## 1 Introduction
### Importance of MSU/AMSU Datasets  
The spatial distribution of meteorological stations providing long-term air temperature data is limited, especially in Himalayan and Tibetan regions, resulting in a lack of comprehensive coverage beyond their immediate vicinity [67]. This spatial data scarcity poses a significant obstacle when examining temperature variations across these heterogeneous regions. The ability to monitor the temperature of the planet and its atmosphere from orbit is made possible by remote sensing sensors. The data collected by Microwave Sounding Unit (MSU) and Advanced Microwave Sounding Unit (AMSU) sensors provide the longest continuous record of tropospheric temperature derived from satellite sensors. A dataset spanning over 45 years has been generated using measurements of brightness temperatures captured by the MSU and AMSU, which allows for long-term climate studies and trend analysis. This extensive dataset provides information on atmospheric temperatures across four distinct layers, including the lower, middle, and upper tropospheric and lower stratospheric regions.  
MSU data sets have emerged as a potentially valuable tool for monitoring global temperatures. However, it monitors a physically different quantity than surface temperature. There is a fairly strong correlation between the temperature anomalies from the MSU and global surface datasets [68]. Recent studies have found a consistent and robust cooling trend of approximately 1-3 \({}^{\circ}\)K in the stratosphere as well as a consistent and robust warming trend of about 0.6-0.8 \({}^{\circ}\)K in the troposphere over the period of four decades from 1979 to 2018 [69]. The results remain consistent when comparing satellite-based average temperature measurements (MSU datasets) with vertically resolved radiosonde records. As most of the Himalayan high terrain plateau is situated above 4 km, the recorded tropospheric temperatures in this area primarily correspond to the mid-tropospheric temperature measured by the MSU.  
The temperatures in mountainous areas are rising at a faster pace compared to the global average [70]. The glacial mass balance trends in the HKH region, being extremely sensitive to temperature changes, call for a holistic assessment of the long-term tropospheric temperature changes over the region and the factors inducing them.  
The present study investigates the tropospheric temperature trends (lower and middle) over the Himalayan (western and eastern), Tibetan Plateau, Indo-Gangetic (IG) plains, and the major dust source regions in northern mid-latitudes that influence the Himalayan and IG plains [71] (Figure 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Ref.** & **Glacier/Region** & **Location** & **Period** & **Retreat Pattern** \\ \hline
[61] & Malan, Hoh Xil & Tibetan Plateau, China & 1970–2000 & 1.0 to 1.7 ma\({}^{-1}\) \\ \hline
[62] & Gangdise Mountains & Trans Himalaya, China & 1970–2016 & 1.09\% a\({}^{-1}\) \\ \hline
[4] & Ladakh (864 glaciers) & W. Himalayas & 1991–2014 & 12.8\% deglication \\ \hline
[63] & Himachal Glacier region & W. Himalayas & 1999–2004 & Increased rate of glacial ice loss compared to 1977–1999 \\ \hline
[64] & Chenab, Parbati, and Baspa basins (466 glaciers) & W. Himalayas & 1962–2007 & 21.0\% deglication \\ \hline
[65] & Milam, Uttaranchal & W. Himalayas & 1849–1957 & 12.5 ma\({}^{-1}\) \\ \hline
[65] & Pindari, Uttarakhand & W. Himalayas & 1845–1966 & 23.0 ma\({}^{-1}\) \\ \hline
[65] & Gangtri, Uttarakhand & W. Himalayas & 1935–1976 & 15.0 ma\({}^{-1}\) \\ \hline
[66] & Gangotri, Uttarakhand & W. Himalayas & 1965–2006 & 0.01 km\({}^{2}\)a\({}^{-1}\) \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._