MyLake (Multi-year simulation model for Lake thermo- and phytoplankton dynamics) is a one-dimensional simple mechanistic model code for predicting daily vertical distribution of lake water temperature and thus stratification (Saloranta and Anderssen 2004). MyLake simulates also the evolution of seasonal lake ice and snow cover as well as nutrient cycle and phytoplankton dynam-ics. The overall structure of the MyLake model code is described in Box 1.
Predictions of lake ice and water temperature
For FINESSI-simulations only lake ice and water temperature were predicted. The model was applied and validated first for two lakes in South Finland. One was a deep strongly stratified Lake Pääjärvi (area= 13.5 km2, maximum depth = 84 m) and the other a shallow Lake Halsjärvi , (area= 0.03 km2, maximum depth = 5 m). For both lakes good calibration and validation data were available. Meteorological data was obtained from Finnish Meteorological Institute (observation site at Jokioinen). For comparing the effects of climate change between northern and southern lakes in Finland the two applications were conducted also such a way that both lakes were placed at Sodankylä location, where again good meteorological data were available .
Base line 1971-1990 and predictions 2071-2100
The base line for applications was 1971-90. For climate change effect simulations of two coupled atmosphere-ocean General Circulation Model (HadCM3 and ECHAM4) with the SRES scenarios A2 and B2 (Nakicenovic et al. 2000) for a period of 2071-2100 were used. Climate model data in monthly time resolution was downloaded from the IPCC Data Distribution Centre.
Delta change approach
The climate change scenarios was combined with observations to construct climate scenarios using the delta-change approach. A future time series was calculated by adding the simulated temperature change to the temperature observations of the location, and applying the percentage change to the observations for the remaining input variables.
One variable, relative humidity, was available only for HadCM3. For the ECHAM4 results humid-ity was estimated by using regression between precipitation and humidity in the base line data. Cloud cover is not directly predicted by the either one of the two GCMs. In this work cloud cover was pre-dicted from the amount of sunshine received and assuming that the change in sunshine received is equal to the change in downward shortwave flux (Mitchell et al. 2003).
The results were presented by using the surface water temperature, water temperature in the deep water (hypolimnion) and the depth of thermocline depth. The changes in ice cover are presented with the dates of the first ice cover formation and the final disappearance of the ice cover in the spring. The amount and mean length of the ice free periods in winter are also presented in the table form.
Box 1. Overview of the MyLake model code structure
For one model time step (24 h):
If no ice
Check for (and possibly proceed) autumn/spring turnover
If ice cover
If Ta < Tf (freezing)
- Calculate ice surface temperature (depending on snow cover, or ice thickness if snow is absent
- Calculate snow ice formation in case of isostatic imbalance
- Calculate congelation ice growth by Stefan's law
- Accumulate new snowfall and subtract formed snow ice from snow cover
If Ta ≥ Tf (melting)
- Melt snow or ice from top with total surface heat flux