Friday, March 2, 2018

                   WHAT  TO  DO  WHEN   MODELS   PUSH  BACK


Correlation between modeled aerosol optical depth (AOD)
 and clear-sky aerosol-radiation interaction (ARI) forcing

Fig. 1. Correlation between mode  AOD  and clear-sky aerosol-radiation interaction (ARI) forcing over Europe in a climate model perturbed-parameter ensemble of 200 members.

Each point in Figure 1a is a model simulation with a different setting of 27 model input parameters that affect aerosol emissions and processes.

Figure 1b shows the range of correlation coefficients that can be generated by randomly selecting many sets of 15 members of the ensemble from the set of 200.
Climate Models Are Uncertain, but We Can Do Something About It
As climate models become more complex, how do we ensure that predictions remain robust? We shift our focus

Model simulations of many climate phenomena remain highly uncertain despite scientific advances and huge amounts of data. Scientists must do more to tackle model uncertainty head-on.
, Lindsay A. Lee, Leighton A. Regayre, and Jill S. Johnson           

Model uncertainty is one of the biggest challenges we face in Earth system science, yet comparatively little effort is devoted to fixing it.
A well-known example of persistent model uncertainty is aerosol radiative forcing of climate, for which the uncertainty range has remained essentially unchanged through all Intergovernmental Panel on Climate Change assessment reports since 1995. From the carbon cycle to ice sheets, each community will no doubt have its own examples.
We argue that the huge and successful effort to develop physical understanding of the Earth system needs to be complemented by greater effort to understand and reduce model uncertainty. Without such reductions in uncertainty, the science we do will not, by itself, be sufficient to provide robust information for governments, policy makers, and the public at large.