Abstract
Persistent abnormal hot weather can cause considerable damage to human society and natural environments. In northern Eurasia, the recent change in summer surface air temperature exhibits a heterogeneous pattern with accelerated warming around the Eastern European Plain and Central Siberia, forming a wave train-like structure. However, the key factors that determine the magnitude and spatial distribution of this summer temperature trend remain unclear. Here, a huge ensemble of general circulation model (GCM) simulations show that the recent summer temperature trend has been intensified by two factors: steady warming induced by external forcing and inhomogeneous warming induced by internal atmosphere–land interactions that amplify quasi-stationary waves. The latter is sensitive to both snow cover and soil moisture anomalies in the spring, suggesting the potential of land surface monitoring for better seasonal prediction of summer temperatures. Dramatic changes in the circumpolar environment, characterised by Eurasian snow variation and Arctic Ocean warming, collectively affect summertime climate via memory effects of the land surface.
Highlights
Social awareness of abnormal weather is growing, regarding the adverse effects of heat waves and droughts
We analyse a dataset derived from a 100-member ensemble experiment called the “database for Policy Decision making for Future climate change”[8] (d4PDF, Methods), which comprises a 60-year integration (1951–2011) using MRI-AGCM3.2 (Meteorological Research Institute Atmospheric General Circulation Model version 3.2) driven by observed sea surface temperature (SST), sea ice, and natural and anthropogenic forcing
The three leading modes for the June–July–August (JJA)-averaged surface air temperature (SAT) over mid-to-high latitudes of Eurasia were computed by empirical orthogonal function (EOF) analysis applied to d4PDF data (Fig. 2; Methods)
Summary
Social awareness of abnormal weather is growing, regarding the adverse effects of heat waves and droughts. The three leading modes for the June–July–August (JJA)-averaged SAT over mid-to-high latitudes of Eurasia were computed by empirical orthogonal function (EOF) analysis applied to d4PDF data (Fig. 2; Methods).
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