Lake surface water temperatures (LSWTs) of 246 globally distributed large lakes were derived from Along-Track Scanning Radiometers (ATSR) for the period 1991 to 2011. These LSWTs, derived in a systematic manner, presents an ideal opportunity to study LSWT behaviour on a global scale. In this thesis, the annual cycles of lake-mean LSWTs derived from these data quantify the responses of large lakes’ surface temperatures to the annual cycle of forcing by solar radiation and the ambient air temperature. Minimum monthly net surface solar irradiance (netSSI) strongly influences minimum LSWTs of non-seasonally ice covered lakes (where lake-mean LSWT remains above 1 ºC throughout the annual cycle), explaining > 0.88 (R2 adj) of the inter-lake variation in both hemispheres. In some regions, for seasonally ice covered lakes (where lake-mean LSWT remains below 1 ºC for part of the annual cycle) the minimum monthly netSSI is a better predictor than latitude, of the length of the frozen period, which shows the importance of local cloud climatological conditions. Additionally, at lake locations between 1º S to 12º N, the netSSI, shown to peak twice annually, is reflected in the LSWT annual cycle. The summer maximum LSWTs of lakes from 25º S to 35º N show a linear decrease with increasing altitude; -3.76 + 0.17 ºC km-1 (R2 adj = 0.95), marginally lower than the corresponding air temperature -4.15 + 0.24 ºC km-1 (R2 adj = 0.95) decrease with altitude. The start and end of the period where the lake-mean LSWT is greater than 4 ºC shows strong correlation with the spring and autumn 0 ºC air temperature crossing days, (R2 adj = 0.74 and 0.80 respectively). The temporally and spatially resolved LSWT observations allows for a greater practical understanding of LSWT behaviour of large lakes. For example, lakes with a greater LSWT annual range have more observed variability in the LSWT extremes, highlighting that they may be more responsive to changes in the climate than lakes with a low annual range. The nighttime LSWT trends show stronger warming than day-night trends in the all regions, except Europe. The lake centre LSWT trends and absolute values can be generally considered representative of the lake-mean LSWT trends and absolute values. The observed LSWT time series are used to tune a 1-dimensional thermodynamic lake model, FLake. By tuning FLake using only 3 basic lake properties, shown by myself to have the most influence over LSWTs (depth, snow and ice albedo and light extinction co-efficient), the daily mean absolute differences for 244 lakes is reduced from 3.38 + 2.74 ºC (untuned model) to 0.85 + 0.61 ºC (tuned model). The effect of wind speed, lake depth, albedo and light extinction co-efficient on LSWTs is demonstrated throughout the tuning process. The modelled summer LSWT response to changes in ice-off is strongly affected by lake depth and latitude explaining 0.50 (R2 adj, p = 0.001) of the inter-lake variation in summer LSWTs. Lake depth alone explains 0.35 (p = 0.003) of the variation, highlighting the sensitivity of the summer LSWTs of deeper lakes to changes in the ice-off. Statistically significant summer/ maximum month modelled LSWT trends, from 1979-2011 are presented for lakes where the modelled LSWTs are strongly supported by observed LSWTs over the period of available observed LSWTs. For these lakes, the trends show LSWT warming of between 0.73 – 2.10 ºC for 29 lakes in northern temperate regions over the 33 year period (1979 – 2011). The modelled regional trends of all lakes over the same period show least warming in Africa of 0.30 ºC and the greatest warming in Europe, 1.35 º.

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.