Lake surface water temperature (LSWT) is sensitive to climate change; however, simulated LSWT and its response to climate change remain uncertain. In this study, FLake, a onedimensional freshwater lake model, is optimized to simulate the LSWTs of 94 large lakes with surface areas greater than 100 km 2 in China. While most of these lakes are seasonally icecovered over the Tibetan Plateau, FLake with default parameters significantly underestimated LSWT in spring and winter and slightly overestimated LSWT in summer and autumn in seasonally ice-covered lakes. We performed sensitivity experiments and calibration in the trial lake (Qinghai Lake). Then, parameter calibrations of three lake-specific properties (albedo, lake mean depth and light extinction coefficient) were performed in all the studied lakes. The optimized FLake substantially improved the simulations of seasonal and interannual variations in LSWT. The root mean square error decreased from 3.64 ± 1.54 ℃ to 1.97 ± 0.72 ℃, and the mean bias of 96% of the lakes decreased to less than 1 ℃. Our study showed that the optimized FLake can reproduce the temporal variations in LSWT across China with optimized parameters, providing the possibility to simulate and project the response of LSWT to rapid climate change.

Abstract. The freshwater 1-D FLake lake model was coupled to the ORCHIDEE land surface model to simulate lake energy balance at the global scale. A multi-tile approach has been chosen to allow the modeling of various types of lakes within the ORCHIDEE grid cell. Thus, three different lake tiles have been defined according to lake depth which is the most influential parameter of FLake, but other properties could be considered in the future. Several depth parameterization strategies have been compared, differing by the way to aggregate the depth of the subgrid lakes, i.e., arithmetical, geometrical, harmonical mean and median. Five atmospheric reanalysis datasets available at 0.5∘ or 0.25∘ resolution have been used to force the model and assess model systematic errors. Simulations have been performed, evaluated and intercompared against observations of lake water surface temperatures provided by the GloboLakes database over about 1000 lakes and ice phenology derived from the Global Lake and River Ice Phenology database. The results highlighted the large impact of the atmospheric forcing on the lake energy budget simulations and the improvements brought by the highest resolution products (ERA5 and E2OFD). The median of the root square mean errors (RMSEs) calculated at global scale ranges between 3.2 and 2.7 ∘C among the forcings, CRUJRA and ERA5 leading respectively to the worst and best results. The depth parameterization strategy appeared to be less influential, with RMSE differences less than 0.1 ∘C for the four aggregation scenarios tested. The simulation of ice phenology presented systematic errors whatever the forcing and the depth parameterization used. Large systematic errors were highlighted such as negative biases on the onset and positive biases on the offset. Freezing onset was shown to be the less sensitive to atmospheric forcing with the median of the errors ranging between 10 and 14 d. Larger errors up to 25 d were observed on the simulation of the end of the freezing period. Such errors, already highlighted in previous works, could be explained by scale effects and deficiencies in the modeling of snow–ice processes not accounting for partial ice cover. Various pathways are drawn to improve the model results, including the use of remote sensing data to better constrain the lake radiative parameters (albedo and extinction coefficient) as well as the lake depth thanks to the recent and forthcoming high-resolution satellite missions.

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