Modeling subgrid lake energy balance in ORCHIDEE terrestrial scheme using the FLake lake model

Abstract

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.

Hydrological modelling on atmospheric grids; using graphs of sub-grid elements to transport energy and water

“…It can then be used to predict water volumes and functioning of these hydrological elements. This can then be combined with the lake energy balance model (Bernus and Ottlé, 2022) in order to represent consistently the lateral transport of water volumes and thermal energy over continents within ESMs.…”

“…In view of the importance of this process a more complete representation of these exchanges should be aimed for. This includes the simulation of lake thermodynamics (Bernus and Ottlé, 2022). It also explains why simple empirical relations relating near surface air temperature and stream temperature work so well (Ducharne, 2008).…”

Reducing the High Mountain Asia cold bias in GCMs by adaptingsnow cover parameterization to complex topography areas

“…The use of the Bayesian framework of the HMASR reanalyses could also enable to provide a range of possible parameters instead of a single optimized value. Despite significant uncertainties in atmospheric forcing datasets in mountain regions, especially for precipitation (e.g., Immerzeel et al, 2015;Lundquist et al, 2019;Gao et al, 2020), additional land surface simulations could provide further insights in the SCF parameterization evaluation by using multiple atmospheric forcing datasets to better understand these uncertainties (e.g., Bernus and Ottlé, 2022). This would allow to quantify the added value of new SCF schemes independently from the influence of the atmosphere.…”

Importance of Parameterizing Lake Surface and Internal Thermal Processes in WRF for Simulating Freeze Onset of an Alpine Deep Lake

“…Biases of forcing variables can lead to about 20% lake evaporation variations for 75 alpine lakes on the TP (Wang, Ma, et al., 2020). Furthermore, a small bias on the air temperature could change the temperature evolution of the lake and then influence the lake ice phenology (Bernus & Ottlé, 2022). The lack of in situ observations at TP alpine lakes has made the simulation of lake thermal structure and lake ice formation more difficult and uncertain (Huang et al., 2017; Lang et al., 2021).…”