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Insignificant effect of Arctic amplification on the amplitude of midlatitude atmospheric waves - PubMed

  • ️Wed Jan 01 2020

Insignificant effect of Arctic amplification on the amplitude of midlatitude atmospheric waves

Russell Blackport et al. Sci Adv. 2020.

Abstract

Whether Arctic amplification has contributed to a wavier circulation and more frequent extreme weather in midlatitudes remains an open question. For two to three decades starting from the mid-1980s, accelerated Arctic warming and a reduced meridional near-surface temperature gradient coincided with a wavier circulation. However, waviness remains largely unchanged in model simulations featuring strong Arctic amplification. Here, we show that the previously reported trend toward a wavier circulation during autumn and winter has reversed in recent years, despite continued Arctic amplification, resulting in negligible multidecadal trends. Models capture the observed correspondence between a reduced temperature gradient and increased waviness on interannual to decadal time scales. However, model experiments in which a reduced temperature gradient is imposed do not feature increased wave amplitude. Our results strongly suggest that the observed and simulated covariability between waviness and temperature gradients on interannual to decadal time scales does not represent a forced response to Arctic amplification.

Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).

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Figures

Fig. 1
Fig. 1. Observed trends in near-surface temperature and waviness.

(A) Trends in zonal mean SAT (°C per decade) as a function of latitude during OND from 1979 to 2018 in ERA-Interim reanalysis. (B) Observed trends in LWA (107 m2 per decade) during OND from 1979 to 2018. Stippling indicates trends that are significant at the 95% confidence interval level. (C and D) As in (A) and (B), but for JFM.

Fig. 2
Fig. 2. Observed trends in the meridional near-surface temperature gradient and waviness as a function of start and end year.

(A) Trends in ΔSAT (°C per decade) during OND as a function of start year (vertical axis) and end year (horizontal axis). Only trends of greater than 15 years in length are plotted. Stippling indicates trends that are statistically significant at the 95% confidence interval level. (B) As in (A), but for JFM. (C) As in (A), but for the LWA (SDs per decade) averaged over 40° to 60°N. (D) As in (C), but for JFM.

Fig. 3
Fig. 3. Comparison of observed and simulated trends in the meridional near-surface temperature gradient and waviness.

(A) Trend in ΔSAT (°C per decade) during OND from 1979 to 2018 in ERA-Interim reanalysis (black) and the three models (blue, red, and magenta). Error bars for ERA-Interim indicate the 95% confidence interval. For the models, the small crosses indicate the trends in individual ensemble members, and large dots indicate the trend of the ensemble mean. (B) As in (A), but for JFM. (C) As in (A), but for LWA (SDs per decade). (D) As in (C), but for JFM.

Fig. 4
Fig. 4. Correlations between the meridional near-surface temperature gradient and waviness in internal variability.

(A) Correlations between ΔSAT and LWA in interannual variability during OND for ERA-Interim reanalysis (black) and the four models (blue, red, magenta, and green). For the models, the small crosses indicate the correlation in individual ensemble members, and the large dots indicate the correlation for concatenated time series of all ensemble members. (B) As in (A), but for JFM. (C) As in (A), but for correlations of 15-year overlapping trends. (D) As in (C), but for JFM.

Fig. 5
Fig. 5. Links between the meridional near-surface temperature gradient and waviness in internal variability versus the forced response.

(A) Zonal mean SAT (°C) regressed onto ΔSAT during OND for ERA-Interim. (B) LWA (107 m2) regressed onto ΔSAT during OND for ERA-Interim. (C and D) As in (A) and (B), but for the HadGEM2 model. (E) The zonal mean near-surface temperature (°C) response to Arctic amplification in HadGEM2. (F) Response of LWA (107 m2) to Arctic amplification in the HadGEM2 simulations. (G to L) As in (A) to (F), but for JFM. The magnitudes of the regression coefficients are scaled by the ΔSAT response in the experiments forced with Arctic amplification (3.06° and 2.17°C in OND and JFM, respectively).

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References

    1. Rahmstorf S., Coumou D., Increase of extreme events in a warming world. Proc. Nat. Acad. Sci. U.S.A. 108, 17905–17909 (2011). - PMC - PubMed
    1. Coumou D., Rahmstorf S., A decade of weather extremes. Nat. Clim. Chang. 2, 491–496 (2012).
    1. Trenberth K. E., Changes in precipitation with climate change. Clim. Res. 47, 123–138 (2011).
    1. Hoskins B., Woollings T., Persistent extratropical regimes and climate extremes. Curr. Clim. Change Rep. 1, 115–124 (2015).
    1. Shepherd T. G., Atmospheric circulation as a source of uncertainty in climate change projections. Nat. Geosci. 7, 703–708 (2014).

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