Analysis of eddy covariance data in alpine terrain: Turbulent heat fluxes and their significance for elevation-dependent climate change
(2) Universität Augsburg, Universitätsstraße 2, 86159 Augsburg
Abstract
Mountain regions experience amplified warming patterns, which significantly impact freshwater security, biodiversity, and both regional and global climate systems. In the European Alps, temperature increases have consistently outpaced the European lowland average. Observations in the Berchtesgaden National Park revealed deviations from local warming trends. The Kühroint station (1,423 m a.s.l.) has a warming rate of 1.6 °C per decade, while nearby valley stations only show 1.2 °C. To identify the drivers of this observed difference, this study investigated the surface energy balance and turbulent heat flux partitioning at Kühroint and a nearby valley station, Schönau (625 m a.s.l.), using the eddy covariance method. The analysis of a three-month dataset spanning from July to September 2025 showed an energy balance ratio of approximately 0.71 for both sites, confirming that reliable energy balance closure rates are possible in complex mountainous terrain, despite topographic challenges. A deviation between the sites was observed in energy partitioning. The valley station showed an evaporative fraction of 0.60, the subalpine Kühroint station showed an evaporative fraction of 0.50. This difference can likely be attributed to (i) soil properties: The lower dry bulk density at Kühroint likely limits water retention capacity, reducing the capacity for latent heat exchange; and (ii) topographic advection: Local wind regimes are influenced by slope–valley wind systems and possible thermal advection from lake Königssee. Although increased sensible heat flux and reduced evapotranspiration have been shown to influence heatwave temperatures in previous research, no conclusions can be drawn regarding the higher decadal temperature increase at Kühroint. Future work will focus on extending these observations to establish a long-term dataset that links turbulent flux dynamics directly to local warming trends.
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