We found that a smooth, quadratic dependence characterizes the response of global precipitation in a set of perturbed physics experiments performed with the GFDL model at 1×1 degree resolution.
Fitting model output to parameter values provides a synthetic way to identify desirable tuning and trade offs for regional optimization, which may be locally different from that of the global average. Opposite to the common practice of weighting model tuning toward an a priori selected region of interest (typically the area in which the code is developed) we show how accounting for atmospheric noise allows reducing the number of trade-offs dramatically, and therefore putting the weighting toward a certain region into a larger context.
The smooth behavior, quantified measure of the sensitivity and the uncertainty introduced by noise we report here are the backbones for the design of computationally effective multi-parameter perturbations and model optimization, which ultimately improve the reliability of climate models.
Lecturer
Laura Zamboni
Argonne National Laboratory, MCS Division, Argonne (US)
Bologna, Italy - Bologna, Italy -
10 Jan 2014
Organized by
- CMCC - Euro-Mediterranean Center on Climate Change