In many numerical weather prediction models, the presence of lakes is simulated crudely, with their effect being neglected in the resulting simulations. However, it has been shown how lakes effect not only their surrounding climate directly, but have an effect to the overall weather evolution and ecosystem. It is therefore vital to improve existing models to take lakes into account, by coupling with smaller models specificaly compiled for a reas with lakes. There have been several sophisticated models to parameterizelakes in a geographical area, which are, on the other hand, computationally expensive and time consuming. A model built specifically on simple physical assumptions, named FLake, aims to provide a solution that is not heavy computationally, but is accurate enough and contains all the necessary physics surrounding the heat budget and temperature of a given lake. For this project, FLake was tried on a lake close to Uppsala, named Erken, where the validity of the model was tested against data archives from Erken Laboratory’s measurement tower. The resulting simulations were very promising regarding the water temperatures, as well as giving out acceptable results for the surface heat fluxes above the lake and the duration of the ice period, as it was modeled by FLake and compared with ice data archives.

Land surfaces have changed as a result of human and natural processes, such asdeforestation, urbanization, desertification and natural disasters like wildfires. Land use and landcover change impacts local and regional climates through various bio geophysical processes acrossmany time scales. More realistic representation of land surface parameters within the land surfacemodels are essential to for climate models to accurately simulate the effects of past, current andfuture land surface processes. In this study, we evaluated the sensitivity and accuracy of theWeather Research and Forecasting (WRF) model though the default MODIS land cover data andannually updated land cover data over southeast of United States. Findings of this study indicatedthat the land surface fluxes, and moisture simulations are more sensitive to the surfacecharacteristics over the southeast US. Consequently, we evaluated the WRF temperature andprecipitation simulations with more accurate observations of land surface parameters over thestudy area. We evaluate the model performance for the default and updated land cover simulationsagainst observational datasets. Results of the study showed that updating land cover resulted insubstantial variations in surface heat fluxes and moisture balances. Despite updated land use andland cover data provided more representative land surface characteristics, the WRF simulated 2- m temperature and precipitation did not improved due to use of updated land cover data. Further,we conducted machine learning experiments to post-process the Noah-MP land surface modelsimulations to determine if post processing the model outputs can improve the land surfaceparameters. The results indicate that the Noah-MP simulations using machine learning remarkablyimproved simulation accuracy and gradient boosting, and random forest model had smaller meanerror bias values and larger coefficient of determination over the majority of stations. Moreover,the findings of the current study showed that the accuracy of surface heat flux simulations byNoah-MP are influenced by land cover and vegetation type.