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Blackening Cryosphere: Revealing Hotspot Shifts and HGB-Based Forecasting of Absorbing Aerosol Threats over the Himalayan Frozen Frontiers

Abira Sengupta, Ayoti Banerjee, Sarbani Palit, Brendon Woodford

Abstract

Black carbon and mineral dust are key absorbing aerosols that influence atmospheric radiation and increasingly threaten global cryospheric stability. This study examines the long-range transport and seasonal variability of these aerosols over Pakistan and their movement toward the western Himalayas. Using satellite-derived Absorption Aerosol Optical Depth (AAOD) data from 2019 to mid-2025, we analyse their spatiotemporal behaviour across Pakistan's urban lowlands and high-altitude regions. Fifteen-day aggregated AAOD fields are used to track seasonal transport into glaciated terrain, where deposited aerosols can darken snow and ice and accelerate melt. For high-AAOD events, a probabilistic forecasting approach based on machine learning (ML) was developed. Using geographical, seasonal, and lagged indicators, a histogram-based gradient boosting classifier was trained to predict AAOD exceedance one step in advance. ROC-AUC, PR-AUC, and the Brier score were used to assess the model's performance. The results show high predictive capacity and good probability calibration, with values of 0.791, 0.269, and 0.028, respectively. Forecasts indicate that areas adjacent to Himalayan glaciers consistently exhibit the highest probability of increasing AAOD, signalling an elevated risk of aerosol-induced snowmelt.

Blackening Cryosphere: Revealing Hotspot Shifts and HGB-Based Forecasting of Absorbing Aerosol Threats over the Himalayan Frozen Frontiers

Abstract

Black carbon and mineral dust are key absorbing aerosols that influence atmospheric radiation and increasingly threaten global cryospheric stability. This study examines the long-range transport and seasonal variability of these aerosols over Pakistan and their movement toward the western Himalayas. Using satellite-derived Absorption Aerosol Optical Depth (AAOD) data from 2019 to mid-2025, we analyse their spatiotemporal behaviour across Pakistan's urban lowlands and high-altitude regions. Fifteen-day aggregated AAOD fields are used to track seasonal transport into glaciated terrain, where deposited aerosols can darken snow and ice and accelerate melt. For high-AAOD events, a probabilistic forecasting approach based on machine learning (ML) was developed. Using geographical, seasonal, and lagged indicators, a histogram-based gradient boosting classifier was trained to predict AAOD exceedance one step in advance. ROC-AUC, PR-AUC, and the Brier score were used to assess the model's performance. The results show high predictive capacity and good probability calibration, with values of 0.791, 0.269, and 0.028, respectively. Forecasts indicate that areas adjacent to Himalayan glaciers consistently exhibit the highest probability of increasing AAOD, signalling an elevated risk of aerosol-induced snowmelt.
Paper Structure (5 sections, 5 figures)

This paper contains 5 sections, 5 figures.

Table of Contents

  1. Introduction
  2. Study Area
  3. Data and Methods
  4. Results and Discussion
  5. Conclusions and Future Outlook

Figures (5)

  • Figure 1: Geographic extent of the study area encompassing Pakistan and the northern high-altitude regions adjoining the western Himalayas, Karakoram and Hindu Kush ranges. The spatial data points are marked in red.
  • Figure 2: AAOD and surface air temperature (in Kelvin) scatter matrix.
  • Figure 3: Hexbin density plot of AAOD versus surface air temperature (in Kelvin). Each hexagonal bin represents the number of observations on a logarithmic scale (color bar).
  • Figure 4: Spatial variance maps of AAOD across Pakistan: Timestep 17 (01–15 September 2019), Timestep 21 (01–15 November 2019), Timestep 28 (16–29 February 2020), Timestep 73 (01–15 January 2022), Timestep 77 (01–15 March 2022), and Timestep 80 (16–30 April 2022).
  • Figure 5: One-step-ahead probabilistic forecast of AAOD exceedance (AAOD $\ge 0.5$) for timestep 157 (1–15 July 2025). Red stars indicate locations with very high risk of exceedance, predominantly over glaciated regions of the western Himalayas.