How well do the CMIP6 models simulate dust aerosols?

Abstract

Abstract. Mineral dust impacts key processes in the Earth system, including the radiation budget, clouds, and nutrient cycles. We evaluate dust aerosols in 16 models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) against multiple reanalyses and satellite observations. Most models, and particularly the multi-model ensemble mean (MEM), capture the spatial patterns and seasonal cycles of global dust processes well. However, large uncertainties and inter-model diversity are found. For example, global dust emissions, primarily driven by model-simulated surface winds, vary by a factor of 5 across models, while the MEM estimate is double the amount in reanalyses. The ranges of CMIP6 model-simulated global dust emission, deposition, burden and optical depth (DOD) are larger than previous generations of models. Models present considerable disagreement in dust seasonal cycles over North China and North America. Here, DOD values are overestimated by most CMIP6 models, with the MEM estimate 1.2–1.7 times larger compared to satellite and reanalysis datasets. Such overestimates can reach up to a factor of 5 in individual models. Models also fail to reproduce some key features of the regional dust distribution, such as dust accumulation along the southern edge of the Himalayas. Overall, there are still large uncertainties in CMIP6 models’ simulated dust processes, which feature inconsistent biases throughout the dust lifecycle between models, particularly in the relationship connecting dust mass to DOD. Our results imply that modelled dust processes are becoming more uncertain as models become more sophisticated. More detailed output relating to the dust cycle in future intercomparison projects will enable better constraints of global dust cycles, and enable the potential identification of observationally-constrained links between dust cycles and optical properties.

Two mega sand and dust storm events over northern China in March 2021: transport processes, historical ranking and meteorological drivers

“…Currently, models still face huge challenges in accurately quantifying dust emissions, due to limitations such as uncertainties in the dust source locations and dust emission parameterization schemes (Kok et al, 2020). Aerosol reanalysis involving the assimilation of a large number of observations is considered a valuable tool for evaluating dust processes in climate models (Wu et al, 2020;Zhao et al, 2021). Admittedly, aerosol reanalysis also carries some uncertainties; however, it was still expected to provide a valuable reference for identifying the sources of these two dust processes.…”

Reduced surface fine dust under droughts over the southeastern United States during summertime: observations and CMIP6 model simulations

“…CNRM-ESM2-1 considerably underestimates the dust concentrations by more than 20 µg/m 3 (80%) regardless of the drought conditions. This is possibly due to its relatively high dry deposition (Zhao et al, 2021). GFDL-ESM4 simulations have a relatively lower underestimation of ~7 µg/m 3 (26%) but do not reproduce the variability as indicated by the negative correlation coefficient (R) and slope.…”

Predominant Type of Dust Storms That Influences Air Quality Over Northern China and Future Projections

“…It is therefore important to project future changes in DS occurrence, although climate change has added complexity to the issue, with simultaneous changes in meteorological factors and dust source conditions under global warming scenarios. Current future projections focus mainly on dust emissions (Wu et al., 2018; Zong et al., 2021), but state‐of‐the‐art climate models participating in CMIP5 and CMIP6 (Climate Models Intercomparison Project phase five/six) are still unable to simulate dust aerosol loadings well for northern China (Pu & Ginoux, 2018; Zhao et al., 2022). For example, Wu et al.…”