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Multi-model assessment of the impact of soil moisture initialization on mid-latitude summer predictability

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Bunzel,  Felix
Decadal Climate Predictions - MiKlip, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

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Müller,  Wolfgang A.
Decadal Climate Predictions - MiKlip, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

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Citation

Ardilouze, C., Batté, L., Bunzel, F., Decremer, D., Déqué, M., Doblas-Reyes, F., et al. (2017). Multi-model assessment of the impact of soil moisture initialization on mid-latitude summer predictability. Climate Dynamics, 49, 3959-3974. doi:10.1007/s00382-017-3555-7.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002C-9287-D
Abstract
Land surface initial conditions have been recognized as a potential source of predictability in sub-seasonal to seasonal forecast systems, at least for near-surface air temperature prediction over the mid-latitude continents. Yet, few studies have systematically explored such an influence over a sufficient hindcast period and in a multi-model framework to produce a robust quantitative assessment. Here, a dedicated set of twin experiments has been carried out with boreal summer retrospective forecasts over the 1992–2010 period performed by five different global coupled ocean–atmosphere models. The impact of a realistic versus climatological soil moisture initialization is assessed in two regions with high potential previously identified as hotspots of land–atmosphere coupling, namely the North American Great Plains and South-Eastern Europe. Over the latter region, temperature predictions show a significant improvement, especially over the Balkans. Forecast systems better simulate the warmest summers if they follow pronounced dry initial anomalies. It is hypothesized that models manage to capture a positive feedback between high temperature and low soil moisture content prone to dominate over other processes during the warmest summers in this region. Over the Great Plains, however, improving the soil moisture initialization does not lead to any robust gain of forecast quality for near-surface temperature. It is suggested that models biases prevent the forecast systems from making the most of the improved initial conditions. © 2017 Springer-Verlag Berlin Heidelberg