Projected changes in persistent extreme summer weather events: The role of quasi-resonant amplification

Abstract

Persistent episodes of extreme weather in the Northern Hemisphere summer have been associated with high-amplitude quasi-stationary atmospheric Rossby waves, with zonal wave numbers 6 to 8 resulting from the phenomenon of quasi-resonant amplification (QRA). A fingerprint for the occurrence of QRA can be defined in terms of the zonally averaged surface temperature field. Examining state-of-the-art [Coupled Model Intercomparison Project Phase 5 (CMIP5)] climate model projections, we find that QRA events are likely to increase by ~50% this century under business-as-usual carbon emissions, but there is considerable variation among climate models. Some predict a near tripling of QRA events by the end of the century, while others predict a potential decrease. Models with amplified Arctic warming yield the most pronounced increase in QRA events. The projections are strongly dependent on assumptions regarding the nature of changes in radiative forcing associated with anthropogenic aerosols over the next century. One implication of our findings is that a reduction in midlatitude aerosol loading could actually lead to Arctic de-amplification this century, ameliorating potential increases in persistent extreme weather events.

Fig. 1. QRA fingerprint and QRA event signatures.

(A) JJA 1000-hPa meridional temperature anomalies over the extratropical region 25°N to 75°N associated with QRA-favorable conditions (i.e., QRA fingerprint) based on composite of events from ERA reanalysis data (1979–2015) along with the actual JJA meridional surface temperature anomalies corresponding to three selected QRA events (July/August 2003, July/August 2010, and August 2010). (B) Upper-troposphere (300 hPa) meridional wind, zonal wave number spectra (computed for the meridional average over 37.5°N to 57.5°N; the climatological mean spectra and associated 1.5σ upper limit are shown for comparison by red solid and red dashed curves, respectively), and zonal mean zonal wind profiles associated with each of the three aforementioned events.

Fig. 2. Analysis of 1PCTCO2 experiments (JJA seasonal means).

(A) Time series of multimodel average zonal mean temperature anomalies. (B) Comparison of multimodel average midlatitude (25°N to 60°N) and high-latitude (65°N to 75°N) mean temperature anomalies. (C) AA index defined as high-latitude minus midlatitude series including individual simulations shown (colored curves) and multimodel mean (black solid curve), along with linear trend (black dashed lines). (D) QRA series for individual series and multimodel mean [conventions as in (C); see Materials and methods for details on scaling of QRA]. (E) Projection of the QRA fingerprint onto zonal wind anomalies.

Fig. 3. Analysis of RCP8.5 future projections (JJA seasonal means).

Included are both (A to C) full multimodel ensemble and (D to F) AIE-only sub-ensemble (JJA seasonal means). (A and D) Time series of multimodel average zonal mean temperature anomalies. (B and E) Comparison of multimodel mean midlatitude (25°N to 60°N) and high-latitude (65°N to 75°N) mean temperature anomalies. (C and F) AA index defined as high-latitude minus midlatitude series (colored curves, individual simulations; black curves, multimodel mean).

Fig. 4. Mean surface temperature trend patterns (JJA seasonal means) over 2006–2050.

(A) multimodel ensemble, (B) most negative QRA-trending ensemble members, and (C) most positive QRA-trending ensemble members (“most” is defined as upper 10th percentile of multimodel ensemble).

Fig. 5. QRA fingerprint series for RCP8.5 future projections.

Both (A) full multimodel ensemble and (B) AIE-only sub-ensemble. Conventions are as in Fig. 3D. The historical QRA series calculated from [Goddard Institute for Space Studies Surface Temperature Analysis (GISTEMP)] surface temperature observations from 1894 to 1916 (cyan) and the actual series of annual QRA event counts (green) from 1979 to 2015 (as diagnosed from ERA reanalysis data; see Materials and methods for further details) are shown for comparison.