Franziska Hellmuth

• Hofer, Stefan; Hahn, Lily; Shaw, Jonah; McGraw, Zachary; Bruno, Olimpia & Hellmuth, Franziska [Show all 11 contributors for this article] (2023). Realistic representation of mixed-phase clouds increases future climate warming. Research Square. ISSN 2693-5015. doi: 10.21203/rs.3.rs-2981113/v1. 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 in NorESM2 SSP5-8.5 climate projections. We anticipate that the difference between cloud top and interior phase in MPCs is an important new target metric for future climate model development, because similar MPC-related biases in future warming are likely present in many climate models.

• 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.

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