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Temporal and spatial influences of precipitation and
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landforms on low visibility in North Africa
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Submitted to Journal of Geophysical Research, Earth Surface
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John-Andrew Ballantine, Department of Geography, University of Connecticut
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Natalie M. Mahowald, Department of Earth and Atmospheric Sciences, Cornell
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University
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Gregory S. Okin, Department of Geography, University of California, Los Angeles
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Thomas Dunne, Donald Bren School of Environmental Science and Management and
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Department of Earth Science, University of California, Santa Barbara
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Index: 0322: Constituent sources and sinks, 1631: Land/atmosphere interactions, 1815:
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Erosion, 1854: Precipitation, 9305: Africa
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0.
Abstract
We analyze the relationship between cumulative antecedent precipitation (CAP),
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landforms, and atmospheric dust in North Africa, as represented by visibility in the
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atmosphere. CAP gives an indication of the influence of precipitation on dynamic surface
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elements that influence erodibility. The seasonality of precipitation and low visibility
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events shows that dust is associated with the rainy season across much of the Sahara
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Desert, but it is associated with dry conditions toward the margins. Rank correlations
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between CAP at lags up to 12 months and the monthly frequency of visibility below 5 km
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show that dust emissions respond to CAP according to one of five spatially distinct
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patterns. In the Sahel and southern Sahara Desert, dust is suppressed following rainfall,
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probably due to the growth of vegetation following periods of rain. CAP has little effect
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in the core of the Sahara Desert, but in the remainder of the study area, dust often is
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enhanced by precipitation, implying that precipitation disturbs the landscape, making it
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vulnerable to wind erosion. Analyses of the statistical relationship between landforms and
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dustiness indicate that alluvial surfaces, bedrock surfaces, dunes, dry lakebeds, regs, and
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sandsheets are all potential sources of dust. These results show that dust sources can
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occur on a range of landforms, but more importantly, they are dynamic features of the
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landscape, responding to precipitation in varying ways across North Africa. The dynamic
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nature of the surface must be more clearly understood to understand large dust sources
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and represent them in atmospheric models.
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1.
Mineral dust in the atmosphere redistributes nutrients throughout the landscape
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Introduction
[McTainsh and Strong, 2007; Li et al. 2008, 2009], affects infrastructure [e.g. Pauley et
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al., 1995], poses health risks [e.g. Griffin et al., 2001], and obscures visibility [N’Tchayi
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et al., 1994; Engelstaedter et al., 2003; Liu et al., 2004; Orlovsky et al., 2005]. At
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regional to global scales, atmospheric dust influences the radiative balance of the
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atmosphere [e.g. Miller and Tegen, 1998; Sokolik et al., 2001; IPCC, 2007] and
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redistributes natural pathogens and nutrients [Swap et al., 1992; Chadwick et al., 1999;
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Goudie and Middleton, 2001; Okin et al., 2004] over intercontinental distances. In all of
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these cases, a clearer understanding of the source conditions during dust erosion is
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important.
Dust sources are distributed across the landscape according to the vulnerability of
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landforms to erosion and factors that influence the wind acting upon the surface such as
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vegetation and local topography [e.g. Raupach et al., 1993; Gillette, 1999; Okin and
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Gillette, 2001]. Major source regions are composed of smaller individual “hotspot”
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sources that contribute to the overall dust plume when the surface is vulnerable and the
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wind shear stress is sufficient to mobilize surface sediments [Gillette, 1999]. In the
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regional context of this paper, the mobilization of dust depends on three factors: 1) the
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landform, which determines long-term sediment availability, 2) the dynamic surface
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cover, particularly vegetation and dynamic elements of the surface such as mobilized
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sediment and crusting, and 3) the force of the wind, which drives erosion. The roles that
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these factors play during dust events needs to be examined in order to understand dust
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emissions at the regional scale.
Previous authors have used satellite imagery [e.g. Middleton and Goudie, 2001;
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Prospero et al., 2002; Prigent et al., 2005; Laurent et al., 2008; Schepanski et al., 2007]
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or models [e.g. Marticorena and Bergametti, 1995; Ginoux et al., 2001; Luo et al., 2003]
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to identify dust sources of regional to global significance. Mahowald et al. [2007] used
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surface meteorological station data to identify factors associated with dust emissions at
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regional and global scales.
In this paper we investigate the relationship between cumulative antecedent
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precipitation (CAP), landforms, and atmospheric dust. We use near-surface
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meteorological measurements and identification of source landforms from satellite
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imagery to characterize the factors important in dust emissions. Surface visibility is used
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as the proxy for atmospheric dust content [N’Tchayi et al., 1994; Seinfeld and Pandis,
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1997; Mahowald et al., 2007]. We focus on the influence of landforms and the dynamic
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surface, as represented by CAP; the influence of wind will be addressed separately.
The data and processing methodology are described in section 2. For both CAP
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and landforms, we begin with exploratory analyses of patterns in the data that indicate
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relationships between the factor and low-visibility conditions during events (sections 3.1
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and 4.1). The influence of CAP on dustiness is further examined with a cluster analysis of
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rank correlation values between CAP and monthly frequency of low visibility events
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(section 3.2) and Mann-Whitney testing of whether CAP is greater or less during dusty
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and non-dusty conditions (section 3.3), after which the precipitation results are discussed
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(section 3.4). Mann-Whitney testing of which landforms are likely to be upwind during
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dusty and non-dusty conditions is presented in section 4.2, followed by a discussion of
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the landform results (section 4.3) and overall conclusions (section 5).
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2.
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2.1 Data
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2.1.1 Meteorological station data
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Methods
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Visibility, wind speed, and wind direction data were retrieved from surface
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meteorological stations. These data were collected from stations within an area extending
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from 10◦ N to 40◦ N and 20◦ W to 40◦ E (Figure 1). This area encompasses hyperarid, arid
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and semiarid locations in North Africa (0-50 mm, 50-200 mm, and 200-400 mm of mean
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annual rainfall, respectively (Tucker et al., 1991)). These wind and visibility data were
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obtained from the National Centers for Environmental Prediction Automated Data
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Processing Global Surface Observations database. Data records from 1931-1977 were
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retrieved from list 463.2 (“early data”) and from 1978-June 2004, from list 464.0 (“recent
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data”) of the National Center for Atmospheric Research data archive (
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http://dss.ucar.edu/catalogs/ranges/range460.html). From the list, 225 stations were
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chosen based on having at least 365 data records per year for at least ten years (Figure 1).
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Each record contained data for wind speed, wind direction, visibility, and time of record.
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Data records were available up to six times per day, usually at regular intervals (e.g. four
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measurements per day at 0:00, 6:00, 12:00, and 18:00). Thus, an event record consists of
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a snapshot of the above variables, and multiple records could be present from a given
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day.
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Figure 1: Data frequency by station
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Thirty-one of the 225 stations were removed because the rank correlation between
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monthly median wind speed and the monthly frequency of dust events was significantly
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negative (p≤0.05). This negative relationship indicated that lower wind speeds were
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responsible for more frequent low-visibility events and therefore it was assumed that non-
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dust factors (e.g. rain, fog, or smoke) dominated the record of low-visibility events at
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these locations. Most of these deleted stations were located near the northern littoral of
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the continent where marine fog and/or rain associated with frontal systems from the north
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could influence visibility. Over long wind speed records, the instrumentation may change
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and structures may be built which would alter the local wind field, creating
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discontinuities in the data. Mahowald et al. [2007] identified two such stations in North
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Africa (WMO stations 607600 (Tozeur, Tunisia) and 612970 (Sikasso, Mali)) and these
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stations were also removed. Thus, 192 stations were used in the following analyses.
At each of the 192 meteorological stations, three datasets were developed from
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the station data to represent dustiness, as derived from visibility. The first dataset is
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referred to as wind speed events (WSEs). The WSEs are comprised of the thirty records
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from the “recent data” with the highest wind speeds for each year at each station. Because
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high winds are necessary to mobilize dust, the WSEs represent those cases where dust
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would be expected if a sufficient dust source existed upwind of the recording
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meteorological station. Mahowald et al. [2007] examined the relationship between the
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number of visibility events per month below a threshold and aerosol optical depth (AOD)
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derived from AERONET [Holben et al., 2001]. Successive visibility thresholds from 1 to
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10 km were tested. They found that the number of visibility events was most closely
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correlated with AOD for thresholds between 3 and 7 km and used 5 km as a threshold for
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studying dust. This 5 km threshold matches the “severe dustiness indicator” of N’Tchayi
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et al. [1994]. Thus, we used a 5 km visibility threshold to separate “dusty” low visibility
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events from “non-dusty” higher visibility events as the two samples of the WSE
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population being tested. The sample of WSEs with visibility less than 5 km is hereafter
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referred to as WSEV-5 and the sample with visibility more than 5 km is referred to as
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non-WSEV-5.
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In the second dataset, for each year and at each station we isolated up to thirty
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“visibility events” where visibility was at or below a threshold of 1 km (VE-1s). These
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VE-1s were drawn only from the “recent data” and represent the most intense visibility
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events recorded between 1978 and 2004. The 1 km threshold corresponds with the dust
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storm frequency measurement of Engelstaedter et al. [2003].
The third visibility-related dataset used the monthly frequency of events when
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visibility dropped below 5 km. This value is referred to as the dust event frequency at 5
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km (DEF-5). To obtain a longer period for calculating the DEF-5 value, both the “early
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data” and “recent data” were used after testing that the long term averages for each
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dataset were statistically equal.
Each of the VE-1 and WSE datasets represents a collection of discrete events that
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occurred between 1978 and 2004. In contrast, the DEF-5 dataset represents the average
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conditions for each month. Taken together, these three datasets, hereafter referred to
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collectively as visibility parameters, shed light on how CAP and landforms are associated
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with dustiness in ways that no one dataset could do alone.
The visibility parameter datasets are each structured as a matrix of values with a
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row representing each event or monthly DEF-5 record and columns representing the date,
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wind speed, wind direction, and visibility. As described below, thirteen columns of CAP
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and ten columns of fractional upwind landform during the event are also part of the VE-1
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and WSE matrices.
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2.1.2 Precipitation data
Monthly precipitation at each meteorological station was derived from a global,
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interpolated, one-degree gridded, precipitation dataset produced using the methods
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described by Dai et al. [2004]. Precipitation was obtained from this interpolated dataset
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because precipitation records at the meteorological stations were unreliable. The
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precipitation time series at each station was used to calculate monthly CAP values at lags
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from zero to twelve months. Each event record in the VE-1 and WSE datasets had
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thirteen CAP values representing the accumulated precipitation from zero to twelve
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months before the event. A lag of zero months indicated that the data record occurred in
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the same month as the month of precipitation record.
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2.1.3 Landform data
Landforms represent the aspects of the Earth’s surface that do not change on time
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scales important to dust mobilization events. These aspects include topography, non-
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erodible surface elements (e.g. bare rock, boulders and desert pavements), and the
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particle size distribution or texture of soils and surface sediments. The texture of surface
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sediments includes the presence or absence of fine particles that are the source material
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for dust, as well as sand-sized particles that act as saltating impactors [Shao et al., 1993;
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Gillette, 1999].
The landform map developed by Ballantine et al., [2005] was used to identify the
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mix of landforms upwind of a station during VE-1s and WSEs. The ten landforms
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identified by Ballantine et al., [2005] were: alluvial surfaces, dunes, sandsheets, dry
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lakebeds, water, regs, basaltic plateaus and cones, other non-mountainous bedrock
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surfaces, mountains, and vegetated surfaces. The basaltic surface, bedrock surface, and
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mountain classes were distinguished from one another based on the geomorphic
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classification of Raisz [1952] and the spectral properties of geomorphic units he
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identified [Ballantine et al., 2005]. The same is true of the distinction between alluvial
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surfaces and regs.
The fractional abundance of each landform within a 45 degree circular wedge
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upwind of the station during a given event was recorded. Upwind direction was
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determined as the direction from the standard 8-point compass rose within which the
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wind direction for the event occurred. The radius of the wedge was defined to be 100 km
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based on the maximum upwind distance that would allow transport of a dust plume from
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a hypothetical source to produce a horizontal visibility of 1 km at the recording
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meteorological station. A Gaussian plume model was used to perform this calculation
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[Seinfeld and Pandis, 1997]. The fractional coverage of each landform associated with a
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given VE-1 or WSE record was determined by calculating the fractional coverage of that
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landform within the circular wedge with direction containing the dominant wind direction
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associated with the record.
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2.2 Analytical methods
The analytical methods used in this study address statistical relationships between
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precipitation or upwind landforms and dustiness. The visibility-related datasets being
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used in this study were not distributed and not transformable to normally distributed data,
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according to a Jarque-Bera test. Thus, non-parametric methods (rank correlation and the
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Mann-Whitney U test) were needed for statistical testing of relationships and differences
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between precipitation or landforms and dustiness [Sanders and Smidt, 2000].
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2.2.1 Rank correlation
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Three basic steps were used to identify a discrete number of separable,
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characteristic patterns in the relationship between the CAP vector and DEF-5. These
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steps were: establishing a vector or “lag spectrum” of correlation coefficients,
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determining the number of clusters/classes to use, and clustering the data using a K-
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means clustering analysis.
For the first step, we used the rank correlation coefficients between each of the 13
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CAP values (current month and previous 12 months) and DEF-5 to form a 13-element
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rank correlation “lag spectrum” vector at each station. As an example, the rank
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correlation between the vector of CAP values at a lag of 12 months and the vector of
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DEF-5 for Bilma, Niger formed the 13th element of the DEF-5 lag spectrum for that
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location.
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The second and third steps involved an iterative procedure of identifying a
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number of classes of individual stations with like behavior, and then applying an
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unsupervised K-means clustering analysis to fit the data into that number of classes. The
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K-means clustering technique grouped samples (each meteorological station’s lag
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spectrum) into distinct classes using an iterative minimum distance technique within the
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13-dimensional data space defined by the 13 rank correlation coefficients at each station
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(Tou and Gonzalez, 1974). Five classes optimized the ability of the classes to represent
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distinct signals while not including superfluous classes. Groups were chosen for
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separability without any attempt to interpret the meaning of each group in the
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classification stage. Thus, each class represents a statistically separable group of stations
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defined by the correlations between CAP and DEF-5.
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2.2.2 Mann-Whitney tests
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The Mann-Whitney U test was used to identify whether the sample of records
during dust events were statistically different from the sample of records when no dust
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event was occurring given a particular CAP lag or upwind landform [Sanders and Smidt,
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2000]. Thus, a given Mann-Whitney test could be phrased as testing the null hypothesis:
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there is no difference in the value of the parameter in question between WSEV-5s and
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non-WSEV-5s. The parameter in question could be CAP at a given lag (one for each
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month lag from 0-12 months) or fractional upwind landform coverage for one of the ten
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landforms. Mann-Whitney tests were performed on the WSE data, using a visibility
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threshold of five kilometers to divide dust events (WSEV-5 sample) from non-dust events
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(non-WSEV-5 sample). For samples determined to be significantly different, the median
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value of the parameter in question (e.g. upwind fractional coverage of dunes) was
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calculated for each sample.
For the landform fractions, the Mann-Whitney test was applied to two groups of
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data from the WSE population: “all data” and “local data”. The “all data” group was
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composed of data from the whole of North Africa, using data from all stations and events
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simultaneously. Tests on the “all data” group produced one result for the whole of the
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study area that represented aspects of the data common to all locations. For tests on the
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“local data”, a separate result was produced for each station. Tests on the local data
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highlighted spatial patterns in wind erosion factors and therefore could be expected to
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illustrate regional differences by showing spatial clusters of stations of similar values. For
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tests on CAP, only the “local data” were used because the influence of precipitation
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varied from region to region.
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3.
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3.1 Seasonal cycle analyses
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Precipitation Results
Comparing the seasonality of the majority of dust events with the seasonality of
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precipitation at locations across the study area provided a qualitative view of the
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relationship between the timing of precipitation and dust. The seasonality of dust events
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is presented in Figure 2a. The Sahel shows a dominance of winter (January-March) low
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visibility events, the western Sahara Desert during the summer (July-September), and the
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band stretching from northern Algeria, through Libya, into Egypt during the spring
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(April-June). The season of greatest mean precipitation (1931-2004) is illustrated in
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Figure 2b. Winter and summer are the dominant seasons of greatest precipitation with the
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former in the north and the latter in the south.
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Figure 2: Seasonality of VE-1s and precipitation
By comparing the seasonality of VE-1s (Figure 2a) and precipitation (Figure 2b),
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it is apparent that the majority of low visibility events in the Sahel, southern Sahara
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Desert, and northern margin of the Sahara Desert do not occur during the season of
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maximum rainfall. The Sahel and southern Saharan Desert results agree with those of
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N’Tchayi Mbourou et al., [1997]. Stations where the majority of dust events occurred in
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the wet season are confined to locations in the central Sahara Desert (e.g. Tessalit, Mali
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and Tamanrasset, Algeria). These results concur with those of N’Tchayi Mbourou et al.,
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[1997], but N’Tchayi Mbourou et al. did not analyze the mechanism behind dust being
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associated with the wet season (see section 3.4).
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3.2 Rank correlation between CAP and DEF-5
The mean correlation value for all stations in each class at each of the thirteen
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time lags in the CAP vector is shown in Figure 3 (i.e. at time lag 0, the mean of the first
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element of each of the CAP vectors for all stations in the class is calculated). The mean
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correlation spectra were calculated by determining the mean of the rank correlation
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values between CAP and DEF-5 for all of the stations in a given class at the time lag
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shown.
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Figure 3: Correlation curves
The mean correlation spectra show distinct patterns characteristic of the regions
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shown below in Figure 4. Although the positive and negative significance thresholds
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indicate lags at which the data are not significant for a given class, the consistent shapes
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of the curves in the non-significant region are still indicative of the seasonal trend of the
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CAP influence. Each curve (aside from class 3) has sections that are significant, but the
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overall shape defines the changing relationship between DEF-5 and CAP over several
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months of lag. Thus, the fact that class 1 shows a trend of non-significant correlations
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between six and eleven months may still reflect a process affecting the evolution of the
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surface influence during the transition from one dominant process to another.
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Figure 4: Map of correlation classes
The correlation classes derived from the lag spectra represent locations that have
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statistically similar correlation spectra and therefore similar temporal relationships
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between CAP and dustiness. The spatial distribution of the five classes, determined by
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the K-means cluster analysis, is shown in Figure 4. There is a clear clustering of class 1
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through the Sahel and of class 2 in the southern Sahara and eastern Sahel. The remaining
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classes have a less clear clustering, but some patterns are apparent. Class 3 dominates
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stations across the central Sahara from the Atlantic Coast to the coast of the Red Sea and
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south into Ethiopia and eastern Sudan. There is also a scattering of stations from class 3
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in North Africa along the Mediterranean Coast and in the Atlas Mountains of Morocco,
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Algeria, and Tunisia. Stations in class 4 are largely located in central and northern
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Algeria as well as along the Mediterranean Coast from Tunisia to Egypt. Stations in class
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5 are also located in the northern part of the study area with clusters in interior Egypt,
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Libya, and eastern Algeria as well as along the western Mediterranean Coast and in the
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Atlas Mountains. The distinction between classes 4 and 5 in the interior of North Africa
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seems to be that class 4 is more common further west.
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3.3 Relating CAP to dusty and non-dusty conditions
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A Mann-Whitney test on the “local data” group was used to determine if there
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was a significant difference in the ranks of antecedent precipitation values for samples
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determined by WSEV-5s and non-WSEV-5s at each station; results are shown in Figures
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5a-d. Antecedent precipitation is cumulative, so each individual antecedent precipitation
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lag represents all of the precipitation accumulated between that number of months before
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the event and the event itself.
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Figure 5: Maps of M-W results for CAP
The spatial and temporal patterns of differences in the WSEV-5 and non-WSEV-5
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samples for CAP at a given lag (Figures 5a-d) illustrate the changing response of
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dustiness to CAP. At a lag of zero months (Figure 5a), lower median values of CAP are
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associated with low visibility events throughout the Sahel and at some locations in
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Morocco and Algeria, indicating that drier conditions are associated with WSEV-5s.
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There are a few cases where larger amounts of precipitation are associated with WSEV-
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5s at locations scattered across the Sahara and in clusters in northern Tunisia and eastern
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Libya. At a lag of three months, the pattern of low precipitation corresponding with
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WSEV-5s is more widespread through the Sahel and a small cluster also appears in
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Tunisia, northeast Algeria, and northern Morocco. High antecedent precipitation, at a lag
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of three months, associated with WSEV-5s occurs in northern Tunisia, Libya and Egypt.
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As the lag is increased to six months, the area of low precipitation that is associated with
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VE-5s moves north into the southern Sahara and up the west coast. The area of high
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precipitation associated with VE-5s is scattered through the northern Sahara east of
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Algeria and a cluster appears in Senegal. At nine months lag, the cluster of stations with
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high precipitation associated with VE-5s in Senegal expands and a similar cluster appears
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in southern Niger while the northern cases of this condition thin out. The cases of lower
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precipitation being associated with WSEV-5s extend further north into the Sahara Desert,
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especially in the northwest.
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3.4 Precipitation discussion
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3.4.1 Precipitation and dust
Precipitation directly influences dust emission through its impact on soil moisture
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[Fecan et al., 1999], surface crusts, and sediment mobilization and deposition [Reid and
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Frostick, 1997; Reheis and Kihl, 1995; Okin and Reheis, 2002; Bryant, 2003; Mahowald
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et al., 2003]. Indirectly, precipitation influences dust emissions by promoting vegetation
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[e.g. Stockton and Gillette, 1990; Raupach et al., 1993], biotic crusts [Belnap and
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Gillette, 1998], and the effluorescence of playa salts (a function of ground water depth
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among other factors) [Reynolds et al., 2007; Elmore et al., 2008]. Each of these
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processes is a response to precipitation at a different timescale. The most likely scenarios
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for dust suppression or enhancement by these processes are shown in Table 1:
Table 1: Hypothetical influences of precipitation
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15
Soil moisture immediately suppresses dust emissions following rainfall, but will
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generally dry quickly so the effect is short-lived unless rains are prolonged [Fecan et al.,
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1999]. Sediment erosion, fluvial transport, and deposition by ephemeral floods will
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produce a dust source for as long as the deposited material remains at the surface
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unprotected by vegetation or crusts. These sediments may be of limited quantity so the
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source will disappear when the fresh material has been exhausted by erosion [e.g. Reid
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and Frostick, 1997; Okin and Reheis, 2002; Bryant, 2003]. Annual vegetation protects
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the soil surface and suppresses dust emission [e.g. Raupach, 1992]. Satellite and field
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observations of vegetation in the Sahara Desert, the Sahel, and Israel found that
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vegetation cover increased in response to rain after a few weeks to 3 months [Nicholson
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et al., 1990; Schmidt and Karnieli, 2000; Herrmann et al., 2005; Lindermann et al., 2005;
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Abdallah and Chaieb, 2006]. Several months after wet periods, vegetation dries and fires
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or grazing cattle can disturb the land surface, leading to dust emissions at longer time lags
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[e.g. Nicholson et al., 1998]. Ephemeral lakes do not produce dust when they are filled
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with water, but may become very active sources when the water table is just below the
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surface [Bryant, 2003; Reynolds et al., 2007; Elmore et al., 2008]. The timing of dust
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suppression or enhancement in ephemeral lakes is difficult to determine from
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precipitation records alone so more extensive analysis of satellite imagery would be
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required to identify wetting and drying of these systems [e.g. Birkett, 2000; Bryant,
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1999].
The theoretical responses of dust to CAP presented in Table 1 match many of the
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patterns presented in the results section (Figures 2-5). Figure 2 only shows VE-1s and
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seasonality of rainfall (effectively short CAP lags), but two basic classes of relationship
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become apparent. At stations in the Sahel, southern Sahara Desert, and along the northern
357
margin of the Sahara Desert, the majority of low visibility events do not occur during the
358
season of maximum rainfall. In these cases, VE-1s occur during the dry season, when one
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might expect the vegetation cover to be lowest. This supports the hypothesis that
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vegetation and/or soil moisture plays a role in decreasing dust production in these
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sparsely vegetated areas of North Africa. The existence of dust in the wet season in the
362
central Sahara Desert supports the hypothesis that precipitation and the ensuing fluvial
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action make sediment available for wind erosion [Reheis and Kihl, 1995; Okin and
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Reheis, 2002; Mahowald et al., 2003]. Strong wind events which mobilize more dust
365
[Engelstaedter and Washington, 2007] can be associated with precipitation early in the
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wet season when the soil is still vulnerable [Sow et al., 2009]. A multiple regression
367
between DEF-5, CAP at zero months lag, and wind speed did not show a significant
368
relationship between these factors. However, it is possible that intense winds occurring
369
concurrently with precipitation are responsible for some dust emission events that have
370
been identified here as being associated with precipitation [Sow et al., 2009].
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3.4.2 Rank correlation between CAP and DEF-5
When plotted geographically, the stations belonging to each correlation class
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clustered in distinct regions (Figure 4). At locations where CAP is associated with
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enhanced or reduced dust production (i.e. not class 3), the correlation class curves (Figure
375
3) illustrate the nature and timing of the role of precipitation. The mechanisms for lags in
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positive or negative relationships between CAP and DEF-5 hypothesized in Table 1
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explain the significant portions of the curves in Figure 3.
17
378
Class 1 has predominantly negative correlations between CAP and DEF-5,
379
indicating that precipitation causes a response on the surface that reduces dust emissions
380
at very short lags (0-5 months). It is likely that these negative correlations are associated
381
with an immediate response to increased soil moisture and/or the growth of annual
382
vegetation (e.g. grasses), at time scales of a few weeks to three months, that prevents dust
383
emissions at these time scales. Annual grasses grow rapidly following the onset of the
384
wet season and remain as groundcover for several months until drying, grazing and
385
trampling by livestock, and fires have removed their effectiveness in sheltering the
386
surface [e.g. Ehrlich et al., 1997; Herrmann et al., 2005; Lindermann et al., 2005]. Class
387
1 occurs almost exclusively in the Sahel where the advance and retreat of the desert (as
388
defined by satellite imagery) in response to precipitation [Tucker et al., 1991] has been
389
shown to be associated with dustiness far downwind [e.g. Prospero 1996; Chiapello et
390
al., 2005; Engelstaedter et al., 2008]. MODIS imagery shows that toward the end of the
391
dry season, widespread burning occurs in the Sahel as documented by Ehrlich et al.
392
[1997]. It is possible that the smoke from these fires reduces visibility, particularly when
393
winds are calm, leading to the slightly positive (though not significant) correlations
394
shown in Figure 3. The year-to-year dynamics of fire are beyond the scope of this paper,
395
but if these positive correlations are associated with smoke from fires, they provide
396
evidence that fires are a consistent factor influencing the landscape over the period of this
397
study.
Class 2 is similar to class 1 in that it has dominantly negative correlations between
398
399
CAP and number of visibility events, but the strongest, significant negative correlations
400
occur at longer lags of 3 to 9 months (Figure 3). This means that the greater the rainfall is
18
401
over the preceding 3-9 months, the lower is the frequency of low-visibility events in the
402
month in question. The position of class 2 in the more arid transition from the Sahel to
403
southern Sahara and in central Sudan, places it in a regime of sparser desert vegetation.
404
The mechanism behind this delayed suppression of dust is harder to interpret than the
405
annual vegetation signal shown in class 1. Zender and Kwon [2005] found a negative
406
correlation between the TOMS aerosol optical depth product and precipitation at a lag of
407
nine months for the Eastern Sahel. They suggested this effect might be due to excess non-
408
photosynthetic vegetation (NPV) suppressing dry season dust, months after an
409
anomalously wet season [Okin et al., 2001]. In other words, a very wet season would
410
create an abundance of dry plant matter that would shelter the surface more than in a dry
411
season that wasn’t preceded by an intense wet season. A similar effect was noted for
412
growth of invasive Saharan Mustard (Brassica tournefortii) at Soda Lake in the Mojave
413
Desert following intense rains in the spring of 2005 [Urban et al., 2009]. The dried
414
remains of Saharan Mustard remained at the surface for well over a year following the
415
rains, and Sensit measurements of saltation activity at the site showed decreased particle
416
mobilization during this period [Urban, et al., 2009]. These results provide circumstantial
417
evidence that residual NPV is responsible for suppressed wind erosion at a lag of several
418
months to a year following rain, but further local testing in the area of study would be
419
necessary to show this conclusively.
In the case of class 3, the correlations at all lags are negligible or very small,
420
421
indicating that CAP is not a major factor associated with visibility at these locations. The
422
stations in this class occur in a large swath of the central and northern Sahara that is
423
characterized by low rainfall (<200 mm/year) with little seasonality other than slightly
19
424
less rainfall in the summer. This lack of precipitation and seasonality of rainfall implies it
425
is too dry for CAP to impact DEF-5 over the long term. There may be influences of CAP
426
in particularly wet periods, but these are not sufficient to change the overall correlation.
Classes 4 and 5 show positive correlations between CAP and DEF-5. Many of
427
428
these correlations are not significant (p < 0.05) in the case of class 4, indicating that they
429
do not represent as strong a trend. In both cases, the positive correlation is significant at a
430
lag of a few months. The positive correlation matches with an enhanced sediment source
431
following rains that has been hypothesized in other studies [Okin and Reheis, 2002;
432
Mahowald et al., 2003; Bryant, 2003; Zender and Kwon, 2005]. A likely mechanism for
433
dust production associated with precipitation discussed by these authors is fine sediment
434
being carried by flash floods into fluvial systems and ephemeral lakes. Once the
435
sediments dry out and any possible annual vegetation has dried or been grazed away,
436
there is fresh sediment available for removal by wind. Thus, the northern Saharan
437
locations covered by these classes are likely to be near locations where fluvial activity
438
can bring fresh sediment to valley floors or sufficiently disturb existing surfaces to make
439
for enhanced vulnerability to wind erosion. In cases where ephemeral lakes are the
440
sources, the complexities of wet playa chemistry and interaction with groundwater and
441
antecedent precipitation likely play a role. The exact timing of highest dust potential from
442
wet playa sources is still uncertain, but seems to be associated with high temperatures
443
following wet periods [Reynolds et al., 2007]. The distinction between classes 4 and 5 is
444
not clear from these data, but it is possible that it is related to whether fluvial or playa
445
dust sources dominate. Further investigation in the field or with satellite imagery of high
446
enough resolution to identify these features is necessary to identify the role of local
20
447
geography. The difficulty of distinguishing between these source types may also explain
448
why classes 4 and 5 are more interspersed than the other classes.
449
While some trends in the influence of contemporaneous and antecedent
450
precipitation on the dustiest months are apparent, the mechanisms are not always clear.
451
Identifying local vegetation cover and sediment responses to rainfall is important in
452
determining the best conditions for dust mobilization, but not possible within the scope of
453
this work. In other words, care should be taken before making general characterizations
454
of dust source mechanisms at the regional to continental scales as is often required in
455
atmospheric modeling. Rainfall stimulating vegetation growth seems to be the major
456
mechanism for dust suppression in the Sahel, and may be predictive enough for use in
457
regional to global scale modeling. Higher rainfall encourages dust production at some
458
northern and central Saharan locations, but the mechanism is not clear without more local
459
understanding. Whether dustiness is associated with sediments mobilized in fluvial
460
systems, effluorescence of salts in playa systems, or some other mechanism will alter the
461
timing and magnitude of dust availability and therefore make predicting or modeling the
462
availability and intensity of dust production difficult.
The relationship between CAP and DEF-5 (Figure 3) illustrates that the
463
464
seasonality of dustiness is at least partly related to rainfall in all classes except for class 3.
465
Figure 4 is similar to the lag spectra (Figure 2) of Zender and Kwon [2005] but for the
466
fact that we used DEF-5, a measure of surface visibility events, instead of the satellite
467
measure of dustiness used by Zender and Kwon. In almost all cases, there are several lag
468
months in which the correlation between antecedent precipitation and number of
469
visibility events is significant at the p<0.01 level. This contrasts with the results of
21
470
Zender and Kwon [2005] where the only significant (p<0.01) lag correlation shown for
471
the eastern Sahel was a negative correlation at nine months. This result implies that the
472
DEF-5 is more responsive to the influence of antecedent precipitation than the TOMS
473
aerosol optical depth (AOD) measure used by Zender and Kwon [2005]. The visibility
474
data used here are more closely associated with dustiness at the surface than the TOMS
475
data [Mahowald et al., 2007]. Therefore it is to be expected that a greater range of
476
significant correlations should be apparent from this dataset of the best available surface
477
data.
478
3.4.3 Relating CAP to dusty and non-dusty conditions
The results of the Mann-Whitney tests (Figure 5) agree with the results from the
479
480
correlation analysis shown in Figures 3 and 4. In general, if lower median CAP values
481
were associated with WSEV-5s, the hypothesis that precipitation promotes vegetation
482
and therefore suppresses dust emissions was validated. In cases where the average
483
landform fraction was greater during WSEV-5s, that landform was interpreted to be
484
associated with increased dustiness. For lags of 0-6 months, the influence of precipitation
485
in suppressing or enhancing dust emissions progresses as one might expect based on the
486
correlation curves in Figure 3. Figure 5 provides more spatial detail than can be obtained
487
from the generalized classes in Figure 4. However, this spatial detail confirms the
488
robustness of the classes in Figure 3 and the fact that some general patterns can be drawn
489
in the relationship between CAP and dustiness. In the Mann-Whitney test results of
490
Figure 5d, at a lag of nine months, a Sahelian zone of increased CAP is associated with
491
the WSEV-5 sample. The fact that dustiness is associated with wetter CAP conditions at
492
nine months lag may indicate burning in the Sahel in the dry season following a wet
22
493
season (approximately nine months earlier) and therefore a lack vegetation cover, which
494
would augment the dustiness. Ehrlich et al. [1997] described Sahelian fires as being used
495
at the start of the dry season for land clearing, preparing fields, and hunting, among other
496
reasons, as part of a broader pattern of land-cover change. The timing of Sahelian fires
497
described by Ehrlich et al. matches the nine-month lag. The reduced visibility indicated
498
by the WSEV-5s could be due to either smoke from the fires or dust from the bare
499
surfaces left following the fires. That the results from the Mann-Whitney testing, the
500
seasonal analysis, and the correlation analysis in Figures 3 and 4 all agree, and show
501
stations with similar responses clustering spatially, indicates a robust set of relationships
502
between CAP and dustiness.
The results described here indicate the utility of CAP as a proxy for the response
503
504
of dust to both vegetation and inputs of fresh sediment to arid regions following rains.
505
Most continental to global scale models of dust emission, transport, and deposition use
506
precipitation for determining the influence of soil moisture on dust erosion [e.g. Ginoux
507
et al., 2001; Zender et al., 2003b], and determine the influence of vegetation dynamics
508
based on satellite imagery [Mahowald et al., 2002; Luo et al., 2003]. The surface-based
509
results shown here elaborate (spatially and in terms of temporal lags) the coarse-scale,
510
model- and satellite-derived results of Zender and Kwon [2005] relating dustiness and
511
antecedent precipitation. Using surface visibility clarifies the influence of CAP on
512
dustiness in different parts of North Africa. If measured precipitation were available at
513
each station in conjunction with the visibility measurements, even higher temporal
514
resolution could be obtained. This would be especially useful for clarifying the influence
515
of soil moisture and flash floods on dust emissions.
23
516
4.
517
4.1 Directional analysis of low visibility and high wind speed events
Landform Results
We examined the difference in modal wind direction during VE-1s and WSEs for
518
519
each station as an exploratory analysis of the influence of landforms (Figure 6). There
520
must be an erodible landform upwind during VE-1s to produce dust and reduce visibility,
521
but that is not necessarily the case with all WSEs. Where the modal wind directions for
522
these two populations of events were different, the mix of upwind landforms during VE-
523
1s should be more erodible than the mix of upwind landforms during WSEs. Aggregated
524
over all stations, the net difference in upwind landform fraction for each landform
525
indicated which landforms were more associated with VE-1s (more erodible) and which
526
were more associated with WSEs (less erodible).
527
Figure 6: Modal directions of VE-1s and WSEs
There was some overlap between the VE-1 and WSE datasets, but not as much as
528
529
would be expected if high wind speeds always corresponded with low visibilities. Of all
530
WSEs at all stations, 15% were VE-1s and 35% had visibility of 5 km or less (WSEV-
531
5s). Of all VE-1s at all stations, 30% were WSEs.
The sum of increases and decreases in landform coverage over all stations, for
532
533
each landform, produced a net difference result that highlighted landforms where there
534
was a consistent increase or decrease in landform coverage associated with VE-1s over
535
many stations (Table 2). When normalized by the fractional coverage of the landform
536
over the entire study area, the “Normalized Net Difference” value provided a relative
537
indication of erodibility for comparing the landforms. Both a t-test and its rank-based,
538
non-parametric equivalent, the Mann-Whitney U test, were used to test whether the VE-1
24
539
sample of fractional upwind coverage of a given landform was different from the WSE
540
sample. These results show that only in the case of perennial water (in bold) were the
541
populations of WSEs and VE-1s significantly different. The negative sign of the
542
significant difference for water indicated that water was not erodible.
543
Table 2: Results from wind direction differences
544
4.2 Relating landforms to dusty and non-dusty conditions
Mann-Whitney tests were used to determine whether there was a significant
545
546
difference in the distributions of the ranked fractional coverage of a given upwind
547
landform for the WSEV-5 and non-WSEV-5 samples. The average (mean and median)
548
upwind percent coverages for each landform, for both the WSEV-5 and non-WSEV-5
549
samples, using the “all data” group, are shown in Table 3. In these results, six landforms
550
had significantly higher average, upwind, fractional coverage during WSEV-5s: alluvial
551
surfaces, dunes, lakebeds, regs, bedrock surfaces, and sandsheets. Thus, mountains,
552
basaltic flows and cones, well-vegetated surfaces, and water were not associated with
553
dusty conditions.
554
Table 3: Landforms associated with dusty conditions
The results of the Mann-Whitney test for differences in landform fraction during
555
556
WSEV-5s and non-WSEV-5s on the “local data” group are shown in Figure 7. The
557
results of this Mann-Whitney test provided spatial patterns showing whether the WSEV-5
558
and non-WSEV-5 samples for each landform were significantly different. If the samples
559
were different, these figures show which sample is associated with higher or lower
560
fractional coverage of each of the six erodible landforms.
561
Figure 7: Spatial distribution of landforms associated with dustiness
25
562
The clusters of higher alluvial fraction (Niger and northeast Libya) and lower
563
fraction (western Mediterranean coast) were not coherent groupings (Figure 7a). The
564
dune results showed more cases of lower dune fraction during WSEV-5s in the
565
southwestern Sahara and southern Niger. There were clusters of higher dune association
566
with WSEV-5s near the Mediterranean coast in Algeria, Tunisia, and Egypt (Figure 7b).
567
There were relatively few dry lakebeds across the study area and most had lower
568
fractions associated with WSEV-5s (Figure 7c). Regs did not exhibit strong spatial
569
patterns, although lower reg fractions in the core of the Sahara seemed to be associated
570
with WSEV-5s (Figure 7d). The opposite was true along the Mediterranean coast and in
571
the Sahel. Bedrock surfaces were rare in the landform map (3% of study area) and largely
572
occurred in sparsely populated, arid regions in the Sahara Desert (Figure 7e). Where
573
bedrock was present at all, higher bedrock fractions were associated with WSEV-5s in
574
Morocco, Algeria, the Sahel, and a couple of locations in Mauritania. Lower bedrock
575
fractions were associated with WSEV-5s largely in very arid regions in the core of the
576
Sahara from Mauritania to Sudan and Egypt. Sandsheets showed perhaps the clearest
577
spatial patterns with widespread occurrence of lower fractions associated with WSEV-5s
578
in the Sahel from Chad west to the coast (Figure 7f). The core of the Sahara had a
579
number of cases where higher sandsheet fractions were associated with WSEV-5s. Many
580
of the stations used in the landform testing were clustered in the North and along the
581
margins of the Sahara Desert where VE-1s were associated with lower fractions of
582
sandsheet (Figure 7f).
583
4.3 Landform discussion
26
The results from the directional analysis (Table 2) found that only water was
584
585
significantly associated with a difference in direction. This evidence indicates that water
586
is not a dust source, as expected. However, we would have expected other landforms to
587
also have significantly different fractional coverage upwind during VE-1s and WSEs.
For example, the lakebed landform class has a high value of normalized net
588
589
change from the VE-1 to WSE population. This positive change (greater lakebed
590
coverage during VE-1s than WSEs) is characteristic of an erodible landform. The lack of
591
significance in the difference at even the 0.1 level indicates that lakebeds are not erodible
592
landforms when considered over the whole study area with this method. Lakebeds are
593
often cited as major sources of dust at the global scale [e.g. Prospero et al., 2002; Zender
594
et al., 2003a]. However, Reynolds et al. [2007] showed that the erodibility of lakebeds
595
(playas) in the Mojave Desert depends on many factors including depth to groundwater
596
table, antecedent precipitation, and playa chemistry. In other words, playas actively erode
597
only when specific surface conditions occur and sufficient winds are present to erode the
598
surface.
The Mann-Whitney tests on the landforms using the “all data” group found that
599
600
the samples for each landform were significantly different (p≤0.01), in contrast with the
601
results in Table 2 where only water was shown to be significantly associated with non-
602
dusty conditions. The six landforms found to be associated with dusty conditions for the
603
whole of North Africa were alluvial surfaces, dunes, lakebeds, regs, bedrock surfaces,
604
and sandsheets. Because of the large number of samples involved in the “all data” test,
605
the Kruskal-Wallis test [Sanders and Smidt, 1997] was also applied to these data with the
606
same results as the Mann-Whitney test.
27
Some of the landforms identified here might not be expected to make strong dust
607
608
sources because of sheltering at the surface (e.g. for regs, alluvial surfaces, and crusted
609
lakebeds) or a lack of fine sediment availability (e.g. for bedrock, well sorted dunes and
610
sandsheets composed of coarse sands). Nonetheless, there is evidence that regs could be
611
dust sources in the Gobi Desert [DeFrancis, 1991; Ishizuka, 2005] and alluvial surfaces
612
may have included channel systems with available fine sediment at the surface [Bryant,
613
2003; Engelstaedter et al., 2006]. Lakebeds can be strong dust emitters when salts
614
effluoresce to create a “fluffy surface” [Reynolds et al., 2007; Elmore et al., 2008], when
615
crusts are removed by sandblasting [Gillette et al., 1982], or when there is freshly
616
deposited sediment on the surface after fluvial inputs [Reheis and Kihl, 1995; Okin and
617
Reheis, 2002; Mahowald et al., 2003]. Dune systems can have fine sediments [e.g. Muhs,
618
2004] and sandsheets may be composed of flat clayey or silty surfaces with abundant
619
sand to act as saltators and sufficient fines to create dust plumes [Thomas, 1997]. The six
620
landforms identified by the “all data” Mann-Whitney test results can be used as a guide
621
for identifying which landforms might be sources, but not as a predictor of which
622
landforms generate dust in any given location or the intensity of dust production.
Few systematic spatial patterns emerge from the “local data” Mann-Whitney tests
623
624
shown in the panels of Figure 7. Where there are not systematic spatial patterns, it is
625
likely the generalized results of significance from the “all data” test, shown in Table 3,
626
hold true. The spatial pattern of sandsheets (higher fraction with visibility events in the
627
central Sahara Desert and lower at the more humid desert margins) implies that
628
precipitation plays a role in determining the erodibility of the surface where sandsheets
629
are present. The lack of significant results (aside from water) in Table 2 and the large
28
630
number of non-significant cases in Figure 7 argues for caution in viewing any of these six
631
landforms as being a consistent dust emitter under windy conditions.
One explanation for the uncertainty in the landform results is that the erodibility
632
633
of the landform is modified by the dynamics of vegetation and sediment
634
deposition/mobilization on the surface of the landform. These dynamic factors may
635
dominate over the static sediment availability and sheltering properties of the landform in
636
determining whether dust can be emitted from the landform at a given time. Thus,
637
landforms provide a minimum, first-order condition for erodibility that is modified by
638
second-order dynamic factors. Analyses of CAP, as discussed previously, indicate the
639
role of moisture-driven dynamic processes in suppressing or enhancing dust emissions.
640
Existing studies mostly rely on satellite images to determine landforms associated
641
with dust sources in the Sahara Desert, if the landforms are discussed at all. Most studies
642
have used TOMS-AI to identify that dust source areas occur in topographically low
643
regions with dry lakebeds and ephemeral fluvial systems [Middleton and Goudie, 2001;
644
Prospero et al., 2002; Zender et al., 2003a; Engelstaedter et al., 2006]. Mahowald and
645
Dufresne [2004] noted that the TOMS-AI measurement is sensitive to boundary layer
646
height and therefore may not correctly interpret some source locations and strengths.
647
Schepanski et al. [2007] used Meteosat data to more precisely map both the spatial and
648
temporal characteristics of dust sources in the Sahara Desert.
Through satellite and surface observations, several major dust sources in North
649
650
Africa have been identified. In the case of the major dust source in the Bodélé Depression
651
of northern Chad, a group of researchers identified the dust source as being associated
652
with a large paleo-lake composed of diatomite and traversed by dunes with various
29
653
degrees of mixing between quartz and diatomite [Washington et al., 2006]. A major
654
source region of dust storms has also been identified in the unpopulated region where
655
Mali, Mauritania, and Algeria meet west of the Ahaggar Mountains [Middleton and
656
Goudie, 2001; Prospero et al., 2002]. Dust sources in southern Tunisia and northern
657
Algeria seem to be associated with extensive chotts (ephemeral lakes) and possibly the
658
downwind clay dunes. Prospero et al. [2002] found that other sources in Egypt, Ethiopia,
659
Niger, and Sudan are associated with ephemeral fluvial systems and paleolakes in
660
topographic depressions. The landform and CAP results described here contain the
661
information to analyze any specific location for the surface conditions that may lead to it
662
being a strong dust emitter at any given time. Such a detailed case-by-case analysis is
663
outside of the scope of this work, but future studies of the specific landscapes associated
664
with North Africa’s major dust sources would be useful for creating a physically-based
665
model of dust emissions.
666
5.
Conclusions
667
We have explored the influence of precipitation and landforms on dust
668
mobilization in North Africa. Cumulative antecedent precipitation (CAP) was used to
669
represent dynamic changes in surface conditions, including soil moisture, vegetation, and
670
the availability of fresh, fine-grained sediment for erosion by the wind. Landforms,
671
derived from satellite imagery [Ballantine et al., 2005], were used to represent
672
unchanging aspects of the land surface including topography and availability of fine
673
sediment. The presence or absence of dust in the atmosphere was represented by visibility
674
during either high wind speed events (WSEs), visibility events with visibility less than 1
675
km (VE-1s), or the frequency of visibility events below 5 km in a given month (DEF-5).
30
The seasonality of dust events (VE-1s) was opposite that of precipitation in the
676
677
northern Sahara, southern Sahara and Sahel, but coincided with the seasonality of
678
precipitation in the central Sahara. Clustering of correlations between CAP up to a year
679
before a particular month of dustiness (represented by DEF-5) and DEF-5 produced five
680
classes of dust response to CAP. These classes occurred in particular regions (Figure 4).
681
A Mann-Whitney U test of whether or not WSEs associated with low visibility were also
682
associated with particular CAP conditions found that the influence of antecedent
683
precipitation on dust changed both spatially and with lag of CAP.
684
The CAP results show that in the Sahel, dustiness responds negatively to
685
precipitation at short time scales, probably due to stabilization of the surface by rapidly
686
growing annual vegetation [e.g. Urban et al., 2009]. There is possibly a positive response
687
of dust to CAP at time scales around nine months. This latter effect may be either dust or
688
smoke from fires that occur in the dry season following heavy rains [Ehrlich et al., 1997].
689
A similar pattern emerges in the southern Sahara Desert, but the effect is somewhat
690
delayed, possibly due to different phenology of vegetation in this more arid region. The
691
core of the Sahara Desert shows little relationship between precipitation and dusty
692
conditions, although meteorological stations to record dustiness in this region are sparsely
693
placed. The remainder of the Sahara Desert is characterized by locations where dustiness
694
responds positively to precipitation. The fact that rainfall is associated with dust at
695
timescales of zero to a few months implies that fine sediments in these arid regions are
696
being mobilized following rains and are then more available for erosion [Okin and
697
Reheis, 2002; Mahowald et al., 2003; Bryant, 2003; Zender and Kwon, 2005]. In the
698
more delayed cases, it is possible that dry lakebeds become inundated, but are more
31
699
vulnerable to erosion once surface water has evaporated or infiltrated [Bryant, 2003;
700
Reynolds et al., 2007; Elmore et al., 2008].
The relationship between landforms and dustiness was first explored by
701
702
identifying locations where the plurality of WSEs and VE-1s had different wind
703
directions. In these cases, the windiest events were not associated with dust and therefore,
704
the landforms upwind during WSEs would not be good dust sources. Similarly, the
705
landforms upwind during VE-1s would likely be good dust sources. This analysis found
706
that only water was significantly associated with WSEs and no landforms were
707
significantly associated with VE-1s. Mann-Whitney U testing of the relationship between
708
dusty and non-dusty samples of the WSE dataset for different landforms found that
709
alluvial surfaces, dunes, lakebeds, bedrock, regs, and sandsheets were significantly
710
associated with dusty conditions whereas basaltic flows and cones, mountains,
711
vegetation, and water were significantly associated with non-dusty conditions. These
712
results agree with the findings of Schepanski et al. [2007] that many different types of
713
landforms can be dust sources, given appropriate conditions. These results do not
714
necessarily agree with results based on the Total Ozone Mapping Spectrometer (TOMS)
715
[Prospero et al., 2002], which found that dust sources preferentially occur in topographic
716
lows. The difference in these results may be related to a bias of boundary layer height in
717
the TOMS data which causes dust to preferentially be seen in topographic lows
718
[Mahowald and Dufresne, 2005].
Spatial results of the Mann-Whitney test found that these landforms did not have
719
720
a strong spatial pattern of where they were or were not associated with dust, except in the
721
case of regs and sandsheets. In the reg case, lower fractions of upwind regs were
32
722
associated with dust in the core of the Sahara. The opposite was true with sandsheets,
723
implying that they were erodible in hyperarid regions. However, around the margins of
724
the Sahara Desert, sandsheets had lower fractional coverage during dusty conditions,
725
implying that in these areas some other factor controls erodibility. It is likely that these
726
spatial patterns were associated with the influence of precipitation on these landforms in
727
the regions in question.
The results from this study suggest that the influence of landform features can
728
729
only be defined by whether or not they are potential dust sources, not the degree to which
730
they are vulnerable. Thus, landforms provide a first-order indication of whether source
731
material for dust might be available. The timing and intensity of dust emissions are
732
dependent on dynamic, second-order effects including CAP, winds, and disturbance of
733
the surface. The positive or negative influence and timing of CAP on dust in most parts
734
of North Africa creates a consistent picture from these analyses. The influence of wind
735
speed was not explicitly addressed in this study and will be explored in a separate study.
Further work on the link between CAP, landforms and dust requires a more
736
737
detailed knowledge of the unique circumstances associated with the landscape near each
738
meteorological station. Previous studies have used empirical observations of dust
739
intensity to identify source regions, but have not investigated the nature of the surface in
740
these source regions beyond observations from global maps [Prospero et al., 2002] or
741
simple topographic parameterizations [e.g. Ginoux et al., 2001; Zender et al., 2003b].
742
Some authors have used satellite imagery [Prigent et al., 2005; Laurent et al., 2008] or
743
global datasets [Marticorena and Bergametti, 1995; Tegen et al., 2002] to model the
744
nature of the surface.
33
More detailed studies of the surface, coupled with dynamic maps of vulnerability
745
746
to dust emissions would improve our understanding of the vegetative and geomorphic
747
processes at work in determining the dynamics of dust emissions while providing a
748
stronger basis for modeling dust emissions. Schepanski et al. [2007] developed a dynamic
749
map of dust sources from Meteosat Second Generation imagery and showed that dust
750
emission model results were significantly improved. Coupling such observations to the
751
surface properties (e.g. CAP and landforms) responsible for changing erodibility would
752
allow these empirical results to be transferred into a more physically-based and predictive
753
model framework.
754
755
Acknowledgements: We thank Dar Roberts and Oliver Chadwick for their
756
contributions to early drafts of the manuscript. Aiguo Dai was generous in sharing global
757
precipitation datasets. We also thank NASA for funding an Earth System Science
758
fellowship and grant NNG06G127G that supported this work.
759
760
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987
44
988
Tables
989
Table 1: Surface responses to precipitation that may suppress or enhance wind erosion
990
and the approximate timing associated with each effect. The Process column indicates the
991
physical process responsible for suppressing or enhancing erosion. The Timing column
992
indicates the timing of the responsiveness to rainfall measured in a number of months of
993
lag time between the process and the effect of the process on erodibility. The Effect on
994
Erodibility column indicates whether the process suppresses or enhances erodibility. Note
995
that ephemeral lakes flood and then dry out, creating a suppression effect followed by
996
enhanced erodibility.
Process
Timing
Effect on Erodibility
Soil Moisture
0-1 months
Suppress
Seasonal Vegetation
1-3 months
Suppress
Remnant Vegetation (NPV)
4-10 months
Suppress
Flood deposits
0-5 months
Enhance
Ephemeral Lakes
0-3/2-9 months
Suppress/Enhance
997
998
Table 2: The net increase or decrease in the fraction of each landform (VE-1 – WSE)
999
summed over the stations. The “Net Change” column is the net fractional cover change
1000
summed over all stations. The “Normalized Net Change” column is the value in the “Net
1001
Change” column divided by the total fractional coverage of that landform within the
1002
overall study area. The “Fractional Coverage” column gives the fractional coverage of
1003
each landform within the whole study area, as calculated in Ballantine et al., [2005]. The
1004
“t-test p” column gives the p-values for the two-tailed t-tests comparing the VE-1s and
45
1005
WSEs. The “Mann-Whitney p” column gives the p-values for the Mann-Whitney tests
1006
comparing VE-1s and WSEs. The row presenting the data for the water landform is in
1007
bold because that is the only landform where the difference in the fractional coverage
1008
populations for VE-1s and WSEs is significant.
Landforms
Alluvial
Dunes
Lakebed
Water
Basalt
Mountain
Reg
Bedrock
Sandsheet
Vegetation
Net
Change
-0.69
0.56
1.08
-2.49
0.08
1.03
2.32
-0.13
-0.16
-1.63
Normalized Net
Difference
-4.93
2.55
108
-249
8.00
11.4
11.1
-4.33
-1.07
-12.5
Fractional
Coverage
0.14
0.22
0.01
0.01
0.01
0.09
0.21
0.03
0.15
0.13
t-test p
0.98
0.62
0.12
0.01
0.46
0.42
0.35
0.77
0.89
0.36
MannWhitney p
0.83
0.75
0.43
<0.01
0.64
0.36
0.22
0.27
0.94
0.50
1009
1010
Table 3: The average fractional coverage of each upwind landform for the WSEV-5 and
1011
non-WSEV-5 samples using data from all stations. All differences between the samples
1012
using the Mann-Whitney tests were significant at the p=0.05 or better level. Cases where
1013
the median fraction of landform was greater during low visibility events are marked in
1014
bold. The lakebed class is included as bold because the mean landform fraction was
1015
greater during low visibility events, even though the median was negligible. The number
1016
in parentheses in the mean column is the coefficient of variation. The n column
1017
represents the number of records used in each sample.
Landform
Median
Alluvial
Dunes
Lakebed
Water
Basalt
Mountain
46
0.15
0.08
0
0
0
0.03
WSEV-5s
Mean
(Coefvar)
0.26 (1.06)
0.20 (1.27)
0.03 (2.50)
0.04 (3.11)
0.01 (3.86)
0.12 (1.57)
n
38001
37954
17627
13220
15578
36029
Non-WSEV-5s
Mean
Median
(Coefvar)
0.09
0.21 (1.21)
0.04
0.13 (1.62)
0
0.02 (2.82)
0
0.07 (2.67)
0
0.02 (2.97)
0.07
0.18 (1.20)
n
83259
77050
46816
45370
51212
85299
Reg
Bedrock
Sandsheet
Vegetation
1018
1019
47
0.18
0.01
0.07
0.02
0.23 (0.94)
0.03 (2.34)
0.13 (1.21)
0.10 (1.93)
39201
11936
38909
31322
0.09
0
0.06
0.07
0.18 (1.14)
0.02 (2.81)
0.11 (1.22)
0.23 (1.27)
85110
27969
82138
85112
1020
Figures
1021
Figure 1: Station coverage frequency for North Africa. Circles show the average number
1022
of records per year. Records from the NCAR data archive were used to determine
1023
frequency of records. Countries with stations used in this study are labeled. Stations
1024
mentioned in the text are labeled as follows: 1 = Bilma, Niger, 2 = Tessalit, Mali, 3 =
1025
Tamanrasset, Algeria.
1026
1027
Figure 2: a) Seasonality of VE-1s. Large circles indicate that the majority (> 50%) of VE-
1028
1s at the station occur in the season indicated by the color. Smaller circles indicate a
1029
weaker seasonality with only a plurality (< 50% of all VE-1s, but largest fraction
1030
occurred during the season indicated) of VE-1s occurring during the season indicated. b)
1031
Season of greatest mean precipitation between 1931 and 2004.
1032
1033
48
1034
2a) Seasonality of VE-1s
1035
1036
2b) Seasonality of maximum mean monthly precipitation
1037
49
1038
Figure 3: The mean of correlations for the stations in each lag correlation class. The p <
1039
0.05 significance thresholds (±0.16) are shown by dashed lines. The significance
1040
thresholds are based on stations with a number of samples within two standard deviations
1041
of the mean (eleven stations with fewer samples had higher thresholds).
1042
1043
1044
1045
Figure 4: Map of the spatial distribution of the five classes derived from the clustering of
1046
correlations between CAP and DEF-5. The color of each class matches the colors from
1047
the curves in Figure 3.
50
1048
1049
Figure 5: Results of the Mann-Whitney test of whether there is a difference in the CAP
1050
ranks for the WSEV-5 and non-WSEV-5 samples at each location and CAP lag. Black
1051
circles represent cases where the WSEV-5 sample is significantly different from the non-
1052
WSEV-5 sample and the median CAP associated with the WSEV-5 sample is lower than
1053
for the non-WSEV-5 sample. The white circles indicate significant differences between
1054
the two samples where median CAP is higher for WSEV-5s than for non-WSEV-5s. X
1055
indicates no significant (p>0.05) difference in the samples based on the Mann-Whitney
1056
tests. The results from one time step to the next changed only a small amount, so only the
1057
results at lags of 0, 3, 6, and 9 months are shown.
1058
51
1059
5a) Lag = 0 months
1060
1061
5b) Lag = 3 months
1062
1063
52
1064
5c) Lag = 6 months
1065
1066
5d) Lag = 9 months
1067
53
1068
Figure 6: Modal directionality of a) events with visibility less than 1 km, and b) wind
1069
speed events. The arrows point downwind. Red arrows indicate a greater than 45 degree
1070
difference in mode direction between VE-1s and WSEs and black arrows indicate less
1071
than this amount (the mode directions of the wind during VE-1s and WSEs are close to
1072
the same direction).
1073
6a: VE-1s
1074
1075
54
1076
6b: WSEs
1077
1078
Figure 7: The results of the Mann-Whitney test comparing distributions of the ranked
1079
upwind fractional landform coverages for the WSEV-5 and non-WSEV-5 samples at
1080
each station. Large black circles indicate that the mean landform fraction is lower during
1081
WSEV-5s (dusty) than non-WSEV-5s (not dusty). Locations where the mean landform
1082
fraction is higher during WSEV-5s than non-WSEV-5s are shown with white circles.
1083
Thus, a black circle means that the Mann-Whitney test found that the WSEV-5 sample is
1084
associated with significantly lower wind speeds than the non-WSEV-5 sample. A white
1085
circle means that WSEV-5s are associated with higher mean wind speeds (i.e. high winds
1086
are probably mobilizing dust from the landform). An asterisk indicates no significant
1087
difference (p=0.05) between the WSEV-5 and non-WSEV-5 samples according to the
1088
Mann-Whitney test. Small black circles inside white circles indicate that the landform in
55
1089
question was not present in any upwind direction at that station. The background
1090
landform map is from Figure 7 of Ballantine et al., 2005.
1091
7a) Alluvial:
1092
1093
7b) Dunes
56
1094
1095
7c) Lakebed
1096
1097
57
1098
7d) Reg
1099
1100
7e) Bedrock
1101
1102
58
1103
7f) Sandsheet
1104
59