This PhD project investigates land-atmosphere interactions, i.e., the combined effect of land surface conditions and the atmospheric circulation for the formation of heatwaves in Northern Europe. It aims to disentangle the extent to which different land cover properties affect the severity of heatwaves in the region of Norway, Sweden and Finland.
Heatwaves have become more frequent in Northern Europe during recent years, in alignment with global warming which is the most prominent in high-latitude regions. High near-surface air temperature extremes in summer are a result of a combination of both the local-scale thermodynamic factors - the exchange of the energy and water fluxes at the land surface level, and the large-scale dynamic factors - atmospheric circulation, primarily, blocking events. The main variables of interest for the assessment of the land-atmosphere feedbacks in this region are changes in near-surface air temperature, snowpack and soil moisture.
In order to evaluate the relative contribution of the major drivers for the occurrence of heatwaves and examine their most susceptible areas, a coupled land and atmosphere climate model WRF-CTSM (Weather Research and Forecasting model, WRF and Community Terrestrial Systems Model, CTSM) is chosen as the main tool for analysis. WRF-CTSM represents a forefront in the climate modelling community, unifies the recent climate model development activities across the fields of climate, weather, water and ecosystem research and is thus a viable tool for studying the feedback mechanisms between the land surface and the atmosphere that present key sources of uncertainty in climate models.
WRF-CTSM has been recently developed and has not yet been tested over the European domain so the first stage of this project was devoted to setting it up on the Norwegian high-performance computing system. Furthermore, several test simulations were performed using WRF-CTSM, WRF Noah and WRF Noah-MP coupled climate models. All three model configurations were run for the summer of 2018 and the selected climate variable results were evaluated against the observational and reanalysis datasets.