Master & SIE Projects

Description:

• Helmut will be the future French/Swiss flagship land surface model and is currently developed between CEN (MeteoFrance) and SLF Davos. The numerical core has been developed and the work will be to execute first model simulations and compare to the state of the art model SNOWPACK. Work will be at SLF Davos.

Description:

• The Arctic is experiencing an accelerated warming with respect to global mean and lower latitudes. Surface temperatures in the Arctic are a direct consequence of the surface radiation balance, which is modulated by clouds and aerosol dynamics. The sublimation of salty blowing snow particles (BSP) is an important source of sea salt aerosol (SSA), one of the most abundant natural aerosols globally.
• An accurate prediction of SSA in numerical models therefore requires capturing the dynamics of BSP, including their transport and interaction with cloud microphysics. This research employs the numerical model CRYOWRF, which includes prognostic blowing-snow equations coupled with the advanced land-surface snow model SNOWPACK, to investigate how BSP are transported and how they influence the highly nonlinear cloud microphysics. Numerical results will be validated using data from the MOSAiC expedition, which demonstrated that BSP can reach cloud levels and therefore potentially interact with cloud microphysics.
• This master project benefits from the expertise of two EPFL labs: CRYOS in numerical modeling and EERL in field measurements. The objectives are:
  • Detailed literature review
  • Set up initial and boundary conditions for CRYOWRF for different blowing snow events
  • Run simulations with sensitivity analyses
  • Compare numerical results with observations

Description:

The turbulent fluxes of sensible and latent heat between the surface of the Earth and the Atmosphere are main contributors to the total energy and mass balance and determine boundary layer dynamics and surface properties for diverse land covers from vegetation to snow and ice.

Based on the recent observation that current models heavily underestimate latent heat fluxes, this master thesis investigates the hypothesis whether radiation penetration into the snow can explain this. The figure shows a temperature profile, for which penetration of shortwave radiation into the snow causes the maximum temperature to be below the snow surface. In this case, most water vapor will be produced at the depth of the maximum temperature and the vapor gradient based on the (lower) surface temperature will be underestimated. The master thesis will explore this mechanism using a combination of numerical modelling and data analysis. Own measurements of temperature profiles in the snow will validate the modelling efforts. Modelling will be done with SNOWPACK and with OpenFOAM, trying to explicitly capture vapor transport through the snow – atmosphere interface.

Description:

High-latitude regions such as Greenland and Svalbard are characterized by complex orography, strong surface heterogeneity, and persistent stable boundary layers most of the year, which challenge the accurate representation of near-surface winds in atmospheric reanalyses of our climate models. While Copernicus Arctic Regional Reanalysis (CARRA) provides high-resolution wind fields (2.5km) over the Arctic, systematic biases remain, particularly in regions affected by the mountains and extreme wind events.

This project aims to develop an Arctic Wind Atlas for Greenland (and possibly Svalbard, Northern Scandinavia and Novaya Zemlya) based on CARRA reanalysis data, using machine-learning-based bias correction methods. You will be able to work on long-term CARRA wind statistics with available in situ observations (e.g. automatic weather stations) to quantify and correct systematic biases in near-surface wind speed and direction. Following the methodology of our recent Antarctic Wind Atlas study, statistical or machine learning approaches will be applied using time-invariant predictors describing sub-grid-scale orography and surface characteristics.

The project focuses on deriving spatially continuous, bias-corrected probability distributions of the wind and associated wind energy metrics across Greenland and Svalbard. Extreme wind regimes, including downslope wind systems such as Piteraq events, may be briefly examined as part of the broader wind climate.

The project is suited for an advanced Semester project or a Master’s thesis. At the end of your project, you will have gained skills in analysing Arctic climate data, statistical and machine learning methods, and the construction and plotting of regional wind atlases. Your results may  contribute to future scientific publications which is relevant to both the scientific and engineering communities.

Description:

• The tasks consist on deploying a LIDAR system in the Flüela valley during winter and use the data of the new Tschuggen station to study the effects of mesoscale circulations (katabatic winds, rotors, etc.) on the snow in an Alpine Valley.

Description:

Extreme atmospheric heatwaves in Antarctica are known to be exacerbated by climate change. This rise concerns on the implications that these extreme events have for cryosphere stability. Our initial modelling results of selected case study also suggest that these heatwaves have important implications on the snow and firn, including enhanced melt, changes in densification processes and alterations of surface energy balance. The project will systematically investigate these processes at large scales.

The tasks will consist in:

• Define and track extreme heatwaves across the Antarctic continent, by developing objective detection criteria and applying them to atmospheric reanalysis to characterize their spatial and temporal distribution.
• Assess their impacts on snow and firn by using long-term, distributed simulation with SNOWPACK across Antarctica in order to quantify changes in melt occurrence, refreezing, firn structure and related processes under extreme temperature conditions.

Description:

• The tasks will be compiling heat fluxes by different datasets in Antarctica and analyze them to study the uncertainty of reanalysis and climate models in Antarctica. Also set simulations with CRYOWRF to understand the importance of grid resolution.