[Paper Review] Effective radiative forcing in the aerosol-climate model CAM5.3-MARC-ARG

Benjamin S. Grandey, Daniel Rothenberg+7 more|arXiv (Cornell University)|Apr 17, 2018

Atmospheric chemistry and aerosols55 references21 citations

TL;DR

This study evaluates the effective radiative forcing (ERF) of anthropogenic aerosols using CAM5.3-MARC-ARG, a modified version of the Community Atmosphere Model 5.3 that replaces the default MAM3 aerosol module with the MARC model, which resolves aerosol mixing states and multi-modal size distributions. The MARC configuration yields a stronger global mean net ERF of −1.75 ± 0.04 W m⁻² compared to −1.57 ± 0.04 W m⁻² in MAM3, primarily due to enhanced direct and surface albedo radiative effects, with regional ERF differences driven by cloud shortwave radiative effect variability.

ABSTRACT

We quantify the effective radiative forcing (ERF) of anthropogenic aerosols modelled by the aerosol-climate model CAM5.3-MARC-ARG. CAM5.3-MARC-ARG is a new configuration of the Community Atmosphere Model version 5.3 (CAM5.3) in which the default aerosol module has been replaced by the two-Moment, Multi-Modal, Mixing-state-resolving Aerosol model for Research of Climate (MARC). CAM5.3-MARC-ARG uses the default ARG aerosol activation scheme, consistent with the default configuration of CAM5.3. We compute differences between simulations using year-1850 aerosol emissions and simulations using year-2000 aerosol emissions in order to assess the radiative effects of anthropogenic aerosols. We compare the aerosol column burdens, cloud properties, and radiative effects produced by CAM5.3-MARC-ARG with those produced by the default configuration of CAM5.3, which uses the modal aerosol module with three log-normal modes (MAM3). Compared with MAM3, we find that MARC produces stronger cooling via the direct radiative effect, stronger cooling via the surface albedo radiative effect, and stronger warming via the cloud longwave radiative effect. The global mean cloud shortwave radiative effect is similar between MARC and MAM3, although the regional distributions differ. Overall, MARC produces a global mean net ERF of -1.75$\\pm$0.04 W m$^{-2}$, which is stronger than the global mean net ERF of -1.57$\\pm$0.04 W m$^{-2}$ produced by MAM3. The regional distribution of ERF also differs between MARC and MAM3, largely due to differences in the regional distribution of the cloud shortwave radiative effect. We conclude that the specific representation of aerosols in global climate models, including aerosol mixing state, has important implications for climate modelling.

Motivation & Objective

• To quantify the effective radiative forcing (ERF) of anthropogenic aerosols in a new CAM5.3 configuration with improved aerosol microphysics.
• To assess how aerosol mixing state and multi-modal size distribution affect radiative forcing and cloud properties.
• To compare MARC-based simulations with the default MAM3 configuration in CAM5.3 to isolate the impact of aerosol representation on ERF.
• To evaluate the sensitivity of ERF to aerosol activation schemes and cloud microphysics parameterizations.

Proposed method

• Simulations were conducted using CAM5.3-MARC-ARG, replacing the default MAM3 aerosol module with the two-Moment, Multi-Modal, Mixing-state-resolving Aerosol model (MARC).
• The model uses the ARG aerosol activation scheme and prescribed year-1850 and year-2000 aerosol emissions for ERF calculation.
• Effective radiative forcing was computed as the top-of-atmosphere radiative flux perturbation under fixed sea surface temperatures.
• Radiative effects were decomposed into direct, cloud shortwave, cloud longwave, surface albedo, and net ERF components using radiative diagnostics.
• Model output was analyzed for column-integrated aerosol and cloud properties, including CCN concentration, cloud droplet number, and liquid/ice water path.
• Sensitivity to model configuration was tested via timing simulations to assess computational cost and diagnostic overhead.

Experimental results

Research questions

• RQ1 How does replacing MAM3 with MARC in CAM5.3 affect the global and regional distribution of effective radiative forcing from anthropogenic aerosols?
• RQ2 What is the relative contribution of direct, cloud albedo, and cloud longwave radiative effects to the total ERF in the MARC configuration?
• RQ3 How do differences in aerosol mixing state and size distribution between MARC and MAM3 alter cloud condensation nuclei (CCN) and cloud microphysical properties?
• RQ4 To what extent does the regional distribution of ERF differ between MARC and MAM3, and what drives these differences?
• RQ5 How does the inclusion of clean-sky radiation diagnostics affect simulation cost and ERF quantification in the MARC configuration?

Key findings

• The MARC configuration produces a global mean net effective radiative forcing (ERF) of −1.75 ± 0.04 W m⁻², which is stronger than the −1.57 ± 0.04 W m⁻² produced by the default MAM3 configuration.
• MARC yields stronger cooling from the direct radiative effect and surface albedo radiative effect compared to MAM3, due to improved representation of aerosol mixing state and size distribution.
• The cloud longwave radiative effect is warmer in MARC than in MAM3, indicating a stronger greenhouse-like effect from aerosol-induced cloud changes.
• The global mean cloud shortwave radiative effect is similar between MARC and MAM3, but regional distributions differ significantly, particularly over industrial and downwind regions.
• Regional ERF differences are primarily driven by variations in the cloud shortwave radiative effect, which are linked to differences in CCN concentration and cloud microphysics.
• The MARC model increases simulation cost by approximately 5.8% compared to MAM3 when clean-sky diagnostics are off, and by 10.9% when diagnostics are enabled.