Human Fingerprints on Daily Temperatures in 2022

Daniel M. Gilford Climate Central, Princeton, New Jersey;

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Andrew J. Pershing Climate Central, Princeton, New Jersey;

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Joseph Giguere Climate Central, Princeton, New Jersey;

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Friederike E. L. Otto Grantham Institute of Climate Change, Imperial College London, London, United Kingdom

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© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: D. M. Gilford, dgilford@climatecentral.org

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: D. M. Gilford, dgilford@climatecentral.org

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

We quantify the attributable climate influence on observed daily and multiday temperatures with a metric called the “Change in Information due to Perspective” (ChIP) based on the definition of Shannon information content from information theory (MacKay 2003; Giguere et al. 2024). ChIP compares the occurrence likelihood of daily temperature T in the modern climate (Pmod; +1.27 K global mean air temperature since preindustrial) with that from a counterfactual climate without anthropogenic forcing (Pcf; +0 K):
ChIP(T)log2[Pmod(T)/Pcf(T)].

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.

To derive variance-scaled ChIP, we assume temperatures are normally distributed, and the likelihood of T is given by PN(T,μ,σ), with mean μ and standard deviation σ. The attributable change in likelihood between modern and counterfactual periods can then be described by a change in the mean, μ + δ, where δ is linearly related to attributable global mean temperature (GMT) changes in the framework’s median method in the online supplemental material. Rewriting Eq. (1),
ChIP(T)log2 [Nmod(T,μ+δ, σ)/ Ncf(T,μ, σ)],
δ2ln(2)σ2(2μ+δ2T).
Assuming μ, δ, and daily σ are representative over an n-day period, then the ChIP of n-day average temperatures [T¯=(1/n)j=1nTj] is
ChIPn(T¯)=(σ2σn2)ChIP¯(Tj),
where σn is the standard deviation of the n-day means. The resulting variance-scaled ChIP, ChIPn(T¯), quantifies climate change’s attributable influence on multiday average 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 ChIPn(T¯) around the world. The multimethod approach uses observed trends from ERA5 (Hersbach et al. 2020) and climate simulations from CMIP6 (Eyring et al. 2016) to generate an ensemble of modern and counterfactual distributions. For each observed daily 2-m maximum temperature Tmax, average temperature Tavg, and minimum air temperature Tmin, we calculate empirical- and model-derived Pmod and Pcf, which are synthesized to produce a ChIP for each daily temperature observation in 2022.

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.

Fig. 1.
Fig. 1.

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 |ChiP|4 on each method’s output, so the maximum equivalent PR is 16 (if the empirical- and model-based methods both reach this maximum). Altogether, while ChIP values are often a conservative underestimate, results agree with WWA that human-caused climate change made the U.K. event much more likely. Note that daily ChIP average standard errors—estimated from the spread of CMIP6 simulations and regression uncertainties between local temperatures and GMT (supplemental material)—are <0.5 on 0.3% of days/locations in 2022 (not shown); for example, the 40°S–60°N mean standard error during 17–18 July was 0.22.

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 ChIPn(T¯) reached 16.0 along India’s northwest coastal region and ChIPn(T¯) 5 stretched into the interior during the event. The ChIPn(T¯)=16 implies that the 2-month average temperature was made 65 536 × more likely because of climate change. Region-average equivalent PRs show that these event anomalies were 2(3.1) = 8.6× more likely because of human-caused climate change, lower than the average but falling within the range of WWA PR estimates, 30× (2–140×). Despite cooler anomalies during the remainder of 2022, 2-month-average Tmax was robustly attributable throughout the year; this result implies that the signal of climate change in India/Pakistan 2-month-mean temperatures has effectively emerged from the baseline climate.

Fig. 2.
Fig. 2.

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 ChIPn(T¯) estimates indicate these events are strongly attributable, consistently averaging ≥4.0.

We also examined estimates of attributable Tmin over India/Pakistan. Despite cooler anomalies overall, regionally averaged ChIPn(T¯) estimates of 2 monthly Tmin are reliably larger than those of Tmax (except in January/February), with a regional average of 7.0 in March/April (i.e., made 128× more likely by climate change). In September/October, cooler overall Tmin values had attribution estimates of equivalent PR > 18 000×, consistent with climate change’s strong overnight influence (Karl et al. 1993; Doan et al. 2022).

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.

References

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    • Search Google Scholar
    • Export Citation
  • Clarke, B., F. Otto, R. Stuart-Smith, and L. Harrington, 2022: Extreme weather impacts of climate change: An attribution perspective. Environ. Res., 1, 012001, https://doi.org/10.1088/2752-5295/ac6e7d.

    • Search Google Scholar
    • Export Citation
  • Doan, Q. V., F. Chen, Y. Asano, Y. Gu, A. Nishi, H. Kusaka, and D. Niyogi, 2022: Causes for asymmetric warming of sub-diurnal temperature responding to global warming. Geophys. Res. Lett., 49, e2022GL100029, https://doi.org/10.1029/2022GL100029.

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  • EM-DAT, 2008: EM-DAT: The international disaster database. Accessed 11 April 2023, http://www.emdat.be/Database/Trends/trends.html.

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    • Search Google Scholar
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    • Search Google Scholar
    • Export Citation
  • Gilford, D. M., A. Pershing, B. H. Strauss, K. Haustein, and F. E. L. Otto, 2022: A multi-method framework for global real-time climate attribution. Adv. Stat. Climatol. Meteor. Oceanogr., 8, 135154, https://doi.org/10.5194/ascmo-8-135-2022.

    • Search Google Scholar
    • Export Citation
  • He, C., and Coauthors, 2022: The effects of night-time warming on mortality burden under future climate change scenarios: A modelling study. Lancet Planet. Health, 6, e648e657, https://doi.org/10.1016/S2542-5196(22)00139-5.

    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

    • Search Google Scholar
    • Export Citation
  • IPCC, 2021: Climate Change 2021: The Physical Science Basis. V. Masson-Delmotte et al., Eds., Cambridge University Press, 3949 pp., https://www.ipcc.ch/report/sixth-assessment-report-working-group-i/.

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  • Karl, T. R., and Coauthors, 1993: Asymmetric trends of daily maximum and minimum temperature. Bull. Amer. Meteor. Soc., 74, 10071023.

    • Search Google Scholar
    • Export Citation
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    • Search Google Scholar
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  • MacKay, D. J. C., 2003: Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 628 pp.

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    • Search Google Scholar
    • Export Citation
  • Otto, F. E. L., and E. Raju, 2023: Harbingers of decades of unnatural disasters. Commun. Earth Environ., 4, 280, https://doi.org/10.1038/s43247-023-00943-x.

    • Search Google Scholar
    • Export Citation
  • Philip, S., and Coauthors, 2020: A protocol for probabilistic extreme event attribution analyses. Adv. Stat. Climatol. Meteor. Oceanogr., 6, 177203, https://doi.org/10.5194/ascmo-6-177-2020.

    • Search Google Scholar
    • Export Citation
  • Roye, D., and Coauthors, 2021: Effects of hot nights on mortality in southern Europe. Epidemiology, 32, 487498, https://doi.org/10.1097/EDE.0000000000001359.

    • Search Google Scholar
    • Export Citation
  • Swain, D. L., D. Singh, D. Touma, and N. S. Diffenbaugh, 2020: Attributing extreme events to climate change: A new frontier in a warming world. One Earth, 2, 522527, https://doi.org/10.1016/j.oneear.2020.05.011.

    • Search Google Scholar
    • Export Citation
  • Tobias, A., D. Roye, and C. Iniguez, 2023: Heat-attributable mortality in the summer of 2022 in Spain. Epidemiology, 34, e5e6, https://doi.org/10.1097/EDE.0000000000001583.

    • Search Google Scholar
    • Export Citation
  • van Oldenborgh, G. J., and Coauthors, 2021: Pathways and pitfalls in extreme event attribution. Climatic Change, 166, 13, https://doi.org/10.1007/s10584-021-03071-7.

    • Search Google Scholar
    • Export Citation
  • Wang, Y., X. Shen, M. Jiang, S. Tong, and X. Lu, 2022: Daytime and nighttime temperatures exert different effects on vegetation net primary productivity of marshes in the western Songnen plain. Ecol. Indic., 137, 108789, https://doi.org/10.1016/j.ecolind.2022.108789.

    • Search Google Scholar
    • Export Citation
  • World Weather Attribution Initiative, 2023: Heatwave reports. Accessed 17 August 2023, https://www.worldweatherattribution.org/analysis/heatwave/.

Supplementary Materials

Save
  • Ballester, J., and Coauthors, 2023: Heat-related mortality in Europe during the summer of 2022. Nat. Med., 29, 18571866, https://doi.org/10.1038/s41591-023-02419-z.

    • Search Google Scholar
    • Export Citation
  • Clarke, B., F. Otto, R. Stuart-Smith, and L. Harrington, 2022: Extreme weather impacts of climate change: An attribution perspective. Environ. Res., 1, 012001, https://doi.org/10.1088/2752-5295/ac6e7d.

    • Search Google Scholar
    • Export Citation
  • Doan, Q. V., F. Chen, Y. Asano, Y. Gu, A. Nishi, H. Kusaka, and D. Niyogi, 2022: Causes for asymmetric warming of sub-diurnal temperature responding to global warming. Geophys. Res. Lett., 49, e2022GL100029, https://doi.org/10.1029/2022GL100029.

    • Search Google Scholar
    • Export Citation
  • EM-DAT, 2008: EM-DAT: The international disaster database. Accessed 11 April 2023, http://www.emdat.be/Database/Trends/trends.html.

  • Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, 2016: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 19371958, https://doi.org/10.5194/gmd-9-1937-2016.

    • Search Google Scholar
    • Export Citation
  • Giguere, J., D. M. Gilford, and A. J. Pershing, 2024: Attributing daily ocean temperatures to anthropogenic climate change. Environ. Res., 3, 035003, https://doi.org/10.1088/2752-5295/ad4815.

    • Search Google Scholar
    • Export Citation
  • Gilford, D. M., A. Pershing, B. H. Strauss, K. Haustein, and F. E. L. Otto, 2022: A multi-method framework for global real-time climate attribution. Adv. Stat. Climatol. Meteor. Oceanogr., 8, 135154, https://doi.org/10.5194/ascmo-8-135-2022.

    • Search Google Scholar
    • Export Citation
  • He, C., and Coauthors, 2022: The effects of night-time warming on mortality burden under future climate change scenarios: A modelling study. Lancet Planet. Health, 6, e648e657, https://doi.org/10.1016/S2542-5196(22)00139-5.

    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

    • Search Google Scholar
    • Export Citation
  • IPCC, 2021: Climate Change 2021: The Physical Science Basis. V. Masson-Delmotte et al., Eds., Cambridge University Press, 3949 pp., https://www.ipcc.ch/report/sixth-assessment-report-working-group-i/.

    • Search Google Scholar
    • Export Citation
  • Karl, T. R., and Coauthors, 1993: Asymmetric trends of daily maximum and minimum temperature. Bull. Amer. Meteor. Soc., 74, 10071023.

    • Search Google Scholar
    • Export Citation
  • Kim, S. E., M. Hashizume, B. Armstrong, A. Gasparrini, K. Oka, Y. Hijioka, A. M. Vicedo-Cabrera, and Y. Honda, 2023: Mortality risk of hot nights: A nationwide population-based retrospective study in Japan. Environ. Health Perspect., 131, 057005, https://doi.org/10.1289/EHP11444.

    • Search Google Scholar
    • Export Citation
  • MacKay, D. J. C., 2003: Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 628 pp.

  • National Academies of Sciences, Engineering, and Medicine, 2016: Attribution of Extreme Weather Events in the Context of Climate Change. The National Academies Press, 186 pp., https://doi.org/10.17226/21852.

    • Search Google Scholar
    • Export Citation
  • Otto, F. E. L., and E. Raju, 2023: Harbingers of decades of unnatural disasters. Commun. Earth Environ., 4, 280, https://doi.org/10.1038/s43247-023-00943-x.

    • Search Google Scholar
    • Export Citation
  • Philip, S., and Coauthors, 2020: A protocol for probabilistic extreme event attribution analyses. Adv. Stat. Climatol. Meteor. Oceanogr., 6, 177203, https://doi.org/10.5194/ascmo-6-177-2020.

    • Search Google Scholar
    • Export Citation
  • Roye, D., and Coauthors, 2021: Effects of hot nights on mortality in southern Europe. Epidemiology, 32, 487498, https://doi.org/10.1097/EDE.0000000000001359.

    • Search Google Scholar
    • Export Citation
  • Swain, D. L., D. Singh, D. Touma, and N. S. Diffenbaugh, 2020: Attributing extreme events to climate change: A new frontier in a warming world. One Earth, 2, 522527, https://doi.org/10.1016/j.oneear.2020.05.011.

    • Search Google Scholar
    • Export Citation
  • Tobias, A., D. Roye, and C. Iniguez, 2023: Heat-attributable mortality in the summer of 2022 in Spain. Epidemiology, 34, e5e6, https://doi.org/10.1097/EDE.0000000000001583.

    • Search Google Scholar
    • Export Citation
  • van Oldenborgh, G. J., and Coauthors, 2021: Pathways and pitfalls in extreme event attribution. Climatic Change, 166, 13, https://doi.org/10.1007/s10584-021-03071-7.

    • Search Google Scholar
    • Export Citation
  • Wang, Y., X. Shen, M. Jiang, S. Tong, and X. Lu, 2022: Daytime and nighttime temperatures exert different effects on vegetation net primary productivity of marshes in the western Songnen plain. Ecol. Indic., 137, 108789, https://doi.org/10.1016/j.ecolind.2022.108789.

    • Search Google Scholar
    • Export Citation
  • World Weather Attribution Initiative, 2023: Heatwave reports. Accessed 17 August 2023, https://www.worldweatherattribution.org/analysis/heatwave/.

  • Fig. 1.

    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.

  • Fig. 2.

    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).

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