Norbert Pirk

• Alonso-Gonzalez, Esteban; Aalstad, Kristoffer; Pirk, Norbert; Mazzolini, Marco; Treichler, Désirée & Leclercq, Paul [Vis alle 9 forfattere av denne artikkelen] (2023). Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation. Hydrology and Earth System Sciences (HESS). ISSN 1027-5606. 27(24), s. 4637–4659. doi: 10.5194/hess-27-4637-2023. Fulltekst i vitenarkiv Vis sammendrag

  Data assimilation techniques that integrate available observations with snow models have been proposed as a viable option to simultaneously help constrain model uncertainty and add value to observations by improving estimates of the snowpack state. However, the propagation of information from spatially sparse observations in high-resolution simulations remains an under-explored topic. To remedy this, the development of data assimilation techniques that can spread information in space is a crucial step. Herein, we examine the potential of spatio-temporal data assimilation for integrating sparse snow depth observations with hyper-resolution (5 m) snow simulations in the Izas central Pyrenean experimental catchment (Spain). Our experiments were developed using the Multiple Snow Data Assimilation System (MuSA) with new improvements to tackle the spatio-temporal data assimilation. Therein, we used a deterministic ensemble smoother with multiple data assimilation (DES-MDA) with domain localization. Three different experiments were performed to showcase the capabilities of spatio-temporal information transfer in hyper-resolution snow simulations. Experiment I employed the conventional geographical Euclidean distance to map the similarity between cells. Experiment II utilized the Mahalanobis distance in a multi-dimensional topographic space using terrain parameters extracted from a digital elevation model. Experiment III utilized a more direct mapping of snowpack similarity from a single complete snow depth map together with the easting and northing coordinates. Although all experiments showed a noticeable improvement in the snow patterns in the catchment compared with the deterministic open loop in terms of correlation (r=0.13) and root mean square error (RMSE = 1.11 m), the use of topographical dimensions (Experiment II, r=0.63 and RMSE =  0.89 m) and observations (Experiments III, r=0.92 and RMSE =  0.44 m) largely outperform the simulated patterns in Experiment I (r=0.38 and RMSE = 1.16 m). At the same time, Experiments II and III are considerably more challenging to set up. The results of these experiments can help pave the way for the creation of snow reanalysis and forecasting tools that can seamlessly integrate sparse information from national monitoring networks and high-resolution satellite information.

• Tømmervik, Hans; Julitta, Tommaso; Nilsen, Lennart; Park, Taejin; Burkart, Andreas & Ostapowicz, Katarzyna Anna [Vis alle 10 forfattere av denne artikkelen] (2023). The northernmost hyperspectral FLoX sensor dataset for monitoring of high-Arctic tundra vegetation phenology and Sun-Induced Fluorescence (SIF). Data in Brief. ISSN 2352-3409. 50. doi: 10.1016/j.dib.2023.109581. Fulltekst i vitenarkiv Vis sammendrag

  A hyperspectral field sensor (FloX) was installed in Adventdalen (Svalbard, Norway) in 2019 as part of the Svalbard Integrated Arctic Earth Observing System (SIOS) for monitoring vegetation phenology and Sun-Induced Chlorophyll Fluorescence (SIF) of high-Arctic tundra. This northernmost hyperspectral sensor is located within the footprint of a tower for long-term eddy covariance flux measurements and is an integral part of an automatic environmental monitoring system on Svalbard (AsMovEn), which is also a part of SIOS. One of the measurements that this hyperspectral instrument can capture is SIF, which serves as a proxy of gross primary production (GPP) and carbon flux rates. This paper presents an overview of the data collection and processing, and the 4-year (2019–2021) datasets in processed format are available at: https://thredds.met.no/thredds/catalog/arcticdata/infranor/NINA-FLOX/raw/catalog.html associated with https://doi.org/10.21343/ZDM7-JD72 under a CC-BY-4.0 license. Results obtained from the first three years in operation showed interannual variation in SIF and other spectral vegetation indices including MERIS Terrestrial Chlorophyll Index (MTCI), EVI and NDVI. Synergistic uses of the measurements from this northernmost hyperspectral FLoX sensor, in conjunction with other monitoring systems, will advance our understanding of how tundra vegetation responds to changing climate and the resulting implications on carbon and energy balance. Chlorophyll fluorescenceSolar Induced Fluorescence (SIF)ReflectancePhotosynthetic functionMERIS terrestrial chlorophyll index (MTCI)High-Arctic tundra

• van Hove, Alouette; Aalstad, Kristoffer & Pirk, Norbert (2023). Using reinforcement learning to improve drone-based inference of greenhouse gas fluxes. Nordic Machine Intelligence (NMI). ISSN 2703-9196. 3, s. 1–6. doi: 10.5617/nmi.9897. Fulltekst i vitenarkiv Vis sammendrag

  Accurate mapping of greenhouse gas fluxes at the Earth's surface is essential for the validation and calibration of climate models. In this study, we present a framework for surface flux estimation with drones. Our approach uses data assimilation (DA) to infer fluxes from drone-based observations, and reinforcement learning (RL) to optimize the drone's sampling strategy. Herein, we demonstrate that a RL-trained drone can quantify a CO_2 hotspot more accurately than a drone sampling along a predefined flight path that traverses the emission plume. We find that information-based reward functions can match the performance of an error-based reward function that quantifies the difference between the estimated surface flux and the true value. Reward functions based on information gain and information entropy can motivate actions that increase the drone's confidence in its updated belief, without requiring knowledge of the true surface flux. These findings provide valuable insights for further development of the framework for the mapping of more complex surface flux fields.

• 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.

• 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.

• 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.

• Van Der Valk, Luuk D.; Teuling, Adriaan J.; Girod, Luc; Pirk, Norbert; Stoffer, Robin & Van Heerwaarden, Chiel C. (2022). Understanding wind-driven melt of patchy snow cover. The Cryosphere. ISSN 1994-0416. 16(10), s. 4319–4341. doi: 10.5194/tc-16-4319-2022. Fulltekst i vitenarkiv
• Oehri, Jacqueline; Schaepman-Strub, Gabriela; Kim, Jin-Soo; Grysko, Raleigh; Kropp, Heather & Grünberg, Inge [Vis alle 74 forfattere av denne artikkelen] (2022). Vegetation type is an important predictor of the arctic summer land surface energy budget. Nature Communications. ISSN 2041-1723. 13. doi: 10.1038/s41467-022-34049-3. Fulltekst i vitenarkiv
• Seco, Roger; Holst, Thomas; Davie-Martin, Cleo L.; Simin, Tihomir; Guenther, Alex & Pirk, Norbert [Vis alle 8 forfattere av denne artikkelen] (2022). Strong isoprene emission response to temperature in tundra vegetation. Proceedings of the National Academy of Sciences of the United States of America. ISSN 0027-8424. 119(38). doi: 10.1073/pnas.2118014119.
• Lindroth, Anders; Pirk, Norbert; Jónsdóttir, Ingibjörg S.; Stiegler, Christian; Klemedtsson, Leif & Nilsson, Mats B. (2022). CO<inf>2</inf> and CH<inf>4</inf> exchanges between moist moss tundra and atmosphere on Kapp Linné, Svalbard. Biogeosciences. ISSN 1726-4170. 19(16), s. 3921–3934. doi: 10.5194/bg-19-3921-2022.
• Ramtvedt, Eirik Næsset & Pirk, Norbert (2022). A Methodology for Providing Surface-Cover-Corrected Net Radiation at Heterogeneous Eddy-Covariance Sites. Boundary-Layer Meteorology. ISSN 0006-8314. 184, s. 173–193. doi: 10.1007/s10546-022-00704-x. Fulltekst i vitenarkiv
• Jentzsch, K.; Schulz, A.; Pirk, Norbert; Foken, T.; Crewell, S. & Boike, Julia (2021). High Levels of CO<inf>2</inf> Exchange During Synoptic-Scale Events Introduce Large Uncertainty Into the Arctic Carbon Budget. Geophysical Research Letters. ISSN 0094-8276. 48(9). doi: 10.1029/2020GL092256. Fulltekst i vitenarkiv
• Pastorello, Gilberto; Trotta, Carlo; Canfora, Eleonora; Chu, Housen; Christianson, Danielle & Cheah, You-Wei [Vis alle 287 forfattere av denne artikkelen] (2020). The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Scientific Data. ISSN 2052-4463. 7. doi: 10.1038/s41597-020-0534-3. Fulltekst i vitenarkiv Vis sammendrag

  The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

• Natali, Susan M.; Watts, Jennifer D.; Rogers, Brendan M.; Potter, Stefano; Ludwig, Sarah M. & Selbmann, Anne-Katrin [Vis alle 75 forfattere av denne artikkelen] (2019). Large loss of CO2 in winter observed across the northern permafrost region. Nature Climate Change. ISSN 1758-678X. 9, s. 852–857. doi: 10.1038/s41558-019-0592-8. Fulltekst i vitenarkiv
• Qiu, Chunjing; Zhu, Dan; Ciais, Philippe; Guenet, Bertrand; Krinner, Gerhard & Peng, Shushi [Vis alle 54 forfattere av denne artikkelen] (2018). ORCHIDEE-PEAT (revision 4596), a model for northern peatland CO2, water, and energy fluxes on daily to annual scales. Geoscientific Model Development. ISSN 1991-959X. 11(2), s. 497–519. doi: 10.5194/gmd-11-497-2018. Fulltekst i vitenarkiv Vis sammendrag

  Peatlands store substantial amounts of carbon and are vulnerable to climate change. We present a modified version of the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface model for simulating the hydrology, surface energy, and CO2 fluxes of peatlands on daily to annual timescales. The model includes a separate soil tile in each 0.5° grid cell, defined from a global peatland map and identified with peat-specific soil hydraulic properties. Runoff from non-peat vegetation within a grid cell containing a fraction of peat is routed to this peat soil tile, which maintains shallow water tables. The water table position separates oxic from anoxic decomposition. The model was evaluated against eddy-covariance (EC) observations from 30 northern peatland sites, with the maximum rate of carboxylation (Vcmax) being optimized at each site. Regarding short-term day-to-day variations, the model performance was good for gross primary production (GPP) (r2 =  0.76; Nash–Sutcliffe modeling efficiency, MEF  =  0.76) and ecosystem respiration (ER, r2 =  0.78, MEF  =  0.75), with lesser accuracy for latent heat fluxes (LE, r2 =  0.42, MEF  =  0.14) and and net ecosystem CO2 exchange (NEE, r2 =  0.38, MEF  =  0.26). Seasonal variations in GPP, ER, NEE, and energy fluxes on monthly scales showed moderate to high r2 values (0.57–0.86). For spatial across-site gradients of annual mean GPP, ER, NEE, and LE, r2 values of 0.93, 0.89, 0.27, and 0.71 were achieved, respectively. Water table (WT) variation was not well predicted (r2 < 0.1), likely due to the uncertain water input to the peat from surrounding areas. However, the poor performance of WT simulation did not greatly affect predictions of ER and NEE. We found a significant relationship between optimized Vcmax and latitude (temperature), which better reflects the spatial gradients of annual NEE than using an average Vcmax value.

• Pirk, Norbert; Sievers, Jakob; Mertes, Jordan Robert; Parmentier, Frans-Jan W.; Mastepanov, Mikhail & Christensen, Torben R (2017). Spatial variability of CO2 uptake in polygonal tundra: assessing low-frequency disturbances in eddy covariance flux estimates. Biogeosciences. ISSN 1726-4170. 14(12), s. 3157–3169. doi: 10.5194/bg-14-3157-2017. Fulltekst i vitenarkiv
• Pirk, Norbert; Mastepanov, Mikhail; López-Blanco, Efrén; Christensen, Louise H.; Christiansen, Hanne H & Hansen, Birger Ulf [Vis alle 10 forfattere av denne artikkelen] (2017). Toward a statistical description of methane emissions from arctic wetlands. Ambio. ISSN 0044-7447. 46, s. 70–80. doi: 10.1007/s13280-016-0893-3. Fulltekst i vitenarkiv Vis sammendrag

  Methane (CH4) emissions from arctic tundra typically follow relations with soil temperature and water table depth, but these process-based descriptions can be difficult to apply to areas where no measurements exist. We formulated a description of the broader temporal flux pattern in the growing season based on two distinct CH4 source components from slow and fast-turnover carbon. We used automatic closed chamber flux measurements from NE Greenland (74°N), W Greenland (64°N), and Svalbard (78°N) to identify and discuss these components. The temporal separation was well-suited in NE Greenland, where the hypothesized slow-turnover carbon peaked at a time significantly related to the timing of snowmelt. The temporally wider component from fast-turnover carbon dominated the emissions in W Greenland and Svalbard. Altogether, we found no dependence of the total seasonal CH4 budget to the timing of snowmelt, and warmer sites and years tended to yield higher CH4 emissions.

• Pirk, Norbert; Tamstorf, Mikkel P.; Lund, Magnus; Mastepanov, Mikhail; Pedersen, Stine Højlund & Mylius, Maria R. [Vis alle 9 forfattere av denne artikkelen] (2016). Snowpack fluxes of methane and carbon dioxide from high Arctic tundra. Journal of Geophysical Research (JGR): Biogeosciences. ISSN 2169-8953. 121(11), s. 2886–2900. doi: 10.1002/2016JG003486.
• Pirk, Norbert; Mastepanov, Mikhail; Parmentier, Frans-Jan W.; Lund, Magnus; Crill, Patrick & Christensen, Torben R. (2016). Calculations of automatic chamber flux measurements of methane and carbon dioxide using short time series of concentrations. Biogeosciences. ISSN 1726-4170. 13(4), s. 903–912. doi: 10.5194/bg-13-903-2016.
• Pirk, Norbert; Santos, Telmo; Gustafson, Carl; Johansson, Anders J.; Tufvesson, Fredrik & Parmentier, Frans-Jan W. [Vis alle 8 forfattere av denne artikkelen] (2015). Methane emission bursts from permafrost environments during autumn freeze-in: new insights from ground-penetrating radar. Geophysical Research Letters. ISSN 0094-8276. 42(16), s. 6732–6738. doi: 10.1002/2015GL065034.

Se alle arbeider i Cristin

• Vatne, Astrid; Vollsnes, Ane Victoria; Pirk, Norbert & Tallaksen, Lena M. (2023). Drought effects on dwarf birch leaves.
• 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.
• 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 and reanalyses .
• 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.

• 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.
• Pastorello, Gilberto; Trotta, Carlo; Canfora, Eleonora; Chu, Housen; Christianson, Danielle & Cheah, You-Wei [Vis alle 288 forfattere av denne artikkelen] (2021). Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data (Scientific Data, (2020), 7, 1, (225), 10.1038/s41597-020-0534-3). Scientific Data. ISSN 2052-4463. 8(1). doi: 10.1038/s41597-021-00851-9.
• Aalstad, Kristoffer; Alonso-Gonzalez, Esteban; Bazilova, Varvara; Bertino, Laurent; Fiddes, Joel & Filhol, Simon [Vis alle 9 forfattere av denne artikkelen] (2021). Unmixing satellite imagery using data assimilation to map snow and albedo.
• Aalstad, Kristoffer; Alonso-Gonzalez, Esteban; Bertino, Laurent; Fiddes, Joel; van Hove, Alouette & Pirk, Norbert [Vis alle 8 forfattere av denne artikkelen] (2021). Learning from Earth observations using data assimilation.
• Aalstad, Kristoffer; Westermann, Sebastian; Pirk, Norbert; Fiddes, Joel & Bertino, Laurent (2021). Retrieving fractional snow-covered area from optical satellites using data assimilation. Vis sammendrag

  Mapping from noisy observations to the latent states that may have generated them falls under the umbrella of inverse problems. These problems are abundant in Earth system science since our uncertain mechanistic models need to be fully specified while the system is only partially and imperfectly observed. Combined with a steadily growing observing system, this abundance has fueled the development of probabilistic Data Assimilation (DA) schemes that use Bayesian inference to fuse uncertain information from models and observations. Widely used applications of DA include the production of global atmospheric reanalyses and initializing numerical weather predictions. At the same time, the added value that DA can bring to remote sensing as a generalized framework for building retrieval algorithms remains largely untapped. In our contribution, we demonstrate the potential of data assimilation in the task of retrieving fractional snow-covered area (fSCA) from multispectral satellite imagery from moderate (MODIS) and higher (Sentinel-2 MSI, Landsat 8 OLI) optical sensors. In this analysis, we build on our previous work by focusing on the Bayelva catchment near Ny-Ålesund we have access to independent high-quality validation data obtained from terrestrial photography. We show how the general problem of linear spectral unmixing that is widely used for land cover classification can be recast as a Bayesian inverse problem. This can then be readily solved using ensemble-based data assimilation schemes, where we test both vanilla and sophisticated flavors of the particle filter and the ensemble Kalman filter, as well as Markov chain Monte Carlo benchmarks. By solving the problem in a transformed parameter space, the physical constraints of spectral unmixing are satisfied while reducing the need for ad hocery. The Bayesian data assimilation fSCA retrieval approach lets us deal with ill-posedness, incorporate physical knowledge, and account for uncertainty in the observed reflectances. It performs favorably compared to widely used techniques for fSCA retrieval such as thresholding of the NDSI, regression on the NDSI, and classical (non-negative least squares) spectral unmixing. This method is also much more scalable than classical unmixing since iterations are pre-determined and can fully exploit vectorization. Furthermore, it does not require any tuning on in-situ observations and it can also be used to solve the endmember selection problem using the concept of model evidence. Crucially, the retrieved fSCA includes dynamic uncertainty estimates that are required for satellite retrievals to be of any use in dynamic data assimilation frameworks. We envisage further validation by leveraging the network of terrestrial cameras operated by our partners in the PASSES consortium (Salzano et al., 2021; SESS Report 2021, Ch. 10). Our aim is to exploit these satellite retrievals in our ongoing efforts to produce tailored high resolution permafrost and snow reanalyses in cold regions, including Svalbard. At the same time, the approach outlined here could also be modified to retrieve surface albedo and (sub-pixel) land cover globally with even broader implications to Earth system science.

• Aalstad, Kristoffer; Alonso-Gonzalez, Esteban; Bazilova, Varvara; Bertino, Laurent; Fiddes, Joel & Pirk, Norbert [Vis alle 7 forfattere av denne artikkelen] (2021). Leveraging emerging Earth observations using data assimilation. Vis sammendrag

  The task of mapping from noisy observations to the states that (given a forward model) may have generated them falls under the umbrella of inverse problems. These problems are abundant in Earth system science since our uncertain mechanistic models need to be fully specified while the system is only partially and imperfectly observed. This abundance has, combined with a steadily growing observing system, fueled the development of probabilistic Data Assimilation (DA) schemes that use Bayesian inference to fuse uncertain information from models and observations. Notable operational applications of DA include the production of global atmospheric reanalyses and initializing numerical weather predictions. Perhaps less appreciated is the added value that DA can bring to Earth Observations (EO) as a generalized framework for building retrieval algorithms. In our work we present two completely different inverse problems that show how DA can help us to make the most out of emerging EO. The first problem is snowpack reconstruction where we constrain snow models using highly informative observations of the dynamics of snow cover depletion retrieved using satellite remote sensing. We provide examples assimilating retrievals from moderate (MODIS) and higher resolution (Landsat, Sentinel-2, PlanetScope cubesats) optical sensors as well as from all-weather radar data (Sentinel-1) in the Californian Sierra Nevada, Svalbard, Finse, the Pyrenees, the Swiss Alps, and Lebanon. This method is being developed to build tailored snow and permafrost reanalysis frameworks that lead to improved global cryospheric mapping capabilities and provide new benchmarks for Earth system models. The second problem is flux inversion where we seek to infer surface fluxes of carbon, water, and heat using a drone swarm that provides distributed measurements of temperature, gas concentrations, and wind in the atmospheric boundary layer. To achieve this, we assimilate drone data into various boundary layer models, building up complexity from analytical flux-profile relationships based on the widely used Monin-Obukhov Similarity Theory to turbulence resolving large eddy simulations. Through flux inversion with the latter the hope is that we will be able to map fluxes in highly heterogeneous landscapes at the scale of Earth system models (10 km). This is not possible with existing methods like eddy covariance and can thereby shed new light on the role of flux heterogeneities in land-atmosphere coupling. When solving these problems we test various probabilistic DA schemes including variants of the ensemble Kalman filter, the particle filter, and Markov chain Monte Carlo. These schemes have been adapted to our problems by casting them as smoothers that condition the model on future observations, rather than as sequential filters, which crucially allows information to propagate backwards in time. We also emphasize how we can use DA to move beyond the usual first level of inference where we “fit” our model to data, up to the second level of inference where we can compare different competing model structures and parametrizations using the model evidence framework.

• Aalstad, Kristoffer; Fiddes, Joel; Martin, Leo Celestin Paul; Alonso-Gonzalez, Esteban; Yilmaz, Yeliz A. & Pirk, Norbert [Vis alle 7 forfattere av denne artikkelen] (2021). Workshop on downscaling with TopoSCALE for cryospheric applications. Vis sammendrag

  Meteorological forcing data is a crucial ingredient when modeling the past, present, and future state of the cryosphere. At the same time, cold regions (i.e. the high elevations and/or latitudes of our planet) where most of the cryosphere is found are typically remote with a very low density of in-situ observations. The few meteorological stations that do exist are typically quite unrepresentative due to the often extreme surface heterogeneity and complex terrain in these regions. This makes empirical-statistical and geostatistical downscaling techniques that rely heavily on station data somewhat impractical for cryospheric applications. On the other side of the scale, we have dynamical downscaling techniques that rely on using sophisticated regional climate models to downscale historical global reanalysis data or projections from global climate models. Although these are not as dependent on local observations in that they are able to mechanistically model the state of the atmosphere, they are prohibitively expensive to run at the decadal timescales and hillslope (1 km - 100 m) spatial scale that is often sought in cryospheric applications. In this workshop, we will provide an overview of various downscaling techniques and introduce the topography-based downscaling routine TopoSCALE as well as its many applications and downstream methods listed below. TopoSCALE was developed to provide a computationally feasible technique for generating hourly hillslope scale atmospheric forcing for cryospheric modeling from global reanalysis data or other atmospheric model outputs, without the need for in-situ data. It relies heavily on topographic parameters derived from digital elevation models to be able to scale the input atmospheric forcing to the local topography. It has been tested quite extensively in a variety of environments, including: the Swiss Alps, Svalbard, the California Sierra Nevada, and High Mountain Asia. TopoSCALE is also being used as part of ongoing permafrost reanalysis efforts in the ESA Permafrost_CCI project. The scheme can be coupled to a clustering framework (TopoSUB) which can speed up distributed simulations by orders of magnitude compared to the more traditional gridded approach to land surface modeling. TopoSCALE can also be used together with bias-correction techniques to help downscale future regional climate projections to the hillslope scale with applications to cryospheric climate impact studies. Recent and ongoing applications of TopoSCALE together with snow data assimilation have been particularly fruitful in being able to handle biases in solid precipitation, which has been one of the most challenging problems for downscaling in cryospheric applications. Importantly, TopoSCALE lets modellers shift limited computational resources away from the downscaling exercise to probing uncertainties through ensemble simulations and constraining these with Earth observations.

• Althuizen, Inge; Christiansen, Casper Tai; Michelsen, Anders; Westermann, Sebastian; Pirk, Norbert & Risk, David [Vis alle 7 forfattere av denne artikkelen] (2021). Annual ecosystem carbon budgets across an abrupt permafrost thaw gradient in Northern Norway .
• Bryn, Anders; Dalen, Thea Grobstok; Finne, Eirik Aasmo; Heiberg, Hanne; Keetz, Lasse Torben & Nilsen, Irene Brox [Vis alle 30 forfattere av denne artikkelen] (2021). Natur i endring - samspillet mellom klima og økosystemene.
• Althuizen, Inge; Christiansen, Casper Tai; Michelsen, Anders; Pirk, Norbert; Risk, David & Westermann, Sebastian [Vis alle 7 forfattere av denne artikkelen] (2020). Annual ecosystem carbon budgets across an abrupt permafrost thaw gradient in Northern Norway .
• Althuizen, Inge; Christiansen, Casper Tai; Michelsen, Anders; Pirk, Norbert; Risk, David & Westermann, Sebastian [Vis alle 7 forfattere av denne artikkelen] (2020). Annual ecosystem carbon budgets across an abrupt permafrost thaw gradient in Northern Norway .
• Aalstad, Kristoffer; Westermann, Sebastian; Pirk, Norbert; Fiddes, Joel; Karsten, Logan & McCreight, James [Vis alle 8 forfattere av denne artikkelen] (2020). Constraining land surface models with emerging observations.
• Christiansen, Casper Tai; Michelsen, Anders; Pirk, Norbert; Risk, David; Westermann, Sebastian & Lee, Hanna (2020). Annual ecosystem carbon budgets across an abrupt permafrost thaw gradient in Northern Norway .
• Christiansen, Casper Tai; Michelsen, Anders; Westermann, Sebastian; Pirk, Norbert; Risk, David & Lee, Hanna (2020). Annual ecosystem carbon budgets across an abrupt permafrost thaw gradient in Northern Norway .
• Bryn, Anders; Dalen, Thea Grobstok; Finne, Eirik Aasmo; Heiberg, Hanne; Nilsen, Irene Brox & Parmentier, Frans-Jan W. [Vis alle 22 forfattere av denne artikkelen] (2020). Natur i endring - en vandreutstilling.
• Natali, Susan M.; Watts, Jennifer D.; Rogers, Brendan M.; Potter, Stefano; Ludwig, Sarah & Selbmann, Anne-Katrin [Vis alle 75 forfattere av denne artikkelen] (2019). Author Correction: Large loss of CO<inf>2</inf> in winter observed across the northern permafrost region (Nature Climate Change, (2019), 9, 11, (852-857), 10.1038/s41558-019-0592-8). Nature Climate Change. ISSN 1758-678X. 9(12). doi: 10.1038/s41558-019-0644-0.
• Pirk, Norbert; Ramtvedt, Eirik Næsset; Decker, Sven; Cassiani, Massimo; Burkhart, John & Stordal, Frode [Vis alle 7 forfattere av denne artikkelen] (2019). Causes of surface energy imbalances of eddy covariance measurements in mountainous terrain.
• Natali, Sue; Watts, Jennifer; Rogers, Brendan M.; Potter, Stefano; Abbott, Benjamin & Arndt, Kyle [Vis alle 72 forfattere av denne artikkelen] (2018). A pan-arctic synthesis of nongrowing season respiration: Key drivers and responses to a changing climate .
• Filhol, Simon; Pirk, Norbert; Schuler, Thomas & Burkhart, John (2017). The Evolution of a Snow Dune Field.
• Filhol, Simon; Pirk, Norbert; Schuler, Thomas & Burkhart, John (2017). The morphological evolution of a wind-shaped snow surface during a storm event at Finse, Norway.

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