experimental warming has a modest but significant effect on plant community functional trait composition and suggests that intraspecific trait variability is a key feature underlying high Arctic ecosystem resistance to climate warming.

• Lambert, Marius; Tang, Hui; Aas, Kjetil Schanke; Stordal, Frode; Fisher, Rosie & Fang, Yilin [Vis alle 8 forfattere av denne artikkelen] (2022). Inclusion of a cold hardening scheme to represent frost tolerance is essential to model realistic plant hydraulics in the Arctic-boreal zone in CLM5.0-FATES-Hydro. Geoscientific Model Development. ISSN 1991-959X. 15(23), s. 8809–8829. doi: 10.5194/gmd-15-8809-2022. Fulltekst i vitenarkiv Vis sammendrag

  As temperatures decrease in autumn, vegetation of temperate and boreal ecosystems increases its tolerance to freezing. This process, known as hardening, results in a set of physiological changes at the molecular level that initiate modifications of cell membrane composition and the synthesis of anti-freeze proteins. Together with the freezing of extracellular water, anti-freeze proteins reduce plant water potentials and xylem conductivity. To represent the responses of vegetation to climate change, land surface schemes increasingly employ “hydrodynamic” models that represent the explicit fluxes of water from soil and through plants. The functioning of such schemes under frozen soil conditions, however, is poorly understood. Nonetheless, hydraulic processes are of major importance in the dynamics of these systems, which can suffer from, e.g., winter “frost drought” events. In this study, we implement a scheme that represents hardening into CLM5.0-FATES-Hydro. FATES-Hydro is a plant hydrodynamics module in FATES, a cohort model of vegetation physiology, growth, and dynamics hosted in CLM5.0. We find that, in frozen systems, it is necessary to introduce reductions in plant water loss associated with hardening to prevent winter desiccation. This work makes it possible to use CLM5.0-FATES-Hydro to model realistic impacts from frost droughts on vegetation growth and photosynthesis, leading to more reliable projections of how northern ecosystems respond to climate change.

• Rixen, Christian; Høye, Toke Thomas; Macek, Petr; Aerts, Rien; Alatalo, Juha M. & Andeson, Jill T. [Vis alle 68 forfattere av denne artikkelen] (2022). Winters are changing: snow effects on Arctic and alpine tundra ecosystems. Arctic Science. ISSN 2368-7460. 8(3), s. 572–608. doi: 10.1139/as-2020-0058. Fulltekst i vitenarkiv
• Beigaitė, Rita; Tang, Hui; Bryn, Anders; Skarpaas, Olav; Stordal, Frode & Bjerke, Jarle W. [Vis alle 7 forfattere av denne artikkelen] (2022). Identifying climate thresholds for dominant natural vegetation types at the global scale using machine learning: Average climate versus extremes. Global Change Biology. ISSN 1354-1013. 28(11), s. 3557–3579. doi: 10.1111/gcb.16110. Fulltekst i vitenarkiv Vis sammendrag

  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 processbased 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. climate extremes , climate thresholds , decision trees , DGVMs , machine learning , vegetation distribution

• Haider, Sylvia; Lembrechts, Jonas J.; McDougall, Keith; Pauchard, Aníbal; Alexander, Jake M. & Barros, Agustina [Vis alle 58 forfattere av denne artikkelen] (2022). Think globally, measure locally: The MIREN standardized protocol for monitoring plant species distributions along elevation gradients. Ecology and Evolution. ISSN 2045-7758. 12(2). doi: 10.1002/ece3.8590.
• Zhao, Junbin; Lange, Holger; Meissner, Helge Rainer & Bright, Ryan M. (2022). Comparing sap flow calculations from Heat Field Deformation (HFD) and Linear Heat Balance (LHB) methods. Agricultural and Forest Meteorology. ISSN 0168-1923. 321, s. 1–10. doi: 10.1016/j.agrformet.2022.108974. Fulltekst i vitenarkiv Vis sammendrag

  Heat Field Deformation (HFD) is a widely used method to measure sap flow of trees based on empirical relationships between heat transfer within tree stems and the sap flow rates. As an alternative, the Linear Heat Balance (LHB) method implements the same instrumental configuration as HFD but calculates the sap flow rates using analytical equations that are derived from fundamental conduction-convection heat transfer theories. In this study, we systematically compared the sap flow calculated using the two methods based on data that were recorded using the same instrument. The measurements were conducted on four Norway spruce trees. We aimed to evaluate the discrepancies between the sap flow estimates from the two methods and determine the underlying causes. Diurnal and day-to-day patterns were consistent between the sap flow estimates from the two methods. However, the magnitudes of the estimated sap flow were different between them, where LHB resulted in much lower estimates in three trees and slightly higher estimates in one compared to HFD. We also observed larger discrepancies in negative (reversed flow) than in positive sap flow, where the LHB resulted in lower reversed flow than HFD. Consequently, the seasonal budget estimated by LHB can be as low as ∼20% of that estimated by HFD. The discrepancies can be mainly attributed to the low wood thermal conductivities for the studied trees that lead to substantial underestimations using the LHB method. In addition, the sap flow estimates were very sensitive to the value changes of the empirical parameters in the calculations and, thus, using a proper case-specific value is recommended, especially for the LHB method. Overall, we suggest that, despite the strong theoretical support, the correctness of LHB outputs depends largely on the tree individuals and should be carefully evaluated.

• Bryn, Anders & Potthoff, Kerstin (2022). Assessing the impact of climate change versus land use on tree- and forest line dynamics in Norway. I Schickhoff, Udo; Singh, R.B. & Mal, Suraj (Red.), Mountain Landscapes in Transition. Effects of Land Use and Climate Change. Springer Nature. ISSN 978-3-030-70237-3. s. 613–626. doi: 10.1007/978-3-030-70238-0_29.
• Wang, You-Ren; Hessen, Dag Olav; Samset, Bjørn Hallvard & Stordal, Frode (2022). Evaluating global and regional land warming trends in the past decades with both MODIS and ERA5-Land land surface temperature data. Remote Sensing of Environment. ISSN 0034-4257. 280. doi: 10.1016/j.rse.2022.113181. Fulltekst i vitenarkiv
• Hessen, Dag Olav & Vandvik, Vigdis (2022). Buffering climate change with nature. Weather, Climate, and Society. ISSN 1948-8327. 14(2), s. 439–450. doi: 10.1175/WCAS-D-21-0059.1. Fulltekst i vitenarkiv
• Erlandsson, Rasmus Ingel; Bjerke, Jarle W.; Finne, Eirik Aasmo; Myneni, Ranga B.; Piao, Shilong & Wang, Xuhui [Vis alle 12 forfattere av denne artikkelen] (2022). An artificial intelligence approach to remotely assess pale lichen biomass. Remote Sensing of Environment. ISSN 0034-4257. 280. doi: 10.1016/j.rse.2022.113201. Fulltekst i vitenarkiv Vis sammendrag

  Although generally given little attention in vegetation studies, ground-dwelling (terricolous) lichens are major contributors to overall carbon and nitrogen cycling, albedo, biodiversity and biomass in many high-latitude ecosystems. Changes in biomass of mat-forming pale lichens have the potential to affect vegetation, fauna, climate and human activities including reindeer husbandry. Lichens have a complex spectral signature and terricolous lichens have limited growth height, often growing in mixtures with taller vegetation. This has, so far, prevented the development of remote sensing techniques to accurately assess lichen biomass, which would be a powerful tool in ecosystem and ecological research and rangeland management. We present a Landsat based remote sensing model developed using deep neural networks, trained with 8914 field records of lichen volume collected for >20 years. In contrast to earlier proposed machine learning and regression methods for lichens, our model exploited the ability of neural networks to handle mixed spatial resolution input. We trained candidate models using input of 1 × 1 (30 × 30 m) and 3 × 3 Landsat pixels based on 7 reflective bands and 3 indices, combined with a 10 m spatial resolution digital elevation model. We normalised elevation data locally for each plot to remove the region-specific variation, while maintaining informative local variation in topography. The final model predicted lichen volume in an evaluation set (n = 159) reaching an R2 of 0.57. NDVI and elevation were the most important predictors, followed by the green band. Even with moderate tree cover density, the model was efficient, offering a considerable improvement compared to earlier methods based on specific reflectance. The model was in principle trained on data from Scandinavia, but when applied to sites in North America and Russia, the predictions of the model corresponded well with our visual interpretations of lichen abundance. We also accurately quantified a recent historic (35 years) change in lichen abundance in northern Norway. This new method enables further spatial and temporal studies of variation and changes in lichen biomass related to multiple research questions as well as rangeland management and economic and cultural ecosystem services. Combined with information on changes in drivers such as climate, land use and management, and air pollution, our model can be used to provide accurate estimates of ecosystem changes and to improve vegetation-climate models by including pale lichens. Remote sensing Lichens Terricolous lichens Deep neural networks Artificial intelligence cladonia Reindeer lichen Light lichens Light coloured lichens Pale lichens Landsat

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• Bryn, Anders; Van der Wal, René; Norton, Lisa & Hofmeester, Tim R. (2023). Citizen science: data collection by volunteers. Routledge. ISBN 978-1-003-17924-5. 14 s.

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• Falk, Stefanie; Vollsnes, Ane Victoria; Berglen Eriksen, Aud; Emberson, Lisa D.; O’Neill, Connie & Stordal, Frode [Vis alle 7 forfattere av denne artikkelen] (2023). Modelling the effects of ozone pollution on coniferous and deciduous forests.
• Vandvik, Vigdis (2023). Biologens blikk på bærekraft i kommunen.
• Vandvik, Vigdis (2023). The nature crisis – a crash course.
• Rosnes, Oddbjørn Nils & Vandvik, Vigdis (2023). Forskar på den naturen ingen av oss ser - men som alt liv er er avhengig av. [Avis]. Avisa Nordhordland.
• Bjerke, Jarle Werner & Finne, Eirik Aasmo (2023). Arctic Climate Change and its impacts.
• Bryn, Anders (2023). Ståan til Norsk skog. [Radio]. NRK Sommeråpent. Vis sammendrag

  15 minutters radiointervju med Tande-P på NRK P1, Sommeråpent, om tilstanden i Norsk skog.

• Bryn, Anders (2023). Natur i endring - hva skjer med den norske naturen?
• Bjerke, Jarle Werner & Finne, Eirik Aasmo (2023). Variasjon i albedo og andre karakteristikker av vegetasjon i åpne og nordlige økosystemer: rollen til lav og moser.
• Bryn, Anders (2023). Slik endres Norge. [Avis]. VG.
• Bryn, Anders (2023). The history of east-central Oslo.
• Bjerke, Jarle W. & Tømmervik, Hans (2023). Synergies and trade-offs between provisioning and climate-regulating ecosystem services from reindeer herding areas .
• Bjerke, Jarle W.; Tallaksen, Lena M.; Stordal, Frode & Yilmaz, Yeliz A. (2023). EMERALD Open Science Day - Summary of Group Discussions. Vis sammendrag

  Group discussions, key questions: How to move from single discipline to multidisciplinary studies? Which processes are missing in terms of plant-climate interactions for improving models?

• Engeland, Kolbjørn; Erlandsen, Helene B.; Gelati, Emiliano; Huang, Shaochun; Narayanappa, Devaraju & Pirk, Norbert [Vis alle 10 forfattere av denne artikkelen] (2023). Intercomparison of local evapotranspiration estimates at high latitudes.
• Althuizen, Inge; Jaroszynska, Francesca Orinda Holl; Halbritter, Aud Helen; Klanderud, Kari; Lee, Hanna & Telford, Richard James [Vis alle 7 forfattere av denne artikkelen] (2023). Keeping it cool - How mosses regulate soil microclimate.
• Vandvik, Vigdis; Sørheim, Maja Dineh; Skrindo, Astrid Brekke; Løken, Anders Magnus & Juel, Tone Margrethe Karlander (2023). Naturavtalen - hvordan ta vare på "hverdagsnaturen"? [Internett]. Tekna.
• Vandvik, Vigdis (2023). Naturmangfold og økosystemer – det må vi vel lenger ut på landet med?
• Vandvik, Vigdis (2023). Forvaltning av natur for fremtiden.
• Vandvik, Vigdis (2023). Hvorfor fokusere mer på hverdagsnaturen?
• Vandvik, Vigdis (2023). Cheat sheet to the Kunming-Montréal Global Biodiversity Framework/Jukselapp til den globale Kunming-Montreal Naturavtalen.
• Bjerke, Jarle W. (2023). Klimaet i Norge nå og i framtiden - effekter på vegetasjon og reinbeiter.
• Vandvik, Vigdis (2023). Målkonflikter – å sette pris på naturen.
• Vatne, Astrid; Vollsnes, Ane Victoria; Pirk, Norbert & Tallaksen, Lena M. (2023). Drought effects on dwarf birch leaves.
• Finne, Eirik Aasmo (2023). On the climatic effects of lichens.
• Bright, Ryan M. (2023). Carbon in Norwegian Forests: Historical developments, recent trends, and future outlooks.
• Erlandsson, Rasmus Ingel (2022). Some aspects of the use of artifical intelligence for remote sensing of lichens.
• Bryn, Anders (2022). Willy (75) svinger saga for å rydde fram turistperle. [Internett]. www.nrk.no.
• Bryn, Anders (2022). Flere trær i fjellet gjør det varmere: – Ikke utelukkende positivt med trær. [Internett]. www.nrk.no.
• Torgersen, Eivind; Vatne, Astrid & Vollsnes, Ane Victoria (2022). Planter er ikke bare passive mottakere av klimaendringer. Titan.uio.no.
• Vandvik, Vigdis (2022). The role of vegetation in regulating the microclimate of northern grasslands.
• Vandvik, Vigdis (2022). NRK nyhetene: Ekspertkommentar om tørke og effekter på matproduksjon i Europa. [TV]. NRK.
• Tang, Hui; Aas, Kjetil Schanke; Althuizen, Inge; Geange, Sonya Rita; Berntsen, Terje Koren & Vandvik, Vigdis (2022). Update on the implementation of moss in CTSM-FATES.
• Parmentier, Frans-Jan W. (2022). Vinterskader: Hvordan påvirkes naturen av ekstremt vintervær og vintertørke?
• Bryn, Anders (2022). Utfordrer tregrensa: Se Oppdals kanskje høyestvoksende gran. [Avis]. https://www.opp.no/.
• Bryn, Anders (2022). Historical lines and recent development at the Natural History Museum in Oslo.
• Lambert, Marius S. A.; Tang, Hui; Aas, Kjetil Schanke; Stordal, Frode; Fisher, Rosie & Bjerke, Jarle W. [Vis alle 7 forfattere av denne artikkelen] (2022). Towards realistic plant hydraulics and frost damage in the Arctic-Boreal Zone by modelling cold acclimation in CTSM5-Fates (hydro).
• Engeland, Kolbjørn; Aas, Kjetil Schanke; Erlandsen, Helene Birkelund; Gelati, Emiliano; Huang, Shaochun & Narayanappa, Devaraju [Vis alle 11 forfattere av denne artikkelen] (2022). LATICE MIP evapotranspiration – A model intercomparison project for evapotranspiration estimates at high latitudes.
• Bjerke, Jarle W. & Steen, Kristian Wollen (2022). Store matter med reinlav kjøler ned klimaet. [Avis]. Nationen.
• Bryn, Anders (2022). Tree- and forest line dynamics in Norway: causes and consequences.
• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon; Gascoin, Simon; Pirk, Norbert & Remmers, Janneke [Vis alle 8 forfattere av denne artikkelen] (2022). Evaluating modeled snow cover dynamics over Fennoscandia using Earth observations. Vis sammendrag

  The snow cover is an essential part of the climate system in cold regions through its effects on the terrestrial water, energy, and carbon balance. Due to the high spatiotemporal variability of snow, it is challenging to resolve snow cover dynamics in models. Thus, our ability to improve the representation of these dynamics in Earth System Models (ESMs) leans heavily on the accuracy and representativeness of the observational data sets used for model evaluation. The big picture provided by the long-term climate data record from satellites helps us to monitor changes in land cover over large areas. At the same time, rapidly developing drone and terrestrial imaging technology provides higher resolution information over specific areas. This complimentary information from spaceborne, airborne, and terrestrial Earth observations is invaluable for better understanding the complex processes in the climate system. In our work, we are currently exploiting estimates of snow-covered area from different optical sensors onboard polar orbiting satellites that are imaging the Nordic region. Drone and terrestrial images are being explored as a source of validation and calibration data for the satellite products.  Having representative snow cover maps enables us to better evaluate the terrestrial component of the Norwegian Earth System Model (NorESM), namely the Community Land Model (CLM5). With a focus on snow processes, we are conducting an analysis using satellite-based estimates of snow-covered area (MODIS, Sentinel-2, and Landsat 8), snow simulations from CLM5, snow variables from several climate reanalyses (ERA5, ERA5-Land, and MERRA-2), and in-situ data from eddy covariance stations (LATICE flux sites). Two offline CLM5 simulations are conducted with different atmospheric forcing, namely the default data set (GSWP3) and ERA5. We are investigating trends in the snow cover phenology, which we characterize using snow cover duration, first and last days of the snow cover, and consecutive snow cover days for each snow season over the last two decades. This work illuminates a path to integrate Earth observations with Earth system modeling in cold environments to both identify and constrain sources of uncertainty. Acknowledgement: This ongoing study is supported by the LATICE (Land-ATmosphere Interactions in Cold Environments) strategic research initiative funded by the University of Oslo, and the project EMERALD (294948) funded by the Research Council of Norway.

• Aas, Kjetil Schanke; Lambert, Marius; Tang, Hui; Althuizen, Inge; Berntsen, Terje Koren & Fisher, Rosie [Vis alle 11 forfattere av denne artikkelen] (2022). Recent high-latitude vegetation developments in CLM-FATES.
• Ursin, Lars & Vandvik, Vigdis (2022). Skal vi fikse klimaet, må vi spille på lag med naturen. [Fagblad]. Energy og Klima <2C.
• Finne, Eirik Aasmo; Tallaksen, Lena M.; Stordal, Frode & Bjerke, Jarle W. (2022). Effects of winter warming events on vegetation ecophysiology on a low-alpine ridge.
• Bryn, Anders (2022). Arealkonflikter - trender, utvikling og påvirkning.
• Bryn, Anders; Horvath, Peter; Torma, Michal; Naas, Adam Eindride; Volden, Inger Kristine & Liahjell, Gunnar Thorsen [Vis alle 10 forfattere av denne artikkelen] (2022). Treeline research at Natural history museum (UiO) .
• Bryn, Anders (2022). Terrestrial ecosystem-climate interactions of our EMERALD planet.
• Bryn, Anders (2022). Skogsbeiter i tørkeperioder.
• Vollsnes, Ane Victoria; Bryn, Anders & Stordal, Frode (2022). Fotosyntesen redder ikke klimaet. Nationen. ISSN 0805-3782. s. 19–19. Vis sammendrag

  Gjennom fotosyntesen tar plantene opp mye av de menneskeskapte klimagassutslippene. Tørke, brann, insektutbrudd og vindfall skaper imidlertid problemer for økosystemenes opptak og lagring av karbon.

• Bryn, Anders; Vollsnes, Ane Victoria & Stordal, Frode (2022). Skogen utfordres i nytt klima. Nationen. ISSN 0805-3782. s. 20–20. Vis sammendrag

  Politikk som skal være grønn og klimavennlig kan ikke lene seg for kraftig på de grønne økosystemene. Eller satse på at de kan endres til vår fordel. Det er for risikabelt.

• Vollsnes, Ane Victoria (2022). Klimaendringer og karbonlagring i myrene.
• Zhao, Junbin; Lange, Holger; Meissner, Helge Rainer & Bright, Ryan M. (2022). Heat Field Deformation (HFD) vs Linear Heat Balance (LHB): A critical comparison of two sap flow methods based on the same instrumentation. EGU General Assembly. Vis sammendrag

  As a way to estimate evapotranspiration (ET), Heat Field Deformation (HFD) is a widely used method to measure sap flow of trees based on empirical relationships between heat transfer within tree stems and the sap flow rates. As an alternative, the Linear Heat Balance (LHB) method implements the same instrumental configuration as HFD but calculates the sap flow rates using analytical equations that are derived from fundamental conduction-convection heat transfer equations.

• Bryn, Anders (2022). Utmark, beiting og klima.
• Vandvik, Vigdis (2022). Å spå om tørke. [Avis]. Morgenbladet forskningsspalte.
• Vandvik, Vigdis; Vetlesen, Arne Johan & Christensen, Tor Bjarne (2022). Hvorfor ødelegger vi naturen? [Fagblad]. Naturvernforbundet.
• Vandvik, Vigdis; Seglsten, Liv-Hege & Langehaug, Helene R. (2022). Trilogien om Vindkraft 1 – klima, natur og energi. [Radio]. GreenPod.
• Vandvik, Vigdis (2022). Skal vi fikse klimaet, må vi spille på lag med naturen. Energi og Klima : Norsk klimastiftelses nettmagasin.
• Vandvik, Vigdis (2022). Naturkrisen – hva den er og hvordan vi løser den?
• Henmo, Jonas Bergvall & Vandvik, Vigdis (2022). Økolog etterspør nye grep for å stanse naturkrisa: VIL SETTE PRIS PÅ NATUREN. [Avis]. Klassekampen.
• Vandvik, Vigdis (2022). Arealforvaltning på lag med folk, klima og natur?
• Vandvik, Vigdis (2022). Klima- og naturkriser må løses sammen.
• Lieungh, Eva (2022). The Land Sites Platform makes CLM-FATES modelling easy. Now what? Vis sammendrag

  https://www.mn.uio.no/geo/english/research/projects/emerald/events/webinar/webinar-dec-2022.html

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