Dust in the Earth System EESC G9910

Transcription

1 Dust in the Earth System EESC G9910 Chemical transforma0ons in the atmosphere and deposi0on Jean Guo 2/10/2016

2 Impact of air pollu<on on wet deposi<on of mineral dust aerosols (Fan et al., 2004)

3 Introduc<on Dust aerosol removal Gravita0onal se=ling à Dry deposi0on to the surface Scavenged à Dust aerosols act as ice nuclei (seeding cloud condensa0on and colliding with rain and snow) Mineral dust is insoluble, but can be coated with soluble materials (sulfate and nitrate)

4 Methods Used the GFDL global chemical transport model (GCTM) 265km resolu0on Uses NCEP reanalysis winds every 6 hours on 28 ver0cal levels Dust entrainment (E, kg/m 2 s 1 ) is predicted with Where C is a scaling factor (= kgm - 5 s 2 ) u* (m/s) is the surface fric0on velocity derived from the surface momentum flux S(x,y) is a func0on of longitude and la0tude that specifies the spa0al distribu0on of dust sources (from Ginoux et al. 2001) u * t is a threshold of u* below which there is no entrainment (= 0.35m/s everywhere)

5 Methods Dust source size distribu0on based on average observa0ons made over the Sahara during wind events and separated into 8 size bins Dry deposi0on parameterized according to Giorgi [1986] with velocity dependent on u* and par0cle size and density. Incloud wet- deposi0on for highly soluble aerosols is based on Kasibhatla et al. [1991]. In- cloud scavenging is assumed to be 100% for hydrophilic aerosols Scavenging of hydrophobic aerosols is set to zero by droplet nuclea0on, and is 100% when ice nuclea0on occurs (ambient temperature below 258 K)

6 Three simula<ons in the model S1: Hydrophobic S2: Hydrophilic S3: Transformed from hydrophobic to hydrophilic by reac0on with SO 2 (Chemical aging) Represents combined effect of dust reac0ons with SO 2, N 2 O 5, H 2 SO 4, HNO 3, and organic acids Compared models with observed annual dust concentra0ons at North Atlan0c (numeric symbols 4 sites) and North Pacific sites (alphabe0cal symbols - 18)

7 Figure 1a. Model vs. observed annual average dust concentra0ons for a) hydrophobic simula0on. Numerical symbols indicate North Atlan0c sites, and alphabe0cal symbols indicate North Pacific sites Figure 1a There is increasing error with distance North Atlan0c (numbers) is well simulated, but North Pacific (alphabe0c) is not

8 Figure 1b. Model vs. observed annual average dust concentra0ons for b) hydrophilic simula0on. Numerical symbols indicate North Atlan0c sites, and alphabe0cal symbols indicate North Pacific sites Figure 1b North Pacific (alphabe0c) is well simulated (within a factor of 2), but the North Atlan0c (numbers) is not

9 Figures 1a and 1b Saharan dust remain largely hydrophobic in the Atlan0c region. Dust from Asia have become hydrophilic in the Pacific region likely due to dust aerosols being coated with soluble ions (ammonium, sulfate, and nitrate).

10 Figure 1c. Model vs. observed annual average dust concentra0ons for c) chemical aging simula0on. Numerical symbols indicate North Atlan0c sites, and alphabe0cal symbols indicate North Pacific sites Figure 1c - S3 simula0on Good agreement with sta0ons near the source and with sta0ons at Atlan0c sites and at Pacific sites near the Asian coast Results are more than twice as high as observa0ons at the remote Pacific sites - More rapid chemical again by reac0ons with compounds like N 2 O 5 and HNO 3 than in the model

11 Desert dust is transformed by air pollu0on from hydrophobic to hydrophilic par0cles Forms CCN Hydrophilic dust aerosols are more rapidly removed from the atmosphere Concentra0on of mineral dust in rain water vs surface air is higher in the North Pacific than in the tropical North Atlan0c by a factor of 5. This is reflected in the S3 simula0on à Asian dust is aged chemically in air pollu0on plumes and is more readily removed by droplet nuclea0on

12 S2/S1 averaged over five years of deposi0on to the North Pacific Ocean Deposi0on to the coastal oceans off of East Asia is increased May be due to rise of air pollu0on in East Asia. Or soluble iron may be increased when sulfate coats mineral dust, increasing iron supply to the surface ocean Deposi0on to NE NPO is decreased by as much as 50% from S1 to S2

13 Conclusion Chemical reac0ons of air pollutants on the surface of mineral dust may be changing the life0me of dust aerosols in the atmosphere and its deposi0on to the oceans Air pollu0on increases the scavenging of dust by producing high levels of readily soluble materials on dust surfaces à Makes dust effec0ve CCN Air pollu0on could have caused an increase of dust deposi0on to the coastal oceans of East Asia and a decrease in the eastern North Pacific (Figure 2) Air pollu0on may influence iron supply to remote oceans, affec0ng diatom biomass and causing poten0al ecological changes in marine ecosystems

14 Chapter 4: Processing and Ageing in the Atmosphere Review physical and chemical processes that alter dust proper0es and their impacts on dust s direct and indirect impacts on climate Examine these processes using data from field observa0ons (assesses the net effect of the various complex processes involved) and laboratory work (means to study fundamental chemical and physical mechanisms under controlled condi0ons)

15 Heterogeneous chemistry on dust surfaces During long range transport, dust may alter in hygroscopicity (material's ability to absorb or release water as a func0on of humidity) and reac0vity of dust cons0tuents, changing the composi0on of the atmosphere

16 Physical processing Winnowing changes size spectrum of suspended dust during transport with large par0cles (> 10um) removed first (i.e. large quartz grains) Long range transport 1-3um with excep0ons

17 Chemical Processing Impact on Physical Proper<es Sulfate coa0ngs Sca=ers radia0on and promotes CCN ac0vity à Cooling effect Sulfuric acid decreases IN ability Interac0on with sea- salt and anthropogenic pollutants forms hygroscopic species (absorbs water) Calcium carbonate (CaCO 3 ) reacts with HNO 3 in presence of water to form calcium nitrate droplets à Changes par0cle morphology, size, sca=ering proper0es, and hygroscopic growth rela0ve to CaCO 3

18 Chemical Processing Impact on Physical Proper<es Changing chemical composi0on may change op0cal proper0es, cloud condensa0on nuclei (CCN), and ice nuclei (IN) ac0vity, and hygroscopicity Coa0ng with soluble materials like sulfate, nitrate, and chloride increases CCN ac0vity Fig 4.2a Par0cles increase in size and change to a spherical shape aler reac0on with nitric acid to form nitrate Fig 4.2b Nitrate par0cles sca=er light to a greater extent at higher rela0ve humidity due to increased size and water content (cooling effect) Fig 4.2c Carbonate par0cles do not grow at different RH while nitrate par0cles growth with increasing RH

19 Chemical Processing Impact on Dust Reac<vity Chemical processing can affect reac0vity of dust Interac0on with mineral and organic acids increases solubility of nutrients (i.e. Fe and P) à Enhances bioavailability Winnowing Decreasing par0cle size increases surface area to volume ra0os à Higher solubility

20 Fig 4.3 There is a hyperbolic increase in Fe solubility as dust concentra0on (or total Fe concentra0on as a proxy) decreases Pa=ern consistent under four different condi0ons for the extrac0on of soluble Fe Ultrapure water Ammonium acetate Formate Seawater leaching solu0on

21 Some authors suggested that non- dust Fe might contribute to Fe solubility Fe solubility increases as total Fe decreases even when non- dust courses of Fe are unlikely to be significant Using tracers (V and Ni), Fe solubility has been linked to anthropogenic influences (combus0on related) likely significant at low dust concentra0ons

22 Hyperbolic increase in Fe solubility as dust concentra0on has been seen in other compounds Fig 4.5 Fe, Al, Mn, P, and Si from samples collected over the Atlan0c Ocean 5- day back trajectories Mn solubility varies more than other elements and is less dependent on total Mn concentra0on à May be due to tendency of Mn to form oxide coa0ngs on other par0cles

23 Chemical processes - Impacts on Atmospheric Composi<on Laboratory experiments show that chemical processes on dust surfaces is sensi0ve to RH, photolysis, and the nature of the reac0ve surface Nitric Acid and Nitrogen Oxides Efficiently taken up onto dust samples, forming surface nitrate + gas- phase NO TiO 2 on dust leads to NO x produc0on NO reac0on with nitric acid is a significant source of HONO in polluted areas and also produces NO x Sulfur species Sulfur dioxide adsorbs onto mineral oxides and aerosol samples, especially in presence of adsorbed water Forms sulfate ions on the surface of dust par0cles

24 Alterna<ve pathway for sulfur dioxide In presence of sunlight, gaseous sulfuric acid is formed as an intermediate product Caused by semiconductor metal oxides in mineral dust with a band gap. When excited, these minerals act as photocatalysts, forming reac0ve species such as OH radicals in the gas phase OH ini0ates conversion of SO 2 to H 2 SO 4 and a new par0cle

25 Impacts on Atmospheric Composi<on Con<nued O 3 TiO 2 and SiO 2 together increases O 3 decomposi0on in presence of sunlight Photodecomposi0on of O 3 may be an important path of O 3 loss in the troposphere Other inorganic and organic species Uptake of hydrogen peroxide by TiO 2 decreases at higher RH In the high troposphere, H 2 CO 3 may be adsorbed on mineral dust (acid stable even at low temperatures) Organics can be adsorpted onto minerals

26 Chapter 8: Dust Deposi<on Dust removed by either dry deposi0on (gravity, impac0on, and diffusion) or wet removal (in or below clouds) Removal is size dependent Direct measurements, especially for dry deposi0on, are difficult to perform Limits ability to test model simula0ons and constrain dust mass budget in models

27 Introduc<on Assessing impacts of dust on Earth system requires knowledge of Dust concentra0on in atmosphere Variability in 0me and space Dust size distribu0on Dust composi0on Models must represent Dust emissions Transport Deposi0on (Dry dep. dominant near source. Wet dep. dominant far from source) Atmospheric dust content is the most precise presently due to remote- sensing of AOD Deposi0on is the least studied

28 Deposi<on Processes Fig 8.1 Number and mass size distribu0ons of dust from wind- tunnel measurements. Representa0ve of dust size distribu0on near source regions Large par0cles are removed quickly

29 Dry Deposi<on Major sink for dust par0cles Par0cles < 0.1um Controlled by Brownian diffusion Par0cles > 5um Gravita0onal se=ling Par0cles between 0.1um and 5um Deposi0on velocity at a min. and controlled by turbulent processes Dry deposi0on velocity is inverse of total equivalent resistance (8.2) Steady- state gravita0onal se=ling velocity (terminal velocity) Cunningham (slip) correc0on factor Aerodynamic resistance (effects of diffusion by turbulent processes at surface) Quasi- laminar resistance (transfer of par0cles through viscous layer)

30 Factors Affec<ng Gravita<onal SeTling Velocity

31 Removal of aerosols due to presence of water (clouds, snow, fog) Importance increases with distance from source Two wet removal pathways: 1) Rainout or in- cloud scavenging (Condensa0on or collision in clouds) Important for submicron par0cles 2) Washout or below- cloud scavenging (Aerosol impact with falling droplets) Important for coarser par0cles In models, dust par0cles are represented either as: 1) Purely hydrophobic with no in- cloud scavenging 2) In- cloud scavenging is considered with efficiency assumed to be that of sulfate aerosols Below- cloud scavenging size dependency is be=er understood Below Cloud Scavenging Removal of par0cles by washout Scavenging Coefficients E represents the capacity of falling droplets to catch aerosols Ra0o between number of collisions between droplets and par0cles and the number of par0cles in the column Collision efficiency due to Brownian diffusion

32 Collision Processes Some Key Points Number of collisions between droplet and par0cles in atmospheric column is dependent on par0cle size Efficiency lowest for par0cles with a diameter of ~0.5 um When par0cle size decreases E (collision efficiency) increases When par0cle size increases, E increases due to intercep0on and iner0al impac0on Intercep0on Iner0al Impac0on

33 Par<cle Size Distribu<on and Deposi<on in Models Most dust size distribu0on measurements are derived from wind- tunnel measurements Models now bin par0cles according to size Two methods: 1) Iso- log Size bins have equal ranged in log D p. Requires at least 12 iso- log bins for accuracy 2) Iso- gradient Places more size bins in the size domain where removal processes are strongly size dependent. Fig 8.2 The iso- gradient method has less errors for low bin numbers

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35 Dry Deposi<on Measurements Wet Deposi<on Measurements Hard to measure dry deposi0on especially for par0cles between um Hard to mimic surface and turbulent flux Larger par0cles depend on sedimenta0on (less sensi0ve to surface proper0es) Easier to measure Long- term measurements of red rains (precipita0on associated w/ Saharan dust) in an area close to Barcelona, Spain Deposi0on of Saharan dust is discon0nuous over W. Mediterranean Up to 30% of the annual deposi0on flux of dust occur in only two or three days

36 Dust Deposi<on Measurements Rare around the Sahara and Sahel region despite being the most important dust sources of the world Fig 8.3 Shows exis0ng measurement sites in the region Annual dust deposi0on fluxes high in or close to source regions (100gm - 2 y - 1 ) Deposi0on decreases as a func0on of distance from Saharan sources Much of the dust is deposited before reaching the Mediterranean basin

37 AeroCom Project Models AeroCom analyzes global aerosol simula0ons by comparing between models and with observa0ons Fig 8.4 Mean deposi0on fluxes for 2000 Dust flux highest near source regions Sahara gm - 2 y - 1 (Consistent with Fig 8.3 observa0ons) Oceanic regions downwind of source regions 20-50gm - 2 y - 1

38 AeroCom Uncertain<es between models Large uncertain0es in simulated dust emissions Used 7 models with similar size distribu0on for dust from the AeroCom project Dust load has best observed constraints Opposite pa=ern from emissions with similar uncertain0es Rela0ve agreement among models

39 AeroCom Experiment A and B Experiment A: Each model worked with its own parameteriza0ons Experiment B: Kept emission strength, ini0al dust size distribu0on, and injec0on height the same across all models Look at annual dry and wet deposi0on mass across 6 models Dry deposibon: Less difference between models in Experiment B than in Experiment A Difference between models in Experiment A may be due to differences in dust size distribu0on Wet deposibon: More difference between models in Experiment B than in Experiment A Conclusion: Dust cycle terms not constrained enough to es0mate dust mass budget accurately à Large uncertain0es in dust emissions, size distribu0on, and wet and dry deposi0on

40 Conclusion Dust load is the only term in the mass budget that is rela0vely well- constrained by observa0ons Large uncertain0es in dust emission and deposi0on Uncertain0es limits ability to asses radia0ve or biogeochemical impacts of dust Need to improve dust deposi0on modeling and measurements to constrain dust mass budget