Changes in snow depth under elevation‐dependent warming over the Tibetan Plateau

Shen, Liucheng; Zhang, Yuqing; Ullah, Safi; Pepin, Nick

doi:10.1002/asl.1041

Cited by 26 publications

(9 citation statements)

References 59 publications

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“…The rapid increase in the number of HW events in the northwestern parts of Pakistan can be attributed to an elevation‐dependent increase in temperature since these areas are located at relatively high altitudes. Several studies have reported more warming in the northwestern parts of SA than in the low‐lying plains areas (Pepin et al., 2015; Shen et al., 2021; You et al., 2020). Interestingly, the Indo‐Pak border is expected to have consistent increases in both the number of daytime and nighttime HWs during 2061–2100, relative to the historical period (1975–2014).…”

Section: Resultsmentioning

confidence: 99%

Future Population Exposure to Daytime and Nighttime Heat Waves in South Asia

et al. 2022

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Climate change is expected to result in more frequent and intense heat waves (HWs) in South Asia (SA). The simultaneous increases in temperature and population will exacerbate the population exposure to future HWs. Here we estimate the future population exposure to daytime and nighttime HWs in SA using the Coupled Model Intercomparison Project 6 (CMIP6) models under four Shared Socioeconomic Pathways (SSPs) during 2061–2100, relative to 1975–2014. The results show that the projected frequency and spatial extent of the daytime (nighttime) HWs will be higher under scenario SSP5‐8.5, followed by SSP2‐4.5, SSP3‐7.0, and SSP1‐2.6 (SSP5‐8.5, followed by SSP3‐7.0, SSP2‐4.5, and SSP1‐2.6), relative to the historical period. The approach presented here allows decomposing the effects of climate change and future population on the overall exposure. The results reveal that the compounding effects of projected trends in population and HWs will significantly escalate the population exposure to HWs. Under the selected SSPs, the total population exposure to daytime and nighttime HWs ranges from 185 to 492 and 204–555 million people‐event, respectively, with the maximum exposure occurring in the Indo‐Gigantic Plain. The wide range of exposed populations highlights the sensitivity of the overall exposure to our future socioeconomic pathway decisions, emphasizing the importance of curbing anthropogenic greenhouse gas emissions and adopting sustainable urban planning solutions to minimize the potential socioeconomic and health impacts of HWs.

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Section: Resultsmentioning

confidence: 99%

Future Population Exposure to Daytime and Nighttime Heat Waves in South Asia

et al. 2022

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“…Many recent studies reported good agreement of the GCMs with CRU data over Pakistan and neighbouring countries (Adnan et al ., 2015; Asmat and Athar, 2015; Abbasian et al ., 2018; Das et al ., 2018; Latif et al ., 2018; Ahmed et al ., 2019d; Almazroui et al ., 2020; Ullah et al ., 2020; Guo et al ., 2021; Shen et al ., 2021). Hence, the statistical techniques used in the current study for comparing the historical performance of the GCMs assumed CRU as reference data.…”

Section: Study Area Data and Methodsmentioning

confidence: 99%

Evaluation and projection of precipitation in Pakistan using the Coupled Model Intercomparison Project Phase 6 model simulations

Abbas

Ullah

et al. 2022

Intl Journal of Climatology

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This study aimed to evaluate the performance of global climate models (GCMs) from the family of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in the historical simulation of precipitation and select the best performing GCMs for future projection of precipitation in Pakistan under multiple shared socioeconomic pathways (SSPs). The spatiotemporal performance of GCMs was evaluated against the Climate Research Unit (CRU) data in simulating annual precipitation during 1951–2014, using the Taylor diagram and interannual variability skill (IVS). Moreover, the modified Mann–Kendall (mMK) and Sen's slope estimator (SSE) tests were employed to estimate significant trends in future precipitation for the period 2015–2100. Based on the comprehensive ranking index (CRI), the HadGEM3‐GC31‐MM model has the highest skill in simulating precipitation distributions followed by EC‐Earth3‐Veg‐LR, CNRM‐ESM2‐1, MPI‐ESM1‐2‐HR, CNRM‐CM6‐1, MRI‐ESM2‐0, CNRM‐CM6‐1‐HR, EC‐Earth3‐Veg, MCM‐UA‐1‐0, INM‐CM5‐0, KACE‐1‐0‐G, CAMS‐CSM1‐0, and HadGEM3‐GC31‐LL models. Furthermore, the projections of the best models ensemble mean (BMEM) showed that the study region will experience a substantial increase in precipitation under SSP3‐7.0 and SSP5‐8.5 but an indolent rise under SSP1‐2.6 and SSP2‐4.5 scenarios. The summer and annual precipitations exhibit a statistically significant increasing trend relative to the winter season under most scenarios. Moreover, the magnitude of monotonic trends in seasonal and annual precipitation progresses from low forcing scenario (SSP1‐2.6) to high forcing scenario (SSP5‐8.5). The findings of the study could provide a benchmark in selecting appropriate GCMs for future projection over a data scare region, like Pakistan. Moreover, the projected trends of future precipitation are crucial in devising adaption and mitigation actions towards sustainable planning of water resource management, food security, and disaster risk management.

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“…Particularly, in this study, we observed that zone 2 and zone 3 have an increasing trend of temperature and decreasing trend of rainfall, which is the reason for decreasing trend of SCA (Supplementary Table S4). Annual SCA at mid-altitude zones decreases but insignificantly which is closely related to the elevation-dependent temperature change as compared to precipitation (Misra et al, 2020;Shen et al, 2021) (Supplementary Table S4).…”

Section: Topographic Parameter and Snow Cover Areamentioning

confidence: 99%

Trend of snow cover under the influence of climate change using Google Earth Engine platform: A case study of Astore (Western Himalayas) and Shigar (Karakoram region)

Moazzam

Rahman²,

Lee³

et al. 2022

Front. Environ. Sci.

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Freshwater resources present in the Upper Indus Basin (UIB) supply water to Pakistan’s irrigation. Half of the annual water discharge in the UIB is contributed from the glacier and snow-fed basins in the Hindu Kush, Karakoram, and Himalaya (HKH) region, and it is currently under the threat of climate change. Therefore, it is very necessary to study and monitor the spatiotemporal changes in the snow cover area (SCA) and its response to climate variables to efficiently manage water reservoirs. Thus, keeping this problem in mind, we conducted this study using the Google Earth Engine Application Programming Interface (GEE API) for Astore and Shigar. We used mean annual Landsat data between 1991 and 2021 to derive the SCA using the normalized difference snow index (NDSI). ASTER GDEM data was used to extract the elevation and analyze the distribution of SCA on different elevation zones. Eventually, we used the climate research unit (CRU) data (rainfall and temperature) to analyze them with SCA. The results revealed that SCA in Astore and Shigar has an increasing trend with a rate of 11.16 km2/year and 4.27 km2/year, respectively. Mean annual precipitation and temperature also confirmed the increasing trend of SCA because mean annual precipitation is increasing and temperature is decreasing in both regions. SCA and elevation analysis revealed that SCA is decreasing on foothills while increasing at the valley top. This is because temperature is increasing and precipitation is decreasing from zone 1 to zone 3 and vice versa for zone 4. It is found that Astore and Shigar in UIB have an increasing trend of SCA and are not affected by global warming. Therefore, it is necessary to conduct studies on large scale to efficiently evaluate the impact of climate change on SCA.

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Changes in snow depth under elevation‐dependent warming over the Tibetan Plateau

Cited by 26 publications

References 59 publications

Future Population Exposure to Daytime and Nighttime Heat Waves in South Asia

Future Population Exposure to Daytime and Nighttime Heat Waves in South Asia

Evaluation and projection of precipitation in Pakistan using the Coupled Model Intercomparison Project Phase 6 model simulations

Trend of snow cover under the influence of climate change using Google Earth Engine platform: A case study of Astore (Western Himalayas) and Shigar (Karakoram region)

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