Laura Mack

• 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.

• Mack, Laura; Rudolph, Annette & Névir, Peter (2026). Detecting atmospheric fronts based on diabatic processes using the dynamic state index (DSI). Meteorology and atmospheric physics (Print). ISSN 0177-7971. 138(3). doi: 10.1007/s00703-026-01141-y. Full text in Research Archive Show summary

  Atmospheric fronts are associated with precipitation and strong diabatic processes. Therefore, detecting fronts objectively from reanalyses is a prerequisite for the long-term study of their weather impacts. For this purpose, several algorithms exist, e.g., based on the thermal front parameter (TFP) or the F diagnostic that combines relative vorticity and horizontal temperature gradient. It is shown that both methods have problems to identify weak warm fronts since they are characterized by low baroclinicity. To avoid this inaccuracy, a new algorithm is developed that considers fronts as deviation from an adiabatic and steady state. These deviations can be accurately measured using the dynamic state index (DSI). The DSI shows a coherent dipole structure along fronts and is strongly correlated with precipitation sums. Using the DSI, a new front detection algorithm is developed (called DSI method), which allows to clearly identify the global storm track regions. The properties of the identified fronts depend on the applied front detection method, whereby fronts identified with the DSI method have particularly high specific humidity. Using a simple estimate for front speed, it is shown that also the front speed depends on the front detection method and that fronts identified using the DSI method have a higher front speed than fronts identified with the TFP method. This can be attributed to the dipole structure of the DSI and thus demonstrates the potential of the DSI to inherently indicate the movement speed and direction in atmospheric flows.

• 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.

• 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.

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• Mack, Laura (2026). Boundary-Layer Processes in Heterogeneous Arctic and Alpine Environments: Integrating Observational, Numerical and Probabilistic Approaches. Universitetet i Oslo. Full text in Research Archive Show summary

  Arctic and alpine regions play an important role in shaping atmospheric circulations, the Earth’s energy and water balance, and host unique ecosystems. Their landscape structure is often highly heterogeneous, comprising tundra, lakes, wetlands, and seasonal or perennial snow cover. This land surface heterogeneity drives complex atmosphere-cryosphere-vegetation interactions that govern the exchange of momentum, heat and moisture between land and atmosphere. In winter, nocturnal radiative cooling over snow induces the formation of stable boundary layers characterized by a pronounced surface-based temperature inversion, which suppresses turbulence, leading to anisotropic and intermittent flows. Such conditions challenge standard boundary-layer theories and make it difficult to represent arctic and alpine boundary-layer processes in numerical weather prediction (NWP) models, introducing systematic errors, especially during cold extremes. This thesis complements observational, numerical and probabilistic approaches to investigate land-atmosphere coupling in heterogeneous arctic and alpine environments and to develop new parameterizations. Multiple observational techniques, including eddy-covariance measurements, fiber-optic distributed temperature sensing, and land-cover classifications, are used for model verification and development. A software package for analyzing eddy-covariance measurements, designed specifically for heterogeneous terrain, is developed and used to investigate the role of coherent structures in turbulent transport over alpine tundra. A data-driven probabilistic framework for land-atmosphere coupling based on a copula Bayesian network is introduced, which improves the representation of cold temperature extremes and explicitly accounts for uncertainty due to subgrid-scale thermal heterogeneity. Large eddy simulations initialized with stochastic land cover maps are set up to study the full three-dimensional, non-linear interactions between surface heterogeneities and atmosphere. Their outputs are subsequently emulated using Gaussian process regression to predict uncertainty-aware extended patch profiles up to the blending height. Together, these probabilistic and process-based perspectives deepen our understanding of surfaceatmosphere interactions and pave the way for improved representation of subgridscale surface heterogeneities in NWP models.

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