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.

Highlights

  • Mineral dust, a key component of the Earth system, has important impacts on the global climate and environment through a number of pathways (Mahowald et al, 2010; Gassó et al, 2010; Knippertz and Stuut, 2014; Shao et al, 2011; Mahowald et 25 al., 2014; Kok et al, 2018; Jin et al, 2021)

  • The spatial pattern and magnitudes of dust emission in the model ensemble mean (MEM) are in broad agreement with Copernicus Atmosphere Monitoring Service (CAMS) and MERRA2

  • The CMIP6 models estimate that between 1.4 (INM-CM4-8) and 7.6 (MIROC-ES2L) Pg dust is emitted into the atmosphere annually, producing a MEM estimate of around 3.5 Pg yr-1

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Summary

Introduction

A key component of the Earth system, has important impacts on the global climate and environment through a number of pathways (Mahowald et al, 2010; Gassó et al, 2010; Knippertz and Stuut, 2014; Shao et al, 2011; Mahowald et 25 al., 2014; Kok et al, 2018; Jin et al, 2021). Incomplete understanding and representations of the driving mechanisms of dust emission, transport and deposition, dust 35 particle size and shape, as well as model structural differences It remains a challenge for climate models to accurately simulate the meteorological processes that play critical roles in dust processes. Pu and Ginoux (2018) compared seven CMIP5 models’ simulated DOD to the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue aerosol product They found that the multi-model mean was better than most individual models in capturing the climatology and seasonal cycles of DOD over most dust source regions, but that it still underestimated the mean value and the amplitude of the seasonal cycle. Given the large uncertainties in previous generations of climate models discussed 75 above, it is important to evaluate the performance of the CMIP6 models.

CMIP6 AMIP models and simulations 90 We examine dust in 16 climate and Earth
Satellite aerosol optical depth products
CAMS and MERRA2 reanalyses
Results
Climatology
Drivers
Dust loading, deposition and lifetime
AOD and DOD
Conclusions and discussions
425 Acknowledgements
Full Text
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