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Abstract:
The land surface forms an important component of Earth system models and interacts nonlinearly with
other parts such as ocean and atmosphere. To capture the complex and heterogeneous hydrology of the land
surface, land surface models include a large number of parameters impacting the coupling to other components
of the Earth system model.
Focusing onECMWF’s land surfacemodel Hydrology TiledECMWF Schemeof Surface Exchanges over Land
(HTESSEL), the authors present in this study a comprehensive parameter sensitivity evaluation using multiple
observational datasets in Europe. The authors select six poorly constrained effective parameters (surface runoff
effective depth, skin conductivity, minimum stomatal resistance, maximum interception, soil moisture stress
function shape, and total soil depth) and explore their sensitivity to model outputs such as soil moisture, evapotranspiration,
and runoff using uncoupled simulations and coupled seasonal forecasts. Additionally, the authors
investigate the possibility to construct ensembles from the multiple land surface parameters.
In the uncoupled runs the authors find that minimum stomatal resistance and total soil depth have the most
influence on model performance. Forecast skill scores are moreover sensitive to the same parameters as
HTESSEL performance in the uncoupled analysis. The authors demonstrate the robustness of these findings
by comparing multiple best-performing parameter sets and multiple randomly chosen parameter sets. The
authors find better temperature and precipitation forecast skill with the best-performing parameter perturbations
demonstrating representativeness of model performance across uncoupled (and hence less computationally
demanding) and coupled settings.
Finally, the authors construct ensemble forecasts from ensemble members derived with different bestperforming
parameterizations of HTESSEL. This incorporation of parameter uncertainty in the ensemble
generation yields an increase in forecast skill, even beyond the skill of the default system.
