GFDL - Geophysical Fluid Dynamics Laboratory

An Investigation of the Connections among Convection, Clouds, and Climate Sensitivity in a Global Climate Model

Key Findings

  • A novel bulk quantity, the convective precipitation efficiency or equivalently the convective detrainment efficiency, is found to be a simple measure of the aggregated properties of parameterized convection important to the GCM simulated clouds.
  • As the convective precipitation efficiency increases in the perturbed physics experiments, both liquid and ice water path decrease, with low and middle cloud fractions diminishing at a faster rate than high cloud fractions. This asymmetry results in a large sensitivity of top-of-atmosphere net cloud radiative forcing to changes in convective precipitation efficiency.
  • For global warming experiments, inter-model variations in the response of cloud condensate, low cloud fraction, and total cloud radiative forcing are well explained by model variations in response to total precipitation (or detrainment) efficiency.
  • Despite significant variability, all of the perturbed-physics models produce a sizable increase in precipitation efficiency to warming. A substantial fraction of the increase is due to its convective component, which depends on the parameterization of cumulus mixing and convective microphysical processes.
  • The increase in convective precipitation efficiency and associated change in convective cloud height distribution owing to warming explains the increased cloud feedback and climate sensitivity in recently developed Geophysical Fluid Dynamics Laboratory GCMs.
  • The results imply that a cumulus scheme using fractional removal of condensate for precipitation and inverse calculation of the entrainment rate tends to produce a lower climate sensitivity than a scheme using threshold removal for precipitation and the entrainment rate formulated inversely dependent on convective depth.

Summary

Uncertainty in cloud feedback is a leading cause of disagreement in GCM predictions of climate change. The differences in parameterizations of convection, clouds, and boundary layer processes are known to be critical. Yet, there is still a lack of in-depth investigation and
understanding of what processes are responsible for the difference and how/why the different schemes may have led to the diversity of cloud feedback. The two recently developed GFDL models (HIRAM and AM3) are found to acquire considerable increase in climate sensitivity compared to the earlier AM2 model. The replacement of the Relaxed Arakawa?Schubert (RAS) convection scheme by the University of Washington Shallow Cumulus (UWShCu) scheme is found to be the cause. Owing to the simpler change from AM2 to HIRAM, we use the HIRAM model as a base model to explore the connections between process-level modeling of convection and GCM simulated clouds and cloud feedback to global warming through a set of perturbed-physics (varying cumulus mixing rate and precipitation microphysics parameters) and perturbed sea surface temperature (SST) experiments.

A bulk diagnostic approach is constructed, and a set of variables is derived and demonstrated to be useful in understanding the simulated relationship. In particular, a novel bulk quantity, the convective precipitation efficiency or equivalently the convective detrainment efficiency, is proposed as a simple measure of the aggregated properties of parameterized convection important to the GCM simulated clouds. As the convective precipitation efficiency increases in the perturbed physics experiments, both liquid and ice water path decrease, with low and middle cloud fractions diminishing at a faster rate than high cloud fractions. This asymmetry results in a large sensitivity of top-of-atmosphere net cloud radiative forcing to changes in convective precipitation efficiency in this limited set of models.

For global warming experiments, inter-model variations in the response of cloud condensate, low cloud fraction, and total cloud radiative forcing are well explained by model variations in response to total precipitation (or detrainment) efficiency. Despite significant variability, all of the perturbed-physics models produce a sizable increase in precipitation efficiency to warming. A substantial fraction of the increase is due to its convective component, which depends on the parameterization of cumulus mixing and convective microphysical processes. The increase in convective precipitation efficiency and associated change in convective cloud height distribution owing to warming explains the increased cloud feedback and climate sensitivity in recently developed Geophysical Fluid Dynamics Laboratory GCMs. The results imply that a cumulus scheme using fractional removal of condensate for precipitation and inverse calculation of the entrainment rate tends to produce a lower climate sensitivity than a scheme using threshold removal for precipitation and the entrainment rate formulated inversely dependent on convective depth.

FIG. 1. (a) Scatterplot of climate sensitivity parameter ? vs cloud feedback parameter ?TCRF/G (TCRF: top-of-atmosphere total cloud radiative forcing, G: change in net TOA radiative flux) from the control (0), the perturbed HIRAM (1?8) models, and AM2 (star). The line shows linear regression and legend shows correlation coefficient. (b) As in (a), but showing changes in total cloud radiative forcing (?TCRF) vs changes in total condensate detrainment efficiency (??). Changes are for the globe means and are normalized for per degree warming.