A Stochastic Representation of Sub-Grid Uncertainty for Dynamical Core Development

Numerical weather prediction and climate models comprise of a
  • dynamical core describing resolved parts of the climate system, 
  • parameterizations describing unresolved components. 
Separate teams of scientists usually develop these two parts of the model in weather and climate centers. 

While representing resolved scales is inherently well posed, development of new parameterizations is inherently imprecise and uncertain and currently represented by stochastic approaches in many operational weather models currently. Because of the nonlinearity of the climate, this stochasticity will inevitably percolate into the dynamical core, and therefore determine a lower bound to the accuracy with which dynamical cores should be formulated. Here we describe a low-cost stochastic scheme, which can be bolted onto any existing deterministic dynamical core. The key point of the study is that there is no point trying to develop dynamical cores that are more precise than the level of uncertainty provided by such a stochastic scheme. Therefore, we present a fundamentally new paradigm to adjust the accuracy of what would otherwise be deterministic dynamical cores. Overall, this will ensure that the climate model is computationally efficient by taking irreducible errors into account. We show some results based on the ECMWF IFS dynamical core. 

We run a test case for stochastic perturbations in the ECMWF IFS dynamical core using the OpenIFS code in a Held-Suarez experiment setup. The datasets used for perturbations in the dynamical core are given below:

Experiment Description and Results

  1. FIGURE 1. Schematic of thunderstorms and large convective cells embedded in a numerical grid for a dynamical core. The color contours show the upper atmosphere potential vorticity field from a long climate simulation in the Held-Suarez experiment setup. The interaction of convective  clouds with the largescale wave indicates instabilities and error propagations that can arise from these fundamental physical processes in the atmosphere 

FIGURE. 2. Zonal mean wind profile as a function of vertical pressure levels and latitude. The field is time- 36 averaged over the last 1000-day period in a 1500 day simulation.