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. Dust aerosol plays an important role in the climate system byaffecting the radiative and energy balances. Biases in dust modeling mayresult in biases in simulating global energy budget and regional climate. Itis thus very important to understand how well dust is simulated in theCoupled Model Intercomparison Project Phase 5 (CMIP5) models. Here sevenCMIP5 models using interactive dust emission schemes are examined againstsatellite-derived dust optical depth (DOD) during 2004–2016. It is found that multi-model mean can largely capture the global spatialpattern and zonal mean of DOD over land in present-day climatology in MAM andJJA. Global mean land DOD is underestimated by −25.2  % in MAM to −6.4  % in DJF. While seasonal cycle, magnitude, and spatial pattern aregenerally captured by the multi-model mean over major dust source regions such asNorth Africa and the Middle East, these variables are not so well representedby most of the models in South Africa and Australia. Interannual variationsin DOD are not captured by most of the models or by the multi-model mean.Models also do not capture the observed connections between DOD and localcontrolling factors such as surface wind speed, bareness, and precipitation.The constraints from surface bareness are largely underestimated while theinfluences of surface wind and precipitation are overestimated. Projections of DOD change in the late half of the 21st century under theRepresentative Concentration Pathways 8.5 scenario in which the multi-modelmean is compared with that projected by a regression model. Despite theuncertainties associated with both projections, results show somesimilarities between the two, e.g., DOD pattern over North Africa in DJF andJJA, an increase in DOD in the central Arabian Peninsula in all seasons, anda decrease over northern China from MAM to SON.

How reliable are CMIP5 models in simulating dust optical depth?