the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Nov 2021
Weaknesses in dust emission modelling hidden by tuning to dust in the atmosphere
Abstract. Dust emissions influence global climate while simultaneously reducing the productive potential and resilience of landscapes to climate stressors, together impacting food security and human health. Vegetation is a major control on dust emission because it extracts momentum from the wind and shelters the soil surface, protecting dry and loose material from erosion by winds. Many of the current dust emission models (TEM) assume that the Earth’s land surface is constantly devoid of vegetation, then adjust the dust emission using a vegetation cover reciprocal, and finally calibrate to dust in the atmosphere. We compare this approach with an albedo-based dust emission model (AEM) which calibrates Earth’s land surface shadow to shelter depending on wind speed, to represent aerodynamic roughness spatio-temporal variation. We also compare these dust emission models with estimates of dust in the atmosphere using dust optical depth frequency (DOD). Using existing datasets of satellite observed dust emission from dust point sources (DPS), we show that during the same period, DOD frequency exceeds DPS frequency by up to two orders of magnitude (RMSEDOD = 67 days). Relative to DPS frequency, both models over-estimated dust emission frequency by up to one order of magnitude (RMSETEM = 6 days; RMSEAEM = 4 days) but showed strong relations with DPS frequency suitable for calibrating models to observed dust emission. Theoretically, the TEM is incomplete in its formulation, which despite the pragmatic adjustment using the vegetation cover reciprocal, causes dust emission to be highly dependent on wind speed and over-estimates large (> 0.1 kg m−2 a−1) dust emission over vast vegetated areas. Consequently, the TEM produces considerable falsely positive change in dust emission, relative to the AEM. Since the main difference between the dust emission models is the treatment of aerodynamic roughness we conclude that its crude representation in the TEM has caused large, previously unknown, uncertainty in Earth System Models (ESMs). Our results indicate that tuning dust emission models to dust in the atmosphere has hidden for more than two decades, these TEM modelling weaknesses and its poor performance. The AEM overcomes these weaknesses and improves performance without tuning. In ESMs the AEM can be driven by available prognostic albedo to represent the fidelity of drag partition physics to reduce uncertainty of aerosol effects on, and responses to, contemporary and future environmental change.
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CC1: 'Comment on gmd-2021-337', Paul Ginoux, 08 Dec 2021
Dear Adrian and co-authors,
I made below a few comments related to your submitted manuscript as I found several cases of mis-representation or inaccurate description of the model and data I have been developing with my collaborators. Hopefully, you will find them useful to improve the manuscript.
Regards,
Paul Ginoux
Lines 29-31: “Many of the traditional dust emission models (TEM) assume that the Earth’s land surface is devoid of vegetation, then adjust the dust emission using a vegetation cover complement, and finally calibrate the magnitude of simulated emissions to dust in the atmosphere”
The calibration is mostly related to numerical discretization of the momentum and continuity equations. Emission of dust in numerical models depends on the discretization of surface winds. The surface winds are inferred from the pressure level wind vectors derived by solving numerically the momentum equations. The numerical discretization of these equations will be affected by the numerical resolution. Obviously higher resolution will resolve sharp topographic variations with stronger downslope winds. On the other hand, flat terrain without roughness elements using low resolution will generate stronger gustiness. So, changing model resolution has a non-trivial effect on surface winds. Concerning dust emission, the flux depends on the cubic power of surface winds (see Equation 2), which will amplify wind bias related to model resolution. This implies that “tuning” dust emission is a required method to simulate scale-aware tracer with numerical model. This is also true for other tracers, such as sea salt emission from the oceans.
Lines 82-83: “The common approach to modeling dust emission in ESMs
uses globally constant values of aerodynamic roughness length (z0), which are static over time and fixes R(z0) ≈ 0.91.”This is incorrect. In ESMs the momentum roughness length is calculated at every time steps and in every grid cells as a function of terrain variations, vegetation cover, snow cover, etc.
Line 81-85: “The common approach to modelling dust emission in ESMs… This emission is then reduced by a function of vegetation cover and ultimately ‘tuned’ down to match observed in the atmosphere.”
I am unaware of any ESMs who have implemented dust emission as described. I can certainly speak for NASA and GFDL models (Ginoux et al., 2001; Evans et al., 2016).
Lines 103-104: “The 𝑢𝑠∗ is obtained directly from 𝜔𝑛𝑠, the normalised and rescaled shadow (1-albedo), enabling an albedo-based dust emission model (AEM; see Appendix for full description of the implementation)”
Do I read correctly that you are scaling the friction velocity using 3 parameters with an exponential function of 𝜔𝑛𝑠? Am I right that you will have to rescale 𝜔𝑛𝑠 for any other satellite instruments with different viewing angles or radiometric characteristics? Is this not a global tuning?
Line 128-132: “Evans et al., 2016”
The characterization of GFDL model (Evans et al. 2016) is not correct. You may want to read the paper. We are not using E=1-Av. The bare surface is calculated using an exponential function of the canopy (LAI) and stems, twigs, litters (SAI). The dust emission is calculated in each land tiles (primary, secondary vegetation, pasture and cropland) independently. Then the flux of dust is passing through a flux-exchanger into the atmosphere while a flux down from turbulence and settling is going in the land model. The latest ESM4 includes also tiles from fires and rangeland, in addition for taking into account slopes (Dunne et al., 2020; Horowitz et al., 2020). I will disagree with you when calling such detailed and consistent modeling of dust cycle a “crude model representation”
Lines 133-135: “When the TEMs are applied in dust-climate ESMs it is assumed that this parameterization is adequate for climate projections. In contrast, the albedo-based scheme for sediment flux and dust emission (AEM; Eqs. 3, 4 & 5) represents the drag partition physics without pre-tuning to a fixed land surface condition, without the need for E, and thereby removes these additional sources of uncertainty.”
The main point of using climate model has been missed here. Despite their approximations, ESMs simulate the different Earth’s climate systems consistently over time using different projection scenarios. While the proposed used of 𝜔𝑛𝑠 (the normalized and rescaled shadow) is considered fixed (beyond MODIS period), ignoring vegetation and land use changes. The AEM technique is inadequate for future or past climates.
Lines 145-149: “To understand the extent to which AOD estimates the spatial variation in dust emission magnitude and frequency we calculated the probability of dust occurrence modeled by the dust optical depth (DOD>0.2) using the criteria established previously (Ginoux et al., 2012). We note the stated limitations of DOD to be largely restricted to bright land surfaces in the visible wavebands which implies reduced performance over areas where
vegetation is present.”This sentence contains several misunderstandings of our latest method developed with my co-authors to derive DOD.
In our 2012, we used the collection 5.1 of MODIS Deep Blue (DB), which provided aerosol products over bright surfaces. Since 2013 Collection 6 MODIS DB aerosol products have been extend to cover most (without snow or cloud cover) land surfaces (Sayer et al., 2014). All subsequent papers deriving DOD is using Collection 6.1 MODIS DB (e.g. Pu and Ginoux, 2017, 2018a, 2018b, 2020; Yu and Ginoux, 2021). A second update is the method to calculate DOD. Since Pu and Ginoux (2017), DOD is calculated using a quadratic function of aerosol optical depth (AOD) and the single scattering albedo (SSA). In our 2012 paper, DOD is calculated using an on/off switch depending on the value of the Angstrom Exponent (AE). Then a threshold is applied to detect the highest frequency to correspond to actual dust sources. The method has been compared to independent geomorphological data over the Chihuahuan desert (Baddock et al., 2016) to prove that MODIS DB DOD is able to successfully detect high-resolution dust sources. It will be necessary to add a note the text stating that you are referring to an old dataset long replaced by thoroughly validated values using the latest MODIS aerosol products. Preferably, you replace the sentence by referring to more recent thoroughly validated values using the latest MODIS aerosol products.
Line 149-150: “To calculate DOD, we used wavebands available from monthly Moderate Resolution Imaging Spectroradiometer (MODIS; MOD08 M3 V6.1) at a 1-degree pixel resolution (Platnick, 2015)”
These are monthly gridded products at 1 x1 degree resolution from 1 km daily pixels. Using coarse resolution monthly aerosol products is totally inadequate to compare with point source dust plumes. The difference with MODIS DB DOD from my team is that it is 0.1 x 0.1 degree twice-daily products, which was shown to be appropriate to detect tiny dust sources (Baddock et al., 2016).
It is unclear how you get these DOD. Citing Platnick, 2015 is not helpful. What MODIS Level 3 products are you using? Are you using Dark Target or Deep Blue algorithm? Maybe you are using a blend of the two algorithms? DOD is not part of these products, so a description on how you obtain DOD from MODIS would be helpful, especially that you assimilate it to Ginoux et al. (2012) which is outdated.
Line 154: “We also provided a theoretical basis for TEMs formulation to be incorrect.”
I commented earlier that your description of TEMs formulation is mostly incorrect.
Line 166: “using MODIS data at 250 m spatial resolution with visible to thermal infrared wavebands”
This is incorrect. Only bands 1 and 2 are provided at 250 m. Bands 3 to 7 are at 500 m resolution. Bands 8 to 36 are at 1 km resolution. Red is band 2, blue is band 3 and green band 4. Deep blue is band 8 or 1 km pixel. Then 10 x 10 pixels are aggregated to provide 10x10 km Level 2 daily aerosol products.
Line 171: “DOD modelled frequency describes DOD > 0.2”
In Baddock et al. (2016), we used DOD>0.75 over the Chihuahuan desert, but Pu et al. (2020) used 2 threshold values (0.2 and 0.02) depending on the continent. Choosing a DOD threshold should be adapted to the objectives of the study but using gridded 1-degree monthly products is too coarse spatially and temporally to study dust sources.
Lines 248-249: “the TEM is driven by wind speed attenuated by aerodynamic roughness which is fixed over space and static over time,…”
If the surface conditions don’t change (no snow, no vegetation or land use changes) this will be true, but most ESMs (or TEMs) include such changes when resolving the boundary layer properties.
References
Baddock, M. C., Ginoux, P., Bullard, J. E., and Gill, T. E.: Do MODIS-defined dust sources have a geomorphological signature?, Geophys. Res. Lett., 43, 2606–2613, https://doi.org/10.1002/2015GL067327, 2016.
Dunne, J. P., Horowitz, L. W., Adcroft, A. J., Ginoux, P., Held, I. M., John, J. G., et al.: The GFDL Earth System Model Version 4.1 (GFDL-ESM 4.1): Overall coupled model description and simulation characteristics. Journal of Advances in Modeling Earth Systems, 12, e2019MS002015. https://doi.org/10.1029/2019MS002015, 2020.
Evans, S., Ginoux, P., Malyshev, S., and Shevliakova, E.: Climate-vegetation interaction and amplification of Australian dust variability, Geophys. Res. Lett., 43, 11823–11830, https://doi.org/10.1002/2016gl071016, 2016.
Ginoux, P., M. Chin, I. Tegen, J. M. Prospero, B. Holben, O. Dubovik, and S.-J. Lin: Sources and distributions of dust aerosols simulated with the GOCART model, J. Geophys. Res., 106, 20,255– 20,274, 2001.
Ginoux, P., Prospero, J. M., Gill, T. E., Hsu, N. C., and Zhao, M.: Global-scale attribution of anthropogenic and natural dust sources and their emission rates based on MODIS Deep Blue aerosol products, Rev. Geophys., 50, RG3005, https://doi.org/10.1029/2012RG000388, 2012.
Horowitz, L. W., Naik, V., Paulot, F., Ginoux, P. A., Dunne, J. P., & Mao, J., et al.: The GFDL global atmospheric chemistry-climate model AM4.1: Model description and simulation characteristics. Journal of Advances in Modeling Earth Systems, 12, e2019MS002032. https://doi.org/10.1029/2019MS002032, 2020
Platnick, S.: MODIS Atmosphere L3 Monthly Product, NASA MODIS Adaptive Processing System, Goddard Space Flight Center,
10.5067/MODIS/MOD08_M3.006, 2015.GPu, B. and Ginoux, P.: Projection of American dustiness in the late 21st century due to climate change, Sci. Rep., 7, 5553, https://doi.org/10.1038/s41598-017-05431-9, 2017.
Pu, B. and Ginoux, P.: Climatic factors contributing to long-term variations in surface fine dust concentration in the United States, Atmos. Chem. Phys., 18, 4201–4215, https://doi.org/10.5194/acp-18-4201-2018, 2018a.
Pu, B. and Ginoux, P.: How reliable are CMIP5 models in simulating dust optical depth?, Atmos. Chem. Phys., 18, 12491–12510, https://doi.org/10.5194/acp-18-12491-2018, 2018b.
Pu, B., Ginoux, P., Guo, H., Hsu, N. C., Kimball, J., Marticorena, B., Malyshev, S., Naik, V., O'Neill, N. T., Pérez García-Pando, C., Paireau, J., Prospero, J. M., Shevliakova, E., and Zhao, M.: Retrieving the global distribution of the threshold of wind erosion from satellite data and implementing it into the Geophysical Fluid Dynamics Laboratory land–atmosphere model (GFDL AM4.0/LM4.0), Atmos. Chem. Phys., 20, 55–81, https://doi.org/10.5194/acp-20-55-2020, 2020.
Sayer, A. M., Munchak, L. A., Hsu, N. C., Levy, R. C., Bettenhausen, C., and Jeong, M.-J. (2014), MODIS Collection 6 aerosol products: Comparison between Aqua's e-Deep Blue, Dark Target, and “merged” data sets, and usage recommendations, J. Geophys. Res. Atmos., 119, 13,965– 13,989, doi:10.1002/2014JD022453.
Yu, Y. and Ginoux, P.: Assessing the contribution of the ENSO and MJO to Australian dust activity based on satellite- and ground-based observations, Atmos. Chem. Phys., 21, 8511–8530, https://doi.org/10.5194/acp-21-8511-2021, 2021.
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AC1: 'Reply on CC1', Adrian Chappell, 18 Dec 2021
Dear Adrian and co-authors,
I made below a few comments related to your submitted manuscript as I found several cases of mis-representation or inaccurate description of the model and data I have been developing with my collaborators. Hopefully, you will find them useful to improve the manuscript.
Regards,
Paul Ginoux
Hi Paul,
Thanks for taking the time to make comments on our manuscript. We are a little surprised that none of your comments relate to the achievements described in our manuscript.
We did not intend to mis-represent or inaccurately describe dust emission modelling. We make no special case about the model and data that you have been developing. Our results show an inherent bias when calibrating a dust emission model to dust in the atmosphere and illustrate this using dust optical depth.
We answer each of your remarks below using bold, indented bullet points.
Regards,
Adrian and co-authors
Lines 29-31: “Many of the traditional dust emission models (TEM) assume that the Earth’s land surface is devoid of vegetation, then adjust the dust emission using a vegetation cover complement, and finally calibrate the magnitude of simulated emissions to dust in the atmosphere”
The calibration is mostly related to numerical discretization of the momentum and continuity equations. Emission of dust in numerical models depends on the discretization of surface winds. The surface winds are inferred from the pressure level wind vectors derived by solving numerically the momentum equations. The numerical discretization of these equations will be affected by the numerical resolution. Obviously higher resolution will resolve sharp topographic variations with stronger downslope winds. On the other hand, flat terrain without roughness elements using low resolution will generate stronger gustiness. So, changing model resolution has a non-trivial effect on surface winds. Concerning dust emission, the flux depends on the cubic power of surface winds (see Equation 2), which will amplify wind bias related to model resolution. This implies that “tuning” dust emission is a required method to simulate scale-aware tracer with numerical model. This is also true for other tracers, such as sea salt emission from the oceans.
- This comment refers to the italicised excerpt from our manuscript (Lines 29-31) which states clearly that the calibration of traditional modelled dust emission magnitude is to dust in the atmosphere. It is evident from many publications (Zender et al., 2003; Woodward, 2001; Tegen et al., 2002) that the dust emission magnitude is adjusted by a calibration factor. That factor is obtained by converting dust emission magnitude to optical properties that are then compared with measurements of aerosol optical properties. In our manuscript, we show the discrepancy between satellite observed dust emission and dust optical depth in the spatio-temporal frequency of occurrence. Consequently, we explain that dust models calibrated to dust in the atmosphere do not require fidelity in dust emission modelling.
Lines 82-83: “The common approach to modeling dust emission in ESMs uses globally constant values of aerodynamic roughness length (z0), which are static over time and fixes R(z0) ≈ 0.91.”
This is incorrect. In ESMs the momentum roughness length is calculated at every time steps and in every grid cells as a function of terrain variations, vegetation cover, snow cover, etc.
- This comment refers to the italicised excerpt from our manuscript (Lines 82-83) which should describe one common approach in ESM dust emission described in the Appendix of our manuscript.
- We accept that traditional dust models (TDMs) include roughness lengths but those roughness lengths values may not represent the heterogeneous vegetation that occurs within large (up to 50 km pixels).
- We accept that traditional dust models allow those roughness lengths to vary in time / spatially due to snow, vegetation etc. What is held constant, uniformly and globally in traditional dust emission models is the roughness length of each surface type. The roughness length of bare ground never changes, but the ground may not be bare all year. The same is true of desert shrubland which may not be bare everywhere within the land cover type. So in many dust-prone parts of the world, especially those that are bare soil, the roughness length never changes in TDMs. This is the roughness length that the mid-layer (resolved) wind speed is solved for.
- With that wind, some TDMs that we are familiar with construct a wind friction speed appropriate for erodible surfaces based on the assumption that erodible surfaces are bare (constant roughness length). That wind friction speed is then used to deflate dust. This "dust friction speed" need not be the same as the friction speed for turbulent fluxes.
- It is evident from this discussion alone that the implementation of TDMs varies model-by-model. There is commonality between models as illustrated in the dust emission schemes illustrated in our Appendix.
- In response to this comment we will revise our manuscript to ensure that we refer to “one” of the common approaches in dust emission modelling. As we stated in our opening paragraph, our work is not specifically focused on the model and data that Paul and his team has been developing. Our aim is to raise awareness of weaknesses in modelling that provide opportunities for model development for the benefit of the community consistent with the aims this journal’s scope.
Line 81-85: “The common approach to modelling dust emission in ESMs… This emission is then reduced by a function of vegetation cover and ultimately ‘tuned’ down to match observed in the atmosphere.”
I am unaware of any ESMs who have implemented dust emission as described. I can certainly speak for NASA and GFDL models (Ginoux et al., 2001; Evans et al., 2016).
- The italicised excerpt (above) refers to a common approach in ESM dust emission which is described in the Appendix of our manuscript. For clarity, our description of the method is that which once (and possibly is still) used in CESM and E3SM.
- As described above (Para 7) we will clarify in our revised manuscript that we refer to one common approach to modelling dust emission and will cease where appropriate, using definite articles. This we hope will provide a more balanced representation of TDMs that may do things slightly differently than TDMs with which we are more familiar.
Lines 103-104: “The 𝑢𝑠∗ is obtained directly from 𝜔𝑛𝑠, the normalised and rescaled shadow (1-albedo), enabling an albedo-based dust emission model (AEM; see Appendix for full description of the implementation)”
Do I read correctly that you are scaling the friction velocity using 3 parameters with an exponential function of 𝜔𝑛𝑠? Am I right that you will have to rescale 𝜔𝑛𝑠 for any other satellite instruments with different viewing angles or radiometric characteristics? Is this not a global tuning?
- Our albedo-based method was developed from a series of ground-based bi-directional spectral reflectance experiments in the laboratory and in the field (Chappell et al., 2005, 2006; Chappell et al., 2007). Over the last ten years or so, the approach has shifted from spectral reflectance to waveband independent albedo focused on land surface structure (Chappell et al., 2010). Our approach was shown to be similar to the widely accepted sheltering concept and its implementation (Raupach, 1992; Raupach et al., 1993). We established the bases for how the albedo-based approach could improve drag partition, sediment transport and hence dust emission (Chappell and Webb, 2016; Chappell et al., 2019). Since then we have confirmed the structural information content and demonstrated its scale invariance from ground-based measurements of albedo to MODIS-based estimates (Chappell et al., 2018; Ziegler et al., 2020).
- The shadow (1-albedo) is normalized to provide structural information for an area and is directly related to the wind friction velocity (of the area) measured in wind tunnel experiments (Chappell and Webb, 2016). To enable this grain-scale areal relation to be used at any other areal scale requires a measurement of albedo. Albedo scales linearly with increasing area in contrast to the wind friction velocity. To explicitly tackle sub-grid scale heterogeneity (Raupach and Lu, 2004) albedo measurements (or indeed any area-weighted estimates) must be rescaled before being used with the same calibration.
Line 128-132: “Evans et al., 2016”
The characterization of GFDL model (Evans et al. 2016) is not correct. You may want to read the paper. We are not using E=1-Av. The bare surface is calculated using an exponential function of the canopy (LAI) and stems, twigs, litters (SAI). The dust emission is calculated in each land tiles (primary, secondary vegetation, pasture and cropland) independently. Then the flux of dust is passing through a flux-exchanger into the atmosphere while a flux down from turbulence and settling is going in the land model. The latest ESM4 includes also tiles from fires and rangeland, in addition for taking into account slopes (Dunne et al., 2020; Horowitz et al., 2020). I will disagree with you when calling such detailed and consistent modeling of dust cycle a “crude model representation”
- The full sentence (quoted below from the manuscript at lines 128-132) reveals that the citation of Evans et al. (2016) is one of several which correctly cite dust models which use NDVI.
“This E is used in some ESMs so that leaf area index (LAI) or satellite ‘greenness’ observations e.g., normalized difference vegetation indices (NDVI) can be used as a surrogate of the land surface fraction occupied by green vegetation (Evans et al., 2016; Galloza et al., 2018; Zender et al., 2003a; Sellar et al., 2019).”
- The exponential relation between bare area and LAI (or NDVI) is an empirical representation of the drag partition which does not account for the physical aerodynamics that fast wind speeds are influenced less by a given roughness than slow wind speeds (Raupach et al., 1993; Chappell and Webb, 2016; Mayaud and Webb, 2017; Webb et al., 2014; Walter, 2012). Consequently, changing vegetation cover (in planform) is not itself sufficient to represent a change in sediment transport and dust emission.
- We acknowledge that vegetation indices can be used to obtain a first order correction to the erodible surface area, whether through thresholds and ramps (as CESM once did) or through more sophisticated canopy schemes like GFDL.
- We note that the full sentence also refers to some ESMs and as we have described (in our opening paragraph) there is no attempt in our manuscript to comment on model and data that Paul and his team has been developing.
Lines 133-135: “When the TEMs are applied in dust-climate ESMs it is assumed that this parameterization is adequate for climate projections. In contrast, the albedo-based scheme for sediment flux and dust emission (AEM; Eqs. 3, 4 & 5) represents the drag partition physics without pre-tuning to a fixed land surface condition, without the need for E, and thereby removes these additional sources of uncertainty.”
The main point of using climate model has been missed here. Despite their approximations, ESMs simulate the different Earth’s climate systems consistently over time using different projection scenarios. While the proposed used of 𝜔𝑛𝑠 (the normalized and rescaled shadow) is considered fixed (beyond MODIS period), ignoring vegetation and land use changes. The AEM technique is inadequate for future or past climates.
- We recognise and appreciate the experience that Paul’s comment provides. We agree that ESMs play a critical role in understanding Earth’s systems and that they must be designed not to rely on observations.
- Our scheme is albedo-based from any source or scale as evident in our recent investigation of scale invariance using both ground-based albedometers and satellite-retrieved albedo (Ziegler et al., 2020).
- Since albedo is one of the main drivers of energetic change in ESMs there is considerable potential for ESM-based albedo (not MODIS-based albedo) to drive the albedo-based dust emission model which will enable multiple internal consistencies in the modelling of the energy and dust cycles.
- In the excerpt above there is no mention that albedo is taken from MODIS.
Lines 145-149: “To understand the extent to which AOD estimates the spatial variation in dust emission magnitude and frequency we calculated the probability of dust occurrence modeled by the dust optical depth (DOD>0.2) using the criteria established previously (Ginoux et al., 2012). We note the stated limitations of DOD to be largely restricted to bright land surfaces in the visible wavebands which implies reduced performance over areas where vegetation is present.”
This sentence contains several misunderstandings of our latest method developed with my co-authors to derive DOD. In our 2012, we used the collection 5.1 of MODIS Deep Blue (DB), which provided aerosol products over bright surfaces. Since 2013 Collection 6 MODIS DB aerosol products have been extended to cover most (without snow or cloud cover) land surfaces (Sayer et al., 2014). All subsequent papers deriving DOD is using Collection 6.1 MODIS DB (e.g. Pu and Ginoux, 2017, 2018a, 2018b, 2020; Yu and Ginoux, 2021). A second update is the method to calculate DOD. Since Pu and Ginoux (2017), DOD is calculated using a quadratic function of aerosol optical depth (AOD) and the single scattering albedo (SSA). In our 2012 paper, DOD is calculated using an on/off switch depending on the value of the Angstrom Exponent (AE). Then a threshold is applied to detect the highest frequency to correspond to actual dust sources. The method has been compared to independent geomorphological data over the Chihuahuan desert (Baddock et al., 2016) to prove that MODIS DB DOD is able to successfully detect high-resolution dust sources. It will be necessary to add a note the text stating that you are referring to an old dataset long replaced by thoroughly validated values using the latest MODIS aerosol products. Preferably, you replace the sentence by referring to more recent thoroughly validated values using the latest MODIS aerosol products.
- Satellite observed dust emission point sources (DPS) have an uncertainty of around +/-2 km (Kandakji et al., 2020) due to the phase difference between timing of dust emission and availability of the imagery. At the point scale, this unexplained variance is reduced by aggregating information at a larger scale. This is well-established in geostatistical literature used across multiple disciplines and the issue of incompatible scales is summarised (Gotway and Young, 2002). We reduce the unexplained spatial variance by aggregating the DPS data.
- The excerpt above describes clearly that we used the established criteria (Ginoux et al., 2012). We then used those criteria with monthly MODIS Deep Blue (MOD08 M3 V6.1) data to establish dust optical depth across 1-degree grid boxes. Those grid boxes coincide with the occurrence of satellite observed dust emission point sources (DPS). These DPS data were aggregated to months across the same 1-degree grid boxes to provide compatibility with the DOD data. We note that these DPS data occur mainly in bright land surfaces which include vegetation.
- In our revised manuscript, we are happy to modify the description of our method to ensure that it is balanced. For clarity, we have used the latest MODIS Deep Blue Collection 6 dataset and apply the established criteria (Ginoux et al., 2012).
- In response to Paul’s query about the method, we looked up the recent methods (Pu and Ginoux, 2017) which cites (Ginoux et al., 2012) for their method. Apart from some small alternations to the Angstrom component, we could not find any no difference between our method, what we described in the manuscript and the method described recently (Pu and Ginoux, 2017).
Line 149-150: “To calculate DOD, we used wavebands available from monthly Moderate Resolution Imaging Spectroradiometer (MODIS; MOD08 M3 V6.1) at a 1-degree pixel resolution (Platnick, 2015)”
These are monthly gridded products at 1 x1 degree resolution from 1 km daily pixels. Using coarse resolution monthly aerosol products is totally inadequate to compare with point source dust plumes.
- As described above (para 20) there are compelling reasons to use aggregated DPS data. Consequently, we aggregated the DPS data over space. For compatibility with the monthly, 1 degree grid we aggregated the DPS to that same resolution.
- In our assessment, frequency of occurrence from either the model, or the DPS observations are described by the occurrence (modelled or observed) of dust emission at any of the DPS pixels within the 1° grid cell. i.e., should one DPS produce dust, that cell is considered on (FoO = 1) for that day.
- The DOD data describe the probability of any part of the 1° cell producing DOD > 0.2 consistent with the DPS FOO. If the reviewer is arguing that DOD data are ‘totally inadequate’ to verify emission from dust point sources, why do many studies use DOD results to therefore describe the spatial variability in dust emission (Ginoux et al., 2012: Pu and Ginoux 2017). The purpose of this analysis was to demonstrate this very point, highlighting the inadequacy of DOD by using actual dust observations to verify simulated dust emission.
- Consistent with previous studies using DOD to display spatial variability, we show the discrepancy between spatial variation in DOD and dust emission observed using satellites.
- That discrepancy between DOD and observed dust emission does not occur when DOD is compared with simulated dust emission.
- The cause of that discrepancy is that DOD measures dust in the atmosphere which is not directly related to dust emission.
The difference with MODIS DB DOD from my team is that it is 0.1 x 0.1 degree twice-daily products, which was shown to be appropriate to detect tiny dust sources (Baddock et al., 2016).
- We make no comparison with the work done by your team comparing dust in the atmosphere at 10 km pixels with satellite observed dust emission point source (DPS) data. In our approach we tackled explicitly the incompatibility of spatial scales (Gotway and Young, 2002) by upscaling DPS data to 1 degree pixels consistent with the DOD data and simulated dust emissions.
It is unclear how you get these DOD. Citing Platnick, 2015 is not helpful. What MODIS Level 3 products are you using? Are you using Dark Target or Deep Blue algorithm? Maybe you are using a blend of the two algorithms? DOD is not part of these products, so a description on how you obtain DOD from MODIS would be helpful, especially that you assimilate it to Ginoux et al. (2012) which is outdated.
- As described in the manuscript and in our response above (para 9) we used the established criteria (Ginoux et al., 2012) with monthly MODIS Deep Blue (MOD08 M3 V6.1) data to establish dust optical depth across 1-degree grid boxes. In the revised manuscript we will include the specific wavebands to make this clearer.
Line 154: “We also provided a theoretical basis for TEMs formulation to be incorrect.”
I commented earlier that your description of TEMs formulation is mostly incorrect.
- We described above (para 9) that the original manuscript erred by implying that all TEMs are formulated in the same way when in fact there is a spectrum of implementations for each process. The revised manuscript will avoid this overgeneralization when using indefinite articles e.g., "many TEMs". Nevertheless, it is important to give example process formulations and in those cases we have double-checked and the references previously given for TEM process descriptions are correct. We cannot find in your above text the argument by which our description in the Introduction of the manuscript of the theoretical basis for TEMs formulation is incorrect. The basis for the incorrect formulation is already published (Webb et al., 2020) and here we are extending that work to dust emission modelling.
Line 166: “using MODIS data at 250 m spatial resolution with visible to thermal infrared wavebands”
This is incorrect. Only bands 1 and 2 are provided at 250 m. Bands 3 to 7 are at 500 m resolution. Bands 8 to 36 are at 1 km resolution. Red is band 2, blue is band 3 and green band 4. Deep blue is band 8 or 1 km pixel. Then 10 x 10 pixels are aggregated to provide 10x10 km Level 2 daily aerosol products.
- To clarify this statement we will remove from the revised manuscript mention of “…at 250 m…” .
Line 171: “DOD modelled frequency describes DOD > 0.2”
In Baddock et al. (2016), we used DOD>0.75 over the Chihuahuan desert, but Pu et al. (2020) used 2 threshold values (0.2 and 0.02) depending on the continent. Choosing a DOD threshold should be adapted to the objectives of the study but…
- The DOD data are describing dust in the atmosphere and the DPS data are describing dust emission. The discrepancy between DOD data and DPS data is not impacted by the choice of threshold.
… using gridded 1-degree monthly products is too coarse spatially and temporally to study dust sources.
- We explained above (para 20-29) that we ensured that the gridded 1-degree monthly DOD is at the same scale (space and time) as the DPS data. In this approach we tackle explicitly the incompatibility of spatial scales. Our methodology explains and our results demonstrate that these 1-degree monthly products are not too coarse to study dust sources.
Lines 248-249: “the TEM is driven by wind speed attenuated by aerodynamic roughness which is fixed over space and static over time,…”
If the surface conditions don’t change (no snow, no vegetation or land use changes) this will be true, but most ESMs (or TEMs) include such changes when resolving the boundary layer properties.
- We have described in our responses above that there are at least two perspectives on this issue and we think that they converge. We will revise our manuscript using definite articles and describe some of the different ways in which surface conditions are represented in dust emission models.
References cited in our responses
Chappell, A. and Webb, N. P.: Using albedo to reform wind erosion modelling, mapping and monitoring, Aeolian Research, 23, 63-78, https://doi.org/10.1016/j.aeolia.2016.09.006, 2016.
Chappell, A., Zobeck, T. M., and Brunner, G.: Using on-nadir spectral reflectance to detect soil surface changes induced by simulated rainfall and wind tunnel abrasion, Earth Surface Processes and Landforms, 30, 489-511, 10.1002/esp.1185, 2005.
Chappell, A., Zobeck, T. M., and Brunner, G.: Using bi-directional soil spectral reflectance to model soil surface changes induced by rainfall and wind-tunnel abrasion, Remote Sensing of Environment, 102, 328-343, https://doi.org/10.1016/j.rse.2006.02.020, 2006.
Chappell, A., Strong, C., McTainsh, G., and Leys, J.: Detecting induced in situ erodibility of a dust-producing playa in Australia using a bi-directional soil spectral reflectance model, Remote Sensing of Environment, 106, 508-524, https://doi.org/10.1016/j.rse.2006.09.009, 2007.
Chappell, A., Van Pelt, S., Zobeck, T., and Dong, Z.: Estimating aerodynamic resistance of rough surfaces using angular reflectance, Remote Sensing of Environment, 114, 1462-1470, https://doi.org/10.1016/j.rse.2010.01.025, 2010.
Chappell, A., Webb, N. P., Leys, J. F., Waters, C. M., Orgill, S., and Eyres, M. J.: Minimising soil organic carbon erosion by wind is critical for land degradation neutrality, Environmental Science & Policy, 93, 43-52, https://doi.org/10.1016/j.envsci.2018.12.020, 2019.
Chappell, A., Webb, N. P., Guerschman, J. P., Thomas, D. T., Mata, G., Handcock, R. N., Leys, J. F., and Butler, H. J.: Improving ground cover monitoring for wind erosion assessment using MODIS BRDF parameters, Remote Sensing of Environment, 204, 756-768, https://doi.org/10.1016/j.rse.2017.09.026, 2018.
Ginoux, P., Prospero, J. M., Gill, T. E., Hsu, N. C., and Zhao, M.: Global-scale attribution of anthropogenic and natural dust sources and their emission rates based on MODIS Deep Blue aerosol products, Reviews of Geophysics, 50, 10.1029/2012rg000388, 2012.
Gotway, C. A. and Young, L. J.: Combining Incompatible Spatial Data, Journal of the American Statistical Association, 97, 632-648, 10.1198/016214502760047140, 2002.
Kandakji, T., Gill, T. E., and Lee, J. A.: Identifying and characterizing dust point sources in the southwestern United States using remote sensing and GIS, Geomorphology, 353, 107019, https://doi.org/10.1016/j.geomorph.2019.107019, 2020.
Mayaud, J. R. and Webb, N. P.: Vegetation in Drylands: Effects on Wind Flow and Aeolian Sediment Transport, Land, 6, 64, 2017.
Pu, B. and Ginoux, P.: Projection of American dustiness in the late 21st century due to climate change, Scientific Reports, 7, 5553, 10.1038/s41598-017-05431-9, 2017.
Raupach, M. R.: Drag and drag partition on rough surfaces, Boundary-Layer Meteorology, 60, 375-395, 10.1007/BF00155203, 1992.
Raupach, M. R. and Lu, H.: Representation of land-surface processes in aeolian transport models, Environmental Modelling & Software, 19, 93-112, https://doi.org/10.1016/S1364-8152(03)00113-0, 2004.
Raupach, M. R., Gillette, D. A., and Leys, J. F.: The effect of roughness elements on wind erosion threshold, Journal of Geophysical Research: Atmospheres, 98, 3023-3029, 10.1029/92jd01922, 1993.
Tegen, I., Harrison, S. P., Kohfeld, K., Prentice, I. C., Coe, M., and Heimann, M.: Impact of vegetation and preferential source areas on global dust aerosol: Results from a model study, Journal of Geophysical Research: Atmospheres, 107, AAC 14-11-AAC 14-27, 10.1029/2001jd000963, 2002.
Walter, B.: Wind Tunnel Studies of Shear Stress Partitioning in Live Plant Canopies,
Webb, N. P., Okin, G. S., and Brown, S. E.: The effect of roughness elements on wind erosion: The importance of surface shear stress distribution, Journal of Geophysical Research, 119, 6066-6084, 2014.
Webb, N. P., Chappell, A., LeGrand, S. L., Ziegler, N. P., and Edwards, B. L.: A note on the use of drag partition in aeolian transport models, Aeolian Research, 42, 100560, https://doi.org/10.1016/j.aeolia.2019.100560, 2020.
Woodward, S.: Modeling the atmospheric life cycle and radiative impact of mineral dust in the Hadley Centre climate model, Journal of Geophysical Research: Atmospheres, 106, 18155-18166, 10.1029/2000jd900795, 2001.
Zender, C. S., Bian, H., and Newman, D.: Mineral Dust Entrainment and Deposition (DEAD) model: Description and 1990s dust climatology, Journal of Geophysical Research: Atmospheres, 108, 10.1029/2002jd002775, 2003.
Ziegler, N. P., Webb, N. P., Chappell, A., and LeGrand, S. L.: Scale Invariance of Albedo-Based Wind Friction Velocity, Journal of Geophysical Research: Atmospheres, 125, e2019JD031978, 10.1029/2019jd031978, 2020.
Citation: https://doi.org/10.5194/gmd-2021-337-AC1
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AC1: 'Reply on CC1', Adrian Chappell, 18 Dec 2021
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RC1: 'Comment on gmd-2021-337', Anonymous Referee #1, 17 Dec 2021
The authors present experiments on dust emission, in which they conclude that an approach using an albedo-based representation of surface roughness (called AEM) outperforms “traditional” approaches (called TEM) for three reasons: 1. The formulation of streamwise sediment/saltation flux was incorrect in TEMs; 2. spatio-temporal dynamics of roughness elemental coverage were not considered in TEMs; 3. TEMs required calibration while AEM did not. Additionally, MODIS satellite-based estimates of dust optical depth (DOD) and dust emission point sources (DPS) and their probabilities of occurrence are compared with one another and the AEM and TEM results. It is concluded that DOD was not suitable for evaluation/calibration of dust models.
All these aspects are interesting and worthwhile to explore, however, the listed points do not support the authors’ conclusions, because a) the correctness/completeness of a formulation of streamwise saltation flux is independent of the use of albedo-based or another roughness representation, b) a dynamical representation of surface roughness can also be used with TEMs using methods other than the proposed albedo-approach, c) the albedo-approach is also calibrated. The authors aim to proof the advantages of AEM using uneven comparisons with what they described as a TEM: AEM uses dynamical vegetation, while TEM does not; AEM uses an updated streamwise flux formulation, while TEM does not; AEM uses variable soil texture/clay content, according to the proposed conversion of streamwise saltation to vertical dust emission flux, while the TEM does not (about the latter, there is contradictory information in the manuscript).
To evaluate the calculated dust emissions, the authors use two observational data sets: MODIS DOD at a spatial resolution of 1 degree and MODIS-based DPS at a spatial resolution of 250 m for a region in the southwestern USA. Again, the conclusion of DOD not being a suitable reference is not supported by this comparison due to the substantial difference in spatial resolution (approximately 400-fold higher resolution for DPS compared to DOD) in the presented comparison, and due to the definition of DOD used in the study, which seems to be simply a grid-point selection of AOD for DPS locations and likely includes aerosol other than dust as well.
Besides these previous aspects, the description of a TEM seems inaccurate and outdated at many places, and often not supported by recent references.
In light of these aspects, I cannot recommend publication of this manuscript. Additional comments are added below.
* L56-57 “total wind friction velocity ustar created by all scales of roughness” sounds like surface roughness were solely sufficient to describe ustar. Shouldn’t the atmospheric flow be an even more important aspect?
* L64 There is no “reanalysis model”. Reanalyses are based on model runs and observations.
* L67-76 This discussion is repeated from Webb et al. (2020). A reference to Kok et al. (2014), who have previously brought up this issue, should be added. It is not clear why – if this problem has been identified – the authors are not simply using the updates expression for both estimates with AEM and TEM, as this issue has nothing to do with the proposed albedo-approach. If the authors wish to test the sensitivity to the streamwise saltation flux formulation, this test needs to be performed holding all other settings constant.
* L80 Why is it that the correct values of R are not known for every pixel and every time step? Parameterizations of drag partition also rely on satellite data as input, as does the presented albedo-approach. While each data set comes with uncertainties and missing data, I do not see a fundamental different in the knowledge obtainable about surface roughness from satellite data for use with one approach or another.
* L81-82 Please give specific references instead of making general statements about what models do and don’t do. Models more and more use spatially and temporally (e.g. monthly) variable z0, e.g. ECHAM5-HAM (Cheng et al., 2008), EC-Earth3-AerChem (van Noije et al., 2021).
* L82 What is meant by values of z0 are pre-tuned and tend to maximize dust emission? No reference is provided for this statement. Values in the aforementioned references were obtained based on satellite data and ground-based measurements, as the authors also mention in the following sentences.
* L85 The use of preferential source areas, e.g. as in Ginoux et al. (2001), in some models has the purpose to generally specify soil erodibility and circumvent the need to prescribe detailed soil-surface properties. It is not specific to a description of surface roughness or z0.
* L95 Again, the “correct” Equation (3) is not related to the use of albedo to describe roughness. Hence a comparison of the albedo-approach and Eq. (3) with another approach and Eq. (2) is inconsistent.
* L101-102 Why would the albedo-approach be inconsistent with a grain-scale entrainment threshold? Is that because the albedo-derived u* is also resolution-dependent?
* L116-117 If the calculation of dust emission flux from streamwise sediment flux depends on %clay, then why a fixed clay content is used in the TEM (L537), but a spatially variable clay content is used in the AEM (L586-587). This, again, is an uneven comparison, which is unjustified. Then in L200, you claim that a soil clay content map was used with both models. In the appendix again, it is claimed that soil clay content was fixed in the TEM. Please explain.
* L120-121 Marticorena and Bergametti (1995) use the adjustment of dust emission according to the bare soil fraction in addition to a drag partition scheme. In the implementation of a TEM in the present study, this adjustment is used alone. Why is no drag partition applied, in combination with dynamic surface roughness as for the AEM? This would provide much better insight into the performance of the albedo-roughness approach.
* L130 Whether E includes brown vegetation depends on what data is used to define it.
* L131 This sentence is lacking foundation and in my humble opinion also dispassion.
* L135 Not clear which pre-tuning is meant.
* L140-142 The authors claim that using AOD for evaluation or calibration of a dust model includes the assumptions that 1. dust in the atmosphere represents the dust emission process, and that 2. the spatial variation of magnitude and frequency of modeled dust emission is correct. This is incorrect. First, for model evaluation/comparison, observed AOD (better DOD, dust optical depth) is compared with modeled AOD/DOD, and not with modeled emissions. Second, the goal of comparing modeled with observed atmospheric dust fields is to determine how well the observed fields can be reproduced with the model. Indirectly, but not unambiguously, this also sheds light on modeled dust emissions. No direct observations of dust emission or surface dust concentration are available on a global scale; hence this indirect evaluation is made. If emissions were assumed to be correct, model evaluation would only test dust transport and deposition processes and their parameterizations, which is not the case.
* L150 Do I understand right that DOD was obtained from AOD by selecting pixels which coincided with a DPS? Over North America, I expect that even at DPS locations, this DOD contains a significant contribution of other aerosol, leading to a larger value and therefore higher frequency of DOD > 0.2 than from dust alone.
* L155 I would see it the other way round: The correct probability of occurrence of (any) sediment flux depends on the correct (magnitude and) frequency of dust emission.
* L160 How are these assumptions circumvented? Do you mean that you evaluate the frequency of emission instead of the magnitude?
* L168 How is the aggregation performed? Are the frequencies aggregated or the original data? What is meant by “normalizing the results to the lowest resolution data”?
* L169 Please provide more detail about how the DPS observations have been obtained.
* L197 Do I understand correctly that the us*/u10 from albedo is completely decoupled from the atmospheric model and that, to calculate dust emission, you calculate us* by multiplying us*/u10 with the ERA5 winds, which correspond to a totally different modeled u* calculated with a different roughness? If so, I very much wonder about consistency of the obtained us* with both, the original albedo-approach and the atmospheric model. If the us*/u10 does not need ancillary data to calculate dust emissions, then – if used in an ESM – I wonder about consistency with the model winds responsible for dust transport after emission. It is also mentioned that the albedo-based us*/u10 from polar-orbiting MODIS data has incomplete coverage. I believe you argued earlier that for this same reason, the R ratio for use in TEMs cannot be estimated accurately. So this applies also to the albedo-based us*/u10?
* L214-215 If I understand well, wind speeds are obtained from ERA5 and in the AEM combined with the albedo-based us*/u10 ratio. Do I also understand well, that the AEM obtains one us*/u10 value each day from which dust emission is estimated, combined with the hourly ERA5 input? How are the different temporal resolutions treated? What is the motivation to select a narrow wind speed range between 8.5 and 9.5 m/s?
* L224-232 This paragraph seems redundant as there is no noteworthy drag partition used in the TEM as described here, hence (vegetation) roughness is not considered, but only surface coverage. The same applies to the discussion in L254-256.
* L238-239 I agree that the interplay of friction velocity and roughness is critical for dust emission, but this can be easily implemented also for the TEM. One option is described in the appendix, but not used.
* Fig. 2 What does “changed” mean in the axis titles? Does this refer to a difference or normalization? I also assume that uf should be u10 in the x-axis.
* L295 I am impressed that despite the severe limitations in the presented TEM implementation, it gives a similar (actually higher) R^2 than the AEM when compared with DPS.
* L316 The pattern similarity between TEM and u10 is most likely a direct result of how u* was calculated and of not including roughness in the TEM implementation.
* L322 Do the daily maxima used in both models refer to wind speed data?
* L355 Please provide a reference for this statement.
* L358-362 Please give the RMSE also at Fig. 4.
* L362-364 I do not see how the use of a fixed z0 can be called a tuning of the TEM. Most importantly, the calculation of us*/u10 is also calibrated/tuned, so there is no difference between AEM and TEM in that regard.
* Table 1 The left and center columns contain a large amount of overlap. Conceptually, it is not clear to me why u*ts at the grain scale should be inconsistent with the albedo-approach unless us* is not correctly retrieved. This may be related to resolution as indicated by the authors, a problem similar to the model calculation of u*. At the same time, the albedo-approach is claimed to be scale-invariant which appears contradictory. The authors also suggest that modeled u10 may be too large. While this can be the case, models typically underestimate strong winds, in particular with decreasing resolution. In line 4 of the table, it is noted that DPS may not include all dust emissions. Shouldn’t this also be a reason why the modeled dust emission frequency is higher? Finally, in the last line of the table, the calibration (tuning) of the albedo-approach is questions. Unfortunately, the assessment of this issue in the center column is not clear. It is also not clear why research on the applicability of the approach for a range of conditions is of low priority. This should be first priority when proposing a new parameterization.
* L393 It seems that the authors consider the dependence of dust emissions in the TEM on u10 (or better u*) negative and uncertainty arising from it more problematic than the fact that the albedo-based us*/u10 ratio was calibrated only against a data set covering a very limited range of conditions.
* L431 While the works from Marticorena and Bergametti (1995) and Shao et al. (1996) have certainly been major advances, there have been many additional advances since. Generally and in contrast to what is described in this paragraph, the importance of vegetation dynamics for dust emission has been well recognized for a long time and has been implemented in several global models/ESMs, also for climate simulations (see also previous comments).
* L480 I presume you are considering a neutral, and not stable, wind profile.
* L490 Please include reference for u*ts.
* L516 Please include reference for R.
* L517 Input data to calculate R are not absent. You have already referenced, for example, Prigent et al. (2012).
* L527 Which previous work?
* L530 Which challenge to estimate R are you referring to?
Citation: https://doi.org/10.5194/gmd-2021-337-RC1 -
AC2: 'Reply on RC1', Adrian Chappell, 21 Dec 2021
Thank you for taking the time to provide comments on our manuscript. To each of your comments, we provide responses below using bold, indented bullet points.
The authors present experiments on dust emission, in which they conclude that an approach using an albedo-based representation of surface roughness (called AEM) outperforms “traditional” approaches (called TEM) for three reasons: 1. The formulation of streamwise sediment/saltation flux was incorrect in TEMs; 2. spatio-temporal dynamics of roughness elemental coverage were not considered in TEMs; 3. TEMs required calibration while AEM did not.
- We do not agree that your summary above adequately represents our manuscript conclusion.
- In the manuscript abstract we state “…It is difficult to avoid our conclusion, also raised by others, that tuning dust emission models to dust in the atmosphere has hidden for more than two decades, these TEM modelling weaknesses and its poor performance.”
- The purpose of our manuscript is not to evaluate performance between dust emission models per se. There are many perspectives and metrics of performance that are required for that type of evaluation and that is not what is done in this manuscript.
- We can find no mention in our manuscript that the AEM does not require calibration. In constrast to the reviewer’s comment, we recognise that all dust emission models require calibration of some sort because of simplifying model assumptions necessary for implementation. The albedo-based dust emission model (AEM) is no different in this respect to other dust emission models as illustrated in our recent publication (Hennen et al., 2021) which is calibrated.
Additionally, MODIS satellite-based estimates of dust optical depth (DOD) and dust emission point sources (DPS) and their probabilities of occurrence are compared with one another and the AEM and TEM results. It is concluded that DOD was not suitable for evaluation/calibration of dust models.
- We can find no mention in our manuscript where we describe that DOD was not suitable for evaluation/calibration of dust models.
- Our manuscript demonstrates that dust emission modelling should be evaluated against satellite observed dust emission point source (DPS) data and not dust in the atmosphere.
All these aspects are interesting and worthwhile to explore, however, the listed points do not support the authors’ conclusions,
- The reviewer’s listed points are not those which represent the manuscript or the conclusions of our manuscript. We justify our perspective on the manuscript in response to the following reviewers’ points.
because a) the correctness/completeness of a formulation of streamwise saltation flux is independent of the use of albedo-based or another roughness representation,
- We disagree, the streamwise saltation flux is dependent on the albedo-based roughness representation.
- Our manuscript describes in the Introduction, and makes clear using equations, that the incorrect formulation is the use of the total wind friction velocity (u_star_cubed; Eq. 2) in the calculation of the streamwise saltation flux magnitude. We describe in the manuscript that term should be replaced with the soil surface wind friction velocity (u_s_star_cubed; Eq. 3) which in the manuscript is retrieved directly using the albedo-based approach.
b) a dynamical representation of surface roughness can also be used with TEMs using methods other than the proposed albedo-approach,
- Traditional dust emission models (TEMs) include aerodynamic roughness lengths (z0) but those z0 values may not represent the heterogeneous vegetation that occurs within large (up to 50 km pixels). We accept that some TEMs may allow those z0 values to vary in time / spatially due to snow, vegetation etc. In some TEMs, the z0 of each surface type is uniformly and globally held constant. The z0 value of bare ground never changes, but the ground may not be bare all year. The same is true of desert shrubland which may not be bare everywhere within the land cover type. So, in many dust-prone parts of the world, especially those that are bare soil, the roughness length never changes in TEMs. We will revise our manuscript to make these perspectives clear.
c) the albedo-approach is also calibrated.
- Agreed. We can find no mention in our manuscript where we state otherwise.
The authors aim to proof the advantages of AEM using uneven comparisons with what they described as a TEM: AEM uses dynamical vegetation, while TEM does not; AEM uses an updated streamwise flux formulation, while TEM does not; AEM uses variable soil texture/clay content, according to the proposed conversion of streamwise saltation to vertical dust emission flux, while the TEM does not (about the latter, there is contradictory information in the manuscript).
- As we mentioned above we are not evaluating the performance of the models per se. We are demonstrating that weaknesses in dust emission modelling have been hidden because dust emission models are evaluated against dust in the atmosphere. Due to the role of mineral dust in the climate system, we see this work as an important contribution towards reducing uncertainties in Earth System Models (ESMs) and increasing the models’ accuracy in reflecting past, current and future climate scenarios.
- Previous attempts to publish the results of the AEM compared with the TEM have been unsuccessful because reviewers have complained that the TEM is not correctly represented when using an ‘even’ comparison. Hence, here we implement the TEM following the scheme described in the appendix of the manuscript (Marticorena and Bergametti, 1995).
To evaluate the calculated dust emissions, the authors use two observational data sets: MODIS DOD at a spatial resolution of 1 degree and MODIS-based DPS at a spatial resolution of 250 m for a region in the southwestern USA. Again, the conclusion of DOD not being a suitable reference is not supported by this comparison due to the substantial difference in spatial resolution (approximately 400-fold higher resolution for DPS compared to DOD) in the presented comparison,…
- We can find no mention in our manuscript where we conclude “DOD not being a suitable reference”. Our manuscript demonstrates that dust emission modelling should be evaluated against satellite observed dust emission point source (DPS) data and not dust in the atmosphere.
- After we aggregate the satellite observed dust emission point source (DPS) data to 1 degree pixels to tackle the incompatible spatial scales (Gotway and Young, 2002) there is no “substantial difference in spatial resolution” in our results.
- Our manuscript states clearly (Lines 167-172) that “Modelled (AEM and TEM) and observed frequencies are aggregated by a 1°x1° grid matrix, normalizing the results to the lowest resolution data (MODIS DOD) (Figure 1). For each grid box location, the observed frequency is calculated as the number of DPS observations per year during observation period (2001 – 2016). The AEM and TEM modelled dust emission frequency describes (F>0) at DPS locations in each grid cell per year during the same period. DOD modelled frequency describes DOD > 0.2 in each grid pixel per year for the same period.”
…and due to the definition of DOD used in the study, which seems to be simply a grid-point selection of AOD for DPS locations and likely includes aerosol other than dust as well.
- We disagree with this statement and refer the reviewer to our manuscript (Lines 145-147) which states clerly that dust optical depth (DOD) is calculated following the established criteria: “To understand the extent to which AOD estimates the spatial variation in dust emission magnitude and frequency we calculated the probability of dust occurrence modelled by the dust optical depth (DOD>0.2) using the criteria established previously (Ginoux et al., 2012)”.
Besides these previous aspects, the description of a TEM seems inaccurate and outdated at many places, and often not supported by recent references.
- With this comment, there is no explanation of where in the manuscript the description of a TEM seems inaccurate and outdated. Please provide additional clarification and we will happily respond in this discussion.
- In our manuscript we refer to long-established dust emission model schemes which were developed more than two decades ago (Marticorena and Bergametti, 1995; Zender et al., 2003; Tegen et al., 2002). We also refer to very recent dust emission model results including our own (Hennen et al., 2021).
In light of these aspects, I cannot recommend publication of this manuscript. Additional comments are added below.
- We have responded to every comment provided by the reviewer and have described and illustrated using examples from the manuscript, how each comment has misrepresented the manuscript.
- Consequently, we find that there is no basis to support the reviewer’s conclusion and do not agree with the reviewer’s recommendation.
- We endeavoured to provide clarity in our manuscript but recognize that there may be room for improvement, and we are happy to revise it accordingly.
* L56-57 “total wind friction velocity ustar created by all scales of roughness” sounds like surface roughness were solely sufficient to describe ustar. Shouldn’t the atmospheric flow be an even more important aspect?
- Near the land surface, the total wind friction velocity (u_star) is influenced by the (freestream) wind speed of the inner boundary layer and the aerodynamic roughness of the land surface. We use a scale invariant calibration between shadow (1-albedo) and wind tunnel wind friction velocity (normalized by freestream wind speed) to calculate the soil surface wind friction velocity (u_s_star). This is the albedo-based approach and avoids the need for aerodynamic roughness length and roughness height, properties which are unknown and approximated by roughness factors attributed to land cover classes.
* L64 There is no “reanalysis model”. Reanalyses are based on model runs and observations.
- By ‘reanalysis models’ we are referring to Reanalysis wind fields. We will clarify this in the revised manuscript.
* L67-76 This discussion is repeated from Webb et al. (2020).
- For clarity, those lines (67-76) referred to by the reviewer are not a repeat of an already published paper (Webb et al., 2020). In describing this issue here we are demonstrating the significance of this incorrect formulation for dust emission modelling.
A reference to Kok et al. (2014), who have previously brought up this issue, should be added.
- We could not find any information in Kok et al., (2014) which describes how this issue was raised earlier. If the reviewer could direct us to page / paragraph we will happily respond in this Discussion.
It is not clear why – if this problem has been identified – the authors are not simply using the updates expression for both estimates with AEM and TEM, as this issue has nothing to do with the proposed albedo-approach.
- In a previous manuscript, we tried using the correct formulation in both AEM and TEM and reviewers would not accept that formulation correctly represented the TEM.
- In contrast to the reviewer’s perspective, we see the incorrect formulation of the sediment flux as central to the albedo-based approach. The soil surface wind friction velocity (u_s_star) is retrieved directly from a calibration with shadow (1-albedo) which avoids the need for aerodynamic roughness length and roughness height, properties of the heterogeneous land surface which are unknown and approximated by homogeneous roughness factors attributed to land cover classes.
If the authors wish to test the sensitivity to the streamwise saltation flux formulation, this test needs to be performed holding all other settings constant.
- The manuscript does not intend to test the sensitivity to the streamwise saltation flux formulation. The manuscript demonstrates (Figure 2 and related text) the significance for dust emission models of using the incorrect formulation. As described in L80 of our manuscript the incorrect formulation have been widely adopted in the TEMs.
* L80 Why is it that the correct values of R are not known for every pixel and every time step? Parameterizations of drag partition also rely on satellite data as input, as does the presented albedo-approach. While each data set comes with uncertainties and missing data, I do not see a fundamental different in the knowledge obtainable about surface roughness from satellite data for use with one approach or another.
- The explanation of why the correct values of the drag partition R are not known for every pixel and every time step is described in line 81: “…common approach to modelling dust emission in ESMs uses globally constant values of aerodynamic roughness length (z0), which are static over time and fixes R(z0) ≈ 0.91.” In other words, if a land cover class does not change over time and an aerodynamic roughness length is attributed to it then the drag partition is static over time and fixed across all pixels given that land cover class.
- In contrast, the albedo-based approach uses shadow (1-albedo) of the land surface (e.g., using MODIS at 500 m) which may be different for every pixel and changes significantly every few weeks with the vegetation phenology, changes abruptly with any rapid land cover change, and changes over long-time periods with extrinsic factors like global ‘greening’. Consequently, dust emission calculations will respond to these spatio-temporal changes in the land surface.
* L81-82 Please give specific references instead of making general statements about what models do and don’t do. Models more and more use spatially and temporally (e.g. monthly) variable z0, e.g. ECHAM5-HAM (Cheng et al., 2008), EC-Earth3-AerChem (van Noije et al., 2021).
- Thanks, we will attempt to do that in the revised manuscript. Of course, it is quite hard to do that for every point we might make as that would require a list of differences / similarities based on the different models. That difficulty in why in this manuscript we are attempting to make a more universal assessment using one of the common dust emissions schemes (Marticorena and Bergametti, 1995) which underpins many of the dust emission models.
* L82 What is meant by values of z0 are pre-tuned and tend to maximize dust emission? No reference is provided for this statement. Values in the aforementioned references were obtained based on satellite data and ground-based measurements, as the authors also mention in the following sentences.
- Models which fix the aerodynamic roughness length (z0) across all barren regions need to set a value. That value is typically set to a value which ensured that the dust emission from North Africa is large (consistent with dust in the atmosphere above North Africa) because models tended to under-estimate dust from the region because of the reduced wind speeds and the soil having about half the clay content. In the revised manuscript, we will include citations (Zender et al., 2003; Woodward, 2001; Tegen et al., 2002).
* L85 The use of preferential source areas, e.g. as in Ginoux et al. (2001), in some models has the purpose to generally specify soil erodibility and circumvent the need to prescribe detailed soil-surface properties. It is not specific to a description of surface roughness or z0.
- Our sentence is referring to generic nature of preferential source areas reducing the magnitude of dust emission. In the revised manuscript we will make this point clearer.
* L95 Again, the “correct” Equation (3) is not related to the use of albedo to describe roughness. Hence a comparison of the albedo-approach and Eq. (3) with another approach and Eq. (2) is inconsistent.
- We have explained above that the correct formulation is related directly to the albedo-based approach and which controls the magnitude of sediment flux and hence dust emission.
* L101-102 Why would the albedo-approach be inconsistent with a grain-scale entrainment threshold? Is that because the albedo-derived u* is also resolution-dependent?
- The albedo-based approach can use any source of shadow (1-albedo). In the manuscript we use shadow from MODIS at 500 m. The entrainment threshold is established from wind tunnel measurements and is effectively a point scale which is incompatible with area (Gotway and Young, 2002). The entrainment threshold needs to be developed like the albedo-based approach so that it can be upscaled from a point to an area (as described in the following paragraph).
- Shadow (1-albedo) scales linearly over area in contrast to the total wind friction velocity (u_star) which is non-linear (Raupach and Lu, 2004). Therefore, shadow from any source can be upscaled with sufficient measurements / estimates to any coarser pixel resolution. In this case, the shadow is then calibrated with total wind friction velocity (u_star) to determine the area-weighted, coarse resolution total wind friction velocity (u_star). This ability cut across scales is one of the key benefits of the albedo-based approach (Chappell and Webb, 2016).
* L116-117 If the calculation of dust emission flux from streamwise sediment flux depends on %clay, then why a fixed clay content is used in the TEM (L537), but a spatially variable clay content is used in the AEM (L586-587). This, again, is an uneven comparison, which is unjustified.
- In a previous manuscript, we used variable clay content in both AEM and TEM. Reviewers would not accept that variable clay was an accurate representation of the original TEM.
Then in L200, you claim that a soil clay content map was used with both models. In the appendix again, it is claimed that soil clay content was fixed in the TEM. Please explain.
- A soil clay content map was used in both models. In the AEM, the soil clay content map varied over space (SoilGrids) and in the TEM a map used fixed values.
* L120-121 Marticorena and Bergametti (1995) use the adjustment of dust emission according to the bare soil fraction in addition to a drag partition scheme. In the implementation of a TEM in the present study, this adjustment is used alone. Why is no drag partition applied, in combination with dynamic surface roughness as for the AEM? This would provide much better insight into the performance of the albedo-roughness approach.
- In previous manuscripts we have tried to do exactly as the reviewer suggests here. However, those previous results were not accepted because previous reviewers would not accept that was an accurate representation of the original TEM. Consequently, we implement the traditional dust emission scheme described in our manuscript Appendix.
* L130 Whether E includes brown vegetation depends on what data is used to define it.
- Brown vegetation cannot be described using vegetation indices which are designed to measure ‘greenness’. In our manuscript we are referring to a common way of representing vegetation in dust emission models using Normalized Difference Vegetation Index (NDVI). Consequently, the use of NDVI cannot represent brown rough vegetation.
* L131 This sentence is lacking foundation and in my humble opinion also dispassion.
- We think the reviewer is referring to the end of that line 131 where the sentence begins “This crude model representation of process…”. We think the statement is accurate, but we are happy to rephrase to avoid causing offense. We are passionate about our science.
* L135 Not clear which pre-tuning is meant.
- In the response to the same point above, we described above that models which fix the aerodynamic roughness length (z0) across all barren regions need to set a value. That parameter is typically set to a value which ensured that the dust emission from North Africa is large consistent with dust in the atmosphere above North Africa. Models tended to under-estimate dust from North Africa because of the reduced wind speeds and the soil having about half the clay content. In this respect some TEMs are pre-tuned before they are tuned to dust in the atmosphere.
* L140-142 The authors claim that using AOD for evaluation or calibration of a dust model includes the assumptions that 1. dust in the atmosphere represents the dust emission process, and that 2. the spatial variation of magnitude and frequency of modeled dust emission is correct. This is incorrect. First, for model evaluation/comparison, observed AOD (better DOD, dust optical depth) is compared with modeled AOD/DOD, and not with modeled emissions.
- At these lines in our manuscript we describe the intrinsic comparison being made between observed AOD and modelled AOD/DOD: modelled dust emission is compared to dust in the atmosphere which assumes that dust in the atmosphere represents the dust emission process.
Second, the goal of comparing modeled with observed atmospheric dust fields is to determine how well the observed fields can be reproduced with the model.
- The problem with comparing dust emission to dust in the atmosphere, is that any weaknesses in the dust emission model are avoided in favour of reproducing dust in the atmosphere. That preference to dust in the atmosphere may appear adequate, but it assumes that the model produces the correct magnitude and frequency of dust emission as we described (line 141). We explained in previous comments above, how aerodynamic roughness length fixed over space and static over time, has been used to maximise dust emission in North Africa to match dust in the atmosphere.
- “However, we know a priori that dust in the atmosphere is only partially related to dust emission because dust concentration is controlled by dust emission magnitude and frequency which varies over space and time, by residence time of dust near the surface which itself is dependent on wind speed, and on dust deposition in the dust source region, a size dependent process.” (Lines 142-145).
Indirectly, but not unambiguously, this also sheds light on modeled dust emissions. No direct observations of dust emission or surface dust concentration are available on a global scale; hence this indirect evaluation is made.
- We have shown in our recent publication (Hennen et al., 2021) and used in this manuscript, direct satellite observations of dust emission point sources (DPS) data.
If emissions were assumed to be correct, model evaluation would only test dust transport and deposition processes and their parameterizations, which is not the case.
- We are not sure what point is being made here.
* L150 Do I understand right that DOD was obtained from AOD by selecting pixels which coincided with a DPS? Over North America, I expect that even at DPS locations, this DOD contains a significant contribution of other aerosol, leading to a larger value and therefore higher frequency of DOD > 0.2 than from dust alone.
- We calculated the probability of dust occurrence modelled by the dust optical depth (DOD>0.2) using the criteria established previously (Ginoux et al., 2012) using MODIS Deep Blue (MOD08 M3 V6.1). The DOD was retrieved from those pixels in which dust emission was observed from point sources (DPS) in space and time throughout 2001-2016.
- DOD uses a specific Deep Blue algorithm, which utilizes single-scattering albedo, AOD and the reflectance properties from specific spectral bands (412, 470, and 670 nm) to differentiate the mixing ratio of dust and various other atmospheric aerosols (e.g., smoke) (Hsu et al., 2013). However, as DOD is a measurement of atmospheric dust, it is entirely possible that the DOD frequencies are affected by transported dust aerosols from upwind dust sources. This is another reason for evaluating dust emission models against satellite observed dust emission point source (DPS) data.
* L155 I would see it the other way round: The correct probability of occurrence of (any) sediment flux depends on the correct (magnitude and) frequency of dust emission.
- We think that difference in perspective amounts to the same outcome.
* L160 How are these assumptions circumvented? Do you mean that you evaluate the frequency of emission instead of the magnitude?
- Since the 1960s (Wolman and Miller, 1960) it has been established that the magnitude of sediment flux (in fluvial and aeolian systems) is controlled by the fluid friction velocity. That magnitude is adjusted by the probability of occurrence which depends on whether the entrainment threshold is exceeded. A key and long-standing limitation with dust emission models is that they assume an infinite supply of loose, dry, erodible, and available sediment. Consequently, whenever and wherever the entrainment threshold is exceeded sediment flux and dust emission occurs. Hence, the probability of occurrence is central to calculating the correct dust emission. In the absence of any way to constrain this assumption, we circumvent the need for this assumption by using satellite observed dust emission point source (DPS) data which describes when and where dust emission occurs (without the need for the assumption). We can explain this more fully in the revised manuscript.
* L168 How is the aggregation performed? Are the frequencies aggregated or the original data? What is meant by “normalizing the results to the lowest resolution data”?
- The DPS data are aggregated per grid box, per day. Therefore, in each grid box, if any of the identified dust sources recognizes an event, either modelled or observed, then that grid box is described as active for that day (FoO =1). Each grid box has a maximum of 1 FoO per day, i.e., if more than one DPS event occurs within a single grid box on a single day, then the grid box FoO is normalized to 1. This is the case for both observed frequencies and modelled frequencies.
* L169 Please provide more detail about how the DPS observations have been obtained.
- The DPS data are made available from previously published studies and those extant papers provide detail on how they have been obtained. Our recently published paper cited in this manuscript describes the way in which the DPS observations have been obtained. In any case, we can provide additional limited information in the main text of the revised manuscript or more fully in an extension to the manuscript Appendix.
* L197 Do I understand correctly that the us*/u10 from albedo is completely decoupled from the atmospheric model and that, to calculate dust emission, you calculate us* by multiplying us*/u10 with the ERA5 winds, which correspond to a totally different modeled u* calculated with a different roughness? If so, I very much wonder about consistency of the obtained us* with both, the original albedo-approach and the atmospheric model. If the us*/u10 does not need ancillary data to calculate dust emissions, then – if used in an ESM – I wonder about consistency with the model winds responsible for dust transport after emission.
- Modelled wind fields are themselves decoupled from the actual varying in space and time land surface. For example, we use here ERA5-Land data which we have shown recently to be one of the wind fields which most closely represents wind speed measurements (Fan et al., 2021). ERA5-Land wind fields take planetary boundary layer winds down to a blending height of 40 m before using roughness factors (not aerodynamic roughness length) for each land cover type to extrapolate wind to 10 m height.
- We use shadow to produce a calibrated soil surface wind friction velocity normalized by wind speed (u_s_star/Uh) which we multiply by ERA5-Land wind speed (at 10 m height) to retrieve the soil surface wind friction.
- There are many weaknesses / inconsistencies in dust emission modelling which include combining incompatible spatial scales of wind, roughness, entrainment threshold and avoid assuming an infinite supply of sediment. First we need to ensure that these weaknesses are evident by evaluating dust emission model simulations with satellite observed dust emission point source (DPS) data.
It is also mentioned that the albedo-based us*/u10 from polar-orbiting MODIS data has incomplete coverage. I believe you argued earlier that for this same reason, the R ratio for use in TEMs cannot be estimated accurately. So this applies also to the albedo-based us*/u10?
- We think the reviewer is referring to Line 80 “The substantive issues for dust emission modelling are that the incomplete form of QTEM (Eq. 2) has been widely adopted in TEMs in which large area estimates of wind speed are typically used, the correct values of R are not known (for every pixel and every time step) …” As we described previously the correct values of R varying over space and time are not known because aerodynamic roughness length is attributed to land cover which is homogeneous over space and fixed over time.
* L214-215 If I understand well, wind speeds are obtained from ERA5 and in the AEM combined with the albedo-based us*/u10 ratio. Do I also understand well, that the AEM obtains one us*/u10 value each day from which dust emission is estimated, combined with the hourly ERA5 input? How are the different temporal resolutions treated?
- In the manuscript (Lines 322-323) we describe how the albedo-based dust emission model (AEM) uses the daily maximum of all hourly wind speed data from the ERA5-Land data.
What is the motivation to select a narrow wind speed range between 8.5 and 9.5 m/s?
- To emphasise the difference in the modelling approaches we selected that narrow range of wind speeds (8.5-9.5 m/s). We identified that narrow range of wind speeds in the experiment (symbols Fig 2a) where the soil surface wind friction velocity was varied to show the response of dust emission. The albedo-based dust emission model (AEM) varied along the same curve and produced a large difference for the changed roughness (for the same wind speed). A representation of the traditional dust emission model (TEM) varied along different curves and produced approximately the same dust emission regardless of the changed roughness (for the same wind speed). In other words, fixing over space and holding static over time the aerodynamic roughness length (and hence making R constant) causes dust emission to occur regardless of roughness when wind is sufficient to overcome the entrainment threshold. When roughness varies realistically over space and time it attenuates the wind speed and dust emission responds accordingly. This model behaviour is illustrated spatially using the map in Figure 5.
- In short, much less adjustment of magnitude, frequency and spatial extent of dust is required if the soil wind friction velocity is allowed to vary over space and time.
* L224-232 This paragraph seems redundant as there is no noteworthy drag partition used in the TEM as described here, hence (vegetation) roughness is not considered, but only surface coverage. The same applies to the discussion in L254-256.
- We stated in that section of text that the reason for the comparison with a restricted drag partition is that is how some traditional dust emissions model have been implemented (as also described in the Appendix).
- We recognize that some dust emission models use a geometric relation between leaf area index (or a vegetation index) and the drag partition. Whilst this approach may describe change in the drag partition depending on changed planform geometry, that approach does not change with wind speed. Consequently, that geometric drag partition is not aerodynamic (Raupach, 1992; Raupach et al., 1993). As we have shown here (Fig. 2 & Fig. 4), the significance for dust emission models of using this approach is that dust emission is larger and more extensive than when including aerodynamics.
* L238-239 I agree that the interplay of friction velocity and roughness is critical for dust emission, but this can be easily implemented also for the TEM. One option is described in the appendix, but not used.
- We agree that changes can be made to the TEMs. We think one of the main challenges for dust emission modelling is to recognize the need for change in the TEMs. We think that the main mechanism for identifying the need for change is the routine evaluation of dust emission modelling with satellite observed dust emission point source (DPS) data.
* Fig. 2 What does “changed” mean in the axis titles? Does this refer to a difference or normalization? I also assume that uf should be u10 in the x-axis.
- The normalized soil surface wind friction velocity is the soil wind friction velocity divided by the wind speed as shown by the term in brackets (u_s_star / Uf). In this case, Uf is correct since this is the calibrated value and applicable to any freestream (f) height which is unaffected by the land surface roughness. The word ‘changed’ indicates how much dust emission changes when normalized soil surface wind friction velocity changes within the known range (0-0.04 m/s)
* L295 I am impressed that despite the severe limitations in the presented TEM implementation, it gives a similar (actually higher) R^2 than the AEM when compared with DPS.
- The larger correlation coefficient between the TEM and the satellite observed dust emission point source (DPS) data is created by the ‘pre-tuning’ of the aerodynamic roughness length used to fix the drag partition. If that value were perturbed the correlation would change. Furthermore, the correlation is with available DPS data which predominantly occur in relatively sparsely vegetated conditions for which the TEM is well suited given its ‘pre-tuned’ value for barren land.
* L316 The pattern similarity between TEM and u10 is most likely a direct result of how u* was calculated and of not including roughness in the TEM implementation.
- Yes, that is exactly our point. Recall from descriptions above, that even when the drag partition of a pixel is approximated by the geometric relation with the leaf area index (or a vegetation index) it is not wind speed dependent and therefore not aerodynamic. Consequently, it will respond in the same way as having a fixed roughness unless the geometric relation causes a change.
* L322 Do the daily maxima used in both models refer to wind speed data?
- Yes, thanks for the query. We will include that clarification in the revised manuscript.
* L355 Please provide a reference for this statement.
- This is our perspective which is why there is no citation. We will clarify that in the revised manuscript.
* L358-362 Please give the RMSE also at Fig. 4.
- Thanks for the suggestion. We will include on Fig 4 the RMSE values for each of the relations.
* L362-364 I do not see how the use of a fixed z0 can be called a tuning of the TEM. Most importantly, the calculation of us*/u10 is also calibrated/tuned, so there is no difference between AEM and TEM in that regard.
- We have explained in our responses (above) that the value of z0 may be set to produce a sufficiently large amount of dust emission to match that of measured dust in the atmosphere in North Africa. This is done before the dust model is calibrated.
- The shadow (1-albedo) retrieved from MODIS is calibrated to the soil surface wind friction velocity. It is not calibrated to produce a particular magnitude or frequency of dust emission.
* Table 1 The left and center columns contain a large amount of overlap. Conceptually, it is not clear to me why u*ts at the grain scale should be inconsistent with the albedo-approach unless us* is not correctly retrieved. This may be related to resolution as indicated by the authors, a problem similar to the model calculation of u*.
- We have explained above in response to a similar query that the entrainment threshold is at the grain scale because it is derived using wind tunnel measurements. It is important to recognize that this grain scale does not represent an area and is sufficiently relatively small to be considered at the point scale.
- Reflectance is measured over an area. Consequently, the calibration of shadow (1-albedo) to wind tunnel measurements of soil surface wind friction velocity provides an area-weighted estimate which when albedo is scaled linearly overcomes the non-linearity of scaling the wind friction velocity. As the albedo-based dust emission model (AEM) stands the point scale entrainment threshold is inconsistent with the new area-weighted albedo-based wind friction velocity as described in Table 1.
At the same time, the albedo-approach is claimed to be scale-invariant which appears contradictory.
- We have described in the original derivation of the albedo-based approach the potential for combining incompatible spatial scales (Gotway and Young, 2002; Chappell and Webb, 2016). We have also recently demonstrated how the albedo-based approach is scale invariant using ground-based albedometers and MODIS albedo (Ziegler et al., 2020).
The authors also suggest that modeled u10 may be too large. While this can be the case, models typically underestimate strong winds, in particular with decreasing resolution.
- The comparison in our manuscript between dust emission model and satellite observed dust emission point source (DPS) data (Figure 4) demonstrates that there are too many occurrences when the wind friction velocity exceeds the entrainment threshold. Assuming that the wind friction velocity is well constrained using the albedo-based (calibrated) approach, too many occurrences are caused by wind speeds being too large and / or entrainment threshold being too small.
- We agree with the reviewer that model wind fields e.g., ERA5-Land at 11 km may under-estimate fine-scale wind gusts e.g., low-level jets. Since the wind friction velocity can be calculated at any area-weighted resolution we can test this and other inconsistencies in scale. However, we first need the community to recognize that these weaknesses / inconsistencies in the dust emission modelling have endured because dust emission models are not being compared against DPS data.
In line 4 of the table, it is noted that DPS may not include all dust emissions. Shouldn’t this also be a reason why the modeled dust emission frequency is higher?
- In the fifth row of Table 1 we describe how some dust emission may not be included in the satellite observations described. That description could explain why the modelled dust emission frequency is larger than the DPS data. However, we think that the first order explanation is around scale difference and the assumed infinite supply of sediment.
Finally, in the last line of the table, the calibration (tuning) of the albedo-approach is questions. Unfortunately, the assessment of this issue in the center column is not clear. It is also not clear why research on the applicability of the approach for a range of conditions is of low priority. This should be first priority when proposing a new parameterization.
- We think that the use of shadow to establish the calibration with wind friction velocity is the first-order explanation of variability in dust emission modelling. There is some uncertainty in the function fitted to that calibration (cf. RMSE in Chappell & Webb, 2016). However, consistent with our previous point we think that the first order improvement of dust emission model is around scale difference and the assumed infinite supply of sediment. Therefore, we have given these points in the table low priority. In the revised manuscript we will make this point clearer.
* L393 It seems that the authors consider the dependence of dust emissions in the TEM on u10 (or better u*) negative and uncertainty arising from it more problematic than the fact that the albedo-based us*/u10 ratio was calibrated only against a data set covering a very limited range of conditions.
- The sentence prior to line 393 provides the context for our description “These contrasting estimates emphasise TEM dependency on variability in U10, due to the use of u*3 and the inability of R(z0)=0.91 fixed over space and time to correctly attenuate wind speeds by aerodynamic roughness”.
- A dust emission model is dependent on the wind friction velocity which in turn must be dependent on both wind speed and aerodynamic roughness to a lesser or greater extent controlled by extrinsic factors.
- We have used the best available data to calibrate shadow (1-albedo) to wind friction velocity (Marshall, 1971) and which has been examined and tested extensively in the aeolian research community (Raupach et al., 1993). These data cover the entire range of conditions under which wind friction velocity causes dust emission. We are happy to discuss this further if the reviewer would like to clarify what they mean by “very limited”.
* L431 While the works from Marticorena and Bergametti (1995) and Shao et al. (1996) have certainly been major advances, there have been many additional advances since. Generally and in contrast to what is described in this paragraph, the importance of vegetation dynamics for dust emission has been well recognized for a long time and has been implemented in several global models/ESMs, also for climate simulations (see also previous comments).
- The manuscript text at lines 431-433 is not about vegetation dynamics: “There is also a great risk that the major scientific advances made in developing dust emission schemes (Marticorena and Bergametti, 1995; Shao et al., 1996) and newly developed data / parameterizations (Prigent et al., 2012) are being overlooked by an over-reliance on simplistic assumptions about dust source location and erodibility to implement dust emission models”.
- There is no mention of vegetation dynamics in that section of text because the broad point we are making is that major developments in dust emission modelling are being overlooked by comparing dust emission models to dust in the atmosphere.
* L480 I presume you are considering a neutral, and not stable, wind profile.
- Consistent with other dust emission models we are assuming neutral buoyancy and a well-mixed. In the revised manuscript we will improve this description.
* L490 Please include reference for u*ts.
- Thanks. It is encompassed by the earlier citation (Marticorena and Bergametti, 1995). In the revised manuscript we will repeat that citation at the description of the entrainment threshold.
* L516 Please include reference for R.
- Thanks, we will include the omitted citation in the revised manuscript.
* L517 Input data to calculate R are not absent. You have already referenced, for example, Prigent et al. (2012).
- The sentence states “…the absence of regional and global spatio-temporal dynamics of R….” Those data cited by the reviewer do not vary over time (Prigent et al., 2012; Prigent et al., 2005).
* L527 Which previous work?
- Thanks for the query. In the revised manuscript we will list again all the citations included in the earlier sentences.
* L530 Which challenge to estimate R are you referring to?
- Thanks for the query. In the revised manuscript we will replace the phrase “a challenge” with “challenging” to improve the clarity of the sentence.
References used in our responses
Chappell, A. and Webb, N. P.: Using albedo to reform wind erosion modelling, mapping and monitoring, Aeolian Research, 23, 63-78, https://doi.org/10.1016/j.aeolia.2016.09.006, 2016.
Fan, W., Liu, Y., Chappell, A., Dong, L., Xu, R., Ekström, M., Fu, T.-M., and Zeng, Z.: Evaluation of Global Reanalysis Land Surface Wind Speed Trends to Support Wind Energy Development Using In Situ Observations, Journal of Applied Meteorology and Climatology, 60, 33-50, 10.1175/jamc-d-20-0037.1, 2021.
Ginoux, P., Prospero, J. M., Gill, T. E., Hsu, N. C., and Zhao, M.: Global-scale attribution of anthropogenic and natural dust sources and their emission rates based on MODIS Deep Blue aerosol products, Reviews of Geophysics, 50, 10.1029/2012rg000388, 2012.
Gotway, C. A. and Young, L. J.: Combining Incompatible Spatial Data, Journal of the American Statistical Association, 97, 632-648, 10.1198/016214502760047140, 2002.
Hennen, M., Chappell, A., Edwards, B. L., Faist, A. M., Kandakji, T., Baddock, M. C., Wheeler, B., Tyree, G., Treminio, R., and Webb, N. P.: A North American dust emission climatology (2001–2020) calibrated to dust point sources from satellite observations, Aeolian Research, 100766, https://doi.org/10.1016/j.aeolia.2021.100766, 2021.
Hsu, N. C., Jeong, M.-J., Bettenhausen, C., Sayer, A. M., Hansell, R., Seftor, C. S., Huang, J., and Tsay, S.-C.: Enhanced Deep Blue aerosol retrieval algorithm: The second generation, Journal of Geophysical Research: Atmospheres, 118, 9296-9315, https://doi.org/10.1002/jgrd.50712, 2013.
Kok, J. F., Albani, S., Mahowald, N. M., and Ward, D. S.: An improved dust emission model – Part 2: Evaluation in the Community Earth System Model, with implications for the use of dust source functions, Atmos. Chem. Phys., 14, 13043-13061, 10.5194/acp-14-13043-2014, 2014.
Marshall, J. K.: Drag measurements in roughness arrays of varying density and distribution, Agricultural Meteorology, 8, 269-292, https://doi.org/10.1016/0002-1571(71)90116-6, 1971.
Marticorena, B. and Bergametti, G.: Modeling the atmospheric dust cycle: 1. Design of a soil-derived dust emission scheme, Journal of Geophysical Research: Atmospheres, 100, 16415-16430, 10.1029/95jd00690, 1995.
Prigent, C., Jiménez, C., and Catherinot, J.: Comparison of satellite microwave backscattering (ASCAT) and visible/near-infrared reflectances (PARASOL) for the estimation of aeolian aerodynamic roughness length in arid and semi-arid regions, Atmos. Meas. Tech., 5, 2703-2712, 10.5194/amt-5-2703-2012, 2012.
Prigent, C., Tegen, I., Aires, F., Marticorena, B., and Zribi, M.: Estimation of the aerodynamic roughness length in arid and semi-arid regions over the globe with the ERS scatterometer, Journal of Geophysical Research: Atmospheres, 110, https://doi.org/10.1029/2004JD005370, 2005.
Raupach, M. R.: Drag and drag partition on rough surfaces, Boundary-Layer Meteorology, 60, 375-395, 10.1007/BF00155203, 1992.
Raupach, M. R. and Lu, H.: Representation of land-surface processes in aeolian transport models, Environmental Modelling & Software, 19, 93-112, https://doi.org/10.1016/S1364-8152(03)00113-0, 2004.
Raupach, M. R., Gillette, D. A., and Leys, J. F.: The effect of roughness elements on wind erosion threshold, Journal of Geophysical Research: Atmospheres, 98, 3023-3029, 10.1029/92jd01922, 1993.
Shao, Y., Raupach, M., and Leys, J.: A model for predicting aeolian sand drift and dust entrainment on scales from paddock to region, Soil Research, 34, 309-342, https://doi.org/10.1071/SR9960309, 1996.
Tegen, I., Harrison, S. P., Kohfeld, K., Prentice, I. C., Coe, M., and Heimann, M.: Impact of vegetation and preferential source areas on global dust aerosol: Results from a model study, Journal of Geophysical Research: Atmospheres, 107, AAC 14-11-AAC 14-27, 10.1029/2001jd000963, 2002.
Webb, N. P., Chappell, A., LeGrand, S. L., Ziegler, N. P., and Edwards, B. L.: A note on the use of drag partition in aeolian transport models, Aeolian Research, 42, 100560, https://doi.org/10.1016/j.aeolia.2019.100560, 2020.
Wolman, M. G. and Miller, J. P.: Magnitude and Frequency of Forces in Geomorphic Processes, The Journal of Geology, 68, 54-74, 10.1086/626637, 1960.
Woodward, S.: Modeling the atmospheric life cycle and radiative impact of mineral dust in the Hadley Centre climate model, Journal of Geophysical Research: Atmospheres, 106, 18155-18166, 10.1029/2000jd900795, 2001.
Zender, C. S., Bian, H., and Newman, D.: Mineral Dust Entrainment and Deposition (DEAD) model: Description and 1990s dust climatology, Journal of Geophysical Research: Atmospheres, 108, 10.1029/2002jd002775, 2003.
Ziegler, N. P., Webb, N. P., Chappell, A., and LeGrand, S. L.: Scale Invariance of Albedo-Based Wind Friction Velocity, Journal of Geophysical Research: Atmospheres, 125, e2019JD031978, 10.1029/2019jd031978, 2020.
Citation: https://doi.org/10.5194/gmd-2021-337-AC2
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AC2: 'Reply on RC1', Adrian Chappell, 21 Dec 2021
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RC2: 'Comment on gmd-2021-337', Anonymous Referee #2, 14 Feb 2022
General Comments
This study introduced a novel method based on dynamic land cover change (albedo or earth’s shadow) to quantify dust emission with grid precision and thus overcomes the biases from the traditional approach that estimated dust emissions based on constant spatial vegetation distribution from bare soil assumptions. The aim is to investigate point source emission detected by satellites observation varying with time and space. They found that both approaches/models overestimated the occurrence of dusty days, which is mainly from soil wind friction velocity. The more the model overestimates the soil wind friction velocity, the more it entrains high sediment flux once the threshold is exceeded. Therefore, the albedo-based model generates lower emissions than the traditional model due to the new formulation of soil wind friction velocity obtained as function albedo, roughness and horizontal wind velocity. This newly developed albedo-based model suggested to mimic the soil bareness and vegetation cover before and after dust emissions, and the results proved moderately good performance. This study is important and has potential impact for modelling community especially in quantifying effective emission of dust. Some questions for the experiment design and results are need to be addressed prior to the publication.
Specific Comments
- Dust Point Source locations are only shown on a small-domain map in Figure 1; Later, the authors described the roughness, wind speed, and dust flux in Figure 5 over a larger-domain map, would the authors show the DPS over a larger-domain map, such as the continental US (CONUS)? And, if possible, a map of North America is preferred to display district boundaries, deserts, vegetation and etc.
- AOD represents the total aerosol burden in the atmosphere. DOD is meant for detecting dust particles in the atmosphere. In this study, the authors preferred to employ a threshold of 0.2 for DOD (DOD>0.2) as from previous study of Ginoux et al (2012) to separate dust from background over North America during spring season. However, Ginoux et al (2012) used the threshold DOD>0.25 for most of the regions, and the threshold from this previous study is retrieved from the MODIS-DB L2 product at the 10 km x10 km grid resolution that is much finer than 1 degree resolution used in this study. Given that the authors in this study overestimated the frequency (Figure 3) even with the lower DOD threshold of 0.2 without quality control, why didn’t the authors sample DOD with quality flag at higher resolution and then average over one degree resolution? Otherwise, is 0.2/0.25 reasonable for one-degree resolution? Over such a large grid, smaller value may be preferred? please clarify.
- The results showed that high dust emissions were generated mainly from the Great Plains extending from Montana, Wyoming, Dakota, Colorado, New Mexico, and Texas, and slight dust emission were from the semi-arid and arid regions of the western deserts (Sonoran, Chihuahua, Mohave and great basin deserts). Therefore, I think the authors should also explain why those semi-arid and arid regions did not have any DPS.
- How does the study incorporate the soil texture/ soil type especially particle size threshold for starting the dust saltation? More explanation is preferred.
Minor Comments
- The paragraph from 231-237 describes Figure 2a for the albedo-based model. It seems that the results from the smooth and rough cases overlap. Please clarify if they are identical.
- In Figure 5 and 6, Uh should be replaced by U10. Please also correct others if any.
Citation: https://doi.org/10.5194/gmd-2021-337-RC2 -
AC3: 'Reply on RC2', Adrian Chappell, 18 Feb 2022
Dear Reviewer,
Many thanks for taking the time to provide comments on our manuscript. I've attached a document containing our responses to your queries / comments because we have used figures to illustrate some of our responses.
Regards,
Adrian, on behalf of the authors.
Status: closed
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CC1: 'Comment on gmd-2021-337', Paul Ginoux, 08 Dec 2021
Dear Adrian and co-authors,
I made below a few comments related to your submitted manuscript as I found several cases of mis-representation or inaccurate description of the model and data I have been developing with my collaborators. Hopefully, you will find them useful to improve the manuscript.
Regards,
Paul Ginoux
Lines 29-31: “Many of the traditional dust emission models (TEM) assume that the Earth’s land surface is devoid of vegetation, then adjust the dust emission using a vegetation cover complement, and finally calibrate the magnitude of simulated emissions to dust in the atmosphere”
The calibration is mostly related to numerical discretization of the momentum and continuity equations. Emission of dust in numerical models depends on the discretization of surface winds. The surface winds are inferred from the pressure level wind vectors derived by solving numerically the momentum equations. The numerical discretization of these equations will be affected by the numerical resolution. Obviously higher resolution will resolve sharp topographic variations with stronger downslope winds. On the other hand, flat terrain without roughness elements using low resolution will generate stronger gustiness. So, changing model resolution has a non-trivial effect on surface winds. Concerning dust emission, the flux depends on the cubic power of surface winds (see Equation 2), which will amplify wind bias related to model resolution. This implies that “tuning” dust emission is a required method to simulate scale-aware tracer with numerical model. This is also true for other tracers, such as sea salt emission from the oceans.
Lines 82-83: “The common approach to modeling dust emission in ESMs
uses globally constant values of aerodynamic roughness length (z0), which are static over time and fixes R(z0) ≈ 0.91.”This is incorrect. In ESMs the momentum roughness length is calculated at every time steps and in every grid cells as a function of terrain variations, vegetation cover, snow cover, etc.
Line 81-85: “The common approach to modelling dust emission in ESMs… This emission is then reduced by a function of vegetation cover and ultimately ‘tuned’ down to match observed in the atmosphere.”
I am unaware of any ESMs who have implemented dust emission as described. I can certainly speak for NASA and GFDL models (Ginoux et al., 2001; Evans et al., 2016).
Lines 103-104: “The 𝑢𝑠∗ is obtained directly from 𝜔𝑛𝑠, the normalised and rescaled shadow (1-albedo), enabling an albedo-based dust emission model (AEM; see Appendix for full description of the implementation)”
Do I read correctly that you are scaling the friction velocity using 3 parameters with an exponential function of 𝜔𝑛𝑠? Am I right that you will have to rescale 𝜔𝑛𝑠 for any other satellite instruments with different viewing angles or radiometric characteristics? Is this not a global tuning?
Line 128-132: “Evans et al., 2016”
The characterization of GFDL model (Evans et al. 2016) is not correct. You may want to read the paper. We are not using E=1-Av. The bare surface is calculated using an exponential function of the canopy (LAI) and stems, twigs, litters (SAI). The dust emission is calculated in each land tiles (primary, secondary vegetation, pasture and cropland) independently. Then the flux of dust is passing through a flux-exchanger into the atmosphere while a flux down from turbulence and settling is going in the land model. The latest ESM4 includes also tiles from fires and rangeland, in addition for taking into account slopes (Dunne et al., 2020; Horowitz et al., 2020). I will disagree with you when calling such detailed and consistent modeling of dust cycle a “crude model representation”
Lines 133-135: “When the TEMs are applied in dust-climate ESMs it is assumed that this parameterization is adequate for climate projections. In contrast, the albedo-based scheme for sediment flux and dust emission (AEM; Eqs. 3, 4 & 5) represents the drag partition physics without pre-tuning to a fixed land surface condition, without the need for E, and thereby removes these additional sources of uncertainty.”
The main point of using climate model has been missed here. Despite their approximations, ESMs simulate the different Earth’s climate systems consistently over time using different projection scenarios. While the proposed used of 𝜔𝑛𝑠 (the normalized and rescaled shadow) is considered fixed (beyond MODIS period), ignoring vegetation and land use changes. The AEM technique is inadequate for future or past climates.
Lines 145-149: “To understand the extent to which AOD estimates the spatial variation in dust emission magnitude and frequency we calculated the probability of dust occurrence modeled by the dust optical depth (DOD>0.2) using the criteria established previously (Ginoux et al., 2012). We note the stated limitations of DOD to be largely restricted to bright land surfaces in the visible wavebands which implies reduced performance over areas where
vegetation is present.”This sentence contains several misunderstandings of our latest method developed with my co-authors to derive DOD.
In our 2012, we used the collection 5.1 of MODIS Deep Blue (DB), which provided aerosol products over bright surfaces. Since 2013 Collection 6 MODIS DB aerosol products have been extend to cover most (without snow or cloud cover) land surfaces (Sayer et al., 2014). All subsequent papers deriving DOD is using Collection 6.1 MODIS DB (e.g. Pu and Ginoux, 2017, 2018a, 2018b, 2020; Yu and Ginoux, 2021). A second update is the method to calculate DOD. Since Pu and Ginoux (2017), DOD is calculated using a quadratic function of aerosol optical depth (AOD) and the single scattering albedo (SSA). In our 2012 paper, DOD is calculated using an on/off switch depending on the value of the Angstrom Exponent (AE). Then a threshold is applied to detect the highest frequency to correspond to actual dust sources. The method has been compared to independent geomorphological data over the Chihuahuan desert (Baddock et al., 2016) to prove that MODIS DB DOD is able to successfully detect high-resolution dust sources. It will be necessary to add a note the text stating that you are referring to an old dataset long replaced by thoroughly validated values using the latest MODIS aerosol products. Preferably, you replace the sentence by referring to more recent thoroughly validated values using the latest MODIS aerosol products.
Line 149-150: “To calculate DOD, we used wavebands available from monthly Moderate Resolution Imaging Spectroradiometer (MODIS; MOD08 M3 V6.1) at a 1-degree pixel resolution (Platnick, 2015)”
These are monthly gridded products at 1 x1 degree resolution from 1 km daily pixels. Using coarse resolution monthly aerosol products is totally inadequate to compare with point source dust plumes. The difference with MODIS DB DOD from my team is that it is 0.1 x 0.1 degree twice-daily products, which was shown to be appropriate to detect tiny dust sources (Baddock et al., 2016).
It is unclear how you get these DOD. Citing Platnick, 2015 is not helpful. What MODIS Level 3 products are you using? Are you using Dark Target or Deep Blue algorithm? Maybe you are using a blend of the two algorithms? DOD is not part of these products, so a description on how you obtain DOD from MODIS would be helpful, especially that you assimilate it to Ginoux et al. (2012) which is outdated.
Line 154: “We also provided a theoretical basis for TEMs formulation to be incorrect.”
I commented earlier that your description of TEMs formulation is mostly incorrect.
Line 166: “using MODIS data at 250 m spatial resolution with visible to thermal infrared wavebands”
This is incorrect. Only bands 1 and 2 are provided at 250 m. Bands 3 to 7 are at 500 m resolution. Bands 8 to 36 are at 1 km resolution. Red is band 2, blue is band 3 and green band 4. Deep blue is band 8 or 1 km pixel. Then 10 x 10 pixels are aggregated to provide 10x10 km Level 2 daily aerosol products.
Line 171: “DOD modelled frequency describes DOD > 0.2”
In Baddock et al. (2016), we used DOD>0.75 over the Chihuahuan desert, but Pu et al. (2020) used 2 threshold values (0.2 and 0.02) depending on the continent. Choosing a DOD threshold should be adapted to the objectives of the study but using gridded 1-degree monthly products is too coarse spatially and temporally to study dust sources.
Lines 248-249: “the TEM is driven by wind speed attenuated by aerodynamic roughness which is fixed over space and static over time,…”
If the surface conditions don’t change (no snow, no vegetation or land use changes) this will be true, but most ESMs (or TEMs) include such changes when resolving the boundary layer properties.
References
Baddock, M. C., Ginoux, P., Bullard, J. E., and Gill, T. E.: Do MODIS-defined dust sources have a geomorphological signature?, Geophys. Res. Lett., 43, 2606–2613, https://doi.org/10.1002/2015GL067327, 2016.
Dunne, J. P., Horowitz, L. W., Adcroft, A. J., Ginoux, P., Held, I. M., John, J. G., et al.: The GFDL Earth System Model Version 4.1 (GFDL-ESM 4.1): Overall coupled model description and simulation characteristics. Journal of Advances in Modeling Earth Systems, 12, e2019MS002015. https://doi.org/10.1029/2019MS002015, 2020.
Evans, S., Ginoux, P., Malyshev, S., and Shevliakova, E.: Climate-vegetation interaction and amplification of Australian dust variability, Geophys. Res. Lett., 43, 11823–11830, https://doi.org/10.1002/2016gl071016, 2016.
Ginoux, P., M. Chin, I. Tegen, J. M. Prospero, B. Holben, O. Dubovik, and S.-J. Lin: Sources and distributions of dust aerosols simulated with the GOCART model, J. Geophys. Res., 106, 20,255– 20,274, 2001.
Ginoux, P., Prospero, J. M., Gill, T. E., Hsu, N. C., and Zhao, M.: Global-scale attribution of anthropogenic and natural dust sources and their emission rates based on MODIS Deep Blue aerosol products, Rev. Geophys., 50, RG3005, https://doi.org/10.1029/2012RG000388, 2012.
Horowitz, L. W., Naik, V., Paulot, F., Ginoux, P. A., Dunne, J. P., & Mao, J., et al.: The GFDL global atmospheric chemistry-climate model AM4.1: Model description and simulation characteristics. Journal of Advances in Modeling Earth Systems, 12, e2019MS002032. https://doi.org/10.1029/2019MS002032, 2020
Platnick, S.: MODIS Atmosphere L3 Monthly Product, NASA MODIS Adaptive Processing System, Goddard Space Flight Center,
10.5067/MODIS/MOD08_M3.006, 2015.GPu, B. and Ginoux, P.: Projection of American dustiness in the late 21st century due to climate change, Sci. Rep., 7, 5553, https://doi.org/10.1038/s41598-017-05431-9, 2017.
Pu, B. and Ginoux, P.: Climatic factors contributing to long-term variations in surface fine dust concentration in the United States, Atmos. Chem. Phys., 18, 4201–4215, https://doi.org/10.5194/acp-18-4201-2018, 2018a.
Pu, B. and Ginoux, P.: How reliable are CMIP5 models in simulating dust optical depth?, Atmos. Chem. Phys., 18, 12491–12510, https://doi.org/10.5194/acp-18-12491-2018, 2018b.
Pu, B., Ginoux, P., Guo, H., Hsu, N. C., Kimball, J., Marticorena, B., Malyshev, S., Naik, V., O'Neill, N. T., Pérez García-Pando, C., Paireau, J., Prospero, J. M., Shevliakova, E., and Zhao, M.: Retrieving the global distribution of the threshold of wind erosion from satellite data and implementing it into the Geophysical Fluid Dynamics Laboratory land–atmosphere model (GFDL AM4.0/LM4.0), Atmos. Chem. Phys., 20, 55–81, https://doi.org/10.5194/acp-20-55-2020, 2020.
Sayer, A. M., Munchak, L. A., Hsu, N. C., Levy, R. C., Bettenhausen, C., and Jeong, M.-J. (2014), MODIS Collection 6 aerosol products: Comparison between Aqua's e-Deep Blue, Dark Target, and “merged” data sets, and usage recommendations, J. Geophys. Res. Atmos., 119, 13,965– 13,989, doi:10.1002/2014JD022453.
Yu, Y. and Ginoux, P.: Assessing the contribution of the ENSO and MJO to Australian dust activity based on satellite- and ground-based observations, Atmos. Chem. Phys., 21, 8511–8530, https://doi.org/10.5194/acp-21-8511-2021, 2021.
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AC1: 'Reply on CC1', Adrian Chappell, 18 Dec 2021
Dear Adrian and co-authors,
I made below a few comments related to your submitted manuscript as I found several cases of mis-representation or inaccurate description of the model and data I have been developing with my collaborators. Hopefully, you will find them useful to improve the manuscript.
Regards,
Paul Ginoux
Hi Paul,
Thanks for taking the time to make comments on our manuscript. We are a little surprised that none of your comments relate to the achievements described in our manuscript.
We did not intend to mis-represent or inaccurately describe dust emission modelling. We make no special case about the model and data that you have been developing. Our results show an inherent bias when calibrating a dust emission model to dust in the atmosphere and illustrate this using dust optical depth.
We answer each of your remarks below using bold, indented bullet points.
Regards,
Adrian and co-authors
Lines 29-31: “Many of the traditional dust emission models (TEM) assume that the Earth’s land surface is devoid of vegetation, then adjust the dust emission using a vegetation cover complement, and finally calibrate the magnitude of simulated emissions to dust in the atmosphere”
The calibration is mostly related to numerical discretization of the momentum and continuity equations. Emission of dust in numerical models depends on the discretization of surface winds. The surface winds are inferred from the pressure level wind vectors derived by solving numerically the momentum equations. The numerical discretization of these equations will be affected by the numerical resolution. Obviously higher resolution will resolve sharp topographic variations with stronger downslope winds. On the other hand, flat terrain without roughness elements using low resolution will generate stronger gustiness. So, changing model resolution has a non-trivial effect on surface winds. Concerning dust emission, the flux depends on the cubic power of surface winds (see Equation 2), which will amplify wind bias related to model resolution. This implies that “tuning” dust emission is a required method to simulate scale-aware tracer with numerical model. This is also true for other tracers, such as sea salt emission from the oceans.
- This comment refers to the italicised excerpt from our manuscript (Lines 29-31) which states clearly that the calibration of traditional modelled dust emission magnitude is to dust in the atmosphere. It is evident from many publications (Zender et al., 2003; Woodward, 2001; Tegen et al., 2002) that the dust emission magnitude is adjusted by a calibration factor. That factor is obtained by converting dust emission magnitude to optical properties that are then compared with measurements of aerosol optical properties. In our manuscript, we show the discrepancy between satellite observed dust emission and dust optical depth in the spatio-temporal frequency of occurrence. Consequently, we explain that dust models calibrated to dust in the atmosphere do not require fidelity in dust emission modelling.
Lines 82-83: “The common approach to modeling dust emission in ESMs uses globally constant values of aerodynamic roughness length (z0), which are static over time and fixes R(z0) ≈ 0.91.”
This is incorrect. In ESMs the momentum roughness length is calculated at every time steps and in every grid cells as a function of terrain variations, vegetation cover, snow cover, etc.
- This comment refers to the italicised excerpt from our manuscript (Lines 82-83) which should describe one common approach in ESM dust emission described in the Appendix of our manuscript.
- We accept that traditional dust models (TDMs) include roughness lengths but those roughness lengths values may not represent the heterogeneous vegetation that occurs within large (up to 50 km pixels).
- We accept that traditional dust models allow those roughness lengths to vary in time / spatially due to snow, vegetation etc. What is held constant, uniformly and globally in traditional dust emission models is the roughness length of each surface type. The roughness length of bare ground never changes, but the ground may not be bare all year. The same is true of desert shrubland which may not be bare everywhere within the land cover type. So in many dust-prone parts of the world, especially those that are bare soil, the roughness length never changes in TDMs. This is the roughness length that the mid-layer (resolved) wind speed is solved for.
- With that wind, some TDMs that we are familiar with construct a wind friction speed appropriate for erodible surfaces based on the assumption that erodible surfaces are bare (constant roughness length). That wind friction speed is then used to deflate dust. This "dust friction speed" need not be the same as the friction speed for turbulent fluxes.
- It is evident from this discussion alone that the implementation of TDMs varies model-by-model. There is commonality between models as illustrated in the dust emission schemes illustrated in our Appendix.
- In response to this comment we will revise our manuscript to ensure that we refer to “one” of the common approaches in dust emission modelling. As we stated in our opening paragraph, our work is not specifically focused on the model and data that Paul and his team has been developing. Our aim is to raise awareness of weaknesses in modelling that provide opportunities for model development for the benefit of the community consistent with the aims this journal’s scope.
Line 81-85: “The common approach to modelling dust emission in ESMs… This emission is then reduced by a function of vegetation cover and ultimately ‘tuned’ down to match observed in the atmosphere.”
I am unaware of any ESMs who have implemented dust emission as described. I can certainly speak for NASA and GFDL models (Ginoux et al., 2001; Evans et al., 2016).
- The italicised excerpt (above) refers to a common approach in ESM dust emission which is described in the Appendix of our manuscript. For clarity, our description of the method is that which once (and possibly is still) used in CESM and E3SM.
- As described above (Para 7) we will clarify in our revised manuscript that we refer to one common approach to modelling dust emission and will cease where appropriate, using definite articles. This we hope will provide a more balanced representation of TDMs that may do things slightly differently than TDMs with which we are more familiar.
Lines 103-104: “The 𝑢𝑠∗ is obtained directly from 𝜔𝑛𝑠, the normalised and rescaled shadow (1-albedo), enabling an albedo-based dust emission model (AEM; see Appendix for full description of the implementation)”
Do I read correctly that you are scaling the friction velocity using 3 parameters with an exponential function of 𝜔𝑛𝑠? Am I right that you will have to rescale 𝜔𝑛𝑠 for any other satellite instruments with different viewing angles or radiometric characteristics? Is this not a global tuning?
- Our albedo-based method was developed from a series of ground-based bi-directional spectral reflectance experiments in the laboratory and in the field (Chappell et al., 2005, 2006; Chappell et al., 2007). Over the last ten years or so, the approach has shifted from spectral reflectance to waveband independent albedo focused on land surface structure (Chappell et al., 2010). Our approach was shown to be similar to the widely accepted sheltering concept and its implementation (Raupach, 1992; Raupach et al., 1993). We established the bases for how the albedo-based approach could improve drag partition, sediment transport and hence dust emission (Chappell and Webb, 2016; Chappell et al., 2019). Since then we have confirmed the structural information content and demonstrated its scale invariance from ground-based measurements of albedo to MODIS-based estimates (Chappell et al., 2018; Ziegler et al., 2020).
- The shadow (1-albedo) is normalized to provide structural information for an area and is directly related to the wind friction velocity (of the area) measured in wind tunnel experiments (Chappell and Webb, 2016). To enable this grain-scale areal relation to be used at any other areal scale requires a measurement of albedo. Albedo scales linearly with increasing area in contrast to the wind friction velocity. To explicitly tackle sub-grid scale heterogeneity (Raupach and Lu, 2004) albedo measurements (or indeed any area-weighted estimates) must be rescaled before being used with the same calibration.
Line 128-132: “Evans et al., 2016”
The characterization of GFDL model (Evans et al. 2016) is not correct. You may want to read the paper. We are not using E=1-Av. The bare surface is calculated using an exponential function of the canopy (LAI) and stems, twigs, litters (SAI). The dust emission is calculated in each land tiles (primary, secondary vegetation, pasture and cropland) independently. Then the flux of dust is passing through a flux-exchanger into the atmosphere while a flux down from turbulence and settling is going in the land model. The latest ESM4 includes also tiles from fires and rangeland, in addition for taking into account slopes (Dunne et al., 2020; Horowitz et al., 2020). I will disagree with you when calling such detailed and consistent modeling of dust cycle a “crude model representation”
- The full sentence (quoted below from the manuscript at lines 128-132) reveals that the citation of Evans et al. (2016) is one of several which correctly cite dust models which use NDVI.
“This E is used in some ESMs so that leaf area index (LAI) or satellite ‘greenness’ observations e.g., normalized difference vegetation indices (NDVI) can be used as a surrogate of the land surface fraction occupied by green vegetation (Evans et al., 2016; Galloza et al., 2018; Zender et al., 2003a; Sellar et al., 2019).”
- The exponential relation between bare area and LAI (or NDVI) is an empirical representation of the drag partition which does not account for the physical aerodynamics that fast wind speeds are influenced less by a given roughness than slow wind speeds (Raupach et al., 1993; Chappell and Webb, 2016; Mayaud and Webb, 2017; Webb et al., 2014; Walter, 2012). Consequently, changing vegetation cover (in planform) is not itself sufficient to represent a change in sediment transport and dust emission.
- We acknowledge that vegetation indices can be used to obtain a first order correction to the erodible surface area, whether through thresholds and ramps (as CESM once did) or through more sophisticated canopy schemes like GFDL.
- We note that the full sentence also refers to some ESMs and as we have described (in our opening paragraph) there is no attempt in our manuscript to comment on model and data that Paul and his team has been developing.
Lines 133-135: “When the TEMs are applied in dust-climate ESMs it is assumed that this parameterization is adequate for climate projections. In contrast, the albedo-based scheme for sediment flux and dust emission (AEM; Eqs. 3, 4 & 5) represents the drag partition physics without pre-tuning to a fixed land surface condition, without the need for E, and thereby removes these additional sources of uncertainty.”
The main point of using climate model has been missed here. Despite their approximations, ESMs simulate the different Earth’s climate systems consistently over time using different projection scenarios. While the proposed used of 𝜔𝑛𝑠 (the normalized and rescaled shadow) is considered fixed (beyond MODIS period), ignoring vegetation and land use changes. The AEM technique is inadequate for future or past climates.
- We recognise and appreciate the experience that Paul’s comment provides. We agree that ESMs play a critical role in understanding Earth’s systems and that they must be designed not to rely on observations.
- Our scheme is albedo-based from any source or scale as evident in our recent investigation of scale invariance using both ground-based albedometers and satellite-retrieved albedo (Ziegler et al., 2020).
- Since albedo is one of the main drivers of energetic change in ESMs there is considerable potential for ESM-based albedo (not MODIS-based albedo) to drive the albedo-based dust emission model which will enable multiple internal consistencies in the modelling of the energy and dust cycles.
- In the excerpt above there is no mention that albedo is taken from MODIS.
Lines 145-149: “To understand the extent to which AOD estimates the spatial variation in dust emission magnitude and frequency we calculated the probability of dust occurrence modeled by the dust optical depth (DOD>0.2) using the criteria established previously (Ginoux et al., 2012). We note the stated limitations of DOD to be largely restricted to bright land surfaces in the visible wavebands which implies reduced performance over areas where vegetation is present.”
This sentence contains several misunderstandings of our latest method developed with my co-authors to derive DOD. In our 2012, we used the collection 5.1 of MODIS Deep Blue (DB), which provided aerosol products over bright surfaces. Since 2013 Collection 6 MODIS DB aerosol products have been extended to cover most (without snow or cloud cover) land surfaces (Sayer et al., 2014). All subsequent papers deriving DOD is using Collection 6.1 MODIS DB (e.g. Pu and Ginoux, 2017, 2018a, 2018b, 2020; Yu and Ginoux, 2021). A second update is the method to calculate DOD. Since Pu and Ginoux (2017), DOD is calculated using a quadratic function of aerosol optical depth (AOD) and the single scattering albedo (SSA). In our 2012 paper, DOD is calculated using an on/off switch depending on the value of the Angstrom Exponent (AE). Then a threshold is applied to detect the highest frequency to correspond to actual dust sources. The method has been compared to independent geomorphological data over the Chihuahuan desert (Baddock et al., 2016) to prove that MODIS DB DOD is able to successfully detect high-resolution dust sources. It will be necessary to add a note the text stating that you are referring to an old dataset long replaced by thoroughly validated values using the latest MODIS aerosol products. Preferably, you replace the sentence by referring to more recent thoroughly validated values using the latest MODIS aerosol products.
- Satellite observed dust emission point sources (DPS) have an uncertainty of around +/-2 km (Kandakji et al., 2020) due to the phase difference between timing of dust emission and availability of the imagery. At the point scale, this unexplained variance is reduced by aggregating information at a larger scale. This is well-established in geostatistical literature used across multiple disciplines and the issue of incompatible scales is summarised (Gotway and Young, 2002). We reduce the unexplained spatial variance by aggregating the DPS data.
- The excerpt above describes clearly that we used the established criteria (Ginoux et al., 2012). We then used those criteria with monthly MODIS Deep Blue (MOD08 M3 V6.1) data to establish dust optical depth across 1-degree grid boxes. Those grid boxes coincide with the occurrence of satellite observed dust emission point sources (DPS). These DPS data were aggregated to months across the same 1-degree grid boxes to provide compatibility with the DOD data. We note that these DPS data occur mainly in bright land surfaces which include vegetation.
- In our revised manuscript, we are happy to modify the description of our method to ensure that it is balanced. For clarity, we have used the latest MODIS Deep Blue Collection 6 dataset and apply the established criteria (Ginoux et al., 2012).
- In response to Paul’s query about the method, we looked up the recent methods (Pu and Ginoux, 2017) which cites (Ginoux et al., 2012) for their method. Apart from some small alternations to the Angstrom component, we could not find any no difference between our method, what we described in the manuscript and the method described recently (Pu and Ginoux, 2017).
Line 149-150: “To calculate DOD, we used wavebands available from monthly Moderate Resolution Imaging Spectroradiometer (MODIS; MOD08 M3 V6.1) at a 1-degree pixel resolution (Platnick, 2015)”
These are monthly gridded products at 1 x1 degree resolution from 1 km daily pixels. Using coarse resolution monthly aerosol products is totally inadequate to compare with point source dust plumes.
- As described above (para 20) there are compelling reasons to use aggregated DPS data. Consequently, we aggregated the DPS data over space. For compatibility with the monthly, 1 degree grid we aggregated the DPS to that same resolution.
- In our assessment, frequency of occurrence from either the model, or the DPS observations are described by the occurrence (modelled or observed) of dust emission at any of the DPS pixels within the 1° grid cell. i.e., should one DPS produce dust, that cell is considered on (FoO = 1) for that day.
- The DOD data describe the probability of any part of the 1° cell producing DOD > 0.2 consistent with the DPS FOO. If the reviewer is arguing that DOD data are ‘totally inadequate’ to verify emission from dust point sources, why do many studies use DOD results to therefore describe the spatial variability in dust emission (Ginoux et al., 2012: Pu and Ginoux 2017). The purpose of this analysis was to demonstrate this very point, highlighting the inadequacy of DOD by using actual dust observations to verify simulated dust emission.
- Consistent with previous studies using DOD to display spatial variability, we show the discrepancy between spatial variation in DOD and dust emission observed using satellites.
- That discrepancy between DOD and observed dust emission does not occur when DOD is compared with simulated dust emission.
- The cause of that discrepancy is that DOD measures dust in the atmosphere which is not directly related to dust emission.
The difference with MODIS DB DOD from my team is that it is 0.1 x 0.1 degree twice-daily products, which was shown to be appropriate to detect tiny dust sources (Baddock et al., 2016).
- We make no comparison with the work done by your team comparing dust in the atmosphere at 10 km pixels with satellite observed dust emission point source (DPS) data. In our approach we tackled explicitly the incompatibility of spatial scales (Gotway and Young, 2002) by upscaling DPS data to 1 degree pixels consistent with the DOD data and simulated dust emissions.
It is unclear how you get these DOD. Citing Platnick, 2015 is not helpful. What MODIS Level 3 products are you using? Are you using Dark Target or Deep Blue algorithm? Maybe you are using a blend of the two algorithms? DOD is not part of these products, so a description on how you obtain DOD from MODIS would be helpful, especially that you assimilate it to Ginoux et al. (2012) which is outdated.
- As described in the manuscript and in our response above (para 9) we used the established criteria (Ginoux et al., 2012) with monthly MODIS Deep Blue (MOD08 M3 V6.1) data to establish dust optical depth across 1-degree grid boxes. In the revised manuscript we will include the specific wavebands to make this clearer.
Line 154: “We also provided a theoretical basis for TEMs formulation to be incorrect.”
I commented earlier that your description of TEMs formulation is mostly incorrect.
- We described above (para 9) that the original manuscript erred by implying that all TEMs are formulated in the same way when in fact there is a spectrum of implementations for each process. The revised manuscript will avoid this overgeneralization when using indefinite articles e.g., "many TEMs". Nevertheless, it is important to give example process formulations and in those cases we have double-checked and the references previously given for TEM process descriptions are correct. We cannot find in your above text the argument by which our description in the Introduction of the manuscript of the theoretical basis for TEMs formulation is incorrect. The basis for the incorrect formulation is already published (Webb et al., 2020) and here we are extending that work to dust emission modelling.
Line 166: “using MODIS data at 250 m spatial resolution with visible to thermal infrared wavebands”
This is incorrect. Only bands 1 and 2 are provided at 250 m. Bands 3 to 7 are at 500 m resolution. Bands 8 to 36 are at 1 km resolution. Red is band 2, blue is band 3 and green band 4. Deep blue is band 8 or 1 km pixel. Then 10 x 10 pixels are aggregated to provide 10x10 km Level 2 daily aerosol products.
- To clarify this statement we will remove from the revised manuscript mention of “…at 250 m…” .
Line 171: “DOD modelled frequency describes DOD > 0.2”
In Baddock et al. (2016), we used DOD>0.75 over the Chihuahuan desert, but Pu et al. (2020) used 2 threshold values (0.2 and 0.02) depending on the continent. Choosing a DOD threshold should be adapted to the objectives of the study but…
- The DOD data are describing dust in the atmosphere and the DPS data are describing dust emission. The discrepancy between DOD data and DPS data is not impacted by the choice of threshold.
… using gridded 1-degree monthly products is too coarse spatially and temporally to study dust sources.
- We explained above (para 20-29) that we ensured that the gridded 1-degree monthly DOD is at the same scale (space and time) as the DPS data. In this approach we tackle explicitly the incompatibility of spatial scales. Our methodology explains and our results demonstrate that these 1-degree monthly products are not too coarse to study dust sources.
Lines 248-249: “the TEM is driven by wind speed attenuated by aerodynamic roughness which is fixed over space and static over time,…”
If the surface conditions don’t change (no snow, no vegetation or land use changes) this will be true, but most ESMs (or TEMs) include such changes when resolving the boundary layer properties.
- We have described in our responses above that there are at least two perspectives on this issue and we think that they converge. We will revise our manuscript using definite articles and describe some of the different ways in which surface conditions are represented in dust emission models.
References cited in our responses
Chappell, A. and Webb, N. P.: Using albedo to reform wind erosion modelling, mapping and monitoring, Aeolian Research, 23, 63-78, https://doi.org/10.1016/j.aeolia.2016.09.006, 2016.
Chappell, A., Zobeck, T. M., and Brunner, G.: Using on-nadir spectral reflectance to detect soil surface changes induced by simulated rainfall and wind tunnel abrasion, Earth Surface Processes and Landforms, 30, 489-511, 10.1002/esp.1185, 2005.
Chappell, A., Zobeck, T. M., and Brunner, G.: Using bi-directional soil spectral reflectance to model soil surface changes induced by rainfall and wind-tunnel abrasion, Remote Sensing of Environment, 102, 328-343, https://doi.org/10.1016/j.rse.2006.02.020, 2006.
Chappell, A., Strong, C., McTainsh, G., and Leys, J.: Detecting induced in situ erodibility of a dust-producing playa in Australia using a bi-directional soil spectral reflectance model, Remote Sensing of Environment, 106, 508-524, https://doi.org/10.1016/j.rse.2006.09.009, 2007.
Chappell, A., Van Pelt, S., Zobeck, T., and Dong, Z.: Estimating aerodynamic resistance of rough surfaces using angular reflectance, Remote Sensing of Environment, 114, 1462-1470, https://doi.org/10.1016/j.rse.2010.01.025, 2010.
Chappell, A., Webb, N. P., Leys, J. F., Waters, C. M., Orgill, S., and Eyres, M. J.: Minimising soil organic carbon erosion by wind is critical for land degradation neutrality, Environmental Science & Policy, 93, 43-52, https://doi.org/10.1016/j.envsci.2018.12.020, 2019.
Chappell, A., Webb, N. P., Guerschman, J. P., Thomas, D. T., Mata, G., Handcock, R. N., Leys, J. F., and Butler, H. J.: Improving ground cover monitoring for wind erosion assessment using MODIS BRDF parameters, Remote Sensing of Environment, 204, 756-768, https://doi.org/10.1016/j.rse.2017.09.026, 2018.
Ginoux, P., Prospero, J. M., Gill, T. E., Hsu, N. C., and Zhao, M.: Global-scale attribution of anthropogenic and natural dust sources and their emission rates based on MODIS Deep Blue aerosol products, Reviews of Geophysics, 50, 10.1029/2012rg000388, 2012.
Gotway, C. A. and Young, L. J.: Combining Incompatible Spatial Data, Journal of the American Statistical Association, 97, 632-648, 10.1198/016214502760047140, 2002.
Kandakji, T., Gill, T. E., and Lee, J. A.: Identifying and characterizing dust point sources in the southwestern United States using remote sensing and GIS, Geomorphology, 353, 107019, https://doi.org/10.1016/j.geomorph.2019.107019, 2020.
Mayaud, J. R. and Webb, N. P.: Vegetation in Drylands: Effects on Wind Flow and Aeolian Sediment Transport, Land, 6, 64, 2017.
Pu, B. and Ginoux, P.: Projection of American dustiness in the late 21st century due to climate change, Scientific Reports, 7, 5553, 10.1038/s41598-017-05431-9, 2017.
Raupach, M. R.: Drag and drag partition on rough surfaces, Boundary-Layer Meteorology, 60, 375-395, 10.1007/BF00155203, 1992.
Raupach, M. R. and Lu, H.: Representation of land-surface processes in aeolian transport models, Environmental Modelling & Software, 19, 93-112, https://doi.org/10.1016/S1364-8152(03)00113-0, 2004.
Raupach, M. R., Gillette, D. A., and Leys, J. F.: The effect of roughness elements on wind erosion threshold, Journal of Geophysical Research: Atmospheres, 98, 3023-3029, 10.1029/92jd01922, 1993.
Tegen, I., Harrison, S. P., Kohfeld, K., Prentice, I. C., Coe, M., and Heimann, M.: Impact of vegetation and preferential source areas on global dust aerosol: Results from a model study, Journal of Geophysical Research: Atmospheres, 107, AAC 14-11-AAC 14-27, 10.1029/2001jd000963, 2002.
Walter, B.: Wind Tunnel Studies of Shear Stress Partitioning in Live Plant Canopies,
Webb, N. P., Okin, G. S., and Brown, S. E.: The effect of roughness elements on wind erosion: The importance of surface shear stress distribution, Journal of Geophysical Research, 119, 6066-6084, 2014.
Webb, N. P., Chappell, A., LeGrand, S. L., Ziegler, N. P., and Edwards, B. L.: A note on the use of drag partition in aeolian transport models, Aeolian Research, 42, 100560, https://doi.org/10.1016/j.aeolia.2019.100560, 2020.
Woodward, S.: Modeling the atmospheric life cycle and radiative impact of mineral dust in the Hadley Centre climate model, Journal of Geophysical Research: Atmospheres, 106, 18155-18166, 10.1029/2000jd900795, 2001.
Zender, C. S., Bian, H., and Newman, D.: Mineral Dust Entrainment and Deposition (DEAD) model: Description and 1990s dust climatology, Journal of Geophysical Research: Atmospheres, 108, 10.1029/2002jd002775, 2003.
Ziegler, N. P., Webb, N. P., Chappell, A., and LeGrand, S. L.: Scale Invariance of Albedo-Based Wind Friction Velocity, Journal of Geophysical Research: Atmospheres, 125, e2019JD031978, 10.1029/2019jd031978, 2020.
Citation: https://doi.org/10.5194/gmd-2021-337-AC1
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AC1: 'Reply on CC1', Adrian Chappell, 18 Dec 2021
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RC1: 'Comment on gmd-2021-337', Anonymous Referee #1, 17 Dec 2021
The authors present experiments on dust emission, in which they conclude that an approach using an albedo-based representation of surface roughness (called AEM) outperforms “traditional” approaches (called TEM) for three reasons: 1. The formulation of streamwise sediment/saltation flux was incorrect in TEMs; 2. spatio-temporal dynamics of roughness elemental coverage were not considered in TEMs; 3. TEMs required calibration while AEM did not. Additionally, MODIS satellite-based estimates of dust optical depth (DOD) and dust emission point sources (DPS) and their probabilities of occurrence are compared with one another and the AEM and TEM results. It is concluded that DOD was not suitable for evaluation/calibration of dust models.
All these aspects are interesting and worthwhile to explore, however, the listed points do not support the authors’ conclusions, because a) the correctness/completeness of a formulation of streamwise saltation flux is independent of the use of albedo-based or another roughness representation, b) a dynamical representation of surface roughness can also be used with TEMs using methods other than the proposed albedo-approach, c) the albedo-approach is also calibrated. The authors aim to proof the advantages of AEM using uneven comparisons with what they described as a TEM: AEM uses dynamical vegetation, while TEM does not; AEM uses an updated streamwise flux formulation, while TEM does not; AEM uses variable soil texture/clay content, according to the proposed conversion of streamwise saltation to vertical dust emission flux, while the TEM does not (about the latter, there is contradictory information in the manuscript).
To evaluate the calculated dust emissions, the authors use two observational data sets: MODIS DOD at a spatial resolution of 1 degree and MODIS-based DPS at a spatial resolution of 250 m for a region in the southwestern USA. Again, the conclusion of DOD not being a suitable reference is not supported by this comparison due to the substantial difference in spatial resolution (approximately 400-fold higher resolution for DPS compared to DOD) in the presented comparison, and due to the definition of DOD used in the study, which seems to be simply a grid-point selection of AOD for DPS locations and likely includes aerosol other than dust as well.
Besides these previous aspects, the description of a TEM seems inaccurate and outdated at many places, and often not supported by recent references.
In light of these aspects, I cannot recommend publication of this manuscript. Additional comments are added below.
* L56-57 “total wind friction velocity ustar created by all scales of roughness” sounds like surface roughness were solely sufficient to describe ustar. Shouldn’t the atmospheric flow be an even more important aspect?
* L64 There is no “reanalysis model”. Reanalyses are based on model runs and observations.
* L67-76 This discussion is repeated from Webb et al. (2020). A reference to Kok et al. (2014), who have previously brought up this issue, should be added. It is not clear why – if this problem has been identified – the authors are not simply using the updates expression for both estimates with AEM and TEM, as this issue has nothing to do with the proposed albedo-approach. If the authors wish to test the sensitivity to the streamwise saltation flux formulation, this test needs to be performed holding all other settings constant.
* L80 Why is it that the correct values of R are not known for every pixel and every time step? Parameterizations of drag partition also rely on satellite data as input, as does the presented albedo-approach. While each data set comes with uncertainties and missing data, I do not see a fundamental different in the knowledge obtainable about surface roughness from satellite data for use with one approach or another.
* L81-82 Please give specific references instead of making general statements about what models do and don’t do. Models more and more use spatially and temporally (e.g. monthly) variable z0, e.g. ECHAM5-HAM (Cheng et al., 2008), EC-Earth3-AerChem (van Noije et al., 2021).
* L82 What is meant by values of z0 are pre-tuned and tend to maximize dust emission? No reference is provided for this statement. Values in the aforementioned references were obtained based on satellite data and ground-based measurements, as the authors also mention in the following sentences.
* L85 The use of preferential source areas, e.g. as in Ginoux et al. (2001), in some models has the purpose to generally specify soil erodibility and circumvent the need to prescribe detailed soil-surface properties. It is not specific to a description of surface roughness or z0.
* L95 Again, the “correct” Equation (3) is not related to the use of albedo to describe roughness. Hence a comparison of the albedo-approach and Eq. (3) with another approach and Eq. (2) is inconsistent.
* L101-102 Why would the albedo-approach be inconsistent with a grain-scale entrainment threshold? Is that because the albedo-derived u* is also resolution-dependent?
* L116-117 If the calculation of dust emission flux from streamwise sediment flux depends on %clay, then why a fixed clay content is used in the TEM (L537), but a spatially variable clay content is used in the AEM (L586-587). This, again, is an uneven comparison, which is unjustified. Then in L200, you claim that a soil clay content map was used with both models. In the appendix again, it is claimed that soil clay content was fixed in the TEM. Please explain.
* L120-121 Marticorena and Bergametti (1995) use the adjustment of dust emission according to the bare soil fraction in addition to a drag partition scheme. In the implementation of a TEM in the present study, this adjustment is used alone. Why is no drag partition applied, in combination with dynamic surface roughness as for the AEM? This would provide much better insight into the performance of the albedo-roughness approach.
* L130 Whether E includes brown vegetation depends on what data is used to define it.
* L131 This sentence is lacking foundation and in my humble opinion also dispassion.
* L135 Not clear which pre-tuning is meant.
* L140-142 The authors claim that using AOD for evaluation or calibration of a dust model includes the assumptions that 1. dust in the atmosphere represents the dust emission process, and that 2. the spatial variation of magnitude and frequency of modeled dust emission is correct. This is incorrect. First, for model evaluation/comparison, observed AOD (better DOD, dust optical depth) is compared with modeled AOD/DOD, and not with modeled emissions. Second, the goal of comparing modeled with observed atmospheric dust fields is to determine how well the observed fields can be reproduced with the model. Indirectly, but not unambiguously, this also sheds light on modeled dust emissions. No direct observations of dust emission or surface dust concentration are available on a global scale; hence this indirect evaluation is made. If emissions were assumed to be correct, model evaluation would only test dust transport and deposition processes and their parameterizations, which is not the case.
* L150 Do I understand right that DOD was obtained from AOD by selecting pixels which coincided with a DPS? Over North America, I expect that even at DPS locations, this DOD contains a significant contribution of other aerosol, leading to a larger value and therefore higher frequency of DOD > 0.2 than from dust alone.
* L155 I would see it the other way round: The correct probability of occurrence of (any) sediment flux depends on the correct (magnitude and) frequency of dust emission.
* L160 How are these assumptions circumvented? Do you mean that you evaluate the frequency of emission instead of the magnitude?
* L168 How is the aggregation performed? Are the frequencies aggregated or the original data? What is meant by “normalizing the results to the lowest resolution data”?
* L169 Please provide more detail about how the DPS observations have been obtained.
* L197 Do I understand correctly that the us*/u10 from albedo is completely decoupled from the atmospheric model and that, to calculate dust emission, you calculate us* by multiplying us*/u10 with the ERA5 winds, which correspond to a totally different modeled u* calculated with a different roughness? If so, I very much wonder about consistency of the obtained us* with both, the original albedo-approach and the atmospheric model. If the us*/u10 does not need ancillary data to calculate dust emissions, then – if used in an ESM – I wonder about consistency with the model winds responsible for dust transport after emission. It is also mentioned that the albedo-based us*/u10 from polar-orbiting MODIS data has incomplete coverage. I believe you argued earlier that for this same reason, the R ratio for use in TEMs cannot be estimated accurately. So this applies also to the albedo-based us*/u10?
* L214-215 If I understand well, wind speeds are obtained from ERA5 and in the AEM combined with the albedo-based us*/u10 ratio. Do I also understand well, that the AEM obtains one us*/u10 value each day from which dust emission is estimated, combined with the hourly ERA5 input? How are the different temporal resolutions treated? What is the motivation to select a narrow wind speed range between 8.5 and 9.5 m/s?
* L224-232 This paragraph seems redundant as there is no noteworthy drag partition used in the TEM as described here, hence (vegetation) roughness is not considered, but only surface coverage. The same applies to the discussion in L254-256.
* L238-239 I agree that the interplay of friction velocity and roughness is critical for dust emission, but this can be easily implemented also for the TEM. One option is described in the appendix, but not used.
* Fig. 2 What does “changed” mean in the axis titles? Does this refer to a difference or normalization? I also assume that uf should be u10 in the x-axis.
* L295 I am impressed that despite the severe limitations in the presented TEM implementation, it gives a similar (actually higher) R^2 than the AEM when compared with DPS.
* L316 The pattern similarity between TEM and u10 is most likely a direct result of how u* was calculated and of not including roughness in the TEM implementation.
* L322 Do the daily maxima used in both models refer to wind speed data?
* L355 Please provide a reference for this statement.
* L358-362 Please give the RMSE also at Fig. 4.
* L362-364 I do not see how the use of a fixed z0 can be called a tuning of the TEM. Most importantly, the calculation of us*/u10 is also calibrated/tuned, so there is no difference between AEM and TEM in that regard.
* Table 1 The left and center columns contain a large amount of overlap. Conceptually, it is not clear to me why u*ts at the grain scale should be inconsistent with the albedo-approach unless us* is not correctly retrieved. This may be related to resolution as indicated by the authors, a problem similar to the model calculation of u*. At the same time, the albedo-approach is claimed to be scale-invariant which appears contradictory. The authors also suggest that modeled u10 may be too large. While this can be the case, models typically underestimate strong winds, in particular with decreasing resolution. In line 4 of the table, it is noted that DPS may not include all dust emissions. Shouldn’t this also be a reason why the modeled dust emission frequency is higher? Finally, in the last line of the table, the calibration (tuning) of the albedo-approach is questions. Unfortunately, the assessment of this issue in the center column is not clear. It is also not clear why research on the applicability of the approach for a range of conditions is of low priority. This should be first priority when proposing a new parameterization.
* L393 It seems that the authors consider the dependence of dust emissions in the TEM on u10 (or better u*) negative and uncertainty arising from it more problematic than the fact that the albedo-based us*/u10 ratio was calibrated only against a data set covering a very limited range of conditions.
* L431 While the works from Marticorena and Bergametti (1995) and Shao et al. (1996) have certainly been major advances, there have been many additional advances since. Generally and in contrast to what is described in this paragraph, the importance of vegetation dynamics for dust emission has been well recognized for a long time and has been implemented in several global models/ESMs, also for climate simulations (see also previous comments).
* L480 I presume you are considering a neutral, and not stable, wind profile.
* L490 Please include reference for u*ts.
* L516 Please include reference for R.
* L517 Input data to calculate R are not absent. You have already referenced, for example, Prigent et al. (2012).
* L527 Which previous work?
* L530 Which challenge to estimate R are you referring to?
Citation: https://doi.org/10.5194/gmd-2021-337-RC1 -
AC2: 'Reply on RC1', Adrian Chappell, 21 Dec 2021
Thank you for taking the time to provide comments on our manuscript. To each of your comments, we provide responses below using bold, indented bullet points.
The authors present experiments on dust emission, in which they conclude that an approach using an albedo-based representation of surface roughness (called AEM) outperforms “traditional” approaches (called TEM) for three reasons: 1. The formulation of streamwise sediment/saltation flux was incorrect in TEMs; 2. spatio-temporal dynamics of roughness elemental coverage were not considered in TEMs; 3. TEMs required calibration while AEM did not.
- We do not agree that your summary above adequately represents our manuscript conclusion.
- In the manuscript abstract we state “…It is difficult to avoid our conclusion, also raised by others, that tuning dust emission models to dust in the atmosphere has hidden for more than two decades, these TEM modelling weaknesses and its poor performance.”
- The purpose of our manuscript is not to evaluate performance between dust emission models per se. There are many perspectives and metrics of performance that are required for that type of evaluation and that is not what is done in this manuscript.
- We can find no mention in our manuscript that the AEM does not require calibration. In constrast to the reviewer’s comment, we recognise that all dust emission models require calibration of some sort because of simplifying model assumptions necessary for implementation. The albedo-based dust emission model (AEM) is no different in this respect to other dust emission models as illustrated in our recent publication (Hennen et al., 2021) which is calibrated.
Additionally, MODIS satellite-based estimates of dust optical depth (DOD) and dust emission point sources (DPS) and their probabilities of occurrence are compared with one another and the AEM and TEM results. It is concluded that DOD was not suitable for evaluation/calibration of dust models.
- We can find no mention in our manuscript where we describe that DOD was not suitable for evaluation/calibration of dust models.
- Our manuscript demonstrates that dust emission modelling should be evaluated against satellite observed dust emission point source (DPS) data and not dust in the atmosphere.
All these aspects are interesting and worthwhile to explore, however, the listed points do not support the authors’ conclusions,
- The reviewer’s listed points are not those which represent the manuscript or the conclusions of our manuscript. We justify our perspective on the manuscript in response to the following reviewers’ points.
because a) the correctness/completeness of a formulation of streamwise saltation flux is independent of the use of albedo-based or another roughness representation,
- We disagree, the streamwise saltation flux is dependent on the albedo-based roughness representation.
- Our manuscript describes in the Introduction, and makes clear using equations, that the incorrect formulation is the use of the total wind friction velocity (u_star_cubed; Eq. 2) in the calculation of the streamwise saltation flux magnitude. We describe in the manuscript that term should be replaced with the soil surface wind friction velocity (u_s_star_cubed; Eq. 3) which in the manuscript is retrieved directly using the albedo-based approach.
b) a dynamical representation of surface roughness can also be used with TEMs using methods other than the proposed albedo-approach,
- Traditional dust emission models (TEMs) include aerodynamic roughness lengths (z0) but those z0 values may not represent the heterogeneous vegetation that occurs within large (up to 50 km pixels). We accept that some TEMs may allow those z0 values to vary in time / spatially due to snow, vegetation etc. In some TEMs, the z0 of each surface type is uniformly and globally held constant. The z0 value of bare ground never changes, but the ground may not be bare all year. The same is true of desert shrubland which may not be bare everywhere within the land cover type. So, in many dust-prone parts of the world, especially those that are bare soil, the roughness length never changes in TEMs. We will revise our manuscript to make these perspectives clear.
c) the albedo-approach is also calibrated.
- Agreed. We can find no mention in our manuscript where we state otherwise.
The authors aim to proof the advantages of AEM using uneven comparisons with what they described as a TEM: AEM uses dynamical vegetation, while TEM does not; AEM uses an updated streamwise flux formulation, while TEM does not; AEM uses variable soil texture/clay content, according to the proposed conversion of streamwise saltation to vertical dust emission flux, while the TEM does not (about the latter, there is contradictory information in the manuscript).
- As we mentioned above we are not evaluating the performance of the models per se. We are demonstrating that weaknesses in dust emission modelling have been hidden because dust emission models are evaluated against dust in the atmosphere. Due to the role of mineral dust in the climate system, we see this work as an important contribution towards reducing uncertainties in Earth System Models (ESMs) and increasing the models’ accuracy in reflecting past, current and future climate scenarios.
- Previous attempts to publish the results of the AEM compared with the TEM have been unsuccessful because reviewers have complained that the TEM is not correctly represented when using an ‘even’ comparison. Hence, here we implement the TEM following the scheme described in the appendix of the manuscript (Marticorena and Bergametti, 1995).
To evaluate the calculated dust emissions, the authors use two observational data sets: MODIS DOD at a spatial resolution of 1 degree and MODIS-based DPS at a spatial resolution of 250 m for a region in the southwestern USA. Again, the conclusion of DOD not being a suitable reference is not supported by this comparison due to the substantial difference in spatial resolution (approximately 400-fold higher resolution for DPS compared to DOD) in the presented comparison,…
- We can find no mention in our manuscript where we conclude “DOD not being a suitable reference”. Our manuscript demonstrates that dust emission modelling should be evaluated against satellite observed dust emission point source (DPS) data and not dust in the atmosphere.
- After we aggregate the satellite observed dust emission point source (DPS) data to 1 degree pixels to tackle the incompatible spatial scales (Gotway and Young, 2002) there is no “substantial difference in spatial resolution” in our results.
- Our manuscript states clearly (Lines 167-172) that “Modelled (AEM and TEM) and observed frequencies are aggregated by a 1°x1° grid matrix, normalizing the results to the lowest resolution data (MODIS DOD) (Figure 1). For each grid box location, the observed frequency is calculated as the number of DPS observations per year during observation period (2001 – 2016). The AEM and TEM modelled dust emission frequency describes (F>0) at DPS locations in each grid cell per year during the same period. DOD modelled frequency describes DOD > 0.2 in each grid pixel per year for the same period.”
…and due to the definition of DOD used in the study, which seems to be simply a grid-point selection of AOD for DPS locations and likely includes aerosol other than dust as well.
- We disagree with this statement and refer the reviewer to our manuscript (Lines 145-147) which states clerly that dust optical depth (DOD) is calculated following the established criteria: “To understand the extent to which AOD estimates the spatial variation in dust emission magnitude and frequency we calculated the probability of dust occurrence modelled by the dust optical depth (DOD>0.2) using the criteria established previously (Ginoux et al., 2012)”.
Besides these previous aspects, the description of a TEM seems inaccurate and outdated at many places, and often not supported by recent references.
- With this comment, there is no explanation of where in the manuscript the description of a TEM seems inaccurate and outdated. Please provide additional clarification and we will happily respond in this discussion.
- In our manuscript we refer to long-established dust emission model schemes which were developed more than two decades ago (Marticorena and Bergametti, 1995; Zender et al., 2003; Tegen et al., 2002). We also refer to very recent dust emission model results including our own (Hennen et al., 2021).
In light of these aspects, I cannot recommend publication of this manuscript. Additional comments are added below.
- We have responded to every comment provided by the reviewer and have described and illustrated using examples from the manuscript, how each comment has misrepresented the manuscript.
- Consequently, we find that there is no basis to support the reviewer’s conclusion and do not agree with the reviewer’s recommendation.
- We endeavoured to provide clarity in our manuscript but recognize that there may be room for improvement, and we are happy to revise it accordingly.
* L56-57 “total wind friction velocity ustar created by all scales of roughness” sounds like surface roughness were solely sufficient to describe ustar. Shouldn’t the atmospheric flow be an even more important aspect?
- Near the land surface, the total wind friction velocity (u_star) is influenced by the (freestream) wind speed of the inner boundary layer and the aerodynamic roughness of the land surface. We use a scale invariant calibration between shadow (1-albedo) and wind tunnel wind friction velocity (normalized by freestream wind speed) to calculate the soil surface wind friction velocity (u_s_star). This is the albedo-based approach and avoids the need for aerodynamic roughness length and roughness height, properties which are unknown and approximated by roughness factors attributed to land cover classes.
* L64 There is no “reanalysis model”. Reanalyses are based on model runs and observations.
- By ‘reanalysis models’ we are referring to Reanalysis wind fields. We will clarify this in the revised manuscript.
* L67-76 This discussion is repeated from Webb et al. (2020).
- For clarity, those lines (67-76) referred to by the reviewer are not a repeat of an already published paper (Webb et al., 2020). In describing this issue here we are demonstrating the significance of this incorrect formulation for dust emission modelling.
A reference to Kok et al. (2014), who have previously brought up this issue, should be added.
- We could not find any information in Kok et al., (2014) which describes how this issue was raised earlier. If the reviewer could direct us to page / paragraph we will happily respond in this Discussion.
It is not clear why – if this problem has been identified – the authors are not simply using the updates expression for both estimates with AEM and TEM, as this issue has nothing to do with the proposed albedo-approach.
- In a previous manuscript, we tried using the correct formulation in both AEM and TEM and reviewers would not accept that formulation correctly represented the TEM.
- In contrast to the reviewer’s perspective, we see the incorrect formulation of the sediment flux as central to the albedo-based approach. The soil surface wind friction velocity (u_s_star) is retrieved directly from a calibration with shadow (1-albedo) which avoids the need for aerodynamic roughness length and roughness height, properties of the heterogeneous land surface which are unknown and approximated by homogeneous roughness factors attributed to land cover classes.
If the authors wish to test the sensitivity to the streamwise saltation flux formulation, this test needs to be performed holding all other settings constant.
- The manuscript does not intend to test the sensitivity to the streamwise saltation flux formulation. The manuscript demonstrates (Figure 2 and related text) the significance for dust emission models of using the incorrect formulation. As described in L80 of our manuscript the incorrect formulation have been widely adopted in the TEMs.
* L80 Why is it that the correct values of R are not known for every pixel and every time step? Parameterizations of drag partition also rely on satellite data as input, as does the presented albedo-approach. While each data set comes with uncertainties and missing data, I do not see a fundamental different in the knowledge obtainable about surface roughness from satellite data for use with one approach or another.
- The explanation of why the correct values of the drag partition R are not known for every pixel and every time step is described in line 81: “…common approach to modelling dust emission in ESMs uses globally constant values of aerodynamic roughness length (z0), which are static over time and fixes R(z0) ≈ 0.91.” In other words, if a land cover class does not change over time and an aerodynamic roughness length is attributed to it then the drag partition is static over time and fixed across all pixels given that land cover class.
- In contrast, the albedo-based approach uses shadow (1-albedo) of the land surface (e.g., using MODIS at 500 m) which may be different for every pixel and changes significantly every few weeks with the vegetation phenology, changes abruptly with any rapid land cover change, and changes over long-time periods with extrinsic factors like global ‘greening’. Consequently, dust emission calculations will respond to these spatio-temporal changes in the land surface.
* L81-82 Please give specific references instead of making general statements about what models do and don’t do. Models more and more use spatially and temporally (e.g. monthly) variable z0, e.g. ECHAM5-HAM (Cheng et al., 2008), EC-Earth3-AerChem (van Noije et al., 2021).
- Thanks, we will attempt to do that in the revised manuscript. Of course, it is quite hard to do that for every point we might make as that would require a list of differences / similarities based on the different models. That difficulty in why in this manuscript we are attempting to make a more universal assessment using one of the common dust emissions schemes (Marticorena and Bergametti, 1995) which underpins many of the dust emission models.
* L82 What is meant by values of z0 are pre-tuned and tend to maximize dust emission? No reference is provided for this statement. Values in the aforementioned references were obtained based on satellite data and ground-based measurements, as the authors also mention in the following sentences.
- Models which fix the aerodynamic roughness length (z0) across all barren regions need to set a value. That value is typically set to a value which ensured that the dust emission from North Africa is large (consistent with dust in the atmosphere above North Africa) because models tended to under-estimate dust from the region because of the reduced wind speeds and the soil having about half the clay content. In the revised manuscript, we will include citations (Zender et al., 2003; Woodward, 2001; Tegen et al., 2002).
* L85 The use of preferential source areas, e.g. as in Ginoux et al. (2001), in some models has the purpose to generally specify soil erodibility and circumvent the need to prescribe detailed soil-surface properties. It is not specific to a description of surface roughness or z0.
- Our sentence is referring to generic nature of preferential source areas reducing the magnitude of dust emission. In the revised manuscript we will make this point clearer.
* L95 Again, the “correct” Equation (3) is not related to the use of albedo to describe roughness. Hence a comparison of the albedo-approach and Eq. (3) with another approach and Eq. (2) is inconsistent.
- We have explained above that the correct formulation is related directly to the albedo-based approach and which controls the magnitude of sediment flux and hence dust emission.
* L101-102 Why would the albedo-approach be inconsistent with a grain-scale entrainment threshold? Is that because the albedo-derived u* is also resolution-dependent?
- The albedo-based approach can use any source of shadow (1-albedo). In the manuscript we use shadow from MODIS at 500 m. The entrainment threshold is established from wind tunnel measurements and is effectively a point scale which is incompatible with area (Gotway and Young, 2002). The entrainment threshold needs to be developed like the albedo-based approach so that it can be upscaled from a point to an area (as described in the following paragraph).
- Shadow (1-albedo) scales linearly over area in contrast to the total wind friction velocity (u_star) which is non-linear (Raupach and Lu, 2004). Therefore, shadow from any source can be upscaled with sufficient measurements / estimates to any coarser pixel resolution. In this case, the shadow is then calibrated with total wind friction velocity (u_star) to determine the area-weighted, coarse resolution total wind friction velocity (u_star). This ability cut across scales is one of the key benefits of the albedo-based approach (Chappell and Webb, 2016).
* L116-117 If the calculation of dust emission flux from streamwise sediment flux depends on %clay, then why a fixed clay content is used in the TEM (L537), but a spatially variable clay content is used in the AEM (L586-587). This, again, is an uneven comparison, which is unjustified.
- In a previous manuscript, we used variable clay content in both AEM and TEM. Reviewers would not accept that variable clay was an accurate representation of the original TEM.
Then in L200, you claim that a soil clay content map was used with both models. In the appendix again, it is claimed that soil clay content was fixed in the TEM. Please explain.
- A soil clay content map was used in both models. In the AEM, the soil clay content map varied over space (SoilGrids) and in the TEM a map used fixed values.
* L120-121 Marticorena and Bergametti (1995) use the adjustment of dust emission according to the bare soil fraction in addition to a drag partition scheme. In the implementation of a TEM in the present study, this adjustment is used alone. Why is no drag partition applied, in combination with dynamic surface roughness as for the AEM? This would provide much better insight into the performance of the albedo-roughness approach.
- In previous manuscripts we have tried to do exactly as the reviewer suggests here. However, those previous results were not accepted because previous reviewers would not accept that was an accurate representation of the original TEM. Consequently, we implement the traditional dust emission scheme described in our manuscript Appendix.
* L130 Whether E includes brown vegetation depends on what data is used to define it.
- Brown vegetation cannot be described using vegetation indices which are designed to measure ‘greenness’. In our manuscript we are referring to a common way of representing vegetation in dust emission models using Normalized Difference Vegetation Index (NDVI). Consequently, the use of NDVI cannot represent brown rough vegetation.
* L131 This sentence is lacking foundation and in my humble opinion also dispassion.
- We think the reviewer is referring to the end of that line 131 where the sentence begins “This crude model representation of process…”. We think the statement is accurate, but we are happy to rephrase to avoid causing offense. We are passionate about our science.
* L135 Not clear which pre-tuning is meant.
- In the response to the same point above, we described above that models which fix the aerodynamic roughness length (z0) across all barren regions need to set a value. That parameter is typically set to a value which ensured that the dust emission from North Africa is large consistent with dust in the atmosphere above North Africa. Models tended to under-estimate dust from North Africa because of the reduced wind speeds and the soil having about half the clay content. In this respect some TEMs are pre-tuned before they are tuned to dust in the atmosphere.
* L140-142 The authors claim that using AOD for evaluation or calibration of a dust model includes the assumptions that 1. dust in the atmosphere represents the dust emission process, and that 2. the spatial variation of magnitude and frequency of modeled dust emission is correct. This is incorrect. First, for model evaluation/comparison, observed AOD (better DOD, dust optical depth) is compared with modeled AOD/DOD, and not with modeled emissions.
- At these lines in our manuscript we describe the intrinsic comparison being made between observed AOD and modelled AOD/DOD: modelled dust emission is compared to dust in the atmosphere which assumes that dust in the atmosphere represents the dust emission process.
Second, the goal of comparing modeled with observed atmospheric dust fields is to determine how well the observed fields can be reproduced with the model.
- The problem with comparing dust emission to dust in the atmosphere, is that any weaknesses in the dust emission model are avoided in favour of reproducing dust in the atmosphere. That preference to dust in the atmosphere may appear adequate, but it assumes that the model produces the correct magnitude and frequency of dust emission as we described (line 141). We explained in previous comments above, how aerodynamic roughness length fixed over space and static over time, has been used to maximise dust emission in North Africa to match dust in the atmosphere.
- “However, we know a priori that dust in the atmosphere is only partially related to dust emission because dust concentration is controlled by dust emission magnitude and frequency which varies over space and time, by residence time of dust near the surface which itself is dependent on wind speed, and on dust deposition in the dust source region, a size dependent process.” (Lines 142-145).
Indirectly, but not unambiguously, this also sheds light on modeled dust emissions. No direct observations of dust emission or surface dust concentration are available on a global scale; hence this indirect evaluation is made.
- We have shown in our recent publication (Hennen et al., 2021) and used in this manuscript, direct satellite observations of dust emission point sources (DPS) data.
If emissions were assumed to be correct, model evaluation would only test dust transport and deposition processes and their parameterizations, which is not the case.
- We are not sure what point is being made here.
* L150 Do I understand right that DOD was obtained from AOD by selecting pixels which coincided with a DPS? Over North America, I expect that even at DPS locations, this DOD contains a significant contribution of other aerosol, leading to a larger value and therefore higher frequency of DOD > 0.2 than from dust alone.
- We calculated the probability of dust occurrence modelled by the dust optical depth (DOD>0.2) using the criteria established previously (Ginoux et al., 2012) using MODIS Deep Blue (MOD08 M3 V6.1). The DOD was retrieved from those pixels in which dust emission was observed from point sources (DPS) in space and time throughout 2001-2016.
- DOD uses a specific Deep Blue algorithm, which utilizes single-scattering albedo, AOD and the reflectance properties from specific spectral bands (412, 470, and 670 nm) to differentiate the mixing ratio of dust and various other atmospheric aerosols (e.g., smoke) (Hsu et al., 2013). However, as DOD is a measurement of atmospheric dust, it is entirely possible that the DOD frequencies are affected by transported dust aerosols from upwind dust sources. This is another reason for evaluating dust emission models against satellite observed dust emission point source (DPS) data.
* L155 I would see it the other way round: The correct probability of occurrence of (any) sediment flux depends on the correct (magnitude and) frequency of dust emission.
- We think that difference in perspective amounts to the same outcome.
* L160 How are these assumptions circumvented? Do you mean that you evaluate the frequency of emission instead of the magnitude?
- Since the 1960s (Wolman and Miller, 1960) it has been established that the magnitude of sediment flux (in fluvial and aeolian systems) is controlled by the fluid friction velocity. That magnitude is adjusted by the probability of occurrence which depends on whether the entrainment threshold is exceeded. A key and long-standing limitation with dust emission models is that they assume an infinite supply of loose, dry, erodible, and available sediment. Consequently, whenever and wherever the entrainment threshold is exceeded sediment flux and dust emission occurs. Hence, the probability of occurrence is central to calculating the correct dust emission. In the absence of any way to constrain this assumption, we circumvent the need for this assumption by using satellite observed dust emission point source (DPS) data which describes when and where dust emission occurs (without the need for the assumption). We can explain this more fully in the revised manuscript.
* L168 How is the aggregation performed? Are the frequencies aggregated or the original data? What is meant by “normalizing the results to the lowest resolution data”?
- The DPS data are aggregated per grid box, per day. Therefore, in each grid box, if any of the identified dust sources recognizes an event, either modelled or observed, then that grid box is described as active for that day (FoO =1). Each grid box has a maximum of 1 FoO per day, i.e., if more than one DPS event occurs within a single grid box on a single day, then the grid box FoO is normalized to 1. This is the case for both observed frequencies and modelled frequencies.
* L169 Please provide more detail about how the DPS observations have been obtained.
- The DPS data are made available from previously published studies and those extant papers provide detail on how they have been obtained. Our recently published paper cited in this manuscript describes the way in which the DPS observations have been obtained. In any case, we can provide additional limited information in the main text of the revised manuscript or more fully in an extension to the manuscript Appendix.
* L197 Do I understand correctly that the us*/u10 from albedo is completely decoupled from the atmospheric model and that, to calculate dust emission, you calculate us* by multiplying us*/u10 with the ERA5 winds, which correspond to a totally different modeled u* calculated with a different roughness? If so, I very much wonder about consistency of the obtained us* with both, the original albedo-approach and the atmospheric model. If the us*/u10 does not need ancillary data to calculate dust emissions, then – if used in an ESM – I wonder about consistency with the model winds responsible for dust transport after emission.
- Modelled wind fields are themselves decoupled from the actual varying in space and time land surface. For example, we use here ERA5-Land data which we have shown recently to be one of the wind fields which most closely represents wind speed measurements (Fan et al., 2021). ERA5-Land wind fields take planetary boundary layer winds down to a blending height of 40 m before using roughness factors (not aerodynamic roughness length) for each land cover type to extrapolate wind to 10 m height.
- We use shadow to produce a calibrated soil surface wind friction velocity normalized by wind speed (u_s_star/Uh) which we multiply by ERA5-Land wind speed (at 10 m height) to retrieve the soil surface wind friction.
- There are many weaknesses / inconsistencies in dust emission modelling which include combining incompatible spatial scales of wind, roughness, entrainment threshold and avoid assuming an infinite supply of sediment. First we need to ensure that these weaknesses are evident by evaluating dust emission model simulations with satellite observed dust emission point source (DPS) data.
It is also mentioned that the albedo-based us*/u10 from polar-orbiting MODIS data has incomplete coverage. I believe you argued earlier that for this same reason, the R ratio for use in TEMs cannot be estimated accurately. So this applies also to the albedo-based us*/u10?
- We think the reviewer is referring to Line 80 “The substantive issues for dust emission modelling are that the incomplete form of QTEM (Eq. 2) has been widely adopted in TEMs in which large area estimates of wind speed are typically used, the correct values of R are not known (for every pixel and every time step) …” As we described previously the correct values of R varying over space and time are not known because aerodynamic roughness length is attributed to land cover which is homogeneous over space and fixed over time.
* L214-215 If I understand well, wind speeds are obtained from ERA5 and in the AEM combined with the albedo-based us*/u10 ratio. Do I also understand well, that the AEM obtains one us*/u10 value each day from which dust emission is estimated, combined with the hourly ERA5 input? How are the different temporal resolutions treated?
- In the manuscript (Lines 322-323) we describe how the albedo-based dust emission model (AEM) uses the daily maximum of all hourly wind speed data from the ERA5-Land data.
What is the motivation to select a narrow wind speed range between 8.5 and 9.5 m/s?
- To emphasise the difference in the modelling approaches we selected that narrow range of wind speeds (8.5-9.5 m/s). We identified that narrow range of wind speeds in the experiment (symbols Fig 2a) where the soil surface wind friction velocity was varied to show the response of dust emission. The albedo-based dust emission model (AEM) varied along the same curve and produced a large difference for the changed roughness (for the same wind speed). A representation of the traditional dust emission model (TEM) varied along different curves and produced approximately the same dust emission regardless of the changed roughness (for the same wind speed). In other words, fixing over space and holding static over time the aerodynamic roughness length (and hence making R constant) causes dust emission to occur regardless of roughness when wind is sufficient to overcome the entrainment threshold. When roughness varies realistically over space and time it attenuates the wind speed and dust emission responds accordingly. This model behaviour is illustrated spatially using the map in Figure 5.
- In short, much less adjustment of magnitude, frequency and spatial extent of dust is required if the soil wind friction velocity is allowed to vary over space and time.
* L224-232 This paragraph seems redundant as there is no noteworthy drag partition used in the TEM as described here, hence (vegetation) roughness is not considered, but only surface coverage. The same applies to the discussion in L254-256.
- We stated in that section of text that the reason for the comparison with a restricted drag partition is that is how some traditional dust emissions model have been implemented (as also described in the Appendix).
- We recognize that some dust emission models use a geometric relation between leaf area index (or a vegetation index) and the drag partition. Whilst this approach may describe change in the drag partition depending on changed planform geometry, that approach does not change with wind speed. Consequently, that geometric drag partition is not aerodynamic (Raupach, 1992; Raupach et al., 1993). As we have shown here (Fig. 2 & Fig. 4), the significance for dust emission models of using this approach is that dust emission is larger and more extensive than when including aerodynamics.
* L238-239 I agree that the interplay of friction velocity and roughness is critical for dust emission, but this can be easily implemented also for the TEM. One option is described in the appendix, but not used.
- We agree that changes can be made to the TEMs. We think one of the main challenges for dust emission modelling is to recognize the need for change in the TEMs. We think that the main mechanism for identifying the need for change is the routine evaluation of dust emission modelling with satellite observed dust emission point source (DPS) data.
* Fig. 2 What does “changed” mean in the axis titles? Does this refer to a difference or normalization? I also assume that uf should be u10 in the x-axis.
- The normalized soil surface wind friction velocity is the soil wind friction velocity divided by the wind speed as shown by the term in brackets (u_s_star / Uf). In this case, Uf is correct since this is the calibrated value and applicable to any freestream (f) height which is unaffected by the land surface roughness. The word ‘changed’ indicates how much dust emission changes when normalized soil surface wind friction velocity changes within the known range (0-0.04 m/s)
* L295 I am impressed that despite the severe limitations in the presented TEM implementation, it gives a similar (actually higher) R^2 than the AEM when compared with DPS.
- The larger correlation coefficient between the TEM and the satellite observed dust emission point source (DPS) data is created by the ‘pre-tuning’ of the aerodynamic roughness length used to fix the drag partition. If that value were perturbed the correlation would change. Furthermore, the correlation is with available DPS data which predominantly occur in relatively sparsely vegetated conditions for which the TEM is well suited given its ‘pre-tuned’ value for barren land.
* L316 The pattern similarity between TEM and u10 is most likely a direct result of how u* was calculated and of not including roughness in the TEM implementation.
- Yes, that is exactly our point. Recall from descriptions above, that even when the drag partition of a pixel is approximated by the geometric relation with the leaf area index (or a vegetation index) it is not wind speed dependent and therefore not aerodynamic. Consequently, it will respond in the same way as having a fixed roughness unless the geometric relation causes a change.
* L322 Do the daily maxima used in both models refer to wind speed data?
- Yes, thanks for the query. We will include that clarification in the revised manuscript.
* L355 Please provide a reference for this statement.
- This is our perspective which is why there is no citation. We will clarify that in the revised manuscript.
* L358-362 Please give the RMSE also at Fig. 4.
- Thanks for the suggestion. We will include on Fig 4 the RMSE values for each of the relations.
* L362-364 I do not see how the use of a fixed z0 can be called a tuning of the TEM. Most importantly, the calculation of us*/u10 is also calibrated/tuned, so there is no difference between AEM and TEM in that regard.
- We have explained in our responses (above) that the value of z0 may be set to produce a sufficiently large amount of dust emission to match that of measured dust in the atmosphere in North Africa. This is done before the dust model is calibrated.
- The shadow (1-albedo) retrieved from MODIS is calibrated to the soil surface wind friction velocity. It is not calibrated to produce a particular magnitude or frequency of dust emission.
* Table 1 The left and center columns contain a large amount of overlap. Conceptually, it is not clear to me why u*ts at the grain scale should be inconsistent with the albedo-approach unless us* is not correctly retrieved. This may be related to resolution as indicated by the authors, a problem similar to the model calculation of u*.
- We have explained above in response to a similar query that the entrainment threshold is at the grain scale because it is derived using wind tunnel measurements. It is important to recognize that this grain scale does not represent an area and is sufficiently relatively small to be considered at the point scale.
- Reflectance is measured over an area. Consequently, the calibration of shadow (1-albedo) to wind tunnel measurements of soil surface wind friction velocity provides an area-weighted estimate which when albedo is scaled linearly overcomes the non-linearity of scaling the wind friction velocity. As the albedo-based dust emission model (AEM) stands the point scale entrainment threshold is inconsistent with the new area-weighted albedo-based wind friction velocity as described in Table 1.
At the same time, the albedo-approach is claimed to be scale-invariant which appears contradictory.
- We have described in the original derivation of the albedo-based approach the potential for combining incompatible spatial scales (Gotway and Young, 2002; Chappell and Webb, 2016). We have also recently demonstrated how the albedo-based approach is scale invariant using ground-based albedometers and MODIS albedo (Ziegler et al., 2020).
The authors also suggest that modeled u10 may be too large. While this can be the case, models typically underestimate strong winds, in particular with decreasing resolution.
- The comparison in our manuscript between dust emission model and satellite observed dust emission point source (DPS) data (Figure 4) demonstrates that there are too many occurrences when the wind friction velocity exceeds the entrainment threshold. Assuming that the wind friction velocity is well constrained using the albedo-based (calibrated) approach, too many occurrences are caused by wind speeds being too large and / or entrainment threshold being too small.
- We agree with the reviewer that model wind fields e.g., ERA5-Land at 11 km may under-estimate fine-scale wind gusts e.g., low-level jets. Since the wind friction velocity can be calculated at any area-weighted resolution we can test this and other inconsistencies in scale. However, we first need the community to recognize that these weaknesses / inconsistencies in the dust emission modelling have endured because dust emission models are not being compared against DPS data.
In line 4 of the table, it is noted that DPS may not include all dust emissions. Shouldn’t this also be a reason why the modeled dust emission frequency is higher?
- In the fifth row of Table 1 we describe how some dust emission may not be included in the satellite observations described. That description could explain why the modelled dust emission frequency is larger than the DPS data. However, we think that the first order explanation is around scale difference and the assumed infinite supply of sediment.
Finally, in the last line of the table, the calibration (tuning) of the albedo-approach is questions. Unfortunately, the assessment of this issue in the center column is not clear. It is also not clear why research on the applicability of the approach for a range of conditions is of low priority. This should be first priority when proposing a new parameterization.
- We think that the use of shadow to establish the calibration with wind friction velocity is the first-order explanation of variability in dust emission modelling. There is some uncertainty in the function fitted to that calibration (cf. RMSE in Chappell & Webb, 2016). However, consistent with our previous point we think that the first order improvement of dust emission model is around scale difference and the assumed infinite supply of sediment. Therefore, we have given these points in the table low priority. In the revised manuscript we will make this point clearer.
* L393 It seems that the authors consider the dependence of dust emissions in the TEM on u10 (or better u*) negative and uncertainty arising from it more problematic than the fact that the albedo-based us*/u10 ratio was calibrated only against a data set covering a very limited range of conditions.
- The sentence prior to line 393 provides the context for our description “These contrasting estimates emphasise TEM dependency on variability in U10, due to the use of u*3 and the inability of R(z0)=0.91 fixed over space and time to correctly attenuate wind speeds by aerodynamic roughness”.
- A dust emission model is dependent on the wind friction velocity which in turn must be dependent on both wind speed and aerodynamic roughness to a lesser or greater extent controlled by extrinsic factors.
- We have used the best available data to calibrate shadow (1-albedo) to wind friction velocity (Marshall, 1971) and which has been examined and tested extensively in the aeolian research community (Raupach et al., 1993). These data cover the entire range of conditions under which wind friction velocity causes dust emission. We are happy to discuss this further if the reviewer would like to clarify what they mean by “very limited”.
* L431 While the works from Marticorena and Bergametti (1995) and Shao et al. (1996) have certainly been major advances, there have been many additional advances since. Generally and in contrast to what is described in this paragraph, the importance of vegetation dynamics for dust emission has been well recognized for a long time and has been implemented in several global models/ESMs, also for climate simulations (see also previous comments).
- The manuscript text at lines 431-433 is not about vegetation dynamics: “There is also a great risk that the major scientific advances made in developing dust emission schemes (Marticorena and Bergametti, 1995; Shao et al., 1996) and newly developed data / parameterizations (Prigent et al., 2012) are being overlooked by an over-reliance on simplistic assumptions about dust source location and erodibility to implement dust emission models”.
- There is no mention of vegetation dynamics in that section of text because the broad point we are making is that major developments in dust emission modelling are being overlooked by comparing dust emission models to dust in the atmosphere.
* L480 I presume you are considering a neutral, and not stable, wind profile.
- Consistent with other dust emission models we are assuming neutral buoyancy and a well-mixed. In the revised manuscript we will improve this description.
* L490 Please include reference for u*ts.
- Thanks. It is encompassed by the earlier citation (Marticorena and Bergametti, 1995). In the revised manuscript we will repeat that citation at the description of the entrainment threshold.
* L516 Please include reference for R.
- Thanks, we will include the omitted citation in the revised manuscript.
* L517 Input data to calculate R are not absent. You have already referenced, for example, Prigent et al. (2012).
- The sentence states “…the absence of regional and global spatio-temporal dynamics of R….” Those data cited by the reviewer do not vary over time (Prigent et al., 2012; Prigent et al., 2005).
* L527 Which previous work?
- Thanks for the query. In the revised manuscript we will list again all the citations included in the earlier sentences.
* L530 Which challenge to estimate R are you referring to?
- Thanks for the query. In the revised manuscript we will replace the phrase “a challenge” with “challenging” to improve the clarity of the sentence.
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Citation: https://doi.org/10.5194/gmd-2021-337-AC2
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AC2: 'Reply on RC1', Adrian Chappell, 21 Dec 2021
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RC2: 'Comment on gmd-2021-337', Anonymous Referee #2, 14 Feb 2022
General Comments
This study introduced a novel method based on dynamic land cover change (albedo or earth’s shadow) to quantify dust emission with grid precision and thus overcomes the biases from the traditional approach that estimated dust emissions based on constant spatial vegetation distribution from bare soil assumptions. The aim is to investigate point source emission detected by satellites observation varying with time and space. They found that both approaches/models overestimated the occurrence of dusty days, which is mainly from soil wind friction velocity. The more the model overestimates the soil wind friction velocity, the more it entrains high sediment flux once the threshold is exceeded. Therefore, the albedo-based model generates lower emissions than the traditional model due to the new formulation of soil wind friction velocity obtained as function albedo, roughness and horizontal wind velocity. This newly developed albedo-based model suggested to mimic the soil bareness and vegetation cover before and after dust emissions, and the results proved moderately good performance. This study is important and has potential impact for modelling community especially in quantifying effective emission of dust. Some questions for the experiment design and results are need to be addressed prior to the publication.
Specific Comments
- Dust Point Source locations are only shown on a small-domain map in Figure 1; Later, the authors described the roughness, wind speed, and dust flux in Figure 5 over a larger-domain map, would the authors show the DPS over a larger-domain map, such as the continental US (CONUS)? And, if possible, a map of North America is preferred to display district boundaries, deserts, vegetation and etc.
- AOD represents the total aerosol burden in the atmosphere. DOD is meant for detecting dust particles in the atmosphere. In this study, the authors preferred to employ a threshold of 0.2 for DOD (DOD>0.2) as from previous study of Ginoux et al (2012) to separate dust from background over North America during spring season. However, Ginoux et al (2012) used the threshold DOD>0.25 for most of the regions, and the threshold from this previous study is retrieved from the MODIS-DB L2 product at the 10 km x10 km grid resolution that is much finer than 1 degree resolution used in this study. Given that the authors in this study overestimated the frequency (Figure 3) even with the lower DOD threshold of 0.2 without quality control, why didn’t the authors sample DOD with quality flag at higher resolution and then average over one degree resolution? Otherwise, is 0.2/0.25 reasonable for one-degree resolution? Over such a large grid, smaller value may be preferred? please clarify.
- The results showed that high dust emissions were generated mainly from the Great Plains extending from Montana, Wyoming, Dakota, Colorado, New Mexico, and Texas, and slight dust emission were from the semi-arid and arid regions of the western deserts (Sonoran, Chihuahua, Mohave and great basin deserts). Therefore, I think the authors should also explain why those semi-arid and arid regions did not have any DPS.
- How does the study incorporate the soil texture/ soil type especially particle size threshold for starting the dust saltation? More explanation is preferred.
Minor Comments
- The paragraph from 231-237 describes Figure 2a for the albedo-based model. It seems that the results from the smooth and rough cases overlap. Please clarify if they are identical.
- In Figure 5 and 6, Uh should be replaced by U10. Please also correct others if any.
Citation: https://doi.org/10.5194/gmd-2021-337-RC2 -
AC3: 'Reply on RC2', Adrian Chappell, 18 Feb 2022
Dear Reviewer,
Many thanks for taking the time to provide comments on our manuscript. I've attached a document containing our responses to your queries / comments because we have used figures to illustrate some of our responses.
Regards,
Adrian, on behalf of the authors.
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- Editorial: Atmospheric dust: How it affects climate, environment and life on Earth? S. Parajuli et al. 10.3389/fenvs.2022.1058052
- A new process-based and scale-aware desert dust emission scheme for global climate models – Part I: Description and evaluation against inverse modeling emissions D. Leung et al. 10.5194/acp-23-6487-2023
- Modelled direct causes of dust emission change (2001–2020) in southwestern USA and implications for management M. Hennen et al. 10.1016/j.aeolia.2022.100852