Grid-independent High Resolution Dust Emissions (v1.0) for Chemical Transport Models: Application to GEOS-Chem (version 12.5.0)

Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, B3H 4R2, Canada 2 Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States Smithsonian Astrophysical Observatory, Harvard-Smithsonian Center for Astrophysics, 10 Cambridge, MA 02138, USA NOAA Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey 08540, USA School of Engineering and Applied Science, Harvard University, Cambridge, MA 02138, USA California Environmental Protection Agency, Sacramento, CA 95814, USA 15

Abstract. The nonlinear dependence of the dust saltation process on wind speed poses a challenge for models of varying resolutions. This challenge is of particular relevance for the next 20 generation of chemical transport models with nimble capability for multiple resolutions. We develop and apply a method to harmonize dust emissions across simulations of different resolutions by generating offline grid independent dust emissions driven by native high resolution meteorological fields. We implement into the GEOS-Chem chemical transport model a high resolution dust source function to generate updated offline dust emissions. These 25 updated offline dust emissions based on high resolution meteorological fields strengthen dust emissions over relatively weak dust source regionsThese updated offline dust emissions based on high resolution meteorological fields can better resolve weak dust source regions, such as in southern South America, southern Africa and the southwestern United States. Identification of an appropriate dust emission strength is facilitated by the resolution independence of offline 30 emissions. We find that the performance of simulated aerosol optical depth (AOD) versus measurements from the AERONET network and satellite remote sensing improves significantly when using the updated offline dust emissions with the total global annual dust emission strength of 2,000 Tg yr -1 rather than the standard online emissions in GEOS-Chem. The offline high resolution dust emissions are easily implemented in chemical transport models. The 35 source code isand available online through GitHub: https://github.com/Jun-Meng/geoschem/tree/v11-01-Patches-UniCF-vegetation. The global offline high-resolution dust emission inventory are publicly is freely available.e (see Code and Data Availability section).

Introduction
Mineral dust, as one of the most important natural aerosols in the atmosphere, has significant impacts on weather and climate by absorbing and scattering solar radiation (Bergin et al., 2017;Kosmopoulos et al., 2017), on atmospheric chemistry by providing surfaces for heterogeneous 45 reaction of trace gases (Chen et al., 2011;Tang et al., 2017), on the biosphere by fertilizing the tropical forest (Bristow et al., 2010;Yu et al., 2015), and on human health by increasing surface fine particulate matter (PM2.5) concentrations (De Longueville et al., 2010;Fairlie et al., 2007;Zhang et al., 2013). Dust emissions are primarily controlled by surface wind speed to the third or fourth power, vegetation cover and soil water content. The principal mechanism for natural 50 dust emissions is saltation bombardment (Gillette and Passi, 1988;Shao et al., 1993), in which sand-sized particles creep forward and initiate the suspension of smaller dust particles when the surface wind exceeds a threshold. The nonlinear dependence of dust emissions on meteorology ity of this process introduces an artificial dependence of simulations upon model resolution (Ridley et al., 2013). For example, dust emissions in most numerical models are 55 parameterized with an empirical method (e.g. Ginoux et al., 2001;Zender et al., 2003), which requires a critical wind threshold to emit dust particles. Smoothing meteorological fields to coarse resolution can lead to wind speeds falling below the emission threshold in regions that do emit dust. Methods are needed to address the artificial dependence of simulations upon model resolution that arises from nonlinearity in dust emissions. 60 Addressing this nonlinearity is especially important for the next generation of chemistry transport models that is emerging with nimble capability for a variety of resolutions at the global scale. For example, the high performance version of GEOS-Chem (GCHP) (Eastham et al., 2018) currently offers simulation resolutions that vary by over a factor of 100 from C24 (~ 4°x45°) to C360 (~0.25°x0.25°), with progress toward even finer resolution and toward a 65 variable stretched grid capability (Bindle et al., 2020). Resolution-dependent mineral dust emissions would vary by a factor of 3 from C360 to C24, and inhibit interpretation (Ridley et al., 2013). Such large resolution-dependent biases would undermine applications of CTMs to assess dust effects, and would lead to large within-simulation inconsistency for stretched grid simulations that can span the entire resolution range simultaneously. Grid-independent high 70 resolution dust emissions offer a potential solution to this concernissue.
An important capability in global dust evaluation is ground-based and satellite remote sensing. The Aerosol Robotic Network (AERONET), a global ground-based remote sensing aerosol monitoring network of Sun photometers (Holben et al., 1998), has been widely used to evaluate dust simulations. Satellite remote sensing provides additional crucial information 75 across arid regions where in-situ observations are sparse (Hsu et al., 2013). Satellite aerosol retrievals have been used extensively in previous studies to either evaluate the dust simulation (Ridley et al., 2012 or constrain the dust emission budget (Zender et al., 2004). Satellite aerosol products have been used to identify dust sources worldwide (Ginoux et al., 2012;Schepanski et al., 2012;Yu et al., 2018), especially for small-scale sources (Gillette, 1999). 80 The objective of this study is to develop a method to mitigate the large inconsistency of total dust emissions across different resolutions of simulations by generating and archiving an offline dust emissions using native high resolution meteorological fields. We apply this method to the GEOS-Chem chemical transport model. As part of this effort, we implement an updated high resolution satellite-identified dust source function into the dust mobilization module of 85 GEOS-Chem to better represent the spatial structure of dust sources. We apply this new capability to assess the source strength that best represents observations.

Description of Observations 90
We use both ground-based and satellite observations to evaluate our GEOS-Chem simulations.
AERONET is a global ground-based remote sensing aerosol monitoring network of sun photometers with direct sun measurements every 15 minutes (Holben et al., 1998). We use Level 2.0 Version 3 data that has improved cloud screening algorithms (Giles et al., 2019).
Aerosol optical depth (AOD) at 550 nm is interpolated based on the local Aangstrom exponent 95 at the 440 nm and 670 nm channels.
Twin Moderate-Resolution Imaging Spectroradiometer (MODIS) instruments aboard both on the Terra and Aqua NASA satellite platforms and provide near daily measurements globally. We use the AOD at 550 nm retrieved from Collection 6.1 (C6) of MODIS product (Sayer et al., 2014). We use AOD from the Deep Blue (DB) retrieval algorithm (Hsu et al., 2013;Sayer et 100 al., 2014) designed for bright surfaces, and the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm (Lyapustin et al., 2018), which provides global AOD retrieved from MODIS C6 radiances at a resolution of 1 km. The MAIAC AOD used in this study is interpolated to the AOD value at 550 nm.
We use ground-based surface fine dust concentration measurements over the US from 105 the Interagency Monitoring of Protected Visual Environments (IMPROVE, http://views.cira.colostate.edu/fed/DataWizard/) network. The IMPROVE network provides 24hr average fine dust concentrations data every third day over the national parks in the United States. We also include a climatology of dust surface concentrations measurements over 1981-2000 from independent dust measurement sites over the globe (Kok et al. 2020). We use those 110 sites (12 in total) ( Figure S1) that either in the dust belt across Northern Hemisphere or sites relatively close to the weak emission regions in the Southern Hemisphere to evaluate our dust simulation. (Kok et al., 2020) We compare the simulated fine AOD and dust concentrations with measurements using reduced major axis linear regression. We report root mean square error (E), correlation (R) and 115 slope (M).

Dust mobilization module
We use the dust endetrainment and deposition (DEAD) scheme (Zender et al., 2003) in the GEOS-Chem model to calculate dust emissions. The saltation process is dependent on the 120 critical threshold wind speed, which is determined by surface roughness, soil type and soil moisture. We use a fixed soil clay fraction of 0.2 as suggested in Zender et al. (2003). Dust aerosol is transported in four size bins (0.1-1.0, 1.0-1.8, 1.8-3.0, and 3.0-6.0 µm radius).
Detailed description of the dust emission parameterization is in Sect. S1 of the supplemental material. 125 The fractional area of land with erodible dust is represented by a source function. The dust source function used in the dust emission module plays an important role in determining the spatial distribution of dust emissions. The standard GEOS-Chem model (version 12.5.0) uses a source function at 2° x 2.5° resolution from Ginoux et al. (2001) as implemented by Fairlie et al. (2007). We implement an updated high resolution version of the dust source function in this 130 study at 0.25° x 0.25° resolution (Sect. S2). Figure S21 shows a map of the original and updated version of the dust source function. The updated source function exhibits more spatially resolved information due to its finer spatial resolution resulting in a higher fraction of erodible dust over in the eastern Arabian Peninsula, the Bodélé depression, and the central Asian deserts. The dust module dynamically applies this source function, together with information 135 on soil moisture, vegetation, and land use to calculate hourly emissions using the Harmonized Emissions Component ( HEMCO) module described below.

Offline dust emissions at the native meteorological resolution
HEMCO (Keller et al., 2014) is a stand-alone software module for computing emissions in global 140 atmospheric models. We run the HEMCO standalone version using native meteorological resolution (0.25° x 0.3125°) data for wind speed, soil moisture, vegetation, and land use to archive the offline dust emissions at the same resolution as the meteorological data. The computational time required for calculating offline dust emission fluxes at 0.25° x 0.3125° resolution is around 6 hours for one-year of offline dust emissions on a compute node with 32 145 cores on 2 Intel CPUs at 2.1 GHz. (Graham -CC Doc, 2021) In this study, we generate two offline dust emission datasets at 0.25° x 0.3125° resolution. One, referred to as the default offline dust emissions, uses the existing dust source function in the GEOS-Chem dust module; the other, referred to as the updated offline dust emissions, uses the updated dust source function implemented here. Both datasets are at the hourly resolution of the parent meteorological 150 fields. The archived native resolution offline dust emissions can be conservatively regridded to coarser resolution for consistent input to an input emission inventory for chemical transport models with scalable dust source strengthsat multiple resolutions. We use the GEOS-Chem model to evaluate the dust simulations and the emission strength. GEOS-Chem aerosol simulation includes the sulfate-nitrate-ammonium (SNA) aerosol system 160 (Fountoukis and Nenes, 2007;Park et al., 2004), carbonaceous aerosol (Hammer et al., 2016;Park et al., 2003;Wang et al., 2014), secondary organic aerosols (Marais et al., 2016;Pye et al., 2010), sea salt (Jaeglé et al., 2011) and mineral dust (Fairlie et al., 2007) with updates to aerosol size distribution (Ridley et al., 2012;Zhang et al., 2013). Aerosol optical properties are based on the Global Aerosol Data Set (GADS) as implemented by Martin et al. (2003) for externally mixed 165 aerosols as a function of local relative humidity with updates based on measurements (Drury et al., 2010;Latimer and Martin, 2019). Wet deposition of dust, including the processes of scavenging from convections and large scale precipitations, is presented by the scheme developed byfollows Liu et al. (2001). Dry deposition of dust includes the effects of gravitational settling (Seinfeld and Pandis, 1998) and turbulent resistance to the surface, (Zhang 170 et al., 2001), which are represented with deposition velocities in the parameterization, implemented into GEOS-Chem by Fairlie et al. (2007). 2,000 and 2,500 Tg yr -1 , which areis in the range of the current dust emission estimates of over 426 --2,726 Tg yr -1 (Huneeus et al., 2011). We focus on the simulation with 2,000 Tg yr -1 which and better represents observations as will be shown below.     persists throughout all seasons.  We further evaluate the performance of simulated AOD over major desert regions using 270 the MODIS Deep Blue (DB) and MAIAC AOD products. Figure 4 shows annual and seasonal scatter plots comparing GEOS-Chem simulated AOD using original online dust emissions and updated offline dust emissions against retrieved AOD from MODIS DB and MAIAC satellite products over the three major desert regions outlined in Fig. 3. Figure

Evaluation of the simulations against surface dust concentration measurements
We also evaluate our simulations using different dust emissions against measurements of surface dust concentrations. Figure 5 shows the comparison of modeled fine dust surface concentration against the fine dust concentration observation from the IMPROVE network. The simulations using the updated offline dust emissions can better represent the observed surface 290 fine dust concentration measurements than the simulation using the original online dust emissions with higher correlations and slopes across all seasons. Annually, the correlation between the simulation and observation increases from 0.39 to 0.68 , and the slope increases from 0.31 to 0.71 when using the updated offline dust emissions with annual dust strength of 909 Tg compared to the simulation using the original online dust emissions. Scaling the annual 295 dust strength to 2,000 Tg/yr marginally improves the performance of the model simulation of fine dust concentrations in all seasons except winter, during which the surface fine dust concentrations are overestimated. Given the specificity and density of the dust measurements, and the disconnect of North American dust emissions from the global source, we conduct an additional sensitivity simulation with North American dust emissions reduced by 30%. The right 300 column shows that the annual slope in the resultant simulation versus observations improves to 1.07, minor improvements to annual and seasonal correlations. Future efforts should focus on better representing the seasonal variation of dust emissions. Figure 6 shows the comparison of seasonal averaged modeled and measured surface dust concentrations from 12 independent sites across the globe. The simulation using the 305 updated offline dust emissions with dust strength of 2,000 Tg yr 1 is more consistent with the observations at almost all sites. The remaining bias at sites distant from source regions, for example sites in the Southern Hemisphere and East Asia, likely reflects remaining uncertainty in representing dust deposition. Further research is needed to address remaining knowledge gaps, such as better representing the dust size distribution and deposition during transport. 310

Discussion of the dust source strength
One of the advantages of the offline dust emissions is that the dust source strengths are scalable. We have found that the simulation with global total annual dust emission scaled to 315 2,000 Tg better represents observations than does the default simulation with global total annual dust emission of 909 Tg. We also evaluate simulations with global total annual dust emission scaled to 1,500 Tg and 2,500 Tg. Figure S7 indicates that the simulation with global total annual dust emission scaled to 2,000 Tg is more consistent with satellite observations over the Sahara and Middle East. Although the central Asian deserts and regions with AERONET 320 observations (Fig. S8) are better represented by the simulation with global total annual dust emission scaled to 2,500 Tg, since the Sahara has the highest dust emissions (Huneeus et al., 2011), and AOD over the Sahara is most likely dominated by dust, we scale global total annual dust emissions to best match this source region. Additional development and evaluation should be conducted to further narrow the uncertainty of dust emissions, especially at the regional 325 scale.

Discussion of the dust source strength
One of the advantages of the offline dust emissions is that the same dust source strength can 360 be readily applied to all model resolutions, facilitating evaluation of dust source strength independent of resolutions. We have found that the simulation with global total annual dust emission scaled to 2,000 Tg better represents observations than does the default simulation with global total annual dust emissions of 909 Tg. We also evaluate simulations with global total annual dust emission scaled to 1,500 Tg and 2,500 Tg. Figure S9 indicates that the simulation 365 with global total annual dust emission scaled to 2,000 Tg is more consistent with satellite observations over the Sahara and Middle East. Although the central Asian deserts and regions with AERONET observations (Fig. S10) are better represented by the simulation with global total annual dust emission scaled to 2,500 Tg, since the Sahara has the highest dust emissions (Huneeus et al., 2011), and AOD over the Sahara is most likely dominated by dust, we scale 370 global total annual dust emissions to best match this source region. Additional development and evaluation should be conducted to further narrow the uncertainty of dust emissions, especially at the regional scale.

Summary and Conclusions
The nonlinear dependence of dust emission parameterizations upon model resolution poses a challenge for the next generation of chemical transport models with nimble capability for multiple resolutions. In this paper we have developed and tested aThe method explored here to calculate offline dust emissions at the native meteorological resolution to promotes 390 consistency of dust emissions across different model resolutions. We take advantage of the capability of HEMCO standalone module to calculate dust emission offline at native meteorological resolution using the DEAD dust emission scheme combined with an updated high resolution dust source function. We evaluate the performance of the simulation with native resolution offline dust emissions and an updated dust source function with source 395 strength of 2,000 Tg yr -1 . We find better agreement with measurements, including satellite and AERONET AOD, and surface dust concentrations. The offline fine resolution dust emissions strengthen the dust emissions over smaller better resolve smaller desert regions. The independence of source strength from simulation resolution facilitates evaluation with observations. Sensitivity simulations with an annual global source strength of either 1,500 or 400 2,500 Tg generally degraded the performance. A sensitivity simulation with North American emissions reduced by 30% improved the annual mean slope versus observations. Futureurther work should continue to develop and evaluate the representation of dust deposition and regional seasonal variationemissions.

Information about the Supplement
The supplement related to this article describes the details of the dust emission scheme used in 425 this project, the updated high resolution dust source function, as well as additional figures described in the main text.