Journal of Geophysical Research - Atmospheres
Supporting Information for
Climatic controls on the interannual to decadal variability in Saudi Arabian dust activity:
Towards the development of a seasonal dust prediction model
Yan Yu1, Michael Notaro1, Zhengyu Liu1,2, Fuyao Wang1,
Fahad Alkolibi3, Eyad Fadda3, Fawzieh Bakhrjy3
1. Nelson Institute Center for Climatic Research, University of Wisconsin-Madison, Madison, Wisconsin, USA
2. Laboratory Climate, Ocean and Atmospheric Studies, School of Physics, Peking University, Beijing, 100871, China
3. Geography Department, King Saud University, Riyadh, Saudi Arabia
Contents of this file
Text S1 to S2
Figures S1 to S5
Tables S1 to S3
Introduction
The supporting information contains tables, figures and materials that support the main article.
It includes a detailed description of how the predictors are selected by stepwise regression. It
also provides description and results of an independent statistical method, generalized
equilibrium feedback assessment (GEFA), which support our conclusions made from the
correlation/regression analyses about the mechanism linking each predictor to Saudi Arabian
dust activity. All the datasets used in the supporting information are described in section 2 in
the main text.
Text S1. Stepwise Regression
In order to identify the primary predictors for Saudi Arabian dust activity and minimize
the multicolliniarity among predictors [Yin et al, 2014], stepwise regression is applied to 15
potential precipitation- and SST-based predictors which significantly regulate Saudi Arabian
dust activity as shown in sections 3.2 and 3.3. These potential predictors include:
1) tropical eastern Pacific SST in February [SSTTEP (Feb)]
2) tropical eastern Pacific SST in March [SSTTEP (Mar)]
3) tropical Indian SST in February [SSTTI (Feb)]
4) tropical Indian SST in March [SSTTI (Mar)]
1
5) tropical Atlantic SST in February [SSTTA (Feb)]
6) tropical Atlantic SST in March [SSTTA (Feb)]
7) Mediterranean SST averaged from the prior 36 months [SSTMed (36)]
8) Mediterranean SST averaged from the prior 24 months [SSTMed (24)]
9) Mediterranean SST averaged from the prior 12 months [SSTMed (12)]
10) North African precipitation averaged from the prior 84 months [PCPNA (84)]
11) North African precipitation averaged from the prior 60 months [PCPNA (60)]
12) North African precipitation averaged from the prior 36 months [PCPNA (36)]
13) Arabian Peninsula precipitation averaged from the prior 84 months [PCPAP (84)]
14) Arabian Peninsula precipitation averaged from the prior 60 months [PCPAP (60)]
15) Arabian Peninsula precipitation averaged from the prior 36 months [PCPAP (36)]
for MAM, during 12 successive 25-year periods (1975-1999, 1976-2000, etc). The potential
predictors for JJA dust activity are identical to those for MAM, except that April and May SSTs
across the tropical eastern Pacific, tropical Indian, and tropical Atlantic are used instead of
those in February and March. The length of the periods (25 years) is determined such that it
exceeds the number of potential predictors (15), while the number of periods is sufficient for
identifying the most consistently important predictors. Stepwise regression selects the most
important variables that are not significantly correlated with each other as predicting factors by
an automated procedure [Hocking, 1976]. Akaike Information Criterion [Akaike, 1974], which
measures the relative quality of a statistical model by estimating the goodness of fit and
penalizing the complexity of the model, is used as the selection criterion in stepwise regression.
According to stepwise regression, the most important predictor for Saudi Arabian dust
activity in MAM are SSTTEP (Feb), SSTMed (36), SSTMed (24), PCPNA (84), and PCPAP (60) (Table S2).
The selected predictors are all negatively correlated with MAM dust PC1. Precipitation across
Arabian Peninsula is the dominant predictor for both MAM and JJA dust, as it is the most
frequently selected predictor. In order to eliminate the redundancy, the prediction model will
not include SSTMed (36), which is highly correlated (temporal correlation = 0.89) with SSTMed (24)
but less frequently selected by the stepwise regression for the 12 periods. Based on the same
approach, SSTTI (Apr), SSTMed (24), and PCPAP (60) are selected as the most important factors
and used in the prediction model for JJA dust activity (Table S3).
Text S2. GEFA
The mechanisms linking SSTs to observed Saudi Arabian dust storm frequency
remain uncertain from the simple regression/correlation analysis in section 3, due to
covariance between tropical Pacific and tropical Indian and between the North Atlantic
and Mediterranean. In order to independently validate the regression/correlation –
based impacts of individual oceanic drivers of Saudi Arabian regional climate and dust
storm frequency, the multivariate statistical method, Generalized Equilibrium
Feedback Assessment (GEFA) [Liu et al., 2008; Wang et al., 2013], is applied to
distinguish the influence of individual SST modes on Middle Eastern and North African
climate. Based on the theory by Frankignoual et al. [1998], GEFA assesses the
instantaneous influence of a slowly-evolving variable (e.g. SST) on an atmospheric
variable by estimating the lagged covariance matrices among the atmospheric variable
and the forcing (SST) field. It was previously demonstrated that GEFA can separate the
individual impacts of different ocean basins on climate [Wen et al., 2010; Wang et al.,
2013].
2
According to Frankignoul et al. [1998], at time scales longer than the
atmospheric memory (about one week), the response of an atmospheric variable at
time t, A(t), to an oceanic variable, O(t), can be approximated as:
A(t) = B×O(t) + N(t)
(S1)
Where B is the feedback matrix, and N(t) is the atmospheric internal variability.
Right multiplying OT(t-τ) on both sides of equation (S1) and applying the covariance
yields:
CAO (t ) = B×COO (t ) + CNO (t )
(S2)
Where τ is the time scale, exceeding the atmospheric adjustment time, C is a
covariance matrix, and superscript “T” indicates a transpose. Since oceanic variability
cannot be forced by an atmospheric internal variability at a later time, CNO(τ) = 0. As a
result, the feedback matrix can be estimated as:
-1
B = CAO (t )×COO
(t )
(S3)
Details of the method have been presented in previous studies [Liu et al., 2008;
Wen et al., 2010; Wang et al., 2013]. In order to reduce the sampling error from the
correlation among forcing fields [Wen et al., 2010], GEFA is performed in truncated SST
EOF space. Following the approach from Wang et al. [2013], the global ocean in the
tropics and Northern Hemisphere is divided into five non-overlapping ocean basins: the
tropical Pacific (20˚S-20˚N, 120˚E-60˚W), North Pacific (20˚N-60˚N, 120˚E-60˚W),
tropical Indian (20˚S-20˚N, 35˚E-120˚E), tropical Atlantic (20˚S-20˚N, 65˚W-15˚E), and
North Atlantic (20˚N-60˚N, 100˚W-20˚E). The PC time series corresponding to the two
leading EOF modes in each ocean basin, as well as area-average Mediterranean SSTs,
are combined into a single forcing matrix. Since area-average SSTTEP and SSTTI used in
the prediction model are highly correlated with tropical Pacific PC1 (TP1) (corr = 0.91)
and tropical Indian PC1 (TI1) (corr = 0.87), respectively, TP1 and TI1 in the forcing matrix
are replaced with SSTTEP and SSTTI, respectively. For all observational and reanalysis
data as described in section 2, the seasonal cycle and third order polynomial trend are
removed to focus on interannual to decadal variability. Theoretically, the estimated
feedback matrix does not depend on the time lag by which it is assessed, but the
sampling error increases with the time lag. Therefore, the response in Middle Eastern
and North African climate variables (temperature, precipitation, wind, and pressure)
are assessed by one-month lag. The statistical significance of the response is estimated
using the Monte Carlo bootstrap approach with 500 iterations following Wang et al
[2013].
The results from GEFA and regression/correlation analysis are generally
consistent regarding the influence of SSTTEP, SSTTI, and SSTMed on Middle Eastern and
North African climate. During winter to spring, an anomalously wet southern Arabian
Peninsula is often associated with positive anomalies in SSTTEP, which induce high
pressure over the Arabian Sea and enhance southwesterly flow and moisture transport
to the Arabian Peninsula (Figure S3 (c)). The GEFA response to an anomalously warm
TEP is consistent with simple regression (Figure S3 (a)). The air temperature and SLP
responses to SSTTI are not consistent between the two methods. During summer,
weaker Shamal is associated with warm SSTTI according to regression, but not present
3
in the GEFA response (Figure S3 (b,d)). Both GEFA and regression analysis agree that
an anomalously warm Mediterranean Sea supports a wetter Sahel on the interannual
time scale (Figure S4 (b,e)), a warm spring over Iran and Iraq (Figure S4 (a, d)), and a
weaker summer Shamal (Figure S4 (c, f)). Furthermore, anomalously abundant total
precipitable water over Sahel is associated with warm Mediterranean Sea as shown in
both regression and GEFA response (Figure S5 (a, c)). The GEFA response in outgoing
longwave radiation also shows a northward shift of ITCZ associated with warm
Mediterranean Sea (Figure S5 (d)). As suggested by the modeling work in Gaetani et al
[2010], anomalously warm eastern Mediterranean is responsible for greater moisture
transport to the Sahel from Mediterranean, while anomalously warm western
Mediterranean is responsible for stronger West African Monsoon flow. The GEFA
results regarding vertical integrated moisture transport (Figure S5 (c)) supports the
conclusion in Gaetani et al [2010]. GEFA, as an independent approach, confirms some
of the physical mechanisms supporting each SST-based predictor.
There are several limitations in the comparison of GEFA and
regression/correlation analysis. First of all, the short time period in this study (41 years
for precipitation and temperature, 30 years for pressure and wind) induces large
sampling error, which partly explains the inconsistency in the influence of SSTTI from
GEFA and regression. Using the longer time period 1900-2010, an anomalously warm TI
supports positive anomalies in air temperature over the Arabian Peninsula from both
GEFA and regression analysis. Second, GEFA assesses the instantaneous atmospheric
responses to oceanic forcings, while regression/correlation analysis reveal lagged
responses. In the case of SSTTI, for example, the regression shows the response in JJA
air temperature and circulation to anomalies in April SSTTI, while GEFA assesses the
instantaneous response. Regarding these, dynamical experiments are needed to
understand the key SST drivers of the interannual to decadal variability in Saudi
Arabian dust activity.
4
(a)
(d)
(b)
(e)
(c)
(f)
Figure S1. Leading three EOF patterns of monthly AOD anomalies from MISR (a-c) and station
dust storm frequency (d-f) over Saudi Arabia during 2000-2012. The percentage of variance
explained by each pattern is denoted on the right shoulder of each panel. Both MISR and the
station data show a homogeneous pattern across Saudi Arabia as the dominant EOF mode.
5
Figure S2. Regression of JFMAM SLP (hPa) upon Feb tropical eastern Pacific SST (K) (19802012). Regression coefficients (hPa K-1) that achieve a significance of 90%, based on the
Student’s t-test, are displayed. The resulting pattern is the canonical ENSO see-saw pressure
pattern, which includes anomalously high pressure extending from the Pacific warm pool to
North Africa, Saudi Arabia, and India.
6
Figure S3. Regulation of (a, c) springtime and (b, d) summertime Saudi Arabian dust activity by
tropical eastern Pacific SST and tropical Indian SST, respectively, through their influence on
precipitation (1970-2010), 850 hPa geopotential height and wind (1979-2010) and surface wind
(1979-2010). (a) Regression of JFMAM precipitation (% in climatology, color), JFMAM 850 hPa
geopotential height (m, contour) and 850 hPa wind (m s-1, vector) upon February tropical
eastern Pacific SST (K). (b) Regression of JJA air temperature (K, color), JJA sea-level pressure
(hPa, contour by 0.5 hPa, solid lines for positive values and dashed lines for negative values),
and JJA surface wind (ms-1, vector) upon April tropical Indian SST (K). (a) and (b) come from
Figure 7 (a), (b) respectively. (c) GEFA response in the same variables as in (a) at one-month lag
to warm TEP SST. (d) GEFA response in the same variables as in (b) at one-month lag to warm
TI SST. Regression coefficients (a, b) and GEFA response (c, d) that achieve a significance of
90%, based on Student’s t-test and Monte Carlo test, respectively, are displayed. The results
from GEFA and regression analysis are consistent regarding the regulation of SSTTEP on
springtime dust activity through precipitation over southern Arabian Peninsula. The linkage
between warm SSTTI and weaker Shamal, as shown in the regression map, is absent in the
GEFA results, partly due to the different time lags in the regression analysis and GEFA.
7
Figure S4. Regulation of (a, d) springtime and (b, c, e, f) summertime Saudi Arabian dust
activity Mediterranean SST through impacts on surface air temperature (1970-2010),
precipitation (1970-2010), sea level pressure and surface wind (1979-2010). (a) Regression of
MAM surface air temperature at each grid cell upon instantaneous area-mean Mediterranean
SST (30˚N-50˚N, 0˚E-50˚E). (b) Regression of annual mean precipitation (cm month-1, color) at
each grid cell, upon instantaneous area-mean Mediterranean SST (K). (c) Regression of JJA
surface air temperature (K, color), sea-level pressure (hPa, contour), and surface wind (m s-1,
vector) at each grid cell upon instantaneous area-mean Mediterranean SST (K). (d-f) GEFA
response in the same variables as in (a-c), respectively, at one-month lag to warm Med SST.
Regression coefficients (a-c) and GEFA response (d-f) that achieve a significance of 90%, based
on Student’s t-test and Monte Carlo test, respectively, are displayed. The results from GEFA
and regression analysis are generally consistent.
8
(a) Reg (TQ, Med SST)
(b) Reg (OLR, Med SST)
(kg/m^2/K)
(c) GEFA (TQ, Med SST)
(kg/m^2/SST std)
(W/m^2/K)
(d) GEFA (OLR, Med SST)
(W/m^2/SST std)
Figure S5. Impacts of Mediterranean SST on summertime (June-September, JJAS) (a, c) total
precipitable water (TQ) and vertical integrated moisture transport (VIMT) from MERRA and (b,
d) outgoing longwave radiation (OLR) from National Oceanic and Atmospheric Administration
Climate Data Records during 1979-2010. (a) Regression of TQ (color shading, kg m-2 K-1) and
VIMT (kg m-1 s-1 K-1) at each grid cell upon concurrent area-average Mediterranean SST (30˚N50˚N, 0˚E-50˚E). (b) Regression of OLR (W m-2 K-1) at each grid cell upon concurrent areaaverage Mediterranean SST. (c, d) GEFA response in TQ (kg m-2 SST std-1) and VIMT (kg m-1 s-1
SST std-1), and OLR (W m-2 SST std-1) at one-month lag to warm Med SST. Regression
coefficients (a, b) and GEFA response (c, d) that achieve a significance of 90%, based on
Student’s t-test and Monte Carlo test, respectively, are displayed. Regression and GEFA
analyses are generally consistent. Anomalously abundant total precipitable water over Sahel is
associated with warm Med SST. A northward shift of ITCZ associated with warm Med SST is
implied by the OLR response.
9
Station
Latitude (˚N)
Longitude (˚E)
Elevation (m)
Missing days (%)
Years of data
Abha
18.14
42.39
2616
30.5
1983-2012
Al Ahsa
25.28
49.48
143
26.2
1982-2012
Al Baha
20.30
41.63
2046
34.3
1981-2012
Al Jouf
29.78
40.10
821
28.9
1983-2012
Al Madinah*
24.33
39.42
899
24.5
1958-2012
Al Qaisumah*
28.31
46.13
207
26.9
1974-2012
Al Taif*
21.26
40.21
1677
32.3
1974-2012
Al Wajh*
26.28
36.42
245
35.5
1973-2012
Arar*
30.90
41.43
653
40.6
1973-2012
Bisha
20.00
42.60
1322
35.7
1983-2012
Dhahran*
26.16
50.10
46
17.5
1955-2012
Gassim*
26.30
43.77
478
29.7
1971-2012
Gizan
16.90
42.58
122
30.6
1984-2012
Hafr
27.90
45.53
389
61.5
1982-2012
Hail*
27.26
41.41
703
29.8
1971-2012
Jeddah
21.42
39.11
23
35.3
1982-2012
Khamis
18.30
42.73
2743
32.1
1983-2012
Makkah
21.42
39.82
1489
42.9
1984-2012
Najran
17.62
44.43
1044
26.4
1982-2012
Rafha*
29.63
43.48
402
26.7
1971-2012
Riyadh
24.93
46.72
457
25.6
1983-2012
Sharorah
17.47
47.12
664
36.7
1981-2012
Tabuk*
28.37
36.62
786
24.6
1973-2012
Turaif*
31.69
38.68
563
25.6
1973-2012
Wadi
20.50
45.20
542
37.1
1990-2012
Yenbo*
24.08
38.00
189
30.9
1974-2012
Table S1. NCDC stations with 3-6 hourly dust records across Saudi Arabia. The asterisks (*)
indicate stations that have dust record since the 1970s. The percentage of missing days ranges
from 11.3% to 71.1% across the whole country for different years during 1975-2010.
Observations are largely missing (68%) at all stations during1995-1998.
10
Period
1975-1999
1976-2000
1977-2001
1978-2002
1979-2003
1980-2004
1981-2005
1982-2006
1983-2007
1984-2008
1985-2009
1986-2010
SSTTEP
(Feb)
-0.76
SSTMed
(36)
-0.91
-1.40
-4.22
-6.58
SSTMed
(24)
-5.90
-6.11
-6.27
-7.93
-6.24
-5.19
-0.64
PCPNA
(84)
-2.82
-2.54
-3.16
-2.32
PCPAP
(60)
-5.63
-5.60
-5.28
-2.79
-5.87
-6.43
-8.76
-9.63
-10.03
Table S2. Regression coefficients of MAM dust PC1 upon predictors selected from 15 potential
predictors by stepwise regression in 12 successive 25-year periods. These potential predictors
include tropical eastern Pacific SST in February [SSTTEP (Feb)] and March [SSTTEP (Mar)], tropical
Indian SST in February [SSTTI (Feb)] and March [SSTTI (Mar)], tropical Atlantic SST in February
[SSTTA (Feb)] and March [SSTTA (Feb)], Mediterranean SST averaged over the prior 36 months
[SSTMed (36)], 24 months [SSTMed (24)], and 12 months [SSTMed (12)], North African precipitation
averaged over the prior 84 months [PCPNA (84)], 60 months [PCPNA (60)], and 36 months [PCPNA
(36)], Arabian Peninsula precipitation averaged over the prior 84 months [PCPAP (84)], 60
months [PCPAP (60)], and 36 months [PCPAP (36)]. Only predictors identified by stepwise
regression are listed. The predictors used in the final prediction model are identified in red.
11
Period SSTTEP
(Apr)
19751999
1976- 0.21
2000
19772001
19782002
19792003
19802004
19812005
19822006
19832007
19842008
19852009
19862010
SSTTEP
(May)
0.30
SSTTI SSTTI SSTMed SSTMed SSTMed PCPNA PCPAP
(Apr) (May) (36)
(24)
(12)
(84)
(60)
-0.73
-3.60
-4.95
-0.89
0.22
-3.84
-4.59
-1.02
-6.83
-4.22
-1.20
-6.80
-4.19
-1.17
-5.52
-4.90
-1.84
-4.07
-2.00
-0.97
-3.67
-2.51
-9.27
-5.10
-9.12
-5.10
-1.54
-11.19
-1.03
-5.67
-4.99
-13.79
-5.32
-9.43
Table S3. Regression coefficients of MAM dust PC1 upon predictors selected from 15 potential
predictors by stepwise regression in 12 successive 25-year periods. These potential predictors
include SSTTEP (Apr), SSTTEP (May), SSTTI (Apr), SSTTI (May), SSTTA (Apr), SSTTA (May), SSTMed
(36), SSTMed (24), SSTMed (12), PCPNA (84), PCPNA (60), PCPNA (36), PCPAP (84), PCPAP (60), PCPAP
(36). The acronyms follow those in Table S2. Only predictors identified by stepwise regression
are listed. The predictors used in the final prediction model are identified in red.
12
13