Heat wave frequency variability over North America: Two distinct leading modes

Abstract

[1] Seasonal prediction of heat wave variability is a scientific challenge and of practical importance. This study investigates the heat wave frequency (HWF) variability over North America (NA) during the past 53 summers . It is found that the NA HWF is dominated by two distinct modes: the interdecadal (ID) mode and the interannual (IA) mode. The ID mode primarily depicts a HWF increasing pattern over most of the NA continent except some western coastal areas. The IA mode resembles a tripole HWF anomaly pattern with three centers over the northwestern, central, and southern NA. The two leading modes have different dynamic structures and predictability sources. The ID mode is closely associated with the prior spring sea surface temperature anomaly (SSTA) in the tropical Atlantic and tropical western Pacific that can persist throughout the summer, whereas the IA mode is linked to the development of El Niño-Southern Oscillation. A simplified general circulation model is utilized to examine the possible physical mechanism. For the ID mode the tropical Atlantic SSTA can induce a Gill-type response which extends to NA, while the northwestern Pacific SSTA excites a Rossby wave train propagating eastward toward NA. These two flow patterns jointly contribute to the formation of the large-scale circulation anomalies associated with the ID mode. For the IA mode the corresponding circulation anomalies are basically similar to a Pacific-North America pattern. The subsidence associated with high-pressure anomalies warms and dries the boundary layer, inhibiting cloud formation. The resulting surface radiative heating further warms the surface. For the low-pressure anomalies the situation is just opposite. Through such processes these SSTAs can exert profound influences on the HWF variability over NA.

North American extreme temperature events and related large scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends

“…Cold extremes over Europe and the southeastern United States during recent winters (2009-2010 and 2010-2011) were primarily accounted for by the anomalous blocking associated with persistent episodes of large amplitude negative phase of the NAO (Guirguis et al 2011). There is also evidence of an important role for stationary Rossby wave patterns in contributing to North American temperature extremes during summer Wu et al 2012). These wave patterns appear to arise from internal forcing associated with intraseasonal transient eddies ).…”

Relationships between climate variability, soil moisture, and Australian heatwaves

“…While it is outside the scope of the present study to physically quantify how interannual variability influences Australian heatwaves, some progress has been made on how interannual variability influences heatwave frequency over North America [Wu et al, 2012a] and north China [Wu et al, 2012b]. Over North America, the change of phase of ENSO and the associated sea surface temperature anomalies excites boundary and circulation anomalies that persist through the Boreal summer, influencing heatwave frequency [Wu et al, 2012a].…”

“…Over North America, the change of phase of ENSO and the associated sea surface temperature anomalies excites boundary and circulation anomalies that persist through the Boreal summer, influencing heatwave frequency [Wu et al, 2012a]. Over north China, variability in snow cover over the Tibetan Plateau results in increased (decreased) high-pressure anomalies and more heatwaves when snow cover is lower, inducing positive feedbacks [Wu et al, 2012b]. While the details of the latter relationship do not directly apply to Australia, the results of these studies suggest that the physical influence of low-frequency variability manifests itself in broad-scale circulation changes.…”

Seasonal Prediction of Killing-Frost Frequency in South-Central Canada during the Cool/Overwintering-Crop Growing Season

“…Nevertheless, time or space averages of synoptic events, or the statistical behavior of KFF, may be predictable over time scales of seasons or longer due to interactions between the atmosphere and the more slowly varying oceans and land surface properties, such as sea surface temperature (SST) and snow cover, etc. (e.g., Namias 1959Namias , 1965Charney and Shukla 1981; Barnett et al 1987;Robinson et al 1993;Shabbar and Khandekar 1996;Shukla 1998;Wang et al 2000;Wu et al 2009;Wang et al 2010;Lin and Wu 2011).…”