Contrasting changes in snow cover and its sensitivity to aerosol optical properties in Hindukush-Karakoram-Himalaya region

Journal Pre-proof Contrasting changes in snow cover and its sensitivity to aerosol optical properties in Hindukush-Karakoram-Himalaya region

Maqbool Ahmad, Khan Alam, Shahina Tariq, Thomas Blaschke PII:

S0048-9697(19)34347-5

DOI:

https://doi.org/10.1016/j.scitotenv.2019.134356

Reference:

STOTEN 134356

To appear in:

Science of the Total Environment

Received date:

27 March 2019

Revised date:

23 August 2019

Accepted date:

6 September 2019

Please cite this article as: M. Ahmad, K. Alam, S. Tariq, et al., Contrasting changes in snow cover and its sensitivity to aerosol optical properties in Hindukush-KarakoramHimalaya region, Science of the Total Environment (2018), https://doi.org/10.1016/ j.scitotenv.2019.134356

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© 2018 Published by Elsevier.

Journal Pre-proof Contrasting changes in snow cover and its sensitivity to aerosol optical properties in Hindukush-Karakoram-Himalaya region Maqbool Ahmad1*, Khan Alam2*, Shahina Tariq1, Thomas Blaschke3 1

Department of Meteorology, COMSATS University Islamabad, Pakistan

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Department of Physics, University of Peshawar, Khyber Pakhtunkhwa, Pakistan

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Department of Geoinformatics Z_GIS, University of Salzburg, Schillerstrasse 30, 5020 Salzburg, Austria

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Abstract

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Snow cover plays a major role in the earth’s climate system. The stability of the snow mass over

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Hindukush-Karakoram-Himalaya (HKH) in contrast to the worldwide retreat of mountainous glaciers and its relation to aerosol concentration remains poorly understood. The proposed study

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focused on the understanding of this relationship between various snow parameters and the

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optical properties of atmospheric aerosols over the HKH region of Northern Pakistan between March and June for a prolonged study period from 2005 to 2015. The aerosol’s optical properties

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were retrieved from snow covered pixels in the study area to avoid the contamination of snow

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albedo (SA) by other features of land surfaces. The results revealed an increasing trend in the snow cover area (SCA) at the rate of 577.3, 1090.6 and 652.3 km2/year in March, May and June, respectively, with a decrease in April due to the uneven distribution of SCA during 2005-2015. The results revealed a strong positive correlation (R = 0.77) between SCA and SA, whereas SCA and SST were negatively correlated (R = -0.82) during the study period. The Cloud-Aerosol Lidar and Infrared Pathfinder (CALIPSO) indicated the presence of scattering and absorbing aerosols (e.g., dust, polluted dust, and smoke) both at high and low altitudes. However, the diminution of aerosol concentration was caused by their short time span in atmosphere and the

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Journal Pre-proof occurrence of snowfall that washed them out from the snow at high altitudes. The findings indicated an increased SCA, with contrasting behavior in the ablation period. However, the presence of aerosols demands proper attention, to monitor any future threat to the high-altitude cryosphere.

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Keywords: Snow cover area, aerosol optical depth, absorption aerosol optical depth

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1. Introduction

Snow/ice cover (SC) is crucial to the cryosphere and has an important influence on the local area

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climate through its reflectivity, surface radiation balance, and energy balance. SC vitally

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influences the energy budget between the atmosphere and the land surface (Lee et al., 2017).

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Therefore, it plays a key role in both the regional and global climate system (Jiang et al. 2016). The reflectivity of solar radiation on SC strongly depends on various factors like snow aging,

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snow impurities, and the temperature of the snow surface. Even a minor decrease in snow

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surface albedo (SA) is responsible for a significant increase in the absorption of solar radiation

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by the surface (Lee et al., 2017). Aerosols are minute particles that range in size from a few nanometers to a few hundred micrometers. However, the size of aerosol particles depends on their structure and source (Bibi et al., 2017). Biomass burning and fossil fuels generate aerosols in the form of black carbon (BC) and organics, while mineral dust is transported from nearby sources. As such, these particles have a direct effect on climate based on the scattering or absorption of solar radiation in the atmosphere. Therefore, they strongly modify the earth’s radiation budget. The release of aerosols into the atmosphere contributes to the uncertainty of climate change evaluation, both on a micro and macro scale. Aerosols are a major source of air

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Journal Pre-proof pollution and their spatio-temporal distribution leads to uncertainty in predicting and modeling the earth’s climate (Alam et al., 2014). Due to the rapid increase in urbanization and industrialization, a large variety of aerosols are released into the atmosphere globally (Zhao et al., 2013). The aerosol impurities can deposit on SC directly through dry deposition of air pollutants (aerosols, dust, polluted dust, and minerals etc.) or indirectly through precipitation. The accumulation of these aerosols causes a reduction in

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SA, which, in turn, influences the SC and local climate through its reflectivity, radiation budget,

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energy balance, and melting (Chen et al., 2016; Ma et al., 2016). Absorbing aerosols, such as BC

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and dust, absorb solar radiation and have consequently changed the SA of the Hindukush-

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Himalayan-Karakoram (HKH) glaciers. This has caused a positive climate forcing on a small scale (Gertler et al., 2016). Minora et al. (2016) found an increase in the snow cover area (SCA) the

Central

Karakoram

National

Park

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over

using

Moderate

Resolution

Imaging

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Spectroradiometer (MODIS) snow cover data. This study found that the reduction in the mean summer temperature and snowfall events were associated with an increase in the SCA.

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According to Fosu et al. (2017) and Wang et al. (2017), air pollution and its associated climate

near future.

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variation pattern are severe and annoying environmental issues which must be confronted in the

The SC in Northern Pakistan is considered as the water tower for the river networks in the country. Major variations in SC and the retreat of these areas are expected to raise the sea level and change the climate on a large scale. Therefore, it is important to understand the behavior of snow/ice cover. In this work, we investigate the spatio-temporal variations of SCA in Northern Pakistan based on satellite data. In addition, the effect of aerosols on SC in Northern Pakistan is poorly understood. Therefore, the assessment of interactions between snow cover areas, their

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Journal Pre-proof surface temperature and the aerosols present are of main concern, both for regional and global climate. It is necessary to assess the impacts of aerosols on SA reduction and SC variations in the study area. In addition, the relationships between aerosol optical depth (AOD), absorbing aerosol optical depth (AAOD), snow surface temperature (SST), SA, and SCA are analyzed to assess their climatic implications for SC in the study region. The SCA, AOD, SST, SA, and AAOD are generated using data from MODIS, the MERRA-2 Model, the ERA-Interim Reanalysis site, and

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an Ozone Monitoring Instrument (OMI), respectively during the period from March to June for

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the years 2005 to 2015.

2.1. Site description and meteorology

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2. Data and Methods

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The geographic range of Pakistan is approximately between 24-37 °N latitudes and 62-75 °E

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longitudes. The present study was conducted over Northern Pakistan (see Figure 1). It is considered an important region as it accommodates some of the world’s giant mountain ranges,

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namely the Himalaya, the Karakoram and the Hindukush ranges. These three mountainous

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regions with snow mass and glaciers co-exist in the study area. In addition, these mountain ranges are home to several giant peaks greater than 7000 meters and approximately 700 peaks higher than 6000 meters (www.pakistangeographic.com). The climate of Northern Pakistan is extremely variant due to its hard topography and its dependence on both broad global circulation patterns and local topographic influences. The meteorological conditions of the study area are shown in Figure 2. The annual land surface temperature (LST), mean air temperature (MT), precipitation rate (PPt), and snow line area (SLA) are -5.7 oC, -0.86 oC, 12254.37 mm, and 5067.90 km2, respectively. The assessment of atmospheric aerosol concentrations over SC in

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Journal Pre-proof Northern Pakistan is of great concern, as aerosols disturb the climate system by causing variations in SA and the associated SST (Gautam et al., 2011). The main sources of absorbing aerosols in this region are BC (from biomass burning), mineral dust (due to the long-range transportation of aerosols), and organics from fossil fuels. During the study period, a high magnitude of AOD is observed, which indicates poor air quality, causing a variation in SA in the region of interest. In general, the increasing response of SST may lead to a decreasing pattern in

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SCA (Basang et al., 2017).

Figure 1. Map of the Study Area.

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Figure 2. Annual variations in MT, LST, PPt, and SLA over Northern Pakistan.

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2.2. Instrumentation

The present study utilizes different datasets and their derived properties. The MODIS sensor,

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which is onboard the Terra and Aqua satellites, was launched by NASA in 2000 and 2002,

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respectively. The swath area for MODIS is 2330 km and the overpass time of equator is 10:30 and 13:30 local time for Terra and Aqua, respectively. MODIS provides high radiometric

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resolution sensitivity comprised of 36 spectral bands with a wavelength range from 0.41 µm to 14.4 µm. The daily mean spatial resolution of MODIS ranges from 250-1000 m, with a temporal resolution of 1-2 days. In this study, AOD at a wavelength of 550 nm was retrieved from the Terra MODIS sensor using the Dark Target Deep Blue Combined (DTB) algorithm. The MODIS global dataset has different retrieving approaches for the dark target (DT) and the deep blue (DB) algorithms. DT is unable to retrieve AOD from snow/ice surfaces. The DTB algorithm was used to improve data coverage over land for the enhancement of AOD retrieval in those grids where DT and DB are not valid (Levy et al., 2013; Sayer et al., 2014). Several studies have found the 6

Journal Pre-proof estimated uncertainty of the MODIS AOD to be in the range of

0.05 0.15 over land and

0.03 0.05 over ocean surfaces (Bouaziz et al., 2019; Sayer et al., 2013). In addition, the present study utilized MODIS (Terra) daily and 8-Day snow products, i.e. MOD10A1 and MOD10A2, respectively, at a spatial resolution of 500 m to analyze SC during March, April, May, and June during the study period (2005-2015). The MODIS datasets were obtained from the websites: http://reverb.echo.nasa.gov/reverb and https://lpdaac.usgs.gov/.

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OMI is a space-borne sensor that is mounted on the Aura satellite. It was launched in July 2004

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by the Netherlands Agency for Aerospace programs in collaboration with the Finnish

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Meteorological Institute. Since 2004, OMI has been continuously delivering data. It is a

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substitution for the (Total Ozone Mapping Spectrometer) TOMS satellite, designed to assess and monitor air quality, the earth's climate, and the presence of ozone layer (Bibi et al., 2015). Due to

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the nadir-viewing property of OMI, it measures the top of atmospheric fluxes in the wavelength

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range of 270-500 nm. OMI can also use this range to detect elevated layers of absorbing aerosols. OMI can observe aerosols over the cloudless sky, as well as below thin layers of clouds

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(Geng et al., 2011). The elevated absorbing aerosols can be further assessed by utilizing the

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OMAERUV retrieval algorithm. In this study, Level 2 (0.25o×0.25o) OMT03d (version 003), daily AAOD at a wavelength of 500 nm was used. The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) is the newest aerosol reanalysis system of the modern satellite era, formed at NASAʼs Global Modeling Assimilation Office (GMAO) in 1979. The reanalysis from the MERRA-2 model is radiatively coupled to partially corrected AOD from MODIS sensors on both the Terra and Aqua satellites (Buchard et al., 2015). The MERRA-2 model and analysis system offer ongoing analysis of several climatic parameters. MERRA uses an algorithm based on 3-dimensional

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Journal Pre-proof variational data assimilation (3DVAR) along with a Grid point Statistical Interpolation scheme (GSI) with a 6-hour update cycle (Derber et al., 2003; Wu et al., 2002). The GSI was enhanced by including several advancements over previously used 3DVAR algorithms. Particularly, the observation-minus-background departures were calculated with their associated temporal accuracy. In addition, a dynamic constraint on noise was implemented to enhance the properties of the analysis solution. In this study, MERRA-2 snow albedo data were used for the analysis of

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snow albedo.

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ERA‐Interim is the latest global atmospheric reanalysis data system produced by the European

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Centre for Medium Range Weather Forecasts (ECMWF). Due to the limitation of poor ground

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data availability at higher altitudes, the analysis was conducted based on the reanalysis data. In addition to data obtained from the Pakistan Meteorological Department (PMD), 2-meter

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temperature data was collected from ERA-Interim from 2005 to 2015. The updates of the archive

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take place on a monthly basis (Dee et al., 2011). The mean monthly surface temperature data were obtained from the website: https://www.ecmwf.int/research/era.

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Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) are among the

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collection of earth observing instruments (A-Train). CALIPSO was launched on April 28, 2006, with overpass times of the equator of 1:30 and 13:30 in an iterating cycle of 16 days (Winker et al., 2010). Presently, it is the single satellite in orbit that provides information about vertical aerosol profiles. According to Winker et al., (2003), CALIPSO continuously measures the attenuated back scattered radiations in two wavelengths (532 and 1064 nm). In this study, CALIPSO Level 2.0, version 3.30, total attenuated back scattered color-modulated, altitude-time images at a wavelength of 532 nm were procured for the time of interest. These images were

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Journal Pre-proof utilized to observe and confirm the existence of absorbing aerosols in the atmosphere. The CALIPSO images were downloaded from the website: www-calipso.larc.nasa.gov.

2.3. Methods The Normalized Difference Snow Index (NDSI) is a computer-generated algorithm used in ArcGIS, QGIS, and Erdas Imagine. It works in two spectral bands, namely a visible band

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(Green) and a shortwave near infrared band (SWNIR). Snow is highly reflective in the visible

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band (Green), whereas it is absorptive in SWNIR band of the electromagnetic spectrum. For

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MODIS, NDSI is calculated using Equation 1 (Riggs et al., 2006).

band and

range in micrometers (

) is

for the green

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respectively. The wavelength (

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Where Band 4 and Band 6 represent Green and Short Wave Near-Infrared (SWNIR),

for the SWNIR band. NDSI was used to map SCA in the study area.

(Lee et al., 2012). For this research, MODIS daily and 8-day snow products

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instead of

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It was used regionally to execute SCA, for which the threshold value was adjusted to greater than

(MOD10A1 and MOD10A2) were used in Hierarchical Data Format (HDF). Dimensional analysis was applied to obtain the mean monthly SCA for the period of 2005-2015 to understand the temporal change in the selected months of March, April, May, and June. The specific SCA for the selected months was mapped in Arc GIS v2015. The snow cover area derived from MODIS was validated using associated data obtained from MERRA-2. The validation of these datasets was carried out using Pearson correlation techniques. Similarly, MERRA-2 was used for the extraction of SA. The complete methodology of the present study is summarized in Figure 3.

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Journal Pre-proof Images Acquisition (MODIS)

NDSI

Image Pre-Processing

Snow & non-Snow area Extraction

Masking Area

Analyses of snow cover Change

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Dimensional Analysis

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Classification

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Meteorological Data

SCA analysis

Aerosols Data

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Results & Discussion

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3. Results and Discussion

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Figure 3. Methodology used in the present research study.

This study focused on the assessment of the SC dynamics for the period of March to June from

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2005 to 2015. In addition to SC, the monthly variations in SA, SST, AOD and AAOD were analyzed. Finally, the impacts of aerosol parameters on SCA and SA were investigated, the results of which are discussed in the following sub-sections.

3.1. Trend analysis of mean monthly SCA, SA, SST, AOD and AAOD The daily and 8-Day snow products are obtained from MODIS. Statistical analyses are carried out to mask monthly mean SCA over the study region. A significant variation in the monthly mean SCA for the study period is observed. The results reveal an increasing trend of SCA in 10

Journal Pre-proof March, May and June and a decreasing trend in April, as shown in Figure 4 (a-d). The decreasing trend observed in April is mainly due to the uneven inter-annual variations of SCA and, specifically, the massive decrease from the years 2011 to 2013. Due to the strong fluctuations in April, a net decreasing trend in SCA is observed (see Figure 4b). However, the SCA at the beginning (in April) is comparatively low during the study period. In April, there are not many days on which it snows. In the same days, low AOD is also recorded. This could be meaningful

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regarding the snow and aerosol interactions. Overall, high AOD values are associated with a

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snowfall event, followed by a drop in AOD value as the snow melts (El-Askary et al., 2018).

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Nevertheless, an increasing trend of SCA is mainly associated with the reduction in mean snow

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summer temperature and augmented snowfall events since 1980 (Minora et al., 2016). Likewise, Kaab et al., (2012) also reported that the Himalaya range is receiving increasing attention due to

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its glaciers’ stability in the early twenty first century in contrast to the worldwide phenomenon of

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glacier retreat.

Our study revealed a strong inter-annual variation in SA with a net decreasing trend during

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March and April (see Figure 4, e-f). However, a similar trend can be observed for SCA during

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May and June (see Figure 4, g-h). Snow, by nature, inhibits the heat exchange between the atmosphere and land surface. Normally, the SST is higher than the air temperature in winter with inverse behavior in spring. Therefore, high SA is observed due to the reflection of solar radiation by the snow’s surface (Park et al., 2017). In general, SA is directly related to SCA, as an increase in SCA will indicate an associated increase in SA and vice versa. The evolution of SA was documented in spring, where the snow cover of each pixel is 100% throughout the year. However, the decreasing trend in March and April indicates the accumulation of aerosols on snow/ice surfaces, which may cover the SCA, resulting in a decrease in SA (El-Askary et al.,

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Journal Pre-proof 2018). Likewise, the temporal variation of SST is observed with a decreasing trend (see Figure 4 i-l). This decrease in SST indicates a cooling effect of the snow/ice surface due to the scattering of aerosol particles. Also, the cooling effect may be caused by an increased SCA and the reflection of a large amount of incoming solar radiation on the snow/ice surface with its associated high albedo. The variation in SCA has a direct impact on SST. Therefore, the increase in SCA will inversely impact SST and vice versa. Further, the uneven distribution of SST can be

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linked to mass transfer, migration of moisture, and the growth of snow grains (Miller. 2002).

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The assessment of aerosol optical properties over snow is quite rare and difficult. Figure 4 (m-p)

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and (q-t) shows the time series variations in AOD and AAOD, respectively. Frequent missing

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values were found for daily AOD due to cloud contamination. Therefore, monthly mean AOD values were only obtained for pixels where more than 15 valid values of AOD were found for the

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whole month; otherwise, the statistical values would become unacceptable. Likewise, AOD and

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AAOD depict inhomogeneity in their spatial distribution. As mentioned before, a decreasing trend in AOD and AAOD normally exists. High AOD values (~ 1.0) indicate anthropogenic

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pollution, whereas values exceeding 0.6 are generally associated with local air pollution through

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biomass burning and dust (Che et al., 2015b). Similarly, the lower values of light-absorbing particulates reveal the dominance of black carbon in the snow (Wang et al., 2016). There is a weak but positive relationship between snowfall anomalies and AOD in spring, while a negative correlation is found in summer (El-Askary et al., 2018). The composition of accumulated aerosols on snow mass still requires great attention. Table 1 presents the punctilious comparison of several relevant statistical analyses in various locations worldwide.

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Figure 4. Trend analysis of SCA, SA, SST, AOD, and AAOD.

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Journal Pre-proof Table 1. Snow parameters and aerosol optical properties in different locations of the world. The (+) represents increasing, and (–) represents decreasing values. Site

Parameters

Period

Reference

AAOD/yr

AOD/yr

Northern Pakistan

577-1090 km2

-(3.7-4.3)

-(0.001-6.2)

2005-2015

Present Study

Kashmir Himalayas

43.5 km2

-

-

2000-2016

Shafiq et al., 2019

Norther Pakistan

-

0.0001

0.006

2004-2016

Zeb et al., 2019

Yangtze River Basin

-

-

0.018

2008-2016

He et al., 2018

Central China

-

-

-0.08

2001-2015

Zhang et al., 2017

East Asia

-

0.72

0.04

2005-2016

Kang et al., 2017

Southern Tibetan Plateau

-(0.7-2%)

-

0.1

2001-2012

Lee et al., 2017

Wuhan, China

-

-

0.57-1.52

2007-2013

Wang et al., 2015

Antarctic Sea Ice

0.50-0.95

-

-

1982-2009

Shao et al., 2015

0.10 m

-

-

199-2011

Gardelle et al., 2013

0.4

-

-

2001-2010

Kaab et al., 2012

0.044

2000-2009

Lee et al., 2012

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-0.026

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Pamir Karakoram Himalaya Central Karakoram National Park Sierra Nevada Mountain

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SCA/SA/yr

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3.2. Spatio-temporal distribution of snow parameters

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3.2.1. Assessment of snow/ice cover

Considering that SC is crucial for feeding the river network in Pakistan, its continuous assessment and monitoring require urgent attention (Kaab et al., 2012; Minora et al., 2016). The variation in SCA depends upon climatic parameters such as air temperature, land surface temperature, precipitation, snow line elevation and the accumulation of aerosols on the snow surface. The variations in SCA mainly occur in the spring as the temperature starts to increase in the same season with associated earlier snow melting (Yu et al., 2017). Therefore, to assess the dynamics of SCA for the past 11 years, the variations in snow cover extent during spring and summer were selected. Because, the accumulating period (winter) of snow ends and starts to 14

Journal Pre-proof melt in summer. The spatio-temporal distribution of monthly mean SCA over Northern Pakistan is shown in Figure 5(a). The distribution of SC exhibits a large spatio-temporal variation over the study area. The results revealed that the snow covered area is most extensive in March followed by May, while a contrasting pattern is observed during April and June. The results analysis also revealed that the minimum SCA occurred in April and June as compared to March and May. The maximum SCA among several parameters seems to be associated with increasing and decreasing

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winter precipitation and SST (Shafiq et al., 2019; Yao et al., 2012). In addition, these trends of

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SCA can be a possible source of their greater accumulation and positive mass budgets. In

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contrast, a minimum trend was observed in April due to the uneven distribution of SCA.

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However, the rise in temperature during May and June can cause the depletion of SC at low

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elevations.

Figure 5(a). Mean monthly SCA over Northern Pakistan for the period 2005-2015.

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Figure 5(b). Same as Figure 5(a), but for the MERRA Model.

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The spatial distribution of SCA retrieved from the MERRA Model shows maximum SC at high

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altitudes (see Figure 5b). The spatial distribution of SC from the MERRA Model (see Figure 5b) reveals the same pattern as that of MODIS (see Figure 5a). Finally, the SC fraction procured

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from the MERRA Model was used to validate the MODIS snow product. The results revealed a

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strong positive correlation (R= 0.87), as shown in Figure 6. The MODIS snow images can also be validated with ASTER cloud images, as used by Tahir et al. (2011) in the Hunza River Basin.

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Figure 6. Inter-comparison between the SCA derived from the MODIS and MERRA model over the study region.

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The box-whisker plots of monthly mean SCA show the highest value of 97040 km2 in March

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2011 and the lowest value of 32658 km2 in June 2008 (see Figure 7a). The snow mass in March (end of winter) is based on the preceding accumulation period, while the melting process starts in

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summer (June-August). The analysis depicts an increase in SCA, except in June 2005. The

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monthly mean of the other parameters, namely, SST, SA, AOD, and AAOD, are shown in Figure 7 (b-e). The results revealed a maximum increase in SCA in the selected months of 2009, whereas the same months in 2007, 2008, and 2012 were characterized by decreases in SCA. The analysis showed a net increase in SCA during March, May, and June and a decrease in April from 2005-2015. This net increase in SCA indicates a prolongation of the snow season. Similar to these results, Minora et al. (2016) and Kaab et al. (2012) reported an increase in the snow cover area of the Pamir Karakoram Himalaya Range and the Hindu-Kush-Karakoram Range. Meanwhile, Kosaka and Xie (2013) and Pederson et al. (2013) demonstrated high snowpack

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using MODIS snow data in the Qilian Mountains during 2000-2005.

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Figure 7. Box-whisker plots summarizing the mean monthly variation in SCA over the entire study region from 2005-2015. Each box indicates the 25th and 75th percentiles and the whiskers show the 5th and 95th percentiles. The vertical lines show the standard deviation from the mean value. The small circle and horizontal line enclosed by each box represent the mean and median values, respectively. The maximum and minimum values are represented by the crosses above and below the boxes, respectively. The monthly mean values ( standard deviation) associated with each parameter are also included in their respective panels.

3.2.2. Assessment of snow albedo The results of the monthly mean variation of SA, revealed by applying dimensional analysis to the SA data retrieved from the MERRA-2 model, are shown in Figure 7c. The maximum and minimum SA are found to be 0.47 and 0.32, observed in March 2012 and April 2010,

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Journal Pre-proof respectively. Similarly, maximum and minimum SA values of 0.30 in May 2013 and 0.26 in June 2008 are observed, respectively. The highest values of SA, observed in March and April, are strongly linked to the maximum amount of SC present during that period. Among various factors, the variations in SA are strongly affected by the movement of the intensity of solar radiation. Intense radiance is responsible for high SA values in low latitudes, SA has a negative correlation with snow thickness and snow age in either shallow or deep snow cover (Amaral et

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al., 2017; Xiao et al., 2017). According to Hadley et al. (2012), the presence of impurities and

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the size of snow grains may affect the SA to a great extent. The increase in SA may also be

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caused by the accumulation of non-absorbing aerosols that scatter solar radiation with an

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associated decrease in SST. Except in March, SA has shown similar patterns to SCA.

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3.2.3. Assessment of snow surface temperature

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The monthly mean variations in SST are shown in Figure 7b. The SST was found to be in the range of -7.6 oC to 9.9 oC from March to June of the study period. The maximum and minimum

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SST values were found to be 2.1 oC in April 2008 and -7.6 oC in March 2012, respectively.

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Similarly, a maximum SST value of 9.9 oC is observed in June 2008, while a minimum value of 2.5 oC occurred in May 2005 (see Figure 7b). The SST is mostly linked to snow thickness and SA variation. SST remains low in the months of March and April due to the presence of thick snow cover and the associated SA (Ahmad and Haider, 2015). The decreasing trend in SST is observed due to the consistent reduction in mean summer temperature (Shafiq et al., 2019). The reduction in SST is also dependent upon the increasing trend of SCA and SA, as discussed in previous sections. The increasing trend of SA indicates the scattering of solar radiation over the snow surface, which will consequently cause a cooling effect and increased SCA.

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Journal Pre-proof The observed decreasing trend of SST would support the preservation of SCA during the ablation season. However, in the month of June, snow melting starts, which indicates a decrease in SST. At high altitudes, the variation in temperature have significant implications for SC. High temperatures and increased aerosol presence cause the melting of permanent snow and glaciers in association with consequent impacts on snowfall events and extent of snow season (Lau and Kim, 2018). Due to these variations in snow parameters, Basang et al. (2017) observed a strong

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economic impact on society and ecological impact on the environment in Tibet and the

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surrounding areas. According to Peng et al. (2013), the changing pattern in the extent of the SC

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is linked to the implementing feedback of the temperature trends. Similarly, the pattern of

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decreasing SST in the cold season during November to April was reported in contrast to the

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continuation in the rise of global temperatures (Kosaka and Xie, 2013).

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3.3. Variations in aerosol optical properties

The monthly mean variations in AOD were observed in the range of 0.22-0.47, with an average 0.03, as shown in Figure 7d. The maximum and minimum AOD values were

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value of 0.33

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found to be 0.47 and 0.22 in March 2012 and May 2011, respectively. In addition, the monthly mean variations of AAOD were in the range of 0.01 to 0.03, with corresponding average values of 0.02 ± 0.001 (see Figure 7e). The maximum AAOD was found to be 0.03 in March 2012, while the minimum AAOD was observed to be 0.01 in April 2013. Similarly, the maximum AAOD was found to be 0.03 in June 2012, while a minimum of 0.01 was observed in June 2010. Irrespective of the biomass burning and fossil fuels in the snow covered region, the high concentration of aerosol particles (BC, mineral dust) in the study region is due to their transportation by winds originating from the far side of the Thar Desert region to the west

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Journal Pre-proof (Chand et al., 2016). Maximum concentrations of AOD and AAOD are the result of biomass burning in the winter season. In addition, these concentrations accumulate on the snow cover due to their transportation by winds from the surrounding regions (Kang et al., 2017). The presence of aerosol particles in snow and ice regions could be the main cause of reduced snow albedo and a consequent decrease in SCA. Aside from a small contribution from East Asia, the major

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sources of aerosols were found to be from central, west and East Asia (Gul et al., 2018).

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3.4. Frequency distribution of aerosol subtypes

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In order to assess the presence of different aerosol subtypes, satellite images from CALIPSO

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were retrieved. The aerosol subtype profiles from the images of interest reveal that the dust and polluted dust are the most dominant aerosol types in the study region. Their presence indicates

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that biomass burning is the dominant emission source of aerosol (Figure 8). The aerosol subtypes

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profile revealed the presence of absorbing aerosols during the selected months (March, April, May, and June), extending up to a height of about 10 km from the surface. It is clear from the

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figure that, along with dust and polluted dust, small amounts of smoke, polluted continental and

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clean continental aerosols were also observed. Kumar et al. (2012) determined the dominant aerosols in their study region and stated that smoke and polluted dust, along with a minor contribution of regular dust, were observed during the winter season. However, polluted dust was more distinct during the post-monsoon season, with a subtle amount of existing smoke and dust particles in the study region. The accumulation of dust and polluted dust in the SC depends on the concentration of atmospheric pollution (Gul et al., 2018).

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Figure 8. Aerosol subtypes profile derived from CALIPSO for the selected months of March, April, May, and June over Northern Pakistan.

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3.4. The impacts of SST, AOD, AAOD on SCA and SA The snow and glaciers in the study region are susceptible to high aerosol loadings due to their exposure to wind-carried aerosol particles from South Asia, central Asia, and the Middle East. The concentration of aerosols will change the climate by changing the SA, cloud formation process, and the chemical reaction to affect the climate forcing of various atmospheric aerosols. The seasonal SC covers 30% of the Earth’s land surface on a global scale (Basang et al., 2017). Snow surface, due to its high albedo, reflects a significant amount of sunlight, keeping the snow surface cooler. The results reveal significant correlations of -0.82 and -0.93 between SCA and SA with SST, respectively (see Figure 9 a-b). The decrease in SST is strongly associated with the 22

Journal Pre-proof increase in SC and SA, which is in close agreement with the findings of the present study (Chen et al., 2016). The duration of seasonal snow cover from mid to upper altitudes is significantly linked with the variation of daily mean temperature. In addition, Liu et al. (2007) have also reported a strong negative correlation (R= -0.93) between SA and SST. Lee et al. (2017) have shown a significant correlation of -0.70 between SA and SST. Likewise, the current results reveal a net positive correlation (R=0.77) between SCA and SA (see

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Figure 9g). This correlation indicates that the reflection of incoming solar radiation back to space

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keeps the snow’s surface cooler. Lang et al. (2018) found significant correlation ranges (0.31 to

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0.54) and (0.12 to 0.51) between SCA and SA over the Nam Co Lake and Jingyu Lake.

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However, slightly different correlation results were found in the research by Shao et al. (2015). The variation in SA is strongly dependent on the age of the snow, as SA varies from 0.90 to 0.95

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for fresh snow and gradually decreases to 0.45-0.60 for aged snow (Atlaskina et al., 2015).

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In contrast, AAOD, and AOD have a minor effect on SA with correlations of -0.02 and -0.22, respectively (see Figure 9, d&f). This is because, the atmospheric aerosols scatter the solar

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radiation back to space and keep the snow surface cooler. In general, the increase in AOD from

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March to April is followed by the the transportation of more absorbing aerosols from nearby regions (Hadley et al., 2007). The negative correlations of (-0.30) and (-0.56) between AAOD and AOD with SCA, respectively, have shown the decrease in aerosol concentration over SCA (see Figure 9, c&e). Menegoz et al. (2014) have assumed that the reason for decreased aerosol concentration is their limited residence time over snow at mid altitudes. Further, the continuous reduction in the concentration of aerosol loadings is strongly associated with their removal by snowfall events.

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Journal Pre-proof AOD over snow is considered the prime element to execute variations in climate on a global scale. It is, therefore, statistically illustrated that an increase in AOD has resulted the decrease in snow albedo from 0.75 % to 2.1 % in the southern Tibetan Plateau (Lee et al., 2017). Moreover, accumulation of dust deposition on the snow’s surface can also decrease the SA, which will, consequently, accelerate the snow melting process (Lee et al., 2017). The AOD has shown significant inter-annual variability following high AOD and AAOD values in 2006, 2007, 2008,

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and 2012. Similarly, Lee et al. (2012) have also reported a correlation of -0.40 between AOD

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and SA. In addition, absorbing aerosols (BC, smoke, and mineral dust) have a greater impact on

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the SA of sea ice than surface snow, because sea ice is prone to melting. The aerosols absorb

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more sunlight, leading to a growth of snow grains and associated snowmelt (Lamare et al., 2016). Verma et al. (2009) have reported that 25% of black carbon originates from Asia with an

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average contribution of 87% to the total dust amount in the Sierra Nevada during spring. The

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reduction in SA indicates that lower reflectance and more absorption of sunlight will eventually cause an increase in SST (Schmidt et al., 2017; Lang et al., 2018). The shift in SA pattern can

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also be observed by the mixing of aerosols in the snow that will consequently impact the

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atmosphere through mass and energy exchange.

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4. Conclusion

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Figure 9. Scatter plots of Snow and aerosol optical parameters over Northern Pakistan during the period 2005-2015.

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This study reports on a spatio-temporal assessment of SCA in Northern Pakistan. The inter-

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relationships between SCA, SA and aerosol optical properties were also explored. The results revealed a net positive balance in SCA due to the longevity of snow/ice at mid and high altitudes in the study region. Similarly, the sensitivity of SCA to variations in AOD and AAOD concentration (mineral dust, polluted dust, dust and smoke) was also analyzed. The uneven distribution of SA revealed the existence of aerosols that scatter solar radiations with an associated surface cooling effect. However, the increases in aerosols can threaten the glaciers’ mass budget in Northern Pakistan. The major findings are summarized as follows: 1. SCA increases in association with decreasing SST, with R = -0.82.

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Journal Pre-proof 2. Snow albedo markedly increases with increasing SCA values (R = 0.77). However, the AOD and AAOD have a moderate (R= -0.22) and low (R= -0.02) effect on SA variations, respectively. The accumulation of deposit aerosols (BC, dust, and polluted dust) on snow/ice may enhance or suppress the cooling effect of SC either by reflecting, scattering, or absorbing radiance. 3. Dust, polluted dust, and smoke over SC may absorb solar radiations and, consequently,

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activate the snow melting process earlier, posing a potential threat to downstream

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infrastructure.

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4. The correlations of aerosol optical properties and snow parameters have proven their

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importance in affecting the SC mass budget in the study region. 5. The stability in SCA in the study area is a positive response to the river network system

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of the country; however, the aerosol accumulation at mid altitudes can disturb this

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stability.

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Highlights

Evidence of increased snow cover area over high altitudes of northern Pakistan

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Evidence of contrasting pattern of snow/ice mass at higher altitudes

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Presence of scattering and absorbing aerosols on snow/ice cover at lower altitudes

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Future threat to the stability of SCA from aerosol varying pattern at lower altitudes

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