Examination of Mineral Dust Variability and Linkages to Climate and Land-Cover/Land-Use Change Over Asian Drylands Copyright© 2

EXAMINATION OF MINERAL DUST VARIABILITY AND LINKAGES TO CLIMATE AND LAND-COVER/LAND-USE CHANGE OVER ASIAN DRYLANDS

A Thesis Presented to The Academic Faculty

Xin Xi

In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the School of Earth and Atmospheric Sciences

Georgia Institute of Technology May 2014

EXAMINATION OF MINERAL DUST VARIABILITY AND LINKAGES TO CLIMATE AND LAND-COVER/LAND-USE CHANGE OVER ASIAN DRYLANDS

Approved by:

Dr. Irina N. Sokolik, Advisor Dr. Judith A. Curry School of Earth & Atmospheric Sciences School of Earth & Atmospheric Sciences Georgia Institute of Technology Georgia Institute of Technology

Dr. Michael H. Bergin Dr. Viatcheslav Tatarskii School of Civil & Environmental School of Earth & Atmospheric Sciences Engineering Georgia Institute of Technology School of Earth & Atmospheric Sciences Georgia Institute of Technology Dr. Rodney J. Weber School of Earth & Atmospheric Sciences Georgia Institute of Technology

Date Approved

To My Grandparents

ACKNOWLEDGEMENTS

This thesis would not be possible without the guidance and support from my advisor, Dr. Irina N. Sokolik. It has been a rewarding experience to work with Dr.

Sokolik, whose expertise, guidance and patience paved a path for me to be an independent researcher. She taught me the value of hard work through her diligence and perseverance. She is an example to me as a scientist and educator. I would like to thank

Dr. Bergin, Dr. Curry, Dr. Tatarskii and Dr. Weber for taking the time to serve on the thesis committee. Thanks to the group alumni, colleagues and friends in Dr. Sokolik‘s group, in particular, Dr. Darmenova and Dr. Darmenov, whose work has benefited my thesis study. Finally, I would like to express my deepest gratitude to my family for their support, love and sacrifice. Confucius said: —While your parents are alive, it is better not to travel far away“. Yet, going to college and graduate school brought me thousands of miles away from home. I am in a great debt to my grandparents and parents which I will never be able to repay. I want to thank the love of my life, my wife Shan, for bearing with me through the difficult times. Her love and encouragement have given me the strength to complete this thesis.

TABLE OF CONTENTS

ACKNOWLEDGEMENTS...... iv

LIST OF TABLES ...... viii

LIST OF FIGURES...... x

SUMMARY...... xiv

CHAPTER 1 INTRODUCTION ...... 1

1.1 OVERVIEW ...... 1

1.2 RESEARCH QUESTIONS AND THESIS STRUCTURE ...... 10

CHAPTER 2 SEASONAL DYNAMICS OF THRESHOLD FRICTION VELOCITY AND

DUST EMISSION ...... 14

2.1 INTRODUCTION ...... 14

2.2 DATA AND METHODOLOGY ...... 19

2.2.1 Model Configurations ...... 19

2.2.2 Data ...... 20

2.2.3 Computation of Threshold Friction Velocity ...... 22

2.2.4 Computation of Dust Fluxes ...... 25

2.3 RESULTS ...... 34

2.3.1 Incorporation of Vegetation Phenology into Threshold Friction Velocity34

2.3.2 Seasonality of Threshold Friction Velocity...... 38

2.3.3 Analysis of Surface Winds ...... 43

2.3.4 Seasonality of Dust Emission ...... 45

2.3.5 Comparison with Dust Observations ...... 53

2.4 CONCLUSIONS ...... 58

CHAPTER 3 INTERANNUAL VARIABILITY OF DUST AEROSOL AND LINKAGE

TO CLIMATE AND LAND-COVER/LAND-USE CHANGE ...... 64

3.1 INTRODUCTION ...... 64

3.2 DATA AND METHODOLOGY ...... 69

3.2.1 LCLUC Data...... 70

3.2.2 Satellite Aerosol Products ...... 70

3.2.3 Ground Observations ...... 73

3.2.4 Climate Indices ...... 73

3.2.5 Drought Indices ...... 73

3.3 INCORPORATION OF LAND USE DYNAMICS IN THE DUST MODEL .. 75

3.3.1 Land Use Dynamics in Agriculture and Water Body ...... 75

3.3.2 Modification of Dominant Land Cover and Soil Texture ...... 78

3.4 RESULTS ...... 81

3.4.1 Comparison of Dust Emission with Observations ...... 81

3.4.2 Linkages between Interannual Variability of Dust and Climate ...... 88

3.4.3 Detection of Dust Trend ...... 97

3.4.4 Assessment of the Anthropogenic Fraction of Dust ...... 102

3.5 CONCLUSIONS ...... 107

CHAPTER 4 IMPACT OF ASIAN DUST ON PHOTOSYNTHETICALLY ACTIVE

RADIATION AND SURFACE RADIATIVE BALANCE...... 111

4.1 INTRODUCTION ...... 111

4.2 DATA AND METHODOLOGY ...... 115

4.2.1 Selection of Mineralogical Composition and Particle Size Distributions

Representative of Asian Dust ...... 116

4.2.2 Reconstruction of Spectral Surface Albedo ...... 123

4.3 RESULTS ...... 124

4.3.1 Examination of Asian Dust Optical Characteristics ...... 124

4.3.2 Impact of Dust on Total and Diffuse PAR ...... 127

4.3.3 Impact of Dust on Surface Radiative Balance ...... 132

4.3.4 Impact of Dust on Vegetation Light Use Efficiency ...... 133

4.4 CONCLUSIONS ...... 139

CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS ...... 143

5.1 CONCLUSIONS ...... 143

5.2 RECOMMENDATIONS FOR FUTURE WORK...... 149

REFERENCES ...... 153

VITA ...... 173

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LIST OF TABLES

Table 2.1 List of data used in Chapter 2. 21

Table 2.2 Descriptions of dustœrelated synoptic weather observations. 23

Table 2.3 The soil texture types in Central Asia and associated soil mass size 27 distribution parameters, including tri-modal log-normal parameters (mass fraction n, mass median diameter MMD [Jm], geometric standard deviation L), smooth aeolian roughness length z0s, and clay content.

Table 2.4 Soil and land characteristics of dust source subregions in Central 31 Asia.

Table 2.5 Dry-sieved soil mass size distribution for the dust source subregions 32 in Central Asia, including bi-modal log-normal parameters (mass fraction n, mass median diameter MMD [Jm], geometric standard deviation L), smooth aeolian roughness length (z0s), and clay content.

Table 2.6 Examples of simplified dust schemes. 33

Table 2.7 Model experiments of dust emission simulations. 33

Table 2.8 Land cover types and the associated roughness density (NB) and 36 geometric height (hB) of non-vegetation roughness elements, and monthly geometric height of vegetation elements (hV).

Table 2.9 Monthly dust emissions from different dust source types. 48

Table 2.10 Monthly dust emissions from Aralkum. 53

Table 3.1 Estimates of the anthropogenic fraction (Fad) of dust burden or 68 emission.

Table 3.2 List of data used in Chapter 3. 71

Table 3.3 Major LCLUC events in Central Asia between the 1950s and 2010s. 75

Table 3.4 Correlation coefficients between monthly dust emission, dust 88 frequency index and MODIS aerosol observations. Gray shaded values indicate statistically significant correlations at 95% confidence level.

Table 3.5 Correlation coefficients between monthly dust emission, dust 91 frequency index and MODIS aerosol observations. Gray shaded values indicate statistically significant correlations at 95% confidence level.

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Table 3.6 The annual, wet-season and dry-season dust emission [Mt] averaged 91 for El Nino and La Nina years, and their differences.

Table 3.7 The annual, wet-season and dry-season dust emission [Mt] averaged 92 for positive and negative NAO years, and their differences.

Table 3.8 The annual, wet-season, and dry-season strong wind frequency 94 (u10>6.5 m/s), precipitation, surface bareness and PDSI averaged for El Nino and La Nina years.

Table 3.9 Correlation coefficients between monthly dust emission anomaly and 95 various factors. Gray shaded values indicate statistically significant correlations at 95% confidence level.

Table 3.10 Annual and seasonal trends [Mt yr-1] of dust emission based on 100 Exp_Mean for different dust source regions. ** indicates trends significant at 95% confidence level, * indicates trends significant at 90% confidence level.

Table 4.1 Parameters of dust volume size distributions from four AERONET 119 sites and past studies. The lognormal parameters are volume fraction (Vj, %), volume median radius (rj, Jm) and geometric standard deviation (Lj) for j-th size mode.

Table 4.2 Past studies of aerosol impact on PAR and photosynthesis in cloud- 136 free condition

LIST OF FIGURES

Figure 1.1 Linkages between mineral dust and the carbon (C), energy (E), and 2 water (W) cycles in the Earth system (modified from Shao et al. [2011]).

Figure 1.2 Flowchart of the WRF-Chem-DuMo modeling system. 11

Figure 2.1 Figure Monthly NDVI and snow cover by MODIS/Terra in 2001. 19

Figure 2.2 a) 16-category soil texture and b) 24-category land cover in Central 29 Asia. Boxes represent the dust source subregions described in Table 2.3.

Figure 2.3 POLDER-derived aeolian roughness length of Central Asia. 30

Figure 2.4 Monthly input parameters: a) vegetation fraction AV, b) total 35 roughness density N, and c) aeolian roughness z0.

Figure 2.5 The threshold friction velocity (u*t) at 12:00 of the 15th day in each 39 month: a) MB_Dry, and the difference from b) MB_Wet and c) Shao_Dry.

Figure 2.6 a) The soil moisture correction (fw) for MB_Dry and Shao_Dry 40 experiments, and b) the difference from MB_Wet. Gray color represents no soil moisture effect (fw=1.0).

Figure 2.7 a) The surface roughness corrections (fr) for MB_Dry experiment, 42 and the differences from b) MB_Wet and c) Shao_Dry. Gray color represents no surface roughness effect (fr=1.0).

Figure 2.8 The frequency of modeled 30-min a) wind speed u10≥6.5 m/s, and b) 43 wind directions at eight compass points (N, NE, E, SE, S, SW, W, NW).

Figure 2.9 The frequency of observed 3-hourly a) wind speed u10≥6.5 m/s, and 44 b) wind directions at eight compass points (N, NE, E, SE, S, SW, W, NW).

Figure 2.10 Frequency distribution of modeled 30-min u10 over the northern 45 steppe (red) and southern desert (blue) areas. Frequencies (in percentages) of u10 exceeding 6.5 and 10 m/s over the desert region are shown for each month.

Figure 2.11 a) Monthly dust emissions by a) MB_Dry, b) MB_Wet and c) their 46 difference.

Figure 2.12 Monthly dust emissions by Shao_Dry and b) the difference from 47

MB_Dry.

Figure 2.13 a) The mean estimate of dust emission, and b) dust emission by the 50 simplified scheme assuming a fixed threshold velocity of 6.5 m/s.

Figure 2.14 Monthly domain-integrated dust emission for a) MB_Dry, b) 52 MB_Wet, c) Shao_Dry, d) the mean estimate and d) Const_Uth. Curves represent the frequency of strong wind events (u10≥6.5 m/s) in the southern desert area.

Figure 2.15 Dust source types in Central Asia. 53

Figure 2.16 Monthly observations of dust aerosol in 2001: a) dust frequency 54 index, b) MODIS deep-blue AOD, c) SeaWiFS deep-blue AOD, and d) TOMS AAI.

Figure 2.17 Deep blue AOD by MODIS/Terra and SeaWiFS on April 6 and 9, 56 2001.

Figure 2.18 Frequencies of a) daily MODIS AOD>0.7, and b) daily TOMS 57 AAI>1.0.

Figure 2.19 Relationships between monthly domain-integrated dust emission and 58 dust observations. Correlation coefficients (r) are shown in parenthesis.

Figure 3.1 a) Annual cropland and pasture fractions from the LUH dataset in 76 selected years; b) domain-average annual land use fractions of cropland, pasture and their sum (cropland+pasture), and c) the percentage of land area with cropland plus pasture land use fractions larger than 70%.

Figure 3.2 Landsat images of surface water body changes. 77

Figure 3.3 Flowcharts of reconstruction of dominant land cover (DLC) for a) 79 cropland and b) pasture in the USGS 24-category classification.

Figure 3.4 The original and reconstructed dominant land cover maps for USGS 82 24-category classification. The categories are: 1–Urban and Built- up Land, 2–Urban and Built-up Land, 3–Irrigated Cropland and Pasture, 4–Mixed Dryland/Irrigated Cropland and Pasture, 5– Cropland/Grassland Mosaic, 6–Cropland/Woodland Mosaic, 7– Grassland, 8–Shrubland, 9–Mixed Shrubland/Grassland, 10– Savanna, 11–Deciduous Broadleaf Forest, 12–Deciduous Needleleaf Forest, 13–Evergreen Broadleaf, 14–Evergreen Needleleaf, 15–Mixed Forest, 16–Water Bodies, 17–Herbaceous Wetland, 18–Wooden Wetland, 19–Barren or Sparsely Vegetated, 20–Herbaceous Tundra, 21–Wooded Tundra, 22–Mixed Tundra, 23–Bare Ground Tundra, 24–Snow or Ice.

Figure 3.5 Monthly dust emissions for different dust source types between 83 1999 and 2012.

Figure 3.6 Monthly time series of a) dust frequency index, b) AOD, c) AE and 85 d) AAI.

Figure 3.7 Annual averages of a) dust emission (1999Þ2012) b) dust frequency 86 index (1999Þ2012) and c) MODIS AOD (2001Þ2012). For MarchÞOctober only.

Figure 3.8 Time series of ENSO index and monthly (standardized) anomalies 90 of dust frequency, MODIS AOD and AE. 3-month running means are shown for clarity.

Figure 3.9 Monthly rainfall during wet and dry seasons averaged over 95 1999Þ2012.

Figure 3.10 Monthly time series of ENSO and drought indices for: a) 1950Þ2012 97 and b) 1999Þ2012.

Figure 3.11 Linear square fit on annual dust emissions for the a) MB_Dry, b) 99 MB_Wet, and c) Shao_Dry experiments. The regression equations and standard errors of emission trends are shown.

Figure 3.12 Linear square fit on the mean estimate of annual dust emissions. 99 Also shown are the (normalized) monthly anomalies of the frequencies of modeled u10>6.5 m/s and u10>10 m/s. Three-month running means are shown for clarity.

Figure 3.13 Linear square fit on the annual dust frequency index. Also shown are 100 the (normalized) monthly anomalies of the frequencies of observed u10>6.5 m/s and u10>10 m/s. Three-month running means are shown for clarity.

Figure 3.14 Linear square fit on MODIS annual AOD. 100

Figure 3.15 a) Temporal and b) spatial patterns of the two leading EOFs of 101 monthly AOD anomaly during 2001Þ2012. Red (blue) areas are increasing (decreasing) tendency for a positive temporal coefficient on the corresponding PC time series. Also shown in PC 1 is the ENSO index leading by 8 months to obtain a maximum correlation (r=0.53) with PC 1 time series.

Figure 3.16 The land use intensity (LUI) in 2001. 105

Figure 3.17 The anthropogenic fraction of dust emission (Fad) as a function of 105 the land use intensity threshold (LUIth) in 2001.

Figure 3.18 Distribution of natural and anthropogenic dust source areas for 106

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different LUIth in 2001.

Figure 3.19 Annual anthropogenic fraction of dust emission (Fad) for two 107 different LUIth values.

Figure 4.1 Normalized dust volume size distributions for the dust cases 121 considered in this study. The associated size distribution parameters are given in Table 4.1. The Angstrom Exponent of the size distribution from four AERONET sites is shown in the parenthesis.

Figure 4.2 Land cover map in Central Asia based on IGBP classification in the 122 MODIS land cover CMG product.

Figure 4.3 Narrowband surface albedo of different land type during spring and 122 summer.

Figure 4.4 Constructed spectral albedos for grassland (red solid line) and 124 cropland (blue solid line) by fitting the USGS spectroscopy data (dash lines) to MODIS narrowband albedos (red circles as grassland and blue stars as cropland)

Figure 4.5 a) Extinction coefficient Kext for a dust loading of 250 Jg/m3, and 126 b) single scattering albedo (Z0) computed for Asian dust and OPAC bulk dust (H98) cases. Shaded areas highlight the PAR spectral region.

Figure 4.6 Computed AOD for dust loadings of 250, 500, and 750 Jg/m3. 127

Figure 4.7 a) Surface downwelling PAR as a function of AOD, b) same as a but 129 for the diffuse PAR component (PARdif ). Squares denote the cropland surface albedo and diamonds are for grassland.

Figure 4.8 a) Diffuse fraction (Fdif) of PAR, and b) the ratio of PAR to 131 downwelling SW radiation, PAR/SWdn, as a function of sun elevation angle. The AOD (0.5 µm) for dust cases are shown in parenthesis.

Figure 4.9 Surface radiative balance (SRB) as a function of AOD. Squares 133 denote the cropland surface albedo and diamonds are for grassland.

Figure 4.10 a) Light use efficiency (LUE) and b) carbon assimilation rate (Ac) 137 computed with the Choudhury [2000] LUE model (equation 4.12) as a function of sun elevation angle.

Figure 4.11 Carbon assimilation rate calculated with the LUE model of Roderick 138 et al. [2001] (equation 4.15) as a function of dust loading.

Figure 4.12 a) Light use efficiency (LUE), and b) carbon assimilation rate (Ac) 138 computed for clean and dust conditions using different LUE models.

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SUMMARY

Large uncertainties remain in estimating the anthropogenic fraction of mineral dust and the climatic impact of dust aerosol, partly due to a poor understanding of the dust source dynamics under the influence of climate variability and human-induced land- cover/land-use change (LCLUC). In the drylands of Central Asia, the recurring annual dust outbreaks pose a great threat to the region‘s air quality, water resources, agriculture and human health. So far, the dust dynamics and linkage to climate and LCLUC in

Central Asia have received little attention from the aerosol research community. This thesis comprises a comprehensive study of the dust dynamics in Central Asia focusing on

1) the seasonality of erosion threshold and dust emission affected by soil moisture, vegetation phenology and surface roughness, 2) the dust interannual variability and connections with large-scale climate variation (ENSO) through effects on the atmospheric circulation, precipitation, vegetation dynamics and drought, and 3) the impact of dust aerosol on surface radiative balance and photosynthetically active radiation, and possible effect on dryland ecosystems. We use a coupled dust model WRF-

Chem-DuMo to conduct dust emission simulations from 1999 to 2012. The model incorporates two physically-based (MB and Shao) and one simplified dust schemes, and provides two options for soil size distribution data (soil texture-based and dry-sieved).

We also use multi-year ground and satellite observations of dust frequency, dust loading, wind speed and vegetation index in the analysis.

Based on results for 2001, we find that the threshold friction velocity significantly varies in space and time in response to soil moisture seasonality, surface roughness heterogeneity and vegetation phenology. Spring is associated with a higher threshold velocity due to precipitation peak and growth of steppe vegetation and desert ephemerals,

xiv whereas summer has a low threshold friction due to drier soils and reduced vegetation.

As a result, although more frequent strong winds occur during spring, spring dust emission is less than summer by 46.8% (or 60.4 Mt) on average, because of higher erosion threshold. Compared to the dry-sieved soil size distribution data, the soil texture- based data significantly reduces the total dust emission, but causes a small change in the dust seasonality. The Shao scheme produces a lower (higher) threshold velocity over barren (vegetated) areas than the MB scheme, resulting in a shift of peak dust emission to the summer. Compared to the MB and Shao schemes, the simplified scheme using a fixed threshold velocity fails to capture the distribution of strong and weak sources, and produces 41.1% more spring dust, and 30.1% less summer dust. This suggests ignoring the dependence of the threshold friction velocity on the surface characteristics leads to biased spatial distribution and seasonality of dust emission.

During the 1999Þ2012 period, La Nina years are associated with less frequent strong winds, but cause drought conditions with drier soils and less vegetation. The MB and Shao schemes display opposing responses to the El Nino and La Nina conditions, due to the difference in the model sensitivity to atmospheric and land conditions. The Shao scheme produces much more dust in La Nina years under the dominant influence of drought, whereas the MB scheme produces slightly higher dust emission in El Nino years due to the compensating effects of more frequent strong winds. The resultant mean estimate from the MB and Shao schemes shows enhanced dust emission under La Nina conditions. Dust is more strongly correlated with the drought index (Palmer Drought

Severity Index) than with individual variables (i.e., precipitation, NDVI and surface bareness), suggesting that the drought index is a more integrative measure of the soil erodibility. We demonstrate a strong linkage between dust and ENSO: La Nina years produce drought condition and enhance the dust activity in Central Asia.

The frequency of strong winds is a dominant factor that affects the dust variability. A decline in the strong wind frequency during 1999Þ2012 results in a decreasing trend in the modeled dust emission at a rate of -7.81±2.73 Mt yr-1, as well as a decreasing trend in the ground observed dust frequency index (-0.14±0.04%). The dust loading (AOD) is strongly correlated with ENSO. As a result, the AOD displays an increasing trend due to more La Nina-like conditions from 1999 to 2012. Nonetheless, we find that after removing the ENSO effect, the AOD displays a decreasing tendency.

Therefore, the apparent opposing trends in the dust emission and dust frequency index versus dust loading (AOD) are caused by the difference in the percentage variance explained by ENSO. Thus, a longer AOD record is needed to establish a robust trend.

Using the agricultural fraction data and information of surface water body changes, we derive the land use intensity (LUI), which is used to distinguish the natural and anthropogenic dust source areas. We suggest that a threshold of 80% in the LUI can be used to separate the natural from anthropogenic sources. We estimate that 58.4% of dust emission is caused by human activity during the 1999Þ2012 period. For a conservative LUI threshold of 90%, we find 20.7% of dust emission can be attributed to human land use. Our estimates suggest human plays an important role in the region‘s dust budget through agriculture and water resource usage.

By conducting a suite of radiative transfer experiments, we find that the Asian dust radiative forcing efficiency ranges from -68.8 to -122.1 Wm-2AOD-1 in the surface radiation balance (0.3Þ20 µm), and from -67.7 to -82.2 Wm-2AOD-1 in the photosynthetically active radiation (PAR, 0.4Þ0.7 µm). The diffuse faction of PAR exhibits even larger variations among considered dust cases. Based on several light use efficiency models, we find that dust impact on the plant gross photosynthetic rate strongly depends on dust optical properties and crop types. The photosynthetic rate is

xvi enhanced under a low dust loading due to more diffuse light, but is decreased when the dust optical depth exceeds a certain optimal level. This critical value depends on the dust loading and particle size distribution, particularly the relative proportion of fine and coarse size modes. We recommend that Earth system models be used to comprehensively address the dust impact on the ecosystem functioning through perturbing the radiation environment and other factors.

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CHAPTER 1

INTRODUCTION

1.1 OVERVIEW

There is scientific consensus that human activities have been altering the atmospheric composition and are a key driver to global climate and environmental changes since pre-industrial times [IPCC, 2013]. It is a pressing priority to understand the

Earth system response to atmospheric aerosol input from diverse sources, which so far remain one of the largest uncertainties in climate studies [Forster et al., 2007; Boucher et al., 2013]. As the second most abundant component (in terms of mass) of atmospheric aerosols, mineral dust exerts tremendous impact on Earth‘s climate, weather and environment through various interaction and feedback processes [e.g., Ravi et al., 2011;

Shao et al., 2011].

Once emitted to the atmosphere, dust particles scatter and absorb solar and terrestrial radiation and therefore alter the radiative energy balance at the top of the atmosphere (TOA), as well as at the ground surface [Sokolik et al., 2001]. Globally, dust produces an annual mean TOA radiative forcing of -0.1±0.2 Wm-2, revealing a large uncertainty in the magnitude and sign of dust global forcing [Forster et al., 2007; Boucher et al., 2013]. At local and regional scales, the dust radiative forcing can be much stronger and heterogeneous in space, causing perturbations to the energy redistribution between the surface and atmosphere, with far-reaching consequences on the energy and water cycles in the climate system [Miller et al., 2004; Huang et al., 2009; Lau et al., 2009;

Mallot et al., 2009; Zhao et al., 2010]. In addition to the shortwave and longwave radiative forcing, dust aerosol may modify the total amount, direct/diffuse components and spectral composition of photosynthetically active radiation (PAR, 0.4Þ0.7 µm), and

1 consequently affects the biosphere-atmosphere exchange processes and terrestrial carbon cycle [e.g., Gu et al., 2003; Mercado et al., 2009; Jing et al., 2010]. Further, dust can alter cloud-precipitation processes by serving as cloud condensation or ice nuclei [Yin et al.,

2002; DeMott et al., 2003], accelerate snowmelt by depositing on snow and glaciers

[Painter et al., 2010; Qian et al., 2011], and supply iron to high-nutrient low-chlorophyll oceans through deposition and subsequent dissolution [Jickells et al., 2005; Mahowald et al., 2009]. Some of the key dust-climate interactions are illustrated in Figure 1.1. In addition to the climatic impact, dust poses serious threats to air quality and human health by increasing the ambient particulate matter level, spreading toxics and microorganisms, and deteriorating the visibility [Griffin et al., 2001; Wiggs et al., 2003; Yu et al., 2012].

An improved characterization of the dust emission processes and the dust source variability is required to achieve better understanding of diverse roles of dust aerosol in the Earth system.

Figure 1.1 Linkages between mineral dust and the carbon (C), energy (E), and water (W) cycles in the Earth system (modified from Shao et al. [2011]).

As the source area for mineral dust, global drylands encompassing hyper-arid, arid, semiarid and dry sub-humid areas cover about 41% of Earth‘s terrestrial surface and

2 are home to over two billion people, or one-third of world population [Mortimore et al.,

2009]. Under a warmer and drier climate, the area extent of drylands has been increasing in the last sixty years, and is likely to continue expanding in the twenty-first century

[Feng and Fu, 2013]. Occurrences of drought conditions are likely to become more severe due to global warming, thereby creating more exposed soils susceptible to wind erosion [Dai, 2011, 2012; Sheffield et al., 2012; Trenberth et al., 2014]. Meanwhile, world population is projected to grow from 6.1 billion in 2000 by 47% to 8.9 billion in

2050, with most of the increase taking place in the less developed dryland areas [United

Nations, 2004]. Under the pressure of population growth, global drylands have undergone various forms of land-cover/land-use change (LCLUC) driven by policy, legislation, institution and development interventions, with 10Þ20% drylands affected by varying degrees of land degradation or desertification [Millennium Ecosystem Assessment, 2005;

Reynolds et al., 2007]. Transformations of rangelands and grasslands into cultivated lands, inappropriate irrigation practices, soil salinization and erosion, overgrazing and deforestation are among the anthropogenic drivers that have caused significant changes to the areal extent and emission strength of the dust source areas [Lambin et al., 2001].

While LCLUC is emerging as a fundamental element of climate change science due to its impact on the surface reflectivity and land-atmosphere interactions [Turner et al., 2007;

Muhmood et al., 2010], the interconnections between climate, human-induced LCLUC and mineral dust have not attracted much attention until recent years [e.g., Gutman,

2007].

There is compelling evidence that dust activity is enhanced by human-induced

LCLUC in global source areas. Mineral dust has been considered in the recent IPCC reports as an important anthropogenic climate forcing agent, thereby overturning the conventional view of mineral dust as natural aerosol [Forster et al., 2007; Boucher et al.,

2013]. Mulitza et al. [2010] demonstrated a sharp increase in dust emission and deposition caused by the development of commercial agriculture in the Sahel region since the beginning of the nineteenth century. Neff et al. [2008] suggested that the drastic increase in aeolian dust deposition in the western United States was caused by rapid expansion of agricultural and grazing activities during the nineteenth century. Excessive cultivation and poor farming practices contributed to the Dust Bowl catastrophes over the

Great Plains in the 1930s and the Soviet virgin lands in the 1960s [Goudie and

Middleton, 1992]. Over-irrigation and resultant drying of the Aral Sea created an active dust source with an increasing trend of dust storm frequency in the past few decades

[Indoitu et al., 2012].

The importance of human activities to global dust budget promoted growing efforts to incorporate land use effects in coupled dust-climate models and to quantify the anthropogenic proportion of total dust loading or emission [Tegen and Fung, 1995;

Mahowald and Luo, 2003; Tegen et al., 2004; Yoshioka et al., 2005; Ginoux et al., 2010,

2012]. According to Zender et al. [2004], anthropogenic dust can result from either direct land use disturbance or indirect modifications of climatic factors and land surfaces due to non-land use activities, such as greenhouse gas emissions. Some of these effects however are difficult to separate from natural processes. Although there is no clear definition of anthropogenic proportion of total dust, it is widely acknowledged that agriculture

(cultivation and grazing) and surface water body change are two most important sources of anthropogenic dust [Tegen and Fung, 1995; Mahowald and Luo, 2003; Tegen et al.,

2004; Yoshioka et al., 2005; Ginoux et al., 2010, 2012].

Model assessments of anthropogenic dust often rely on information of agriculture and ephemeral water bodies to identify the location of potential human-made source areas, such as the cultivation map of Matthews [1983] and the more recent agricultural

4 fraction datasets by Ramankutty and Foley [1999] and Klein Goldewijk [2001].

However, there are disagreements on how to treat anthropogenic versus natural sources in models. Considering that land use disturbances tend to produce more erodible materials,

Tegen and Fung [1995] used a higher emission factor for disturbed sources. Similarly,

Tegen et al. [2004] assigned a lower erosion threshold velocity for agricultural lands. In contrast, Ginoux et al. [2012] prescribed a higher threshold velocity (10 m/s) to agricultural lands than natural source areas (6 m/s), in recognition of the vegetation shielding effects over croplands and pastures. These methods are highly subjective, as they are used to tune the model response to the addition of anthropogenic sources, in order to reconcile the discrepancies between modeled dust loading and various observations. As a result, the inferred anthropogenic dust fraction strongly depends on the model and observation data used, and is subject to a large uncertainty. For instance,

Tegen and Fung [1995] forced the modeled dust aerosol optical depth (AOD) to match the observed AOD seasonality from Advanced Very High Resolution Radiometer

(AVHRR), and found that 20œ50% of the dust loading is caused by the disturbed sources.

Tegen et al. [2004] tuned the modeled dust emission to dust frequency ground measurements, based on which they found less than 10% of global dust is due to land use.

Yoshioka et al. [2005] found that adding 20œ25% dust from disturbed sources improved the model comparison with Total Ozone Mapping Spectrometer (TOMS) aerosol index over North Africa. There are a few important caveats in these studies. The modeled 3D dust concentration or optical properties are subject to many sources of uncertainties from model representations of dust emission, entrainment, transport and removal processes, as well as radiative transfer processes. The dependence of dust emission on natural and human-caused changes in surface characteristics is either not accounted for or is represented in a simplified form. Although tuning the strength of ”added‘ anthropogenic

5 sources improves the model performance against a specific observation dataset, such top- down approach does not produce robust estimates of the anthropogenic dust fraction.

Hence, it is desirable to adopt an alternative bottom-up approach by focusing on accurate simulations of dust emission processes, including the total amount and spatiotemporal distributions, from both natural and anthropogenic source areas.

Apart from land use, dust emission is governed by climate variations in atmospheric circulation, precipitation and soil moisture at multiple temporal scales. Dust storm is known to be a seasonal phenomenon at worldwide desert regions, where the source activity and dust transport is governed by seasonal atmospheric circulation pattern, precipitation and vegetation phenology. As world‘s largest dust source, North Africa is characterized by a winter peak from the southern Sahara and Sahel driven mainly by northeasterly harmattan winds, and a summer peak with strong contributions from the

Bodele Depression associated with a number of factors, such as low-level jets, African easterly waves, haboobs and dust devil [Engelstaedter and Washington, 2007; Knippertz et al., 2012]. East Asia dust season peaks in late winter and spring due to the frontal cyclones from Mongolia and northern China, while the dust activity weakens in summer due to weaker winds and increased vegetation cover [Zou and Zhai, 2004; Shao and

Dong, 2006]. It remains a challenging task for dust models to reproduce the observed annual dust cycle, because of insufficient representations of some key atmospheric processes, such as subgrid-scale wind variability and dry convection, and the controlling factors in dust emission processes, such as soil moisture and vegetation [Cakmur et al.,

2004; Shinoda et al., 2011; Klose and Shao, 2012; Knippertz et al., 2012; Heinold et al.,

2013]. Additional complexity comes from representing the seasonal change in soil erodibility in response to the crop cycle, irrigation, residual management, livestock burden, and grazing pattern [Webb and Strong, 2011].

In recent years, there is growing interest on the long-term variability and trend of dust aerosol. Horizontal visibility and synoptic weather records are the longest continuous dust-related measurements at worldwide meteorological ground stations, while multi-sensor aerosol observations from space now provide over thirty years data of aerosol amount and distribution, including from early-generation satellites (TOMS and

AVHRR) and more recent advanced instruments such as Sea-viewing Wide Field-of-view

Sensor (SeaWiFS), Moderate Resolution Imaging and Spectroradiometer (MODIS) and

Visible Infrared Imaging Radiometer Suite (VIIRS). Based on station records, Mahowald et al. [2007] and Shao et al. [2013] found decreasing trends of dust frequency over most desert areas since the 1980s, except for a positive trend in the Middle East. The decreasing dust trend in northern China is due to the weaker cyclonic activity, likely as a result of weakening Siberian High since the 1970s [Panagiotopoulos et al., 2005; Zhu et al., 2008]. Using 13-year (1997Þ2010) SeaWiFS AOD, Hsu et al. [2012] found a strong positive trend of dust emission and transport from Arabian Peninsula and a negative tendency in dust outflow from North Africa. Similar trends were also found in the

MODIS AOD [Zhang and Reid, 2010]. It is often difficult to establish robust dust trends from satellites, because of the relatively short data length and strong correlations between dust and interannual and interdecadal climate variability, such as El Nino/La Nina-

Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO). Complementing the dust-related observations, coupled dust-climate models have been used to generate climatology of dust emission, loading and deposition that allow more detailed analysis of physical linkages between dust and climate [e.g., Zhang et al., 2003; Hara et al., 2006;

Gong et al., 2006; Zhao et al., 2006; Chin et al., 2013; Ridley et al., 2014]. The spring dust emission in East Asia was found to be highly correlated with Asian polar vortex indices, which reflect the strength of cold air intrusion from high latitudes [Hara et al.,

2006; Gong et al., 2006]. Through teleconnections, ENSO modulates the long-range dust transport by affecting the westerly jet and East Asian winter monsoon. In particular, anomalously strong Asian polar front and more dust outflow are found in La Nina years

[Hara et al., 2006; Gong et al., 2006]. Ridley et al. [2014] find that the decline in dust outflow from North Africa is caused by a decreasing trend in the surface winds, which accounts for over 60% of the interannual variability of the dust AOD. They suggest the dust interannual variability does not depend on the vegetation cover change, which is likely due to a simple representation of vegetation in the dust scheme, or out-of-phase of vegetation change with surface winds. We argue that a process-level understanding of dust emission physics and explicit representation in dust models are required to characterize the linkages between dust, climate and LCLUC.

Mineral dust is produced primarily through disintegration of soil aggregates following creeping and saltation of coarse soil grains over the surface [Shao et al., 2008].

Dust emission depends on the meteorological conditions, particularly the difference between the surface friction (shear) velocity and the threshold friction velocity [Shao et al., 2008]. Several dust emission schemes have been developed and incorporated in weather and climate models to account for the dependence of the erosion threshold on surface characteristics (e.g., soil moisture, aeolian roughness) and the relationship between the size-resolved dust vertical flux and the saltation and sandblasting processes

[e.g., Raupach et al., 1993; Marticorena and Bergametti, 1995; Fecan et al., 1999; Shao et al., 1996]. Compared to the physically-based methods, simplified dust schemes mostly used in global models use fixed threshold velocity and compute the dust vertical flux as a power function of wind velocity (e.g., 10-m wind) [e.g., Tegen and Fung, 1995; Tegen and Miller, 1998; Uno et al., 2001]. While dust models are best constrained in simulations of dust loading thanks to the available satellite observations, dust emission

8 and (dry/wet) deposition are subject to great uncertainties. Depending on what model and meteorological data are used, estimates of global dust emission vary from 1000 to 4000

Mt (million tons) [Zender et al., 2004; Cakmur et al., 2006; Textor et al., 2006; Huneeus et al., 2011]. Textor et al. [2007] found that harmonization of dust emission sources had minor effects on the aerosol burden among seven global models, suggesting a compensation effect between dust emission and deposition. This implies that any improvements to the representation of dust emission processes can further lead to improvements on modeling the dust lifetime (e.g., transport, loading and deposition). It is therefore a top priority to develop and test physically-based dust emission parameterizations, and prepare the required soil and surface datasets at appropriate spatial scales. Several dust model inter-comparisons have been conducted to evaluate dust model performance, reconcile model differences, and pinpoint the potential need for improvements [Uno et al., 2006; Todd et al., 2008; Huneeus et al., 2011]. However, the differences in the model configuration, parameterization and input data among the participating models are too overwhelming to identify and rank the specific sources of model errors. In light of this problem, Darmenova et al. [2009] developed a coupled dust modeling system comprising several dust schemes and optimized input data for Asian dust sources, which is a powerful tool to quantify the dust emission uncertainties associated with meteorology and surface characteristics. Similar work is done by Kang et al. [2011] who conducted a comparison of three dust vertical flux parameterizations in the same host model (i.e. WRF). Both studies suggest that dust emission uncertainty is caused by insufficient representation of the physical processes and lack of soil and land data.

1.2 RESEARCH QUESTIONS AND THESIS STRUCTURE

The goal of this thesis is to examine the seasonal and interannual variability of dust aerosol, and the linkage between dust, climate and LCLUC. The study focuses on the drylands of Central Asia (37NÞ55N, 50EÞ80E) where the recurring annual dust events pose a great threat to the region‘s air quality, water resources, agriculture, and human health [Wake et al., 1994; Orlovsky and Orlovsky, 2002; Singer et al., 2003;

Lioubimtseva et al., 2005; Micklin, 2007; Lioubimtseva and Henebry, 2009]. To date, this region has received little attention from the dust research community. This thesis comprises a comprehensive study of the dust dynamics in Central Asia by integrating a coupled physically-based dust model and a wealth of dust-related observations from ground stations and satellite instruments. The thesis focuses on three research questions:

1) How do the erosion threshold and dust emission vary in response to seasonal changes in atmospheric and surface conditions? 2) How does dust change in the past decade under the influence of climate variability and human land use? 3) How does dust alter the surface radiative balance and PAR, and what is the possible effect on the ecosystems?

To address the above questions, we utilize a coupled dust model WRF-Chem-

DuMo, a radiative transfer code SBDART, and observations from meteorological ground stations and multiple satellite instruments. The WRF-Chem-DuMo model incorporates two physically-based (MB and Shao) and one simplified dust schemes, and provides two options for soil size distribution data (soil texture-based and dry-sieved) for individual dust source areas. Including multiple options of dust schemes and input data in one model framework enables us to systematically quantify the sensitivity and uncertainty of the model parameterizations and parameters. The WRF-Chem-DuMo model components are depicted in Figure 1.2. Development of WRF-Chem-DuMo is an ongoing project in Prof.

Irina Sokolik‘s research group at Georgia Tech, with several contributions from this

10 thesis. A novel approach is developed to use the in-situ dry-sieved soil size distribution measurements from Chinese deserts and to remap them to Central Asia dust source regions based on their similarities in land cover, soil texture and surface roughness characteristics. To account for the effect of human land use, we modify the model to reflect annual and decadal changes in cropland and pasture distributions and water bodies by incorporating a global agriculture (cropland and pasture) dataset and geo-referenced maps and images of surface water bodies. Further, multi-year monthly NDVI product

(normalized difference vegetation index) from the MODIS instrument are optimized and incorporated into the model as a critical input to represent the effects of seasonal and interannual vegetation dynamics on the soil exposure, erodible surface fraction, surface roughness and dust emission.

Figure 1.2 Flowchart of the WRF-Chem-DuMo modeling system.

In Chapter 2, we examine the seasonality of dust activity focusing on the threshold friction velocity and dust emission under the effects of soil moisture, surface roughness heterogeneity and vegetation phenology. Model simulation is performed for

11 the dust season (MarchÞOctober) of 2001. Three model experiments are conducted with optimized model configurations and different combinations of dust schemes and soil size distribution data. We compare the simulated threshold friction velocity and dust emission from multiple experiments to pinpoint the differences in the model sensitivity to the atmospheric and land conditions. A mean estimate of dust emission is obtained to bracket the uncertainty regarding selections of dust scheme and soil size distribution data. The mean estimate is compared against a simplified dust scheme, and dust observations from ground stations and satellites, in order to evaluate whether it is necessary to use physically-based but computationally expensive methodology in simulating the dust seasonality in dryland areas.

In Chapter 3, we extend the analysis of dust dynamics to the interannual scale and examine the linkage between dust and large-scale climate variations, focusing on ENSO through effects on precipitation, wind speed, vegetation and drought. We further evaluate the dust trend in the past decade (1999Þ2012) and estimate the anthropogenic proportion of total dust emission. This chapter first presents a methodology on incorporating agriculture (cropland and pasture) and surface water body changes into the WRF-Chem-

DuMo model by reconstructing the land cover, soil texture, and related surface characteristics affected by land use. Model simulations are conducted for the period of

MarchÞOctober 1999Þ2012 following the same model configuration and experiment as

Chapter 2. The modeled dust emissions are compared against the ground observation of dust frequency index, and satellite observations of AOD, Angstrom Exponent (AE) and absorbing aerosol index from a suite of instruments (MODIS/Terra, MODIS/Aqua,

SeaWiFS, TOMS and OMI). We compute the differences in the annual, wet-season, and dry-season dust emission during El Nino and La Nina years, which are related to changes in precipitation, strong wind frequency, areal extent of barren surfaces, and drought

12 conditions. We then derive the annual and seasonal trends of dust aerosol by applying least square fit to the annual dust emission, dust frequency index and AOD. Finally, we estimate the anthropogenic proportion of total dust emission by using the land use intensity to separate the natural and anthropogenic source areas.

In Chapter 4, we examine the dust radiative impact on the surface radiative balance and PAR, and explore the possible effects for dryland ecosystems. The dust optical characteristics are computed using recent data on dust size-resolved mineralogical composition. We obtain several dust size distributions from the literature and AERONET sites to compute the spectral optical properties from the UV to the longwave spectrum.

Remote sensing observations of dust vertical distributions and spectral surface albedo are used to constrain the radiative transfer experiments. MODIS narrowband albedos at seven channels are expanded to the entire shortwave spectrum by using a spectroscopy dataset of vegetation. We then perform radiative transfer model experiments to compute shortwave, visible and longwave fluxes under clean and dusty conditions. From the radiative fluxes, we derive the dust radiative forcing and forcing efficiency. Further, we examine the changes in the total amount and direct/diffuse partitioning of PAR for different cases of dust optical models, and explore the possible effects on the light use efficiency and photosynthetic rate of several vegetation types.

Chapter 5 concludes this thesis with the main findings, and makes recommendations for future research towards an improved characterization of the linkages between dust, climate and LCLUC over dryland areas.

CHAPTER 2

SEASONAL DYNAMICS OF THRESHOLD FRICTION VELOCITY

AND DUST EMISSION

2.1 INTRODUCTION

Dust emission from global drylands involves complex and non-linear aeolian processes that depend on the meteorological conditions (e.g., wind) and land surface state and properties. Dust particles are released into the atmosphere mainly through saltation bombardment (or sandblasting) and aggregate disintegration [Shao, 2008]. To initialize the saltation process, the wind shear stress exerted on soil aggregates needs to exceed the resistance from gravity and inter-particle cohesion. In dust emission schemes, the wind shear is represented by friction velocity (u*), while the resistant force is expressed in terms of threshold friction velocity (u*t), which is the minimum u* required to initialize particle motion. The u*t describes the soil susceptibility to aeolian erosion, and may vary in space and time due to natural and human-induced variability in soil and land characteristics.

Drylands are composed of diverse landscapes including arid and semiarid deserts, temperate grasslands, shrublands, savannas and agricultural lands. These regions experience seasonal wind erosions regulated by seasonal dynamics in atmospheric circulation, soil moisture and vegetation, as well as by human land use [Shinoda et al.,

2011]. For instance, dust outbreaks in East Asia occur most frequently during spring, due to strong winds, dry soils and low vegetation cover, whereas dust activity is inhibited by growing vegetation during summer [Zou and Zhai, 2004; Kimura et al., 2009]. Despite less frequent strong winds in the 2000s than 1990s, dust frequency increased over the grasslands and cultivated lands in East Asia, suggesting that a decrease in u*t due to

14 weakened vegetation protection may have increased the dust activity [Kurosaki et al.,

2011]. In contrast to natural vegetation, agricultural lands undergo seasonal changes in soil erodibility, in response to crop cycle and pasture management [Webb and Strong,

2011]. Land use disturbance can reduce the u*t of agricultural soils, creating intensive anthropogenic dust sources [Tegen and Fung, 1995; Neff et al., 2008; Cook et al., 2009].

Wind tunnel experiments have identified various factors that may affect u*t, including inter-particle cohesion, soil crust, soil moisture, and non-erodible surface roughness elements [Chen et al., 1996; Belnap and Gillette, 1998; Cornelis and Gabriels,

2003; Ravi et al., 2006]. These factors have been incorporated in dust schemes to account for the dependence of u*t on land characteristics, with much focus on the effects of soil moisture and surface roughness [e.g., Raupach et al., 1993; Marticorena and Bergametti,

1995; Fecan et al., 1999; Shao et al., 1996]. However, representation of the effects of dynamic soil moisture and vegetation in dust models faces various challenges. In fact, many models assign a fixed u*t for individual dust sources [Tegen and Fung, 1995; Tegen and Miller, 1998; Uno et al., 2001; Liu and Westphal, 2001; Takemura et al., 2009; Zhao et al., 2010]. Such simplified treatments of u*t may lead to modeling biases in the spatial distribution and seasonality of dust emission, especially under low or moderate wind speeds, when the dust flux becomes highly sensitive to the surface characteristics

[Darmenova et al., 2009].

In climate models, soil moisture is a prognostic variable calculated at several subsurface vertical layers by land surface schemes, and is highly dependent on the model initialization and parameterizations of soil-vegetation processes [Koster et al., 2009]. The simulated soil moisture may not be suitable for direct use in the u*t parameterizations, which are derived from wind tunnel experiments. For instance, Grini et al. [2005] found that their ECMWF-modeled soil moisture was too high to describe the soil water

15 variations required by the Fecan et al. [1999] scheme. In addition, it is a difficult task for current land schemes to capture the sub-seasonal and seasonal variability of soil moisture over dryland regions. Koster et al. [2009] showed large disagreements among several state-of-the-art land surface schemes in simulations of seasonal soil moisture over arid and semiarid areas.

Further, there is a lack of data on the surface aerodynamic properties of dust source areas. There have been attempts to estimate the surface roughness length from satellite observations of surface bi-directional reflectance (BDR) [Marticorena et al.,

2004] and radar backscattering [Prigent et al., 2005; Marticorena et al., 2006]. Due to atmospheric contaminations, the surface roughness is derived from composite scenes for a certain time period, and therefore represents a quasi-mean state of the observed surface.

Marticorena et al. [2004] derived a surface roughness map using cloud- and dust-free

BDR measurements from the ADEOS (Advanced Earth Observing Satellite) POLDER-1

(POLarization and Directionality of the Earth's Reflectances) instrument between

November 1996 and June 1997. This dataset was used as a static roughness map in modeling dust emission from Chinese and Saharan deserts [Laurent et al., 2006, 2008].

To represent the seasonal dynamics of surface roughness due to vegetation phenology,

Pierre et al. [2012] applied a vegetation model to estimate the time-varying vegetation height and resultant surface roughness. They found that the increase in u*t due to vegetation and soil moisture dynamics reduced the total dust emission by 24% during

2004œ2007 in the Sahel.

In addition to u*t, dust emission depends strongly on the wind speed, particularly the frequency of strong winds exceeding the erosion thresholds [Kurosaki and Mikami,

2003; Hara et al., 2006]. Strong dust outbreaks are often associated with seasonal vigorous weather systems that provide favorable wind conditions, such as the cold frontal

16 systems during East Asia winter monsoon [Shao et al., 2008]. As pointed out by

Darmenova et al. [2009], there are a few options of the wind speed parameter in coupled dust-climate models, including 10-meter wind (u10), friction velocity from surface layer schemes, and friction velocity recalculated from u10 or first model layer wind using external datasets of surface roughness. When selecting which wind speed to use, it requires that the wind speed parameter be consistent with computation of the threshold velocity.

The semiarid drylands of Central Asia comprise several types of deserts (e.g., hilly, sandy, loess, saline, etc.) with varying geomorphology and lithology of parent soils

[Lioubimtseva, 2002]. The entire region can be broadly divided into the northern steppe and southern desert areas, both subject to seasonal aeolian erosion affected by atmospheric circulation, snow cover, vegetation phenology and agriculture. The dust season typically lasts from early spring to late autumn, and reaches peak during summer months [Indoitu et al., 2012]. Figure 2.1 shows the monthly NDVI (Normalized

Difference Vegetation Index) and snow cover observed by the MODIS (Moderate

Resolution Imaging Spectroradiometer) instrument onboard Terra satellite. The northern steppe area is mostly covered by severe snow/frost from November to March, and is protected by vegetation from April to October. The southern desert is snow-free during winter, and has a low NDVI (below 0.15) during most of the year. Driven by precipitation and temperature changes, a temperate grassland belt (46°Nœ48°N) between the steppe and desert areas experiences a seasonal cycle of spring greening and summer dry-out. Agricultural fields are mostly located near the water bodies and at the alluvial deposits of southern mountain ranges, which show a high NDVI during summer under the effect of irrigation.

In light of the interplay of atmospheric and land conditions in controlling the dust

17 seasonality, goal of this chapter is to examine the seasonality of dust activity in Central

Asia, focusing on two research questions: 1) How does the threshold friction velocity

(u*t) change in space and time, under the effects of soil moisture, surface roughness heterogeneity and vegetation phenology? 2) How do dust emissions from major source areas vary in response to the seasonality of u*t and surface winds? To address these questions, we use a coupled dust modeling system, WRF-Chem-DuMo, to compute the u*t and vertical dust fluxes from March 1 to October 31, 2001. The WRF-Chem-DuMo model incorporates two physically-based and one simplified dust schemes, and provides two options for soil size distribution data (soil texture-based and dry-sieved) for individual dust source areas. Therefore, the WRF-Chem-DuMo model allows us to examine model uncertainty and sensitivity associated with the model parameters and input data. As an essential input to the dust scheme, the top-layer soil moisture is provided by the Noah land scheme with improved surface exchange coefficient parameterization that may benefit the soil moisture simulation over drylands [Chen and

Zhang, 2009]. Monthly surface roughness length is derived from POLDER-derived static roughness and MODIS monthly NDVI to account for the effects of geomorphological heterogeneity and vegetation phenology.

Figure 2.1 Monthly NDVI and snow cover by MODIS/Terra in 2001.

2.2 DATA AND METHODOLOGY

2.2.1 Model Configurations

The dust modeling system WRF-Chem-DuMo, a modified version of the community WRF-Chem model (version 3.4.1), incorporates two physically-based dust schemes of Marticorena and Bergametti [1995] and Shao et al. [1996] (hereafter as MB and Shao schemes, respectively), and one simplified scheme similar to the one used in

Tegen and Fung [1995]. The physically-based schemes explicitly account for the effects of soil moisture and surface roughness on the u*t, and size-dependent sandblasting efficiency in computing dust vertical fluxes, whereas the simplified scheme uses a fixed threshold velocity that is independent of the soil and land characteristics.

The model domain covers the five former Soviet republics (Kazakhstan,

Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan), extending from the Aral-Caspian

Depression in the west to the Kuldzhuktau and Karatau mountains in the east, and from the Kopet Dagh mountain range in the south to the Kazakh Steppe in the north. We use

NCAR/NCEP reanalysis data to provide the initial and boundary conditions to the model.

The selected model physics options are Noah land surface scheme, MYJ planetary boundary layer (PBL) scheme, Janic Eta surface layer scheme, Thompson microphysics scheme, and Kain-Fritsch cumulus scheme. The model has 42 vertical levels with the 11 levels below 1 km, which has been shown to improve simulations of near-surface winds

[Todd et al., 2008; Shin et al., 2012]. Todd et al. [2008] noted that the PBL configuration, in particular an optimized number of vertical levels in the surface layer, is critical for accurate simulations of low-level wind fields. Model simulations are performed for

March 1œOctober 31, 2001 at a time step of 30 seconds. The grid size (spatial resolution) is set to 10 km. Such a fine resolution is necessary in order to accommodate the complex topography of the region, and to capture the presence of fine-scale dust source areas, such as the Aral Sea bed and patchy croplands. Model outputs are saved every 30 minutes.

2.2.2 Data

Various ground-based and satellite observations are used to derive the key model parameters or to evaluate model simulations of wind fields and dust emissions, which are summarized in Table 2.1. All the data are obtained for the time period of March 1œ

October 31, 2001.

To represent the seasonal changes in vegetation fraction and surface roughness, we obtain the MODIS/Terra NDVI monthly composite level-3 product (MOD13C2), which is available on a 0.05-degree geographic Climate Modeling Grid (CMG), and is archived at the Land Processes Distributed Active Archive Center

(https://lpdaac.usgs.gov).

To evaluate modeled dust emissions on a monthly basis, we use the deep-blue

Aerosol Optical Depth at 550 nm (AOD) from MODIS/Terra and SeaWiFS (Sea-viewing

Wide Field-of-view Sensor), and Absorbing Aerosol Index (AAI) from TOMS/Earth-

Probe (Total Ozone Mapping Spectrometer). MODIS AOD monthly mean is computed by remapping best-quality pixels from the collection-5 level-2 granules onto a

0.25°þ0.25° grid, and averaging the daily AOD over a month. The MODIS level-2 granules are obtained from the Level-1 and Atmosphere Archive and Distribution System

(http://ladsweb.nascom.nasa.gov). SeaWiFS AOD monthly mean is computed from the version-004 level-3 0.5° daily global gridded product

(http://doi.org/10.5067/MEASURES/SWDB/DATA301). Monthly mean AAI is computed from the TOMS version-8 1°þ1.25° daily global gridded product, which is archived at Goddard Earth Sciences Data and Information Services Center

(http://disc.sci.gsfc.nasa.gov/data-holdings/).

Table 2.1 List of data used in Chapter 2. Parameter Data Product Attributes NDVI MODIS/Terra vegetation indices CMG product Monthly, 0.05°þ0.05° (MOD13C2) Wind MIDAS Land and Marine Surface Stations 3- or 6-hourly, Data ground-based Present MIDAS Land and Marine Surface Stations 3- or 6-hourly, Weather Data ground-based AOD MODIS/Terra deep-blue aerosol product 5-min granules, (MOD04 level-2 collection-5) 10þ10 km AOD SeaWiFS deep-blue aerosol product (level-3 Daily, 0.5°þ0.5° v004) AAI TOMS/Earth-Probe aerosol index (version-8) Daily, 1°þ1.25°

In addition, we obtain ground-based wind and dust weather observations from the

Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations data. The dataset is archived at the British Atmospheric Data Center (BADC)

(http://badc.nerc.ac.uk/data/ukmo-midas/). Two variables are used: the 3- or 6-hourly wind (u10) speed and direction, and present weather code (PW). We avoid using the horizontal visibility due to the compounding effects of industrial haze and fog in the study area. Table 2.2 lists the dust-related PW codes according to the visibility level, which can be further divided into severe (PW=33Þ35), moderate (PW=30Þ32 or 98) and weak dust events (PW=06Þ09). O‘Loingsigh et al. [2014] derived a dust storm index by applying to these dust events weighting factors derived from relationships between the dust concentration and visibility. Here we define a dust frequency index (DFI) as:

5 LSD MD 05.0 LWD DFI ,TStn (2.1) PW SD MD WD 5 LSD MD 05.0 LWD ,TStn ,TStn ,TStn where SD, MD and WD represent the number of severe, moderate and weak dust observations, respectively. The denominator in equation (2.1) is the total number of PW records adjusted by the weighted dust observations, so that the DFI falls in the range of

0Þ1.0. The DFI can be calculated for an individual station or a number of stations during a time period (e.g., a month). The definition of DFI takes into account the difference in the intensity of reported dust events, and thus is more accurate in capturing the emission strength than the conventional dust frequency derived from dusty day count.

2.2.3 Computation of Threshold Friction Velocity

Parameterizations of threshold friction velocity (u*t) in the MB and Shao schemes follow the concept of adding a series of correction terms to the threshold friction velocity over an idealized dry and smooth surface (u*ts):

u*t D u*ts D L fw L fr (2.2) where D is the soil particle diameter. Here fw and fr are the correction terms for soil moisture and non-erodible surface roughness elements, respectively. Both fw and fr are

22 larger than 1. The Shao scheme produces a slightly higher u*ts than the MB scheme, for instance, by 0.03 m/s or 13% for a diameter of 100 Jm. Additional differences in the u*t between the two schemes can be caused by the fw and fr parameterizations.

Table 2.2 Descriptions of dustœrelated synoptic weather observations. PW Description 06 Widespread dust in suspension in the air, or —dust in suspension“ 07 Dust or sand raised by wind at or near the station, or —blowing dust“ 08 Well-developed dust or sand whirls at or near the station 09 Duststorm or sandstorm within sight at the time of observation 30 Slight or moderate duststorm or sandstorm - has decreased during the preceding hour 31 Slight or moderate duststorm or sandstorm - no appreciable change during the preceding hour 32 Slight or moderate duststorm or sandstorm - has begun or has increased during the preceding hour 33 Severe duststorm or sandstorm - has decreased during the preceding hour 34 Severe duststorm or sandstorm - no appreciable change during the preceding hour 35 Severe duststorm or sandstorm - has begun or increased during the preceding hour 98 Thunderstorm combined with dust/sandstorm at time of observation

Both the MB and Shao schemes use the approach of Fecan et al. [1999] to account for an increase in the u*t due to soil moisture. Fecan et al. [1999] assumes that the soil moisture effect is attributed to an increase in the inter-particle capillary force, whereas the effect of water adsorption films is assumed to be negligible for all soil texture types. They further assume that the capillary force starts to enhance the u*t after the soil moisture reaches the maximum water amount held by adsorptive forces, defined as the residual soil moisture (wr). The soil moisture correction is therefore expressed as:

w w 68.0 w w w 1 21.1 r , r f w (2.3) ,1 otherwise

2 where wr is a function of soil clay content: wr = 0.0014þ(%clay) + 0.17þ(%clay). The gravimetric soil moisture for the 0œ2 cm soil layer (w) is calculated by multiplying Noah-

23 simulated 0œ10 cm soil moisture by a factor of 0.8. This approximation is used to account for the topsoil moisture during wet season. The dry season soil moisture is found to be below wr and therefore has no effects on u*t.

The effect of surface roughness on the u*t depends on the partition of wind shear stress between the erodible fraction of the surface and the non-erodible roughness elements. In the MB scheme, the original drag partition term of Marticorena and

Bergametti [1995] was modified by MacKinnon et al. [2004] to account for partially vegetated surfaces comprising porous canopies of grasslands and shrublands:

1 lnz z f z 1 0 0s (2.4) r 0 . cm z .80 1 ln70 12255 0s where z0s is the aeolian roughness of an idealized smooth surface, and is often calculated as 1/30 of the coarse-mode mass median diameter (MMD) of the multi-modal log-normal soil size distribution. The aeolian roughness length z0 is an integrative measure of the non-vegetation (e.g., gravels, pebbles) and vegetation roughness elements [Marticorena and Bergametti, 1995; Menut et al., 2013].

In contrast to the MB scheme, the Shao scheme uses a double drag partition method to account for the effects of non-vegetation and vegetation elements separately.

This method is based on the drag partition scheme developed by Raupach et al. [1993] for barren surfaces, and is extended to account for both bare (B) and vegetated (V) parts of the land surface:

2 2 f 2 1 9 m 2 1 . m 2 1 9 m B 1 . m B (2.5) r V V V V VV B B A B B A 1 V 1 V where the L, m and c parameters describe the characteristics of non-vegetation (B) and vegetation (V) roughness elements. These parameters depend strongly on the surface and vegetation characteristics, and are difficult to obtain due to lack of measurements at the

24 appropriate scales of climate models. Here we use the values recommended by

Darmenova et al. [2009] for use in regional dust models: LV=1.45; mV=0.16; cV=202;

LB=1.0; mB=0.5; cB=90. The roughness density, also called lateral cover, of non-erodible elements over the bare (NB) and vegetated (NV) surfaces depend on the number density and geometrical dimensions (i.e., shape, height, width) of the obstacles lying on surfaces

[Marticorena et al., 2006]. Here NB is assumed to be time-invariant, and is prescribed as a function of land cover. NV is estimated from the vegetation fraction (AV), based on the empirical method of Shao et al. [1996]:

V 0.35ln1 AV (2.6)

The z0 and AV required by the MB and Shao schemes, respectively, are determined using

MODIS NDVI data (refer to section 2.3.1).

2.2.4 Computation of Dust Fluxes

Darmenova et al. [2009] provided detailed descriptions and comparisons of the parameterizations of the dust horizontal (or saltation) and vertical dust fluxes in the MB and Shao schemes. Briefly, the MB scheme uses the White [1979] formulation for the horizontal flux, G:

8 u u2 dG D E a u3 1 *t 1 *t dS D * 2 rel (2.7) g u* u* where the erodible fraction E is calculated as the fraction of vegetation- and snow-free land area within a grid cell. u* is the friction velocity, da is the air density, g is the gravitational acceleration. dSsel(D) is the relative surface covered by particles of diameter

D, and is computed from the soil size distribution. The dust vertical flux is calculated from G as a function of a size-dependent sandblasting efficiency, which depends on the balance of the kinetic energy of saltators and the binding energy of soil aggregates

[Alfaro and Gomes, 2001].

In the Shao scheme, the horizontal flux G is computed following the Owen [1964] transport-limited saltation approach:

8 u 2 dG D E a u3 1 *t p D dD * 2 (2.8) g u* where p(D) is the soil size distribution. The dust vertical flux is computed from G using a sandblasting mass efficiency as a function of the sizes of saltating soil aggregates and suspended dust particles [Shao et al., 1996]. Darmenova et al. [2009] showed that the MB and Shao schemes produce similar size-resolved and total horizontal fluxes over dry smooth surfaces, because of similarities in the u*ts and dG(D) parameterizations. Their differences are enlarged significantly over rough surfaces, due to the different surface roughness corrections.

The friction velocity (u*) is computed from the modeled u10 assuming neutral atmospheric stability, so that wind follows a logarithmic profile:

u u 10 (2.9) * z log1000 cm 0 where e is the von Karman constant. The neutral-stability assumption has minor effects on the u*, especially during strong dust events [Darmenova et al., 2009]. From equation

(2.9), surface roughness may affect dust emission in two opposing ways: it consumes a proportion of wind momentum and increases u*t, while it also modifies the boundary- layer wind profile causing an increase in the surface drag, or u*.

In general, there are two different approaches for representing the soil size distribution in dust emission schemes. Many global and regional models use the readily available soil texture data (sand/silt/clay) as a proxy for soil sizes [Zender et al., 2003;

Zakey et al., 2006; Heinold et al., 2007; Pérez et al., 2011; Mokhtari et al., 2012]. Figure

2a shows the soil texture map in Central Asia based on the USDA textural classification.

We identify seven dominant soil texture classes in Central Asia: sand, sandy loam, loam, sandy clay loam, clay loam, clay and bedrock. Each soil texture is associated with a tri- modal log-normal size distribution, z0s and clay content, which are given in Table 2.3

(also see Table 1 of Zakey et al. [2006]). We add the texture class 'bedrock' to the original data of Zakey et al. [2006], and assign it the size distribution data of 'sand' texture, such that the northern part of the Karakum Desert (called Trans-Unguz Karakum) is treated as the sandy desert in the model.

Table 2.3 The soil texture types in Central Asia and associated soil mass size distribution parameters, including tri-modal log-normal parameters (mass fraction n , mass median diameter MMD [Jm], geometric standard deviation L), smooth aeolian roughness length z0s, and clay content.

Soil Texture Coarse Mode Fine Mode 1 Fine Mode 2 z0s Clay (10-4 cm) (%) n MMD L n MMD L n MMD L Sand 0.9 1000 1.6 0.1 100 1.7 - - - 3.3 3 Sandy Loam 0.6 520 1.6 0.3 100 1.7 0.1 10 1.8 17.3 10 Loam 0.35 520 1.6 0.5 75 1.7 0.15 2.5 1.8 17.3 18 Sandy Clay 0.30 210 1.7 0.5 75 1.7 0.2 2.5 1.8 7.0 27 Loam Clay Loam 0.2 125 1.7 0.5 50 1.7 0.3 1 1.8 4.2 34 Clay 0.5 100 1.8 - - - 0.5 0.5 1.8 3.3 58 Bedrock 0.9 1000 1.6 0.1 100 1.7 - - - 3.3 3

Because soil texture is usually obtained using wet sedimentation methods, and may not reflect the natural state of soil aggregation, dry-sieved soil size distributions are recommended for use in dust emission schemes [Laurent et al., 2008]. Mei et al. [2004] conducted dry-sieving size measurements using soil samples collected from major

Chinese deserts. A fitting procedure was applied to derive the parameters of the soil size distribution of each desert type characterized by a bimodal (one coarse mode and one fine mode) log-normal size distribution [Laurent et al., 2006].

To our knowledge, there are no direct measurements of dry-sieved soil size distribution for Central Asia. To overcome this problem, we conduct a comparative analysis of the Central Asian deserts versus Chinese deserts, focusing on the similarity in the land and soil characteristics. Through the comparison, we attempt to remap the dry- sieved soil size distributions of Chinese deserts to Central Asian dust source areas. The rationale behind this method is that the soil textural composition is a good characterization of the emission potential of the soil, and can be used to map dry-sieved soil size measurements from a limited number of soil samples to the soil texture types of dust source areas. Other information including land cover and surface roughness is used to provide additional constraints during the remapping process.

Based on the dry-sieved soil size distributions reported in Laurent et al. [2006], the Chinese deserts can be broadly divided into two groups: sandy deserts located in topographic depressions, such as the Taklamakan Desert, and the vast Gobi Desert comprising temperate grasslands, shrublands, and smaller deserts such as the Badain

Jaran, Mu Us and Tengger. Sandy deserts are associated with a large MMD and small mass fraction in the coarse mode, whereas the Gobi Desert has a large coarse-mode mass fraction. The soil size difference between the two groups is reflected in their land cover and soil texture. The sandy deserts are dominated by 'barren or sparsely vegetated' in land cover and 'sand' in soil texture, whereas the Gobi Desert is dominated by 'grassland' and

'shrubland' in land cover, and vary in soil texture: 'sandy loam', 'loam', 'sandy clay loam', etc. The two groups also differ in surface roughness: POLDER-derived z0 is less than

0.001 cm over the sandy deserts, and ranges from 0.01 cm to 0.5 cm over the Gobi Desert

[Laurent et al., 2005].

Figure 2.2 a) 16-category soil texture and b) 24-category land cover in Central Asia. Boxes represent the dust source subregions described in Table 2.3.

To compare with Chinese deserts, we divide the Central Asia into nine dust source subregions, shown in Figure 2.2. Each subregion has a dominant land cover and soil texture, determined by their distinctive geomorphology and lithology of parent soils, which are described in Lioubimtseva [2002] and summarized in Table 2.4. Subregion I comprises the gravelly and hilly deserts of Ustyurt Plateau and Mangyshlak Peninsula.

Subregion II comprises the Kara-Bogaz-Gol bay and Caspian coasts, which lie below the mean sea level and are covered by saline deserts (or solonchaks) and drifting sand dunes.

Subregion III covers the Karakum sandy desert. Subregion IV covers the Kyzylkum and

Muyunkum sandy deserts. The sandy deserts are of aeolian-alluvial origins. Subregion V comprises the loess deserts formed by the alluvial fan deposits and clayey sediments of mountain ranges. Large parts of the loess deserts are transformed into rainfed and irrigated croplands, as shown in Figure 2.2b. Subregion VI comprises the dried seabed of

Aral Sea, named Aralkum. Aralkum is covered by solonchaks, sands, takyr soils and alluvial deposits, and is a major source of white salty dust [Orlovsky and Orlovsky,

2002]. Based on Landsat images, we modify the land/water mask in WRF-Chem-DuMo to reflect the areal extent of Aral Sea in 2001. Subregion VII covers the stony and hilly

Betpak-Dala Desert located at the boundary between the northern steppe and southern desert areas. Subregion VIII comprises the temperate grasslands of the Kazakh Steppe.

Subregion IX comprises the rainfed cropland belt (wheat, barley, etc.) to the north of

Kazakhstan, and the rainfed and irrigated croplands (cotton, rice, etc.) located in the Amu

Darya and Syr Darya river valleys and mountain alluvial plains. Due to their heterogeneous geomorphology and lithology, the dust source subregions differ greatly in the aeolian roughness. Figure 2.3 shows that, POLDER-derived z0 ranges from 0.05 to

1.0 cm in Subregion I, V, VII and IX, and from 0.001 to 0.01 cm over Subregion III and

IV.

Figure 2.3 POLDER-derived aeolian roughness length of Central Asia.

Table 2.4 Soil and land characteristics of dust source subregions in Central Asia. Subregion Dust Sources Land Cover Soil Texture Description I Ustyurt Plateau, Shrubland (8), Barren or Clay loam (9) Gravelly and Mangyshlak sparsely vegetated (19) hilly desert Peninsula plateau II Caspian coasts, Barren or sparsely Clay (12), Saline deserts Kara-Bogaz-Gol vegetated (19) Sandy loam (3) and drifting bay sand dunes III Karakum Desert Shrubland (8), Barren or Sand (1), Sand dune sparsely vegetated (19) Bedrock (15) ridges and chains IV Kyzylkum Shrubland (8) Sand (1), Clay Sand dune Desert, loam (9) ridges and Muyunkum chains Desert V Loess deserts Shrubland (8), Loam (6), Alluvial Grassland (7), Barren Sandy loam (3) deposits of or sparsely vegetated mountain ranges (19) VI Aralkum Barren or sparsely Sandy loam (3) Saline desert, vegetated (19) alluvial deposits, takyr soils VII Betpak-Dala Shrubland (8) Loam (6) Stony and hilly Desert desert VIII Steppe Grassland (7) Loam (6), Temperate Sandy clay grassland and loam (7), Clay shrubland loam (9) biome IX Cropland Dryland cropland/ Clay (12), Rainfed and pasture (2), Irrigated Loam (6), irrigated cropland/ pasture (3) Sandy loam (3) croplands

Based on similarities in land cover, soil texture, and aeolian roughness, we remap the dry-sieved soil size distributions of Chinese deserts to the dust source subregions in

Central Asia, which are summarized in Table 2.4. Similar to the Gobi Desert, Subregion I and VII are associated with 'shrubland' land cover, loamy soil texture, and large surface roughness. We therefore assign the soil size distribution of Gobi Desert to Subregion I and VII. The sandy deserts in Subregion III and IV are assigned with the soil size

31 distribution of Taklamakan Desert. For the saline deserts in Subregion II and VI, we use the dry-sieved soil size measurements by Argaman et al. [2006] based on soil samples collected from the exposed Aral Sea bottom. The loess deserts in Subregion V are assigned with the soil size distribution of the alluvial deposits at Hexi Corridor. The steppe area in Subregion VIII is assigned with the single-mode soil size distribution of

Horqin Desert, considering that they were both used primarily as grazing lands and have undergone serious desertification due to overgrazing and land reclamation in recent decades [Su et al., 2005]. The rainfed and irrigated croplands in Subregion IX are assigned with the soil size distribution of sandy deserts, in order to account for the effects of human land use, which can produce large amounts of easily erodible fine materials from the disturbed soils.

Table 2.5 Dry-sieved soil mass size distribution for the dust source subregions in Central Asia, including bi-modal log-normal parameters (mass fraction n, mass median diameter MMD [Jm], geometric standard deviation L), smooth aeolian roughness length (z0s), and clay content.

Subregion Coarse Mode Fine Mode z0s Clay (10-4 cm) (%) n MMD L n MMD L I 0.58 457 1.74 0.42 86 1.38 15.2 15.8 II 0.28 271 1.37 0.72 14 1.17 9.03 13.4 III 0.03 442 1.42 0.97 84 1.34 2.8 3.0 IV 0.03 442 1.42 0.97 84 1.34 2.8 3.0 V 0.6 386 1.59 0.4 97 1.26 12.9 11.5 VI 0.28 271 1.37 0.72 14 1.17 9.03 13.4 VII 0.58 457 1.74 0.42 86 1.38 15.2 15.8 VIII 1.0 315 1.29 - - - 10.5 15.6 IX 0.03 442 1.42 0.97 84 1.34 2.8 3.0

In contrast to physically-based schemes, simplified dust schemes often assume a fixed threshold velocity independent of the land and soil properties. Several examples of

32 the simplified schemes are listed in Table 2.6. A threshold u10 velocity (u10t) of 6.5 m/s is widely used in GCMs [Tegen and Fung, 1995; Uno et al., 2001; Liu and Westphal, 2001;

Takemura et al., 2009]. To demonstrate the importance of representing the u*t seasonality in dust models, we incorporate a simplified dust scheme into the model as follows:

CEu 2 u u , u u F 10 10 10t 10 10t ,0 otherwise (2.10) where F is the dust vertical flux. E is the erodible fraction defined as the fraction of vegetation- and snow-free land area within a grid cell. The C is a tuning parameter used to match modeled dust fields with satellite observations. Here, we compute C by tuning the domain-integrated dust emission to be the same as physically-based schemes.

Table 2.6 Examples of simplified dust schemes. Study Model Dust Vertical Flux Threshold Velocity 2 Uno et al. (2001) CFORS/RAMS C(u10-u10t)u10 u10t = 6.5 m/s 2 Liu and Westphal (2001) COAMPS fC(u10- u10t) u10 u10t = 6.3 m/s 4 Liu and Westphal (2001) COAMPS fCu* (u*>u*t) u*t = 0.6 m/s 2 Takemura et al. (2009) SPRINTARS fC(u10- u10t) u10 u10t = 6.5 m/s

Table 2.7 Model experiments of dust emission simulations. Experiment Dust Scheme Soil Size Distribution Data Wind

MB_Dry MB Dry-sieved u*

MB_Wet MB Soil texture-based u*

Shao_Dry Shao Dry-sieved u*

Const_Uth Simplified - u10

Four cases of model experiments are conducted to compute 30-min u*t and dust vertical fluxes, as summarized in Table 2.7. The MB_Dry experiment uses the MB dust scheme and dry-sieved soil size distribution data. The MB_Wet experiment uses the MB scheme and soil texture-based soil size distribution data. The Shao_Dry experiment uses the Shao scheme and dry-sieved soil size distribution data. The Const_Uth experiment

33 uses the simplified dust scheme. The experiment design enables us to address the dust emission uncertainty from model parameterizations and soil size distribution data, and evaluate the necessity and advantage of physically-based dust schemes in simulations of dust seasonality compared to simplified schemes.

2.3 RESULTS

2.3.1 Incorporation of Vegetation Phenology into Threshold Friction Velocity

To account for the effect of vegetation dynamics on u*t, we use MODIS NDVI to derive the key parameters in the surface roughness correction terms of the MB and Shao schemes: the aeolian roughness length (z0), vegetation fraction (AV), and the roughness density of non-erodible vegetation elements (NV). NDVI is a quantitative measure of the amount and state of photosynthetic vegetation, and has been used as proxies for various land and vegetation parameters [Glen et al., 2008]. By using the same MODIS NDVI data, we are able to represent the effect of vegetation dynamics on u*t in the MB and

Shao schemes in a consistent manner.

To incorporate vegetation dynamics into the Shao scheme, we first calculate the

AV using the linear equation from Gutman and Ignatov [1998]:

NDVI t NDVI A t S (2.11) V N V N V D IV D I S where NDVI(t) is the observed time-varying NDVI value; NDVIV and NDVIS represent the NDVI values for dense vegetation and bare soil, respectively. We use NDVIV=0.93 and NDVIS=0.06 suggested by Kimura and Shinoda [2010], based on linear fitting of observed NDVI and AV in the Mongolian and Chinese rangelands. Using equation (2.11), we calculate the monthly AV, which is used to calculate NV based on equation (2.6), and applied to the surface roughness correction for u*t in the Shao scheme.

Figure 2.4 Monthly input parameters: a) vegetation fraction AV, b) total roughness density N, and c) aeolian roughness z0.

Figure 2.4a shows the monthly AV values which exhibit strong variability driven by phenology and agricultural activity. In March, the steppe area is mostly covered by snow. As the steppe grassland greens up in April, AV increases rapidly and exceeds 30%.

At the temperate grassland belt of 46°Nœ48°N, AV exceeds 18% during spring, and drops below 15% during summer. Due to the peak spring rainfall, growth of ephemeral plants causes AV to exceed 12% over large parts of the Karakum and Kyzylkum sandy deserts

35 and the loess deserts. As the desert ephemerals dry up during summer, AV drops below

9%. Irrigated croplands display a crop cycle in the river valleys and loess deserts: green- up onset in April (AV<15%), green-peak in August (AV>30%), and defoliation in October

(AV<24%). The Ustyurt Plateau, southeast (SE) Caspian coasts, Aralkum and central

Karakum Desert are associated with low AV (<6%) during all months. In these barren regions, z0 is contributed mainly by non-vegetation elements, such as gravels and pebbles.

Table 2.8 Land cover types and the associated roughness density (NB) and geometric height (hB) of non-vegetation roughness elements, and monthly geometric height of vegetation elements (hV).

Land Cover Vegetation NB hB hV (cm) Type (cm) M A M J J A S O Dryland Wheat, 0.05 2.0 2 2 10 30 50 50 30 10 cropland/pasture (2) barley Irrigated Cotton, rice 0.05 2.0 2 5 15 30 50 50 40 15 cropland/pasture (3) Grassland (7) Temperate 0.04 2.0 5 10 20 15 12 12 10 8 grasslands Shrubland (8) Permanent 0.03 1.0 15 15 12 12 10 10 10 5 rangelands Barren or sparsely - 0.02 0.5 2 2 2 2 2 2 2 2 vegetated (19)

To incorporate the vegetation dynamics into the MB scheme, we use the empirical relationship between the aeolian roughness length (z0) and the geometric height (h) of roughness elements derived by Marticorena et al. [2006]:

31.1 log ,66.0 2 .0 045 z h 10 log10 0 (2.12) ,16.1 otherwise where N is the total roughness density contributed by non-vegetation and vegetation elements: N=NB + NV. h is the weighted geometric height of the non-vegetation (hB) and vegetation (hV) elements by their roughness densities: h=(hB‡NB+hV‡NV)/N. The z0-h

36 relationship was derived from in-situ measurements conducted in the south of Tunisia covering a range of dryland landscapes similar to those in Central Asia [Marticorena et al., 2006]. We prescribe values of hB and hV as a function of land cover type, given in in

Table 2.8. The hB is assumed to be constant, while hV is assigned monthly values to represent the vegetation growth-decay cycle and crop calendars in Central Asia.

To derive the dynamic z0 caused by vegetation phenology, we divide the study area into three NDVI regimes. Regions with NDVI≤0.1 (or AV≤4.6%) are considered as barren surfaces, where N and z0 are contributed only by non-vegetation elements and do not change with time. We assume that the POLDER-derived static z0 adequately represents sparsely vegetated and barren regions. Therefore, POLDER-derived static z0 is assigned to regions with NDVI≤0.1. Regions with NDVI>0.3 (or AV>27.6%) are assumed to be fully protected by vegetation [Kimura et al., 2009]. Over regions with

0.1

For these regions, we estimate the monthly NV from NDVI based on equation (2.6), and derive the monthly z0 based on equations (2.12). To this end, we merge the POLDER- derived z0 with MODIS monthly NDVI to derive the dynamic z0 for Central Asian dust source areas.

Figure 2.4b shows the monthly total roughness density (N) which varies in response to the seasonality in AV. To the best of our knowledge, there are no measurements of N in Central Asia. Marticorena et al. [2006] reported values of N in the range of 0.025Þ0.233 in their field measurements in south Tunisia. Our estimates range from 0.02 over barren surfaces to >0.12 over temperate grasslands. The contribution (or ratio) of vegetation to the total roughness density varies from 60Þ70% over the northern steppe area to 40Þ60% over the southern desert area. Figure 2.4c shows the derived monthly z0 values. The steppe area is mostly protected by snow or dense vegetation (i.e.,

NDVI>0.3). The z0 remains nearly constant in the barren (unvegetated) regions of

Ustyurt Plateau, Caspian coasts, Aralkum and central Karakum Desert. Due to geomorphological heterogeneity, the gravelly Ustyurt Plateau is associated with a higher z0 (0.2Þ1.0 cm) than the central Karakum Desert (0.001Þ0.01 cm). In contrast to barren regions, the presence of vegetation leads to substantial increase in z0 and a strong seasonality in z0. The z0 exceeds 5.0 cm in the steppe area and exceeds 2.0 over the grassland belt (46°Nœ48°N). In the sandy and loess deserts, z0 falls in the range of

1.0Þ2.0 cm during spring and 0.2Þ1.0 cm during summer, in response to the phenology of desert ephemerals. The derived z0 values are in good agreement with the model estimates by Pierre et al. [2012] for the Sahel vegetated areas (refer to their Figure 4).

2.3.2 Seasonality of Threshold Friction Velocity

Using the derived values of AV, N and z0 in Figure 2.4, we compute the u*t for model experiments MB_Dry, MB_Wet and Shao_Dry, and the associated fw and fr terms, which are equivalent to the ratios of u*t with and without soil moisture and surface roughness corrections, respectively. Here we report results for 1200 of the 15th day in each month. Figure 2.5a shows the u*t varies strongly in space and time, in response to the soil moisture and surface roughness dynamics. In the steppe area, u*t exceeds 1.0 m/s, which can effectively inhibit dust emission. At the grassland belt of 46°Nœ48°N, u*t exceeds 0.8 m/s during spring, and drops to 0.4œ0.6 m/s during summer. In the sandy and loess deserts, u*t exceeds 0.8 m/s during spring, and drops to 0.3œ0.5 m/s during summer.

For the barren deserts of Ustyurt Plateau, Caspian coasts and Aralkum, u*t exceeds 0.6 m/s during spring, and drops to 0.3œ0.5 m/s during summer. The lowest u*t (0.2œ0.3 m/s) occurs in the central Karakum Desert.

Figure 2.5 The threshold friction velocity (u*t) at 12:00 of the 15th day in each month: a) MB_Dry, and the difference from b) MB_Wet and c) Shao_Dry.

Figure 2.6 a) The soil moisture correction (fw) for MB_Dry and Shao_Dry experiments, and b) the difference from MB_Wet. Gray color represents no soil moisture effect (fw = 1.0).

Figure 2.6a and 2.7a show the monthly fw and fr for MB_Dry, respectively.

During spring, fw ranges from 1.5 to 2.0 in the southern desert area, implying that soil moisture causes u*t to be 1.5Þ2.0 times as large as dry soils. During summer, as the soil dries, fw drops to 1.0 over most desert areas, indicating no soil moisture effect on the u*t.

In comparison, surface roughness exerts a stronger enhancing effect on the u*t. At the grassland belt (46°Nœ48°N), growth of steppe vegetation causes u*t to be more than three times as large as smooth surfaces during AprilÞJune, and 2.0Þ2.5 times as large during

JulyÞOctober. For the sandy and loess deserts, growth of ephemeral plants causes u*t to be 2.5 times as large as smooth surfaces during spring. As the desert ephemerals dry up during summer, u*t is twice as large. For the barren deserts such as Ustyurt Plateau, u*t is

1.5Þ2.0 times as large as smooth surfaces. This indicates that vegetation is more effective

40 than non-vegetation elements in enhancing the u*t. Surface roughness has weak effect

(fr<1.5) on the u*t over the central Karakum Desert, where z0 is close to z0s.

Soil size distribution data affects the u*t through the clay content and z0s of individual dust source areas, which affect the fw and fr, respectively. Figure 2.6b shows that the soil texture-based soil size distribution data (MB_Wet) produces similar fw (i.e., within ±0.2) as the dry-sieved soil data (MB_Dry) over most desert areas during summer.

During spring and October, the MB_Wet produces no soil moisture effect, or fw = 1.0, over the Ustyurt Plateau and grassland belt (46°Nœ48°N), compared to fw~1.5Þ2.0 in

MB_Dry. MB_Wet also produces slightly larger fw over the Aralkum and SE Caspian coasts during spring. On the other hand, Figure 2.7b shows that the MB_Wet produces significantly larger fr (by more than 1.0) over the grassland belt, northern Karakum

Desert and Kopet-Dagh mountain alluvial fans, and slightly larger fr over the Ustyurt

Plateau during all months.

Consequently, Figure 2.5b reveals that the MB_Wet produces significantly higher u*t (by more than 0.2 m/s) over the grassland belt, northern Karakum Desert and Kopet-

Dagh mountain alluvial fans during all months. In addition, MB_Wet produces a higher u*t over Aralkum and SE Caspian coasts during spring, and over Ustyurt Plateau during summer. In contrast, the MB_Wet produces a lower u*t over the Ustyurt Plateau during spring.

Compared to the MB scheme, the Shao scheme produces a higher u*ts, and uses a double drag partition on non-vegetation and vegetation elements for surface roughness correction. Figure 2.7c shows that the Shao scheme (Shao_Dry) produces a larger fr than the MB scheme (MB_Dry) over vegetated areas, such as the grassland belt, and a lower fr than MB_Dry over barren areas, such as the Ustyurt Plateau. As a result, Figure 5c shows that the Shao scheme produces a higher u*t over vegetated areas, and a lower u*t over

41 barren areas than the MB scheme. These differences can affect the dust emission strength from vegetated versus barren surfaces, and therefore the seasonality of dust emission in response to vegetation phenology.

Figure 2.7 a) The surface roughness corrections (fr) for MB_Dry experiment, and the differences from b) MB_Wet and c) Shao_Dry. Gray color represents no surface roughness effect (fr=1.0).

2.3.3 Analysis of Surface Winds

This section focuses on the seasonality of surface wind speed and direction, which affects dust emission strength and aeolian transport pathways. Based on equation (2.8), the same u10 can produce different u*, depending on z0. Here we calculate the frequency of modeled 30-min u10>6.5 m/s, considered to be strong wind events. We also calculate the frequency distribution of wind directions of the strong wind events. Figure 2.8a reveals that strong winds tend to occur more frequently during spring than summer, and over the desert rather than the steppe area. More than 30% of modeled winds exceed 6.5 m/s over the Ustyurt Plateau, Caspian coasts, Aralkum, and loess deserts in March, April,

June and July. In contrast, August and October experiences drastic decreases in wind speed, especially over the sandy deserts. Figure 2.8b shows that the strong winds are mostly from east and northeast directions in the desert area.

Figure 2.8 The frequency of modeled 30-min a) wind speed u10≥6.5 m/s, and b) wind directions at eight compass points (N, NE, E, SE, S, SW, W, NW).

Figure 2.9 shows the frequency of strong winds and wind directions, based on 3- hourly ground-based wind observations. Figure 2.9a reveals that a high frequency

(>20%) of strong winds occurs over the Ustyurt Plateau, Caspian coasts and Aralkum during spring, and over the Aralkum and loess deserts during summer. The observations also reveal a decreasing trend of wind speed from spring to summer, in agreement with model results. However, the model produces a higher frequency of strong winds than observations over the high wind areas, probably due to the difference in time sampling.

Observed wind directions (Figure 2.9b) show similar patterns to model results. In particular, the observed prevailing winds are from the north, northeast and east directions in the desert area.

Figure 2.9 The frequency of observed 3-hourly a) wind speed u10≥6.5 m/s, and b) wind directions at eight compass points (N, NE, E, SE, S, SW, W, NW)

Past studies reported violent winds reaching up to 15Þ20 m/s during strong dust outbreaks in the Central Asian deserts [Orlovsky and Orlovsky, 2002]. To examine the occurrence of extreme wind events, we compute the wind speed frequency distributions over the steppe and desert regions. Figure 2.10 shows that strong winds persistently occur more frequently over the desert area than the steppe area for all wind speed bins. April is associated with the most frequent strong wind occurrences: 36% wind events exceed 6.5 m/s over the desert area, followed by June (29%), March (28%), July (27%), October

(25%), September (24%), May (22%), and August (18%). In addition, April reports 11% wind events exceeding 10 m/s, more than twice as frequent as in other months. Such rare but extreme winds carry large momentum and contribute significantly to total dust emission, because of the power law relationship between the dust flux and wind speed.

Figure 2.10 Frequency distribution of modeled 30-min u10 over the northern steppe (red) and southern desert (blue) areas. Frequencies (in percentages) of u10 exceeding 6.5 and 10 m/s over the desert region are shown for each month.

2.3.4 Seasonality of Dust Emission

Here we present results of monthly dust emissions based on the 30-min dust vertical fluxes. Figure 2.11a shows that for the MB_Dry experiment, the steppe area is generally dust-free throughout the year. The grassland belt (46°Nœ48°N) is mostly dust-

45 free during spring, but becomes an active source during summer after vegetation drying.

In the southern desert area, dust activity is inhibited by the high u*t values in March, except the Caspian coasts and loess deserts. From April, dust emission occurs over entire desert area. In particular, strong dust emissions are from the Ustyurt Plateau, Caspian coasts (including KBG), Aralkum, Betpak-Dala Desert and loess deserts, as a result of high winds over these regions. In contrast, the Karakum and Kyzylkum sandy deserts are weaker sources.

Figure 2.11 Monthly dust emissions by a) MB_Dry, b) MB_Wet and c) their difference.

Figure 2.11b shows the result for experiment MB_Wet. Apparently, using soil texture-based soil size distribution data leads to substantial decreases in dust emission.

This seems to be consistent with the MB_Wet producing a higher u*t than MB_Dry over some of these areas. However, the MB_Wet shows significant reductions in dust emission over regions with a lower u*t, such as the SE Caspian coasts during summer and southeast mountain alluvial plains. Therefore, the reduction in dust emission is mainly caused by the difference in the soil size distribution functions (see Table 2.2 and 2.4). As shown in Figure 2.11c, most significant reductions occur at the strong source areas identified in Figure 2.11a, suggesting that dust emission is most sensitive to the soil size distribution data under strong wind conditions.

Figure 2.12 a) Monthly dust emissions by Shao_Dry and b) the difference from MB_Dry.

Compared to the MB scheme (MB_Dry), Figure 2.12 shows that the Shao scheme

(Shao_Dry) tends to produce more dust from the barren areas, such as the Caspian coasts

47 and Aralkum, and less dust from vegetated areas, such as the loess deserts and Betpak-

Dala Desert. This is consistent with the Shao scheme producing a lower (higher) u*t over barren (vegetated) areas than the MB scheme. Therefore, the Shao scheme is more sensitive to changes in vegetation cover than the MB scheme.

To bracket the uncertainty associated with selections of soil size distribution data and dust schemes, we calculate the mean dust emission of the MB_Dry, MB_Wet and

Shao_Dry experiments, hereafter as Exp_Mean. Figure 2.13a reveals that the Ustyurt

Plateau, SE Caspian coasts, Aralkum, Betpak-Dala Desert and loess deserts are strong source areas. The Karakum and Kyzylkum sandy deserts are however weak sources. In comparison, Figure 2.13b shows the dust emission calculated by the simplified scheme

(Const_Uth). The domain-integrated dust mass of Const_Uth is tuned to be the same as

Exp_Mean. As expected from equation (2.10), the spatial distribution of dust emission from Const_Uth agrees with the frequency of u10≥6.5 m/s. Compared to Exp_Mean, the

Const_Uth produces more evenly distributed emissions from different source areas, as well as much more dust from the northern steppe area.

Table 2.9 Monthly dust emissions from different dust source types. Dust Area Efficiency Dust Mass (Mt) Source (103 (Kt/km2) Type km2) M A M J J A S O Total Hilly desert 290.1 0.35 0.68 11.5 5.46 25.7 26.6 7.33 19.7 4.54 101.3 Sandy 795.0 0.05 0.99 7.6 2.78 6.77 8.85 3.13 5.36 3.18 38.7 desert Loess 331.4 0.13 5.57 8.79 6.12 5.85 6.45 2.44 4.28 3.81 43.3 desert Saline 214.5 0.07 0.18 2.36 0.69 3.22 3.39 1.83 2.55 1.73 16.0 desert Steppe 1704.9 0.002 0 0.21 0.42 0.89 0.73 0.24 0.09 0.08 2.7 Cropland 776.1 0.07 2.92 4.07 8.33 12.7 8.63 4.19 5.33 7.42 53.6 Entire 4112 0.06 10.3 34.5 23.8 55.1 54.7 19.2 37.3 20.8 255.6 domain

To examine the seasonality of dust emission, we compute the monthly domain- integrated dust masses for the above model experiments, shown in Figure 2.14. The monthly dust masses exhibits good correlation with the frequency of strong wind events with u10≥6.5 m/s. Although more frequent strong winds occur during spring than summer, spring dust emission is lower because of the higher u*t caused by wetter soils and more vegetation. In the MB_Dry experiment, spring and summer dust account for

35.7% (130.2 Mt) and 44.7% (163.4 Mt) of the total emission (365.0 Mt), respectively.

August has the lowest emission of 21.4 Mt, or 5.9% due to a low frequency of strong winds. For MB_Wet, the domain-integrated dust mass (39.8 Mt) is much lower than

MB_Dry due to the use of soil texture-based soil size distribution data. Spring and summer dust account for 34.8% (or 13.8 Mt) and 45.7% (or 18.2 Mt) of total emission, respectively. This indicates that the dust seasonality remains nearly unchanged by the soil size distribution data. For Shao_Dry, spring and summer dust account for 17.0% (or 61.8

Mt) and 56.7% (or 205.2 Mt) of total emission (361.9 Mt), respectively. The shift of peak dust activity to summer is caused by the Shao scheme producing lower u*t and thus more emission from barren areas during the dry hot summer than the MB scheme. The mean dust emission (Exp_Mean) based on MB_Dry, MB_Wet and Shao_Dry produces 26.9%

(or 68.6 Mt) spring dust and 50.4% (or 129.0 Mt) summer dust. In contrast, the simplified scheme (Const_Uth) produces more dust during spring (37.9% or 96.8 Mt) than summer

(35.3% or 90.2 Mt). Specifically, compared to Exp_Mean, Const_Uth produces 41.1% more dust during spring, and 30.1% less dust during summer. This suggests that using a fixed threshold velocity, in other words, ignoring the seasonality of u*t leads to biased seasonal dust emission.

Figure 2.13 a) The mean estimate of dust emission, and b) dust emission by the simplified scheme assuming a fixed threshold velocity of 6.5 m/s.

To investigate the dust emission strength from different source areas, we group the dust source subregions into six source types. Hilly deserts include the Ustyurt Plateau

(Subregion I) and Betpak-Dala Desert (Subregion VII). Sandy deserts include the

Karakum, Kyzylkum and Muyunkum deserts (Subregion III and IV). Loess deserts include the alluvial plains of mountain ranges (Subregion V) which are not with cropland land cover types. Saline deserts include the Caspian coasts (Subregion II) and Aralkum

(Subregion VI). Steppe includes the grassland of Kazakh Steppe (Subregion VIII).

Cropland includes regions associated with land cover of rainfed or irrigated croplands.

The spatial distribution of dust source types is shown in Figure 2.15. Based on

Exp_Mean, we compute the monthly dust mass for each source type, shown in Table 2.9.

Loess desert most actively produces dust in April, while other source types are most

50 active in June or July. Hilly desert is the largest source, accounting for 39.7% (or 101.4

Mt) of the total domain-integrated dust mass (255.6 Mt), followed by cropland (21.0%), loess desert (16.9%), sandy desert (15.1%), saline deserts (6.2%) and steppe (2.7%). The strong emission of cropland source type is not necessarily caused by agricultural activities, because the dust source types are defined only based on the land cover and soil texture. By dividing the dust mass of each source type by its area, we calculate the area- averaged dust emission, or dust emission efficiency. Hilly desert is the most efficient source type (0.35 Kt/km2), followed by loess desert (0.13 Kt/km2), cropland (0.07

Kt/km2), saline desert (0.07 Kt/km2), sandy desert (0.05 Kt/km2), and steppe (0.002

Kt/km2).

Due to agriculture expansion and over-irrigation, the drying of Aral Sea has become a major environmental disaster in the past few decades [Micklin, 2007]. We estimate that the size of Aralkum is 42 900 km2 in 2001, compared to a full lake size of

67 500 km2 [Micklin, 2007]. Our estimate is in good agreement with estimates of 40 300 km2 for 1999 [Breckle et al. 2001], and 42 000 km2 for 2000 [Singer et al., 2003]. We compute the dust emission from Aralkum, shown in Table 2.10. Estimated dust emission from Aralkum differs greatly among the model experiments: 1.38 Mt (MB_Dry), 0.17 Mt

(MB_Wet), 36.3 Mt (Shao_Dry), 12.8 Mt (Exp_Mean) and 1.82 Mt (Const_Uth). The dust emission efficiency varies from 0.004 Kt/km2 to 0.85 Kt/km2. According to

Orlovsky and Orlovsky [2002], the annual dust emission from Aralkum was about 7.3 Mt during 1966œ1979. Considering that the Aral Sea has been decreasing in size in the past half century, more dust is expected to emit from Aralkum. The estimate estimate (12.8

Mt) is therefore in a reasonable range.

Figure 2.14 Monthly domain-integrated dust emission for a) MB_Dry, b) MB_Wet, c) Shao_Dry, d) the mean estimate and d) Const_Uth. Curves represent the frequency of strong wind events (u10≥6.5 m/s) in the southern desert area.

Figure 2.15 Dust source types in Central Asia.

Table 2.10 Monthly dust emissions from Aralkum. Experiment Efficiency Dust Mass (Mt) 2 (Kt/km ) M A M J J A S O Total MB_Dry 0.03 0 0.19 0.10 0.85 0.64 0.06 0.09 0.09 1.38 MB_Wet 0.004 0 0.01 0 0.07 0.06 0.01 0.02 0 0.17 Shao_Dry 0.85 0.04 5.82 1.75 7.57 6.93 4.45 5.21 4.56 36.3 Exp_Mean 0.30 0.01 2.01 0.62 2.83 2.54 1.51 1.77 1.55 12.8 Const_Uth 0.04 0.69 1.13 0.55 0.79 0.66 0.44 0.50 0.78 1.82

2.3.5 Comparison with Dust Observations

We use ground-based and satellite observations of atmospheric dust to compare with simulated seasonal dust emission. Figure 2.16a shows the monthly dust frequency index derived from station-based present weather observations on a monthly basis.

Frequent dust outbreaks occur over the Aral-Caspian Depression through the year, with highest dust frequency in April, June, August and September. The most affected stations are located at the Amu Darya (e.g., Cimbaj UZB at 41.7N 59.8E; Buzaubaj UZB at

41.8N 62.5E) and Syr Darya (e.g., Dzhusaly KAZ at 45.5N 64.1E; Kyzylorda KAZ at

44.9N 65.5E) river basins, and the Ustyurt Plateau (i.e., Akkuduk, KAZ at 42.9N, 54.1E).

The southern loess desert region experiences more frequent dust during summer than

53 spring. Our model generally produces strong dust emission at the surrounding areas of the stations affected by frequent dust storms.

Figure 2.16 Monthly observations of dust aerosol in 2001: a) dust frequency index, b) MODIS deep-blue AOD, c) SeaWiFS deep-blue AOD, and d) TOMS AAI.

Figure 2.16b shows the MODIS monthly mean AOD. High AOD values (>0.7) occur at the Ustyurt Plateau, SE Caspian coasts, and Aralkum from April to September.

The loess deserts are associated with a higher AOD during spring than summer. In contrast, the AOD is mostly lower than 0.5 over the Karakum and Kyzylkum sandy deserts. Apart from strong local emissions, the lingering high AOD over the Aral-Caspian

Depression may be also caused by the persistent north and northeast winds which carry dust to the area (Figure 2.8b and 2.9b).

Figure 2.16c shows that SeaWiFS captures the same regions with large AOD as

MODIS. However, SeaWiFS produces much lower AOD values, which is likely to be due to the SeaWiFS cloud screening process that may mistreat strong dust plumes as clouds (A. Sayer, personal communication, 2013). As shown in Figure 2.17, SeaWiFS reports 'missing data' over the regions where MODIS produces strong AOD values (>1.5) caused by heavy dust plumes over the Ustyurt Plateau and Aralkum during Apirl 2001.

Due to this caveat, SeaWiFS may fail to capture strong dust outbreaks. In addition,

SeaWiFS produces no AOD retrieval over the Aralkum, because of an outdated land/water mask used in the retrieval algorithm. As a result, an over-ocean algorithm is applied to the Aralkum, and produces no retrieval from the dried seabed (A. Sayer, personal communication, 2013).

Figure 2.16d shows the monthly mean AAI, which is often used to identify UV- absorbing aerosols, such as dust [Prospero et al., 2002]. The AAI falls in the range of

1.0œ1.5 over most desert areas. Higher AAI (1.5œ2.0) values occur near the Kara-Bogaz-

Gol and Aralkum regions, consistent with the findings of Prospero et al. [2002]. These

AAI values are lower than those typically found in other regions, such as the Taklamakan

Desert, where AAI exceeds 2.0. This may be due to the reduced sensitivity of TOMS to dust plumes over elevated areas [Mahowald and Dufresne, 2004]. Also, the rich salt

55 content in the dust from saline deserts may reduce the light absorption capability of dust aerosols [Singer et al., 2003].

Figure 2.17 Deep blue AOD by MODIS/Terra and SeaWiFS on April 6 and 9, 2001.

Past studies suggested that active dust source areas tend to be associated with frequent occurrences of high AOD or AAI values, based on which dust source locations have been derived from satellite aerosol observations [Prospero et al., 2002; Ginoux et al., 2012]. Draxler et al. [2010] developed an empirical dust emission scheme based on multi-year MODIS AOD observations, suggesting that the once-per-day AOD observations to certain extent reflect the emission strength of dust source areas. Indoitu et al. [2012] showed that the daily dust activity peaks between local time 9:00 and 13:00 in

Central Asia, which coincides with the MODIS/Terra overpass. Here we calculate the frequency of daily MODIS AOD>0.7, and daily TOMS AAI>1.0 in each month, shown in Figure 2.18. Over 80% of daily AOD exceed 0.7 at the Ustyurt Plateau, SE Caspian

56 coasts and Aralkum between April and September. In other words, the AOD is greater than 0.7 in at least 24 days out of each month in these regions. Also, more frequent

AOD>0.7 occur over the loess deserts during AprilÞJune than JulyÞSeptember. In contrast, less than 20% of AOD is greater than 0.7 over the Karakum and Kyzylkum sandy deserts. Figure 2.18b shows that the frequency of AAI>1.0 is similar to the AOD frequency. Over 80% of daily AAI exceed 1.0 at the Ustyurt Plateau, SE Caspian coasts and Aralkum between April and October. In the sandy and loess deserts, more frequent

AAI>1.0 occur during AprilÞJune than JulyÞSeptember.

Figure 2.18 Frequencies of a) daily MODIS AOD>0.7, and b) daily TOMS AAI>1.0.

The spatial distribution and seasonality of Exp_Mean dust emission (Figure

2.13a) generally agree with the AOD and AAI frequency statistics. Regions with frequent occurrences of daily AOD>0.7 and AAI>1.0 are associated with strong dust emissions, including the Ustyurt Plateau, Caspian coasts, Aralkum and loess deserts. The loess

57 deserts produce more dust during AprilÞJune than JulyÞSeptember, in agreement with more frequent AOD>0.7 and AAI>1.0 during AprilÞJune than JulyÞSeptember. On the other hand, regions with low frequencies of AOD>0.7 and AAI>1.0 produce weak dust emissions, such as the Karakum and Kyzylkum sandy deserts. Figure 2.19 shows the monthly domain-integrated dust emission is correlated with MODIS AOD, and the percentages of land area with more than 80% occurrences of AOD>0.7 and AAI>1.0.

The biased low SeaWiFS AOD and weak correlation with dust emission reflect the poor performance of SeaWiFS in detecting dust near source areas. The dust frequency index computed from all the stations is highest in April due to a large number of severe dust events. We find there are more present weather observations (i.e., larger sample) in April than other months, which may partly explain the high dust frequency.

Figure 2.19 Relationships between monthly domain-integrated dust emission and dust observations. Correlation coefficients (r) are shown in parenthesis.

2.4 CONCLUSIONS

This study examines the seasonality of threshold friction velocity (u*t) and dust emission of Central Asian drylands, focusing on the effects of soil moisture, surface roughness heterogeneity, vegetation dynamics and wind conditions. We utilize a dust

58 modeling system, WRF-Chem-DuMo, to compute the u*t and dust vertical fluxes during

March 1ÞOctober 31, 2001. The model incorporates two physically-based (MB and

Shao) and one simplified dust schemes, and provides two options of soil size distribution data (soil texture-based and dry-sieved). Dry-sieved soil size distribution data are obtained from Chinese deserts and remapped to Central Asian dust source areas, based on similarities in land cover, soil texture and surface roughness characteristics. POLDER- derived static roughness and MODIS monthly NDVI are used to determine the dynamic aeolian roughness caused by geomorphological heterogeneity and vegetation phenology.

Four model experiments are conducted: MB_Dry (MB scheme plus dry-sieved soil size distribution data), MB_Wet (MB scheme plus soil texture-based soil size distribution data), Shao_Dry (Shao scheme plus dry-sieved soil size distribution data) and Const_Uth

(simplified scheme). A mean estimate (Exp_Mean) is obtained by averaging the

MB_Dry, MB_Wet and Shao_Dry experiments. Ground and satellite observations of dust frequency and loading are used to compare with modeled seasonal dust emission. The main findings are:

1. The u*t significantly varies in space and time, in response to soil moisture variability, surface roughness heterogeneity and vegetation phenology. The peak spring rainfall results in high soil moisture and expedites the growth of steppe vegetation and desert ephemeral plants. Based on the MB_Dry experiment, the u*t exceeds 0.8 m/s in vegetated areas and exceeds 0.6 m/s in barren areas during spring (MAM). The u*t drops to 0.3œ0.5 m/s, as the soil dries and vegetation wilts during the dry hot summer (JJA).

The lowest u*t (0.2œ0.3 m/s) occurs in the central Karakum Desert with low soil moisture and surface roughness.

2. Dust emission is controlled by the seasonality in the u*t and surface winds.

Although more frequent strong winds occur during spring than summer, spring dust

59 emission is less because of the higher u*t. Based on the MB_Dry experiment, spring and summer dust account for 35.7% (130.2 Mt) and 44.7% (163.4 Mt) of the total emission

(365.0 Mt), respectively. The soil texture-based soil size distribution data leads to substantial reductions in total emission. However, there are small changes in dust seasonality: spring and summer dust account for 34.8% and 45.7% of total emission (39.8

Mt), respectively. Compared to the MB scheme, the Shao scheme produces more dust from barren areas, and less dust from vegetated areas. This is due to the Shao scheme producing a lower u*t over barren areas and higher u*t over vegetated areas than the MB scheme. This causes a shift of peak dust activity to summer (58.7% versus spring 14.5%).

The mean estimate (Exp_Mean) generates a total emission of 255.6 Mt, of which 26.9%

(or 68.6 Mt) from spring and 50.4% (or 129.0 Mt) from summer. In contrast to the MB and Shao schemes, the simplified scheme assuming a fixed threshold velocity generates more evenly distributed emission in space, and produces more dust during spring (37.9% or 96.8 Mt) than summer (35.3% or 90.2 Mt). Compared to Exp_Mean, the simplified scheme produces 41.1% more dust during spring, and 30.1% less dust during summer.

This demonstrates that ignoring the dependence of the threshold velocity on the dynamic surface characteristics leads to biased spatial distribution and seasonality of dust emission.

3. Based on the mean estimate of dust emission, the Ustyurt Plateau, Caspian coasts, Aralkum, Betpak-Dala Desert and loess deserts are strong dust sources, whereas the Karakum and Kyzylkum sandy deserts are weak sources. Different dust source types vary in the seasonality of dust emission. Sandy and loess deserts most actively produce dust in April, while the hilly and saline deserts, steppe and cropland are most active in

June or July. The spatial distribution and seasonality of modeled dust emission are in general agreement with ground-based dust frequency observations, and satellite

60 observations of aerosol optical depth (AOD) and absorbing aerosol index (AAI). In particular, the strong source areas are associated with frequent (over 80%) occurrences of daily AOD>0.7 and daily AAI>1.0. The weak source areas of Karakum and Kyzylkum deserts are associated with a low frequency of AOD>0.7 and AAI>1.0. The loess deserts produce more dust during AprilÞJune than JulyÞSeptember, in agreement with the observation of more frequent AOD>0.7 and AAI>1.0 during AprilÞJune than

JulyÞSeptember. The monthly domain-integrated dust emission are correlated with the domain-averaged MODIS AOD and percentage areas with more than 80% occurrences of

AOD>0.7 and AAI>1.0.

Because the seasonal dust activities in dryland areas are regulated by concomitant changes in the land and vegetation characteristics and atmospheric conditions, it is imperative for coupled dust-climate models to improve representations of the key controlling factors on the threshold friction (or wind) velocity in order to capture the seasonality of dust emission. This study demonstrates that accounting for the dependences of threshold velocity on soil moisture and surface roughness is crucial for an accurate simulation of seasonal dust emission. Yet there are a few important issues regarding the use of model parameters in coupled dust-climate models. Koster et al.

[2009] pointed out that soil moisture produced by land surface schemes is a highly model-specific variable, and suggested that the modeled soil moisture should be treated as a wetness index of the true moisture state. It is therefore dangerous to directly transfer the soil moisture predicted by a specific land model for other applications (such as dust simulation) without knowing the limitations of such predictions that may require necessary modifications. On the other hand, existent soil moisture corrections for the threshold velocity were developed based on wind tunnel experiments for specific soil types, which may require recalibrations for large scale applications. This can explain that

61 the ECMWF-modeled soil moisture was found to be too high to describe the soil water variations required by the Fecan et al. [1999] soil moisture correction method [Grini et al.

2005]. In the present study, we use a scale factor (0.8) to adjust the modeled soil moisture to approximate the superficial soil moisture at top 1Þ2 cm layer required by the MB and

Shao schemes. We find that the soil moisture has a significant effect on the u*t during wet season, but a small effect during dry season. This result is reasonable, because the soil is expected to become dry quickly by strong winds during dust events, which has also been observed in wind tunnel experiments [Cornelis and Gabriels, 2003]. In Central Asia, sporadic rainfall and high air temperature (and therefore high potential evapotranspiration) can cause extremely dry soil conditions during summer, which results in a low soil moisture and threshold velocity.

Further, surface roughness corrections for the threshold velocity require data of surface roughness length or roughness density at the pertinent spatial scale of aeolian processes (i.e., aeolian scale). Satellite observations are valuable for deriving high- resolution aeolian roughness to meet the needs of coupled dust-climate models

[Marticorena et al., 2004, 2006; Prigent et al., 2005]. Nonetheless, remote sensing retrievals are subject to atmospheric contaminations (such as by dust) at dust source areas, which may prevent continuous monitoring of the temporal variations in surface aerodynamic properties, especially for the semiarid areas. Pierre et al. [2012] applied for the first time a vegetation model to estimate the dynamic aeolian roughness due to vegetation phenology. In our study, we adopt a similar concept but using satellite observations of monthly vegetation greenness (i.e., NDVI) to derive the aeolian roughness. A limitation of this method is that NDVI is unable to detect the presence of non-photosynthetic vegetation (or vegetation with very low chlorophyll) and (dead) plant residuals. Once the plants enter dormancy under extreme conditions, the photosynthetic

62 activity ceases while they still protect the surface from wind erosion. Some observational studies suggest that plant residuals from the previous growing season increase the erosion threshold and reduce the dust activity [Zou and Zhai, 2004; Kurosaki et al., 2011]. A recent study demonstrates that including the effects of plant residuals in dust models improves simulations of the dust concentration [Kang et al., 2014]. This implies that a correction for non-photosynthetic vegetation (or vegetation residuals) may need to complement the NDVI-based approach for dust simulations of the transient period between vegetation growing seasons, although the importance of the effect for the dust budget remains to be investigated.

CHAPTER 3 INTERANNUAL VARIABILITY OF DUST AEROSOL AND LINKAGE TO CLIMATE AND LAND-COVER/LAND-USE CHANGE 3.1 INTRODUCTION

Mineral dust affects Earth‘s climate and environment by interacting with the energy, biogeochemical and hydrological cycles [Shao et al., 2011]. Large uncertainties remain in assessments of the climatic impact due to dust aerosol, partly due to poor understanding of the natural and anthropogenic dust sources and their interactions

[Forster et al., 2007; Boucher et al., 2013]. For instance, the sign and magnitude of direct radiative forcing (-0.1±0.2 Wm-2) due to anthropogenic dust are poorly constrained. To improve the estimates of dust climate impact, it is necessary to characterize the interannual and decadal variability of dust aerosol in response to climate change and to distinguish the roles of natural processes and human activities.

As the source area for dust aerosol, drylands encompassing hyper-arid, arid, semiarid and dry sub-humid areas cover about 41% of Earth‘s terrestrial surface and are home to more than two billion people [Mortimore et al., 2009]. Dust frequencies at major source areas are closely tied with multi-scale climate variability through changes in precipitation and atmospheric circulation. In particular, the dust activity is highly sensitive to the occurrence of drought events that may result in more exposed area and lower erosion threshold. Moulin and Chiapello [2004] found that dust emission in the

Sahel is highly correlated with previous-year drought conditions. During the

1970sÞ1980s, prolonged precipitation deficit in the Sahara-Sahel region increased the local dust emission, and dust transport to North Atlantic and Caribbean [Prospero and

Lamb, 2003]. The African drought was linked to warmer-than-normal sea surface

64 temperature (SST) in the equatorial Indian Ocean [Giannini et al., 2003; Dai et al.,

2011b]. Dust activity in East Asia, the second largest source after North Africa, is closely linked with cold frontal cyclones in late winter and spring [Shao and Dong, 2006]. As a result, the spring dust emission is highly correlated with the Asian polar vortex indices

[Hara et al., 2006; Gong et al., 2006]. Asian dust transport is also modulated by El Nino

Southern Oscillation (ENSO) teleconnections. La Nina events create an anomalously strong Asian polar front, leading to more dust outflow compared to El Nino conditions

[Hara et al., 2006; Gong et al., 2006]. Based on ground observations of visibility and synoptic weather, Mahowald et al. [2007] and Shao et al. [2013] found decreasing trends of dust frequency over most desert areas since the 1980s. The decreasing trend in North

Africa is accompanied by a positive anomaly of the Atlantic Multidecadal Oscillation

(AMO), which results in less rainfall and more dust in the Sahel region [Wang et al.,

2012]. The decreasing trend in northern China may result from the decreased cyclonic activity, likely due to the weakening of Siberian High [Panagiotopoulos et al., 2005; Zhu et al., 2008]. Similar trends of dust aerosol are also found in satellite aerosol observations near dust source and outflow regions [Zhang and Reid, 2010; Hsu et al. 2012]. Current- generation satellite instruments, such as MODIS, have improved sensor capability and onboard calibration compared to early-generation satellite sensors, such as AVHRR.

However, the short length (~10 years) of data record may prevent establishing robust dust trends under the influence of large-scale climate variations.

Under the pressure of population growth, global drylands have undergone various forms of land-cover/land-use change (LCLUC) driven by policy, legislation, institution and development interventions, with 10Þ20% drylands affected by land degradation

[Millennium Ecosystem Assessment, 2005; Reynolds et al., 2007]. There is compelling evidence that dust emission from drylands is increased by human-induced LCLUC. By

65 comparing the 1970s drought to past mega-droughts, Mulitza et al. [2010] concluded that the increase in African dust since the 19th century was greatly enhanced by the development of commercial agriculture in the Sahel region. Similarly, the extensive cultivation and poor farming practices are important causes for the Dust Bowl events in the US Midwest during the 1930s and in the Soviet virgin lands during the 1960s [Goudie and Middleton, 1992]. Therefore, dust emission from dryland areas, especially the populous semi-arid regions, are regulated by both natural climate variability and human land disturbance. In fact, the interplay between climate, LCLUC and dust is highly complex due to the land-atmosphere interaction and feedback processes [e.g., Yoshioka et al., 2007; Cook et al., 2009; Evan et al., 2012].

Yet, the relative importance of LCLUC and natural climate variability to dust production remains unclear. According to Zender et al. [2004], anthropogenic dust can result from either direct land use disturbance or indirect modifications of climate factors and land surfaces due to non-land use activities, such as greenhouse gas emissions.

Although there is no consensus on the definition of the anthropogenic proportion of total dust, it is widely acknowledged that agriculture (through cultivation and livestock grazing) and surface water body modifications are two most important sources of anthropogenic dust [Tegen and Fung, 1995; Mahowald and Luo, 2003; Tegen et al., 2004;

Yoshioka et al., 2005; Ginoux et al., 2010, 2012]. Past studies have used coupled dust- climate models along with satellite aerosol observations to estimate the anthropogenic fraction (Fad) of total dust. Table 3.1 shows that Fad falls in the range of 10œ60%. These estimates differ greatly in the atmospheric model, dust emission parameterization, wind and soil data, as well as aerosol observations to constrain the model. Agriculture and hydrology data are commonly used to locate the potential anthropogenic dust source areas, such as the cultivation map of Matthews [1983] and the more recent cropland and

66 pasture fraction datasets by Ramankutty and Foley [1999] and Klein Goldewijk [2001].

However, there is disagreement on how to treat anthropogenic versus natural sources.

Considering that land use disturbance tends to produce more erodible materials, Tegen and Fung [1995] prescribed a higher emission factor for disturbed soils. Similarly, Tegen et al. [2004] assigned a lower erosion threshold velocity for agricultural lands. On the contrary, Ginoux et al. [2012] assigned a higher threshold velocity to agricultural lands than natural source areas to account for the vegetation effects over croplands and pastures.

Further, past studies follow the strategy of empirically adding anthropogenic sources into the model to reconcile the discrepancy between modeled dust quantities and dust-related observations. For instance, Tegen and Fung [1995] forced the modeled dust optical depth to match the AOD seasonality from AVHRR, and found that the disturbed sources contributed 20œ50% to the total dust loading. Tegen et al. [2004] tuned the modeled dust emission to dust frequency records from worldwide meteorological stations, based on which they found less than 10% dust is anthropogenic. Yoshioka et al.

[2005] found that adding 20œ25% dust from disturbed sources improved the model comparison with the TOMS aerosol index over North Africa. There are a few important caveats in these model-based estimates. The modeled 3D dust fields are subject to many sources of uncertainties from parameterizations of dust emission, entrainment, transport and removal processes, as well as radiative transfer processes. Although the model comparison against certain observations is improved by tuning the strength of potential anthropogenic dust sources, the estimates of anthropogenic dust using this top-down approach are not robust and may be invalid, because of the intervolving model and data uncertainties. Hence, it is desirable to adopt an alternative bottom-up approach by

focusing on accurate simulations of dust emission from both the natural and

anthropogenic source areas.

Table 3.1 Estimates of the anthropogenic fraction (Fad) of dust burden or emission. Study Region Time Wind Land Use Data Dust Fad Observation Tegen and Global - ECMWF Matthews [1983] AVHRR AOD 20œ50% Fung [1995] TOGA U10

Mahowald Global 1880œ1889; NCEP Matthews [1983] - 14œ60% and Luo 1990œ1999 reanalysis [2003] Zhang et al. East 1960œ2002 NCEP Chinese desert - 14% [2003] Asia reanalysis maps Tegen et al. Global 1983œ1992 ECMWF Ramankutty and Dust storm <10% [2004] ERA U10 Foley [1999]; frequency Klein Goldewijk [2001] Yoshika et North 1984œ1990 NCEP Matthews [1983] TOMS aerosol 20œ25% al. [2005] Africa reanalysis index Ginoux et Global 2005 GFDL Klein MODIS Deep 25% al. [2012] HIRAM Goldewijk, Blue products U10 [2001]

Since the 1950s, the land use policy in the five Central Asian states (Kazakhstan,

Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan) of the former USSR has

undergone significant politic, economic and institutional changes, especially the

socioeconomic transformation from the Soviet collectivism to independent market

economies after the collapse of the USSR. Notable examples of large-scale land

disturbance include the cultivation of virgin lands and abandonment (1950sÞ1960s),

construction of massive irrigation schemes for cotton monoculture (1960s), Aral Sea

desiccation (1960sÞ2000s), and break-up of collective farming and pastoralism resulting

in reduced grazing pressure (1990sÞ2000s) [Glazovsky, 1995; Robinson et al., 2003;

Gintzburger et al., 2005; Lioubimtseva et al., 2005]. Meanwhile, ENSO and NAO

teleconnections significantly affect the region‘s precipitation and atmospheric circulation

[Small et al., 1999; Tippett et al., 2005; Syed et al., 2006, 2010; Mariotti, 2007]. In the

68 past century, there has been a general warming trend in Central Asia; however, precipitation change is more complex with large spatial differences in the aridity change

[Lioubimtseva et al., 2005; Lioubimtseva and Cole, 2006]. So far, there have been few studies on the effects of the climate and land use dynamics on the dust variability in

Central Asia. Based on visibility, Mahowald et al. [2007] and Indoitu et al. [2012] reported a decreasing trend of dust frequency in large parts of Central Asia. The underlying driving factors however remain uninvestigated.

The goal of this chapter is to conduct an integrated study of the dust interannual variability in Central Asia (37NÞ55N, 50EÞ80E) and to examine the connections with the

ENSO and NAO climate variability, wind, drought, vegetation and human land use.

Chapter 2 demonstrated the capability of the WRF-Chem-DuMo model in simulating the dust annual cycle. This chapter advances the model skill by incorporating the interannual and decadal variability of the characteristics of natural and anthropogenic dust sources.

While the model methodology developed in this chapter aims at multi-decadal dust simulations between the 1950s and 2010s, our analysis focuses on the available results for the time period of MarchÞOctober between 1999 and 2012. We use the coupled dust model and ground and satellite observations to address the following questions: 1) What are the linkages between dust and climate variability, wind speed, drought and vegetation dynamics? 2) How does dust change in the past decade? Is there a trend? and 3) How much dust is contributed by human land use?

3.2 DATA AND METHODOLOGY

Ground-based and satellite observations of dust aerosol, atmospheric and surface conditions from various platforms and sources are used in this chapter, as summarized in

Table 3.2.

3.2.1 LCLUC Data

To examine the human land use via agriculture and surface water body modifications, we obtain the global dataset of cropland and pasture fractions from the

Land Use Harmonization project (hereafter as LUH dataset, http://luh.umd.edu/ ) [Hurtt et al., 2006, 2011]. The annual cropland and pasture fractions are available for the time period 1700Þ2005 at 0.5ºþ0.5º resolutions. In addition, we obtain historical maps and satellite images of the major rivers and lakes of Central Asia for the period 1950sÞ2010s, including the contour maps of Kara-Bogaz-Gol bay and Aral Sea [Denisov, 2000;

Varushchenko et al., 2000], and Landsat images (http://glovis.usgs.gov/).

To account for the effect of interannual vegetation dynamics on dust emission, we obtain the MODIS/Terra NDVI monthly composite level-3 product (MOD13C2) for the period 2000Þ2012. The data are available via the Land Processes Distributed Active

Archive Center (https://lpdaac.usgs.gov/ ). The NDVI data are used to derive the roughness density and aeolian roughness (section 2.3.1) in calculating the threshold friction velocity. We create a monthly NDVI climatology by averaging the NDVI data across the 12-year period. This climatology is used as model input for the time prior to

2000 with modifications to reflect land cover changes at different decades.

3.2.2 Satellite Aerosol Products

Multi-year multi-sensor satellite aerosol products are used for analyzing the dust interannual variability. The data include level-2 collection-5 deep-blue AOD from

MODIS/Terra (2001Þ2007), MODIS/Aqua (2003Þ2012), the level-3 version-004 daily deep-blue AOD from SeaWiFS (1998Þ2010), and level-3 daily UV absorbing aerosol index (AAI) from TOMS/Earth-Probe (2001Þ2005) and OMI/Aura (2004Þ2012).

Table 3.2 List of data used in Chapter 3. Parameter Data Source Attributes Time Period LCLUC Data Cropland Hurtt et al. [2011] Annual, 0.5ºþ0.5º 1950Þ2005 and Pasture Fractions NDVI MODIS/Terra (MOD13C2) Monthly, 2000Þ2012 0.05°þ0.05° Ground Observations Present MIDAS ground observation 3- or 6-hourly 1983Þ2012 Weather Wind MIDAS ground observation 3- or 6-hourly 1983Þ2012 Satellite Aerosol Products AOD MODIS/Terra (MOD04) deep-blue 5-min granules, 2001Þ2007 product (level-2 collection 5) 10kmþ10km AOD MODIS/Aqua (MYD04) deep-blue 5-min granules, 2003Þ2012 product (level-2 collection 5) 10kmþ10km AOD SeaWiFS Deep Blue L3 Daily Daily, 0.5°þ0.5° 1998Þ2010 Aerosols AAI TOMS/Earth-Probe aerosol index Daily, 1°þ 1.25° 2001Þ2005 product (version-8) AAI OMI/Aura level-3 aerosol index Daily, 1.0°þ1.0° 2004Þ2012 product (OMTO3d) Climate Indices MEI ESRL, NOAA Monthly 1950Þ2012 NAO Climate Analysis Section, NCAR DJFM, monthly 1950Þ2012 Drought Indices PDSI Dai [2011a] Monthly, 1950Þ2012 2.5°þ2.5° PDSI Sheffield et al. [2012] Monthly, 1950Þ2008 1.0°þ1.0° DSI Mu et al. [2013] 8-day, 0.5°þ0.5° 2000Þ2011

The two MODIS instruments onboard Terra and Aqua provide nearly twice-per- day observations over desert surfaces using the deep-blue algorithm [Hsu et al., 2004,

2006]. The deep-blue AOD on MODIS/Terra is available only through 2007 in the current collection-5 product. The level 2 granules are obtained from the LAADS achieve

(http://ladsweb.nascom.nasa.gov/ ). We preprocess the level-2 granules into daily files consisting of the location (i.e., latitude and longitude) and AOD of best-quality pixels.

The daily files are then remapped onto a predefined latitude/longitude grid of 0.5°þ0.5°

71 resolution. Within each grid, the average value of enclosed pixels is computed as the daily mean AOD, and the number of pixels is recorded. By averaging the MODIS/Terra and MODIS/Aqua daily AOD for the overlapping time period (i.e., 2003Þ2007), we create a daily AOD product for 2001Þ2012. We then calculate the Angstrom Exponent

(AE) based on the MODIS AOD at 412 nm and 470 nm channels. AE describes the spectral dependence of AOD and is inversely related to aerosol particle size [Dubovik et al., 2002]. AE has been widely used to detect the presence of coarse-mode dust aerosol

[e.g., Ginoux et al., 2010, 2012]. Monthly mean AOD and AE are computed by averaging the available daily observations within a month. To ensure the monthly mean is derived from a sufficient number of daily observations, pixels with less than 30% (~9 days) observations are excluded from the calculations. Further, from the daily observations, we compute the frequency of daily AOD>0.7 in each month. As shown in Chapter 2, other than the monthly mean, the frequency of AOD>0.7 is a useful measure to detect the strong dust source areas.

The 13-year SeaWiFS AOD product is produced by applying the deep-blue algorithm to well-calibrated SeaWiFS radiance measurements [Sayer et al., 2012]. The data is available from the MEaSUREs Projects (http://disc.gsfc.nasa.gov/dust). We calculate the monthly AOD as the mean of the available daily observations within a month. Pixels with less than 30% observations are excluded.

The TOMS/Earth-Probe and OMI/Aura daily AAI products employs the same

TOMS version-8 algorithm. The level-3 TOMS data is available at 1°þ1.25°, while the

OMI data is available at 1°þ1° global grid. Both are obtained from Goddard Earth

Sciences Data and Information Services Center (GES DISC)

(http://disc.sci.gsfc.nasa.gov/ ). The monthly AAI is calculated by averaging the daily observations within a month. Pixels with less than 30% observations are excluded.

3.2.3 Ground Observations

Ground observations of wind (u10) and dust frequency are obtained from the Met

Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, which is achieved at the British Atmospheric Data Centre (BADC)

(http://badc.nerc.ac.uk/data/ukmo-midas/). The dataset contains 3- or 6-hourly surface synoptic observations in the variable of present weather code (PW), as summarized in

Table 2.2. Valid observations from about 114 stations are available from 1983 onward.

Based on equation (2.1), we compute the monthly dust frequency index using all stations in the five Central Asian countries. We also compute the frequency of strong (u10≥6.5 m/s) and extreme (u10≥10 m/s) wind events from the wind speed observations.

3.2.4 Climate Indices

The Multivariate ENSO Index (MEI) is an integrative measure of ENSO events by using six observed variables over the tropical Pacific [Wolter and Timlin, 1998]. The

MEI data is obtained from http://www.esrl.noaa.gov/psd/data/climateindices. Negative

MEI values represent the cold ENSO phase (La Nina), while positive MEI values represent the warm ENSO phase (El Nino). The principal component-based indices of

North Atlantic Oscillation (NAO) are obtained from the Climate Analysis Section, NCAR

(https://climatedataguide.ucar.edu/experts/hurrell-james). The NAO indices are the leading Emperical Orthogonal Function (EOF) of sea level pressure anomalies over the

Atlantic sector of 20°Þ80°N, 90°WÞ40°E [Hurrell, 1995]. Both the December-January-

February-March (DFJM) and monthly NAO indices are used in the analysis.

3.2.5 Drought Indices

We obtain three drought index datasets from three prominent investigators, including the Palmer Drought Severity Index (PDSI) by Dai [2011a] (hereafter as

Dai2011, http://www.cgd.ucar.edu/cas/catalog/climind/pdsi.html) and Sheffield et al.

[2012] (hereafter as Sheffield2012, http://hydrology.princeton.edu/data.pdsi.php), and the

Drought Severity Index (DSI) by Mu et al. [2013] (hereafter as Mu2013, http://www.ntsg.umt.edu/project/dsi). We will examine the consistency of these datasets in monitoring the drought conditions in Central Asia, and the relationship between drought and dust activity.

PDSI values range from -10 (dry) to +10 (wet) with values below -3 representing severe to extreme drought. The two PDSI datasets use the Penman-Monteith method for potential evapotranspiration (PET) which accounts for the dependence of atmospheric water demand on wind, radiation and humidity. Both PDSI global datasets are available as monthly values and at coarse resolutions (2.5°þ2.5° for Dai2011 and 1.0°þ1.0° for

Sheffield2012). The main differences between Dai2011 and Sheffield2012 datasets are the baseline time period and atmospheric forcing input data. Due to these differences, Dai et al. [2012] and Sheffield et al., [2012] report different trends of drought in some regions.

Unlike the PDSI data, the Mu2013 DSI is derived from the operational ET/PET and NDVI products from MODIS. DSI is computed as the standardized deviations of

ET/PET and NDVI from their climatology mean of the 2000Þ2011 period, and describes the vegetation response to drought conditions [Mu et al., 2013]. Here we use the 8-day composites DSI at 0.5°þ0.5° resolution. Monthly DSI is obtained by calculating the mean of enclosed 8-day composites within a month. The DSI ranges theoretically from unlimited negative values (drier than normal) to unlimited positive values (wetter than normal).

3.3 INCORPORATION OF LAND USE DYNAMICS IN THE DUST

MODEL

3.3.1 Land Use Dynamics in Agriculture and Water Body

Since the 1950s, LCLUC due to agriculture (cultivation and grazing) and exploitation of water resources has caused tremendous perturbations to the arid environment of Central Asia. Table 3.3 lists the major LCLUC events relevant for the region's dust activity between the 1950s and 2010s. These LCLUC events occurred under the context of institutional and socioeconomic changes in the Central Asian republics.

Table 3.3 Major LCLUC events in Central Asia between the 1950s and 2010s. Region LCLUC Event Cause Aral Sea Shrink and division into north and Expansion of irrigation south parts NE Caspian Sea Drying and refilling Sea level fluctuation Kara-Bogaz-Gol bay Dry-out and refilling Dam construction and removal Sarykamysh lake Expansion in the 1990s Increase in water inflow Kazakh Steppe Cultivation, and abandonment of Virgin Lands Campaign agricultural lands in the 1960s

Under the agricultural collectivization of the former USSR, Central Asia experienced expansion of cultivation and irrigation of the virgin lands starting from the early 1950s. The Virgin Lands Campaign (1954œ1960) led to conversion of large areas of natural steppe into farmlands, which caused severe wind erosion and catastrophic dust bowls due to the monoculture farming practice [Stringer, 2008]. The degraded lands were abandoned, while more new lands were reclaimed. Meanwhile, the normadic pastoralism was replaced by state and collective farms. The pasture lands were grazed by an increasing number of livestock above the carrying capacity, year-round without migration. This has led to depletion of vegetation cover and upper soil layer, making the soil vulnerable to wind erosion [Robinson et al., 2003; Gintzburger et al., 2005].

Following the collapse of USSR in 1991, cereal area and grain production underwent significant declines [de Beurs and Henebry, 2004; Lioubimeseva and Henebry, 2012].

The livestock production system collapsed and resulted in steep declines in the livestock numbers and a slow recovery of the degraded pasture lands [Wilson, 1997; Gintzburger et al., 2005].

Figure 3.1 a) Annual cropland and pasture fractions from the LUH dataset in selected years; b) domain-average annual land use fractions of cropland, pasture and their sum (cropland+pasture), and c) the percentage of land area with cropland plus pasture land use fractions larger than 70%.

Figure 3.1 shows the changes in cropland and pasture fractions in selected years between the 1950s and 2010s. Figure 3.1a shows a dry (or rainfed) crop belt of wheat and barley stretching eastward from Ukraine to the Ural Mountains and eastern Siberia, and southward to the Volga valley and northern Caucasus. Irrigated croplands (cotton and rice) are mostly located in river deltas and loess deposits of southern and southeastern mountains. The desert and steppe landscapes are widely used as pasture lands, including the semi-arid steppe, shrublands and mountainous rangelands [Gintzburger et al., 2005].

Figure 3.1b shows that the cropland fraction increases from 10% in 1950 to 14% in 1960, probably due to cultivation of virgin lands. The cropland fraction remains nearly unchanged till 1990. The cropland fraction started to decrease after 1990, likely due to the USSR collapse. In comparison, the pasture fraction monotonically increases from

34% in 1950 to 48% in 1960 and to 58% in 2005. This implies the pasture fraction simply represents the fraction of land area used for grazing, and does not reflect the decreasing trend of grazing intensity following the collapse of USSR. The cropland plus pasture accounts for 45% of total land surface in 1950 and 70% in 2005. Figure 3.1c shows that less than 2% of total land area has a cropland fraction higher than 70%, whereas there are a lot more land areas mostly (fraction>70%) used as grazing lands.

Figure 3.2 Landsat images of surface water body changes.

Figure 3.2 shows the Landsat images for several inland water bodies displaying significant water level variations. As part of the agricultural collectivism, large scale

77 irrigation systems were built to increase the soil fertility. As a result, diversion of water from Amu Darya and Syr Darya caused persistent shrinkage of the Aral Sea since the

1960s [Micklin, 2007]. The Caspian Sea water level fluctuations resulted in the drying

(e.g., 1975, 1988 and 2011) and inundation (e.g., 1999, 2007) of the shallow northeast shoals [Kravtsova and Lukyanova, 2000]. Once exposed, the northeast Caspian coast turned into a dry solonchak desert susceptible to wind erosion. A dam was built in 1980 to block the water flow from Caspian Sea to the Kara-Bogaz-Gol bay, resulting in a dry salt flat prone to wind erosion [Varushchenko et al., 2000; Leroy et al., 2006]. The Kara-

Bogaz-Gol bay began to refill with water after the dam was destroyed in 1992. The

Sarygamysh Lake located to the southwest of Amu Darya has been progressively increasing in size due to the drainage water.

3.3.2 Modification of Dominant Land Cover and Soil Texture

The land surface in the WRF-Chem-DuMo model is represented by a discrete number of dominant land cover (DLC) types, which are determined from the USGS 24- category (USGS24) land use fractions. The DLC is serves as a linkage to assign land and vegetation parameters through look-up tables. Similarly, soil texture is used to prescribe various soil parameters in a look-up table. The USGS24 classification is derived from

AVHRR NDVI data between April 1992 and March 1993 [Eidenshink and Faundeen,

1994]. This static data may cause biases for different years in case of significant land changes (e.g., land/water mask). Further, the development of USGS24 classification mostly focuses on natural land types. It does not dedicate any land categories to croplands. Instead, cropland is treated with natural vegetation as mosaic land classes.

Also, there are no categories for pasture, which is obscurely represented by land categories of ”grassland‘ and ”shrubland‘. Therefore, in order to account for the effect of agriculture on land cover change and dust emission, it is necessary to incorporate the

78 changes in the land and soil properties through a reconstruction of the DLC and soil texture.

Figure 3.3 Flowcharts of reconstruction of dominant land cover (DLC) for a) cropland and b) pasture in the USGS 24-category classification.

Reconstruction of DLC consist of two parts: 1) reconstruction of the cropland and pasture distributions using the LUH cropland and pasture fractions on annual basis, and

2) reconstruction of the land/water mask using the water body maps and images on decadal basis. Figure 3.3 shows the flowcharts of reconstructing the DLC types for cropland and pasture. By comparing the LUH cropland fraction (LUH_crop) with the

USGS24 land use fractions, we determine that LUH_crop corresponds to the combined field of category #2 ”dryland cropland and pasture‘ (USGS_cat2) and category #3

”irrigated cropland and pasture‘ (USGS_cat3). The LUH pasture fraction (LUH_pasture)

79 corresponds to the combined field of category #7 ”grassland‘ (USGS_cat7) and category

#8 ”shrubland‘ (USGS_cat8). Depending on whether it is rainfed or irrigated cropland, we replace the fraction of USGS_cat2 or USGS_cat3 with LUH_crop. Similarly, the

USGS_cat7 (north of 45°N) or USGS_cat8 (south of 45°N) fraction is replaced by

LUH_pasture. The DLC is then recomputed from the modified USGS24 land use fractions. On the other hand, reconstruction of land/water mask is based on geo- referenced water body contour maps and satellite images. If a water body dries out, it is assigned with category #19 ”barren or sparsely vegetated‘. When a dried water body is refilled with water, it is assigned with category #16 ”water bodies‘. Figure 3.4 shows the original and reconstructed DLC for each decade of 1950sÞ2010s. Major changes in the

1950s include the restoration of Aral Sea to full size and expansion of rainfed croplands.

Abandoned virgin lands in the 1960s are converted to barren surfaces. The Aral Sea area is reduced to reflect its shrinkage in size and division into two separate parts in the 1990s.

Conversion of the northeast Caspian shoals to dry solonchak is reflected by an increase in barren surfaces. The Kara-Bogaz-Gol bay gradually decreases from the 1950s to the

1980s, and becomes completely exposed in the 1980s when a dam was built. After the dam was destroyed, the Kara-Bogaz-Gol bay is replenished in the 1990s.

Reconstruction of soil texture is difficult due to lack of data. Soil texture is described by the soil texture triangle consisting of major textural classes as a function of the compositional percentage of sand, silt and clay. The land use impact on soil texture can involve complex processes which are poorly understood. However, we expect a good linkage between the soil texture and DLC, given the fact that soil texture is of great importance to land use and management. For instance, the suitability of soil for different crops depends on the water permeability and nutrient content which in turn depend on the textural composition. To confirm such a linkage, we compute the most frequent soil

80 texture class associated with the cropland and pasture DLC types. We find that over 40% of the grid cells of each DLC type have the same soil texture class. Based on this, we use the most frequent soil texture classes to modify the soil texture in accordance with land cover change. In the case of change in the land/water mask, there is fundamental change in soil texture. Based on the original soil texture map for regions of ephemeral lakes, grids cells that are converted from water to land are assigned soil texture of ‘silty clay loam‘.

3.4 RESULTS

3.4.1 Comparison of Dust Emission with Observations

Using the WRF-Chem-DuMo model with reconstructed land cover and soil texture, we compute the monthly dust emissions during MarchÞOctober, 1999Þ2012.

Following the experimental design in Table 2.7, four experiments are conducted:

MB_Dry (MB scheme plus dry-sieved soil size distribution data), MB_Wet (MB scheme plus soil texture-based soil size distribution data), Shao_Dry (Shao scheme plus dry- sieved soil size distribution data) and Const_Uth (simplified scheme). A mean estimate

(Exp_Mean) is obtained by averaging the MB_Dry, MB_Wet and Shao_Dry experiments. Const_Uth is tuned to generate the same annual (i.e., MarchÞOctober) emissions as Exp_Mean.

Figure 3.4 The original and reconstructed dominant land cover maps for USGS 24- category classification. The categories are: 1–Urban and Built-up Land, 2–Urban and Built-up Land, 3–Irrigated Cropland and Pasture, 4–Mixed Dryland/Irrigated Cropland and Pasture, 5–Cropland/Grassland Mosaic, 6–Cropland/Woodland Mosaic, 7– Grassland, 8–Shrubland, 9–Mixed Shrubland/Grassland, 10–Savanna, 11– Deciduous Broadleaf Forest, 12–Deciduous Needleleaf Forest, 13–Evergreen Broadleaf, 14–Evergreen Needleleaf, 15–Mixed Forest, 16–Water Bodies, 17– Herbaceous Wetland, 18–Wooden Wetland, 19–Barren or Sparsely Vegetated, 20– Herbaceous Tundra, 21–Wooded Tundra, 22–Mixed Tundra, 23–Bare Ground Tundra, 24–Snow or Ice.

The annual dust emission falls in the range of 188.1Þ519.1 Mt, 21.8Þ77.4 Mt, and

33.2Þ361.9 during 1999Þ2012 for the MB_Dry, MB_Wet and Shao_Dry experiments, respectively. Based on the mean estimate (Exp_Mean), the annual emission ranges from

81.1 Mt to 255.6 Mt, producing a 14-year average of 153.7 Mt yr-1. The AeroCom

(Aerosol Comparisons between Observations and Models) phase I compared 14 global dust models with annual dust emissions in the range of 539Þ1736 Mt yr-1 for North

Africa, 27Þ873 Mt yr-1 for entire Asia, and 1000Þ4000 Mt yr-1 for the globe [Huneeus et al., 2011]. For East Asian sources (Chinese and Mongolian deserts), annual emissions vary between 100 and 460 Mt yr-1 [Laurent et al., 2006]. Our estimates for Central Asia are in a reasonable range, when comparing to the emission strength of the two most important sources: North Africa and East Asia.

Figure 3.5 Monthly dust emissions for different dust source types between 1999 and 2012.

Figure 3.5 shows the monthly dust emissions for different source types based on

Exp_Mean. The locations of dust source types are shown in Figure 2.15. The relative importance of different source types varies from year to year. Hilly desert is the largest source (39.7%) in 2001, but contributes less than 15% to the total emission in other years.

Sandy desert is the second largest source type (27.8%) after cropland (39.6%) in 2006,

83 due to strong emission from the Kyzylkum Desert in August and September. Over the 14- year period, the percentages of dust emission from different source types are 35.1%

(cropland), 25.6% (loess desert), 20.3% (sandy desert), 13.2% (hilly desert), 3.3% (saline desert) and 1.3% (steppe). The percentage emission from Aralkum is 1.8%. In terms of temporal distribution, the percentages of monthly emissions are 3.9%, 8.5%, 9.7%,

14.2%, 14.5%, 21.9%, 18.9% and 8.4% for MarchÞOctober, respectively. Thus, summer is the most active season (50.6%), followed by autumn (27.3%) and spring (21.1%).

To compare with the monthly dust emissions, Figure 3.6 shows the monthly dust frequency index, AOD, AE and AAI averaged over the study domain. All variables display strong annual cycles. The recurring dust events are well captured by the high values of dust frequency, AOD and AAI and the low AE values during summer, which is consistent with the summer peak in dust emission. The SeaWiFS persistently produces lower AOD than MODIS, especially during the peak season. It is likely that SeaWiFS fails to detect the strong dust events due to a systematic bias in the retrieval algorithm, such as mistreatment of strong dust plumes as clouds (see examples in Figure 2.17).

Therefore, the SeaWiFS data may be problematic for studying dust variability near the source areas. Owing to the capability of MODIS on dust detection and the good continuity between the two instruments onboard Terra and Aqua, we will use MODIS aerosol products in the following analysis.

Figure 3.6 Monthly time series of a) dust frequency index, b) AOD, c) AE and d) AAI.

Figure 3.7 Annual averages of a) dust emission (1999Þ2012) b) dust frequency index (1999Þ2012) and c) MODIS AOD (2001Þ2012). For MarchÞOctober only.

Figure 3.7 compares the annual dust emission, dust frequency index and MODIS

AOD averaged over the 1999Þ2012 period (2001Þ2012 for MODIS). There is a general agreement in the long-term statistics of modeled dust emission and observed dust frequency index and dust loading. Areas with strong emission tend to be associated with high dust frequency and strong dust loading (AOD), such as the Caspian coasts, Ustyurt

Plateau, Aralkum and the loess deserts of the southeastern mountain regions. The relative importance of the source regions however differs in the model versus observations. For example, the Kyzlykum Desert and Amu Darya basin are associated with a high frequency index. The Aralkum is associated with strong AOD. Compared to the strong source areas, these regions produce much weaker dust emissions.

However, it is difficult to conclude that the model underestimate dust emission in these areas, because the dust frequency observation and satellite AOD are affected by the transport and removal processes, and, apart from the source strength, depend on the atmospheric lifetime and transport route. Besides, the observations are subject to limitations in spatial and temporal sampling. We find that the stations showing high dust frequency are mainly the manned stations located in big cities. The unmanned stations that are closer to the source areas do not record dust weather, thus reducing the spatial representativeness. The MODIS AOD is subject to biases from various assumptions in the retrieval algorithm. In particular, the deep-blue algorithm in the collection-5 product assumes static surface reflectance, and is only applied to bright surfaces. This may cause biases over regions with strong vegetation dynamics and mixed vegetation/non- vegetation scenes [Hsu et al., 2013]. Surprisingly, Figure 3.7c shows that strong AOD values tend to occur over the regions with year-around low vegetation cover. In contrast, the cropland fields in the river basins and southeastern mountainous loess deserts are associated with low AOD, whilst they produce strong dust emission.

Given the general similarity in the spatial distributions, we conduct linear correlation analysis between the monthly dust emission, dust frequency index and

MODIS aerosol observations. The results are shown in Table 3.4. The dust emission is strongly correlated (r=0.429) with dust frequency index. In comparison, the correlations between domain-integrated dust emission and MODIS observations are weaker.

Nonetheless, they are highly correlated for the source areas with high AOD values, such as the hilly desert, saline desert and Aralkum. These regions are also associated with a high frequency of daily AOD>0.7, indicating that the variability in the dust loading over these areas is linked with the intensity of dust emission from underlying source areas.

Overall, the ground station measurements are more representative for the entire domain‘s

87 dust activity, while MODIS observations only capture part of the domain, likely due to the limitation of AOD retrieval over vegetated areas.

Table 3.4 Correlation coefficients between monthly dust emission, dust frequency index and MODIS aerosol observations. Gray shaded values indicate statistically significant correlations at 95% confidence level. Dust Frequency AOD AE Frequency of Index AOD>0.7 Entire domain 0.429 0.242 -0.129 0.202 Hilly desert - 0.485 -0.585 0.482

Sandy desert - 0.072 0.008 0.033 Dust Loess desert - 0.046 0.091 0.022 Emission Saline desert - 0.474 -0.514 0.482 Steppe - 0.280 -0.275 0.337 Cropland - 0.100 0.104 0.047 Aralkum - 0.523 0.619 0.539

3.4.2 Linkages between Interannual Variability of Dust and Climate

To investigate the linkage between dust and climate, we briefly describe the seasonal controls of atmospheric circulation and precipitation in Central Asia. During winter and early spring, Central Asia is affected by the southwestern periphery of the

Siberian High anticyclone, and experiences cold air intrusions from north, northwest and northeast [Orlovsky et al., 2005]. The annual precipitation peaks in winter and spring, which is associated with the eastward-propagating mid-latitude cyclones generated over the eastern Mediterranean and Middle East [Martyn, 1992]. The westerly cyclones carrying high moisture fluxes are deflated by the Siberia High to southern directions over the Aral-Caspian area [Small et al., 1999; Lioubimtseva et al., 2005; Orlovsky et al.,

2005]. The resultant winter precipitation varies strongly depending on year-to-year changes in the position and intensity of the Siberia High [Small et al., 1999]. As the

Siberia High diminishes in late spring, the cyclonic storm frequency increases over

Central Asia. During summer, cold frontal intrusions from west and northwest become

88 frequent, and cause strong atmospheric instability and post-frontal dust storms [Littmann,

1991; Small et al., 1999]. Due to intensive solar heating, a thermal depression is formed at the southern desert region, and enhances regional advection of violent cyclones

[Orlovsky et al., 2005].

Large-scale climatic variability such as ENSO affects dust emission by altering the atmospheric circulation (e.g., wind speed) and precipitation. Precipitation indirectly affects dust emission in two ways. First, precipitation can increase the soil wetness and therefore suppress dust mobilization, although the soil moisture effect might be limited due to the fast evaporation loss during dust outbreaks. Second, precipitation is critical for the vegetation in the water-limited dryland regions, and therefore inhibits dust emission by decreasing the surface erodible fraction and increasing the aeolian roughness. The impact of climate variability on dust emission may be cumulative in time, due to the land memory of soil moisture and vegetation. For example, Zou and Zhai [2004] found a strong negative relationship between the spring dust activity and previous growing season

NDVI in East Asia.

Several studies suggested that ENSO and NAO are two dominant low frequency modes of interannual climate variability that affect the cyclonic activity and precipitation in Central Asia [Small et al., 1999; Tippett et al., 2005; Syed et al., 2006, 2010; Mariotti,

2007]. Under warm ENSO or El Nino conditions, a below-normal SST anomaly and above-normal SLP anomaly occur over the western Pacific, and reinforces a southwesterly moisture flux from the Arabian Sea and Tropic Africa, bringing more precipitation to Central Asia [Mariotti, 2007]. In contrast, the cold ENSO or La Nina phase leads to a decrease in the wintertime rainfall. The severe drought during

1998Þ2002 in Central Asia is strongly related with the prolonged La Nina event with unusually warm SST in the western Pacific [Barlow et al., 2002]. In addition, NAO is a

89 dominant mode of winter variability in the Northern Hemisphere, and significantly affects the North Atlantic storm track, causing dry and wet anomalies over the

Mediterranean and Northern Europe [Hurrell, 1995]. The positive NAO phase is associated with more cyclones formed over Eastern Mediterranean and transport of extra moisture to Central Asia [Syed et al., 2006, 2010]. Further, the weakening of the Siberian

High accompanied with positive NAO and warm ENSO phases may allow more moisture flow and increase the precipitation [Gong and Ho, 2002; Syed et al., 2010].

Figure 3.8 Time series of ENSO index and monthly (standardized) anomalies of dust frequency, MODIS AOD and AE. 3-month running means are shown for clarity.

To look for correlations between dust and climate variability, we first obtain the monthly dust anomalies by subtracting the month-wise averages from the monthly data.

We find no significant correlation between the dust emission anomalies (from all experiments) and the ENSO or NAO indices. Because there is a 4-month gap in the dust emission anomaly, it is difficult to look for time-lag correlations. Table 3.5 shows the correlation coefficients between the dust ground and satellite observations and ENSO index at various lags. The significant negative dustÞENSO correlation suggests intensified (weakened) dust activity during La Nina (El Nino) years, as shown in Figure

3.8. The correlation is strongest with ENSO leading by 6Þ8 months. We find no

90 significant correlations between dust and the (monthly) NAO index. This might be due to the NAO being dominant only during winter.

Table 3.5 Correlation coefficients between ENSO and dust frequency index (1999Þ2012), and MODIS data (2001Þ2012). Gray shaded values indicate statistically significant correlations at 95% confidence level. ENSO No lead 3-mon 6-mon 9-mon 1-yr lead lead lead lead Dust Frequency -0.041 -0.126 -0.268 -0.083 -0.022 Index AOD -0.252 -0.374 -0.473 -0.484 -0.337 AE 0.022 0.172 0.411 0.451 0.352

Table 3.6 The annual, wet-season and dry-season dust emission [Mt] averaged for El Nino and La Nina years, and their differences. Model Experiment El Nino La Nina Difference [%] MB_Dry 103.5 116.0 10.9 Wet Season MB_Wet 11.8 12.8 7.6 Shao_Dry 10.9 25.2 56.6 Exp_Mean 42.1 51.3 18.1 MB_Dry 246.5 231.6 -6.5 Dry Season MB_Wet 33.8 29.6 -14.3 Shao_Dry 31.4 69.4 54.8 Exp_Mean 103.9 110.2 5.7 MB_Dry 350.0 347.6 -0.68 Annual MB_Wet 45.6 42.3 -7.7 Shao_Dry 42.3 94.6 55.3 Exp_Mean 146.0 161.5 9.6

Given the strong correlation between dust observations and ENSO, we attempt to look for a similar relationship between dust emission and ENSO on an annual basis.

Based on the MEI values, we split the 1999Þ2012 period into El Nino (2002, 2003, 2004,

2005, 2006, 2009, 2012) and La Nina (1999, 2000, 2001, 2007, 2008, 2010, 2011) years.

We consider two separate seasons with distinct differences in atmospheric circulation and precipitation: the wet season of October-March-April-May and the dry season of June-

July-August-September. We then calculate the annual, wet-season, and dry-season dust

91 emission averaged during the El Nino and La Nina years, shown in Table 3.6. Similarly, based on the NAO (DJFM) index, the simulation period is split into positive NAO (1999,

2000, 2002, 2003, 2005, 2007, 2008, 2009, 2012) and negative NAO (2001, 2004, 2006,

2010, 2011) NAO years. The annual, wet-season, and dry-season dust emission averaged during the positive and negative NAO years are shown in Table 3.7.

Table 3.7 The annual, wet-season and dry-season dust emission [Mt] averaged for positive and negative NAO years, and their differences. Model Experiment Positive NAO Negative NAO Difference [%] MB_Dry 106.3 116.0 8.4 Wet Season MB_Wet 12.0 12.8 6.7 Shao_Dry 13.6 26.0 47.7 Exp_Mean 44.0 51.6 14.9 MB_Dry 223.8 266.5 16 Dry Season MB_Wet 29 36.5 20.6 Shao_Dry 33.0 81.8 59.7 Exp_Mean 95.3 128.3 25.7 MB_Dry 330.1 382.5 13.7 Annual MB_Wet 41.0 49.4 17 Shao_Dry 46.6 107.8 56.8 Exp_Mean 139.2 179.9 22.6

Table 3.6 shows that during the wet season, La Nina years produce more dust than

El Nina years for all model experiments. The La Nina phase is likely associated with more favorable conditions for spring dust emission. The difference in dust emission between La Nina and El Nino years is strongest (over 50%) for the Shao_Dry experiment. This indicates that the Shao scheme is more sensitive than the MB scheme to

ENSO-induced changes in the atmospheric and land conditions. Given that the Shao scheme is more sensitive to vegetation than the MB scheme (refer to section 2.3.2), we speculate the La Nina condition causes lower-than-normal vegetation cover during the wet season.

During the dry season, the difference in dust emission between La Nina and El

Nino conditions becomes smaller, and even reverses for the MB_Dry and MB_Wet experiments. As a result, the annual dust emission for MB_Dry and MB_Wet is higher under El Nino than La Nina conditions. Again, this demonstrates a strong contrast in the model responses of the MB and Shao schemes to concurrent changes in the atmospheric

(i.e., wind) and surface (i.e., threshold velocity) conditions. Table 3.7 shows that, for all model experiments, negative NAO is associated with more dust emission than positive

NAO during both the wet and dry seasons. Similar to the ENSO effect, the largest difference between the negative and positive NAO phases occurs in the Shao_Dry experiment. A careful look shows the two most dusty years, 2001 (part of the prolonged

1998Þ2001 La Nina drought event) and 2006 (weak El Nino drought event), are associated with negative NAO. Also, two other La Nina years (2010 and 2011) are with negative NAO. Thus, the ENSO and NAO effects on dust are confounding each other. A longer model simulation is required to pinpoint the NAO effect.

To explore the mechanisms of the ENSO effects, we focus on four variables: the frequency of strong winds (u10>6.5 m/s), precipitation, surface bareness and drought. The surface bareness is defined as the fraction of land area with NDVI<0.1. Table 3.8 shows during the wet season, the El Nino phase is associated with more frequent strong winds, whereas the La Nina phase tends to generate drier conditions with less rainfall, less vegetation and more severe drought. The stronger wet-season dust emission under La

Nina phase implies that the favorable surface conditions dominate the effect of wind speed in all three model experiments. During the dry season, the La Nina phase is associated with slightly less frequent strong winds and more precipitation, which explains the weaker dust emission than El Nino phase in the MB_Dry and MB_Wet experiments.

In contrast, the Shao_Dry experiment persistently produces more dust under La Nina

93 phase due to more barren surfaces. This implies the dust emission in the Shao scheme is more dependent on the surface characteristics (e.g., vegetation) than the MB scheme.

Table 3.8 The annual, wet-season, and dry-season strong wind frequency (u10>6.5 m/s), precipitation, surface bareness and PDSI averaged for El Nino and La Nina years. ENSO El Nino La Nina Wet Season 26.3 25.4 u10>6.5 m/s [%] Dry Season 23.4 23.2 Annual 24.8 24.3 Wet Season 54.0 47.0 Precipitation [mm] Dry Season 17.9 19.4 Annual 35.9 33.2 Wet Season 11.4 14.5 Surface Bareness [%] Dry Season 3.9 7.5 Annual 6.8 10.1 Wet Season 0.52 -0.40 PDSI (Dai2011) Dry Season 0.67 -0.07 Annual 0.60 -0.24 Further, Table 3.9 shows the correlation coefficients between the monthly dust emissions and various factors. All model experiments show strong correlations between dust emission and the frequency of strong (u10>6.5 m/s) and extreme (u10>10 m/s) wind events. The simplified scheme displays the strongest dependence on wind speed due to the use of a fixed threshold velocity. The Shao scheme is less dependent on wind than the

MB scheme. This is expected as dust emission in the Shao scheme is more sensitive to the threshold velocity than the MB scheme. No significant correlations are found between dust and precipitation. Figure 3.9 shows that most of the precipitation is received in the mountain areas (as snow). The southern desert regions receive much less rainfall than the northern steppe area. Thus the precipitation effect on dust emission may be limited.

Further, precipitation affects dust emission indirectly through soil moisture and vegetation. Indeed, the negative correlation between PDSI and dust emission suggests that drought conditions tend to enhance dust emission due to drier soils and lower vegetation cover. The spurious positive dust-NDVI correlations might be due to the

94 dependence of the friction velocity on aeolian roughness. A higher NDVI generates stronger surface shear, while it also increases the threshold velocity and decreases the erodible fraction of land surfaces. The competing effect reduces the sensitivity of the MB scheme to vegetation change. The surface bareness, which is derived from NDVI, appears more appropriate than NDVI itself in depicting the dust-vegetation relationship.

Table 3.9 Correlation coefficients between monthly dust emission anomaly and various factors. Gray shaded values indicate statistically significant correlations at 95% confidence level. Dust Emission MB_Dry MB_Wet Shao_Dry Exp_Mean Const_Uth u10>6.5 m/s [%] 0.513 0.457 0.259 0.546 0.631 u10>10 m/s [%] 0.532 0.509 0.252 0.564 0.632 Precipitation [mm] -0.005 -0.003 0.042 0.012 0.172 NDVI 0.010 0.010 -0.018 0.002 0.024 Surface Bareness [%] 0.008 -0.019 0.170 0.070 0.061 PDSI (Dai2011) -0.086 -0.073 -0.240 -0.167 -0.089

Figure 3.9 Monthly rainfall during wet and dry seasons averaged over 1999Þ2012.

Although the dust-drought correlations are not statistically significant (except for the Shao_Dry experiment), the correlation coefficients between dust and drought index are higher than those between dust and other surface variables (precipitation, NDVI, and surface bareness). This indicates that the dust variability is more connected with drought conditions than individual factors, because the drought index is a more integrative measure of the soil moisture and vegetation conditions. Figure 3.10 shows the monthly

95 times series of ENSO index and three drought indices. There is general good agreement between the three drought indices. The correlation between Dai2011 PDSI and

Sheffield2012 PDSI is 0.74 for the period 1950Þ2008. The correlation between Dai2011

PDSI and Mu2013 DSI is 0.70 for the period 2000Þ2011. From 1999 to 2012, there are three prominent drought periods: 1999Þ2001, 2006Þ2008, and 2010Þ2011, all of which are associated with above-average dust emissions. The prolonged drought condition of

1999Þ2001 was linked with the La Nina-like SST anomaly in the western Pacific

[Barlow et al., 2002, Hoerling and Kumar, 2003]. Indeed, we find strong correlations between ENSO and the drought indices. The correlation coefficients between ENSO and

Dai2011 PDSI are 0.37, 0.46, and 0.42 at no lag, 3-month lag and 6-month lag, respectively. The correlations account for the autocorrelation (or serial correlation) of individual time series, and are statistically significant at 95% confidence level.

To this end, we identify a strong linkage between ENSO, drought, and dust emission in Central Asia. The La Nina-induced dry anomaly causes favorable surface conditions (less precipitation, drier soil and lower vegetation cover), and consequently may enhance the dust emission. The MB and Shao schemes differ in responses to the drought conditions, because of different sensitivities to wind speed and threshold velocity. During the period of 1999Þ2012, the Shao scheme produces much more dust in

La Nina years under the dominant influence of drought events, whereas the MB scheme produces slightly higher dust emission in El Nino years due to the compensating effects of more frequent strong winds. The mean estimate (Exp_Mean) based on the two schemes however shows enhanced dust emission during La Nina than El Nino years.

Figure 3.10 Monthly time series of ENSO and drought indices for: a) 1950Þ2012 and b) 1999Þ2012.

3.4.3 Detection of Dust Trend

Because the dust activity is greatly affected by the ENSO cycle, trend determination will be sensitive to the influence of the climatic factors, and be less likely to pass the significance test. To avoid the outliers in the monthly emission time series, we perform ordinary least square linear regression on the annual dust emissions. The results are shown in Figure 3.11. All model experiments show decreasing trends in dust emission. The trend for the MB_Dry experiment (-14.86±6.06 Mt yr-1) is significant at

90% confidence level, whereas the trends for MB_Wet and Shao_Dry experiments fail to pass the significance test. The dust trends are affected by two outliers: 2001 and 2006.

Figure 3.10b shows that both 2001 and 2006 are drought years associated with above- average dust emissions. In the MB_Dry and MB_Wet experiments, 2006 is the most active year. In the Shao_Dry experiment, 2001 produces much stronger emission than

97 other years. Figure 3.12 shows the dust emission trend from the mean estimate

(Exp_Mean). The dust trend (-7.81±2.73 Mt yr-1) is accompanied by a decreasing tendency in the frequency of strong (u10>6.5 m/s) and extreme (u10>10 m/s) wind events.

The trend is significant at 95% confidence level, meaning that the interference of outliers on trend determination is reduced when using the mean estimate.

Given the strong seasonality in dust emission, we also calculate the seasonal trends for different dust source areas, shown in Table 3.10. To be consistent with the annual trend, seasonal trends are derived from the spring (MAM), summer (JJA) and autumn (SO) emissions for the 14-year period. Generally, all source areas show decreasing annual and seasonal trends. The strong source areas of cropland, hilly, sandy and loess deserts tend to be with larger trends. Only the sandy (-1.57±0.73 Mt yr-1) and loess deserts (-1.88±0.63 Mt yr-1) display significant annual trends at 95% confidence level. The decreasing trend at Aralkum is mainly due to less frequent strong winds, because the areal extent of Aralkum changes little in the past decade, and the source has low vegetation cover. Our finding does not contradict with the increasing trend at Aral

Sea station reported by Indoitu et al. [2012]. They looked at an earlier time period

(1950Þ2000) during which the dust emission has been increasing likely due to the expansion of the Aralkum source area. The summer and autumn trends are not statistically significant for all source areas, likely as a result of large variability in the seasonal distribution of dust emission.

Figure 3.11 Linear square fit on annual dust emissions for the a) MB_Dry, b) MB_Wet, and c) Shao_Dry experiments. The regression equations and standard errors of emission trends are shown.

Figure 3.12 Linear square fit on the mean estimate of annual dust emissions. Also shown are the (normalized) monthly anomalies of the frequencies of modeled u10>6.5 m/s and u10>10 m/s. Three-month running means are shown for clarity.

. Table 3.10 Annual and seasonal trends [Mt yr-1] of dust emission based on Exp_Mean for different dust source regions. ** indicates trends significant at 95% confidence level, * indicates trends significant at 90% confidence level. Source Entire Hilly Sandy Loess Saline Steppe Cropland Aralkum Type domain desert desert desert desert Annual Trend -7.81 ** -1.91 -1.57 * -1.88 ** -0.28 0.05 -2.18 -0.21 Std Err 2.73 1.54 0.73 0.63 0.21 0.05 1.06 0.19 MAM Trend -2.47 -0.27 -0.62 ** -0.82 -0.07 0.06 ** -0.75 * -0.04 Std Err 0.91 0.27 0.13 0.29 0.05 0.02 0.33 0.04 JJA Trend -3.09 * -1.12 -0.6 -0.55 -0.13 -0.01 -0.69 -0.11 Std Err 1.45 0.95 0.33 0.32 0.12 0.04 0.65 0.11 SO Trend -2.25 -0.35 -0.35 -0.51 -0.08 0 -0.75 -0.06 Std Err 1.25 0.37 0.42 0.33 0.06 0 0.52 -0.05

Figure 3.13 Linear square fit on the annual dust frequency index. Also shown are the (normalized) monthly anomalies of the frequencies of observed u10>6.5 m/s and u10>10 m/s. Three-month running means are shown for clarity.

Figure 3.14 Linear square fit on MODIS annual AOD.

100

Figure 3.15 a) Temporal and b) spatial patterns of the two leading EOFs of monthly AOD anomaly during 2001Þ2012. Red (blue) areas are increasing (decreasing) tendency for a positive temporal coefficient on the corresponding PC time series. Also shown in PC 1 is the ENSO index leading by 8 months to obtain a maximum correlation (r=0.53) with PC 1 time series.

Further, we estimate the linear trend of annual mean dust frequency index between 1999 and 2012, as shown in Figure 3.13. The dust frequency displays a significant (at 95% level) decreasing trend, consistent with the dust emission trend. Based on visibility data, several studies reported declines in dust frequency in the Central Asian stations since the 1950s [Orlovsky and Orlovsky, 2002; Mahowald et al., 2007; Indoitu et al., 2012]. Our analysis shows a continuation of the decreasing dust tendency. Figure 3.13 show the wind measurements at the ground stations show a decreasing tendency, which has a significant correlation (r=0.28) with the dust frequency index. The correlation is weaker than that between dust emission and modeled winds, likely due to the limited spatial representativeness of ground-based wind measurements.

101

Using MODIS AOD during 2001Þ2012, we also calculate the AOD trend as shown in Figure 3.14. The AOD shows a small increasing trend with a large standard error. Because the AOD is strongly correlated with ENSO, detection of AOD trend can be subject to a large uncertainty due to the short data length. In fact, the upward AOD trend after 2003 is mainly caused by the La Nina events and resultant drought conditions.

Figure 3.15 further shows the MODIS AOD contains two robust modes of variability, together explaining 58.5% of the total variance. The first mode is associated with ENSO, with a maximum correlation of r=0.53 when ENSO leads by 8 months. Its spatial pattern shows a decreasing (increasing) tendency under warm (cold) ENSO phase in the entire domain. The second mode represents a decreasing trend over the regions with strong

AOD values (see Figure 3.7c), including the Caspian coasts, Kara-Bogaz-Gol bay and

Ustyurt Plateau. An increasing tendency occurs over the southeastern mountain regions, where the AOD has a lower value. Therefore, if the ENSO effect is removed, we obtain a general decreasing AOD trend in Central Asia. The apparent opposing trends in dust emission and loading (AOD) are caused by the difference in the strength of linkage with, in other words, the percentage variance explained by ENSO. In particular, the dust loading is more correlated with ENSO than dust emission. Gong et al. [2006] and Hara et al. [2006] showed similar relationships between East Asian dust emission, transport pathway and ENSO. They found strong connections between the dust loading and ENSO, but no statistically significant correlation between dust emission and ENSO. Therefore, a longer AOD record is required to establish a robust AOD trend.

3.4.4 Assessment of the Anthropogenic Fraction of Dust

To quantify the contribution of human land use to dust emission via agriculture and surface water body changes, we define the anthropogenic fraction of dust Fad on an annual basis:

102

W LM F , ji , ji L100 ad M , ji (3.1) where Mi,j is the annual dust emission. Wi,j is the weight or the fraction of dust at each grid cell attributed to human activity. Fad depends on the grid resolution of Mi,j and Wi,j.

In other words, computation of Fad is affected by the spatial resolutions of dust simulation, and of the weights used to separate the natural and anthropogenic source areas. Our simulations use a grid resolution of 10 km by 10 km to capture the presence of fine-scale dust source areas, such as Aralkum and patchy croplands. Here we use the

LUH agriculture dataset to derive Wi,j. In accordance with the cropland and pasture fractions, we first aggregate the dust emission onto a 0.5°þ0.5° grid. The Wi,j is an empirical function of the land use intensity (LUI, in percentage), as follows:

,0 if LUI , ji LUI th W , ji ,1 if LUI K LUI , ji th (3.2)

The LUI describes the intensity of human activity, in this case, by agriculture and water body change. It is computed as the sum of cropland and pasture fractions. In the case of land/water conversion, such as the dry-out of Aral Sea and Kara-Bogaz-Gol bay,

LUI is assigned to 100%, assuming the land cover change is purely caused by human.

The definition of Wi,j assumes that once the LUI of a grid cell exceeds a threshold value

LUIth, the emitted dust from that grid cell is considered anthropogenic. The selection of such threshold value is subjective without any universal rules, and is often based on the land use conditions of the region of interest. For instance, Tegen et al. [2004] considered a dust source to be natural if there is less than 5% land use on a global scale. Ginoux et al.

[2010] used a threshold of 10% for the West Africa (5Þ20°N, 0Þ20°E), meaning that any dust from a grid cell with at least 10% land use is considered anthropogenic. They showed that the LUI threshold effectively separates the natural sources, mostly the arid

103 areas such as the Bodele Depression, from the anthropogenic sources located in semiarid regions. However, such a low threshold may not work for the majority of global drylands which are extensively used by human for cultivation and grazing. In fact, when extending their study to global scale, Ginoux et al. [2012] adopted a higher threshold of 30% to accommodate the intensive land use over most dryland regions. In addition, the definition of Wi,j depends on the LUI and thus the accuracy of the land use dataset. The LUH agriculture dataset used in this study contains harmonized historical cropland and pasture data and future land use scenarios as part of data effort for the IPCC AR5 Earth system models [Hurtt et al., 2011]. The cropland and pasture fraction data are based on the most recent HYDE 3.1 agricultural dataset of Kleim Goldewijk et al. [2010], which is an improved version compared to those used by Tegen et al. [2004] and Ginoux et al.

[2012].

Figure 3.16 shows the LUI is higher than 70% over most areas in 2001. The majority of dust sources can thus be categorized into anthropogenic if a low LUIth is used. To investigate the sensitivity of the Fad to the selection of LUIth, we calculate the Fad for a range of LUIth values, as shown in Figure 3.17. As expected, the Fad decreases as

LUIth increases. The Fad remains high at low LUIth values (e.g., <35%). For instance, over

77% of dust emission is considered as anthropogenic for LUIth=30%. Figure 3.18 shows that, for LUIth=30% (and even 50%), almost the entire region is unrealistically treated as anthropogenic source. The Fad decreases significantly when LUIth exceeds 80%. Figure

3.18c shows for LUIth=80%, the anthropogenic source areas include the Aralkum, the elevated Trans-Unguz Karakum, loess deserts at southern mountain alluvial deposits, and parts of the Kazakh Steppe. Aralkum is formed due to over-irrigation, while other areas either contains cultivated lands or are used for grazing. On the other hand, the natural source areas include the Ustyurt Plateau, Caspian coasts, Karakum and Kyzylkum deserts

104 and the Betpak-Dala Desert. This division is more realistic compared to the use of low

LUIth values. Nonetheless, the dryland cropland belt and parts of grazing lands are classified into natural sources. For even higher LUIth values such as 90% (Figure 3.18d), the anthropogenic sources only occupy the Aralkum, Trans-Unguz Karakum and part of the loess deserts. We estimate that the Fad is 37.2% and 22.5% for a LUIth of 80% and

90%, respectively. These estimates are perhaps conservative given that the anthropogenic sources may cover more land areas.

Figure 3.16 The land use intensity (LUI) in 2001.

Figure 3.17 The anthropogenic fraction of dust emission (Fad) as a function of the land use intensity threshold (LUIth) in 2001.

105

. Figure 3.18 Distribution of natural and anthropogenic dust source areas for different LUIth in 2001.

Figure 3.19 shows the estimates of Fad for the period 1999Þ2012. Because there is little change in the LUI from 1999 to 2012 (except a small increase of Aralkum during

2011Þ2012), the separation of natural and anthropogenic source areas remains nearly unchanged. Therefore, the change in Fad is caused by the variability in the spatial distribution in dust emission, which determines the relative strength of natural versus anthropogenic sources. For LUIth=80%, the Fad stays within 60±5% in most years. The lowest value (37.2%) occurs in 2001, implying enhanced dust emission from natural source areas. For LUIth=90%, the Fad values drop to around 20%. There is also a decreasing tendency of Fad when comparing the period 2006Þ2012 to 1999Þ2005. Given that a LUIth of 80% gives a realistic separation of natural and anthropogenic source areas, we estimate that 58.4% of dust emission is anthropogenic for the 14-year period. For

LUIth=90%, we obtain a conservative estimate of 20.7%.

106

Figure 3.19 Annual anthropogenic fraction of dust emission (Fad) for two different LUIth values.

3.5 CONCLUSIONS

This study investigates the interannual variability of dust in Central Asia and linkages to climate variations and land-cover/land-use change. Using the WRF-Chem-

DuMo model, we conduct dust emission simulations for the period of MarchÞOctober

1999Þ2012 for four model experiments: MB_Dry (MB scheme plus dry-sieved soil size distribution data), MB_Wet (MB scheme plus soil texture-based soil size distribution data), Shao_Dry (Shao scheme plus dry-sieved soil size distribution data) and Const_Uth

(simplified scheme). A mean estimate (Exp_Mean) is obtained by averaging the MB_Dry,

MB_Wet and Shao_Dry experiments. The model accounts for annual changes in the cropland and pasture distribution based on an agriculture dataset, and decadal changes in surface water bodies based on geo-referenced maps and satellite images. The dominant land cover and soil texture are modified accordingly to reflect changes in dust source characteristics. MODIS monthly NDVI from 2001 to 2012 are used to derive the roughness density, aeolian roughness length and surface erodible fraction for the dust schemes. Ground observation of dust frequency, and satellite observations of dust loading

(including AOD, AE and AAI) are used in conjunction with modeled dust emissions to

107 address three objectives: 1) examine the effects on dust of the ENSO and NAO teleconnections, 2) estimate the trend of dust from 1999 to 2012 and 3) assess the anthropogenic fraction of dust emission.

Based on Exp_Mean, annual dust emissions in Central Asia range from 81.1 Mt to 255.6 Mt during 1999Þ2012, with an average of 153.7 Mt yr-1. The largest dust source types are cropland (35.1%), loess desert (25.6%) and sandy desert (20.3%). The dried seabed of Aral Sea or Aralkum contributes 1.8% to total emission. Dust emission is most active during summer (JJA) which accounts for 50.6% of annual emission, followed by autumn (SO, 27.3%) and spring (MAM, 21.1%). The spatial distribution of dust emission generally resembles that of 14-year (1999Þ2012) mean dust frequency index and 12-year

(2001Þ2012) mean MODIS AOD. The monthly dust emission is strongly correlated

(r=0.429) with dust frequency index. High AOD values occur over the Aralkum, Ustyurt

Plateau and Caspian coasts. As a result, monthly dust emissions from these source areas are highly correlated with the monthly AOD, AE and the frequency of daily AOD>0.7.

The La Nina phase is associated with less frequent strong winds, but cause drought conditions with drier soils and less vegetation cover. The MB and Shao schemes have opposing responses of dust emission to the El Nino and La Nina phases. The Shao scheme is more sensitive to changes in surface characteristics, while the MB scheme is more sensitive to changes in the frequency of strong winds. As a result, during the period of 1999Þ2012, the Shao scheme produces much more dust in La Nina years under the dominant influence of drought conditions, whereas the MB scheme produces slightly higher dust emission in El Nino years due to the compensating effects of more frequent strong winds. The resultant mean estimate (Exp_Mean) based on the two schemes suggests enhanced dust emission under La Nina condition. It is difficult to deduce the

108 effect of NAO on dust emission from the 14-year simulation, due to the confounding effects of ENSO.

Correlation analysis between monthly dust emissions and climate and surface variables suggests that the wind speed, particularly the frequency of strong winds, is the dominant factor that determines the dust emission variability. The level of correlation however differs among dust schemes: the simplified (Shao) scheme is most (least) dependent on the wind speed. During 1999Þ2012, a decline in the strong wind frequency causes a decreasing trend of dust emission and dust frequency index. Based on

Exp_Mean, dust emission decreases at a rate of -7.81±2.73 Mt yr-1, which is statistically significant at 95% confidence level. Dust emission has a higher correlation with drought index than with individual variables (i.e., precipitation, NDVI and surface bareness), because the drought index is a more integrative measure of the soil moisture and vegetation conditions. Nonetheless, only the dust emission from the Shao scheme displays a significant correlation with drought. The occurrence of drought conditions in

Central Asia is strongly related to the ENSO cycle: prolonged La Nina conditions can produce severe drought events and lead to enhanced dust emission, for example, the

1999Þ2001 drought. Compared to the dust emission and dust frequency index, the dust loading (AOD) has a stronger correlation with ENSO. As a result, the AOD displays an increasing trend after 2003 due to the La Nina effects. EOF analysis on the monthly

AODs during 2001Þ2012 shows after removing the ENSO effect, the AOD displays a decreasing tendency. Therefore, the apparent opposing trends of dust emission and loading are caused by the difference in the level of correlation with ENSO. In particular, the dust loading is more correlated than dust emission with ENSO, such that a longer

AOD record is required to establish a robust dust trend. Meanwhile, a longer model simulation is desirable given that dust emission is affected by the low frequency

109 teleconnection modes. The model methodology developed in section 3.3 can meet such need, which however requires long-term input data for the surface parameters, such as vegetation index.

Estimation of the anthropogenic fraction of dust emission depends on 1) the accuracy of simulated total dust emission and 2) separation of natural and anthropogenic source areas. We obtain a climatology of dust emission for 1999Þ2012 by using two physically-based dust schemes and two options of soil size distribution data in one unified modeling framework. The model also benefits from a suite of land and soil datasets developed specifically for the Asian drylands regions. This model-ensemble approach accounts for the differences in physics parameterizations, and their sensitivities to atmospheric and land parameters, as well as the uncertainty of the input data. The uncertainty in the total dust emission is therefore minimized. The assessment of anthropogenic dust involves an empirical method to distinguish the natural and anthropogenic source areas by using the land use intensity data. Based on the land use conditions in Central Asia, we find a threshold of 80% land use fraction can be used to identify the potential anthropogenic source areas. In that case, we estimate about 58.4% of dust emission can be attributed to human activity during the 1999Þ2012 period. If using a more conservative threshold of 90%, we find an anthropogenic dust fraction of

20.7%. Our estimates suggest human plays an important role in the dust budget in Central

Asian through agriculture and perturbing water bodies.

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CHAPTER 4

IMPACT OF ASIAN DUST ON PHOTOSYNTHETICALLY ACTIVE

RADIATION AND SURFACE RADIATIVE BALANCE

4.1 INTRODUCTION

There has been growing interest in the impact of atmospheric aerosols on the terrestrial ecosystems and their role in land-atmosphere interactions in the context of

Earth system science. These interactions are thought to involve multiple, interrelated processes and various feedbacks that remain poorly constrained [Carslaw et al., 2010].

Here we address the impact of mineral dust that involves radiative transfer processes, focusing on aerosol-induced changes in the photosynthetically active radiation (PAR,

0.4Þ0.7 µm) and surface radiative balance (SRB, 0.3Þ20 µm). Light is a vital factor governing the plant photosynthetic activities and hence changes in PAR caused by aerosols can influence the biosphere-atmosphere carbon/water exchanges and ecosystem functioning. Changes in the land surface energy balance are important because they affect the surface evapotranspiration, sensible and latent heat, soil temperature and moisture, and major land-atmosphere processes that along with light availability are all important to the ecosystems.

Dust affects both the shortwave (SW, 0.3Þ2.5 µm) and longwave (LW, 2.5Þ20

µm) components of the radiative energy balance, but in opposing ways [Sokolik and

Toon, 1999]. Many studies have shown that dust aerosols cause significant reductions in the SW radiation and a strong directive radiative forcing over the source and downwind regions [e.g., Takemura et al., 2003; Huang et al., 2009; Mallet et al., 2009]. In contrast to the negative SW forcing, dust increases the LW radiation reaching the surface, partly compensating the SW cooling effect [Markowicz et al., 2003; Vogelmann et al., 2003;

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Huang et al., 2009]. Therefore, dust-induced changes in both the SW and LW radiation should be accounted for in assessing the surface energy balance.

Although there have been numerous studies of the dust SW and LW radiative impact, we are not aware of any study that comprehensively addressed the impact on

PAR. Past studies, however, explored the influence of several other aerosol types on

PAR, including volcanic aerosol [Roderick et al., 2001; Gu et al., 2003], urban pollution aerosols [Chameides et al., 1999; Bergin et al., 2001; Cohan et al., 2002], and biomass burning smoke aerosols [Yamasoe et al., 2006]. Several studies reported reductions in the plant photosynthetic rate and primary production due to less incoming PAR as a result of aerosol attenuation [Bergin et al., 2001; Chameides et al., 1999]. However, while reducing the total PAR, aerosols can enhance the diffuse component of PAR and lead to a higher gross photosynthetic rate. The underlying reasoning is believed to be due to the redistribution of light between the sunlit and shaded leaves within the plant canopy: the aerosol absorption and scattering causes reductions in the PAR received by the sunlit leaves with small changes in their photosynthesis rate, while more scattered diffuse PAR becomes available to the majority of light-limited shaded leaves, such that the gross photosynthetic rate increases. This so-called diffuse radiation fertilization effect due to aerosols has been extensively addressed in modeling [Cohan et al., 2002, Matsui et al.,

2008] and observational [Gu et al., 2003; Niyogi et al., 2003; Yamasoe et al., 2006] studies.

Given that changes in total PAR and its diffuse fraction are controlled by aerosol type, especially by the aerosol amount and optical properties, and because of distinct differences between the optical properties of dust and other aerosols, it is important to understand how dust aerosol can affect PAR. Further, dust-induced changes in both PAR and SRB need to be addressed to understand the net radiative impact of dust on terrestrial

112 ecosystems. Indeed, aerosols can alter the surface net radiation, latent/sensible heat, soil/leaf temperature and atmospheric humidity. These factors are all important for the photosynthesis and respiration processes and the net primary production [Gu et al., 2002;

Steiner and Chameides, 2005].

Assessment of PAR along with SW and LW components of the surface energy balance under dusty conditions necessitates a consistent representation of the dust optical characteristics across the wide spectral range (i.e., from the UV to the IR). Current measurement capabilities cannot provide this information so that optical modeling must be performed to compute the required spectral optical characteristics. However, computations of dust optical characteristics, such as extinction coefficient, single scattering albedo and scattering phase function, are subject to large uncertainties due to the complex nature of mineral aerosols. Dust particles exhibit various non-spherical shapes, mineralogical compositions, and size spectra that depend on dust source characteristics and physicochemical changes (i.e., atmospheric aging) during transport

[Sokolik et al., 2001]. To perform optical modeling for radiative budget assessments, past studies often considered dust as a single generic species and used a spectral refractive index reported for limited dust bulk samples collected in a few geographical regions. For instance, the dust refractive index used by Yoshioka et al. [2007] was a combination of data from Patterson [1981] for the visible, from Sokolik et al. [1993] for the near-infrared

(near-IR), and from Volz [1973] for the IR, despite the fact that these datasets represent three completely different dust samples œ one from Central Asia and the other two from

Northern Africa. The OPAC (Optical Properties of Aerosols and Clouds) library, which is widely used in radiation/climate modeling, also consists of dust bulk models that are based on the Patterson and Volz refractive indices [Hess et al., 1998]. To overcome these limitations, here we explicitly consider size-resolved mineralogical compositions of dust

113 aerosol that allow the use of the spectral refractive indices of major minerals in dust aerosols [Sokolik and Toon, 2001; Lafon et al., 2006; Jeong and Sokolik, 2007]. One key advantage of this approach is to incorporate recent data of dust mineralogical composition and provide an improved representation of region-specific dust optical characteristics across the entire spectrum from the UV to the IR.

The aerosol radiative impact also depends on the properties and state of the underlying land surfaces, such as surface albedo and emissivity. In dryland regions, surface albedo exhibits large spatiotemporal and spectral variability. In particular, for barren and sparsely vegetated areas, surface albedo depends on several factors including soil type, composition and soil moisture [Tsvetsinskaya et al., 2006; Waggoner and

Sokolik, 2010]. For vegetated surfaces, the surface albedo displays a strong seasonality controlled by plant phenology [Gao et al., 2005]. Shrublands and grasslands tend to exhibit larger albedo variations compared to other land categories [Gao et al., 2005].

Although surface albedo varies with wavelength, it is commonly represented in regional and global models by a wavelength-independent constant (often called SW broadband albedo), which is then prescribed to land cover categories as part of the land surface scheme. However, neglecting the spectral dependence of surface albedo, such as in the visible versus near-IR, can lead to significant errors in climate simulations [Roesch et al.,

2002]. Therefore, it is important to account for the spectral surface albedo and its spatiotemporal variability to better understand the regional and temporal (e.g., seasonal) dynamics of dust radiative impact.

The goal of this chapter is to assess the extent to which Asian dust can impact the

PAR and surface radiative balance considering the regional specifics of Asian dust properties and spectral surface albedo in the dryland ecosystems of Central Asia. The objectives are to 1) compute and examine the size- and composition-dependent spectral

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(i.e., from the UV to the IR) behavior of Asian dust optical characteristics, 2) determine the spectral surface albedo of the major dryland ecosystems in Central Asia that are affected by dust, 3) examine the dust radiative impact on the total PAR, diffuse fraction of PAR and surface radiative balance in these ecosystems, and 4) explore implications of the dust radiative impact on the ecosystem functioning using several light use efficiency models. Our approach is to perform a comprehensive one-dimensional radiative transfer modeling constrained by ground-based and satellite observations of dust aerosol and land surface properties.

4.2 DATA AND METHODOLOGY

We use a one-dimensional radiative transfer code SBDART (Santa Barbara

DISORT Atmospheric Radiative Transfer, Ricchiazzi et al. [1998]) to compute the radiative fluxes. SBDART solves the radiative transfer equation in a vertically inhomogeneous plane-parallel atmosphere taking into account scattering, absorption and emission by major gases and aerosols. The SBDART code is modified to allow for the incorporation of a new module to treat the aerosol vertical profile and spectral dust optical characteristics. In addition, spectral surface albedos are reconstructed for the dominant dryland ecosystems in Central Asia and incorporated into SBDART. Radiative transfer calculations are performed for cloud-free conditions with a spectral resolution of

0.05 µm in the SW and 20 cm-1 in the LW. Spectral radiative fluxes are integrated over the wavelength intervals to calculate SW, LW, and PAR fluxes at the surface. We also compute the diffuse component of PAR (PARdif), the diffuse fraction of PAR (Fdif), and the surface radiative balance (SRB) as follows:

AR AR AR P dif P tot P dir (4.1)

PAR F dif (4.2) dif AR P tot

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RB S SWdn SWup LWdn LWup (4.3) where SWdn, SWup, LWdn, and LWup are the downwelling SW flux, upwelling SW flux, downwelling LW flux, and upwelling LW flux at the surface, respectively. PARtot is the total photosynthetically active radiation incident at the surface. PARdir and PARdif are the direct and diffuse PAR components, respectively. We also compute the efficiency of dust radiative forcing in the PAR (mPAR) and in the surface radiative balance (mSRB), defined as the radiative forcing caused by one unit dust AOD (0.5 µm):

R d FPAR 0 AR (4.4) P dAOD

R d FSRB 0 RB (4.5) S dAOD where RFPAR=PARdustœPARclean, and RFSRB=SRBdustœSRBclean, which are the radiative forcing in the PAR and SRB, respectively.

4.2.1 Selection of Mineralogical Composition and Particle Size Distributions

Representative of Asian Dust

We use a Mie code to compute the dust optical characteristics over the wide spectral range (from the UV to thermal IR) that is required for this study. As a necessity, dust particles are assumed to be spheres. A number of studies have demonstrated the validity of the spherical shape assumption in radiative flux calculations. For instance, Fu et al. [2009] showed that this assumption caused less than 5% error in radiative fluxes compared to the spheroidal-shape approximation. Yi et al. [2011] reported the 5Þ10% error in surface radiative fluxes over land by comparing the results for spheres versus ellipsoids.

The dust composition is represented by a mixture of quartz, calcite, and clay-iron oxide aggregates based on recent measurements of size-resolved mineralogical

116 composition of Asian dust [Lafon et al., 2006; Jeong et al., 2007]. Quartz and calcite have negligible absorption in the SW but exhibit significant absorption in the LW [Sokolik and

Toon, 1999]. Clays are often aggregated with iron oxides such that these aggregates have much higher light absorption than individual minerals. In particular, illite is found to be the most abundant type of clay in Asian dust, while goethite and hematite are two most important iron oxides [Lafon et al., 2006]. Therefore we consider two clay-iron oxide aggregates: illite-hematite (IH) and illite-goethite (IG) [Lafon et al. 2006]. The effective refractive indices of the aggregates are computed using the Bruggeman approximation

[Sokolik and Toon, 1999].

Past studies showed that dust mineralogical composition varies with particle size.

We use the measurements of Lafon et al. [2006] to constrain the composition of fine and coarse particle size modes. The number fractions of quartz, calcite, and iron oxide-clay aggregates are 16%, 25% and 59% in the fine mode, and are 28%, 29% and 43% in the coarse mode. In both size modes, IG is assumed to constitute 70% of total aggregates. An important factor that can significantly affect the dust light absorption is the volume fraction (n) of iron oxides (in this case hematite and goethite) within the aggregates. Here we use the values representative of Asian dust recommended by Lafon et al. [2006]: 3.0% for the fine mode and 6.7% for the coarse mode.

Selection of representative dust particle size distributions was performed by examining the size distribution retrieved from sun photometers of the AERONET program. Because there are no AERONET sites in Central Asia, we utilize the data from several locations in northern China, including Dunhuang (DH), InnerMongolia (IM),

Yulin (YL) and Beijing (BJ). These sites are located in the transport route of dust from the Taklamakan and Gobi deserts. Because these stations are located at different distances from the dust source areas, selected aerosol size distributions during a dust

117 event or season can help examine how dust optical properties change during transport and the implications to dust radiative impact.

The aerosol size distribution products are retrieved from the sun and sky radiance measurements and report parameters of a bimodal lognormal function, including the volume fraction, volume median radius and geometric standard deviation [Dubovik et al.,

2002]. AERONET also measures spectral AOD which is used to derive the Angstrom

Exponent (AE). Given that dust events are commonly associated with relatively large

AOD and are dominated by coarse-size particles, we select several representative size distribution cases by examining the daily-average AOD and AE during the dust peak season of 2001, when AERONET was part of the ACE-Asia field campaign [Arimoto et al., 2006]. AE is inversely related to particle sizes such that AE < 0.5 (or 1.0) is often used to identify dust events. We find that the considered AERONET sites in northern

China show frequent high AOD and low AE during 2001 spring, and the retrieved size distributions contain a significant coarse size fraction. The relative proportion of the fine and coarse modes, however, shows large temporal variations at all locations. To address the observed dynamics in aerosol size distributions, for our modeling we select four representative cases shown in Table 4.1. In addition, we consider four dust size distributions from past studies. B02 represents the size distribution averaged from 8-year

AERONET data at the Bahrain site, which was suggested as a representative size distribution of dust [Dubovik et al., 2002]. C04 was measured by Clarke et al. [2004] during the ACE-Asia campaign during a strong dust event. L91 is the single-mode particle size distribution of d‘Almeida [1991], which is included in the OPAC library

[Hess et al., 1998]. L91 is thought to represent the long-range transported dust after preferential removal. We also consider another dust size distribution from OPAC

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consisting of three size modes (H98). Table 4.1 presents all the considered size

distributions in terms of their parameters of the lognormal size distribution expressed as:

V r r 2 dV r j log log 0v j exp (4.6) r j 2 d log 26 log9 j 2log9 j

where j denotes the j-th size mode with volume fraction (Vj), volume median radius (r0vj),

and geometric standard deviation (Lj).

Table 4.1 Parameters of dust volume size distributions from four AERONET sites and past studies. The lognormal parameters are volume fraction (Vj, %), volume median radius (rj, Jm) and geometric standard deviation (Lj) for j-th size mode. Source Mode 1 Mode 2 Mode 3 V1 r1 L1 V2 r2 L2 V3 r3 L3 DH Dunhuang 4 0.254 1.697 96 2.561 2.024 [AERONET] IM InnerMongolia 3.8 0.169 1.757 96.2 1.932 1.623 [AERONET] YL Yulin 3.9 0.235 1.786 96.1 2.23 1.758 [AERONET] BJ Beijing 4.9 0.209 1.815 95.1 2.331 1.736 [AERONET] B02 Dubovik et al. 9.1 0.149 1.52 90.9 2.538 1.84 [2002] C04 Clarke et al. 1.8 0.53 1.46 69.4 2.751 1.85 28.8 7.099 1.5 [2004] L91 d‘Almeida 100 3.228 2.2 [1991] H9 Hess et al. 3.4 0.267 1.95 76.1 1.648 2.0 20.5 11.02 2.15 8 [1998]

These size distributions are further compared in Figure 4.1. Various similarities

and differences are apparent. Some differences in measured size distributions could be

due to the variability in size spectra controlled by the dust emission and transport

processes, sedimentation of large particles, and mixing of dust with other types of

aerosols. For example at the Beijing site, dust aerosol can be mixed with fine particles

originating from urban and industrial sources, so that the size distribution has a larger

fine mode than at other sites. B02 has a larger fine mode than the size distributions from

119 the AERONET sites. This might be due to the multi-year averaging or because of an actual difference in dust sizes between Persian Gulf and East Asia. In addition, considered size distributions are derived by different means, either from the limited sampling at local sources, such as C04 and H98, or from column-averaged optical inversion, such as AERONET.

We first compute the spectral optical characteristics of each mineral species in

each size mode for any size distributions, including the normalized extinction +

and scattering + coefficients, and asymmetry parameter ß, where i denotes the i- th mineral species and j denotes the j-th size mode. Then for the j-th mode, the normalized extinction coefficient is calculated by summing up the normalized extinction coefficient of each species weighted by its number fraction, fi,j:

* * Kext j 2 f , ji Kext , ji 2 (4.7)

For instance for the fine mode, equation (4.7) can be written as:

* * * * * Kext f fcal, f Kcal, f f z, fqt K z, fqt f , fIG K , fIG f , fIH K , fIH (4.8) where the subscripts cal, qtz, IG, and IH represent calcite, quartz, IG aggregate and IH

-1 -3 aggregate, respectively. + (km /cm ) is then weighted by the number concentration Nj of each size mode to give the extinction coefficient of the dust mixture:

N * Kext 2 jKext j 2 (4.9)

The + can also be expressed in terms of the particle mass concentration. The

N m * Nj is related to the total mass concentration (M) as j j M M j . Here mj is the mass

* r3 2 -3 -3 fraction of the j-th mode and M j 4 368 ,0 jv exp 9log9 j 2 (Jg m /cm ), where

3 r0n,j is the volume median radius and d is the particle density (2.5 g/cm ). The scattering

120

coefficient + is calculated in the similar way, so the single scattering albedo Z0 of the dust mixture is given by:

0 2 Ksct 2 Kext 2 (4.10)

By performing Mie calculations for the selected dust size distributions and comparing the results to the aerosol optical depth measurement at the AERONET sites, we select M = 250, 500, and 750 Jg/m3 to represent low, moderate and high dust loadings, respectively.

Figure 4.1 Normalized dust volume size distributions for the dust cases considered in this study. The associated size distribution parameters are given in Table 4.1. The Angstrom Exponent of the size distribution from four AERONET sites is shown in the parenthesis.

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Figure 4.2 Land cover map in Central Asia based on IGBP classification in the MODIS land cover CMG product.

Figure 4.3 Narrowband surface albedo of different land type during spring and summer.

The computed spectral optical characteristics of Asian dust are then incorporated into the SBDART code. The dust vertical profile is specified based on the CALIPSO

(Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) lidar data. We examine the CALIPSO vertical feature mask during spring 2007 to select the

122 representative cases. We find that Asian dust aerosol often extends from the ground up to

4 km in the dust source regions and downwind, although dust layers aloft reaching up to

8 or 9 km were also observed. Here we consider two different profiles: one with a uniform dust layer between 0 and 4 km, (hereafter the mixed-layer) and another one with an elevated layer between 5 and 9 km (hereafter the elevated-layer).

4.2.2 Reconstruction of Spectral Surface Albedo

We use the MODIS land products to obtain the spectral surface albedo for the dryland ecosystems that are affected by dust. We first examine the MODIS CMG land cover product (MOD12C1), which contains fractions of each IGBP (International

Geosphere-Biosphere Program) land type at 1-km resolution. These fractions are used to identify pure (>95% coverage) land cover grids for each land type. Figure 4.2 shows the land cover map of the region for which we identified four dominant land types: open shrublands, grasslands, croplands, and barren or sparsely vegetated surfaces. To assign surface albedo to different land cover types, we collocate land cover with the MODIS

CMG surface albedo (MCD43C) product. The MODIS albedo is generated every 8 days on a geographic latitude/longitude projection at 0.05 degree resolution, and reported at seven channels centered at 0.47, 0.56, 0.65, 0.86, 1.24, 1.64 and 2.13 Jm [Gao et al.,

2005]. We use the while-sky albedo in accordance with the Lambertian surface assumption in SBDART. Snow-free pixels with best retrieval quality are selected to compute the mean spectral albedo for each land type, as shown in Figure 4.3. All the land types tend to have a higher albedo during summer due to less vegetation cover than spring. Unlike croplands, the land types of barren/sparsely vegetation, grasslands and open shrublands exhibit very similar spectral dependence of surface albedo. To focus on dryland ecosystems, we select two cases–grassland and cropland–to bracket the range of surface albedo variations, and use the USGS spectroscopy data to build the detailed

123 spectral dependence of surface albedo. We select the spectroscopy dataset that produces the smallest deviation from the MODIS albedo at the seven channels, and then apply a least-square fitting to compute the spectral surface albedo. The results are shown in

Figure 4.4. The constructed spectral surface albedos are used as input to SBDART simulations.

Figure 4.4 Constructed spectral albedos for grassland (red solid line) and cropland (blue solid line) by fitting the USGS spectroscopy data (dash lines) to MODIS narrowband albedos (red circles as grassland and blue stars as cropland)

4.3 RESULTS

4.3.1 Examination of Asian Dust Optical Characteristics

Using the size distributions presented in Table 4.1, we computed the dust spectral optical characteristics from the UV to the IR. Figure 4.5 shows the extinction coefficient

3 (Kext) for the dust loading M=250 Jg/m and the single scattering albedo (Z0) in the SW.

For comparison, we also show the OPAC (H98) bulk dust optical characteristics. All shown cases have the same composition, except the IM_agg and H98, so the strong influence of size distribution on the magnitude and spectral behavior of Kext and Z0 in the

124 solar and IR (not shown) is apparent. The IM_agg dust has the same size distribution as

IM but consists only of clay-iron oxide aggregates. This case, associated with the largest light absorption compared to other Asian dust cases shown in Figure 4.5, helps to demonstrate the influence of the mineralogical composition on dust optics. This composition difference has little effect on Kext, but it causes a significant decrease in Z0, e.g., from 0.93 to 0.84 at 0.5 µm with even larger decrease across the PAR region towards the shorter wavelength. Comparing to our Asian dust cases, the OPAC bulk dust model (H98) has relatively high Kext values and the lowest Z0.

Even for the same composition, the dust Z0 strongly varies among the considered size distributions, e.g., Z0 (0.5 Jm) ranges from 0.88 to 0.93. These values of Z0 are comparable to those reported by past studies for Asian dust-laden environments:

0.88±0.07 (0.55 Jm) at an urban site in Seoul, Korea [Jung et al., 2010], 0.919±0.056 at

Gosan [Nakajima et al., 2003], 0.957±0.031 (0.55 Jm) from in situ measurements during

ACE-Asia [Doherty et al., 2005], and 0.97±0.01 (0.55 Jm) from aircraft measurements of an Asian dust plume over the Pacific [Clarke et al., 2001].

The large spread of Kext values at 0.5 Jm is clearly seen in Figure 4.5a. One important implication is that, for a given dust loading, this results in large differences in

AOD. Figure 4.6 shows the AOD computed for three different dust loadings for all dust cases. Significant differences seen in AOD values suggest that caution must be exercised in interpreting linkages between AOD and dust loading which will be further addressed below. In terms of the spectral behavior, Kext varies slightly with wavelength in the SW, except for the DH, IM and B02 dust cases, which are associated with very small AE. For the IM case, Kext increases with wavelength up to 0.8 Jm and rapidly decreases in the near-IR, while for DH and B02 cases, Kext decreases with wavelength up to 1.0 Jm and then levels off. The value of Z0 increases with wavelength in the PAR region, except for

125 the DH and B02 cases. In the latter cases, Z0 slightly decreases with wavelength in the

PAR region and then increases in the near-IR. The spike at 0.66 Jm is due to the imaginary part of the refractive index of goethite. Note that Z0 computed for considered

Asian dust cases have spectral behavior similar to that of Saharan dust reported by Otto et al. [2007] (refer to their Figure 6).

3 Figure 4.5 a) Extinction coefficient Kext for a dust loading of 250 Jg/m , and b) single scattering albedo (Z0) computed for Asian dust and OPAC bulk dust (H98) cases. Shaded areas highlight the PAR spectral region.

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Figure 4.6 Computed AOD for dust loadings of 250, 500, and 750 Jg/m3.

Overall, examination of Figure 4.5 clearly shows that both Kext and Z0 strongly depend on the presence of both fine and coarse modes. This finding is in agreement with past studies [e.g., Otto et al., 2007] and reinforces the need for taking into account the broad range of particle sizes and having realistic representation of the fine/coarse mode ratio.

4.3.2 Impact of Dust on Total and Diffuse PAR

To assess the extent to which dust can affect the total PAR and its partitioning into direct and diffuse components, we performed radiative transfer modeling considering different combinations of dust optical cases and dryland ecosystems. PAR is absorbed by green vegetation and converted to biomass through photosynthesis. In ecological and land surface models, PAR is a key factor controlling the biophysical processes that govern photosynthesis and stomatal regulation of water, energy, and biogeochemical cycles.

Figure 4.7 shows how PAR varies with AOD for the considered dust cases. In each case,

127 results are shown for three AOD values corresponding to dust loadings of 250, 500, and

750 Jg/m3. These results are for the mixed-layer dust profile and sun elevation angle of

90°.

Under clean (dust-free) conditions, PAR varies from 490.0 to 495.6 W/m2 among the analyzed ecosystems (surface albedos). For a given dust loading, the dust case with IM size distribution gives the highest Kext in the PAR region and hence the largest AOD, causing the largest reduction in PAR. The DH size distribution gives the lowest Kext values and hence the highest available PAR. For instance, for a dust loading of 250 Jg/m3 and cropland albedo, the reduction in PAR is 12.6 W/m2 for DH (AOD = 0.19), and 88.9

W/m2 for IM (AOD = 1.2), while the PAR reduction is 119.9 W/m2 for IM_agg. The latter indicates that the composition change alone contributes to a reduction in PAR of

2 31.0 W/m . Due to its low Z0 values and relatively high Kext, H98 dust causes a larger reduction in PAR than other dust cases for the same dust loading, with the exception of the IM and IM_agg cases.

As seen in Figure 4.7a, PAR is nearly linearly related to AOD that supports the validity of using the dust forcing efficiency (mPAR) (equation 4.4).The mPAR mainly depends on the dust Z0 and to a lesser extent on the surface albedo. Computed mPAR values range from -67.7 Wm-2AOD-1 for the IM dust case to -82.2 Wm-2AOD-1 for the L91 case over cropland. The dust vertical profile has a negligible effect on mPAR. Here we use the radiation measurements by Bush and Valero [2003] under dust-laden conditions at

Gosan, South Korea to compare with our results. Based on their data, we estimate an

-2 -1 efficiency mPAR of -93.6±12.9 Wm AOD . This value is slightly higher but in general agreement with our results.

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Figure 4.7 a) Surface downwelling PAR as a function of AOD, b) same as a but for the diffuse PAR component (PARdif ). Squares denote the cropland surface albedo and diamonds are for grassland.

As demonstrated in past studies, diffuse PAR (PARdif) is an important factor in assessing the plant gross photosynthetic rate due to the fact that PARdif is often associated with higher light use efficiency than PARdir. Figure 4.7b shows that PARdif drastically increases in the presence of dust, although changes in PARdif differ between dust cases.

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3 2 For dust loading of 250 Jg/m , PARdif reaches 95.3 W/m for the DH case and

2 266.5W/m for the IM case. These differences in PARdif changes are mainly due to the large difference in the AOD between dust cases. The compositional effect is obvious by

2 comparing the IM with IM_agg case: the former causes 30.6 W/m more PARdif than the latter due to the low Z0 associated with IM_agg case. Overall, PARdif tends to increase when dust loading increases from 250 to 750 Jg/m3, except for IM and IM_agg cases, for

3 which PARdif begins to decrease when dust loading increases from 500 to 750 Jg/m .

When the dust profile changes to elevated-layer, PARdif has negligible changes.

The diurnal variation of aerosol loading and solar radiation can affect the ecosystem carbon uptake on a daily basis. Here we examine the solar angle dependence of the diffuse fraction of PAR (Fdif, equation 4.2). Figure 4.8a shows that Fdif remains low

(~10%) during most of the day under dust-free conditions. Values of Fdif become significantly higher in the presence of dust, especially at low sun elevation angles

(measured as the angle above horizon). Here we show the results for dust loading of 250

3 Jg/m and mixed-layer dust profile. For all dust cases, Fdif decreases with increasing sun angle before leveling off around 90o (i.e. local noon). For a given dust loading, considered dust cases have very different AOD and, as a result, significant differences in

o Fdif are clearly seen in Figure 4.8a. For instance at 90 , Fdif ranges from 20.0% for the DH case to 66.4% for the IM case. Due to solely the difference in Z0, Fdif is reduced down to

63.7% for the IM_agg case compared to the IM case. We also found that Fdif depends weakly (within 1.2%) on the ecosystem type (i.e., surface albedo), but this sensitivity increases with dust loading due to the effect of multiple scattering.

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Figure 4.8. a) Diffuse fraction (Fdif) of PAR, and b) the ratio of PAR to downwelling SW radiation, PAR/SWdn, as a function of sun elevation angle. The AOD (0.5 µm) for dust cases are shown in parenthesis.

Because of the lack of direct measurements of PAR, the common way to estimate its value is from measurements of downwelling SW radiation, SWdn. PAR is often computed as a fraction of SWdn. Although the PAR/SWdn ratio remains fairly constant for a given location at daily or longer time scales, it is sensitive to the presence of clouds and

131 aerosols [Frouin and Pinker, 1995]. Clouds generally increase the PAR/SWdn ratio due to the fact that clouds absorb much more in the near-IR than in the PAR spectrum. Aerosols can affect this ratio in more complicated ways, depending on the spectral dependence of the aerosol optical characteristics [Frouin and Pinker, 1995]. Figure 4.8b shows that the

PAR/SWdn ratio remains nearly constant at 0.45 (±4%) for clean and dusty conditions, except for the IM_agg case. This is due to the fact that both the Kext and Z0 of the IM_agg case have contrasting spectral behaviors in the PAR vs. near-IR. Our PAR/SWdn value

(0.45) is slightly lower than that (0.45œ0.50) reported by Frouin and Pinker [1995] and is close to the value (0.443œ0.445) reported by Jacovides et al. [2003] for soot.

4.3.3 Impact of Dust on Surface Radiative Balance

It is well known that dust can reduce the surface net radiation, which controls the available energy for surface turbulent fluxes and affects the surface temperature and boundary layer dynamics [Carslaw et al., 2010; Mallet et al., 2009]. Figure 4.9 shows the surface radiative balance (SRB, equation 4.3) as a function of AOD. Under clean conditions, SRB ranges from 688.4 Wm-2 over grassland to 809.1 Wm-2 over cropland.

We used the same surface emissivity and temperature for grassland and cropland, so the

SRB difference is due to the difference in surface albedo. For dust loading of 250 Jg/m3, the reduction in SRB over cropland ranges from -14.6 W/m2 for the DH dust case to -

128.8 W/m2 for the IM case, and to -168.3 W/m2 for the IM_agg case. The forcing

-2 -1 - efficiency in SRB (mSRB) ranges from -91.4 Wm AOD for the DH case to -122.1 Wm

2AOD-1 for the IM_agg case. As expected, the large differences in the dust forcing in the

SRB (RFSRB) and mSRB are due to the differences in the dust optical characteristics.

When the SW and LW radiation components were examined separately, we find that the reduction in SW net radiation ranges from -18.8 W/m2 for the DH dust case to -

140.6 W/m2 for the IM case, and to -182.7 W/m2 for the IM_agg case. The forcing

132 efficiency in the SW ranges from -110.1 Wm-2AOD-1 for the DH case to -139.4 Wm-

2AOD-1 for the L91 case. These values are higher than those (-73.0±9.6 Wm-2AOD-1) reported by Bush and Valero [2003]. We also find that the LW positive forcing compensates the SW reduction by 7.9œ26.4% depending on the dust case. In the case of elevated-layer, the dust LW forcing decreased by 1.7œ5.6 Wm-2 because of lower temperatures associated with the elevated dust layer.

Figure 4.9 Surface radiative balance (SRB) as a function of AOD. Squares denote the cropland surface albedo and diamonds are for grassland.

4.3.4 Impact of Dust on Vegetation Light Use Efficiency

In Sections 4.3.2, we demonstrated that Asian dust can exert a substantial impact on the total PAR and its direct/diffuse components. To explore the implications for croplands, here we consider several light use efficiency (LUE) models for different crop types (wheat, soybean, and corn) [Anderson et al., 2000; Choudhury, 2000; Choudhury,

2001; Cohan et al., 2002; Roderick, 2001]. It is established that the biomass accumulation

133 is nearly linearly related to PAR absorbed by the vegetation canopy (APAR), so that the carbon assimilation rate of the canopy (Ac) can be expressed as [Monteith, 1972]:

A LUE L APAR c (4.11) LUE L fPARLPAR

- where the ratio of Ac to APAR is called light use efficiency (LUE, mol CO2 (mol APAR)

1) and fPAR is the fraction of PAR absorbed by the canopy. fPAR can be readily estimated from the leaf characteristics and is currently an operational satellite land product from the MODIS instruments

Observations showed that LUE increases with increasing PARdif within the canopy [Gu et al., 2002]. Past studies demonstrated that LUE increases more or less linearly with the diffuse fraction, Fdif o[ Anderson et al., 2000; Choudhury, 2000;

Choudhury, 2001]. For the case of wheat canopy with a leaf area index (LAI) of 2.9,

Choudhury [2000] derived a LUEœ Fdif relationship as follows:

LUE .0 0356LFdif .0 0108 (4.12)

Anderson et al. [2000] derived empirical relationships between LUE and Fdif for

C3 and C4 plants. For soybean (C3 plant)

LUE .0 0251[ (8.0 Fdif )]5.0 : (4.13)

and for corn (C4 plant):

LUE 1[04.0 (3.0 Fdif )]5.0 (4.14)

Based on the modeling results of Choudhury [2001] for a variety of crop and forest canopies in different climatic zones, Roderick et al. [2001] derived the following

LUEœFdif relationship:

LUE .0 024LFdif .0 012 (4.15)

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Given that the latter is based on an ensemble of modeling cases for various vegetation types, this LUE model is not for a specific vegetation type but more generic in nature.

Although LUE is expressed solely as a function of Fdif, these LUE models implicitly account for likely changes in surface radiative balance, given that the models were developed for specific environmental conditions (temperature, soil moisture, etc).

Using the LUE models (equations 4.12œ4.15) and our modeling results, we assess the potential dust impact on the ecosystems. Figure 4.10 presents the LUE and Ac computed for wheat (equation 4.12) under clean and dusty (dust loading of 250 Jg/m3) conditions. Apparently, LUE has the same dependence on sun elevation angle as Fdif. As the sun angle increases, LUE decreases and begins to level off when sun angle > 70°.

Figure 4.10b shows that the gross photosynthetic rate Ac increases with the sun angle.

Generally, Ac is larger in all dust cases compared to clean condition, except that at low sun elevation angles with very high diffuse fraction, the reduction in PAR by dust leads to a decrease in Ac. Although the IM dust case leads to the largest LUE, the largest Ac is associated with the YL case. This is due to the fact that the IM case causes strong reductions in PAR, whose effect dominates the increase in Fdif and LUE.

Previous studies reported that the plant gross photosynthesis can reach a maximum at a critical aerosol loading corresponding to a critical AOD value, as summarized in Table 4.2. When the AOD drops below the critical value, the majority of shaded leaves receive low sunlight while the sunlit leaves are light saturated; On the other hand, when the AOD exceeds the critical value, most sunlight is attenuated such that the plant is light-starved. Using the LUE model of Roderick et al. [2001], we calculated Ac as a function of dust loading for all dust cases shown in Figure 4.11. There are noticeable differences in the behavior of Ac: it decreases with increasing dust loading for the IM and

IM_agg dust cases, while for the OPAC (H98) case, Ac increases with dust loading (up to

135

3 500 Jg/m ) and then decreases. For all other cases, Ac increases with dust loading. These can be explained by the fact that the dust impact on the plant carbon uptake depends on a balance between the reduction in PAR and the increase in PARdif. For example, the IM dust case has a much larger extinction coefficient than other cases, such that at low dust loading of 250 Jg/m3, resultant dust AOD exceeds the optimum value for gross photosynthesis.

Figure 4.12 presents the LUE and Ac computed with all the LUE models at sun elevation angle of 90° for dust loading of 250 Jg/m3. Due to the fact that corn (C4 plant) has a higher light saturation point and is less sensitive to changes in PARdif than C3 plants [Choudhury, 2001], the LUE and Ac values for corn are higher and less sensitive to the dust optical properties compared to other plant types. In addition, for certain crop type, Ac peaks at different values depending on the dust case, e.g., Ac of soybean and

-1 wheat reaches the maximum for the IM (3.4 mol CO2 (mol APAR) ) and YL (5.2 mol

-1 CO2 (mol APAR) ) cases, respectively. Thus, the dust impact on the PAR and plant photosynthesis depends on both the dust properties and ecosystem type (e.g., C3 vs. C4 crops).

Table 4.2 Past studies of aerosol impact on PAR and photosynthesis in cloud-free condition Study Aerosol Type Ecosystem Type Critical AOD Cohan et al. [2002] Urban pollution Crop ~ 0.6 (Z0 = 0.9) Niyogi et al. [2004] - Forest, crop, > 0.8 grassland Yamasoe et al. [2006] Biomass burning Tropical rainforest 1.5œ2.0 (Z0 = 0.93) Jing et al. [2010] - Semi-arid grassland not found, but > 1.2

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Figure 4.10 a) Light use efficiency (LUE) and b) carbon assimilation rate (Ac) computed with the Choudhury [2000] LUE model (equation 4.12) as a function of sun elevation angle.

137

Figure 4.11 Carbon assimilation rate calculated with the LUE model of Roderick et al. [2001] (equation 4.15) as a function of dust loading.

Figure 4.12 a) Light use efficiency (LUE), and b) carbon assimilation rate (Ac) computed for clean and dust conditions using different LUE models.

138

4.4 CONCLUSIONS

In this chapter, we investigate the dust impact on the photosynthetically active radiation (PAR) and surface radiative balance (SRB) considering conditions representative of dryland ecosystems in Central Asia. The spectral (from the UV to thermal IR) optical characteristics of Asian dust are computed based on representative size-resolved mineralogical composition and remote sensing retrievals of dust size distributions. MODIS narrowband albedo products along with the USGS spectroscopy library data are used to reconstruct the spectral surface albedo of different ecosystems.

These albedo data and dust optical models are incorporated in the radiative transfer code

SBDART, which is used to investigate the dust-induced changes in PAR and SRB in terms of forcing and its efficiency. The radiative transfer modeling results are analyzed with several LUE models to examine the implications of the dust radiative impact on the plant gross photosynthetic rate.

For a representative Asian dust composition, our results demonstrate significant variations in the optical characteristics in terms of both the magnitude and spectral dependence caused by variations in size distribution. For instance, at 0.5 Jm, the normalized extinction coefficient ranges from 1.9 x10-4 to 1.2 x10-3 km-1(Jg/m3)-1 and the single scattering albedo Z0 ranges from 0.88 to 0.93 for the dust cases (Figure 4.5). The highest absorption case (IM_agg), which only contains iron-oxide clay aggregates, gives the lowest Z0 of 0.88 and so does the OPAC bulk dust model. However, the OPAC dust has too low Z0 values across the SW spectrum compared to Asian dust cases.

Comparison with the OPAC bulk dust model stresses the limitations of this model in representing regional dust optics, in particular, Asian dust. This also demonstrates the need and advantage of representing the dust mineralogy and size distribution covering fine and coarse modes in assessments of the dust radiative impact.

139

The dust-induced changes in the total PAR, diffuse PAR and SRB are found to exhibit large variations over the dryland ecosystems, depending on the dust optical properties and the surface albedo. The estimated range of the forcing efficiency of Asian dust in SRB is from -68.8 to -122.1 Wm-2AOD-1, while in the total PAR it ranges from -

67.7 to -82.2 Wm-2AOD-1. The OPAC and IM_agg cases give the largest absolute value

(about -110 Wm-2AOD-1 in total PAR) due to higher absorptions. They also give the smallest increase in the diffuse component of PAR compared to Asian dust cases. Similar to other aerosol types, the ratio of total PAR to downwelling SW flux remains nearly constant (0.45 ±4%). However, the diffuse faction of PAR exhibits significant variations among considered Asian dust cases.

Using the light use efficiency (LUE) models for several types of crops (wheat, soybean, and corn), we estimate the influence of dust-induced changes in PAR on the plant photosynthesis. We find that the dust impact on the vegetation gross photosynthetic rate strongly depends on dust optical properties and crop types. The plant photosynthetic rate is enhanced under a low dust loading, but is decreased when dust loading exceeds a certain optimal level. This behavior is consistent with previous studies of other types of aerosols that identified a critical AOD. We demonstrate, however, that the critical AOD depends on both the loading and size distribution of dust. In particular, the relative proportion of fine and coarse modes is a key factor controlling the normalized + so that the same dust loading will result in different AOD depending on the size distribution.

Thus, in the case of dust, both loading and size distribution will need to be considered in determining the optimal regime of plant photosynthesis.

Given that the diffuse radiation fertilization is due to the fact that more scattered sunlight reaches shaded leaves, the extent of this effect on vegetation also depends on

LAI. The lower the LAI is, the less effect of the dust-enhanced diffuse radiation.

140

Wohlfahrt et al. [2008] showed that temperate mountain grassland is less sensitive to the diffuse radiation when the green area is low. They suggested that biomes with LAI<2 such as desert shrublands exhibit little sensitivity to diffuse PAR. Jing et al. [2010] showed that semi-arid grassland exhibits no fertilization effect to the aerosol-enhanced diffuse PAR that is likely caused by the low LAI and low light saturation point of grassland. However, dust can be transported downwind affecting large regions with vegetation having higher LAI values.

In addition to diffuse PAR and dust-induced changes in SRB, there are a number of important factors that affect the vegetation functioning. Under aerosol-laden conditions, concurrent variations in leaf/soil temperature and humidity may occur that can amplify the diffuse PAR effect [Gu et al., 2002]. Specifically, due to less incoming solar radiation, lowering leaf/soil temperature could depress the leaf/soil respiration, while a lower vapor pressure deficit tends to enhance the stomatal conductance and leaf- air exchanges. These environmental changes can exert either significant [Yamasoe et al.,

2006; Matsui et al., 2008] or negligible [Jing et al., 2010] effects on the canopy photosynthesis. Under certain conditions, the changes in the environmental factors can overcome the effect of diffuse PAR. Steiner and Chameides [2005] showed that under high-irradiance condition, the presence of aerosol reduces the incoming sunlight and leaf temperature down to an optimum level and thus enhances the photosynthesis, in which case the effect of increased diffuse PAR is negligible. Accounting for these different mechanisms will require an Earth system framework that couples biosphere with the physical climate system. We suggest that improved representation of dust that takes into account size-resolved composition of fine and coarse modes will be needed to provide more accurate assessments of how dust-induced changes in the radiation regime affect the

141 ecosystem functioning and the role of these processes in overall land-atmosphere interactions.

142

CHAPTER 5

CONCLUSIONS AND RECOMMENDATIONS

5.1 CONCLUSIONS

This thesis investigates the seasonal and interannual variability of dust aerosol and the linkage between dust, climate and land-cover/land-use change (LCLUC) in the dryland region of Central Asia (37NÞ55N, 50EÞ80E). The work focuses on three research questions: 1) How do the erosion threshold and dust emission vary in response to seasonal dynamics in atmospheric and surface conditions? 2) How does dust change in the past decade under the influence of climate variability and human land use? 3) What are the dust radiative impact on surface radiative balance and PAR, and possible effects on the dryland ecosystems?

Chapter 2 examines the dust seasonality focusing on the threshold friction velocity and dust emission under the effects of soil moisture, surface roughness heterogeneity and vegetation phenology. We use a coupled dust modeling system, WRF-

Chem-DuMo, to compute the threshold friction velocity and vertical dust fluxes during the dust season of 2001, which lasts from March 1 to October 31. The model incorporates two physically-based (MB and Shao) and one simplified dust schemes, and provides two options for soil size distribution data (soil texture-based and dry-sieved) for individual dust source areas. The MB and Shao schemes account for the soil moisture and surface roughness enhancement effects on the threshold friction velocity, and the dependence of dust vertical fluxes on the sandblasting efficiency of soil aggregates. The soil moisture is simulated by the Noah land scheme, while the surface roughness parameters (aeolian roughness length for MB scheme and roughness density for Shao schme) are derived from a static roughness map based on POLDER data and monthly NDVI data from the

143

MODIS instrument. We develop a novel approach to map the in situ measurements of dry-sieved soil size distribution data in Chinese deserts onto the dust source areas of

Central Asia, based on the similarities in land and soil characteristics. We conduct four model experiments to bracket the dust emission uncertainty from selection of model schemes and soil input data: MB_Dry (MB scheme plus dry-sieved soil size distribution data), MB_Wet (MB scheme plus soil texture-based soil size distribution data), Shao_Dry

(Shao scheme plus dry-sieved soil size distribution data) and Const_Uth (simplified scheme). The Const_Uth experiment is tuned to produce an annual emission equal to the mean of MB_Dry, MB_Wet and Shao_Dry experiments.

We find the threshold friction velocity significantly varies in space and time, in response to soil moisture variability, surface roughness heterogeneity and vegetation phenology. Based on the MB_Dry experiment, the threshold friction velocity exceeds 0.8 m/s in vegetated areas and exceeds 0.6 m/s in barren areas during spring (MAM) due to the precipitation maximum, which results in high soil moisture and expedites the growth of steppe vegetation and desert ephemeral plants. The threshold friction velocity drops to

0.3œ0.5 m/s during summer (JJA) due to drier soils and less vegetation. The dynamics in the threshold friction velocity determine the seasonality of dust emissions. In particular, although more frequent strong winds occur during spring than summer, spring dust emission is less because of the higher erosion threshold. The MB_Dry experiment produces 130.2 Mt or 35.7% spring dust, and 163.4 Mt or 44.7% summer dust out of annual emission of 365.0 Mt. The MB_Wet experiment produces much less annual emission (39.8 Mt) due to differences in the soil size distributions. Nonetheless, the dust seasonality remains nearly unchanged. The Shao scheme tends to generate a lower

(higher) threshold velocity over barren (vegetated) areas. This results in a shift of the peak dust emission to summer (58.7%), when vegetation cover is lowest. This

144 demonstrates strong differences in the model responses of MB and Shao schemes to changes in the surface characteristics.

By averaging the MB_Dry, MB_Wet and Shao_Dry experiments, we obtain a mean estimate (Exp_Mean) of annual emission for 2001: 255.6 Mt, of which 26.9% from spring and 50.4% from summer. The spatial distribution and seasonality of dust emission are in good agreement with ground-based dust records, and satellite aerosol optical depth

(AOD) and absorbing aerosol index (AAI). In particular, the strong source areas (Ustyurt

Plateau, Caspian coasts, Aralkum, Betpak-Dala Desert and loess deserts) are associated with a high frequency (over 80%) of daily AOD>0.7 and daily AAI>1.0. The weak sources (Karakum and Kyzylkum deserts) are associated with a low frequency of

AOD>0.7 and AAI>1.0.

Compared to the MB and Shao schemes, the simplified scheme using a fixed threshold velocity fails to capture the distribution of strong and weak source areas. There is a strong bias in the dust seasonality as well: the simplified scheme produces more dust during spring (37.9%) than summer (35.3%). Compared to Exp_Mean, the simplified scheme produces 41.1% more dust during spring, and 30.1% less dust during summer.

This suggests that ignoring the dependence of threshold friction velocity on surface characteristics lead to biased spatial distribution and seasonality of dust emission.

Therefore we argue that it is necessary to incorporate the effects of surface properties, particularly soil moisture, roughness length and vegetation, on the erosion threshold in dust models.

Chapter 3 investigates the interannual variability of dust and the linkage to climate and human-induced land-cover/land-use change in Central Asia. Following the same experiment design as Chapter 2, we compute the monthly dust emission for the period of MarchÞOctober 1999Þ2012. To account for the agricultural land use effect, a

145 global agriculture dataset is used to reflect annual changes in the cropland and pasture distribution in the WRF-Chem-DuMo model. To account for decadal changes in water bodies, the land/water mask is modified based on geo-referenced maps and satellite images. In this way, the land use effects on the dominant land cover and soil texture are taken into account, which consequently affect the dust source characteristics, as well as the region‘s climate regime (i.e., atmospheric circulation). We further use MODIS monthly NDVI from 2001 to 2012 to represent the vegetation effects on the soil exposure, erodible surface fraction and surface roughness.

Over the period of 1999Þ2012, annual dust emissions range from 81.1 Mt to

255.6 Mt, with an average of 153.7 Mt yr-1. The strongest dust source types are cropland

(35.1%), loess desert (25.6%) and sandy desert (20.3%). The dried seabed of Aral Sea or

Aralkum accounts for 1.8% of total emission. The spatial distribution of dust emission generally resembles the dust frequency ground observation averaged over 1999Þ2012 and

MODIS AOD averaged over 2001Þ2012. The monthly dust emission is strongly correlated (r=0.429) with the dust frequency index. High AOD values occur over the

Aralkum, Ustyurt Plateau and Caspian coasts. As a result, monthly dust emissions from these source areas are highly correlated with the monthly AOD, AE and frequency of daily AOD>0.7.

By splitting the simulation period into El Nino and La Nina years, we calculate the annual, wet-season (March-Apirl-May-October) and dry-season (June-July-August-

September) dust emissions under El Nino and La Nina conditions. La Nina years are associated with less frequent strong winds, but cause drought conditions with drier soils and less vegetation. The MB and Shao schemes display opposing responses to the El

Nino and La Nina conditions. The Shao scheme is more sensitive to changes in surface characteristics, while the MB scheme is more sensitive to changes in the frequency of

146 strong winds. As a result, during the period of 1999Þ2012, the Shao scheme produces much more dust in La Nina years under the dominant influence of drought, whereas the

MB scheme produces slightly higher dust emission in El Nino years due to the compensating effects of more frequent strong winds. The resultant mean estimate

(Exp_Mean) shows enhanced dust emission under La Nina conditions.

We further conduct correlation analysis between monthly dust emissions and various climate and surface variables. We find the surface wind speed, particularly the frequency of strong winds, is the dominant factor that affects the dust emission variability. The level of correlation however differs among dust schemes: the simplified

(Shao) scheme is most (least) dependent on wind speed. During 1999Þ2012, a decline in the strong wind frequency causes a decreasing trend of dust emission, at a rate of -

7.81±2.73 Mt yr-1. The trend is statistically significant at 95% confidence level. Dust emission has a higher correlation with drought index than with individual variables (i.e., precipitation, NDVI and surface bareness), suggesting that the drought index is a more integrative measure of the soil moisture and vegetation conditions. Nonetheless, only the dust emission from Shao scheme displays a significant correlation with the drought index. The occurrence of drought conditions in Central Asia is strongly related to ENSO: prolonged La Nina conditions can produce severe drought events and lead to enhanced dust emission. For example, the 1999Þ2001 drought is associated with anomalously strong dust emissions. Compared to dust emission, the dust loading (AOD) has a stronger correlation with ENSO. As a result, the AOD displays an increasing trend after 2003 due to more La Nina-like conditions. However, through EOF analysis on the AOD time series, we find that after removing the ENSO effect, the AOD displays a decreasing tendency from 2001 to 2012. Therefore, the apparent opposing trends of dust emission and loading are in fact caused by the difference in the level of correlation with ENSO. In

147 particular, the dust loading is more correlated than dust emission with ENSO. As a result, a longer AOD record is required to establish a robust dust trend.

Based on the agricultural dataset and information of surface water body changes, we derive the land use intensity (LUI) in Central Asia from 1999 to 2012, which is used to aid in distinguishing the natural and anthropogenic dust source areas. We find a strong sensitivity of the anthropogenic dust fraction to the selection of a LUI threshold.

Considering the land use conditions in Central Asia, we suggest that 80% is an optimal threshold that can be used to separate natural from anthropogenic sources. We estimate that about 58.4% of dust emission can be attributed to human activity during the

1999Þ2012 period. If using a more conservative threshold of 90%, we find an anthropogenic dust fraction of 20.7%. Our estimates suggest human plays an important role in the dust budget in Central Asian through agriculture and perturbing water bodies.

In Chapter 4, we investigate the dust impact on the surface radiative balance

(0.3Þ20 µm) and photosynthetically active radiation (PAR, 0.4Þ0.7 µm) and explore how dust may affect the dryland ecosystems. The dust optical properties are computed using representative size-resolved mineralogical composition and remote sensing retrievals of dust size distributions. A spectral surface albedo dataset is created by expanding the

MODIS narrowband albedo to the full shortwave spectrum using the USGS spectroscopy data. We find significant variations in the dust optical characteristics in both the magnitude and spectral dependence caused by variations in the particle size distribution.

At 0.5 Jm, the normalized extinction coefficient ranges from 1.9 x10-4 to 1.2 x10-3 km-

1 3 -1 (Jg/m ) and the single scattering albedo Z0 ranges from 0.88 to 0.93 for considered dust cases. The OPAC dust has too low Z0 values across the SW spectrum compared to Asian dust cases, suggesting a need for representing the dust mineralogy and size distribution covering fine and coarse modes in assessments of the dust radiative impact. Depending

148 on dust optical properties and surface albedo, the dust forcing efficiency ranges from -

68.8 to -122.1 Wm-2AOD-1 in the surface radiation balance, and from -67.7 to -82.2 Wm-

2AOD-1 in PAR. The diffuse faction of PAR exhibits even larger variations among considered Asian dust cases. Based on several light use efficiency models, we find that the dust impact on the vegetation gross photosynthetic rate strongly depends on dust optical properties and crop types. The plant photosynthetic rate is enhanced under a low dust loading, but is decreased when dust loading exceeds a certain optimal level. This critical AOD value depends on both the loading and size distribution of dust, particularly the relative proportion of fine and coarse modes. In light of the low biomass over drylands, we suggest the dust radiative impact on photosynthesis may be small, however, a range of other factors (e.g., temperature, humidity, deposition) that may be affected by dust can modify the ecosystem processes. We suggest Earth system models are needed to address the dust impact on ecosystems by perturbing the radiation environment and environmental factors.

5.2 RECOMMENDATIONS FOR FUTURE WORK

The findings in this thesis lend support to dust model developers by showing a need for more efforts in developing and testing dust parameterizations based on process- level understanding of the dust emission processes. It is not only important to improve simulations of dust emission in terms of spatial distribution and temporal variability, but it has the potential to further improve estimates of the dust loading, transport and deposition. The dust model used in this thesis incorporate two dust schemes that are similar in nature, because both represent two key processes in dust emission: saltation bombardment and soil aggregate disintegration. In that case, soil mobilization is assumed to be driven by the mean wind shear, and dust emission only occurs when the wind shear

149 exceeds a threshold (i.e., threshold friction velocity). However it has been known for long that under certain occasions, dust production is of stochastic nature and can occur as a result of aerodynamic lifting by turbulence flow, often called convective dust emission

[Klose and Shao, 2012, 2013]. Although the strength of convective emission is lower compared to the saltation mechanisms (e.g., post-frontal dust storms), dry hot desert regions, such as the Karakum Desert, are associated with favorable conditions for more frequent convective emission. Klose and Shao [2012] developed a convection-driven dust emission parameterization for regional models, and applied it to a week-long dust event at Taklimakan Desert. They obtained a general agreement between predicted and

LIDAR-measured boundary-layer dust loading. However, the relative importance of convection to total emission is unknown, given that their model could not explain the observed high dust loading above the boundary layer. Still, it may be a great asset to include such a convection-driven dust scheme into our model, because our results show much lower dust emission from the heart of the Karakum and Kyzylkum deserts as a result of weak winds. Including the convection-driven emission processes into our model might be able to account for the dry convection events that are responsible for the lingering dust haze weather observed in the area.

Development and implementation of dust schemes in regional models face one great challenge, that is, lack of dust source data at the appropriate climate model grid scales. This thesis benefits from a previous work by Darmenova et al. [2009] who made recommendations on assigning the values of a set of input parameters for Asian drylands.

Yet, the key input data for soil and vegetation properties are not available for Central

Asia, and most other desert regions as well. We have made an attempt to use soil size measurements of Chinese deserts in our study of Central Asia, which generate reasonable results. We further use an empirical method to derive the surface roughness

150 characteristics from MODIS NDVI, which was derived based on data collected from south Tunisia with similar landscapes to Central Asia. The applicability of these methods to Central Asia are yet to be investigated, as current dust schemes are developed based on data obtained at micro-scales, often from idealized wind tunnel experiments.

Implementation of these schemes at climate model scales often involves tuning of model parameters to match dust loading measurements obtained distant from source regions. It is therefore highly desirable to generate new datasets by conducting targeting campaigns, which can provide full characterization of the dust source erodibility for a range of land forms and climate regimes. Such datasets will greatly improve the applicability of dust schemes across a range of landscapes by minimizing the need for tuning the respective source strength to satellite observations. In particular, Central Asia is associated with complex terrains and land forms with diverse types of dust sources, which can hardly be characterized by a generalized dust parameterization. For example, the presence of large areas of saline deserts may render the clay content-based soil moisture correction inapplicable, because the salt content is highly hygroscopic and can induce strong inter- particle cohesion.

As pointed out in Chapter 3, a longer model simulation is required to establish physical linkages between dust and climate, and to derive a robust dust trend. In distinguishing the El Nino and La Nina years, we did not exclude the weak ENSO events due to short simulation time, which may affect separation of the influence of typical El

Nino and La Nina conditions in the dust variability. We propose an extended model simulation to cover the period of (at least) 1990sÞ2010s. One challenge for this is to obtain the vegetation data which is available from early-generation sensors (e.g.,

AVHRR). To use the vegetation data from a different sensor, the continuity issue needs to be considered [e.g., Beck et al., 2011]. Vegetation affects dust emission not only

151 during growing seasons, but may continue shielding the soil from erosion through the plant residuals, the so-called dead-leaf effect. How important this effect has on regional dust budget is unknown. A recent study by Kang et al. [2014] shows including the dead- leaf effect in a regional dust model improves the predication of dust concentration. We argue that the improvement in dust simulation by accounting for the dead-leaf effect does not necessarily imply the missing protection effect of dead-leaf is the true cause to the under-performance of the model. In fact, the Shao scheme used in their model has been shown in our study to produce anomalous high dust emission from barren surfaces and under dry conditions. Incorporating the dead-leaf effect into our model is therefore not a top priority at this stage.

152

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VITA Xin Xi was born in 1987 in a small town in south China. Upon graduation from high school, Xin was admitted in 2003 to the undergraduate program in Geography of

Beijing Normal University. He received a B.Sc. in June 2007, and two months later, enrolled in the graduate program of Earth and Atmospheric Sciences at Georgia Institute of Technology. Xin worked as a graduate research assistant to Dr. Irina N. Sokolik and studied the variability of mineral dust and connections to climate and land-cover/land-use change in Asia. In May 2014, Xin graduated Georgia Tech with a PhD in Atmospheric

Science. Xin married his long-time friend Shan in 2011. They have a son born in 2012.

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