Improving Weather Predictability by Including Land Surface Model Parameter Uncertainty

Rene Orth Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland

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Emanuel Dutra European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

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Florian Pappenberger European Centre for Medium-Range Weather Forecasts, Reading, and School of Geographical Sciences, University of Bristol, Bristol, United Kingdom

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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 on ECMWF’s land surface model Hydrology Tiled ECMWF Scheme of 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 best-performing 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.

Denotes Open Access content.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/MWR-D-15-0283.s1.

Publisher’s Note: This article was revised on 19 July 2017 to correct an error in the Acknowledgments section when originally published.

Corresponding author address: Rene Orth, Institute for Atmospheric and Climate Science, ETH Zurich, Universitätsstrasse 16, CH-8092 Zurich, Switzerland. E-mail: rene.orth@env.ethz.ch

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 on ECMWF’s land surface model Hydrology Tiled ECMWF Scheme of 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 best-performing 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.

Denotes Open Access content.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/MWR-D-15-0283.s1.

Publisher’s Note: This article was revised on 19 July 2017 to correct an error in the Acknowledgments section when originally published.

Corresponding author address: Rene Orth, Institute for Atmospheric and Climate Science, ETH Zurich, Universitätsstrasse 16, CH-8092 Zurich, Switzerland. E-mail: rene.orth@env.ethz.ch

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