A new approach to the cumulus parameterization issue

Can the superensemble methodology provide improved precipitation forecasts by combining existing physical parameterizations? We recently addressed this question in the context of Numerical Weather Prediction (NWP).We feel, however, that the information provided here may be useful for seasonal climate modeling as well. In theNWPcontext, we have developed multi-model forecasts from six versions of the Florida State University global spectral model (at a horizontal resolution of 170 waves, triangular truncation). These different versions deployed six different cumulus parameterization schemes; these models were identical in all other aspects, including the initial states. Making the assumption that differences in short-range (one day) forecasts of precipitation arise largely from differences in the cumulus parameterization, a superensemble methodology, following a recent study, was deployed to assign geographically distributed weights to convective heating for the different cumulus parameterization schemes. This was done after completion of some 85 experiments for each model for the training phase of the superensemble. A new single spectral model was next designed that included the weighted sum of the six cumulus parameterization schemes strung out within this model. This model was next shown to outperform in NWP forecasts of precipitation compared to any of those models that used a single cumulus parameterization scheme. This merely suggests that no single, present scheme is superior to all other schemes over the entire tropical belt; they all seem to have some virtues over different geographical regions. This Unified collective scheme is physically based since it does carry mechanisms such as mass flux, moisture convergence, cloud detrainment, downdrafts, effects of sea surface temperature etc. that are explicitly carried within one or the other schemes. This collective scheme is, however, based on optimized weights for these processes that vary geographically. It is our premise that even if a new breakthrough in cumulus parameterization were to occur from the development of a new scheme, that scheme, at best, may only achieve a skill ranking of number three for precipitation forecasts. The first place, we noted, still belongs to a multi-model superensemble, based on the optimal combination of six separate models. The second place belongs to the single model that utilizes a strung out weighted sum of many cumulus parameterization schemes within it. The individual member models have larger precipitation forecast errors compared to the two above. The skills, here, are evaluated using standard metrics such as correlations, root mean square errors and the equitable threat scores; finally we also present the vertical profiles of the apparent heat source and the apparent moisture sink that also confirm these above findings.