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Arctic sea-ice loss intensifies aerosol transport to the Tibetan Plateau

Nature Climate Change volume 10, pages 1037–1044 (2020)Cite this article

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Abstract

The Tibetan Plateau (TP) has recently been polluted by anthropogenic emissions transported from South Asia, but the mechanisms conducive to this aerosol delivery are poorly understood. Here we show that winter loss of Arctic sea ice over the subpolar North Atlantic boosts aerosol transport toward the TP in April, when the aerosol loading is at its climatological maximum and preceding the Indian summer monsoon onset. Low sea ice in February weakens the polar jet, causing decreased Ural snowpack via reduced transport of warm, moist oceanic air into the high-latitude Eurasian interior. This diminished snowpack persists through April, reinforcing the Ural pressure ridge and East Asian trough, segments of a quasi-stationary Rossby wave train extending across Eurasia. These conditions facilitate an enhanced subtropical westerly jet at the southern edge of the TP, invigorating upslope winds that combine with mesoscale updrafts to waft emissions over the Himalayas onto the TP.

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Fig. 1: Aerosol and meteorological climatology at Nam Co and QOMS.
Fig. 2: February AASIC change and climatic indices.
Fig. 3: February snowpack, temperature and circulation anomalies linked to low AASIC.
Fig. 4: April snowpack, temperature and circulation anomalies linked to low AASIC.
Fig. 5: April horizontal and vertical circulation anomalies over the Pan-Third Pole linked to low AASIC and schematic representation of the Arctic–Ural–TP teleconnection.

Data availability

The in situ meteorological data at Nam Co and QOMS43,44 are available from the Institute of Tibetan Plateau Research on reasonable request. The in situ meteorological data at the 66 TP stations are available from the National Climate Centre, China Meteorological Administration, on reasonable request. The ERA-Interim/Land reanalysis data (a version without precipitation correction)47 is available from the European Centre for Medium-Range Weather Forecasts on request. The following publicly available data sources were used in this study: AERONET (ref. 42), https://aeronet.gsfc.nasa.gov; HadISST (ref. 45), https://www.metoffice.gov.uk/hadobs/hadisst2; ERA-Interim (ref. 46), https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim; MODIS Aqua (ref. 48), https://giovanni.gsfc.nasa.gov/giovanni. Source data are provided with this paper.

Code availability

All graphics were produced using NCAR Command Language v.6.40 (https://www.earthsystemgrid.org/dataset/ncl.640.html). Scripts are available at Zenodo under the identifier https://doi.org/10.5281/zenodo.3934144.

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Acknowledgements

F.L. was supported by Nordforsk ARCPATH (grant no. 76654) and the National Natural Science Foundation of China (grant no. 41875118). X.W. was supported by the National Natural Science Foundation of China (grant no. 41807389) and the Strategic Priority Research Programme of Chinese Academy of Sciences, Pan-Third Pole Environment Study for a Green Silk Road (Pan-TPE) (XDA20040501). Y.J.O. was supported by the Research Council of Norway (grant no. SNOWGLACE 244166). We acknowledge support from Nam Co and QOMS, Chinese Academy of Sciences, for providing the meteorological datasets.

Author information

Authors and Affiliations

  1. Geophysical Institute, University of Bergen and Bjerknes Centre for Climate Research, Bergen, Norway

    Fei Li

  2. Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

    Xin Wan & Zhiyuan Cong

  3. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China

    Huijun Wang

  4. Climate Change Research Center, Chinese Academy of Sciences, Beijing, China

    Huijun Wang

  5. Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Huijun Wang & Yongqi Gao

  6. NILU−Norwegian Institute for Air Research, Kjeller, Norway

    Yvan Joseph Orsolini

  7. CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing, China

    Zhiyuan Cong & Shichang Kang

  8. Nansen Environmental and Remote Sensing Center and Bjerknes Centre for Climate Research, Bergen, Norway

    Yongqi Gao

  9. State Key Laboratory of Cryosphere Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, China

    Shichang Kang

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  1. Fei Li
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Contributions

F.L., X.W. and H.W conceived of the study. F.L., X.W. and Y.J.O. conducted the analysis. All authors contributed to writing the paper.

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Correspondence to Fei Li or Xin Wan.

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Extended data

Extended Data Fig. 1 February and April snowfall anomalies linked to low AASIC.

a,b, The regressions of February (a) and April (b) snowfall (mm water equivalent day−1) upon the negative February AASIC index for 1979−2018. Those values exceeding 95% CI are denoted by gridding. The brown lines mark the axes of the climatological polar and subtropical westerly jets here and hereafter. The thick black line marks the boundary of the TP, based on the altitude of 2,600 m above sea level here and hereafter.

Extended Data Fig. 2 Lead–lag circulation anomalies linked to low AASIC.

a–d, The regressions of January (a), February (b), March (c) and April (d) Rossby wave source (shaded; 10−10 s−2) and geopotential height (contours; 10 m) at 200 hPa upon the negative February AASIC index for 1979 − 2018. Those values of Rossby wave source exceeding 95% CI are denoted by gridding. The solid and dashed contours indicate positive and negative values, respectively, here and hereafter.

Extended Data Fig. 3 Land–atmosphere coupling in February and April.

a,b,The correlations between SAT and SWE in February (a) and April (b) for 1979−2018. Those values exceeding 99% CI are denoted by gridding.

Extended Data Fig. 4 March snowpack anomalies linked to low AASIC.

The regressions of March SWE (shaded; cm) and SAT (contours; °C) upon the negative February AASIC index for 1979−2018. Those values of SWE exceeding 95% CI are denoted by gridding.

Extended Data Fig. 5 April blocking activity anomalies linked to low AASIC and low Ural SWE.

a, The time evolutions of the normalized negative February AASIC (black), April TP 10-m wind speed from the ERA-Interim (blue) and negative April Ural SWE (red). b,c, The regressions of April frequency of blocking heights (shaded; %) and geopotential height (contours;10 m) at 500 hPa upon the negative February AASIC (b) and negative April Ural SWE (c) indices for 1979−2018. Those values of frequency of blocking heights exceeding 95% CI are denoted by gridding. The red rectangular box marks the region used to define the Ural SWE index in b.

Extended Data Fig. 6 February AASIC, April snowpack and circulation anomalies linked to low Ural SWE.

a–c, The regressions of February sea-ice concentration (shaded; %) and surface turbulent (sensible + latent) heat flux (contours; 105 J m−2) (a), April SWE (shaded; cm) and SAT (contours; °C) (b) and April zonal wind (shaded; m s−1), geopotential height (contours; 10 m) and Rossby wave activity flux (vectors; m2 s−2) at 200 hPa (c) upon the negative April Ural SWE index for 1979−2018. Those values of turbulent heat flux (a), SWE (b) and zonal wind (c) exceeding 95% CI are denoted by gridding. The red line marks the sea-ice edge in a.

Extended Data Fig. 7 April horizontal and vertical circulation climatology over the Pan-Third Pole and linked to TP 10-m wind speed.

a,b, The climatological10-m horizontal wind (vectors; m s−1) and AOD 550 nm observed by MODIS (shaded) (a) and vertical-zonal wind (vectors; m s−1) and vertical velocity (shaded; m s−1) along 28°N (b) in April for 2003−2018. c,d, As a,b except for the regressions upon the April TP 10-m wind speed index from the ERA-Interim. Those values of AOD (c) and vertical-zonal wind (d) exceeding 99% CI are denoted by gridding. The circle and square mark the locations of Nam Co and QOMS, respectively, in a,c. The vertical component is multiplied by 100 in b,d. Topography is shaded by black in b,d. The vectors of horizontal wind or vertical-zonal wind are plotted where the scales are >0.75 m s−1 in a, 0.15 m s−1 in c and 0.4 m s−1 in d.

Extended Data Fig. 8 April backward trajectories at QOMS in 2016 and 2015.

a,b, The April MODIS AOD 550 nm anomalies (shaded), compared to the climatology of 2003 − 2018, and 3-d backward air-mass trajectories, shown by mean backward trajectory for six clusters (colour lines; three-dimensional view shown below) arriving at QOMS (1000 m above ground level) in 2016 (a) and 2015 (b). The numbers indicate the percentages of daily trajectories with the origins. The square marks the location of QOMS.

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Supplementary Table 1 and Figs. 1−6.

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Source Data Fig. 1

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Li, F., Wan, X., Wang, H. et al. Arctic sea-ice loss intensifies aerosol transport to the Tibetan Plateau. Nat. Clim. Chang. 10, 1037–1044 (2020). https://doi.org/10.1038/s41558-020-0881-2

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