Future Projections of the Large‐Scale Meteorology Associated with California Heat Waves in CMIP5 Models

Abstract

Previous work showed that climate models capture historical large‐scale meteorological patterns (LSMPs) associated with California Central Valley heat waves including both ways these heat waves form. This work examines what models predict under the Representative Concentration Pathway (RCP) 4.5 and RCP8.5 scenarios. Model performance varies, so a multimodel average weights each model based on its historical performance in four parameters. An LSMP index (LSMPi) defined using upper atmosphere variables captures dates of past extreme surface temperature maxima. LSMPi correlates well with all values of California Central Valley‐average surface maximum temperature. LSMPi distributions in future simulations shift ~0.6 standard deviations higher between 1961–2000 and 2061–2100 for RCP8.5 data. Based on the historical climatology, future scenarios show a large increase in the frequency and duration of heat waves in every model. Four times as many heat waves occur and their median duration doubles, using historical thresholds. Of the two ways heat waves form, Type 1 has similar frequency in the future. But, Type 2 becomes much more common because Type 2 has a preexisting hot anomaly in Southwestern Canada, much like the historical to future climatological change in that region (a “global warming” signal). The 20‐year return value anomaly increases by 30–40%. The average of the 50 hottest temperatures increases 3.5–6 K depending on the scenario. When extreme values are defined using the future climatology, the models and their average have no consistent increase or decrease of distribution properties such as shape, scale, and return values of the extremes compared to historical values.

Evidence of Specific MJO Phase Occurrence with Summertime California Central Valley Extreme Hot Weather

“…It has also been shown that temperature extremes are accompanied by large displacements of air masses that create specific wave patterns-the socalled large-scale meteorological patterns (LSMPs) reviewed in Grotjahn et al (2016)-which are distinct from the climate modes mentioned above. Regional-scale extreme heat in the CCV has been shown to be linked to LSMPs that are an equivalent barotropic, nearly stationary wave train (ridgetrough-ridge) across the North Pacific and western North America (Grotjahn and Faure, 2008;Grotjahn, 2011Grotjahn, , 2013Grotjahn, , 2016Palipane and Grotjahn, 2018). Looking at the general features of LSMPs prior to the onset of heat waves is a good way to connect those heat waves with associated atmospheric phenomena and therefore begin to understand related mechanisms.…”

Stepwise‐clustered heatwave downscaling and projection for Guangdong Province

“…According to Figures 2, 3, and S2 and Tables S2–S5, it can be found that the performance of SCHW in producing the T max from the model ensemble is superior to any single GCM. In previous studies, multiple model ensemble techniques have been broadly used in post‐processing of surface air temperature due to its significant ability in improving the variability of model outputs (Räisänen, 2007; Palipane and Grotjahn, 2018; Yang et al ., 2018; Hu et al ., 2019). Thus, it is crucial to evaluate the ability of the developed SCHW in reproducing the present‐day climate with multiple model ensemble since the outputs will be used to project station‐scale T max and explore the related future extreme high‐temperature events (i.e., heatwaves).…”

“…Previously, research efforts have been made in studying the future heatwaves (Xu and Xu, 2012; Cowan et al ., 2014; Xu et al ., 2015; Guo et al ., 2017; Palipane and Grotjahn, 2018). For example, Li et al .…”

“…Previously, research efforts have been made in studying the future heatwaves (Xu and Xu, 2012;Cowan et al, 2014;Xu et al, 2015;Guo et al, 2017;Palipane and Grotjahn, 2018). For example, Li et al (2019) explored heatwaves in China in the twenty-first century based on the multi-model ensemble of 10 CMIP5 GCMs; they found that high latitude and altitude regions of China (e.g., the Tibetan Plateau and northern China) are tend to experience a larger increase of frequency and intensity of heatwaves compared with southern China.…”

Meteorological Drivers of Resource Adequacy Failures in Current and High Renewable Western U.S. Power Systems

“…The patterns persist over large spatial and temporal scales, and unlike the high-frequency variations exhibited by surface meteorology, the patterns' spatio-temporal variations are better captured by GCMs. Previous research has sought to link the changes in frequency and return periods of these largescale patterns with the occurrence of extreme events under a changing climate using data from GCMs [26][27][28][29].…”