Norbert Pirk

• Virkkala, Anna-Maria; Wargowsky, Isabel; Vogt, Judith; Kuhn, McKenzie A.; Madaan, Simran & Windholz, Tiffany [Show all 269 contributors for this article] (2026). ABCFlux v2: Arctic–boreal CO2 and CH4 monthly flux observations and ancillary information across terrestrial and freshwater ecosystems. Earth System Science Data. ISSN 1866-3508. 18(9), p. 6357–6409. doi: 10.5194/essd-18-6357-2026. Full text in Research Archive Show summary

  Measurements of surface-atmosphere carbon dioxide (CO 2 ) and methane (CH 4 ) fluxes have been relatively sparse across the Arctic tundra and boreal biomes, causing significant uncertainties in carbon budget estimates from the region. While the availability of Arctic-boreal carbon flux data has increased substantially over the past decade, the data have remained spread across different repositories, scientific articles, and unpublished sources, making it difficult to leverage. Here we present a new dataset of monthly Arctic-boreal carbon fluxes (ABCFlux v2) across terrestrial (wetlands and uplands) and freshwater (lakes and rivers) ecosystems compiled from previous syntheses including the Arctic-boreal CO 2 flux database (ABCFlux v1), the Boreal-Arctic Wetland and Lake Methane Dataset (BAWLD-CH4), and the Global River Methane Database (GRiMeDB). In addition, we consider data from general-purpose (e.g., Zenodo) and flux network repositories, literature, and site principal investigators. The dataset includes surface-atmosphere CO 2 fluxes of gross primary production (GPP), ecosystem respiration (Reco), and net ecosystem exchange (NEE), alongside CH 4 fluxes. For aquatic ecosystems, we split CH 4 fluxes into diffusive and ebullitive flux pathways, and included potential emissions from transient storage in the water column (“storage fluxes”), alongside CO 2 and CH 4 concentrations dissolved in the surface water. Fluxes are measured through a variety of methods including chamber and eddy covariance techniques alongside bubble traps, ice-surveys, and concentration-based turbulence-driven modelling in aquatic ecosystems. The monthly flux data are reported together with supporting methodological and environmental metadata. The resulting ABCFlux v2 has 23 847 flux site-months, 8182 concentration site-months, and 199 seasonal observations from 1024 sites, and includes 56 139 reported fluxes (i.e. sum of GPP, Reco, NEE, and CH 4 fluxes) from the years 1984 to 2024. The majority of monthly observations occurred after 1999. Wetlands had the highest number of site-month observations (8758), followed by boreal forest (6981), lotic ecosystems (6275), lentic ecosystems (3799) and upland tundra (3308). Measurements of CO 2 dominated the dataset across most ecosystem types (25 222) except for lentic ecosystems, where CH 4 flux site-months (3098) were more frequent than CO 2 flux site-months (2915). Overall, ABCFlux v2 includes 160 % more site-months for terrestrial CO 2 flux data compared to ABCFlux v1. Integrating and updating BAWLD-CH4 flux data from growing season averages to monthly fluxes resulted in 5671 site-months of chamber CH 4 data compared to 762 site-years. This collaborative initiative, involving contributions from over 260 researchers, provides a comprehensive overview of the current state of the Arctic-boreal carbon flux network and its data, and serves as an important step in reducing uncertainties in Arctic-boreal carbon budgets and in enhancing our understanding of climate feedbacks. The data can be accessed at ORNL DAAC at https://doi.org/10.3334/ORNLDAAC/2448 (Virkkala et al., 2026).

• Aalstad, Kristoffer; Alonso-González, Esteban; Pirk, Norbert; Westermann, Sebastian; Willmes, Clarissa & Yang, Ruitang (2026). Evolving beyond collapse: an adaptive particle batch smoother for cryospheric data assimilation. Geoscientific Model Development. ISSN 1991-959X. 19(18), p. 8565–8596. doi: 10.5194/gmd-19-8565-2026. Full text in Research Archive Show summary

  We present a new adaptive particle-based data assimilation scheme for cryospheric applications that leverages promising developments in importance sampling. The proposed approach seeks to combine some of the advantages of two widely used classes of schemes: particle methods and iterative ensemble Kalman methods. Specifically, it extends the Particle Batch Smoother (PBS) that is commonly used in cryospheric data assimilation, with the Adaptive Multiple Importance Sampling algorithm. This adaptive formulation transforms the PBS into an iterative scheme with improved resilience against ensemble collapse and the ability to implement early-stopping strategies. As such, computational cost is automatically adapted to the complexity of the problem at hand, even down to the grid-cell and water year level in distributed multiyear simulations. In homage to the schemes that it builds on, we coin this new algorithm the Adaptive Particle Batch Smoother (AdaPBS) and we test it across a range of scenarios. First, we conducted an intercomparison of some of the most commonly used cryospheric data assimilation algorithms using Markov Chain Monte Carlo (MCMC) simulation as a costly gold-standard benchmark in a simplified temperature index model assimilating snow depth observations. We further evaluated AdaPBS by assimilating snow depth observations from the ESMSnowMIP project at 6 different sites spanning 3 continents, using an ensemble of simulations generated with the more complex Flexible Snow Model (FSM2). Our results demonstrate that AdaPBS is a robust and reliable tool, outperforming or at least matching the performance of other commonly used algorithms and successfully handling complex cases with dense observational datasets. All experiments were carried out using the open-source Multiple Snow Data Assimilation System (MuSA) toolbox, which now includes AdaPBS and MCMC among the growing list of available cryospheric data assimilation methods. Beyond our cryospheric focus, the scheme has the potential to be applied directly to the closely related fields of land surface and hydrological data assimilation as well as more general geoscientific Bayesian inference problems.

• Mack, Laura & Pirk, Norbert (2026). Reddy: An Open-Source Toolbox for Analyzing Eddy-Covariance Measurements in Heterogeneous Environments. Boundary-Layer Meteorology. ISSN 0006-8314. 192(9). doi: 10.1007/s10546-026-01003-5. Full text in Research Archive Show summary

  Land–atmosphere exchange is mediated by turbulent fluxes that can be quantified with eddy-covariance (EC) measurements. EC has been widely used to measure ecosystem-scale vertical exchange between atmosphere and vegetation, and to test and refine atmospheric turbulence theories, thereby improving the representation of turbulent fluxes in land surface models. Traditionally, research has focused on idealized, homogeneous and flat surfaces, but recent work increasingly targets turbulent exchange in complex, heterogeneous environments under non-ideal conditions, where challenges include advective fluxes, mesoscale circulations between contrasting surface types, and non-stationary nighttime turbulence. Here, we introduce the open-source R package Reddy, which integrates multiple EC analysis methods into a single modular framework. Reddy enables users to tailor post-processing choices to site-specific conditions by integrating turbulence diagnostics and flux calculations in an iterative re-evaluation spiral, where processing choices are adapted step-by-step. The package supports station management and facilitates detailed scientific analyses, and is accompanied by an extensive documentation and hands-on EC processing tutorials. We demonstrate Reddy using measurements from three Norwegian sites: (1) a morning transition following a strongly stably stratified night at an alpine tundra valley, (2) spectral and ogive analysis before and after ice-cover transition at a boreal lake, and (3) fitting flux–variance relations at a permafrost-affected palsa peatland. Comparison with EddyPro shows close agreement for carbon dioxide fluxes derived from closed-path systems, but increased scatter for near-zero methane fluxes derived from open-path systems. Reddy extends existing EC software and helps bridging micrometeorology and ecology in heterogeneous, real-world environments.

• Mack, Laura; Kähnert, Marvin & Pirk, Norbert (2026). Probabilistic modelling of atmosphere–surface coupling with a copula Bayesian network. Environmental Modelling & Software. ISSN 1364-8152. 202, p. 1–12. doi: 10.1016/j.envsoft.2026.107003. Full text in Research Archive Show summary

  Land–atmosphere coupling is an important process for correctly modelling near-surface temperature profiles, but it involves various uncertainties due to subgrid-scale processes, such as turbulent fluxes or unresolved surface heterogeneities, suggesting a probabilistic modelling approach. We develop a copula Bayesian network (CBN) to interpolate temperature profiles, acting as alternative to T2m-diagnostics used in numerical weather prediction (NWP) systems. The new CBN results in (1) a reduction of the warm bias inherent to NWP predictions of wintertime stable boundary layers allowing cold temperature extremes to be better represented, and (2) consideration of uncertainty associated with subgrid-scale spatial variability. The use of CBNs combines the advantages of uncertainty propagation inherent to Bayesian networks with the ability to model complex dependence structures between random variables through copulas. By combining insights from copula modelling and information entropy, criteria for the applicability of CBNs in the further development of parameterizations in NWP models are derived.

• Pismeniuk, Anfisa; Dörsch, Peter; Ippach, Mats Rouven; Willmes, Clarissa; Sheffield, Sunniva Bratli & Pirk, Norbert [Show all 7 contributors for this article] (2026). Wintertime production and storage of methane in thermokarst ponds of subarctic Norway. Biogeosciences. ISSN 1726-4170. 23(4), p. 1497–1514. doi: 10.5194/bg-23-1497-2026. Full text in Research Archive Show summary

  The ongoing climate change in permafrost areas can trigger abrupt thaw processes, leading to the formation of thermokarst lakes and ponds. These water bodies, especially in organic-rich areas, are recognized as strong methane (CH4) emitters during the ice-free periods and have the potential to accumulate large CH4 amounts in and below the ice, which is released during the ice melt. We estimated winter CH4 bottom flux in nine shallow ponds within two permafrost peatlands in Northern Norway, Iškoras and Áidejávri, during the 2023–2024 ice cover season. The cumulative winter CH4 bottom flux ranged from 0.6 to 24 g CH4-C m−2 which at the Iškoras site contributed up to 40 % to the annual CH4 budget. The heterogeneity of the CH4 wintertime accumulation is related to pond depth, differences in vegetation, and the thermokarst pond formation age. The latter was investigated using a space-for-time substitution approach along chronosequences of thermokarst formation spanning more than 70 years. The winter CH4 bottom flux increased from 2 mg CH4-C m−2 d−1 in two-year-old pond to 106 mg CH4-C m−2 d−1 in a pond formed between 30 and 60 years ago. Pond that formed more than 70 years ago and is currently experiencing sedge regrowth exhibited large winter CH4 bottom flux of 59 mg CH4-C m−2 d−1, while older ponds dominated by Sphagnum mosses showed 4 to 10 times smaller CH4 bottom fluxes.

• Hove, Alouette van; Aalstad, Kristoffer & Pirk, Norbert (2026). Actively inferring methane sources with drones. Environmental Data Science (EDS). 5. doi: 10.1017/eds.2026.10029. Full text in Research Archive Show summary

  This study presents a framework that combines Bayesian inference with reinforcement learning to guide drone-based sampling for methane source estimation. Synthetic gas concentration and wind observations are generated using a calibrated model derived from real-world drone measurements, providing a more representative testbed that captures atmospheric boundary layer variability. We compare three path planning strategies—preplanned, myopic (short-sighted), and non-myopic (long-term)—and find that non-myopic policies trained via deep reinforcement learning consistently yield more precise and accurate estimates of both source location and emission rate. We further investigate centralized multi-agent collaboration and observe comparable performance to independent agents in the tested single-source scenario. Our results suggest that effective source term estimation depends on correctly identifying the plume and obtaining low-noise concentration measurements within it. Precise localization further requires sampling in close proximity to the source, including slightly upwind. In more complex environments with multiple emission sources, multi-agent systems may offer advantages by enabling individual drones to specialize in tracking distinct plumes. These findings support the development of intelligent, data-driven sampling strategies for drone-based environmental monitoring, with potential applications in climate monitoring, emission inventories, and regulatory compliance.

• Bekken, Michael Andrew; Vatne, Astrid; Larsen, Poul; Ibrom, Andreas; Larsen, Klaus Steenberg & Elberling, Bo [Show all 14 contributors for this article] (2025). Carbon dynamics of a controlled peatland rewetting experiment in the Norwegian boreal zone. Scientific Reports. 16(1). doi: 10.1038/s41598-025-30836-2. Full text in Research Archive Show summary

  A controlled peatland rewetting experiment was conducted on two adjacent drained peatland sites in southeastern Norway. Eddy covariance monitoring of CO 2 and CH 4 fluxes at both sites began in 2019. In 2021, the Treatment Site was rewetted while the Control Site remained drained. Using nine environmental variables and the processed flux data as training data, Bayesian Additive Regression Tree (BART) models were used to generate annual flux balances for CO 2 and CH 4 . The 4-year mean annual flux at the Control Site was 17.3 ± 10 g CO 2 -C m − 2 yr − 1 and 4.6 ± 0.1 g CH 4 -C m − 2 yr − 1 . At the Treatment Site, the 2-year mean annual flux before the rewetting was 12.2  ±  3.8 g CO 2 -C m − 2 yr − 1 and 1.8  ±  0.04 g CH 4 -C m − 2 yr − 1 . In the first year after rewetting the annual flux was 53.3  ±  13 g CO 2 -C m − 2 yr − 1 and 3.8  ±  0.3 g CH 4 -C m − 2 yr − 1 , and in the second year after rewetting the annual flux was 41.2  ±  18 g CO 2 -C m − 2 yr − 1 and 3.4  ±  0.4 g CH 4 -C m − 2 yr − 1 . BART counterfactual modeling was able to estimate the effect of the rewetting on CO 2 and CH 4 fluxes. Two years after the rewetting, the BART counterfactual modeling estimated that the cumulative fluxes had increased by 80.3 ± 49 g CO 2 -C m − 2 and 3.4 ± 0.47 g CH 4 -C m − 2 because of the rewetting. Carbon flux monitoring of both sites is ongoing as the Control Site remains drained and the soil and vegetation at the Treatment Site continues to adjust to the altered hydrological regime after rewetting.

• Hove, Alouette van; Aalstad, Kristoffer; Lind, Vibeke; Arndt, Claudia; Odongo, Vincent & Ceriani, Rodolfo [Show all 9 contributors for this article] (2025). Inferring methane emissions from African livestock by fusing drone, tower, and satellite data. Biogeosciences. ISSN 1726-4170. 22(16), p. 4163–4186. doi: 10.5194/bg-22-4163-2025. Full text in Research Archive Show summary

  Considerable uncertainties and unknowns remain in the regional mapping of methane sources, especially in the extensive agricultural areas of Africa. To address this issue, we developed an observing system that estimates methane emission rates by assimilating drone and flux tower observations into an atmospheric dispersion model. We used our novel Bayesian inference approach to estimate emissions from various ruminant livestock species in Kenya, including diverse herds of cattle, goats, and sheep, as well as camels, for which methane emission estimates are particularly sparse. Our Bayesian estimates aligned with Tier 2 emission values of the Intergovernmental Panel on Climate Change. In addition, we observed the hypothesized increase in methane emissions after feeding. Our findings suggest that the Bayesian inference method is more robust under non-stationary wind conditions compared to a conventional mass balance approach using drone observations. Furthermore, the Bayesian inference method performed better in quantifying emissions from weaker sources, estimating methane emission rates as low as 100 g h−1. We found a ± 50 % uncertainty in emission rate estimates for these weaker sources, such as sheep and goat herds, which reduced to ± 12 % for stronger sources, like cattle herds emitting 1000–1500 g h−1. Finally, we showed that radiance anomalies identified in hyperspectral satellite data can inform the planning of flight paths for targeted drone missions in areas where source locations are unknown, as these anomalies may serve as indicators of potential methane sources. These promising results demonstrate the efficacy of the Bayesian inference method for source term estimation. Future applications of drone-based Bayesian inference could extend to estimating methane emissions in Africa and other regions from various sources with complex spatiotemporal emission patterns, such as wetlands, landfills, and wastewater disposal sites. The Bayesian observing system could thereby contribute to the improvement of emission inventories and verification of other emission estimation methods.

• Westergaard-Nielsen, Andreas; Maigaard, R S; Davie-Martin, Cleo Lisa; Seco, Roger; Holst, T & Pirk, Norbert [Show all 8 contributors for this article] (2025). Towards a remote-sensing-driven model of isoprene emissions from Alpine tundra. Environmental Research Letters. 20(10). doi: 10.1088/1748-9326/ae02a9. Full text in Research Archive Show summary

  Abstract This study investigates isoprene emissions in a high-latitude Alpine tundra ecosystem, focusing on using near-field remote sensing of surface temperatures, the photochemical reflectance index (PRI) and normalized difference vegetation index (NDVI), and meteorological measurements to model these emissions. Isoprene is a key biogenic volatile organic compound (BVOC) emitted by select plants, which can impact atmospheric chemistry and climate. Increased temperatures, particularly in high latitudes, may enhance isoprene emissions due to extended growing seasons and heightened plant stress. The research was conducted in Finse, Norway, where isoprene and CO 2 fluxes were measured with eddy covariance alongside spectral and meteorological data, and surface temperature. A random forest (RF) model was developed to predict isoprene fluxes, considering the variable importance of different environmental factors. The results showed that surface temperature and CO 2 flux were consistently important predictors, across three differential temporal data aggregations (hourly, daily, weekly), while the PRI demonstrated low predictive power, possibly due to the heterogeneous vegetation and variable light conditions. The NDVI was more effective than anticipated, likely linked to phenological changes in vegetation. Model performance varied with temporal resolution, with weekly data achieving the highest predictive accuracy ( R 2 up to 0.76). The RF model accurately reflected seasonal emission patterns but underestimated short-term peaks, suggesting the potential to combine machine learning with process-based modelling. This research highlights the promise of proxy data from remote sensing for scaling BVOC emission models to regional levels, essential for understanding climate impacts in Arctic ecosystems.

• Hodson, Andrew; Kleber, Gabrielle Emma; Platt, Stephen Matthew; Kalenitchenko, Dimitri Stanislas Desire; Hensgens, Geert & Irvine-Fynn, Tristram [Show all 20 contributors for this article] (2025). Methane in Svalbard (SvalGaSess). In Neuber, R.; Łupikasza, E.; Hübner, C. & Holmén, K. (Ed.), The State of Environmental Science in Svalbard – an annual report. Svalbard Integrated Arctic Earth Observing System. ISSN 9788293871149. p. 106–137. doi: 10.5281/zenodo.14425572. Full text in Research Archive Show summary

  Methane is a powerful greenhouse gas whose emission into the atmosphere from Arctic environments is increasing in response to climate change. At present, the increase in atmospheric methane concentrations recorded at Ny-Ålesund and globally threatens the Paris Agreement goal of limiting warming to 2 degrees, preferably 1.5 degrees, by increasing the need for abatements. However, our understanding of the physical, chemical and biological processes that control methane in the Arctic are strongly biased towards just a few lowland sites that are not at all like Svalbard and other similar mountainous, ice-covered regions. Svalbard can therefore be used to better understand these locations. Svalbard’s methane stocks include vast reserves of ancient, geogenic methane trapped beneath glaciers and permafrost. This methane supplements the younger, microbial methane mostly produced in waterlogged soils and wetlands during the summer and early winter. Knowledge about the production, removal and migration of these two methane sources in Svalbard’s complex landscapes and coastal environments has grown rapidly in recent years. However, the need to exploit this knowledge to produce reliable estimates of present-day and future emissions of methane from across the Svalbard landscape is now paramount. This is because understanding these quantities is absolutely necessary when we seek to define how society must adjust in order to better manage greenhouse gases in Earth’s atmosphere

• Mack, Laura; Kähnert, Marvin; Rauschenbach, Quentin; Frank, Lukas; Hasenburg, Franziska & Huss, Jannis-Michael [Show all 12 contributors for this article] (2025). Stable Boundary Layers in an Arctic Fjord-Valley System: Evaluation of Temperature Profiles Observed From Fiber-Optic Distributed Sensing and Comparison to Numerical Weather Prediction Systems at Different Resolutions. Journal of Geophysical Research (JGR): Atmospheres. ISSN 2169-897X. 130(2). doi: 10.1029/2024JD042825. Full text in Research Archive Show summary

  Stable boundary layers (SBLs) commonly form during the Arctic polar night, but their correct representation poses a major challenge for numerical weather prediction (NWP) systems. To enable detailed model verification, we performed measurements of the lower atmospheric boundary layer with airborne fiber-optic distributed sensing (FODS), a tethered sonde and ground-based eddy-covariance (EC) measurements during contrasting synoptic forcings in a fjord-valley system in Svalbard. The FODS-derived temperature variances and static stability profiles are used to investigate the spatial and temporal evolution of different inversion types. The strong gradients of the inversions are accompanied by an increased temperature variance, which is related to enhanced buoyancy fluctuations. The observed vertical temperature and wind speed profiles are compared to two configurations of the HARMONIE-AROME system with different horizontal resolutions at 2.5 and 0.5 km. The higher-resolution model captures cold pool and low level jet formation during weak synoptic forcing, resulting in a well-represented vertical temperature profile, while the coarser model exhibits a warm bias in near-surface temperatures of up to 8 K due to underestimated inversion strength. During changing background flow, the higher-resolution model is more sensitive to misrepresented fjord-scale wind directions and performs less well. The results indicate the importance of the ratio between nominal horizontal model resolution and valley width to represent SBL features. Our results underline the substantial benefit of spatially resolving FODS measurements for model verification studies as well as the importance of model and topography resolution for accurate representation of SBLs in complex terrain.

• Keetz, Lasse Torben; Aalstad, Kristoffer; Fisher, Rosie; Terán, Christian Poppe; Naz, Bibi S. & Pirk, Norbert [Show all 8 contributors for this article] (2025). Inferring Parameters in a Complex Land Surface Model by Combining Data Assimilation and Machine Learning. Journal of Advances in Modeling Earth Systems. 17(6). doi: 10.1029/2024MS004542. Full text in Research Archive Show summary

  Complex Land Surface Models (LSMs) rely on a plethora of parameters. These parameters and the associated process formulations are often poorly constrained, which hampers reliable predictions of ecosystem dynamics and climate feedbacks. Robust and uncertainty-aware parameter estimation with observations is complicated by, for example, the high dimensionality of the model parameter space and the computational cost of LSM simulations. Herein, we adapt a novel Bayesian data assimilation (DA) and machine learning framework termed “calibrate, emulate, sample” (CES) to infer parameters in a widely-used LSM coupled with a demographic vegetation model (CLM-FATES). First, an iterative ensemble Kalman smoother provides an initial estimate of the posterior distribution (“calibrate”). Subsequently, a machine-learning-based emulator is trained on the resulting model-observation mismatches to predict outcomes for unseen parameter combinations (“emulate”). Finally, this emulator replaces CLM-FATES simulations in an adaptive Markov Chain Monte Carlo approach enabling computationally feasible posterior sampling with enhanced uncertainty quantification (“sample”). We test our implementation with synthetic and real observations representing a boreal forest site in southern Finland. We estimate a total of six plant-functional-type-specific photosynthetic parameters by assimilating evapotranspiration (ET) and gross primary production (GPP) flux data. CES provided the best estimates of the synthetic truth parameters when compared to data-blind emulator sampling designs while all approaches reduced model-observation errors compared to a default parameter simulation (GPP: - 10% to - 30%, ET: -4% to -6%). Although errors were also consistently reduced with real data, comparing the emulator designs was less conclusive, which we mainly attribute to equifinality, structural uncertainty within CLM-FATES, and/or unknown errors in the data that are not accounted for.

• Chevrollier, Lou-Anne; Wehrlé, Adrien; Cook, Joseph M.; Pirk, Norbert; Benning, Liane G. & Anesio, Alexandre M. [Show all 7 contributors for this article] (2025). Separating the albedo-reducing effect of different light-Absorbing particles on snow using deep learning. The Cryosphere. ISSN 1994-0416. 19(4), p. 1527–1538. doi: 10.5194/tc-19-1527-2025. Full text in Research Archive Show summary

  Several different types of light-absorbing particles (LAPs) darken snow surfaces, enhancing snowmelt on glaciers and snowfields. LAPs are often present as a mixture of biotic and abiotic components at the snow surface, yet methods to separate their respective abundance and albedo-reducing effects are lacking. Here, we present a new optimisation method enabling the retrievals of dust, black carbon, and red algal abundances and their respective darkening effects from spectral albedo. This method includes a deep-learning emulator of a radiative transfer model (RTM) and an inversion algorithm. The emulator alone can be used as a fast and lightweight alternative to the full RTM with the possibility to add new features, such as new light-absorbing particles. The inversion method was applied to 180 ground field spectra collected on snowfields in southern Norway, with a mean absolute error on spectral albedo of 0.0056, and surface parameters that closely matched expectations from qualitative assessments of the surface. The emulator predictions of surface parameters were used to quantify the albedo-reducing effect of algal blooms, mineral dust, and dark particles represented by black carbon. Among these 180 surfaces, the albedo reduction due to light-absorbing particles was highly variable and reached up to 0.13, 0.21, and 0.25 for red algal blooms, mineral dust, and dark particles respectively. In addition, the effect of a single LAP was attenuated by the presence of other LAPs by up to 2–3 times. These results demonstrate the importance of considering the individual types of light-absorbing particles and their concomitant interactions for forecasting snow albedo.

• Hove, Alouette van; Aalstad, Kristoffer & Pirk, Norbert (2024). Guiding drones by information gain. Proceedings of Machine Learning Research (PMLR). 233(89-96). Full text in Research Archive Show summary

  The accurate estimation of locations and emission rates of gas sources is crucial across various domains, including environmental monitoring and greenhouse gas emission analysis. This study investigates two drone sampling strategies for inferring source term parameters of gas plumes from atmospheric measurements. Both strategies are guided by the goal of maximizing information gain attained from observations at sequential locations. Our research compares the myopic approach of infotaxis to a far-sighted navigation strategy trained through deep reinforcement learning. We demonstrate the superior performance of deep reinforcement learning over infotaxis in environments with non-isotropic gas plumes.

• Mack, Laura; Berntsen, Terje Koren; Vercauteren, Nikki & Pirk, Norbert (2024). Transfer Efficiency and Organization in Turbulent Transport over Alpine Tundra. Boundary-Layer Meteorology. ISSN 0006-8314. 190. doi: 10.1007/s10546-024-00879-5. Full text in Research Archive Show summary

  The exchange of momentum, heat and trace gases between atmosphere and surface is mainly controlled by turbulent fluxes. Turbulent mixing is usually parametrized using Monin–Obukhov similarity theory (MOST), which was derived for steady turbulence over homogeneous and flat surfaces, but is nevertheless routinely applied to unsteady turbulence over non-homogeneous surfaces. We study four years of eddy-covariance measurements at a highly heterogeneous alpine valley site in Finse, Norway, to gain insights into the validity of MOST, the turbulent transport mechanisms and the contributing coherent structures. The site exhibits a bimodal topography-following flux footprint, with the two dominant wind sectors characterized by organized and strongly negative momentum flux, but different anisotropy and contributions of submeso-scale motions, leading to a failure of eddy-diffusivity closures and different transfer efficiencies for different scalars. The quadrant analysis of the momentum flux reveals that under stable conditions sweeps transport more momentum than the more frequently occurring ejections, while the opposite is observed under unstable stratification. From quadrant analysis, we derive the ratio of the amount of disorganized to organized structures, that we refer to as organization ratio (OR). We find an invertible relation between transfer efficiency and corresponding organization ratio with an algebraic sigmoid function. The organization ratio further explains the scatter around scaling functions used in MOST and thus indicates that coherent structures modify MOST. Our results highlight the critical role of coherent structures in turbulent transport in heterogeneous tundra environments and may help to find new parametrizations for numerical weather prediction or climate models.

• Pirk, Norbert; Aalstad, Kristoffer; Mannerfelt, Erik Schytt; Clayer, Francois; Wit, Heleen de & Christiansen, Casper Tai [Show all 9 contributors for this article] (2024). Disaggregating the Carbon Exchange of Degrading Permafrost Peatlands Using Bayesian Deep Learning. Geophysical Research Letters. ISSN 0094-8276. 51(10). doi: 10.1029/2024GL109283. Full text in Research Archive Show summary

  Extensive regions in the permafrost zone are projected to become climatically unsuitable to sustain permafrost peatlands over the next century, suggesting transformations in these landscapes that can leave large amounts of permafrost carbon vulnerable to post-thaw decomposition. We present 3 years of eddy covariance measurements of CH4 and CO2 fluxes from the degrading permafrost peatland Iškoras in Northern Norway, which we disaggregate into separate fluxes of palsa, pond, and fen areas using information provided by the dynamic flux footprint in a novel ensemble-based Bayesian deep neural network framework. The 3-year mean CO2-equivalent flux is estimated to be 106 gCO2 m−2 yr−1 for palsas, 1,780 gCO2 m−2 yr−1 for ponds, and −31 gCO2 m−2 yr−1 for fens, indicating that possible palsa degradation to thermokarst ponds would strengthen the local greenhouse gas forcing by a factor of about 17, while transformation into fens would slightly reduce the current local greenhouse gas forcing.

• Alonso-Gonzalez, Esteban; Aalstad, Kristoffer; Pirk, Norbert; Mazzolini, Marco; Treichler, Désirée & Leclercq, Paul [Show all 9 contributors for this article] (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), p. 4637–4659. doi: 10.5194/hess-27-4637-2023. Full text in Research Archive Show summary

  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.

• Keetz, Lasse Torben; Lieungh, Eva; Karimi-Asli, Kaveh; Geange, Sonya Rita; Gelati, Emiliano & Tang, Hui [Show all 24 contributors for this article] (2023). Climate–ecosystem modelling made easy: The Land Sites Platform. Global Change Biology. ISSN 1354-1013. 29(15), p. 4440–4452. doi: 10.1111/gcb.16808. Full text in Research Archive Show summary

  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.

• Hove, Alouette van; Aalstad, Kristoffer & Pirk, Norbert (2023). Using reinforcement learning to improve drone-based inference of greenhouse gas fluxes. Nordic Machine Intelligence (NMI). 3, p. 1–6. doi: 10.5617/nmi.9897. Full text in Research Archive Show summary

  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.

• Tømmervik, Hans; Julitta, Tommaso; Nilsen, Lennart; Park, Taejin; Burkart, Andreas & Ostapowicz, Katarzyna Anna [Show all 10 contributors for this article] (2023). The northernmost hyperspectral FLoX sensor dataset for monitoring of high-Arctic tundra vegetation phenology and Sun-Induced Fluorescence (SIF). Data in Brief. 50. doi: 10.1016/j.dib.2023.109581. Full text in Research Archive Show summary

  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

• Pirk, Norbert; Aalstad, Kristoffer; Yilmaz, Yeliz A.; Vatne, Astrid; Popp, Andrea & Horvath, Peter [Show all 12 contributors for this article] (2023). Snow-vegetation-atmosphere interactions in alpine tundra. Biogeosciences. ISSN 1726-4170. 20(11), p. 2031–2047. doi: 10.5194/bg-20-2031-2023. Full text in Research Archive Show summary

  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.

• 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, p. 173–193. doi: 10.1007/s10546-022-00704-x. Full text in Research Archive Show summary

  Abstract A one-point measurement of net radiation is typically not representative of radiative energy available for the turbulent exchange of latent and sensible heat at eddy-covariance sites with heterogeneous surface cover. We propose a methodology for providing surface-cover-corrected net radiation matching the footprint of turbulent fluxes at a heterogenous eddy-covariance site. This is demonstrated at a complex sub-alpine site in southern central Norway over a week. The methodology is assessed by comparing the energy balance closure calculated with the regular one-point net radiation measurement at the flux tower against the surface-cover-corrected net radiation. The assessment indicates a decrease in the energy imbalance by 8% when assessed with the energy balance ratio, but no improvement is revealed when assessed with regression methods. However, only a small dataset serves as basis for this demonstration, and the findings therefore cannot necessarily be generalized. Further testing and application of the methodology is required to understand the full effect of surface-cover-correcting mismatching footprints of turbulent fluxes and net radiation at heterogeneous eddy-covariance sites.

• Seco, Roger; Holst, Thomas; Davie-Martin, Cleo L.; Simin, Tihomir; Guenther, Alex & Pirk, Norbert [Show all 8 contributors for this article] (2022). Strong isoprene emission response to temperature in tundra vegetation. Proceedings of the National Academy of Sciences of the United States of America (PNAS). ISSN 0027-8424. 119(38). doi: 10.1073/pnas.2118014119. Full text in Research Archive
• 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), p. 3921–3934. doi: 10.5194/bg-19-3921-2022. Full text in Research Archive Show summary

  We measured CO2 and CH4 fluxes using chambers and eddy covariance (only CO2) from a moist moss tundra in Svalbard. The average net ecosystem exchange (NEE) during the summer (9 June–31 August) was negative (sink), with −0.139 ± 0.032 µmol m−2 s−1 corresponding to −11.8 g C m−2 for the whole summer. The cumulated NEE over the whole growing season (day no. 160 to 284) was −2.5 g C m−2. The CH4 flux during the summer period showed a large spatial and temporal variability. The mean value of all 214 samples was 0.000511 ± 0.000315 µmol m−2 s−1, which corresponds to a growing season estimate of 0.04 to 0.16 g CH4 m−2. Thus, we find that this moss tundra ecosystem is closely in balance with the atmosphere during the growing season when regarding exchanges of CO2 and CH4. The sink of CO2 and the source of CH4 are small in comparison with other tundra ecosystems in the high Arctic. Air temperature, soil moisture and the greenness index contributed significantly to explaining the variation in ecosystem respiration (Reco), while active layer depth, soil moisture and the greenness index were the variables that best explained CH4 emissions. An estimate of temperature sensitivity of Reco and gross primary productivity (GPP) showed that the sensitivity is slightly higher for GPP than for Reco in the interval 0–4.5 ∘C; thereafter, the difference is small up to about 6 ∘C and then begins to rise rapidly for Reco. The consequence of this, for a small increase in air temperature of 1∘ (all other variables assumed unchanged), was that the respiration increased more than photosynthesis turning the small sink into a small source (4.5 g C m−2) during the growing season. Thus, we cannot rule out that the reason why the moss tundra is close to balance today is an effect of the warming that has already taken place in Svalbard.

• Oehri, Jacqueline; Schaepman-Strub, Gabriela; Kim, Jin-Soo; Grysko, Raleigh; Kropp, Heather & Grünberg, Inge [Show all 74 contributors for this article] (2022). Vegetation type is an important predictor of the arctic summer land surface energy budget. Nature Communications. 13. doi: 10.1038/s41467-022-34049-3. Full text in Research Archive Show summary

  Abstract Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm −2 ) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.

• Valk, Luuk D. Van Der; Teuling, Adriaan J.; Girod, Luc; Pirk, Norbert; Stoffer, Robin & Heerwaarden, Chiel C. Van (2022). Understanding wind-driven melt of patchy snow cover. The Cryosphere. ISSN 1994-0416. 16(10), p. 4319–4341. doi: 10.5194/tc-16-4319-2022. Full text in Research Archive Show summary

  Abstract. The representation of snow processes in most large-scale hydrological and climate models is known to introduce considerable uncertainty into the predictions and projections of water availability. During the critical snowmelt period, the main challenge in snow modeling is that net radiation is spatially highly variable for a patchy snow cover, resulting in large horizontal differences in temperatures and heat fluxes. When a wind blows over such a system, these differences can drive advection of sensible and latent heat from the snow-free areas to the snow patches, potentially enhancing the melt rates at the leading edge and increasing the variability of subgrid melt rates. To get more insight into these processes, we examine the melt along the upwind and downwind edges of a 50 m long snow patch in the Finseelvi catchment, Norway, and try to explain the observed behavior with idealized simulations of heat fluxes and air movement over patchy snow. The melt of the snow patch was monitored from 11 June until 15 June 2019 by making use of height maps obtained through the photogrammetric structure-from-motion principle. A vertical melt of 23 ± 2.0 cm was observed at the upwind edge over the course of the field campaign, whereas the downwind edge melted only 3 ± 0.4 cm. When comparing this with meteorological measurements, we estimate the turbulent heat fluxes to be responsible for 60 % to 80 % of the upwind melt, of which a significant part is caused by the latent heat flux. The melt at the downwind edge approximately matches the melt occurring due to net radiation. To better understand the dominant processes, we represented this behavior in idealized direct numerical simulations, which are based on the measurements on a single snow patch by Harder et al. (2017) and resemble a flat, patchy snow cover with typical snow patch sizes of 15, 30, and 60 m. Using these simulations, we found that the reduction of the vertical temperature gradient over the snow patch was the main cause of the reductions in vertical sensible heat flux over distance from the leading edge, independent of the typical snow patch size. Moreover, we observed that the sensible heat fluxes at the leading edge and the decay over distance were independent of snow patch size as well, which resulted in a 15 % and 25 % reduction in average snowmelt for, respectively, a doubling and quadrupling of the typical snow patch size. These findings lay out pathways to include the effect of highly variable turbulent heat fluxes based on the typical snow patch size in large-scale hydrological and climate models to improve snowmelt modeling.

• Pirk, Norbert; Aalstad, Kristoffer; Westermann, Sebastian; Vatne, Astrid; Hove, Alouette van & Tallaksen, Lena Merete [Show all 8 contributors for this article] (2022). Inferring surface energy fluxes using drone data assimilation in large eddy simulations. Atmospheric Measurement Techniques. ISSN 1867-1381. 15(24), p. 7293–7314. doi: 10.5194/amt-15-7293-2022. Full text in Research Archive Show summary

  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.

• 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. Full text in Research Archive Show summary

  CO2 release from thawing permafrost is both a consequence of, and a driver for, global warming, making accurate information on the Arctic carbon cycle essential for climate predictions. Eddy covariance data obtained from Bayelva (Svalbard) in 2015, using well-established processing and quality control techniques, indicate that most of the annual net CO2 uptake is due to high CO2 flux events in winter that are associated with strong winds and probably relate to technical limitations of the gas analyzer. Emission events may relate to either (unidentified) instrumental limitations or to physical processes such as CO2 advection. Excluding the high winter uptake events yields an annual CO2 budget close to zero; whether or not these events are included can, therefore, have a considerable effect on carbon budget calculations. Further investigation will be crucial to pinpoint the factors causing these high CO2 flux events and to derive scientifically substantiated flux processing standards.

• Pastorello, Gilberto; Trotta, Carlo; Canfora, Eleonora; Chu, Housen; Christianson, Danielle & Cheah, You-Wei [Show all 287 contributors for this article] (2020). The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Scientific Data. 7. doi: 10.1038/s41597-020-0534-3. Full text in Research Archive Show summary

  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 [Show all 75 contributors for this article] (2019). Large loss of CO2 in winter observed across the northern permafrost region. Nature Climate Change. ISSN 1758-678X. 9, p. 852–857. doi: 10.1038/s41558-019-0592-8. Full text in Research Archive Show summary

  Recent warming in the Arctic, which has been amplified during the winter, greatly enhances microbial decomposition of soil organic matter and subsequent release of carbon dioxide (CO₂). However, the amount of CO₂ released in winter is not known and has not been well represented by ecosystem models or empirically based estimates. Here we synthesize regional in situ observations of CO₂ flux from Arctic and boreal soils to assess current and future winter carbon losses from the northern permafrost domain. We estimate a contemporary loss of 1,662 TgC per year from the permafrost region during the winter season (October–April). This loss is greater than the average growing season carbon uptake for this region estimated from process models (−1,032 TgC per year). Extending model predictions to warmer conditions up to 2100 indicates that winter CO₂ emissions will increase 17% under a moderate mitigation scenario—Representative Concentration Pathway 4.5—and 41% under business-as-usual emissions scenario—Representative Concentration Pathway 8.5. Our results provide a baseline for winter CO₂ emissions from northern terrestrial regions and indicate that enhanced soil CO₂ loss due to winter warming may offset growing season carbon uptake under future climatic conditions.

• Qiu, Chunjing; Zhu, Dan; Ciais, Philippe; Guenet, Bertrand; Krinner, Gerhard & Peng, Shushi [Show all 54 contributors for this article] (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), p. 497–519. doi: 10.5194/gmd-11-497-2018. Full text in Research Archive Show summary

  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; Mastepanov, Mikhail; López-Blanco, Efrén; Christensen, Louise H.; Christiansen, Hanne H & Hansen, Birger Ulf [Show all 10 contributors for this article] (2017). Toward a statistical description of methane emissions from arctic wetlands. Ambio. ISSN 0044-7447. 46(Supplement 1), p. 70–80. doi: 10.1007/s13280-016-0893-3. Full text in Research Archive Show summary

  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; 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), p. 3157–3169. doi: 10.5194/bg-14-3157-2017. Full text in Research Archive Show summary

  The large spatial variability in Arctic tundra complicates the representative assessment of CO₂ budgets. Accurate measurements of these heterogeneous landscapes are, however, essential to understanding their vulnerability to climate change. We surveyed a polygonal tundra lowland on Svalbard with an unmanned aerial vehicle (UAV) that mapped ice-wedge morphology to complement eddy covariance (EC) flux measurements of CO₂. The analysis of spectral distributions showed that conventional EC methods do not accurately capture the turbulent CO₂ exchange with a spatially heterogeneous surface that typically features small flux magnitudes. Nonlocal (low-frequency) flux contributions were especially pronounced during snowmelt and introduced a large bias of -46 gCm^-2 to the annual CO₂ budget in conventional methods (the minus sign indicates a higher uptake by the ecosystem). Our improved flux calculations with the ogive optimization method indicated that the site was a strong sink for CO₂ in 2015 (-82 gCm^-2). Due to differences in light-use efficiency, wetter areas with lowcentered polygons sequestered 47% more CO₂ than drier areas with flat-centered polygons. While Svalbard has experienced a strong increase in mean annual air temperature of more than 2K in the last few decades, historical aerial photographs from the site indicated stable ice-wedge morphology over the last 7 decades. Apparently, warming has thus far not been sufficient to initiate strong ice-wedge degradation, possibly due to the absence of extreme heat episodes in the maritime climate on Svalbard. However, in Arctic regions where ice-wedge degradation has already initiated the associated drying of landscapes, our results suggest a weakening of the CO₂ sink in polygonal tundra.

• 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), p. 903–912. doi: 10.5194/bg-13-903-2016. Full text in Research Archive
• Pirk, Norbert; Tamstorf, Mikkel P.; Lund, Magnus; Mastepanov, Mikhail; Pedersen, Stine Højlund & Mylius, Maria R. [Show all 9 contributors for this article] (2016). Snowpack fluxes of methane and carbon dioxide from high Arctic tundra. Journal of Geophysical Research (JGR): Biogeosciences. ISSN 2169-8953. 121(11), p. 2886–2900. doi: 10.1002/2016JG003486. Full text in Research Archive
• Pirk, Norbert; Santos, Telmo; Gustafson, Carl; Johansson, Anders J.; Tufvesson, Fredrik & Parmentier, Frans-Jan W. [Show all 8 contributors for this article] (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), p. 6732–6738. doi: 10.1002/2015GL065034. Full text in Research Archive

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• Parmentier, Frans-Jan W.; Marchesini, Luca Belelli; Wille, Christian; Hugelius, Gustaf; Siewert, Mathias B. & Aurela, Mika [Show all 20 contributors for this article] (2026). Can soil carbon content explain inter-site differences in carbon flux magnitude across the permafrost region? doi: 10.5194/egusphere-egu26-14668. Full text in Research Archive Show summary

  The permafrost region holds vast amounts of carbon which, upon thaw, may be released to the atmosphere as CO2 through enhanced decomposition. While links between soil carbon content and respiration have been shown by, for example, numerous incubation studies, it remains challenging to establish similar relationships from in-situ data collected in the field – especially at the large landscape-scale of eddy covariance towers. Part of the reason is the high heterogeneity of Arctic landscapes, combined with frequently shifting footprint distributions, and general lack of detailed soil carbon data that make it difficult to separate the signal from the noise. In this study, therefore, we combine detailed surveys of soil carbon content with high resolution footprint analyses to explore whether inter-site differences in carbon fluxes across ten sites spanning the Arctic can be explained by soil carbon content. Soil carbon data at each site was collected across dominant landforms and analyzed with depth. At 5 sites, these data were further developed into high resolution soil carbon maps. At the remaining sites, spatially weighted estimates of soil carbon content were determined proportionally to dominant landforms. Net CO2 fluxes collected by the towers were processed according to the same pipeline and partitioned into GPP and ecosystem respiration. These fluxes were related to the amount of soil carbon in the active layer at dominant landforms through a spatially explicit footprint analysis. Our preliminary results suggest that active layer soil carbon storage is a strong predictor of inter-site differences in ecosystem respiration during the growing season. This study will further explore the robustness of this relationship across the Arctic and throughout the year.

• Yilmaz, Yeliz A.; Pirk, Norbert; Tallaksen, Lena M.; Stordal, Frode; Berntsen, Terje Koren & Bryn, Anders (2026). Cold region land-atmosphere interactions. doi: https:/www.gewexevents.org/meetings/glass2026/program/abstracts/#Y. Full text in Research Archive Show summary

  Environmental change in high latitudes is progressing faster and more intensely than in many other regions of the Earth. A warmer climate has already triggered unprecedented changes in the terrestrial cryosphere, hydrosphere and ecosphere, which feed back to the atmosphere and the hydrologic cycle. Yet, many studies still rely on one-way coupling between the atmosphere and the land surface, thereby neglecting important interactions and feedback mechanisms. To advance our knowledge on climate change in northern environments, LATICE (Land–ATmosphere Interactions in Cold Environments) was established as a strategic research initiative at the University of Oslo in 2015. Since then, it has developed into a collaborative network of researchers from atmospheric and terrestrial research groups. We combine novel observations, process-based model development, and integrated Earth observation frameworks to improve our understanding of high latitude environmental processes and their representation in Earth System Models - particularly the Norwegian Earth System Model (NorESM). The current LATICE infrastructure including flux towers, drone-based measurements, and a cold climate container contributes to expanding our observational data records in cold environments complemented by regionally calibrated satellite products tailored to boreal and Arctic conditions. These coordinated in-situ, airborne, and spaceborne datasets provide important constraints on surface fluxes, snow dynamics, and vegetation processes. On the modelling side, LATICE researchers have been contributing to land surface and hydrological models to better represent cold region processes by including process parameterizations, new plant functional types, and vegetation demography. The integration of observations and models through data assimilation methods, and further exploration of hybrid approaches with machine learning algorithms support improved understanding of land-atmosphere coupling and uncertainty quantification. This presentation highlights key activities and scientific advances within the LATICE community, presents example studies, and discusses future directions for scientific collaboration with the Pan-GLASS community.

• Aalstad, Kristoffer; Alonso-González, Esteban; Fiddes, Joel; Kääb, Andreas; Pirk, Norbert & Treichler, Désirée [Show all 9 contributors for this article] (2026). Global ensemble-based mountain snow reanalysis. doi: https:/snowhydro.unirioja.es/public/files/Snowhydro_abstracts.pdf. Full text in Research Archive Show summary

  Snow is an essential climate variable that regulates the global cycles of energy, carbon, and water. However, the state of the snowpack is only partially observable by satellites due to gaps, noise, and indirect retrieval algorithms. The fusion of uncertain snow models and noisy observations through data assimilation presents a natural solution to this problem. Nonetheless, data assimilation has received relatively little attention from the snow remote sensing community despite its potential to serve as a generalized retrieval method that adds value by inferring gap-free hidden snow state variables with uncertainty quantification. We present ongoing research on applying ensemble-based data assimilation techniques to produce a global mountain snow reanalysis constrained by satellite data. These efforts are motivated by the challenge of estimating seasonal snow mass in mountainous terrain which remains a major unsolved problem in snow hydrology. Taking a step towards solving this problem, we aim to generate the first global, consistent, ensemble-based, daily, kilometer-scale reanalysis of seasonal snow mass (i.e. snow water equivalent) and fractional snow-covered area dynamics for all mountains outside Antarctica. To produce our reanalysis, we assimilate fractional snow-covered area satellite retrievals from ESA Snow_cci into a simple snow model run at a relatively high spatio-temporal resolution. Given the efficiency of our model, we are able to leverage promising iterative ensemble-based schemes while ensuring that the problem remains computationally feasible. Specifically, we build on developments hybridizing ensemble Kalman and particle methods for robust and tractable Bayesian inference. We also demonstrate how to hierarchically infer hyperparameters related to snow climatology. The new reanalysis approach is evaluated using independent spaceborne, airborne, and in-situ validation data. Finally, we benchmark the performance of our reanalysis by comparing it to other global mountain snow products. Unlike existing global snow mass products, our product is uncertainty-aware and is tailored to address a key knowledge gap concerning mountain snow mass. We highlight that, by combining observations with models, data assimilation can transform largely untapped climate data into actionable climate information on essential climate variables. The uncertainty quantification in this probabilistic climate information enables informed decision making in response to climate change and its impacts.

• Ahlbäck, Malin; Pirk, Norbert; Tallaksen, Lena M. & Silantyeva, Olga (2025). Temporal Dynamics and Hydrological Impacts of Snow-Atmosphere Vapor Exchanges in a High-Altitude Catchment. doi: https:/www.hydrologiraadet.no/wp-content/uploads/2025/08/AbstractsLillehammer2025_210825.pdf. Full text in Research Archive Show summary

  The evolution of the snowpack is strongly influenced by turbulent fluxes, where latent heat transfer alters the snowpack water equivalent through processes such as snow sublimation and deposition, in addition to changing the cold content and structure of the snow. In alpine regions, the contribution of sublimation to the water balance varies widely, with results indicating only a negligible part of snowfall is sublimated, to up to 90% locally at wind-exposed ridges and crests. To improve our understanding of these dynamics, this study integrates small-scale micrometeorological observations with catchment-scale modelling, enabling a multi-scale analysis of snow-atmosphere water vapour exchanges and their impacts on the snowpack water balance in a high-altitude catchment in southern Norway. We will outline preliminary insights into the temporal scales governing water vapour fluxes and its linkage to environmental drivers, using micrometeorological site data and wavelet analysis. Compared to the commonly used eddy covariance method, processing the high-frequency site data using wavelet analysis allowed for resolving fluxes over small (<1 min) time scales, and offer a detailed analysis of the eddy distribution over time, and as such, new insights into the dynamics governing sublimation. In addition, the wavelet analysis method handled non-stationary time periods where the eddy covariance method fails, indicating the potential for using wavelet analysis over eddy covariance for processing complex micrometeorological data where traditional methods are not possible. To evaluate the broader hydrological impacts of sublimation and deposition fluxes, we also plan to present results from a snow model framework over the whole catchment and contrast the annual variability between dry and wet winters. Our findings are expected to contribute to a deeper understanding of sublimation and deposition processes in Nordic regions, and how they may change in a future climate. By combining field measurements and modelling approaches, these insights can be used for advancing the parametrisation of winter vapour fluxes for applications in snow hydrology.

• Hove, Alouette van; Aalstad, Kristoffer; Lind, Vibeke; Arndt, Claudia; Odongo, Vincent & Ceriani, Rodolfo [Show all 9 contributors for this article] (2025). Inferring methane emissions from African livestock by fusing drone, tower, and satellite data. 9th International Greenhouse Gas and Animal Agriculture (GGAA2025) conference. Full text in Research Archive
• Aalstad, Kristoffer; Alonso-Gonzalez, Esteban; Fiddes, Joel; Guillet, Gregoire; Kääb, Andreas Max & Mazzolini, Marco [Show all 11 contributors for this article] (2025). Ensemble-based cryospheric reanalysis to infer global snow mass. Full text in Research Archive Show summary

  Snow, glaciers, and permafrost are essential climate variables (ECV) that regulate the global cycles of energy, carbon, and water. At the same time, these cryospheric ECVs are only partially observable by satellites due to gaps, noise, and indirect retrieval algorithms. Data assimilation (DA), namely the Bayesian fusion of uncertain models and noisy observations, presents a natural solution to the problem of partial observability. Nonetheless, unlike in numerical weather prediction, DA has received relatively little attention from the Earth observation (EO) community despite its potential as a generalized retrieval framework that adds value by filling gaps and inferring latent ECVs with uncertainty quantification. This contribution presents active research on applying ensemble-based DA techniques to carry out cryospheric reanalysis constrained by satellite data. Our future goals with these efforts are to generate consistent global high resolution reanalyses of seasonal snow mass (also known as snow water equivalent or simply SWE), glacier mass balance, and the thermal state of permafrost. In doing so, we seek to leverage a myriad of data streams including Earth observing satellites, global atmospheric reanalyses, airborne retrievals, and in-situ observations. Here we focus on seasonal snow since accurate global snow mass estimation, particularly in mountainous terrain, remains a major unsolved problem in snow hydrology. To help tackle this problem, we are developing a global scale ensemble-based snow reanalysis product by assimilating ESA Snow_cci fractional snow-covered area retrievals. Using a simple snow model we are able to devote considerable computational resources to generate a global snow reanalysis at daily temporal resolution and kilometric spatial resolution while still being able to afford using promising iterative ensemble-based DA schemes. For the latter, we explore recent developments in hybridizing iterative ensemble Kalman and particle methods to provide robust Bayesian posterior inference. In particular, we demonstrate how these schemes can be used as nested smoothers to hierarchically infer prior hyperparameters related to snow climatology. The new reanalysis approach is evaluated using independent spaceborne, airborne, and in-situ validation data by comparing its performance to state-of-the-art regional snow reanalyses and existing global snow mass products in the form of ERA5-Land reanalysis data and ESA Snow_cci SWE retrievals. Unlike existing global snow products, this new snow reanalysis product is uncertainty-aware, assimilates snow satellite data, and specifically targets a key knowledge gap concerning mountain snow mass. Concurrent efforts to adapt this DA framework towards the generation of global ensemble-based reanalyses for glaciers and permafrost will also be showcased to emphasize synergies across these terrestrial cryospheric reanalysis efforts. We highlight that, by combining EO with cryospheric models, ensemble-based DA can transform largely untapped climate data into actionable climate information on cryospheric ECVs. The baked-in uncertainty quantification in this probabilistic climate information empowers us to make decisions in response to climate change and its perilous impacts on the mountain cryosphere.

• Aalstad, Kristoffer; Blandini, Giulia; Guidicelli, Matteo; Pirk, Norbert & Hove, Alouette van (2025). Learning to assimilate and assimilating to learn. Full text in Research Archive Show summary

  https://www.hydrologiraadet.no/seminar-om-maskinlaering-innen-hydrologi-og-meteorologi-25-april/

• Silantyeva, Olga; Ahlbäck, Malin; Yilmaz, Yeliz A.; Tallaksen, Lena M.; Lappegard, Gaute & Erlandsen, Helene B. [Show all 11 contributors for this article] (2024). SnowSub: Assessing snow sublimation for hydropower production in Norway. Project results at Snow Bridges workshop. Full text in Research Archive
• Parmentier, Frans-Jan W.; Marchesini, Luca Belelli; Wille, Christian; Hugelius, Gustaf; Siewert, Matthias Benjamin & Aurela, Mika [Show all 21 contributors for this article] (2024). Does soil carbon content explain inter-site differences in ecosystem respiration across the permafrost region? Full text in Research Archive
• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon; Gascoin, Simon; Pirk, Norbert & Remmers, Janneke [Show all 9 contributors for this article] (2024). Leveraging Earth observations to model snow cover dynamics over Scandinavia. Full text in Research Archive
• Tallaksen, Lena M.; Stordal, Frode; Pirk, Norbert & Yilmaz, Yeliz A. (2024). Land-Atmosphere Interactions at High Northern Latitudes. Full text in Research Archive
• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon; Gascoin, Simon; Pirk, Norbert & Remmers, Janneke [Show all 9 contributors for this article] (2024). Using Earth observations to model snow cover over Scandinavia. Full text in Research Archive
• Ahlbäck, Malin; Pirk, Norbert; Tallaksen, Lena M. & Silantyeva, Olga (2024). Assessing the impact of sublimation for hydrological forecasting. Full text in Research Archive
• Aalstad, Kristoffer; Hock, Regine; Mazzolini, Marco; Pirk, Norbert; Treichler, Désirée & Westermann, Sebastian [Show all 9 contributors for this article] (2024). Ensemble-Based Reanalysis of the Terrestrial Cryosphere. Full text in Research Archive Show summary

  The terrestrial cryosphere in the form of glaciers, permafrost, and seasonal snow, plays a vital role in the climate system by regulating the global cycles of energy, carbon, and water. As a result, these components of the cryosphere are considered essential climate variables (ECVs) by the Global Climate Observing System. At the same time, these ECVs are only partially observable by Earth observing satellites due to gaps, noise, and indirect retrieval algorithms. The statistical fusion of uncertain models and noisy observations referred to as data assimilation in geoscience represents a natural solution to this problem of partial observability. Nonetheless, beyond the confines of weather prediction and atmospheric reanalyses, data assimilation has received relatively little attention in broader Earth system science despite its potential for adding value to observations by filling gaps and inferring latent ECVs with uncertainty quantification. In this contribution, we present ongoing efforts in several projects applying ensemble-based (probabilistic) data assimilation techniques to carry out long term and large scale reanalyses of the terrestrial cryosphere. Our lofty goals in these projects are to generate global high resolution multi-decadal ensemble-based reanalyses of glacier mass balance, the thermal state of permafrost, and seasonal snow mass. In doing so, we are leveraging a plethora of emerging data from Earth observing satellites, global atmospheric reanalyses, and in-situ observations. Herein, we will focus mainly on the general implementation of the novel ensemble-based methods that we are using to solve the reanalysis (or reconstruction) problem as opposed to the structure of the specific cryospheric models and data that they are applied to. Considerable emphasis is placed on how these methods can be deployed at unprecedented spatio-temporal scales while retaining uncertainty quantification that is crucial in guiding decisions. Looking further ahead, we will highlight the challenges that remain in pushing the boundaries of terrestrial reanalysis efforts towards the hillslope (100 m) scale and making use of the largely untapped and continuously growing stream of Earth observation data.

• Engeland, Kolbjørn; Erlandsen, Helene B.; Gelati, Emiliano; Huang, Shaochun; Narayanappa, Devaraju & Pirk, Norbert [Show all 10 contributors for this article] (2023). Intercomparison of local evapotranspiration estimates at high latitudes. Full text in Research Archive
• Vatne, Astrid; Vollsnes, Ane Victoria; Pirk, Norbert & Tallaksen, Lena M. (2023). Drought effects on dwarf birch leaves. Full text in Research Archive
• Engeland, Kolbjørn; Aas, Kjetil Schanke; Erlandsen, Helene Birkelund; Gelati, Emiliano; Huang, Shaochun & Narayanappa, Devaraju [Show all 11 contributors for this article] (2022). LATICE MIP evapotranspiration – A model intercomparison project for evapotranspiration estimates at high latitudes. Full text in Research Archive
• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon; Gascoin, Simon; Pirk, Norbert & Remmers, Janneke [Show all 8 contributors for this article] (2022). Evaluating modeled snow cover dynamics over Fennoscandia using Earth observations and reanalyses. Full text in Research Archive
• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon; Gascoin, Simon; Pirk, Norbert & Remmers, Janneke [Show all 8 contributors for this article] (2022). Evaluating modeled snow cover dynamics over Fennoscandia using Earth observations. Full text in Research Archive Show summary

  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.

• Pastorello, Gilberto; Trotta, Carlo; Canfora, Eleonora; Chu, Housen; Christianson, Danielle & Cheah, You-Wei [Show all 288 contributors for this article] (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. 8(1). doi: 10.1038/s41597-021-00851-9. Full text in Research Archive
• Althuizen, Inge; Christiansen, Casper Tai; Michelsen, Anders; Westermann, Sebastian; Pirk, Norbert & Risk, David [Show all 7 contributors for this article] (2021). Annual ecosystem carbon budgets across an abrupt permafrost thaw gradient in Northern Norway. Full text in Research Archive
• Aalstad, Kristoffer; Fiddes, Joel; Martin, Leo Celestin Paul; Alonso-Gonzalez, Esteban; Yilmaz, Yeliz A. & Pirk, Norbert [Show all 7 contributors for this article] (2021). Workshop on downscaling with TopoSCALE for cryospheric applications. Full text in Research Archive Show summary

  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.

• Aalstad, Kristoffer; Alonso-Gonzalez, Esteban; Bazilova, Varvara; Bertino, Laurent; Fiddes, Joel & Pirk, Norbert [Show all 7 contributors for this article] (2021). Leveraging emerging Earth observations using data assimilation. Full text in Research Archive Show summary

  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; Westermann, Sebastian; Pirk, Norbert; Fiddes, Joel & Bertino, Laurent (2021). Retrieving fractional snow-covered area from optical satellites using data assimilation. Full text in Research Archive Show summary

  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 & Filhol, Simon [Show all 9 contributors for this article] (2021). Unmixing satellite imagery using data assimilation to map snow and albedo. Full text in Research Archive
• Aalstad, Kristoffer; Alonso-Gonzalez, Esteban; Bertino, Laurent; Fiddes, Joel; Hove, Alouette van & Pirk, Norbert [Show all 8 contributors for this article] (2021). Learning from Earth observations using data assimilation. Full text in Research Archive
• Bryn, Anders; Dalen, Thea Grobstok; Finne, Eirik Aasmo; Heiberg, Hanne; Nilsen, Irene Brox & Parmentier, Frans-Jan W. [Show all 22 contributors for this article] (2020). Natur i endring - en vandreutstilling. Full text in Research Archive
• Aalstad, Kristoffer; Westermann, Sebastian; Pirk, Norbert; Fiddes, Joel; Karsten, Logan & McCreight, James [Show all 8 contributors for this article] (2020). Constraining land surface models with emerging observations. Full text in Research Archive
• 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. Full text in Research Archive
• 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. Full text in Research Archive
• Althuizen, Inge; Christiansen, Casper Tai; Michelsen, Anders; Pirk, Norbert; Risk, David & Westermann, Sebastian [Show all 7 contributors for this article] (2020). Annual ecosystem carbon budgets across an abrupt permafrost thaw gradient in Northern Norway. Full text in Research Archive
• Althuizen, Inge; Christiansen, Casper Tai; Michelsen, Anders; Pirk, Norbert; Risk, David & Westermann, Sebastian [Show all 7 contributors for this article] (2020). Annual ecosystem carbon budgets across an abrupt permafrost thaw gradient in Northern Norway. Full text in Research Archive
• Pirk, Norbert; Ramtvedt, Eirik Næsset; Decker, Sven; Cassiani, Massimo; Burkhart, John & Stordal, Frode [Show all 7 contributors for this article] (2019). Causes of surface energy imbalances of eddy covariance measurements in mountainous terrain. Full text in Research Archive
• Natali, Susan M.; Watts, Jennifer D.; Rogers, Brendan M.; Potter, Stefano; Ludwig, Sarah & Selbmann, Anne-Katrin [Show all 75 contributors for this article] (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), p. 1005–1005. doi: 10.1038/s41558-019-0644-0. Full text in Research Archive
• Natali, Sue; Watts, Jennifer; Rogers, Brendan M.; Potter, Stefano; Abbott, Benjamin & Arndt, Kyle [Show all 72 contributors for this article] (2018). A pan-arctic synthesis of nongrowing season respiration: Key drivers and responses to a changing climate. Full text in Research Archive
• Filhol, Simon; Pirk, Norbert; Schuler, Thomas & Burkhart, John (2017). The Evolution of a Snow Dune Field. Full text in Research Archive
• 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. Full text in Research Archive

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