Articles | Volume 20, issue 7
Hydrol. Earth Syst. Sci., 20, 2737–2743, 2016
https://doi.org/10.5194/hess-20-2737-2016
Hydrol. Earth Syst. Sci., 20, 2737–2743, 2016
https://doi.org/10.5194/hess-20-2737-2016

Research article 12 Jul 2016

Research article | 12 Jul 2016

Evaluating uncertainty in estimates of soil moisture memory with a reverse ensemble approach

Dave MacLeod1, Hannah Cloke2,3, Florian Pappenberger4,5, and Antje Weisheimer4,6 Dave MacLeod et al.
  • 1Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, Oxford, UK
  • 2Department of Geography and Environmental Science, University of Reading, Reading, UK
  • 3Department of Meteorology, University of Reading, Reading, UK
  • 4European Centre for Medium-Range Weather Forecasts, Reading, UK
  • 5School of Geographical Sciences, Bristol University, Bristol, UK
  • 6Department of Physics, National Centre for Atmospheric Science (NCAS), University of Oxford, Oxford, UK

Abstract. Soil moisture memory is a key component of seasonal predictability. However, uncertainty in current memory estimates is not clear and it is not obvious to what extent these are dependent on model uncertainties. To address this question, we perform a global sensitivity analysis of memory to key hydraulic parameters, using an uncoupled version of the H-TESSEL land surface model.

Results show significant dependency of estimates of memory and its uncertainty on these parameters, suggesting that operational seasonal forecasting models using deterministic hydraulic parameter values are likely to display a narrower range of memory than exists in reality. Explicitly incorporating hydraulic parameter uncertainty into models may then give improvements in forecast skill and reliability, as has been shown elsewhere in the literature. Our results also show significant differences with previous estimates of memory uncertainty, warning against placing too much confidence in a single quantification of uncertainty.

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Short summary
Soil moisture memory is a key aspect of seasonal climate predictions, through feedback between the land surface and the atmosphere. Estimates have been made of the length of soil moisture memory; however, we show here how estimates of memory show large variation with uncertain model parameters. Explicit representation of model uncertainty may then improve the realism of simulations and seasonal climate forecasts.