Climate sensitivity is the global mean surface temperature change in response to changes in radiative forcing, for example, due to increasing greenhouse gases. In the climate modeling communities, the equilibrium climate sensitivity (ECS) is commonly used as the equilibrium change in global mean surface temperature in response to a doubling of carbon dioxide (CO₂). Large uncertainties associated with climate sensitivity from Earth System Models (ESMs) have persisted since the 1970s, which affects the climate simulations and projections by ESMs. The IPCC Fifth Assessment Report (AR5) estimates that “the ECS is likely between 1.5°C and 4.5°C and very unlikely greater than 6°C”. In preparation for the 6th IPCC report, a new generation of ESMs has been developed by modeling centers in the World. ECS in these new ESMs have increased substantially, with values spanning 1.8–5.6 K across 27 ESMs and exceeding 4.5 K in 10 of them. Cloud feedback has been identified as the major cause of the large spread in ECS.

The Department of Energy (DOE)’s Energy Exascale Earth System Model version 1 (E3SMv1), with a state-of-the-art representation of Earth components, has an ECS of 5.3 K, and is not able to reproduce the 20th-century surface temperature warming before 2000 due to its too strong (negative) aerosol radiative forcing, although it captures the temperatures after 2000 due to a combination of its high ECS together with a reduced cooling due to anthropogenic aerosols after 2000. The effective radiative forcing (ERF) due to aerosol-radiation and aerosol-cloud interactions (ERF_ari+aci) in the present-day (1995-2014) relative to 1850 is -1.65 W m^-2, which is substantially larger in magnitude than the IPCC AR5 best estimate of -0.9  W m^-2 (Boucher et al., 2013) and outside the likely range of -1.5 to -0.4 W m^-2.

The overall objective of this proposal is to advance the understanding of physical processes responsible for the large increase in ECS of E3SMv1 from its predecessor, Community Earth System Model version 1 (CESM1) with an ECS of 4.0 K. We will focus on the roles of cloud microphysics and aerosol-cloud interactions in the changes of cloud feedback (λ_cld) and ECS. The change of ERF due to aerosol-cloud interactions (ERF_aci) will also be examined. We will develop efficient machine learning (ML) parameterizations of critical cloud microphysical processes and incorporate them into E3SM to examine the impact on λ_cld and ERF_aci. We will conduct process studies of clouds and aerosols and evaluate the E3SM model simulations with observations. The above E3SM model analysis will be applied to other CMIP6 models with high ECS (>4.5 K) to explore the possible relationships between λ_cld, cloud microphysics, and aerosol-cloud interactions in these models. The improved knowledge will be helpful to reducing the uncertainties in ECS, which is essential for future climate projections. In this proposed work, we will address the following scientific questions:

1. Which physical processes contribute to the increase in λ_cld in the E3SMv1 evolution from CESM1? What is the role of cloud microphysics and aerosol-cloud interaction in the λ_cld change?
2. How does the treatment of clouds and aerosol-cloud interactions in the E3SMv1 evolution affect the aerosol ERF_aci?
3. Will an improved representation of cloud microphysics and aerosol-cloud interactions give both weaker λ_cld and weaker ERF_aci in E3SM?
4. Can the improved understanding drawn from the E3SM analysis be applied to other CMIP6 models with high ECS (>4.5 K)?