Distinct Contributions of Ice Nucleation, Large‐Scale Environment, and Shallow Cumulus Detrainment to Cloud Phase Partitioning With NCAR CAM5

Abstract

Mixed‐phase clouds containing both liquid droplets and ice particles occur frequently at high latitudes and in the midlatitude storm track regions. Simulations of the cloud phase partitioning between liquid and ice hydrometeors in state‐of‐the‐art global climate models are still associated with large biases. In this study, the phase partitioning in terms of liquid mass phase ratio (MPRliq, defined as the ratio of liquid mass to total condensed water mass) simulated from the NCAR Community Atmosphere Model version 5 (CAM5) is evaluated against the observational data from A‐Train satellite remote sensors. Modeled MPRliq is significantly lower than observations on the global scale, especially in the Southern Hemisphere (e.g., Southern Ocean and the Antarctic). Sensitivity tests with CAM5 are conducted to investigate the distinct contributions of heterogeneous ice nucleation, shallow cumulus detrainment, and large‐scale environment (e.g., winds, temperature, and water vapor) to the low MPRliq biases. Our results show that an aerosol‐aware ice nucleation parameterization increases the MPRliq especially at temperatures colder than −20°C and significantly improves the model agreements with observations in the Polar regions in summer. The decrease of threshold temperature over which all detrained cloud water is liquid from 268 to 253 K enhances the MPRliq and improves the MPRliq mostly over the Southern Ocean. By constraining water vapor in CAM5 toward reanalysis, modeled low biases in many geographical regions are largely reduced through a significant decrease of cloud ice mass mixing ratio.

Cloud Phase and Relative Humidity Distributions over the Southern Ocean in Austral Summer Based on In Situ Observations and CAM5 Simulations

“…From -20°--10°C, the CAM-collocated data also contain 53% of liquid phase, which is three times of that in the Obs-200s (16%), and underestimate frequencies of ice and mixed phase samples. Previously, CAM5 simulations have been reported to underestimate SLW content and overproduce ice at temperatures relevant for mixed phase conditions compared with satellite observations (e.g., Komurcu et al 2014;Cesana et al 2015;Kay et al 2016b;Wang et al 2018). Comparing these previous findings with our results on phase frequencies indicates that satellite observations may be biased to detect a layer of SLW at the top of cold clouds (e.g., Rauber and Tokay 1991) and underestimate ice phase occurrence frequency below cloud top, and/or the underestimation of SLW content in simulations is more likely attributed to underestimating mixed phase frequencies than overestimating liquid phase frequencies.…”

“…Climate models show large deficiencies in simulating radiative fluxes in the Southern Ocean region (~50°-80°S), and often underestimate reflected shortwave radiation on the order of 10 W m -2 (e.g., Bodas-Salcedo et al 2014;Li et al 2013; Kay et al 2012). This is in part due to the fact that climate models (e.g., Trenberth and Fasullo 2010;Kay et al 2016a;Bodas-Salcedo et al 2016;Kay et al 2016b;Cesana and Chepfer 2013;Wang et al 2018) as well as higher-resolution regional models (e.g., Huang et al 2014Huang et al , 2015 generally show lower cloud fraction and less supercooled liquid water (SLW, i.e., liquid water existing at temperatures below 0°C) than the observations in the mid-and high southern latitudes. The amount of SLW plays a critical role in determining cloud radiative forcing (e.g., Ceppi et al 2014;Lawson and Gettelman 2014;Shupe and Intrieri 2004), cloud feedbacks (e.g., Gettelman and Sherwood 2016;Tsushima et al 2006;McCoy et al 2014b), and equilibrium climate sensitivity (e.g., Frey and Kay 2017).…”

Toward Understanding the Simulated Phase Partitioning of Arctic Single‐Layer Mixed‐Phase Clouds in E3SM

“…First, EAMv1 adopts the CNT ice nucleation scheme to replace the previous temperature dependent heterogeneous ice nucleation scheme (Meyers et al, 1992) used in CAM5 in mixed-phase cloud regime. As shown in Wang et al (2018), this change leads to a large increase in SLF at the temperatures colder than −20°C in the polar regions because the Meyers scheme predicts much higher INP number concentrations than CNT (Liu et al, 2011;Xie et al, 2013) in clean environments. This may cause the model to significantly overestimate INP number concentrations compared to observations (Prenni et al, 2007).…”

“…For example, the detrainment of liquid from shallow convection to stratiform clouds increases the amount of cloud liquid water in simulated mixed-phase clouds over the Southern Ocean. This leads to a large reduction of the surface shortwave radiative fluxes over that region as demonstrated in the Community Atmosphere Model version 5 (CAM5) (Kay et al, 2016;Wang et al, 2018).…”

Characterizing the Occurrence and Spatial Heterogeneity of Liquid, Ice, and Mixed Phase Low‐Level Clouds Over the Southern Ocean Using in Situ Observations Acquired During SOCRATES

“…Clouds over the Southern Ocean (SO) strongly influence the energy budget over this region, with satellite observations showing an annual mean spatial fraction around 80%–90% (e.g., Kay et al., 2012; Matus & L'Ecuyer, 2017; McCoy et al., 2014). Climate models struggle to correctly simulate radiative fluxes over the SO (50°S–80°S), commonly underestimating reflected shortwave radiation in part because they (e.g., Bodas‐Salcedo et al., 2016; Cesana & Chepfer, 2013; Kay et al., 2016; Trenberth & Fasullo, 2010; Wang et al., 2018) produce lower cloud fraction and less supercooled liquid water (SLW, liquid water at temperatures below 0°C) than observed. Similar problems have been noted in output from higher‐resolution models (e.g., Huang et al.…”