Intercomparison of the cloud water phase among global climate models

Abstract

Mixed-phase clouds (clouds that consist of both cloud droplets and ice crystals) are frequently present in the Earth's atmosphere and influence the Earth's energy budget through their radiative properties, which are highly dependent on the cloud water phase. In this study, the phase partitioning of cloud water is compared among six global climate models (GCMs) and with Cloud and Aerosol Lidar with Orthogonal Polarization retrievals. It is found that the GCMs predict vastly different distributions of cloud phase for a given temperature, and none of them are capable of reproducing the spatial distribution or magnitude of the observed phase partitioning. While some GCMs produced liquid water paths comparable to satellite observations, they all failed to preserve sufficient liquid water at mixed-phase cloud temperatures. Our results suggest that validating GCMs using only the vertically integrated water contents could lead to amplified differences in cloud radiative feedback. The sensitivity of the simulated cloud phase in GCMs to the choice of heterogeneous ice nucleation parameterization is also investigated. The response to a change in ice nucleation is quite different for each GCM, and the implementation of the same ice nucleation parameterization in all models does not reduce the spread in simulated phase among GCMs. The results suggest that processes subsequent to ice nucleation are at least as important in determining phase and should be the focus of future studies aimed at understanding and reducing differences among the models.

The importance of mixed-phase clouds for climate sensitivity in the global aerosol-climate model ECHAM6-HAM2

“…As shown in Figure 3, ECHAM6-HAM2 still underestimates SLF but on average simulates twice as high SLFs as presented in Komurcu et al (2014). At -10…”

“…SLF has been obtained from the CALIOP satellite and has been compared to an earlier version of ECHAM6-HAM2 (Komurcu et al, 2014). At that time ECHAM6-HAM2 seriously underestimated SLF.…”

“…This can partly be explained by differences in the definition of SLF in the satellite data and in the model. In addition, some of the model improvements mentioned above were added after the study by Komurcu et al (2014). As the lidar signal gets attenuated at cloud optical depth (COD) > 3, the CALIOP satellite data are only representative for thin clouds with COD < 3, to which we now limit the model data as well.…”

Anthropogenic Aerosol Influences on Mixed-Phase Clouds

“…On a global scale, the fraction of supercooled water in MPCs at various temperatures from the ECHAM6 and different versions of the CAM GCM has been compared to CALIOP satellite estimates. None of the models captured the observed variation in the supercooled fraction with temperature correctly but either simulated too small or too high values at all temperatures [46]. This is partly related to differences in INP concentrations and partly to differences in the involved parameterized microphysical processes.…”

Numerical framework and performance of the new multiple-phase cloud microphysics scheme in RegCM4.5: precipitation, cloud microphysics, and cloud radiative effects

“…Recently, several studies have illustrated the importance of using a more realistic representation of cloud microphysics in climate models. For example, Cesana et al (2015) and Komurcu et al (2014) showed that climate models tend to underestimate the supercooled liquid clouds, and models that prognose separately the liquid and ice mixing ratios give a better representation of cloud properties. The RegCM version 4 (or RegCM4) regional climate model of the International Centre for Theoretical Physics (ICTP) is a widely used system that has been applied to local and regional seasonal forecasting and climate change problems for all regions of the globe (e.g.…”