Impacts of projected climate change on the thermodynamics of a shallow and a deep lake in Finland: model simulations and Bayesian uncertainty analysis

Abstract

A lake model (MyLake) has been used to simulate the impacts of the projected future (2071–2100) climate on the thermodynamic properties of a shallow and a deep lake in Finland. The model has been calibrated using a Bayesian Markov chain Monte Carlo (MCMC) simulation method. The model results show, among others, that the largest change in water temperature in a projected future climate occurs in April–May when a 4.6–7.6°C increase in epilimnion and 2.7–4.4°C increase in whole-lake monthly mean temperature occurs compared to the climate of 1961–1990. This corresponds to a large decrease in the probability of ice cover in March–April. In winter and early spring a negative correlation was obtained between lake water and air temperatures in a projected future climate due to the thermal buffering feature of the lake ice and snow cover. The uncertainties connected to the choice of the general circulation model and the boundary condition forcing for the regional atmospheric climate model were often significantly larger than the uncertainties connected to MyLake model parameter values.