Abstract

Lake Baikal (Southern Siberia) is the world's oldest, deepest and largest freshwater body by volume. In spite of its enormous depth, episodically (i.e. almost twice a year) large volumes of surface, cold and oxygenated water sink towards the bottom of the lake. This phenomenon is known as deep ventilation and determines the periodical, partial renewal of deep water, playing a key role in the ecology of the whole lacustrine system. Despite deep ventilation has been widely observed, still significant uncertainties exist about the detailed characterization of deep downwellings. In order to tackle this issue, a simplified, one-dimensional numerical model has been developed, which allows for a suitable simulation of deep ventilation in profound lakes. Three main algorithms are at the basis of the model: a reaction-diffusion equation for temperature and other tracers (e.g. dissolved oxygen), and two Lagrangian algorithms, the first to handle buoyancy-driven convection due to density instability (including thermobaric effects) and the other to reproduce the deep downwelling mechanism. Thanks to its simple structure, such a model ensures a considerable computational speed that makes it suitable to perform long-term simulations (i.e. decades, centuries). At the same time, it has been shown to be appropriate for quantitatively and qualitatively simulating deep ventilation, well capturing the relative contribution of the different processes involved. The model has been applied to investigate deep ventilation in the South Basin of Lake Baikal. The numerical results have been shown to be in good agreement with observed data (concerning temperature, CFC-12 and dissolved oxygen profiles), indicating a proper performance of the core algorithms. The analysis of results allowed for a detailed description of the major mixing and thermal dynamics of the lake, and for an in-depth characterization of deep water renewal (e.g. typical downwelling temperatures and volumes, vertical distribution of sinking water, energy balance). Numerical simulations have been performed under current conditions and climate change scenarios, thus permitting to assess the future behavior of the lake and the possible impact on deep ventilation, in response to the expected evolution of climate. In addition to the main results discussed above, this study provided some additional outcomes: a simplified lumped model to convert air temperature into surface water temperature of lakes, and a novel downscaling procedure to transform meteorological data (i.e. wind speed and air temperature) from the global scale to the lake scale. In the light of the proven performance of the deep ventilation model, further improvements of the model could bring to the development of a suitable module to simulate biogeochemical processes in the lake, thus providing valuable information to assess the role of deep ventilation in affecting the lake ecosystem.