Understanding Cloud Feedback and Climate Sensitivity in E3SMv1 and CMIP6 Models: Roles of Cloud Microphysics and Aerosol-Cloud Interactions

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).

carbon dioxide (CO2)

An important heat-trapping gas, also known as a greenhouse gas, that comes from the extraction and burning of fossil fuels (such as coal, oil, and natural gas), from wildfires, and natural processes like volcanic eruptions.

Source: NASA Source

Large uncertainties associated with climate sensitivity from Earth System Models

earth system model (ESM)

A model that simulates how chemistry, biology, and physical forces work together. These models are similar to, but much more comprehensive than, global climate models.

Source: U.S. Department of Energy Source

(ESMs) have persisted since 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 have 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

Energy Exascale Earth System Model (E3SM)

The Energy Exascale Earth System Model (E3SM) project includes Earth System Models, simulations, and predictions to investigate energy-relevant science using code optimized for DOE's advanced supercomputers.

Source: NVCL Web Page Source

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

aerosols

Small particles or droplets, such as wildfire smoke, volcanic gases, or salty sea spray, that float in the air. They are emitted by both natural events and human activities.

Source: NASA Global Climate Change Source

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

anthropogenic aerosol

Tiny particles suspended in the atmosphere that are primarily generated by human activities.

Source: Source

after 2000. The effective radiative forcing (ERF) due to aerosol-radiation and aerosol-cloud interactions

aerosol-cloud interaction

The interaction of airborne particles (aerosols) with clouds. Scientists study how aerosols affect clouds and how these interactions can impact the Earth's climate.

Source: DOE Source

(ERFari+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 (?cld) and ECS. The change of ERF due to aerosol-cloud interactions (ERFaci) will also be examined. We will develop efficient machine learning (ML)

machine learning (ML)

Machine Learning (ML) refers to the field and practice of using algorithms that are able to “learn” by extracting patterns from a large body of data.

Source: Center of Excellence Source

parameterizations of critical cloud microphysical processes and incorporate them into E3SM to examine the impact on ?cld and ERFaci. 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 ?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 ?cld in the E3SMv1 evolution from CESM1? What is the role of cloud microphysics and aerosol-cloud interaction in the ?cld change?

(2)   How does the treatment of clouds and aerosol-cloud interactions in the E3SMv1 evolution affect the aerosol ERFaci?

(3)   Will an improved representation of cloud microphysics and aerosol-cloud interactions give both weaker ?cld and weaker ERFaci in E3SM?

(4)   Can the improved understanding drawn from the E3SM analysis be applied to other CMIP6 models with high ECS (>4.5 K)?