1. Contribution à l’étude du cycle des aérosols désertiques : Des processus d’émission à la modélisation tridimensionnelle Béatrice Marticorena Habilitation à Diriger des Recherches

2. A contribution to mineral dust cycle studies : from dust emission processes to tridimensional modelling Béatrice Marticorena Habilitation à Diriger des Recherches

3.  Terrigeneous particles emitted by aeolian erosion in arid and semi-arid areas of the Earth  About 40% of the total mass of particulate matter injected each year in the atmosphere 1. Introduction

4. The mineral dust cycle (Mahowald, Webpage) EMISSIONS TRANSPORT DEPOSITION 1. Introduction

5. (Mahowald, Webpage) EMISSIONS TRANSPORT DEPOSITION Reduction of visibility Impact on health Soil loss, fertility Input of nutrients to remote ecosystems Direct and indirect radiative effects Impact on atmospheric chemistry Impacts of the mineral dust cycle 1. Introduction

6. data from Chester (1986) and Duce (1995)  Concentrations vary over several orders of magnitude Average measured concentrations Spatial distribution of mineral dust concentration 1. Introduction

7. - Events : sporadic variations - Annual : a clear seasonal cycle; max in summer - Interannual : strong variability in the mean and max (Trapp et al., 2010) Monthly mean measured concentrations Temporal variability of mineral dust concentration  A strong variability at all time-scales (daily to multiannual) 1. Introduction

8. Mean Aerosol Optical Depth (2001-2005)  A valuable information on dust distribution in transport regions  No sufficient information in and around source regions No retrieval over bright surfaces 1. Introduction

9. Emissions (Mt/yr) × 1.7 Dust load (Mt) × 4.1 Lifetime (day) Global models of the mineral dust cycle AEROCOM Intercomparison exercise (Results adapted from the AEROCOM simulations (Aerosol Model Comparison; http://dataipsl.ipsl.jussieu.fr/cgi-bin/AEROCOM/; Textor et al., Atmos. Chem. Phys., 2006; 2007; Huneus, ACPD, 2011; G. Bergametti, Mineral Dust Workshop, Leipzig, 2009)http://dataipsl.ipsl.jussieu.fr/cgi-bin/AEROCOM/  The mass budget is not constrained  Still large uncertainties on dust emissions 1. Introduction

10. - What are the main processes involved in dust emission by aeolian erosion ? - How to model these processes ?

11. 2. Dust emission processes P : Weight Ip : Cohesion forces  : Wind shear-stress  A process with a wind velocity threshold depending on particle size P Ip  U * ² Initiation of soil particle movement (loose particles; dry and smooth surface)

12. Size-dependent threshold wind friction velocity Optimum size = minimum threshold Weight Interparticles Cohesion Forces  Uncertainties on the small particles (<10 µm) ( =high threshold wind friction velocities) Comparable agreement with measurements 2. Dust emission processes

13. P : Weight Ip : Cohesion forces  : Wind shear-stress Fc : Capillary forces  Soil moisture increases the threshold wind velocity P Ip Fc  U * ² 2. Dust emission processes Initiation of soil particle movement over a WET surface (loose particles; smooth surface)

14. (Fécan et al., 1999) Influence of soil moisture on the threshold wind friction velocity  A parameterisation that perfoms well in the field (Ishisuka et al. 2005) 2. Dust emission processes

15.   Surface roughness increases the threshold wind friction velocity and protects the surface against erosion RR RR RR SS  s  R 2. Dust emission processes Initiation of soil particle movement over a ROUGH (loose and dry particles)

16. (Marticorena et al., 1997) Roughness dependent threshold wind friction velocities  Good agreement for relatively low roughness density and solid obstacle 2. Dust emission processes

17. (Darmenova et al., 2009) Sensitivity of the threshold wind friction velocities to the drag partition scheme  Drag partition over vegetated surfaces is a major source of uncertainty on the threshold wind velocities 2. Dust emission processes Equivalent surface roughness × 4 × 2

18. Direct dust uplift Dust release by impact of saltating grains D ~1-10 µm D ~100 µm Dust release by desagregation of of saltating grains D ~100 µm Sand-Blasting D ~1-10 µm 2. Dust emission processes Sandblasting is much more efficient :  saltation is a pre-requisit for dust emission

19. Horizontal and vertical fluxes The ratio of vertical to horizontal flux defines the sandblasting efficiency (  =F/G) SALTATION = Horizontal flux SANDBLASTING = Vertical flux 2. Dust emission processes

20. Parametrization of the saltation fluxes General expression (i.e. White, 1979) Complete expressions (Marticorena et Bergametti, 1995) including : -the size and roughness dependence of the threshold wind friction velocity - the relative contribution of the different soil grain sizes - the fraction of erodible surface 2. Dust emission processes

21. Saltation as a size-segregating processe 2. Dust emission processes Soil mass size distribution Saltation flux size distribution  Size segregation as a function of wind velocity is not verified in the field (with natural soils)

22.  Conceptual understanding of sandblasting Kinetic energy provided by the saltating particles Binding energy of the dust particles 2. Dust emission processes  Physical parameterization - Lu and Shao (2001)  = f(p) ; p: plastic flow pressure = soil hardness - Alfaro et al. (1996;1998)  = f(e d ) ; Cohesion energy of the particles - Shao et al. (2004)  = f(p; soil pdf) ; undisturbed and fully disturbed pdf - Kok (2011a, 2011b)  = f(soil texture) ; fragmentation theory  Empirical parameterization - Marticorena and Bergametti (1995) :  = f(%clay)

23. Simulated dust emission mass fluxes  The orders of magnitude of the dust mass fluxes are reasonably well reproduced (Shao, 2001; data from Gillette, 1979) 2. Dust emission processes

24. Size-resolved dust emission fluxes X 2 X 10 (Sow et al., 2009) 2. Dust emission processes  A dependence with the wind velocity (in agreement with Alfaro and Gomes (2001))  Differences in the level of dependence and in the dust modes compared to wind-tunnel

25. Threshold Drag partition on vegetated surfaces Saltation Further investigation on the dependence with the soil size-distribution Dust flux Further investigation on the dust size distribution Combination mass/size flux measurements - A well-established understanding of the main processes - Available operational parameterizations 2. Dust emission processes

26. How to apply such physical models at a larger scale (regional)?

27. 3. Regional dust emission modeling EROSION THRESHOLD SALTATION SANDBLASTING PROCESS

28. EROSION THRESHOLD SALTATION SANDBLASTING PROCESS Threshold wind friction velocity Soil grain size distribution (D p ) Roughness length (Z 0, Z 0s ) Soil moisture (w, w’) PARAMETERIZATIONSINPUT DATA 3. Regional dust emission modeling

29. EROSION THRESHOLD SALTATION SANDBLASTING PROCESS Threshold wind friction velocity Soil grain size distribution (D p ) Precip., T°, alb. Roughness length (Z 0, Z 0s ) Texture (%clay, %sand,%silt) Soil moisture (w, w’) PARAMETERIZATIONSINPUT DATA 3. Regional dust emission modeling

30. EROSION THRESHOLD SALTATION SANDBLASTING PROCESS Threshold wind friction velocity Surface wind (U10m) Soil grain size distribution (D p ) Precip., T°, alb. Roughness length (Z 0, Z 0s ) Texture (%clay, %sand,%silt) Soil moisture (w, w’) PARAMETERIZATIONS Wind friction velocity INPUT DATA 3. Regional dust emission modeling

31. EROSION THRESHOLD SALTATION SANDBLASTING PROCESS Threshold wind friction velocity Surface wind (U10m) Snow cover Soil grain size distribution (D p ) Precip., T°, alb. Roughness length (Z 0, Z 0s ) Texture (%clay, %sand,%silt) Horizontal flux Soil moisture (w, w’) % covered surface PARAMETERIZATIONS Wind friction velocity INPUT DATA 3. Regional dust emission modeling

32. EROSION THRESHOLD SALTATION SANDBLASTING PROCESS Threshold wind friction velocity Surface wind (U10m) Snow cover Soil grain size distribution (D p ) Precip., T°, alb. Roughness length (Z 0, Z 0s ) Texture (%clay, %sand,%silt) Horizontal flux Vertical mass flux Size-distribution Soil moisture (w, w’) %clay % covered surface PARAMETERIZATIONS Wind friction velocity INPUT DATA 3. Regional dust emission modeling

33. SURFACE PROPERTIESSURFACE PROPERTIES METEOROLOGICAL DATAMETEOROLOGICAL DATA Surface wind (U10m) Snow cover Soil grain size distribution (D p ) Precip., T°, alb. % covered surface (E) Roughness length (Z 0, Z 0s ) Texture (%clay, %sand,%silt) Specific parameters not available in distributed data sets : soil maps, land- use maps, atmospheric model inputs.. Provided by global or regional meteorological and/or climate models INPUT DATA 3. Regional dust emission modeling

34. Surface roughness and soil mapping A geomorphologic approach IGN topographic map (Callot et al, 2000) Manual file production  A time consuming approach whose reliability depends on the quantity and quality of available initial maps/information 3. Regional dust emission modeling Aerodynamic roughness map (cm)

35. Roughness mapping by radar measurements Synthetic Aperture Radar  High resolution (~10m) mapping for local applications In situ measurements (Marticorena al., 2006) 3. Regional dust emission modeling

36. Roughness mapping with POLDER BRDF 3. Regional dust emission modeling × × × × × × × Empirical relationship Protrusion coefficient (*) from POLDER-1 (Marticorena et al., 2004; 2006) *(Roujean et al., 1992]  Medium resolution mapping for regional applications Threshold wind velocities (at 10m)

37. Threshold wind velocities (at 10m) over East Asia U(10m) 50% (Kurosaki and Mikami, 2007) 10m wind velocities with a 50% probability of occurrence of dust storm (synoptic data) (m/s)  The estimated thresholds are very consistent with synoptic measurements 3. Regional dust emission modeling

38. SURFACE PROPERTIESSURFACE PROPERTIES METEOROLOGICAL DATAMETEOROLOGICAL DATA Surface wind (U10m) Snow cover Soil grain size distribution (D p ) Precip., T°, alb. % covered surface (E) Roughness length (Z 0, Z 0s ) Texture (%clay, %sand,%silt) - Roughness maps derived from remote sensing - Soil characteristics derived from geomorphological interpretations and/or in-situ sampling Meteorological fields from ECMWF (European Center for Medium-range Weather Forecast) 3. Regional dust emission modeling INPUT DATA

39. Takl NE deserts GobiOther Sahara North-East Asia Simulated mean annual dust emissions (1996-2000) 670 + /- 60 Mt.yr -1 240 + /- 131 Mt.yr -1 3. Regional dust emission modeling

40. Simulated dust emissions INTENSITY (Laurent et al., 2006)  The interannual variability of the dust emission is due a few unfrequent but extremely intense dust events 3. Regional dust emission modeling North-East Asia (1996-2001) Simulated dust emissions OCCURENCE

41. Validation : Comparison to satellite aerosol products  Infrared Dust Index (IDDI; Meteosat)  UV Absorbing Aerosol Index (TOMS) Simulated dust emission frequency (Laurent et al., 2008) 3. Regional dust emission modeling  A good agreement on the source location  Under(over) estimations due to bias in the surface winds

42. Comparison to frequency of reduction of the horizontal visibility measured at meteorological stations Western Sahara Eastern Sahara SimulationObservations Contrasted seasonal patterns in the simulations and in the observations 3. Regional dust emission modeling  The seasonal patterns of the simulated dust emission occurrence are consistent with the observations

43. Soils Informations on the undisturbed soil size distribution Link between undisturbed and disturbed (texture) soil size distribution Roughness Test of roughness maps on vegetated surfaces Meteo Critical analysis and evaluation of the biais or errors induced by meteorological forcing (surface winds, soil moisture) - Explicit emission schemas that can be used in 3-D models - Consistent estimation of the dust emissions and of their dependance with surface properties 3. Regional dust emission modeling

44. LIMITATIONS  Location of the dust emission (sources)  Temporal pattern of dust emission frequency = No direct validation of the emitted dust amount and of the relative intensity of the emissions by the different sources 3. Regional dust emission modeling

45. How to go further in the investigation of the variability of the mineral dust cycle ?

46. 4. Regional simulation of the dust cycle STRATEGY « Chemistry and Transport Model » (+ + +) Meteorology = external forcing (+ + + ) Low computing cost = long period of simulations with a large domain and a relevant time resolution (1h) (- - -) No coupling radiative/dynamics

47. Dust production model (DPM) + surface data sets (Laurent et al., 2008) Optimisation of the representation of the size-dependent deposition processes (Forêt et al., 2006) The CHIMERE-Dust regional model 4. Regional simulation of the dust cycle

48. First simulation : March 2004 Sao Nicolau, 1304m Santiago, 1392m and 1063m Below 2000m  No simultaneous measurements of AODs, concentration, deposition, vertical distribution 4. Regional simulation of the dust cycle

49. « Sahelian Dust Transect » Geophysical station of IRDIER/SRAC Cinzana Banizoumbou, Niger Cinzana, Mali M'Bour, Senegal mm.yr -1 Longitude (°) Latitude (°) IRD Niamey = 3 stations along the main transport pathways of Saharan and Sahelian dust 4. Regional simulation of the dust cycle

50. Banizoumbou Cinzana M’Bour Comparison between measured and simulated AODs (Observed : dark color; Simulated : light color) (Schmechtig et al., 2011) 4. Regional simulation of the dust cycle  Magnitude of the monthly AODs is OK  A similar seasonal cycle is observed and simulated at the three stations  The West to East gradient is retrieved  The observed and simulated AODs are significantly correlated (n=36; r=0.53)

51. Comparison between the measured and simulated daily surface concentrations (Observed : dark color; Simulated : light color) Cinzana, Mali (Schmechtig et al., 2011) 4. Regional simulation of the dust cycle  The order of magnitude of the surface concentrations is retrieved  The seasonal cycle is well reproduced at the three stations  The level of agreement with observations is similar than for an air quality PM model (Normalized Mean Error = 75%; Normalized Mean Bias = -36 %)

52. Measured and simulated deposition fluxes - 2006 Year 2006 Total deposition (g.m -2 ) MeasuredSimulated M'Bour83,259.6 Cinzana10580.2 Banizoumbou127,742.8  Annual total deposition fluxes are reasonable but slighly underestimated  The observed Est-West gradient is not reproduced Monthly deposition in Cinzana (Mali) 4. Regional simulation of the dust cycle  Significant underestimation of the dry deposition ?  Significant bias due to uncertainties on the spatial and temporal distribution of the precipitation

53. Meteorology Simulations are sensitive to the meteorological forcings that control dust emissions, transport and deposition Validation Validation against AOD is not sufficient, deposition and size distribution must be further constrained Emissions in arid regions Emissions in arid regions are impacted by land-use and local meteorological processes  Regional simulations of the mineral dust cycle give reasonable estimation of the atmospheric dust load 4. Regional simulation of the dust cycle

54. Are we able to « close » the dust mass budget at the regional scale ?

55. Sensitivity and validation ASTRID, 2013-2015, coll. Numtech, LMD Coupled WRF-OASIS-CHIMERE model Sensitivity to the meteorological forcing Evaluation/validation Size-distribution, deposition fluxes, AODs, etc., from field campaigns (AMMA; SAMUM;...); long-term measurements (SDT, AERONET, IDAF) Sources, spatial distribution : satellite products (MODIS deep-Blue; AOT IR et altitude IASI,...) ISSUE : CHIMERE : A transverse tool 3-D used in LISA to evaluate dust impacts : soil loss, direct radiative effect, nutrient inputs  The different terms of the dust cycle and the properties controling dust impacts must be correctly reproduced STRATEGY : 4. Regional simulation of the dust cycle

56. Are we able to simulate dust emission from semi-arid regions ? How are they going to evolve due to changes in climate and land-use ?

57. 5. Sahelian dust emissions Simulation of the dust emissions in a « natural » Sahel (Pierre et al., JGR, 2012) Simulations of dust emissions AND vegetation Difference of emissions with and without vegetation and moisture Dust emission model– STEP vegetation model  Seasonal vegetation and soil moisture decrease dust emissions by - 20 to - 39 %  Simulated dust emissions are low (no cultivation; no convection)

58. Dynamic of wind erosion on cultivated fields in the Sahel 5. Sahelian dust emissions Cultivated fields Cumulated erosion fluxes (g/m²) Fallows Beginning of the wet season  Wind erosion is recorded almost only on the cultivated fields  It is due to high winds in the front of convective systems

59. Dynamic of wind erosion on cultivated fields in the Sahel (Abdourhamane Touré et al., 2011) Max of wind erosion in the wet season = convection Intensity linked with crops residues Erosion in the dry season over bare surfaces  Importance of cultivated surfaces and agricultural practices Bare Cultivated 5. Sahelian dust emissions

60. How has aeolian erosion evolved in the Sahel ? from Dumay et al. (2002) Nouakchott (Mauritanie) Precipitation deficit Enhanced wind erosion  Wind erosion events increased dramatically during the drought period  Their frequency remains higher after the drought than before 5. Sahelian dust emissions

61. C A V I A R S Climate Agriculture and Vegetation Impacts on Aeolian eRosion in the Sahel Issue Describe the evolution of aeolian erosion in the Sahel due to changes in climate and land- use in the recent past (50 years) STRATEGY -Modeling natural and cultivated vegetation and its influence on wind erosion - Representing high winds in convective systems - Intensive validation for present conditions (AMMA) 5. Sahelian dust emissions Soc. & Env., 2013-2016, coll/. GET, CNRM, CIRAD, Bioemco, JEAI ADE

62. CONCLUSION (1/4) Dust emission processes  Huge progresses have been achieved in the last 20 years  Functional parameterization and models Remaining issues: - Dust size distribution and its link with soil properties - Erosion thresholds and fluxes over vegetated surfaces (cultivated and natural) for quantification, prevention and remediation

63. CONCLUSION (2/4) Regional dust emission  Significant progresses in the application of dust model  Available maps of surface properties Remaining issues : - Document the soils size-distributions -Estimation of surface winds and soil moisture biais ; possibilities of biais corrections ?

64. Regional modeling of the dust cycle  Almost all aerosol models include mineral dust  A relevant scale to test simulations against experimental data (size distribution, …) Remaining issues: - Quantitative evaluation of their performances with a focus on the mass budget and the size distribution - Identification of the biais due to the meteorological forcing or to mis-represented dynamical processes CONCLUSION (3/4)

65. CONCLUSION (4/4) Changes in the mineral dust cycle in the past and the future  Significant changes have occured and are occuring due to climate variability (or climate change) and to land-use change Remaining issues: - Document land-use change in existing and future semi-arid regions and their evolution (surface and practices) - Try to link regional dynamics and climate for present, to represent the impact of climate variability (or climate change) in the past and the future

66. Thank you for your attention