Global Dust Variability Explained by Drought Sensitivity in CMIP6 Models. Issue 6 (8th June 2021)

• Record Type: Journal Article
• Authors: Aryal, Yog N.; Evans, Stuart
• Is Part Of: Journal of geophysical research. Volume 126:Issue 6(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-06-08
• Subjects: CMIP6 -- drought -- dust -- linear regression -- SMI -- SPI; Geomorphology -- Periodicals; 551.3
• DOI: 10.1029/2021JF006073
• Languages: English
• ISSNs: 2169-9003

Abstract

Abstract: Both the atmospheric and land surface conditions affect the dust cycle in the climate system. In particular, the occurrence of drought can modulate the emission of dust on interannual time scales. Studies have shown, however, that models generally do not represent dust variability, and that there is a large intermodel spread in the simulation of dust. In this study, we compare the relationship between drought and dust in 19 Global Circulation Models participating in Phase Six of the Coupled Model Intercomparison Project for historical (1950–2014) and future (2050–2100: SSP585) scenarios and MERRA‐2 reanalysis. The relationships between drought and dust (dust sensitivity to drought) are based on linear regression analysis. Our results show that MERRA‐2 reanalysis highly underestimates models' average dust emission. The Standardized Soil Moisture Index better explains the dust variability over most regions than the Standardized Precipitation Index, highlighting the importance of the condition of the land surface. Across models, the strength of the dust‐drought relationship explains much of the spread in interannual variability of dust emission over Southern Africa, Sahel, India, Australia, and North America, indicating models that capture this relationship generate greater variability. We also find that the correlation between models' dust‐drought relationship and mean emission is generally weaker compared to that with dust variability. In future scenarios, the intermodel spread in the projected changes in the dust variability is correlated to the intermodel spread in the projected changes in the models' dust sensitivity to drought in Australia, India, Middle East, South America, and Southern Africa. Plain Language Summary: Dust emission depends on meteorological conditions and land surface conditions. Drought indices provide information on both of these factors. Dust emissions from most dust source regions exhibit large year‐to‐year variability, but due to the lack of long‐term observations, studies on dust are often based on climate models. However, dust emission estimated from climate models varies widely in annual average, and especially in year‐to‐year variability. Here, we examine the sensitivities of dust emission to drought in 20 global climate models. We attribute climate models' differences in dust emission and its year‐to‐year variability to their differences in the dust sensitivity to drought. We find a significant correlation between dust emission and drought in a large number of climate models in all dust source regions. Compared to precipitation drought, soil moisture drought is more strongly correlated with dust emission. Among the climate models, differences in the year‐to‐year variability of dust emission are correlated with the climate models' differences in the sensitivity of dust emission to drought. In future scenarios, changes to the dust‐drought relationship can explain the changes to dust emission variability in some regions. The findings are valuable for climate model evaluation and understanding the future of dust in the climate system. Key Points: Standardized Soil Moisture Index better characterizes dust emission and its variability than Standardized Precipitation Index The intermodel spread in dust emission variability is well explained by the intermodel spread in dust emission sensitivity to drought There is a strong intermodel correlation between projected changes in dust variability and changes in dust sensitivity to drought