Increase in seasonal precipitation over the Tibetan Plateau in the 21st century projected using CMIP6 models

Chen, Rong; Duan, Keqin; Wang, Shang; Shi, Ping‐Ping; Meng, Yali; Zhang, Zhaopeng

doi:10.1016/j.atmosres.2022.106306

Cited by 22 publications

(15 citation statements)

References 37 publications

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“…However, it also can be seen that the performance is relatively limited in adjusting precipitation using the EQM method in the HRB due to the spatial-temporal variability and uncertainty of precipitation, as shown in Figure 4. This result has also been reported in previous studies (Almazroui et al, 2020;R. Chen et al, 2022;Yue et al, 2021).…”

Section: Discussionsupporting

confidence: 92%

Projection of future precipitation change using CMIP6 multimodel ensemble based on fusion of multiple machine learning algorithms: A case in Hanjiang River Basin, China

Wang,

Liu,

Luan

et al. 2023

Meteorological Applications

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Projecting precipitation changes is essential for researchers to understand climate change impacts on hydrological cycle. This study projected future precipitation over the Hanjiang River Basin (HRB) based on the multimodel ensemble (ME) of six global climate models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6). An ME method using the fusion of four machine learning (ML) algorithms (random forest [RF], K‐nearest neighbors [KNN], extra tree [ET], and gradient boosting decision tree [GBDT]) was proposed in this study. The future precipitation changes were investigated during 2023–2042 (Near‐term), 2043–2062 (Mid‐term), and 2081–2100 (Long‐term) periods, with reference to the base period 1995–2014, under three integrated scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5) of the Shared Socioeconomic Pathways (SSPs) and the representative concentration pathways (RCPs). The results show that: (1) the proposed ME method performs better than the ME mean and individual ML algorithms, with a correlation coefficient value reaching 0.88 and Taylor skill score reaching 0.764. (2) The precipitation under SSP5‐8.5 has the largest upward trend with the annual precipitation variation range of −9.27% to 112.84% from 2023 to 2100, followed by SSP2‐4.5 with −30.48% to 44.67%, and the smallest under SSP1‐2.6 with −37.19% to 37.78%, which show a significant trend of humidification over the HRB in the future. (3) The precipitation changes over the HRB are projected to increase over time, with the largest in the Long‐term, followed by Mid‐term, and the smallest in the Near‐term. (4) The northeastern parts of the HRB are projected to experience a large precipitation in the future, and the southeastern parts are smaller. (5) Uncertainties in the projected precipitation over the HRB still exist, which can be reduced by ME. The findings obtained in this study have important implications for hydrological policymakers to make adaptive strategies to reduce the risks of climate change.

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Section: Discussionsupporting

confidence: 92%

Projection of future precipitation change using CMIP6 multimodel ensemble based on fusion of multiple machine learning algorithms: A case in Hanjiang River Basin, China

Wang,

Liu,

Luan

et al. 2023

Meteorological Applications

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“…5-8°C) over the Himalayan-Tibetan Highlands by the end of the 21st century (Mishra, et al, 2019). Furthermore, annual mean precipitation over the TP is projected to increase by 10.4%-11.0% over the shortterm (2010-2050), and 14.2%-21.4% over the long-term (2051-2100) under RCP4.5 and RCP8.5 relative to 1961-1990 values (Figure 19b) (R. Chen et al, 2022;Gao et al, 2018;Hu & Zhou, 2021;K. Jia et al, 2019;Lun et al, 2021;Y.…”

Section: Future Of Tp Climatementioning

confidence: 99%

“…Furthermore, annual mean precipitation over the TP is projected to increase by 10.4%–11.0% over the short‐term (2010–2050), and 14.2%–21.4% over the long‐term (2051–2100) under RCP4.5 and RCP8.5 relative to 1961–1990 values (Figure 19b) (R. Chen et al., 2022; Gao et al., 2018; Hu & Zhou, 2021; K. Jia et al., 2019; Lun et al., 2021; Y. Zhao et al., 2022). Specifically, the largest precipitation increases are projected to occur in the summer, and the smallest in the winter (D. Chen et al., 2015).…”

Section: Future Change Of Tp Climate and Forcingsmentioning

confidence: 99%

Global Climate Impacts of Land‐Surface and Atmospheric Processes Over the Tibetan Plateau

Huang

Zhou

et al. 2023

Reviews of Geophysics

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The Tibetan Plateau (TP) impacts local and remote atmospheric circulations, wherein it mechanically and thermally affects air masses or airflows. Moreover, the TP provides a key channel for substance transport between the troposphere and the stratosphere. This study reviews recent advances in research regarding land–atmosphere coupling processes over the TP. The TP experiences climate warming and wetting. Climate warming has caused glacier retreat, permafrost degradation, and a general increase in vegetation density, while climate wetting has led to a significant increase in the number of major lakes, primarily through increased precipitation. Local and regional climates are affected by interactions between the land and the atmosphere. Namely, the TP drives surface pollutants to the upper troposphere in an Asian summer monsoon (ASM) anticyclone circulation, before spreading to the lower stratosphere. Further, the thermal forcing of the TP plays an essential role in the ASM. TP forcing can modulate hemispheric‐scale atmospheric circulations across all seasons. The TP interacts with remote oceans through a forced atmospheric response and is substantially affected by the evolution of the Earth’s climate via promoting Atlantic meridional overturning circulation and eliminating Pacific meridional overturning circulation. The extensive influence of the TP is facilitated by its coupling with the ASM in the summer; whereas its winter influence on climate mainly occurs through Rossby waves. The observed increasing trends of temperature and precipitation over the TP are projected to continue throughout the 21st century.

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“…Precipitation is projected to increase at a faster rate than the global mean but slower than that in the Arctic. Furthermore, larger precipitation increases are expected under higher emission scenarios (Chen et al 2022). Consequently, it raises questions about how increased precipitation could jointly affect permafrost thermal dynamics along with atmospheric warming.…”

Section: Introductionmentioning

confidence: 99%

Divergent responses of permafrost degradation to precipitation increases at different seasons on the eastern Qinghai–Tibet Plateau based on modeling approach

Yang,

Wang,

Yang

2023

Environ. Res. Lett.

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The Qinghai-Tibet Plateau (QTP) has responded to remarkable climate warming with dramatic permafrost degradation over the past few decades. Previous studies have mostly focused on permafrost responses to rising air temperature, while the effects of accompanying increases in precipitation remain contentious and largely unknown. In this study, a distributed process-based model was applied to quantify the impacts of increased precipitation on permafrost thermal regimes in a warming climate by employing model experiments in the source region of Yellow River (SRYR) on the eastern QTP. The results showed that the active layer thickness (ALT) of permafrost increased by 0.25 m during 2010-2019 compared to 2000 across the SRYR, which was primarily driven by climate warming. In contrast, the increased annual precipitation played a relatively limited role and just slightly mitigated active layer thickening by 0.03 m. Intriguingly, increased precipitation in the cold and warm seasons exerted opposite effects on permafrost across the SRYR. The increased precipitation in the cold season mainly promoted ALT increases, while the increased precipitation in the warm season mitigated ALT increases. In ~81.0% of the permafrost across the SRYR, the cooling effects of warm season wetting outweighed the warming effects of cold season wetting; while at the transition zone where permafrost was unstable and degrading to seasonally frozen ground, the warming effects of cold season wetting played a relatively larger role which contributed to permafrost degradation. This study explored the physical mechanisms of permafrost thermal responses to climate wetting, thus providing a better understanding of permafrost change in a warmer and wetter climate on the QTP.

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Increase in seasonal precipitation over the Tibetan Plateau in the 21st century projected using CMIP6 models

Cited by 22 publications

References 37 publications

Projection of future precipitation change using CMIP6 multimodel ensemble based on fusion of multiple machine learning algorithms: A case in Hanjiang River Basin, China

Projection of future precipitation change using CMIP6 multimodel ensemble based on fusion of multiple machine learning algorithms: A case in Hanjiang River Basin, China

Global Climate Impacts of Land‐Surface and Atmospheric Processes Over the Tibetan Plateau

Divergent responses of permafrost degradation to precipitation increases at different seasons on the eastern Qinghai–Tibet Plateau based on modeling approach

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