Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Atmospheric dynamic constraints on Tibetan Plateau freshwater under Paris climate targets

Abstract

Rivers originating in the Tibetan Plateau provide freshwater to downstream populations, yet runoff projections from warming are unclear due to precipitation uncertainties. Here, we use a historical atmospheric circulation–precipitation relationship to constrain future modelled wet-season precipitation over the Tibetan Plateau. Our constraint reduces precipitation increases to half of those from the unconstrained ensemble and reduces spread by around a factor of three. This constrained precipitation is used with estimated glacier melt contributions to constrain future runoff for seven rivers. We estimate runoff increases of 1.0–7.2% at the end of the twenty-first century for global mean warming of 1.5–4 °C above pre-industrial levels. Because population projections diverge across basins, this runoff increase will reduce the population fraction living under water scarcity conditions in the Yangtze and Yellow basins but not in the Indus and Ganges basins, necessitating improved water security through climate change adaptation policies in these regions at higher risk.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Simulated and constrained projections of wet-season precipitation change at different warming levels.
Fig. 2: The intermodel relationship between runoff changes and precipitation changes at different warming levels.
Fig. 3: Simulated and constrained projections of wet-season runoff change at different warming levels.
Fig. 4: Projected changes in percentage of population living above the absolute water scarcity level at different warming levels.

Data availability

The data that support the findings of this study are publicly available. The observational grid precipitations are available from https://www.dwd.de/EN/ourservices/gpcc/gpcc.html, http://www.cru.uea.ac.uk/data and https://www.esrl.noaa.gov/psd/data/gridded/data.UDel_AirT_Precip.html. The SLP dataset is available from https://www.esrl.noaa.gov/psd/gcos_wgsp/Gridded/data.hadslp2.html. The CMIP output is available from the Earth System Grid Federation at https://esgf-node.llnl.gov/projects/cmip5/, https://esgf-node.llnl.gov/search/cmip6/. The population data are available from https://sedac.ciesin.columbia.edu/data/set/popdynamics-1-km-downscaled-pop-base-year-projection-ssp-2000-2100-rev01. The glacier runoff data were extracted from https://www.nature.com/articles/s41558-017-0049-x#Sec14. The GLDAS Noah product is available from https://ldas.gsfc.nasa.gov/gldas/model-output. The Japanese 55-year reanalysis data are available from http://jra.kishou.go.jp/.

Code availability

The codes used to perform the calculation and produce all figures are available from GitHub at https://github.com/zhaoyutong/.

References

  1. 1.

    Immerzeel, W. W. et al. Importance and vulnerability of the world’s water towers. Nature 577, 364–369 (2020).

    CAS  Google Scholar 

  2. 2.

    Tian, L. et al. Stable isotopic variations in west China: a consideration of moisture sources. J. Geophys. Res. 112, D10112 (2007).

    Google Scholar 

  3. 3.

    Schiemann, R., Lüthi, D. & Schär, C. Seasonality and interannual variability of the westerly jet in the Tibetan Plateau region. J. Clim. 22, 2940–2957 (2009).

    Google Scholar 

  4. 4.

    Yao, T. et al. A review of climatic controls on δ18O in precipitation over the Tibetan Plateau: observations and simulations. Rev. Geophys. 51, 525–548 (2013).

    Google Scholar 

  5. 5.

    Huss, M. & Hock, R. Global-scale hydrological response to future glacier mass loss. Nat. Clim. Change 8, 135–140 (2018).

    Google Scholar 

  6. 6.

    Shea, J. M. & Immerzeel, W. W. An assessment of basin-scale glaciological and hydrological sensitivities in the Hindu Kush-Himalaya. Ann. Glaciol. 57, 308–318 (2016).

    Google Scholar 

  7. 7.

    Kraaijenbrink, P. D. A., Bierkens, M. F. P., Lutz, A. F. & Immerzeel, W. W. Impact of a global temperature rise of 1.5 degrees Celsius on Asia’s glaciers. Nature 549, 257–260 (2017).

    CAS  Google Scholar 

  8. 8.

    Immerzeel, W. W., Van, B. L. P. & Bierkens, M. F. Climate change will affect the Asian water towers. Science 328, 1382–1385 (2010).

    CAS  Google Scholar 

  9. 9.

    Bookhagen, B. & Burbank, D. W. Toward a complete Himalayan hydrological budget: spatiotemporal distribution of snowmelt and rainfall and their impact on river discharge. J. Geophys. Res. F 115, F03019 (2010).

    Google Scholar 

  10. 10.

    Mukhopadhyay, B. & Khan, A. A reevaluation of the snowmelt and glacial melt in river flows within upper Indus basin and its significance in a changing climate. J. Hydrol. 527, 119–132 (2015).

    Google Scholar 

  11. 11.

    Yao, T. et al. Different glacier status with atmospheric circulations in Tibetan plateau and surroundings. Nat. Clim. Change 2, 663–667 (2012).

    Google Scholar 

  12. 12.

    Yang, K. et al. Response of hydrological cycle to recent climate changes in the Tibetan plateau. Climatic Change 109, 517–534 (2011).

    Google Scholar 

  13. 13.

    Yang, W., Guo, X., Yao, T., Zhu, M. & Wang, Y. Recent accelerating mass loss of southeast Tibetan glaciers and the relationship with changes in macroscale atmospheric circulations. Clim. Dynam. 47, 805–815 (2016).

    Google Scholar 

  14. 14.

    Cuo, L., Zhang, Y., Zhu, F. & Liang, L. Characteristics and changes of streamflow on the Tibetan Plateau: a review. J. Hydrol. 2, 49–68 (2014).

    Google Scholar 

  15. 15.

    Wang, Y. et al. Contrasting runoff trends between dry and wet parts of eastern Tibetan Plateau. Sci. Rep. 7, 15458 (2017).

    Google Scholar 

  16. 16.

    Lutz, A. F., Immerzeel, W. W., Shrestha, A. B. & Bierkens, M. F. P. Consistent increase in high Asia’s runoff due to increasing glacier melt and precipitation. Nat. Clim. Change 4, 587–592 (2014).

    Google Scholar 

  17. 17.

    Lutz, A. F., Immerzeel, W. W., Kraaijenbrink, P. D., Shrestha, A. B. & Bierkens, M. F. Climate change impacts on the upper Indus hydrology: sources, shifts and extremes. PLoS ONE 11, e0165630 (2016).

    CAS  Google Scholar 

  18. 18.

    Immerzeel, W. W. & Bierkens, M. F. P. Asia’s water balance. Nat. Geosci. 5, 841–842 (2012).

    CAS  Google Scholar 

  19. 19.

    Pepin, N. et al. Elevation-dependent warming in mountain regions of the world. Nat. Clim. Change 5, 424–430 (2015).

    Google Scholar 

  20. 20.

    Turner, A. G. & Annamalai, H. Climate change and the South Asian summer monsoon. Nat. Clim. Change 2, 587–595 (2012).

    Google Scholar 

  21. 21.

    Schott, F. A. & McCreary Jr, J. P. The monsoon circulation of the Indian Ocean. Prog. Oceanogr. 51, 1–123 (2001).

    Google Scholar 

  22. 22.

    Gao, J., Masson-Delmotte, V., Risi, C., He, Y. & Yao, T. What controls precipitation δ18O in the southern Tibetan Plateau at seasonal and intra-seasonal scales? A case study at Lhasa and Nyalam. Tellus B 65, 21043–21055 (2013).

    Google Scholar 

  23. 23.

    Zhang, L., Su, F., Yang, D., Hao, Z. & Tong, K. Discharge regime and simulation for the upstream of major rivers over Tibetan Plateau. J. Geophys. Res. D 118, 8500–8518 (2013).

    Google Scholar 

  24. 24.

    Immerzeel, W. W., Droogers, P., De Jong, S. M. & Bierkens, M. F. P. Large-scale monitoring of snow cover and runoff simulation in Himalayan river basins using remote sensing. Remote Sens. Environ. 113, 40–49 (2009).

    Google Scholar 

  25. 25.

    Kääb, A., Berthier, E., Nuth, C., Gardelle, J. & Arnaud, Y. Contrasting patterns of early twenty-first-century glacier mass change in the Himalayas. Nature 488, 495–498 (2012).

    Google Scholar 

  26. 26.

    Falkenmark, et al. On the Verge of a New Water Scarcity: A Call for Good Governance and Human Ingenuity (Stockholm International Water Institute, 2007).

  27. 27.

    Pritchard, H. D. Asia’s shrinking glaciers protect large populations from drought stress. Nature 569, 649–654 (2019).

    CAS  Google Scholar 

  28. 28.

    Falkenmark, M. Meeting water requirements of an expanding world population. Philos. Trans. R. Soc. Lond. B 352, 929–936 (1997).

    Google Scholar 

  29. 29.

    Jones, B. & O’Neill, B. C. Spatially explicit global population scenarios consistent with the shared socioeconomic pathways. Environ. Res. Lett. 11, 084003 (2016).

    Google Scholar 

  30. 30.

    Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    Google Scholar 

  31. 31.

    O’Neill, B. C. et al. The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).

    Google Scholar 

  32. 32.

    Van Vuuren, D. P. et al. The representative concentration pathways: an overview. Climatic Change 109, 5–31 (2011).

    Google Scholar 

  33. 33.

    Hawkins, E. & Sutton, R. The potential to narrow uncertainty in regional climate predictions. Bull. Am. Meteorol. Soc. 90, 1095–1107 (2009).

    Google Scholar 

  34. 34.

    Anav, A. et al. Evaluating the land and ocean components of the global carbon cycle in the CMIP5 earth system models. J. Clim. 26, 6801–6843 (2013).

    Google Scholar 

  35. 35.

    Navarro, R. C. et al. High-resolution and bias-corrected CMIP5 projections for climate change impact assessments. Sci. Data 7, 1–14 (2020).

    Google Scholar 

  36. 36.

    Seager, R., Naik, N. & Vecchi, G. A. Thermodynamic and dynamic mechanisms for large-scale changes in the hydrological cycle in response to global warming. J. Clim. 23, 4651–4668 (2010).

    Google Scholar 

  37. 37.

    Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58, 267–288 (1996).

    Google Scholar 

  38. 38.

    Allan, R. & Ansell, T. A new globally complete monthly historical gridded mean sea level pressure dataset (HadSLP2): 1850–2004. J. Clim. 19, 5816–5842 (2006).

    Google Scholar 

  39. 39.

    Cox, P. et al. Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature 494, 341–344 (2013).

    CAS  Google Scholar 

  40. 40.

    O’Callaghan, J. F. & Mark, D. M. The extraction of drainage networks from digital elevation data.Computer Vision Graphics Image Process. 28, 323–344 (1984).

    Google Scholar 

  41. 41.

    Rogelj, J. et al. Energy system transformations for limiting end-of-century warming to below 1.5 °C. Nat. Clim. Change 5, 519–527 (2015).

    Google Scholar 

  42. 42.

    Samir, K. C. & Lutz, W. The human core of the shared socioeconomic pathways: population scenarios by age, sex, and level of education for all countries to 2100. Glob. Environ. Change 42, 181–192 (2017).

    Google Scholar 

Download references

Acknowledgements

This study was supported by the Second Tibetan Plateau Scientific Expedition and Research Programme (2019QZKK0208), the NSFC project Basic Science Centre for Tibetan Plateau Earth System (41988101-04), Key Research and Development Programmes for Global Change and Adaptation (2017YFA0603604) and the National Natural Science Foundation of China (41922004 and 41871104). We acknowledge the support of Kathmandu Centre for Research and Education, Chinese Academy of Sciences–Tribhuvan University.

Author information

Affiliations

Authors

Contributions

T.W. designed the study and wrote the paper. Y.Z. performed the analysis. All authors contributed to the interpretation of the results and to the text.

Corresponding author

Correspondence to Tao Wang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Daniel Farinotti, Santosh Nepal and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Simulated wet-season temperature change over the upper river basins sourced from the Tibetan Plateau at different warming levels.

The multimodel ensemble mean temperature difference between pre-industrial period (1850–1900) and future periods (2070–2099) at different warming levels over the upper Indus (a), Ganges (b), Brahmaputra (c), Salween and Mekong (d), Yangtze (e) and Yellow river basins (f). The dark grey region denotes the geographical location of each river basin. Blue, yellow and red represent the 1.5, 2 and 4 °C, respectively. Error bars indicate one standard deviation of intermodel spread. The number of CMIP5 and CMIP6 ESMs used to calculate projected temperature change is 6 and 4 for 1.5 °C of global warming (13 and 4 for 2 °C, and 6 and 5 for 4 °C). The wet season here is defined as the period from June to September.

Extended Data Fig. 2 Partitioning of intermodel uncertainty in projected wet-season runoff change over upper river basins sourced from the Tibetan Plateau at the three warming levels.

The multi-circular bar shows the intermodel uncertainty in projected runoff changes that can be partitioned into the individual components of projected changes in precipitation (blue), evapotranspiration (green) and the residual term (for example snow and soil water storage). Contributions are calculated as a percentage (%), estimated by means of analysis of variance. Dark grey, white, orange and light blue represent the Tibetan Plateau, the glaciered region, the upper river basin and ocean, respectively. The wet season here is defined as the period from June to September. The number of CMIP5 and CMIP6 ESMs used to calculate projected temperature change is 6 and 4 for 1.5 °C of global warming (13 and 4 for 2 °C, and 6 and 5 for 4 °C).

Extended Data Fig. 3 Same as Extended Data Fig. 2, except that the uncertainty partitioning is performed using the period from January to September in upper Indus basin.

The multi-circular bar shows the intermodel uncertainty in projected runoff changes that can be partitioned into the individual components of projected changes in precipitation (blue), evapotranspiration (green) and the residual term (for example snow and soil water storage). Contributions are calculated as a percentage (%), estimated by means of analysis of variance.

Extended Data Fig. 4 The coefficient of determination between interannual variabilities of precipitation and atmospheric moisture flux components over the Tibetan Plateau from 1960 to 2005 for JRA-55 and ESMs.

Q, Dyn and Thermo represent the moisture flux convergence, the dynamic component and the thermodynamic component of moisture flux convergence, respectively. JRA-55 indicates the Japanese 55-year reanalysis data, and ESM indicates the Earth System Model from CMIP5 and CMIP6. The number of models from CMIP5 and CMIP6 historical experiments are 18 and 11, respectively.

Extended Data Fig. 5 Simulated and statistical-predicted precipitation over Tibetan Plateau under 1.5 °C.

Wet season (June to September) precipitation averaged over the Tibetan Plateau, simulated by CMIP ESMs (black line) and statistically predicted (red) from 1960 to 2099. All time series are smoothed with a 21-yr running mean filter. R indicates the correlation coefficient between simulated and statistical-predicted precipitation.

Extended Data Fig. 6 Simulated and constrained projections of wet-season precipitation changes over the Tibetan Plateau at different warming levels using the historical observations ensemble mean precipitation (GPCC, CRU and UDEL) and SLP relationship as a constraint on model projections of future precipitation changes.

The multimodel ensemble mean precipitation difference (in percentage) is calculated between historical period (1960–2005) and future periods (2070–2099) from CMIP original (a, c, and e) and constrained model projections (b, d and f) at different temperature levels. SLP is sea-level pressure. Dots indicates grids where all models agree on the sign of change. The bars indicate the frequency of distribution of the precipitation change. The wet season here is defined as the period from June to September. The number of CMIP5 and CMIP6 ESMs is 6 and 4 for 1.5 °C of global warming (13 and 4 for 2 °C, and 6 and 5 for 4 °C).

Extended Data Fig. 7 Same as Fig. 4, but for projected changes in percentage of population living above chronic water scarcity level (>1000 m3 per person per year) at the three warming levels.

The bars denote the percentage of the population living in the highly dependent area that could be satisfied by wet-season upstream runoff for a full year at the chronic water scarcity level (1000 m3 per person per year), and brown points represent the population (in millions) integrated over the highly dependent area.

Supplementary information

Supplementary Information

Supplementary Figs. 1–15 and Tables 1 and 2.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wang, T., Zhao, Y., Xu, C. et al. Atmospheric dynamic constraints on Tibetan Plateau freshwater under Paris climate targets. Nat. Clim. Chang. 11, 219–225 (2021). https://doi.org/10.1038/s41558-020-00974-8

Download citation

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing