Climate‐vegetation interaction and amplification of Australian dust variability

Abstract

Observations show that Australian dust activity varies by a factor of 4 on decadal timescales. General circulation models, however, typically fail to simulate this variability. Here we introduce a new dust parameterization into the NOAA/Geophysical Fluid Dynamics Laboratory climate model CM3 that represents land surface processes controlling dust sources including soil water and ice, snow cover, vegetation characteristics, and land type. In an additional novel step, we couple this new dust parameterization to the dynamic vegetation model LM3. In Australia, the new parameterization amplifies the magnitude and timescale of dust variability and better simulates the El Niño–Southern Oscillation‐dust relationship by more than doubling its strength. We attribute these improvements primarily to the slow response time of vegetation to precipitation anomalies and show that vegetation changes account for approximately 50% of enhanced dust emission during El Niño events. The amplified dust leads to radiative forcing over Australia greater than −1 and −20 W/m2 at top of atmosphere and surface, respectively.

Retrieving the global distribution of threshold of wind erosion from satellite data and implementing it into the GFDL AM4.0/LM4.0 model

“…Australia. In general, DOD simulated by the V thresh Ann run using a constant annual mean V threshold is higher than that simulated by the V thresh 12mn run, consistent with the higher dust emission in the V thresh Ann run ( constraint in the model, which is a very important element in capturing the variation of DOD in Australia (Evans et al, 2016), may contribute to the large overestimation of DOD in Australia.…”

Quantifying Anthropogenic Dust Emissions

“…Pattern analysis of remotely sensed dust optical depth has provided further insights into the distribution of source areas in relation to land use and land cover classifications, but has omitted the influence of LUI (Ginoux et al, ; Lee et al, ; Prospero et al, ). Research into the temporal dynamics of dust source areas has sought to explain the relative significance of regional changes in wind erosivity and vegetation for emissions (Cowie et al, ; Evans et al, ; Moulin & Chiapello, ; Ridley et al, ). Model analyses of anthropogenic dust emissions of the second kind, resulting from climate change, have shed light on potential future emissions as a function of wind erosivity and vegetation responses to warming and elevated atmospheric CO 2 concentrations (e.g., Ashkenazy et al, ; Mahowald & Luo, ; Stanelle et al, ).…”

“…Within assessment frameworks, analyses of anthropogenic dust emissions would also benefit from the integration of dust models with LSMs and agricultural systems models that enable human‐dust cycle interactions to be quantified. Building dust emission, transport and deposition processes into LSMs (e.g., the Joint UK Land Environment Simulator [JULES], ORganizing Carbon and Hydrology in Dynamic EcosystEms [ORCHIDEE], and Noah models) and agricultural systems models (e.g., the Agricultural Policy/Environmental eXtender [APEX], the Agricultural Production Systems sIMulator [APSIM], and Decision Support System for Agrotechnology Transfer [DSSAT]) would enable more advanced scenario development to test the effects of LULCC and land management change on the dust cycle than have previously been achieved at the field scale or globally (Evans et al, ; Pierre et al, , ). Research seeking to establish climate change impacts on dust emission could incorporate agricultural adaptation scenarios to represent management change, as well as considering the uncertainty in climate projections (Pelletier et al, ).…”

The Impacts of the Dust Radiative Effect on Vegetation Growth in the Sahel

“…We perform our experiments using the NOAA/GDFL coupled Atmosphere‐Ocean‐Land‐Sea Ice CM3 model (Donner et al, ; Griffies et al, ; Milly et al, ; Shevliakova et al, ) that includes interactive dust emission within the land model (Evans et al, ). Comparison of CM3 precipitation to Global Precipitation Climatology Project (GPCP) data show that the model simulates precipitation in the Sahel well (Donner et al, ).…”

“…Comparison of CM3 precipitation to Global Precipitation Climatology Project (GPCP) data show that the model simulates precipitation in the Sahel well (Donner et al, ). Dust emission is calculated in LM3 for five size classes (spanning 0.1–10 μm radius particles) as a function of the presence of topographic depressions, friction velocity, near surface soil wetness, vegetation cover, land use type, and whether the soil is frozen or snow covered (Evans et al, ). Once in the atmosphere, dust is transported by advection, convection, and vertical diffusion.…”