Franziska Hellmuth

• Dekhtyareva, Alena; Sodemann, Harald; Carlsen, Tim; Thurnherr, Iris; Johannessen, Aina Marie & Seidl, Andrew [Show all 14 contributors for this article] (2026). Spatially distributed measurements of aerosols and stable isotopes in water vapour and precipitation in coastal Northern Norway during the ISLAS2021 campaign. Earth System Science Data. ISSN 1866-3508. 18(4), p. 2573–2607. doi: 10.5194/essd-18-2573-2026. Full text in Research Archive Show summary

  Precipitation from mixed-phase clouds at high-latitudes is difficult to represent correctly in numerical weather prediction models. Paired water vapour and precipitation isotope measurements provide a constraint on the integrated effect of evaporation and condensation processes, but have rarely been collected in a way that allows to use these for model validation and improvement. Here we present a collection of spatially distributed measurements of water isotopes in the different phases at high time resolution during the ISLAS2021 field campaign over the period 15 to 30 March 2021. The main observational site of this campaign was Andenes, Norway (69.3144° N, 16.1194° E). Isotopic measurements were conducted simultaneously at sea level and a mountain observatory, as well as additional coastal sites at distances of 120 km (Tromsø, Norway) and 1100 km (Bergen, Norway), enabling the assessment of spatial representativeness of vapour isotope measurements. Precipitation samples for water isotope analysis were collected on site at sub-event time resolution, and along a transect across the Vesterålen archipelago. These measurements were complemented by a suite of aerosol measurements, including ice-nucleating particles, and additional in situ and remote sensing observations of meteorological variables. During the two weeks of the ISLAS2021 field campaign, frequent alternations between mid-latitude and arctic weather systems were encountered, providing a range of different cases for more detailed process studies. The dataset is available at https://doi.org/10.1594/PANGAEA.984616 (Sodemann et al., 2025), and can serve as a test bed for assessing the spatial representativeness and sampling strategies for water isotope measurements on meteorological time scales. Furthermore, we anticipate our data to be useful in various aspects related to cloud microphysics, for example the quantification of riming processes in convective clouds, the role of ice nucleating particles in marine cold-air outbreaks, and on the condensation efficiency of mid-latitude storms.

• Hellmuth, Franziska; Carlsen, Tim; Daloz, Anne Sophie; David, Robert Oscar; Che, Haochi & Storelvmo, Trude (2025). Evaluation of biases in mid-to-high-latitude surface snowfall and cloud phase in ERA5 and CMIP6 using satellite observations. Atmospheric Chemistry and Physics (ACP). ISSN 1680-7316. 25(2), p. 1353–1383. doi: 10.5194/acp-25-1353-2025. Full text in Research Archive Show summary

  Supercooled liquid-containing clouds (sLCCs) play a significant role in Earth's radiative budget and the hydrological cycle, especially through surface snowfall production. Evaluating state-of-the-art climate models with respect to their ability to simulate the frequency of occurrence of sLCCs and the frequency with which they produce snow is, therefore, critically important. Here, we compare these quantities as derived from satellite observations, reanalysis datasets, and Earth system models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) and find significant discrepancies between the datasets for mid- and high latitudes in both hemispheres. Specifically, we find that the ERA5 reanalysis and 10 CMIP6 models consistently overestimate the frequency of sLCCs and snowfall frequencies from sLCCs compared to CloudSat–CALIPSO satellite observations. The biases are very similar for ERA5 and the CMIP6 models, which indicates that the discrepancies in cloud phase and snowfall stem from differences in the representation of cloud microphysics rather than the representation of meteorological conditions. This, in turn, highlights the need for refinements in the models’ parameterizations of cloud microphysics in order for them to represent cloud phase and snowfall accurately. The thermodynamic phase of clouds and precipitation has a strong influence on simulated climate feedbacks and, thus, projections of future climate. Understanding the origin(s) of the biases identified here is, therefore, crucial for improving the overall reliability of climate models.

• Gjelsvik, Astrid Bragstad; David, Robert Oscar; Carlsen, Tim; Hellmuth, Franziska; Hofer, Stefan & McGraw, Zachary [Show all 8 contributors for this article] (2025). Using a region-specific ice-nucleating particle parameterization improves the representation of Arctic clouds in a global climate model. Atmospheric Chemistry and Physics (ACP). ISSN 1680-7316. 25(3), p. 1617–1637. doi: 10.5194/acp-25-1617-2025. Full text in Research Archive Show summary

  Projections of global climate change and Arctic amplification are sensitive to the representation of low-level cloud phase in climate models. Ice-nucleating particles (INPs) are necessary for primary cloud ice formation at temperatures above approximately −38 °C and thus significantly affect cloud phase and cloud radiative effect (CRE). Due to their complex and insufficiently understood variability, INPs constitute an important modelling challenge, especially in remote regions with few observations, such as the Arctic. In this study, INP observations were carried out at Andenes, Norway, in March 2021. These observations were used as a basis for an Arctic-specific and purely temperature-dependent INP parameterization, which was implemented into the Norwegian Earth System Model (NorESM). This implementation results in an annual average increase in cloud liquid water path (CLWP) of 70 % for the Arctic and improves the representation of cloud phase compared to satellite observations. The change in CLWP in boreal autumn and winter is found to likely be the dominant contributor to the annual average increase in net surface CRE of 2 W m−2. This large surface flux increase brings the simulation into better agreement with Arctic ground-based measurements. Despite the fact that the model cannot respond fully to the INP parameterization change due to fixed sea surface temperatures, Arctic surface air temperature increases by 0.7 °C in boreal autumn. These findings indicate that INPs could have a significant impact on Arctic climate and that a region-specific INP parameterization can be a useful tool to improve cloud representation in the Arctic region.

• Hofer, Stefan; Hahn, Lily; Shaw, Jonah; McGraw, Zachary; Bruno, Olimpia & Hellmuth, Franziska [Show all 11 contributors for this article] (2024). Realistic representation of mixed-phase clouds increases projected climate warming. Communications Earth & Environment. 5, p. 1–12. doi: 10.1038/s43247-024-01524-2. Full text in Research Archive Show summary

  Clouds are the main source of uncertainties when projecting climate change. Mixed-phase clouds (MPCs) that contain ice and supercooled-liquid particles are especially hard to constrain, and climate models neither agree on their phase nor their spatial extent. This is problematic, as models that underestimate contemporary supercooled-liquid in MPCs will underestimate future warming. Furthermore, it has recently been shown that supercooled-liquid water in MPCs is not homogeneously-mixed, neither vertically nor horizontally. However, while there have been attempts at observationally constraining MPCs to constrain uncertainties in future warming, all studies only use the phase of the interior of MPCs. Using novel satellite observations, and contrary to current knowledge, we show that MPCs are more liquid at the cloud top globally. We use these observations to constrain, for the first time, the cloud top phase in addition to the interior of MPCs in a global climate model, leading to +1C more 21st century warming i

• Cooper, Steven J.; L'Ecuyer, Tristan S.; Wolff, Mareile Astrid; Kuhn, Thomas; Pettersen, Claire & Wood, Norman B. [Show all 14 contributors for this article] (2022). Exploring Snowfall Variability through the High-Latitude Measurement of Snowfall (HiLaMS) Field Campaign. Bulletin of The American Meteorological Society - (BAMS). ISSN 0003-0007. 103(8), p. E1762–E1780. doi: 10.1175/BAMS-D-21-0007.1. Full text in Research Archive Show summary

  The High-Latitude Measurement of Snowfall (HiLaMS) campaign explored variability in snowfall properties and processes at meteorologically distinct field sites located in Haukeliseter, Norway, and Kiruna, Sweden, during the winters of 2016/17 and 2017/18, respectively. Campaign activities were founded upon the sensitivities of a low-cost, core instrumentation suite consisting of Micro Rain Radar, Precipitation Imaging Package, and Multi-Angle Snow Camera. These instruments are highly portable to remote field sites and, considered together, provide a unique and complementary set of snowfall observations including snowflake habit, particle size distributions, fall speeds, surface snowfall accumulations, and vertical profiles of radar moments and snow water content. These snow-specific parameters, used in combination with existing observations from the field sites such as snow gauge accumulations and ambient weather conditions, allow for advanced studies of snowfall processes. HiLaMS observations were used to 1) successfully develop a combined radar and in situ microphysical property retrieval scheme to estimate both surface snowfall accumulation and the vertical profile of snow water content, 2) identify the predominant snowfall regimes at Haukeliseter and Kiruna and characterize associated macrophysical and microphysical properties, snowfall production, and meteorological conditions, and 3) identify biases in the HARMONIE-AROME numerical weather prediction model for forecasts of snowfall accumulations and vertical profiles of snow water content for the distinct snowfall regimes observed at the mountainous Haukeliseter site. HiLaMS activities and results suggest value in the deployment of this enhanced snow observing instrumentation suite to new and diverse high-latitude locations that may be underrepresented in climate and weather process studies.

• Hellmuth, Franziska; Engdahl, Bjørg Jenny Kokkvoll; Storelvmo, Trude; David, Robert Oscar & Cooper, Steven J. (2021). Snowfall Model Validation Using Surface Observations and an Optimal Estimation Snowfall Retrieval. Weather and forecasting. ISSN 0882-8156. 36(5), p. 1827–1842. doi: 10.1175/WAF-D-20-0220.1. Full text in Research Archive Show summary

  In the winter, orographic precipitation falls as snow in the mid to high latitudes where it causes avalanches, affects local infrastructure, or leads to flooding during the spring thaw. We present a technique to validate operational numerical weather prediction model simulations in complex terrain. The presented verification technique uses a combined retrieval approach to obtain surface snowfall accumulation and vertical profiles of snow water at the Haukeliseter test site, Norway. Both surface observations and vertical profiles of snow are used to validate model simulations from the Norwegian Meteorological Institute’s operational forecast system and two simulations with adjusted cloud microphysics. Retrieved surface snowfall is validated against measurements conducted with a double-fence automated reference gauge (DFAR). In comparison, the optimal estimation snowfall retrieval produces + 10.9% more surface snowfall than the DFAR. The predicted surface snowfall from the operational forecast model and two additional simulations with microphysical adjustments (CTRL and ICE-T) are overestimated at the surface with +41.0 %, +43.8 %, and +59.2 %, respectively. Simultaneously, the CTRL and ICE-T simulations underestimate the mean snow water path by -1071.4% and -523.7 %, respectively. The study shows that we would reach false conclusions only using surface accumulation or vertical snow water content profiles. These results highlight the need to combine ground-based in-situ and vertically-profiling remote sensing instruments to identify biases in numerical weather prediction.

View all works in NVA

• Hellmuth, Franziska; Storelvmo, Trude & Daloz, Anne Sophie (2026). Understanding Snowfall Processes Across Scales: Integrating Microphysical Observations, Remote Sensing, and Atmospheric Models. Universitetet i Oslo. Full text in Research Archive Show summary

  Snowfall plays a critical role in mid- to high-latitude regions because it is a key component of the extra-tropical hydrological cycle and influences important variables like surface albedo, ice sheet mass balance and soil moisture. However, snowfall remains poorly represented in atmospheric models because key processes occur at scales that are smaller than the resolution of numerical weather prediction (NWP) and global climate models (GCMs). This thesis integrates multi-platform observations and atmospheric model simulations to evaluate the physical consistency of snowfall representation across scales. The topics progress from individual snowflake observations at micrometer-scale, to weather models and GCMs at the regional or even global scale. The papers combine ground based observations from field campaigns in the Norwegian mountains and the Arctic, including microphysical observations of snowflakes, vertical profiling radar measurements, and NWP. In addition, satellite observations are used to evaluate cloud- and snowfall representation in reanalysis and GCM simulations in mid-to high-latitude regions. Together, these datasets contribute to enhanced understanding of snowfall processes across microphysical, synoptic, and global climate scales. The results of the papers show that while radar reflectivity alone is non-unique for snowfall, the transferability of retrieval relationships is limited by regional microphysics for the maritime Arctic coastal site. Surface snowfall is overestimated, whereas snow water content aloft is underestimated in NWPs at the Norwegian mountain site. Similar microphysical biases are also seen in reanalysis and GCMs. They overestimate the frequency of supercooled liquid containing clouds and produce too frequent and too light snowfall. The findings of this thesis suggest that biases in snowfall representation originating at the microphysical scale propagate through to NWP and GCM scales. These results highlight the importance of integrating observations across scales to identify biases and improve the representation of mixed-phase clouds and hydrometeors in atmospheric models to eventually reduce uncertainty in mid- to high-latitude climate projections.

View all works in NVA