An extreme heat event impacted western North America (WNA) in September 2022 (Andone 2022), including many U.S. cities in California, Utah, Arizona, and Nevada. Several all-time record high temperatures were also broken (Graff 2022; Sistek 2022). In California, both Merced and Sacramento reached 116°F (46.7°C), their highest temperatures since recordkeeping began in 1899 and 1877, respectively. The Sacramento record, which previously stood at 110°F (43.3°C), was broken by a significant margin. During this heat event, more than 61 million people were under active extreme heat advisories, watches, and warnings on 7 September 2022 (National Weather Service 2022). Furthermore, this persistent heat throughout the month (Fig. 1a) affected the agriculture industry, fueled destructive wildfires, and caused rolling power shutoffs (Kang and Newman 2022; Graff 2022; Sistek 2022).
September 2022 monthly (a) mean 2-m temperature anomaly (relative to 1900–2010 climatology) using the HadCRUTv5 data and (b) 500-hPa geopotential height anomaly (relative to 1948–2010 climatology) taken from the NCEP–NCAR reanalysis 1. (c) September 2-m temperature anomaly time series (relative to 1900–2010 climatology; black line) and its trend (dash line; calculated by linear least squares) for the western North America (WNA) regional mean [averaged over the black box in (a)] from 1900 to 2022. (d) General Pareto Distribution (GPD) return period (unit: years; red bold line) of the WNA regional mean 2-m temperature, and 5%–95% uncertainty range (red dashed lines), with the black line denoting the 2022 September event.
Citation: Bulletin of the American Meteorological Society 105, 2; 10.1175/BAMS-D-23-0148.1
Extreme heat events are expected to become more frequent under global warming (Perkins-Kirkpatrick and Lewis 2020; Bartusek et al. 2022; IPCC 2022). Studies have shown that anthropogenic forcing and natural variability amplified the recent temperature extremes (Xie et al. 2019; Hu et al. 2020; Bartusek et al. 2021; Zhou et al. 2023). In this study, we examine the 2022 record September heat event in the historical context and investigate the effects of anthropogenic climate change and natural climate variability on the likelihood of this record high heat event. This study focuses on two questions: 1) How statistically extreme is this event in the historical context? 2) What is the relative importance of the natural variability versus human-induced forcing to this temperature extreme?
Data and methods
The datasets used in the study include monthly 2-m temperature from HadCRUTv5 (Morice et al. 2021; Osborn et al. 2021). It is a global temperature dataset that includes CRUTEM5 and HadSST4 that provides gridded temperature across the world and spans 1850 to the present. Considering the sparsity of observation before 1900, we focus on HadCRUTv5 data from 1900 to 2022. Geopotential height data from 1951 to 2022 are taken from NCEP reanalysis data (Kalnay et al. 1996). For model data, we used the Energy Exascale Earth Model version 2 (E3SMv2; Golaz et al. 2022) simulations (produced with a 110-km atmosphere grid spacing and 60–30-km ocean grid spacing) under the protocol of Diagnostic, Evaluation and Characterization of Klima (DECK) and Detection and Attribution Model Intercomparison Phase (DAMIP; Gillett et al. 2016) for Coupled Model Intercomparison phase 6 (CMIP6) including historical runs (ALL), natural forcing run (NAT) for 1900–2014, and 500 years of piControl run. As a reference, we also used historical simulation produced using the fully coupled North America regional refined model (NARRM; Tang et al. 2019) configuration of E3SMv2 under DECK protocol (ALL_NARRM). It is a first-of-its-kind set of global climate simulations for RRM. The NARRM features finer-horizontal-resolution grids over North America and coarser resolution elsewhere over the globe—consisting of 25–100-km for the atmosphere/land, and 14–60-km for the ocean (Tang et al. 2023). The resolution is ∼25 km over the contiguous United States (CONUS). The simulations are listed in Table 1. Five pairs of ensemble simulations are used. A Kolmogorov–Smirnov (KS) two-sample test is used to evaluate if the simulations and the observation come from the same distribution (p > 0.05; failure to reject the null hypothesis that the modeled temperature has the same distribution as the observed). To be consistent with our observational and simulation analysis, all data are area averaged over the west North America region (WNA; black dashed box shown in Fig. 1a, incorporating the core region of anomalous high temperature in WNA); both the observed and simulated temperature anomalies are obtained relative to the period of 1900–2010. The 500-hPa geopotential height anomalies are relative to the period of 1948–2010.
List of E3SMv2 model runs used in this study. Numbers represent the ALL, ALL-R_NARRM, and NAT simulation ensemble sizes or the number of 115-yr chunks for the piControl simulations.
To estimate the return period in this study, the generalized Pareto distribution (GPD) (Philip et al. 2020) is used to fit the tail distribution of the observed and simulated temperature. GPD is a well-established choice for statistical modeling of extreme occurrence over high thresholds (Davison and Smith 1990), and has been widely applied to tail distribution fit and return period estimates (Schaller et al. 2016; Philip et al. 2020; Zhou et al. 2020; Xie 2022). The input data used for fitting both observation and simulation are the highest 30% September mean temperature in the historical period following Zhou et al. (2021). Following Van Oldenborgh. (2015), the location parameter shifts proportionally to the smoothed global mean temperature, μ = μ0 + αT', where μ0 is the location parameter corresponding to when global temperature set to 14°C; T' is 0, and α is the trend (1.24°C °C−1) computed as the regression of September monthly mean temperature of WNA onto the GISTEMPv4 global mean temperature smoothed with a 4-yr running mean (Lenssen et al. 2019; GISTEMP Team 2022). We calculate the fraction of attributable risk (FAR; Stott et al. 2004, 2016) and attributable risk ratio (RR; Fischer and Knutti 2015) to quantitatively assess the contributions of anthropogenic influence on 2022 September mean temperature. The definition for FAR is FAR = 1 – PNAT/PALL (Stott et al. 2016), and for RR is RR = PALL/PNAT (Fischer and Knutti 2015); PNAT here denotes the probability of exceeding magnitude of the 2022 September mean value (above 2.35°C) in the natural-forcing scenarios and PALL denotes the equivalent for the all-forcings scenarios. Bootstrapping resampling is performed 1,000 times to estimate the 5%–95% uncertainty range (Xie et al. 2019). In addition, the attributable magnitude change is also calculated following Wehner et al. (2018).
We also used detection and attribution method—optimal fingerprint total least squares (TLS) method to further evaluate the human influence (Hegerl et al. 1997; Allen and Stott 2003; Ribes et al. 2013). The September monthly mean temperature for the WNA region is derived for ALL and NAT simulation for the year 1900–2014. We first evaluate natural and anthropogenic factors separately using single-signal tests. The combined two-signal test is then used to attribute their contribution to the historical climate. To estimate the internal variability, a total of 32 overlapping chunks of 115 years (with a 10-yr shift between neighboring chunks) is taken from the preindustrial control simulations (see online supplement; https://doi.org/10.1175/BAMS-D-23-0148.2). The signal of human-induced warming is considered detected if the uncertainty ranges (5%–95%) for the scaling factors using the fingerprint method is significantly greater than zero (Min et al. 2011; Xie et al. 2016).
Results
The anomalous 2022 September heat event is shown in Figs. 1a and 1b. From the anomalous 2022 September mean temperature, we can see that the anomalous September mean warmth spanned most of the western United States and some of western Canada, with the center in the Midwest (Fig. 1a). This temperature anomaly was largely associated with an enhanced ridge in this region that was stagnant for the whole month that trapped a hot air mass and contributed to the high temperature (Fig. 1b). The area average (dashed black box in Fig. 1a) of the temperature anomaly shows that the September anomaly is about 2.35°C (Fig. 1c), making it the highest-ranked temperature anomaly since 1900. Aside from the temperature, analysis shows that the anomalous 500-hPa geopotential heights in the region were the second highest since 1948 (not shown). The estimate of the return period for September mean temperature using GPD estimates that this event was a 1-in-450-yr event (Fig. 1d). This event also shows an upper bound of 1 in 80 years to a lower bound of nearly unlikely to happen based on observational estimate. We use the E3SMv2 simulations with ALL and NAT to attribute anthropogenic influence in this extreme event. The ALL_NARRM results are used as an additional support for the low-resolution ALL and NAT simulations. We choose unusually hot months defined as the highest 30% September temperature in the historical period to fit the GPD probability density function (PDF) for the ALL, NAT, and ALL_NARRM simulations. The times series are all obtained by averaging over the same dashed line region in Fig. 1a. While the highest September anomalies in the ALL simulations (over 1900–2014) can be used to estimate the return period of the observed 2022 September event of about 106 years (within the bound of the observational estimate), the return period from the NAT simulations is estimated to be about 1,460 years (Fig. 2a). The upper and lower bounds of the return period for the ALL and NAT are from 80–120 to 130 years—highly unlikely, respectively. For the ALL_NARRM simulations, the PDF shifts to a more extreme phase than ALL with the return period around 20–90 years (Fig. 2a, orange lines). This suggests an even higher contribution of human-induced warming than estimated by the lower-resolution E3SMv2 simulations. This may be due to the better resolved western North American topography (including the Rocky Mountains; Tang et al. 2023). Better representation of the heating associated with blocking and local land–atmosphere coupling in ALL_NARRM may contribute to enhanced heat events (Jiménez-Esteve and Domeisen 2022). The FAR for the events higher than 2022 September extreme (2.35°C) based on ALL and NAT is 0.93, with an uncertainty estimated by bootstrap from 0.985 (+0.055) to 0.820 (–0.110) under the influence of the anthropogenic climate change (Fig. 2a). RR, on the other hand, is 14.3 with a range of 6–67. This means that the human influence is estimated to make the 2022 September extreme heat event to be 6–67 times more likely than the climate condition without human influence. The increased magnitude of the long-period return value (Wehner et al. 2018; defined here as value of the period longer than 100 years) by anthropogenic influence (e.g., ALL-NAT) is around 0.6–1 K (Fig. S1).
(a) Return period (unit: years) of WNA September monthly mean maximum temperature anomaly (average over the black dashed box in Fig. 1a) for generalized pareto distribution (GPD) fit (best estimate) of the ALL_NARRM (orange), ALL (red), and NAT (green) simulations from E3SMv2 simulations. The black line denotes the 2022 September observation. (b) Best estimates of the two-signal scaling factors of WNA September monthly mean temperature anomaly (averaged over the black dashed box in Fig. 1a) for anthropogenic forcing (ANT; x axis), natural forcing (NAT; y axis), and their 5%–95% uncertainty range (red bar for ANT, green bar for NAT, and gray circle for ANT–NAT joint confidence). Please refer to the supplement for more details.
Citation: Bulletin of the American Meteorological Society 105, 2; 10.1175/BAMS-D-23-0148.1
Furthermore, for the two-signal regression of observed September mean temperature anomaly times series (averaged over the black dashed box in Fig. 1a) with the correspondent ANT and NAT, the best estimate scaling factor for ALL is 0.97 with an uncertainty range of (0.05–2.45) that is greater than zero and includes unity, and for NAT is –0.37 with uncertainty range of (–2.03, 0.80) that includes zero (Fig. 2b; red bar for ALL and green bar for NAT, gray circle shows the joint 5%–95% confidence ellipse for ANT and NAT). The ellipse of the two-signal shows similar results in that it is above the 0 line for ANT but includes 0 for NAT, indicating ANT influence on observation and not NAT. It is also the case for single regression scaling factor of observed anomaly with ANT (pass) and NAT (fail) (not shown). This detection of the human influence based on the climate model’s patterns using optimal fingerprint analysis suggests that the WNA September mean temperature in observations contains an attributable anthropogenic component. Our findings for the WNA September-mean temperature, including the GPD results, suggest a robust human influence, leading to increasing likelihood for such an extreme event as the September 2022 mean temperature anomaly.
Conclusions
Heat events can have a significant impact on the society. They are the number one cause of weather-related death in the United States (Weant 2019; Major et al. 2022). Excessive heat also can damage crops, injure or kill livestock, and increase wildfire risk. Additionally, it can lead to power outages as increased demands for air conditioning strain the power grid (Witze 2022). In September 2022, a record high heat event hit western North America, leading to one of the hottest Septembers on record. This long-lasting temperature dome throughout the month affected agriculture, fueled destructive wildfires, and threatening rolling power shutoffs (Kang and Newman 2022; Graff 2022; Sistek 2022).
Based on the attribution analysis using E3SMv2 simulations with and without anthropogenic forcings, the likelihood (magnitude) of extremely high September mean temperature in the western United States like this event increased by about 6–67 times (0.6–1 K) due to anthropogenic climate change. Results from the coupled NARRM simulations suggest even higher factors from human-induced warming due to better resolved topography and heating associated process. A detection and attribution analysis using the optimal fingerprint two-signal attribution process that utilizes the E3SMv2 ALL, NAT (piControl run chunks as noise estimate) further confirmed the robustness of the human influence.
However, there are certain caveats to the study. Due to the large inhomogeneity of the data in the study region, we limited our study to the post-1900 period, which excludes anthropogenic influence before that. It would also be desirable to test more ensemble members/models for more robust results (Milinski et al. 2020). For example, the attribution study could be done for a narrow period of years closer to 2022, instead of on the top 30% of years in the record. With these limitations in mind, we could potentially underestimate the influence of anthropogenic forcing on September mean temperature in the region. Consequently, our estimate of anthropogenic influence may be a conservative one. As an outcome of global warming, increasing extreme heat events in the future are anticipated (Perkins-Kirkpatrick and Lewis 2020; Bartusek et al. 2021; IPCC 2022). This calls for serious actions in the management of hazardous events and environmental protection.
Acknowledgments.
The HadCRUTv5 data can be retrieved from https://crudata.uea.ac.uk/cru/data/temperature/. The NOAA data can be retrieved from Research Data Archive at https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html. GISTEMPv4 data are provided by the NOAA/OAR/ESRL PSL, Boulder, Colorado, from their website at https://psl.noaa.gov/data/gridded/data.gistemp.html. The E3SM project is funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research. E3SMv2 simulation data can be retrieved from https://pcmdi.llnl.gov/CMIP6/. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344; the IM release number is LLNL-JRNL-849792. J. X. was supported by the LLNL Institutional Postdoctoral Program. Part of the work is supported by the LLNL LDRD Project 22-ERD-008, “Multiscale Wildfire Simulation Framework and Remote Sensing.”
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