# Long-term variability and trends in the Caspian Sea - Hindu Kush Index: Influence on atmospheric circulation patterns, temperature and rainfall over the Middle East and Southwest Asia

Dimitris G. Kaskaoutis, Elias E Houssos, Fanny Minvielle, Alireza Rashki, Isabelle Chiapello, Umesh Chandra Dumka, Matthieu Legrand

► To cite this version:

Dimitris G. Kaskaoutis, Elias E Houssos, Fanny Minvielle, Alireza Rashki, Isabelle Chiapello, et al. Long-term variability and trends in the Caspian Sea - Hindu Kush Index: Influence on atmospheric circulation patterns, temperature and rainfall over the Middle East and Southwest Asia. Global and Planetary Change, 2018, 169, pp.16-33. 10.1016/j.gloplacha.2018.07.004. insu-03686252

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https://insu.hal.science/insu-03686252v1

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1 Long-term variability and trends in the Caspian Sea – Hindu Kush

2 Index: Influence on atmospheric circulation patterns, temperature
3 and rainfall over the Middle East and southwest Asia

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5 D.G. Kaskaoutis¹, E.E. Houssos², F. Minvielle³, A. Rashki⁴ I. Chiapello³, U.C.

6 Dumka⁵, M. Legrand³

7 ¹Institute for Environmental Research and Sustainable Development, National

8 Observatory of Athens, 11810 Athens, Greece

9 ²Laboratory of Meteorology, Department of Physics, University of Ioannina, 45110

10 Ioannina, Greece

11 ³LOA, University of Lille-1, 59655 Villeneuve d'Ascq, France

12 ⁴Natural Resources and Environment College, Ferdowsi University of Mashhad,

13 Mashhad, Iran

14 ⁵Aryabhatta Research Institute of Observational Science, Nainital 263 001, India

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16 Abstract

17 The Caspian Sea – Hindu Kush Index (CasHKI) has been introduced as an indicator of
18 modulation of the wind regime and dust activity over southwest (SW) Asia. This study
19 analyzes the long-term series in CasHKI values and the associated mean sea-level
20 pressure (MSLP) anomalies over the Caspian Sea (CS) and Hindu Kush (HK) domains
21 during the period 1963 – 2014, aiming at examining the trends in CasHKI and the
22 associated climate implications. An overall negative trend (-0.12 hPa per decade) in
23 CasHKI is revealed during 1963 – 2014, **mostly driven by** the large decrease in
24 CasHKI during 1963 – 1980, whereas an increasing trend is found during the 2000s.

25 The CasHKI values are mostly related to the MSLP anomalies over the CS rather than
26 HK domain; however, the negative anomalies in MSLP over HK during the 1960s
27 determine the corresponding CasHKI trends. The MSLP values and anomalies show a
28 regional shift in the **core** maximum and higher increasing trends at the southern
29 compared to northern latitudes within the CS domain. The synoptic maps of the MSLP,
30 geopotential height at 700 hPa (Z700) and vertical profile of the meridional wind are
31 compared between the months with highest and lowest CasHKI values, **revealing a**
32 significant intensification in the **north** wind over the SW Asia and **a** deepening of the
33 Indian/Pakistan thermal low for the high-CasHKI months in summer. In addition, the
34 high-CasHKI months are mostly associated with a decrease in Z700 over SW Asia and
35 the Middle East, an intensification of the Indian monsoon trough, a decrease in
36 temperature over the SW Asia and a **slight** increase in rainfall over parts of SW Asia,
37 Middle East and east Africa. Finally, ENSO variability seems not to be significantly
38 linked with CasHKI - but this issue requires further examination.

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**Keywords:** CasHKI; trend analysis; meteorology; climate dynamics; NCEP/NCAR; SW Asia

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# 1. Introduction

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Several atmospheric and oceanic teleconnection patterns have been identified over the globe that are associated with meteorological changes at regions hundreds-to-thousands of kilometers away from the teleconnection centers. Several of these teleconnection patterns have been quantified into climatic indexes like the El-Nino Southern Oscillation (ENSO, Yadav et al., 2009), the North Atlantic Oscillation (NAO, Mahowald et al., 2003), the Eastern Atlantic (EA, Esbensen, 1984), the Atlantic Multi-decadal Oscillation (AMO, Skliris et al., 2012) the Southern Europe-North Atlantic (SENA, Kutiel and Kay, 1992), the North Sea - Caspian Pattern (NCP, Kutiel and Benaroch, 2002), the North African Dipole (NAFDI, Rodriguez et al., 2015), among many others (Del Rio et al., 2013). Short-to-long term variabilities in these indexes are associated with large-scale modification or re-distribution in rainfall (Gadgil et al., 2003; Wang et al., 2012; Donat, 2013; Notaro et al., 2015), temperature (Kutiel and Benaroch, 2002; Brunetti and Kutiel, 2011), and natural hazards like droughts and dust storms, especially over the north Africa and the Middle East (Prospero and Lamb, 2003; Chiapello et al., 2005; Yu et al., 2015, 2016; Cuevas et al., 2016). On the other hand, dust emission and motion may be also strongly associated with short-scale meteorological dynamics, like frontal systems and development of thermal lows, buoyancy, advection and low-level jets (LLJ) over the arid and semi-arid areas (Todd et al., 2008; Basart et al., 2012; Israelevich et al., 2012; Awad and Mashat, 2013; Pey et al., 2013; Hamidi et al., 2013; Najafi et al., 2014; Fiedler et al., 2014; Salvador et al., 2014).

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Sistan in southeast Iran is a closed topographic-low and extremely dry basin, far away from urban centers (Kaskaoutis et al., 2015a; Alsaaidah et al., 2017), where dust aerosols dominate especially in the summer season. This basin constitutes one of the windiest and active dust sources in SW Asia and over the globe (Rashki et al., 2013, 2015). The Caspian Sea – Hindu Kush Index (CasHKI) has been recently introduced as a regulatory factor for the wind regime and dust activity over the southwest (SW) Asia. Initially, Kaskaoutis et al. (2015a) noticed that the summer dusty days over Zabol (31°N, 61°E), located in the Sistan Basin, were associated with a “dipole” of positive mean sea-level pressure (MSLP) anomalies over the Caspian Sea and negative MSLP anomalies over the Hindu Kush mountains that **intensified** the pressure gradient between these regions and the northern wind over **the** SW Asia. Later on, by examining a dust event along the eastern Iranian borders during 1 - 3 July 2014, Kaskaoutis et al. (2015b) found that the same “dipole” has been intensifying during the dust event and was strongly related ($r = 0.93$) with the wind speed at 10 meteorological stations in east Iran and with the aerosol loading over the northern Arabian Sea. These evidences triggered Kaskaoutis et al. (2016) to quantify this “dipole” and to introduce a new climatology index, named CasHKI, which has been shown to be tightly related to the dust activity over **the** SW Asia **during summer** and slightly related to the **summer** monsoon rainfall over northern India and the Himalayas. More recently,
Kaskaoutis et al. (2017) examined the influence of CasHKI variations on the synoptic atmospheric circulation over the SW Asia and on the meteorological conditions (wind speed, temperature, pressure, RH) in the station of Zabol for both summer and winter periods. Furthermore, they studied the impact of CasHKI on visibility records over Zabol and on dust emissions and concentrations along the east Iran by means of SPRINTARS simulations, concluding that higher CasHKI values are significantly related to increase in the northerly flow and in dust activity over the SW Asia.

However, some aspects about multi-decadal CasHKI trends and variations in the pressure levels over the CS and HK domains remain unexplored in the previous studies. The present study analyses the long-term (1963 – 2014) series of CasHKI values along with the respective ones of the MSLP over the CS and HK domains, aiming to examine the CasHKI climatology and trends on annual and monthly time scales. Furthermore, the MSLP variations and trends of the different pixels within the CS and HK domains are studied in order to reveal possible shifts that may be able to modify the gradient between the Caspian Sea and Hindu Kush. Therefore, this analysis sheds light to some queries like (i) what is the difference in MSLP values and variability between the various pixels in CS and HK domains? (ii) is(are) there any favourite pixel(s) for maximum or minimum MSLP values in each domain? (iii) what about the covariance and the correlations between the MSLP values in the different pixels? (iv) is there any tendency of shifting of the MSLP maxima or remarkable changes in the MSLP trends between the pixels of each domain, which might cause changes in the centres of action over the Caspian Sea and Hindu Kush? All these issues are very important for examining the variability, trends and changes in CasHKI and associated climate dynamics over the SW Asia.

Finally, the atmospheric circulation patterns and meteorological dynamics over the SW Asia are examined for months with high and low CasHKI values aiming to reveal differences associated with CasHKI variations. The current results constitute also a basis for examining the relations between CasHKI and other climatic indexes or teleconnection patterns like NCP and ENSO.

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## 2. Databases and methodology

Daily values of MSLP obtained from the National Center for Environmental Prediction / National Center for Atmospheric Research (NCEP/NCAR) re-analysis (Kalnay et al., 1996) have been used for the calculations of CasHKI during 1963 – 2014 (52 years). More specifically, the MSLP values, and anomalies from the mean climatology, have been obtained at 15 pixels over the Caspian Sea domain (40° – 50°N, 50° – 55°E) and 9 pixels over the Hindu Kush domain (35° – 40°N, 70° – 75°E) with 2.5° x 2.5° spatial resolution. The MSLP anomalies were then spatially averaged over the two domains and the CasHKI values were calculated after subtracting the HK MSLP anomalies from the respective ones over the CS domain, as CasHKI = MSLP<sub>anomCS</sub> – MSLP<sub>anomHKI</sub>. The CasHKI values followed a Gaussian distribution with a high accuracy both for the whole period (1963 – 2014) and for each month separately and, therefore, they classified into four modes using thresholds defined by the mean and standard deviation (stdev) values, as described in previous studies (Kaskaoutis et al., 2016, 2017). The low
and high CasHKI modes correspond to CasHKI values below the mean - 1 stdev and above the mean + 1 stdev, respectively. Furthermore, each of the 52 individual months is characterized by a mean CasHKI value. **From these months, the ones with the 5-highest and 5 lowest CasHKI values were considered into contrasting groups of high and low CasHKI months, respectively. Finally, the composite means (and anomalies) of the MSLP, Z700, temperature, rainfall and meridional wind for these high- and low-CasHKI classes, were obtained from NCEP/NCAR re-analysis in order to reveal differences in atmospheric dynamics related or even driven by the changes in CasHKI intensity.**

In addition, the Oceanic Nino Index (ONI) was obtained from NOOA (http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.sht ml) during 1963 – 2014. The ONI constitutes a measure of ENSO variation and is examined against CasHKI in order to explore possible covariance in the time series and/or linkages between high or low CasHKI values with changes in ENSO (e.g., El-Nino or La-Nina phases). The ONI values were computed as 3-month running means of the ERSST.v3b Sea Surface Temperature (SST) anomalies in the El-Nino 3.4 region (5°N – 5°S, 120°–170°W), based on centered 30-year periods (Smith et al., 2008). The warm (El-Nino) and cold (La-Nina) ENSO phases are based on the ONI thresholds of > +0.5 °C and < -0.5 °C, respectively; these thresholds should be met for a minimum of 5 consecutive over-lapping months.

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## 3. Results and discussions

### 3.1. CasHKI values and MSLP anomalies

Figure 1 shows the annual pattern of the daily CasHKI values, along with the MSLP anomalies over the CS and HK domains (N = 18993) for all the 52 examined years, which gives valuable information about the fluctuation and range of the variables during the year. The CasHKI values range widely from -25 hPa to +35 hPa during the winter months (Dec - Feb), while in summer (Jun – Aug), the CasHKI range is limited to ~ -10 hPa to +12 hPa. The distribution of all CasHKI values follows the Gaussian curve around the mean of 0.63 ± 6.56 hPa (median of 0.6 hPa) and the same feature was found in each month (see Fig. 1 in Kaskaoutis et al., 2017). The monthly means of the CasHKI values remain close to zero, ranging from 1.29 hPa in February to 0.36 hPa in December. The larger range in CasHKI values during winter is mostly attributed to the large variability in the MSLP anomalies over the CS domain, which is in the range of -29.1 hPa to +31.1 hPa (mean of 0.00 ± 6.28 hPa, median = -0.09 hPa). The monthly-averaged MSLP anomalies over the CS domain range from -0.31 hPa in April to +0.76 hPa in February. In contrast, the MSLP anomalies over the HK domain are much lower, ranging from -20.9 hPa to +15.8 hPa with a mean of -0.63 ± 3.61 and a median of -0.56 during 1963 - 2014. It is characteristic that the monthly means of the HK MSLP anomalies are always negative, i.e., from -0.96 in October to -0.28 in November. The results indicate that the main centre of action is the Caspian Sea and the large daily, intra-seasonal and inter-annual variabilities in MSLPs and CasHKI are attributed to the remarkable changes in the pressure systems over that region, which are strongly influenced by the Siberian anticyclone during winter and the Iranian ridge moving
172 northward during summer (Kaskaoutis et al., 2017). Therefore, changes in the intensity
173 of the core of the Siberian anticyclone or in its possible movement may strongly
174 influence the CasHKI values, which **modulate** the climate dynamics and dust activity
175 over SW Asia (Kaskaoutis et al., 2016, 2017).

176 The strong relationship between the CasHKI values and the MSLP anomalies over the
177 CS domain is verified in Fig. 2, **as function of the HK** MSLP anomalies. The linear
178 regression is associated with 71% of the variance and is statistically significant at 99.9%
179 level. Note also that for zero MSLP-anomaly over the CS domain, CasHKI equals to -
180 0.51, which is close to the mean and median values of the HK MSLP anomalies. In
181 contrast, a negative correlation (as expected from the CasHKI definition) was found
182 between CasHKI and HK MSLP anomalies, but with much larger scatter of the data
183 points (R² = 0.12). The covariance between CasHKI and the CS MSLP anomalies (34.8)
184 is much larger than that between CasHKI and HK MSLP anomalies (-8.3), confirming
185 the **atmospheric** dynamics over the Caspian Sea as the main regulatory factor for the
186 temporal evolution of CasHKI. **This shows a** similarity with the variability and phases
187 of the NCP, **which are** mostly controlled by the evolution of the geopotential heights
188 at 500 hPa (Z500) over the Caspian Sea **rather than** those over the North Sea (Kutiel
189 and Benaroch, 2002). This points out the Caspian Sea as a centre of MSLP and Z500
190 changes, which play a major role in climate dynamics over the central, SW Asia and
191 the Middle East, and may also affect the temperature and precipitation over the
192 Levantine Basin in combination with the east Mediterranean troughs (Brunetti and
193 Kutiel, 2011).

194 Figure 3 presents the inter-annual variation of the monthly CasHKI values (a), along
195 with the MSLP anomalies over the CS (b) and the HK (c) domains. Much larger
196 variability in CasHKI values is shown in the winter months, since anomalous high (+12
197 hPa) or low (-9 hPa) monthly means are observed. The analysis shows a significant
198 variability in CasHKI for both intra-seasonal and inter-annual time scales with larger
199 values in April – October during ~1963 – 1970 and ~2004 – 2008 and a long period
200 (~1980 – 1999) of low-to-negative CasHKI values. The MSLP anomalies over the CS
201 (Fig. 3b) show large inter-annual variability in the winter months and an increase during
202 the 2000s, while the MSLP variability in HK (Fig. 3c) is much weaker, exhibiting a
203 reversible trend with negative values during the 2000s. The most impressive finding is
204 the large negative MSLP anomalies over HK in April – October for the years 1963 to
205 ~1966, which results in the large positive CasHKI values during that period.

206 Figure 4 shows the temporal evolution of the frequency of occurrence for the low (a)
207 and the high (b) CasHKI modes on monthly basis during the period 1963 – 2014. It
208 should be noted that the thresholds for the low and high CasHKI modes were used
209 separately for each month. The maximum of the frequency of occurrence for the low-
210 CasHKI mode is observed during the period ~1977 – 1999, especially for the **months**
211 June to December, while a very low frequency is observed for the same months during
212 the period 1963 - 1970 and after 2003. As expected, a reverse pattern is observed for
213 the high-CasHKI mode (Fig. 4b). **From February to May**, the frequency of occurrence
214 for both low and high CasHKI modes remains at low-to-moderate levels (~5-10)
215 exhibiting a small yearly variability and low occurrence of extreme values. Long-
216 lasting periods of high or low CasHKI modes or even a sudden transition between low
and high and vice versa may cause climatic perturbations over the SW Asia and the
Middle East. In this respect, Notaro et al. (2015) indicated that the positive MSLP
anomaly over the Caspian Sea associated with an anomalous subsidence at 500 hPa
during 2006 – 2007, caused intensification of the northerlies and drought in the Fertile
Crescent (Syria-Iraqi plains). This period is associated with a shift to larger CasHKI
values (Fig. 3a) and an increase in frequency of the high-CasHKI mode (Fig. 4b).

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224 **3.2. Multi-decadal CasHKI variations and trends**

This section analyses the long-term fluctuation and trends of CasHKI on annual and
monthly time scales, as well as the trends in the MSLP anomalies over the CS and HK
domains during the whole period 1963 – 2014 and three selected sub-periods. Figure
5a-c show the time series of the daily CasHKI values and the MSLP anomalies over the
CS and HK domains, respectively. **In addition**, the 30-days and the 365-days running
means are plotted. The trend analysis estimated an overall decrease in CasHKI of -0.63
hPa during 1963 – 2014 corresponding to -0.12 hPa per decade, which is not considered
as statistically significant at the 95% Confidence Level (C.L.). Moreover, the CasHKI
evolution reveals different tendencies within the studied period, with mostly positive
values and a declining trend till the mid-1970s, which are shifted to mostly negative
with rather neutral variability (yearly-run averages) during the 1980s and the 1990s and
followed by an increasing trend **maximized** in the mid-2000s. On the other hand, the
CS MSLP anomaly exhibits a slight increasing trend (+1.65 hPa during 1963 – 2014:
+0.32 hPa per decade), which is not statistically significant at 95% **C.L.** and **is** mostly
attributed to the dominance of positive values after the 2000s (see also Fig. 3b). The
variability in the HK MSLP anomaly is significantly lower and smoother (Fig. 5c).
Much lower values with a strong increasing trend are shown from the mid-1960s to the
mid-1970s, followed by a neutral variation till the 2000s and a slight declining tendency
afterwards. This variability results in a statistically-significant increasing trend (**95% C.L.**)
of +0.44 hPa per decade for the HK MSLP anomaly, **corresponding to** an
average increase of +2.30 hPa during 1963 – 2014. An important finding from the trend
analysis is that although CasHKI values are mostly driven by the CS MSLP anomalies
(see Figs. 1 and 2), the HK MSLP anomaly trends seem to play the major role in the
CasHKI trends, especially during the beginning of the examined period. In spite of the
rather small tendency in MSLP anomalies over the CS (Fig. 5b), Kutiel and Benaroch
(2002) reported a significant increasing trend in the Z500 hPa over the Caspian Sea
from 1958 to 1998, which impacted the meteorological conditions over the eastern
Mediterranean and Europe.

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The absence of a homogeneous tendency in CasHKI variability during the whole period
and the existence of distinct sub-periods of increasing or decreasing trends, lead us to
examine the CasHKI trends by changing both the starting year as well as the window
period used in the linear regressions. The 10-years period has been considered as the
minimum window, which has been then repeatedly increased by one year. The slope
values of these linear regression analyses are plotted in Fig. 6, while the dots indicate
**the statistically significant trends.** The results show that the slopes become **weaker**
with increasing the window period, since for periods above 30 years the slopes are
within ± 0.16 hpa/year, **but remain statistically significant at the most cases**. During the first years of the examined period, CasHKI presents a **systematic** declining trend independently from the start year and the window periods (– 0.35 to -0.44 hPa/year during the period 1963 – 1975 and for window periods of 10 to 20 years). This negative trend is progressively weakening during the 1980s **(not statistically significant)**, and for larger windows (>25-30 years) within this decade the CasHKI trend shifts to positive, but with rather low slopes (~0.2 hPa/year). The high positive and statistically significant CasHKI trends (slope values above ~0.32 hPa/year) start occurring by the mid-1990s and maximize in the period 1998 - 2008, since after 2008 a slight decreasing trend in CasHKI begins. The graphical representation in Fig. 6 is characterised by the existence of three sub-periods (i.e., 1960s-1970s, 1980s-1990s, and 2000s) with clearly distinct signs and intensities of the trends.

The variations and trends in CasHKI values are also examined on monthly basis (Fig. 7) and in further detail by dividing the whole period into three sub-periods i.e., 1963 – 1980, 1981 – 2000 and 2001 – 2014, with the respective linear regressions summarized in Table 1. Figure 7 shows that on most of the months, the variability in CasHKI values follows the general annual pattern (Fig. 5a), revealing a negative tendency during 1963 – 1980, a rather neutral variability during 1980 – 2000 and an increasing trend afterwards, but with a decreasing tendency after ~2008. This pattern is not followed in March and November, which are the only months with statistically significant trends in CasHKI during the whole period (1963 – 2014) i.e., negative for March and positive for November (Table 1). According to the student T-test, the annual CasHKI trends are not statistically significant at 95% C.L. at any sub-period. Statistically significant decreasing trends are found in the months of April to October during 1963 – 1980 and for February and November during 2001 – 2014 (positive trends). The increasing trends in July and September during 1981 – 2000 are at the limits of statistical significance at 95% (P = 0.049). Merging the trends in each sub-period, the general conclusion is that the overall decreasing trend in CasHKI during 1963 – 2014 is mostly attributed to the significant decreasing trend during 1963 – 1980, which is mostly driven by the HK MSLP anomalies (Figs. 3, 5).

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## 3.3. MSLP patterns over the Caspian Sea and Hindu Kush

Analysis of the temporal variation and trends in the MSLP values and anomalies at the different pixels over the CS and HK domains has remained unexplored in previous studies. Figure 8 shows the annual-mean variability of the MSLP values (a) and MSLP anomalies from each monthly mean (b) for the 15 pixels of the CS domain. The rainbow coloured scale was selected to represent mostly the latitudinal variation from north to south, (blue to red) respectively, **while the statistically significant (95% C.L.) trends correspond to thick (bold) curves**. The time series of the MSLP show a range of ~2 - 6 hPa between the pixels in the CS domain, with maximum values at the northern latitudes, 47.5°N - 50°N, (green and blue colours). Another characteristic is the large inter-annual variability, which can reach at 5 hPa **for the same pixels**, whereas in certain years like 1984 and 1996, the MSLP at the northern latitudes is particularly high (**compared to the rest of the years**), but without a respective **increase** at the southern
latitudes. Since the annual variation of the MSLP at each pixel exhibited much larger values (1022 – 1026 hPa) in the winter months compared to summer (Suppl. Fig. 1a), the highest annual-means in 1984 and 1996 could be attributed to abnormal strengthening of the Siberian anticyclone at the northern pixels of the CS domain, where its influence is much stronger. However, the MSLP time series are progressively changing from the end of the 1990s and the beginning of the 2000s, when the MSLP values at the pixels 45°N and 47.5°N (yellow and green colours) became equal or even larger **than those at 50°N** (Fig. 8). **The MSLP increasing trends are statistically significant (95% C.L.) for the latitudes 40° – 45°, but not for the northern ones.** Moreover, during the 2000s there is a tendency of decreasing **in** MSLP range between the CS pixels, due to both a decrease in MSLP at the northern latitudes and a concurrent increase in MSLP at the southern ones. The important **finding** obtained from this analysis is the southward shift (~ 2.5° - 5°) of the MSLP maximum that creates a new centre of high-pressure core at the middle of the Caspian Sea and closer to the regions that are most impacted by CasHKI. Further analysis is needed in order to examine the cause of this MSLP-max shift during the 2000s, i.e., if it is related to a weakening of the Siberian anticyclone or to a southward movement of its core or even to an **intensification** and expansion of the Iranian high-pressure system toward northern latitudes.

The respective temporal evolution of the MSLP anomalies over the CS domain from each monthly mean (Fig. 8b) shows, in general, larger anomalies (positive or negative) for the northern pixels, like the abnormal highs in 1984 and 1996 and the lows in 1997, 2002, 2013. In contrast, the central and southern pixels exhibit more homogeneous variabilities (**apart from** the abnormal lows during ~1963 – 1968). The graph clearly reveals that the increase in MSLP anomalies during the 2000s, which resulted in an increase of CasHKI (Figs. 3, 4, 5), is mostly attributed to the larger increase in MSLP at central and southern pixels of the CS domain (**statistically significant trends**). In contrast, from the end of the 1960s till the end of the 1990s (except of few years), the MSLP anomalies are mostly similar between the northern and southern pixels corresponding to a smoother variability within the domain-averaged MSLP anomalies (see Figs. 3b, 5b). Linear regression analyses in the MSLP time series showed higher slope values in the range of 0.03 to 0.07 hPa/year for the southern pixels (40°N and 42.5°N) compared to 0.002 - 0.02 hPa/year found for the northern ones (47.5°N and 50°N).

The temporal evolutions of the MSLP (Fig. 9a) and MSLP anomalies from the monthly means (Fig. 9b) for the 9 pixels of the HK domain also reveal some interesting features although a shift of the position of the maximum MSLP is not observed, contrary to the case of the CS domain. The maximum annual MSLP always occurs at central-north and east pixels (i.e., 37.5°N – 40°N and 72.5°E – 75°E), which lie over the mountainous ranges of Hindu Kush and Pamir. The difference in annual MSLP between the pixels is about 3 to 5 hPa, while the annual pattern of the MSLP at each pixel exhibited a winter maximum and a summer minimum (Suppl. Fig. 1b), similarly to the CS domain. **The MSLP shows statistically significant increasing trends in all the pixels over the HK domain.** It is worth to be noted that the very low values and the steep increase in MSLP during ~1963 – 1980 is shown at all pixels of the HK domain, while the MSLP remained
much below the mean values till about 1980. These low values resulted in high CasHKI during the 1960s-1970s and in the pronounced **CasHKI** decreasing trend in 1963 - 1980. Another important result is the decrease in MSLP at all pixels during the 2000s, which in combination with the increase in MSLP over the southern CS pixels, resulted in the increase in CasHKI during that period (Figs. 5-7).

The southward shift of the highest MSLP core in the CS domain does not impact the CasHKI values, as they defined as differences between the spatially-averaged MSLP anomalies, but may have an impact on the gradient strength between the Caspian Sea and Hindu Kush. Possible changes in the pressure-gradient strength may affect the wind regime (intensity, core and area of expansion) in the SW Asia and modify the relative strengths of the various dust sources, i.e., Karakum desert, Dasht-e-Lut, Margo and Registan deserts, Sistan Basin, Jazmurian depression (Kaskaoutis et al., 2015a, 2016; Rashki et al., 2015, 2017). Spatial oscillations in the intensity of NAFDI over the west Africa during the last six decades have modulated changes in the dust-transport pathways over the sub-tropical Atlantic (Rodriguez et al., 2015). Therefore, further investigation is needed to explore possible impacts on the wind regime and dust activity over SW Asia related to the southward movement of the MSLP core over the Caspian Sea.

## 3.4. Changes in MSLP and Z700 between months of low and high CasHKI values

This section examines the differences in the atmospheric circulation patterns over the Middle East, central and SW Asia between contrasting months associated with the lowest and highest CasHKI during 1963 – 2014. In this respect, Figs. 10-13 summarize the MSLP and Z700 composite means for the five months of lowest and for the five months of highest CasHKI values during winter, spring, summer and autumn, respectively. The analysis aims to detect **the** characteristics in the MSLP and Z700 **that** are associated or even favoured by the low or high CasHKI values. It should be noted that the differences in the atmospheric circulation patterns between the contrasting months are lower **than those found** by examining separately the high and the low CasHKI modes in each month (Kaskaoutis et al., 2017). **Figures 10-13 give a qualitative indication of the synoptic meteorology. Further analysis showed that statistically significant differences between the two groups of CasHKI values occur for MSLP differences of ± ~3 hPa and for Z700 of ± ~25 gpm.**

In winter, the months with the lowest CasHKI values (first row in Fig. 10) generally exhibit higher pressures over the Hindu – Kush, Himalayas and Tibetan plateau compared to the respective months with highest CasHKI (second row in Fig. 10). In contrary, the high-CasHKI winter months exhibit an expansion of the high pressure system from the central Asia to the Siberia, indicating an intensification of the Siberian anticyclone. On the other hand, there are no important changes between the high- and low-CasHKI months over the Arabian Sea and northern Indian Ocean, with the exception of January that exhibits increased (decreased) MSLP anomalies for the low (high) CasHKI cases in Arabia (Suppl. Fig. 2). The differences in the Z700 hPa between the months of low (third row in Fig. 10) and high (fourth row in Fig. 10) CasHKI are mostly observed at the northern latitudes, where significantly lower Z700 fields are
found for the low-CasHKI months, especially over the areas northwest of the Caspian Sea. In contrast, there is a tendency for higher Z700 levels over northwestern China and the elevated terrains of Hindu Kush, Himalayas and Tibet for the months of low CasHKI. Furthermore, these months show an important increase in Z700 over the Arabian Peninsula, Arabian Sea and Indian subcontinent with a northward shift of the subtropical high Z700 hPa. The Z700 anomaly field (Suppl. Fig. 2) reveals variations of up to +20 - 30 hPa in Z700 over the Tibetan Plateau during low-CasHKI months against the large negative (-30 hPa to - 40 hPa) Z700 anomalies over the same regions for the high-CasHKI months.

In spring, the synoptic conditions start to change rapidly, **with** main characteristics the weakening of the high MSLP system over the Caspian Sea and central/north Asia and the formation of the Indian-Pakistan thermal low (in May) with expansions over the north Arabian Sea (Fig. 11). At Z700 level, there is an increase in the geopotential heights over the northern latitudes, compared to the winter case. Such an increase is **also** shown over the Arabian Sea, Arabian Peninsula and India, **with remarkable changes between the two CasHKI groups**. The low-CasHKI months are associated with lower MSLP values over the Caspian Sea and central Asia, while the MSLP anomaly maps (Suppl. Fig. 3) reveal more negative anomalies over the Hindu Kush and western China for the high-CasHKI months. Over the rest of the regions, the MSLP anomalies seem to be rather random, depending on specific months. In general similarity to the winter case, the low-CasHKI months **exhibit an intensification and a northward shift of the sub-tropical high Z700**, carrying warmer air over the Himalayas and the Tibetan plateau. In addition, there is a significant increase in the core and expansion of the Z700 **indicating intensification of the Arabian ridge for the low-CasHKI months in spring** (Fig. 11).

During summer, the Indian-Pakistan thermal low **is** well developed and **expanded** over the whole south Asia forming a secondary low over the eastern Arabian Peninsula (Fig. 12). The high-CasHKI months are associated with a deepening of the thermal-low core and an expansion of the MSLP low over the SW Asia and Arabia (Kaskaoutis et al., 2017). The MSLP anomalies in all the summer months are positive (negative) over the Caspian Sea (Hindu Kush) for the high-CasHKI months and reverse for the low-CasHKI months, and rather negligible over the other parts of the studied domain (Suppl. Fig. 4). The time of development, core intensity and westward expansion of the Indian-Pakistan thermal low was found to influence the onset of the “Shamal period” over the eastern Arabian Peninsula, while the end of the “Shamal period” was associated with the time of weakening and the eastward recession of this low-pressure system (Yu et al., 2016). Therefore, possible linkage between CasHKI and Indian-Pakistan thermal low should be examined in more detail in order to explore **changes** on the onset of Levar wind and **on Sistan dust outflows over the Arabian Sea that strongly affect the phytoplankton and chlorophyll blooming, the enzymatic activity and fishery** (Singh et al., 2008; Yang et al., 2017; Shamsuzzaman et al., 2017). At Z700 hPa, the high-CasHKI months show a general strengthening of the Indian monsoon trough over the Tibetan plateau and the Indian subcontinent, whereas an intensification and northward expansion of the Arabian/Iran ridge is observed for the low-CasHKI months (Fig. 12). In general, the central and south Asia are mostly covered by positive
(negative) Z700 anomalies in the low- (high-) CasHKI months in summer (Suppl. Fig. 4).

Autumn is a transition season as the MSLP and Z700 hPa synoptic maps are similar to summer for the month of September and close to winter for the months of October and November (Fig. 13). Therefore, in September, the high-CasHKI exhibits deeper Indian-Pakistan thermal low compared to the low-CasHKI situation, while in October and November, the high-CasHKI presents higher pressure systems over the Caspian Sea and central Asia compared to the low-CasHKI situation. The MSLP anomaly maps reveal dipoles of high and low MSLP anomalies between the Caspian Sea and the Hindu Kush/western China and negligible anomalies over the southern latitudes (Suppl. Fig. 5). The Z700 hPa maps for the low- and high-CasHKI months in autumn are in general agreement with the other seasons, characterized by intensification and expansion of the Arabian ridge and by northward movement of the sub-tropical high Z700 over the Indian sub-continent for the low-CasHKI months.

The changes in the synoptic meteorological patterns between the low- and high-CasHKI months may drive changes in meteorological parameters and dust activity over the arid SW Asia, as shown in previous studies (Kaskaoutis et al., 2016, 2017). Similarly, a positive (negative) NCP phase was associated with anomalous trough (ridge) of the Z500 over Arabia, Iran and the Caspian Sea (Kutiel and Benaroch, 2002). Furthermore, Notaro et al. (2015) noticed that the shift from the mostly positive NCP during 2001–2005 to mostly negative NCP during 2008–2012 might be responsible for a shift to a drier and dustier period in 2008 – 2012 over Arabia, but without clear verification about this linkage. Although the positive phase of the NCP seems to correspond to the high CasHKI mode, the linkage and interrelation between NCP and CasHKI have not been evaluated so far and this constitutes a challenging issue for the future.

464

### 3.5. Changes in wind regime between months of low and high CasHKI values

The Levar or 120-days wind is one of the most featured meteorological phenomena in SW Asia during summer, blowing with great violence, often above 25-30 ms⁻¹, along the east Iran and causing massive dust storms (Middleton, 1986; Alizadeh Choobari et al., 2013, 2014; Rashki et al., 2012, 2015). Kaskaoutis et al. (2015a) noticed much stronger surface winds in Zabol during the summer dust-storm days compared to the monthly climatology, which were associated with higher CasHKI values, while a strong correlation (r = 0.93) was found between the wind speeds at 10 stations in east Iran and the CasHKI values during a case dust storm (1-3 July 2014) (Kaskaoutis et al., 2015b). Furthermore, the high CasHKI modes were associated with stronger northern winds over Sistan both in summer and winter, with a strong linkage between CasHKI and meridional wind in Zabol (Kaskaoutis et al., 2016, 2017). Therefore, this section examines the differences in the vertical profiles of the meridional wind along east Iran (for the red box area in Fig. 10) between the months with highest and lowest CasHKI values (Fig. 14).

The latitude-altitude cross sections show a distinct change in the orientation (north = +, south = -) and intensity of the meridional winds within the studied domain between
the warm (May – October) and cold (November – April) periods (Fig. 14). So, the meridional wind during the cold period is mostly slightly positive, indicating southern flow of low intensity over the continental SW Asia in winter, **which** shifts to northerly flow with higher **intensity** in the summer (negative values). More or less similar patterns are shown at all the altitudes, while at the southern latitudes the meridional winds are mostly from northern directions (negative values of the wind speed) in winter. In contrast, **during summer, the southerlies dominate over the Arabian Sea near the surface (below ~800 hPa)** due to the southwest summer monsoon flow. This general pattern is strongly modified between the low- and high-CasHKI months, both near the surface as well as at higher altitudes. Focusing at lower altitudes (e.g. < 700 hPa), the meridional wind component is rather low from January to April, without pronounced difference between the low- and high-CasHKI months. However, especially at near-surface and upper-levels **over the continental Asia (latitudes above 25°N)**, the meridional wind for the high-CasHKI months tends to have lower values, indicating a much weaker southern component. From May to October, the high-CasHKI months exhibit much stronger northern meridional winds (blue to violet colours) than the low-CasHKI months, **especially** over Sistan and the northern latitudes. This verifies **the** intensification of the northern Levar wind over the continental SW Asia for the high CasHKI values in the summer months (Kaskaoutis et al., 2016, 2017). Focusing on altitudes below ~800 hPa, a zone of drastic change in the wind direction lies around 25°N, along the northern coast of the Arabian Sea, which defines the position of the Inter-Tropical Convergence Zone (ITCZ) in summer (Kaskaoutis et al., 2015b). This zone delineates the area of influence of the Levar wind near the surface, which seems to reach at **southern** latitudes at upper levels, even **south of** 20°N (outside of the domain covered by Fig. 14). The position and the northward shift of the ITCZ were found to play an important role in the capability of the Sistan dust storms to reach the central part of the Arabian Sea (Kaskaoutis et al., 2014, 2015b). After October, the Levar wind is absent over SW Asia and, therefore, changes in CasHKI values do not seem to significantly affect the meridional wind pattern.

The Levar wind is closely associated with dust emissions across the SW Asia and, more specifically, from the Sistan Basin. Previous study (Kaskaoutis et al., 2017) showed that enhanced wind speeds winds (> ~15 ms⁻¹) under higher CasHKI intensities increased the probability of dust storms with associated reduction in visibility (< 1 - 2 km) in Zabol. Behrooz et al. (2017a, b) reported a tremendous increase in Total Suspended Particulate (~10000 µgm⁻³), PM₁₀ (~3000 µgm⁻³) and PM₂.₅ (~1000 µgm⁻³) concentrations in Sistan for wind speeds exceeding 8-9 ms⁻¹, in accordance with the results concerning dust storms triggered by the Shamal wind in Arabia (Yu et al., 2016). Analysis of the vector winds over SW Asia for the low- and high-CasHKI months (not shown) did not reveal significant differences in the intensity of the Shamal winds **over Arabia**. However, on certain cases, changes in CasHKI intensity may also regulate the summer Shamal and dust activity over the Middle East and Arabian Peninsula, as shown in Yu et al. (2015). Although the genesis of Levar seems to be associated with the development of the Indian/Pakistan thermal low, as well as several other limited thermal lows over the topographic-low basins in SW Asia, more detailed investigation is required in order to assess the meteorological conditions that are associated with an
early or late onset and the inter-annual variability of Levar, as examined for the Shamal wind over Arabia (Yu et al., 2016).

529

## 3.6. Changes in temperature and precipitation between months of low and high CasHKI values

This section examines possible changes on the distribution of rainfall and temperature anomalies over the SW Asia, Middle East and Arabia between the low and high-CasHKI groups. In this respect, the composite anomalies of the rainfall (in mm/day) (Fig. 15) and temperature (Fig. 16) obtained from the NCEP/NCAR re-analysis are plotted for the five months of lowest and highest CasHKI values. The coloured areas correspond in practical to statistical significant differences between the two CasHKI groups. The rainfall anomalies did not reveal a clear pattern due to low precipitation over the Middle East and SW Asia throughout the year. Furthermore, negligible to very low rainfall anomalies are also shown over areas with large seasonal rainfall, like the northern latitudes during winter and spring, and the Indian subcontinent during summer. However, the high-CasHKI months present positive rainfall anomalies (2-6 mm/day) over scarce areas in northern Afghanistan, western Iran, Syria-Iraqi plains, eastern Turkey and parts of the east Africa (Sudan, Ethiopia), which may affect forest ecosystems over Zagros mountains (Moradi et al., 2017; Varnosfaderani et al., 2017), Sahel vegetation and the greenery over the study area (Hamidi et al., 2014; Song et al., 2017). In contrast, nearly zero rainfall anomalies from the mean climatology are shown over the same areas in the low-CasHKI months. The differences in rainfall between the low- and high-CasHKI months over these areas are more pronounced in spring, while no remarkable changes between the low- and high-CasHKI months are seen over the Indian sub-continent and adjoining oceans during the summer monsoon months. A positive (negative) NCP phase was associated with positive (negative) rainfall anomalies in the Middle East and the east Mediterranean (Kutiel and Benaroch, 2002). The current results are in general consistency with those findings, since the high-CasHKI months exhibited larger rainfall amounts over parts of the SW Asia, Middle East and east Africa. However, CasHKI variations do not seem to significantly affect the rainfall distribution over the SW Asia, verifying Kaskaoutis et al. (2016) who reported low-to-moderate CasHKI impact on the Indian summer monsoon rainfall.
months over central Asia and parts of Iran and Afghanistan. The temperature anomalies can be easily explained by the changes in atmospheric circulation and wind regime associated with changes in CasHKI intensity. Therefore, the high-CasHKI mode was associated with anomalously-high northern wind **over the** central Asia, east Iran and Afghanistan (see Fig. 14), bringing colder air masses **that drop** the temperature, while the low-CasHKI months were associated with intensification of the Arabian ridge and enhancement of the southerlies over Iran, Afghanistan and central Asia. These contrasting atmospheric circulation patterns are also related to negative and positive phases of the NCP and important changes in temperature. In this respect, Ghanghermeh et al. (2015) reported negative correlation between NCP and temperature over the northeast Iran during winter, indicating that the positive NCP phase was associated with enhancement of the northerly flow and drop in temperature (as in the conditions of the high-CasHKI months). In accordance to the current results, Kaskaoutis et al. (2017) reported lower temperatures over Zabol for the higher CasHKI modes, which were more pronounced during winter. The positive temperature anomalies over Iran and Afghanistan for the low-CasHKI months are likely to cause negative pressure anomalies, thus enhancing the cyclonic flow and increase the northwestern Shamal winds and the probability of dusty days along the eastern Arabian Peninsula (Yu et al., 2016). Despite the fact that Levar and Shamal winds seem to be triggered by different atmospheric dynamics, more detailed examination is needed for common factors that may modify these wind regimes, like the Indian-Pakistan thermal low, the Iranian low and the MSLP variations over the Caspian Sea along with large-scale climate dynamics, like ENSO (Notaro et al., 2015; Rashki et al., 2015; Yu et al., 2015, 2016).

594

## 3.7. Large-scale dynamics and further challenges

Previous **analysis** shown that changes in atmospheric dynamics and teleconnection patterns were associated with meteorological and dust variabilities over the Middle East and SW Asia. However, possible impacts of the large-scale atmospheric circulation patterns and SST anomalies on CasHKI variability and dust activity over the SW Asia remain largely unknown, although such issues have been addressed for the Arabian dust (Mahowald et al., 2003; Hsu et al., 2012; Abish and Mohanakumar, 2013; Notaro et al., 2015; Yu et al., 2015, 2016). In this respect, Fig. 17 analyses the monthly evolution of the CasHKI and Oceanic Nino Index (ONI) values during 1963 – 2014, aiming to assess any co-variance and linkage between them. The light magenta and light cyan colours correspond to periods with at least 5 consecutive overlapping seasons with ONI values above 0.5 and below -0.5, respectively, corresponding to El-Nino and La-Nina phases.

Regression analysis did not reveal any correlation between CasHKI and ONI, either during the whole period or during the summer (June – September) dusty season. It is characteristic that CasHKI exhibits similar ranges, i.e., from -6.3 to 12.6 hPa for N = 145 cases with ONI > 0.5 (El-Nino) and from -7.5 to 10.3 hPa for N = 154 with ONI < -0.5 (La-Nina). However, the CasHKI mean is slightly lower (0.58) during the El-Nino months compared to La-Nina (1.11). In several periods like 1969 – 1970, 1972 – 1973, 1987 – 1988, 1998 – 1999, 2009 – 2010, there is a shift from El-Nino (positive ONI) to
La-Nina (negative ONI) phase, but without any significant **shift** in the CasHKI values (Fig. 17). Therefore, the MSLP time series over the CS and HK domains that define the CasHKI are apparently not related to ENSO variations, and any change in dust activity over the SW Asia that is ascribed to ENSO should be mostly arise with variations in precipitation, since La-Nina was related to drought **conditions** over **the** central and SW Asia (Mathew et al., 2002).

Most studies dealing with meteorology and dust over the Middle East, Arabia and SW Asia, refer to the period after the 2000s during which a shift in ENSO has been observed (from El-Nino to La-Nina phase around 2006 – 2007), marking the beginning of a prolonged drought in the Fertile Crescent and an abnormal dusty period (Notaro et al., 2015; Solmon et al., 2015; Yu et al., 2016). Furthermore, La-Nina was found to be associated with anomalous early onset and longer duration of the Shamal wind due to favourable development of the Iranian heat low, while El-Nino seemed to inhibit the formation of this thermal low resulting in late onset and earlier dissipation of the Shamal wind (Yu et al., 2016). In addition, a shift from an increasing to a decreasing trend in CasHKI was observed around 2007 - 2008 (Figs. 5a, 17). The long-lasting La-Nina phase from mid-1998 to the beginning of 2001 was associated with the most extreme drought period in Sistan leading to remarkable increase in dust-storm frequency during 2001 – 2003 (Rashki et al., 2013, 2014; Sharifikia, 2013). Similarly, Yu et al. (2015) noticed that the La-Nina phase reduced the springtime rainfall over Arabia favouring the dust emissions, and was also associated with cooler tropical Indian ocean, which favoured the development of anomalous anticyclonic circulation over the central Saudi Arabia that intensified the Shamal wind. In spite of the absence of a significant association between CasHKI and ONI (Fig. 17), the atmospheric circulation patterns that are associated with variations in the onset and intensity of Levar and possible linkage between them and ENSO have not been examined so far and this constitutes a real challenge for future research.

The intra-seasonal to inter-annual dust variability over Sistan is strongly related to several factors, like local and regional precipitation, early or late dryness of the Hamoun lakes, soil characteristics, greenness of the area, intensity of Levar and CasHKI variations (Rashki et al., 2013; Kaskaoutis et al., 2016, 2017; Natsagdorj et al., 2017), which may be also linked to large-scale climate dynamics. Due to several factors that are involved with dust **activity** and meteorology over SW Asia, a seasonal dust prediction model needs to be developed via multi-linear regressions or artificial neural networks based on variations of the climatic control factors, as performed for dust in Arabia (Yu et al., 2013, 2015) and China (Lang et al., 2008; Gao and Han, 2010).

## 4. Conclusions

This study analysed the long-term variations and trends in CasHKI during the period 1963 – 2014, focusing on examining the causes for the CasHKI variability and the differences in atmospheric circulation patterns, on monthly basis, that were associated with high and low CasHKI values. Furthermore, this study explored the impact of CasHKI variations on the rainfall distribution and temperature over the SW Asia, the Middle East and Arabia and possible linkage between large-scale climate dynamics
659 (i.e., ENSO) and CasHKI. CasHKI was defined as the difference in MSLP anomalies
660 over specified domains in the Caspian Sea and Hindu Kush and its values exhibited
661 much larger range (-25 hPa to +35 hPa) in winter compared to summer (-12 hPa to +14
662 hPa) due to more intense variability in the atmospheric circulation systems during
663 **wintertime** at northern mid-latitudes. The MSLP anomalies over the Caspian Sea were
664 **found to** control the CasHKI values, while the Hindu Kush MSLP anomalies **played**
665 an important role in the CasHKI trends due to abnormal negative MSLP anomalies
666 during the 1960s. The CasHKI values were relatively high during 1963 – mid-1970s
667 exhibiting a decreasing trend, they were mostly negative with low variability during the
668 1980s and 1990s and increased during the 2000s. The overall trend in CasHKI during
669 1963 – 2014 was -0.12 hPa per decade and was not considered as statistically
670 significant, while statistically significant trends were found during 1963 – 1980 for the
671 months April – October, and for few months (e.g. February, November) during 2001 –
672 2014. Further analysis in the MSLP anomalies over the Caspian Sea and Hindu Kush
673 domains revealed that the maximum MSLP over the Caspian Sea was shifted to
674 southern latitudes during the 2000s and this may impact the intensity of the pressure
675 gradient between the two domains and **may** cause changes in Levar wind.

676 The intra-seasonal to inter-annual variability in CasHKI values may cause significant
677 changes in meteorological and climate dynamics over the SW Asia and the Middle East,
678 since the high-CasHKI months were associated with intensification of the northern
679 wind speed across east Iran, significant drop in temperature and slight increase in
680 precipitation over parts of Iran, Afghanistan and the Middle East. The high-CasHKI
681 months exhibited increase in MSLP at regions north and northwest of the Caspian Sea
682 and decrease over the mountainous ranges of Hindu Kush, Himalayas and Tibetan
683 Plateau, while the opposite MSLP anomalies were observed over these areas for the
684 low-CasHKI months. Another important finding was the deepening of the
685 Indian/Pakistan thermal low and the intensification of the Indian summer monsoon
686 trough for the months of high CasHKI values, while the low-CasHKI months exhibited
687 an intensification of the Arabian-Iran ridge and a northern shift of the sub-tropical high
688 Z700 over south Asia. CasHKI variations were not found to be related to ENSO, so
689 changes in dust activity over SW Asia, Arabia and the Middle East that have been
690 ascribed to CasHKI and/or ENSO variations should be considered mostly on
691 local/regional changes in the wind regime (mostly affected by CasHKI) and or
692 prolonged dry conditions mostly driven by La-Nina phases. However, more analysis is
693 needed in this issue in order to explore possible amplification of ENSO and CasHKI
694 impacts on meteorological conditions and dust activity over the SW Asia.

695

696 Acknowledgements

697 NCEP/NCAR reanalysis is gratefully acknowledged for providing the meteorological
698 fields over SW Asia that are used in the current study. In addition, we are thankful to
699 the NOAA Climate Prediction Center for providing the Oceanic Nino Index (ONI)
700 values. This work is supported by the project “*Le Sistan, source singulière d’aérosols
701 désertiques: sous quelle influence dynamique locale et synoptique?* 2016 - 2018”
702 funded by CNRS, France (Ref No: AO2016-997442).
703

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<table><caption>**Table 1:** Linear regressions of CasHKI trends during the whole period (1963-2014) and three sub-periods. The statistically significant trends at 95% Confidence Level (C.L.) are highlighted in bold. The \* asterisk stands for statistical significance at 99% C.L.</caption><thead><tr><th></th><th>1963 – 2014</th><th>1963 – 1980</th><th>1981 – 2000</th><th>2001 – 2014</th></tr></thead><tbody><tr><td>Jan</td><td>Y = -0.007x + 1.19</td><td>Y = -0.151x + 1.05</td><td>Y = 0.078x – 3.38</td><td>Y = 0.057x – 0.40</td></tr><tr><td>Feb</td><td>Y = -0.025x + 1.94</td><td>Y = -0.076x + 3.34</td><td>Y = -0.152x + 3.97</td><td><b>Y = 0.693x – 29.63</b></td></tr><tr><td>Mar</td><td><b>Y = -0.083x + 2.64</b></td><td>Y = -0.033x + 2.06</td><td>Y = -0.051x + 2.19</td><td>Y = 0.063x – 4.49</td></tr><tr><td>Apr</td><td>Y = -0.035x + 1.35</td><td><b>Y = -0.349x + 4.59</b></td><td>Y = -0.047x + 0.71</td><td>Y = -0.050x + 3.08</td></tr><tr><td>May</td><td>Y = -0.035x + 1.66</td><td><b>Y = -0.199x + 3.82</b></td><td>Y = 0.037x – 1.51</td><td>Y = 0.153x – 6.11</td></tr><tr><td>Jun</td><td>Y = -0.001x + 0.53</td><td><b>Y = -0.325x + 4.19*</b></td><td>Y = -0.015x – 0.52</td><td>Y = 0.213x – 7.87</td></tr><tr><td>Jul</td><td>Y = 0.012x + 0.39</td><td><b>Y = -0.473x + 5.81*</b></td><td><b>Y = 0.113x – 4.46</b></td><td>Y = 0.120x – 2.77</td></tr><tr><td>Aug</td><td>Y = -0.028x + 1.43</td><td><b>Y = -0.377x + 5.31*</b></td><td>Y = 0.016x – 1.26</td><td>Y = 0.049x – 0.77</td></tr><tr><td>Sep</td><td>Y = -0.017x + 1.08</td><td><b>Y = -0.511x + 6.48*</b></td><td><b>Y = 0.134x – 4.96</b></td><td>Y = 0.014x + 1.26</td></tr><tr><td>Oct</td><td>Y = -0.013x + 0.99</td><td><b>Y = -0.419x + 5.40*</b></td><td>Y = 0.031x – 1.57</td><td>Y = 0.349x – 14.32</td></tr><tr><td>Nov</td><td><b>Y = 0.064x – 1.52</b></td><td>Y = -0.049x + 0.06</td><td>Y = 0.141x – 4.52</td><td><b>Y = 0.380x – 15.45</b></td></tr><tr><td>Dec</td><td>Y = 0.026x – 0.34</td><td>Y = -0.233x + 2.71</td><td>Y = -0.091x + 1.28</td><td>Y = -0.021x + 3.54</td></tr><tr><td>Annual</td><td>Y = -0.012x + 0.95</td><td>Y = -0.241x + 3.74</td><td>Y = 0.017x – 1.19</td><td>Y = 0.165x – 6.00</td></tr></tbody></table>

<figure><img src="image_2.png" /><figcaption>**Figure 1:** Annual variability of the daily MSLP anomalies over the Caspian Sea and Hindu Kush domains, along with the CasHKI values during the period 1963 – 2014. Total number of daily values, N=18993.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>**Figure 2:** Correlation between the daily CasHKI and CS MSLP<sub>anom.</sub> values (in hPa) during the period 1963-2014. The HK MSLP<sub>anom.</sub> values are presented in the coloured scale. **Total number of daily values, N=18993.**</figcaption></figure>
<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

**Figure 3:** Temporal variation of the monthly CasHKI values (a), MSLP anomalies over the Caspian Sea (b) and Hindu Kush (c) domains during the period 1963 – 2014.
<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

**Figure 4:** Temporal variation of the frequency of occurrence (number of days in a month) for the low (a) and high (b) CasHKI modes on monthly scale during the period 1963 – 2014.
<figure><img src="image_9.png" /><figcaption>**Figure 5:** Daily variation and trends of CasHKI (a), Caspian Sea MSLP anomaly (b) and Hindu Kush MSLP anomaly (c) during the period 1963 – 2014. The 30-day (**corresponding** to a month) and 365-day (corresponding to a year) running averages are shown. The total variations during the examined period as well as the trends per decade are also given in the graphs. **Total number of daily values, N=18993.**</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Figure 6:** Graphical representation of the CasHKI trends (slope values of the linear regressions in hPa/year) using different starting years and windows (time periods) within the period 1963 – 2014. The dots correspond to statistical significant (95% confidence level) trends.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**Figure 7:** Inter-annual variability of the monthly-mean CasHKI values along with the stdev (shaded) for each month during the period 1963 - 2014. The linear trends in the CasHKI values are shown in red, while the 95% lower and upper limits of the regression lines are shown in green. The whole period is divided into three sub-periods, 1963-1980, 1981-2000, 2001-2014.</figcaption></figure>
<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

**Figure 8:** MSLP values (a) and anomalies (b) (in hPa) from the mean 1963 - 2014 climatology for the 15 pixels over the Caspian Sea domain. The pixels are colour-scaled from north (blue colours) to south (red colours). **Thick lines correspond to statistical significant trends (95% C.L.).**
<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /><figcaption>**Figure 9:** Same as in Fig. 8, but for the 9 pixels over the Hindu Kush domain. The trends at all pixels are statistical significant (95% C.L.).</figcaption></figure>
Latitude

<figure><img src="image_16.png" /><figcaption>**Figure 10:** Mean composites of the MSLP (upper 2 rows) and Z700 hPa (bottom two rows) for the winter months with the lowest (1st, 3rd rows) and the highest (2nd, 4th rows) CaSHKI values during the period 1963 – 2014. The red domain corresponds to the area that is mostly impacted by the Levar wind and is used for the altitude cross sections of the meridional winds (see Fig. 14).</figcaption></figure>
Latitude

<figure><img src="image_17.png" /></figure>

Longitude

<caption>**Figure 11:** Same as in Fig. 10, but for the spring months.</caption>
Latitude

<figure><img src="image_18.png" /><figcaption>**Figure 12:** Same as in Fig. 10, but for the summer months.</figcaption></figure>
Latitude

<figure><img src="image_19.png" /></figure>

Longitude

<caption>**Figure 13:** Same as in Fig. 10, but for the autumn months.</caption>
Pressure

<figure><img src="image_20.png" /></figure>

Latitude

**Figure 14:** Composite-means of the latitudinal vertical cross sections of the meridional wind component along the longitudes 55° – 65° E for the months of the five lowest and the five highest monthly-CasHKI values during the period 1963 - 2014.
<figure><img src="image_21.png" /></figure>

**Figure 15:** Composite anomalies of the rainfall rate (mm/day) from the mean climatology 1981 – 2010 for the five months with lowest and highest CasHKI values during 1963 – 2014.
<figure><img src="image_22.png" /><figcaption>**Figure 16:** Same as in Fig. 15, but for the air temperature at 1000 hPa.</figcaption></figure>
<figure><img src="image_23.png" /><figcaption>**Figure 17:** Monthly variability of CasHKI and Oceanic Nino Index during the period 1963 – 2014. The red line corresponds to 3-month running means for the CasHKI values. The light magenta and light cyan areas correspond to 5 consecutive months of high (>0.5) and low (<-0.5) ONI values corresponding to El-Nino and La-Nina phases, respectively.</figcaption></figure>