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

Abstract

Abstract. Clouds are important in the climate system because of their large influence on the radiation budget. On the one hand, they scatter solar radiation and with that cool the climate. On the other hand, they absorb and re-emit terrestrial radiation, which causes a warming. How clouds change in a warmer climate is one of the largest uncertainties for the equilibrium climate sensitivity (ECS). While a large spread in the cloud feedback arises from low-level clouds, it was recently shown that also mixed-phase clouds are important for ECS. If mixed-phase clouds in the current climate contain too few supercooled cloud 5 droplets, too much ice will change to liquid water in a warmer climate. As shown by Tan et al. (2016), this overestimates the negative cloud phase feedback and underestimates ECS in the CAM global climate model (GCM). Here we are using the newest version of the ECHAM6-HAM2 GCM to investigate the importance of mixed-phase clouds for ECS.Although we also considerably underestimate the fraction of supercooled liquid water globally in the reference version of ECHAM6-HAM2 GCM, we do not obtain increases in ECS in simulations with more supercooled liquid water in the present-

Elucidating ice formation pathways in the aerosol-climate model ECHAM6-HAM2

“…As 15 discussed above, the TOA energy balance constraints in the new model require a lower tuning parameter for the autoconversion of cloud droplets to rain (γ r ) and thus thicker liquid clouds that reflect more SW radiation. We consider this the main reason for the higher liquid water path in the 2M as compared to the REF simulation, consistent with the findings of Lohmann and Neubauer (2018), as the warm phase parameterizations are the same in both models.…”

“…This leads to an overestimation of the magnitude of the cloud phase feedback (Li and Le Treut, 1992;Terai et al, 2016;Tan et al, 2016) in a greenhouse induced, warmer climate in some models. Recent modeling efforts challenge 5 the universality of this finding (Lohmann and Neubauer, 2018;Bodas-Salcedo, 2018), calling for a comprehensive description of ice formation pathways in GCMs.…”

The chemistry–climate model ECHAM6.3-HAM2.3-MOZ1.0

“…Figure 13 shows the difference between a decadal ECHAM6.3 simulation (i.e., the ECHAM-HAMMOZ model without the chemistry and aerosol schemes) versus the ERA-Interim reanalysis (Dee et al, 2011), which confirms the statement of Stevens et al (2013). In addition, the amounts of cloud liquid water and cloud ice are underestimated in the tropics (Lohmann and Neubauer, 2018), pointing to problems either with the parameterization of convection or detrained condensate with implications for the wet scavenging of trace gases and aerosol par-ticles. A more detailed investigation of this issue is beyond the scope of this paper.…”

“…Interactions with clouds are implemented through an explicit activation scheme based on Köhler theory (Abdul-Razzak and Ghan, 2000), with an empirical estimation of maximum supersaturation derived from explicit parcel model calculations. Activated droplet numbers are passed on to a two-moment cloud microphysics scheme (Lohmann et al, 2007;Lohmann and Hoose, 2009) with prognostic variables for cloud droplet number concentration (CDNC) and ice crystal number concentration (ICNC). Emissions and dry and wet deposition are handled consistently between the aerosol scheme and the gas-phase chemistry scheme MOZ (see Sect.…”

“…Stratiform clouds are computed diagnostically based on a relative humidity threshold (Sundqvist et al, 1989). Cloud water and cloud ice are treated prognostically according to Lohmann and Roeckner (1996). In the base model version, the cloud droplet number concentration is parameterized as a function of altitude with higher values over land than over the ocean.…”