@misc{79955,
  keywords = {workshop, model uncertainty},
  author = {Lauriane Batte and Michel Déqué},
  title = {Representing atmospheric model uncertainties: Applications in seasonal forecasts with CNRM-CM},
  abstract = {<p>
  To account for atmospheric model uncertainties in the seasonal forecasting system based on CNRM-CM (Voldoire et al. 2013), two stochastic perturbation methods have been introduced in the ARPEGE-Climate atmospheric model, namely:</p>

<ul><li>stochastic dynamics (Batté and Déqué, 2016): prognostic variables (T, q, ψ) are perturbed with random simultaneous corrections (δX) of model errors estimated by nudged seasonal runs over the hindcast period;</li><li>SPPT (Palmer et al. 2009): multiplicative physical parameterization tendencies perturbations using a random spectral pattern generator. In ARPEGE-Climate only u,v tendencies are perturbed.</li></ul>
<p>
  We present here separate assessments of the impact of these methods on seasonal forecast quality.</p>
},
  year = {2016},
  month = {2016},
  language = {English},
}