1. Introduction
The year 2022 was an exceptional year for heat worldwide. Heat-related disasters worsened droughts and forest fires and threatened millions of people’s health (EM-DAT 2008; Ballester et al. 2023). While human-induced climate change is no doubt responsible for the globally increasing rate and intensity of extreme heat (IPCC et al. 2021), there is an ongoing need to investigate and communicate the extent of this human influence depending on time of year, region, and event persistence (Swain et al. 2020).
The rapid advancement of climate attribution science is enabling quantitative and confident attribution of human influences on the likelihood of individual heat events within days of occurrence (National Academies of Sciences, Engineering, and Medicine 2016; IPCC et al. 2021; Clarke et al. 2022). The World Weather Attribution (WWA) initiative has pioneered rapid attribution approaches and regularly publishes detailed attribution reports of specific events using peer-reviewed methods (e.g., Philip et al. 2020). These self-consistent reports reliably inform which 2022 heat events were potentially most noteworthy and attributable (World Weather Attribution Initiative 2023; Otto and Raju 2023). But WWA’s in-depth studies require limited resources and days-to-weeks to produce, which restricts the number of heat events that can be assessed and attributed over a given year.
A new automated attribution system has been developed to enable real-time climate attribution of heat events every day, everywhere (G22; Gilford et al. 2022). We implement this system to expand on WWA’s capacity, producing a hindcast of daily attribution estimates for globally resolved air temperatures in 2022. We also evaluate the system by comparing with WWA reports for two events: a 2-day event over the United Kingdom (July 2022) and a 2-month-long event over India/Pakistan (March/April 2022). Using these as a benchmark, we demonstrate the attributable scale and spatial–temporal scope of similarly defined events around the world in 2022.
2. Approach and data
ChIP has several advantages compared to traditional attribution metrics. The occurrence ratio in Eq. (1) considers changes in the likelihood of observing T, rather than commonly employed “probability ratios” (PRs; e.g., Philip et al. 2020) that consider changes in the likelihood of exceeding T. This approach enables attribution assessments for not only extremely hot days but also all days, allowing negative ChIP values to be assigned to conditions made less likely by climate change. Furthermore, ChIP’s logarithmic form allows its daily values to be averaged or summed, providing a meaningful attribution estimates for multiday events. We use this feature to derive a variance-scaled ChIP that can be directly compared with WWA’s PRs estimated from multiday mean temperatures.
We implement G22’s multimethod attribution framework (Gilford et al. 2022; Giguere et al. 2024; supplemental material) following established attribution protocols (Philip et al. 2020) to create a 2022 daily hindcast of ChIP and
3. Results
Figure 1 summarizes analyses of United Kingdom’s 2-day extreme heat event during 17–18 July 2022. WWA analyzed two extreme event definitions averaged over the region (black box): the 2-day mean Tavg and the annual maximum of Tmax. Both metrics were observed above their 1991–2020 climatological 99th percentiles.
The 17–18 July 2022 (a) average temperature anomalies and (b) the associated ChIP (i.e., this study’s daily attribution estimate). The accompanying table includes temperatures (the defining basis for similar extreme events, see text) and compares WWA range of *lower bound probability ratios against this study’s ChIP estimates and the equivalent PR. (c) Number of 2-day average temperatures in 2022 consistent with the WWA U.K. event definition in each 2° × 2° land pixel and (d) the zonal-mean ChIP across these 2-day events.
Citation: Bulletin of the American Meteorological Society 105, 7; 10.1175/BAMS-D-23-0264.1
Mean ChIP values during the U.K. event were 3.0 (Tavg) and 2.8 (Tmax), indicating the extreme temperatures were made 8× more likely because of climate change. This equivalent ratio is smaller than WWA’s final PR estimate (10×), but given the near-record temperatures, the underestimate is consistent with G22’s conservative system design. Because ChIP is constructed from occurrence likelihoods, the ratio in Eq. (1) will always be lower than the PR. Second, to enable autonomous real-time attribution, G22’s framework evaluates a continuous skew-normal fit across each temperature distribution rather than using extreme value theory in the tails (e.g., van Oldenborgh et al. 2021). This effectively bounds reliable ChIP calculations, because tail probabilities will be undersampled and hence uncertain. We codify this limitation by fixing an absolute upper bound of
To screen for comparable events in 2022, we regrid temperature and ChIP to a resolution comparable to the U.K. event (2° × 2°; black box in Fig. 1a) and then search for when/where 2-day rolling-mean Tavg values exceeded their 1991–2020 climatological 99th percentile. Without a climate-shifted distribution, we would expect 3.7 exceedances per year, but globally we find these events were much more common in 2022. Hotspots with 20+ events include central/west North America, Argentina/Paraguay, central Africa, western Europe, China, and Papua New Guinea. These events were robustly attributable (ChIP > 0.5; shading in Fig. 1c) with some reaching the maximum (ChIP = 4.0). Zonal-mean ChIP over these hotspots was typically between 1 and 2.5.
Figure 2 summarizes analyses of India and Pakistan’s 2-month-long extreme heat during March/April 2022. Two-month-average daily Tmax anomalies peaked during the second warmest March/April since 1991, ranging from +1 to +6 K across the averaging region (black polygon in Fig. 2a); concurrent
March/April-mean 2022 (a) maximum temperature anomalies and (b) the associated variance-scaled ChIP. (c) Number of 2 monthly mean maximum temperatures in 2022 (of 12 two-monthly periods, January–February through December–January) consistent with the WWA India/Pakistan event definition (see text) in each 2° × 2° land pixel. (d) The zonal-mean variance-scaled ChIP associated with these events. (e) The 2022 seasonal cycle of 2 monthly mean maximum (red lines) and minimum (blue lines) temperature anomalies (dashed lines) and the zonal-mean variance-scaled ChIP levels across these 2-month events (solid lines).
Citation: Bulletin of the American Meteorological Society 105, 7; 10.1175/BAMS-D-23-0264.1
To find events similar to the WWA event definition, we search for places and periods around the world where the rolling 2 monthly average temperatures in 2022 were ranked in the top two since 1991. The mapped number of monthly pair events meeting these criteria (out of 12) shows many places globally where persistent heat stretched across multiple months. The most prominent hotspots include south-central United States, western Europe, Mediterranean coasts, central and eastern Africa, most of China, northern Australia, and Papua New Guinea. The
We also examined estimates of attributable Tmin over India/Pakistan. Despite cooler anomalies overall, regionally averaged
4. Discussion
A hindcast attributing daily 2022 temperatures to human-caused climate change shows that the WWA definitions of short- (2-day) and long-lived (2-month) extreme temperature events were both relatively common across the globe and highly attributable. Using WWA event definitions, this study demonstrates good agreement between WWA attribution estimates and the Gilford et al. (2022) automated attribution system over two distinct extreme heat events: a 2-day event over the United Kingdom (July 2022) and a 2-month-long event over India/Pakistan (March/April 2022). While the framework’s conservative design often underestimates the climate influence compared with WWA’s numbers, we find the approach is capable of rapidly identifying and confidently attributing these events. It has also been extended to evaluate similar events on a daily, global basis and can serve as an early warning system to support immediate climate change communications.
There are clear and robust human fingerprints on 2022’s daily weather. For instance, our results expose the powerful emergence of human influence on overnight temperatures, a well-known (but often undercommunicated and understudied) result of climate change with potentially critical impacts on global health and economics (Roye et al. 2021; Wang et al. 2022; Kim et al. 2023; He et al. 2022). While a thorough examination of the negative impacts associated with these events is beyond our scope, multiple lines of early evidence indicate that widespread attributable heat had human consequences during 2022 (e.g., Ballester et al. 2023; Tobias et al. 2023). Our analyses reveal that there are still many outstanding opportunities to study and communicate attributable temperature events throughout the world each year.
Acknowledgments.
Funding was provided by the Bezos Earth Fund, The Schmidt Family Foundation, High Meadows Foundation, and the William and Flora Hewlett Foundation.
Data availability statement.
Hindcast data are available in a Zenodo repository at https://doi.org/10.5281/zenodo.12667013.
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