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Home » Processed-Oriented Diagnostics of Aerosol-Cloud Interactions in CMP6 Models

Processed-Oriented Diagnostics of Aerosol-Cloud Interactions in CMP6 Models

Aerosols represent a key source of uncertainty in global climate models. Through
the absorption and scattering of shortwave radiation, aerosols reduce the incoming
solar radiation at the surface and thus offset part of the warming resulting from
increases in anthropogenic greenhouse gases. In addition to this direct radiative effect,
certain types of aerosols are known to act as cloud condensation nuclei, altering the
cloud albedo and lifetime. Differences in modeling the effective radiative forcing
from aerosol-cloud interactions (ERFaci) are a substantial source of uncertainty in
predicting climate change.

Aerosol-climate interactions (ACI) play an important role in climate projections
despite the limited ability of models to represent aerosol and cloud processes accurately.
Indeed, climate models can disagree on both the sign and magnitude of the radiative
effects from aerosol-cloud interactions. This disagreement reflects, in part, the absence of
a consistent methodology to quantify their effects in models. Indeed, even the direct
radiative effects of aerosols are rarely calculated explicitly. The lack of a coherent
framework to quantify the radiative impact of aerosol-cloud interactions limits our
ability to compare its importance across different models, or even between
different versions of the same model. This is compounded by the lack of regionally-
resolved observations of ACI on a global scale, that account for the presence of co-
varying meteorological conditions on ACI. Thus, despite their fundamental role in
determining both historical and future climate change, the magnitude of ACI remains
poorly constrained in models.

This proposal aims to fill this gap by developing a set of diagnostics for evaluating
aerosol-cloud interactions in models that can be derived from existing
CMIP6 simulations, or from standard model performed by labs runs during
the model development cycle, and can be applied to both historical and future
emission scenarios. The model diagnostics will be compared to observationally-
constrained estimates of ERFaci for low (warm) marine clouds which are the
dominant source of uncertainty of ACI in models. These estimates use satellite
measurements to provide observational constraints on the cloud susceptibility to
aerosols within a framework that accounts for the role of varying environmental
factors in modulating the strength of aerosol–cloud interactions.
Through these diagnostics, we aim to both quantify and better constrain
the representation of aerosol-cloud processes in CMIP6 models. This will directly support
the MAPP program goal to “advance understanding of biases generally affecting CMIP6-
era and next-generation models and to identify targeted model improvements that
can improve model fidelity.”

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