Title: Detectable global temperature responses to wildfires and volcanic eruptions
Author: Richard Delahaye (LLE - Staff)

Pinatubo (16-month mean)	Australian Fires (4-month mean) Hunga Tonga (20-month mean)

PNAS
RESEARCH ARTICLE
EARTH, ATMOSPHERIC, AND PLANETARY SCIENCES

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Significance
Major volcanic eruptions and wildfires provide natural experiments for testing how Earth's atmosphere responds to sudden disturbances. While the tropospheric cooling and stratospheric warming effects of sulfate particles from the 1991 Pinatubo eruption are well established, the climate impacts of wildfire smoke and volcanic water vapor have been far less clear. Here, we show that the 2019-2020 Australian wildfires and the 2022 Hunga Tonga eruption also produced statistically detectable global atmospheric temperature signals. Using a robust detection framework, we evaluate whether, when, and for how long these signals can be distinguished from internal climate variability ("noise"). Our results highlight emerging forcings that must be included in climate models to better interpret past variability and anticipate impacts of future perturbations.

Author contributions: Y.L., B.D.S., and S.S. designed research; Y.L. performed research; Y.L. contributed new reagents/analytic tools; Y.L., B.D.S., S.S., D.W.J.T., and Q.F. analyzed data; and Y.L., B.D.S., S.S., D.W.J.T., and Q.F. wrote the paper.
The authors declare no competing interest.
This article is a PNAS Direct Submission.
Copyright © 2026 the Author(s). Published by PNAS. This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
To whom correspondence may be addressed, Email: yaoweili@mit.edu.
This article contains supporting information online at https://www.pnas.0rg/l00kup/suppl/d0i:l 0.1073/pnas. 2525500123/-/DCSupplemental.
Published February 23, 2026.

Detectable global temperature responses to wildfires and volcanic eruptions
Yaowei Li^a''C, Benjamin D. Santerb 0, Susan Solomon^3 D, David W.J. Thompson^, and Qiang Fu^d )
Affiliations are included on p. 8.
Edited by Mark Thiemens, University of California San Diego, La Jolla, CA; received September 12, 2025; accepted December 23, 2025
Large volcanic eruptions and intense wildfires perturb Earths atmospheric temperature. Understanding the climate response to such natural forcings is essential for obtaining reliable estimates of its response to anthropogenic greenhouse gas emissions. While the climate impacts of volcanic sulfate aerosols are well documented, other natural forcings—including wildfire smoke reaching the stratosphere and water vapor injections from a submarine eruption—pose new challenges for detecting and attributing their atmospheric temperature impacts. Here, we demonstrate robust detection of statistically significant temperature anomalies in the troposphere and stratosphere using multidecadal satellite observations and internal variability estimates from a climate model ensemble and from observations. We analyze three landmark events: the 1991 Pinatubo eruption, the 2019-2020 Australian wildfires, and the 2022 Hunga Tonga eruption. Each leaves a fingerprint with distinct altitudinal, geographical, and temporal structure. The global-mean stratospheric signal from Australian wildfires is detectable even in time averages extending beyond 10 mo, despite injecting only ~5% of Pinatubo’s aerosol mass. For Hunga Tonga, we detect significant and prolonged stratospheric cooling, but no robust tropospheric signal in the first 2 y. These findings show that both sulfate and nonsulfate stratospheric perturbations produce distinct, statistically identifiable global temperature signals. Accounting for such forcings in climate model simulations is therefore essential for improving comparisons of simulated and observed variability.
atmospheric temperature | climate forcing | satellite and model data | wildfires | volcanic eruptions
Major volcanic eruptions and intense wildfires inject large quantities of aerosol particles and gases into the atmosphere (1—5), disrupting radiative balances and circulation, and thereby altering atmospheric temperature profiles (6—9). These events serve as natural experiments for evaluating the climate system’s response to abrupt forcings (10). Volcanic eruptions have long been recognized as key drivers of climate variability (7, 11). The 1991 Pinatubo eruption (15°N) injected approximately 10 teragrams of sulfur dioxide into the stratosphere (12), where it oxidized to form sulfate aerosols, causing well-documented global cooling near the surface and warming in the lower stratosphere (13-17). By contrast, the 2019-2020 Australian wildfires (~34 to 38°S) generated intense pyroconvective activity that lofted about 1 teragram of smoke aerosols into the upper troposphere and lower stratosphere (18, 19). These smoke aerosols were largely confined to the Southern Hemisphere mid-latitudes, with partial transport into the tropics in the first several months of 2020. The smoke aerosols were strongly light-absorbing and radiatively distinct from volcanically generated sulfate (20, 21). This explains why lower stratospheric warming was reported after the Australian fires (9, 22-24) despite the fact that they injected substantially less material than Pinatubo. More recently, the 2022 Hunga Tonga eruption (21°S) injected both sulfate aerosol precursors and an estimated 150 teragrams of water vapor into the stratosphere (2, 25). The water vapor injection was unprecedented in the satellite era and dispersed globally within a year, causing persistent stratospheric cooling through both radiative and dynamical effects (8, 26—32), and raising questions about potential indirect tropospheric impacts (33).
Although there is growing evidence of the importance of volcanic and wildfire forcings, few studies have quantitatively assessed the detectability of their temperature signals relative to internal variability, or have analyzed the vertical structure of their signals across the troposphere and stratosphere (6, 34). Variations in radiative properties of the injected material, injection altitudes, and the latitudinal location of the injection site yield vertically and meridionally heterogeneous signals. This spatial heterogeneity in the signals is partly obscured in observed temperature records (35—38) by natural variability from the El Nino-Southern Oscillation (ENSO), the Quasi-Biennial Oscillation (QBO), and

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Fig. 1. Global-mean observed temperature anomalies across six different atmospheric layers. Monthly anomalies are shown relative to the 1986-2024 climatology, derived from gap-free satellite observations (MSU and SSU records). Panels (A-F) display results for SSU3, SSU2, SSU1, MSU TLS, MSU ΤΤΤ, and MSU TLT channels, respectively. Approximate altitude ranges of the weighting functions for these layers are given in brackets. Green lines show raw anomalies; black lines show residual anomalies after removing the linear trend and the influences of ENSO, the solar cycle, and the QBO using multiple linear regression. Vertical lines mark the onset of three major events: the 1991 Pinatubo eruption (orange, 15 June 1991; labeled PIN), the 2019-2020 Australian wildfires (red, around 01 January 2020; labeled ANY), and the 2022 Hunga Tonga eruption (blue, 15 January 2022; labeled ΗΤ).

solar activity, necessitating rigorous statistical treatment for signal identification.
To address these challenges, we develop a robust detection framework that estimates temperature signals and internal variability across vertical, meridional, and temporal dimensions. This method relies on multidecadal satellite observations and internal variability estimates from simulations in the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble (39) and from observations. By removing confounding influences (the long-term trend, ENSO, solar irradiance changes, and QBO effects) from the satellite data and comparing observed residual anomalies to model and observational estimates of internal variability, we assess the statistical significance of the temperature impact of three specific perturbations: the 1991 Pinatubo eruption, the 2019-2020 Australian wildfires, and the 2022 Hunga Tonga eruption. This assessment is performed on timescales ranging from 1 mo to 3 y. It facilitates consistent cross-event comparison of signal properties and improved attribution of recent atmospheric temperature anomalies (40).

Results
Global-Mean Temperature Responses. We examine global-mean temperature anomalies from 1986 to 2024 across six atmospheric layers using observations from satellite-based microwave sounders (Fig. 1), including the Stratospheric Sounding Unit (SSU) and the Microwave and Advanced Microwave Sounding Units (MSU) (41, 42). SSU channels 1, 2, and 3 cover the middle-to-upper stratosphere (approximately 25 to 50 km), while MSU provides lower stratospheric temperature (TLS, approximately 15 to 20 km), total tropospheric temperature (ΤΤΤ, approximately 0 to 13 km), and lower tropospheric temperature (TLT, approximately 0 to 8 km) (37, 43, 44). The MSU data used here are the ensemble mean of three available datasets, which show close agreement in both high- and low-frequency variability (SI Appendix, Fig. S1).
Over this 39-y period, raw anomalies reveal pronounced stratospheric cooling and tropospheric warming, consistent with expected long-term responses to greenhouse gas increases and time-evolving changes in anthropogenic aerosol emissions and

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ozone-depleting substances (38, 45—48). In the troposphere, raw anomalies also reflect ENSO influences (35, 36), notably during the major El Νΐήο events of 1997-98, 2015-16, and 2023-24, all of which contributed to marked tropospheric warming during these periods. The three SSU layers reveal stratospheric temperature modulation by the roughly 11-y solar cycle (37, 49). Superimposed on these long-term trends and ENSO, QBO, and solar-induced variability are distinct temperature anomalies after the Pinatubo eruption (June 1991), the Australian wildfires (December 2019-January 2020), and the Hunga Tonga eruption (January 2022).
To isolate the anomalies associated with these three events, we first remove the linear trend and the influences of ENSO, solar variability, and the QBO using multiple linear regression. We explore lags of 0 to 12 mo between the ENSO, solar, and QBO predictor variables and the temperature responses in each of our six atmospheric layers. We test various combinations of commonly used ENSO indices (such as Nino 3 and 3.4 sea surface temperatures) and solar indices, including the Total Solar Irradiance (TSI) and the solar radio flux at 10.7 cm (F10.7). F10.7 is a good proxy for solar ultraviolet emissions and explains more temperature variance in the stratosphere than TSI (SI Appendix, Fig. S2), likely due to the ultraviolet sensitivity of stratospheric ozone. We find that the combination of Νίήο 3.4 and Fl0.7 predictor variables, when each is used with the lag that maximizes the correlation with the temperature anomalies for a given layer, yields the highest correlations (SI Appendix, Fig. S2). SI Appendix, Fig. S3 shows the regression-based estimates of the ENSO, solar, and QBO signals, confirming that solar variability dominates in the stratosphere while ENSO influence dominates in the troposphere. The QBO plays a relatively small role in global-mean temperature variability, but a larger role in the tropical and subtropical stratosphere (SI Appendix, Fig. S4).
The residual anomalies (Fig. 1, black curves) reveal clearer event-related signals than the raw data. The 1991 Pinatubo eruption induced sharp and sustained warming in the stratosphere, lasting over 2 y, with maximum global-mean anomalies exceeding +1.5 °C in TLS and +0.8 °C in SSU3. Pinatubo also drove persistent cooling in the troposphere. Despite injecting only about 5% of the aerosol mass of Pinatubo (13, 19), the 2019-2020 Australian wildfires produced about half of Pinatubo’s peak TLS warming (~0.8 °C). This peak-response comparison implies up to an order of magnitude higher heating efficiency for carbonaceous smoke than for sulfate aerosol in the lower stratosphere, consistent with the strong light-absorbing content of smoke aerosols (50). The smoke aerosols absorb strongly in the solar shortwave spectrum, whereas volcanic sulfate primarily scatters visible light and absorbs in the thermal infrared. However, the approximately 4-mo TLS warming anomaly associated with the wildfires was much shorter in duration than the Pinatubo TLS anomaly, mainly due to the reduced aerosol loading, lower injection altitude, and higher-latitude injection (19). SSU1 also shows warming after the Australian wildfires, but warming is smaller than after Pinatubo. In contrast, the 2022 Hunga Tonga eruption was associated with cooling in the stratosphere, especially in the SSU layers, with the response persisting for over 2 y. This is consistent with the substantial stratospheric water vapor injection from Hunga Tonga (2, 28—30).
Robust Signal Detection against Variability. To assess whether observed global-mean temperature signals significantly exceed internal variability (“noise”), we calculate signal-to-noise (S/N) ratios for each of the three events and each of the six broad atmospheric layers (Fig. 2). Ratios are a function of averaging window length. Signals are defined as the average of the monthlymean residual anomalies over specific event windows. Noise is estimated as the SD of mean anomalies over windows matching the event window length, using information from three different sources: 1) CMIP6 preindustrial control runs (CTL); 2) CMIP6 extended historical simulations (HISTJ; and 3) satellite observations excluding the three event periods (Materials and Methods) . Points outside the gray shading in Fig. 2 indicate signals that exceed the 5% significance threshold, defined as |S/N| > 1.96.
Our results reveal distinct vertical and temporal signatures of detectability for each event considered. Using CTL-based noise estimates, the stratospheric warming signals from the Pinatubo eruption differ significantly from internal variability (at the 5% level) in all stratospheric layers, and for all averaging windows from 1 to 36 mo. The Pinatubo-induced tropospheric cooling signal first becomes significant for a 13-mo averaging window and remains significant for all longer averaging windows up to 3 y after the event. The markedly longer “time to significance” for Pinatubo’s tropospheric cooling signal, relative to its stratospheric warming signal, arises primarily from the large thermal inertia of the ocean (35, 51). Warming signals from the Australian wildfires are also detectable early in TLS and SSU1, with the strongest and most persistent detectability (on timescales of 1 to 10 mo) in TLS, consistent with the fact that most of the smoke aerosol burden from the wildfires was in the lower stratosphere (22, 24). In contrast, the global-mean wildfire signals in the troposphere and upper stratosphere remain indistinguishable from internal variability.
Stratospheric cooling from the Hunga Tonga eruption is initially masked by short-term variability but becomes statistically significant in all SSU layers for averaging windows ranging from 10 to 35 mo. There are no significant tropospheric temperature anomalies after Hunga Tonga over timescales of at least 2 y, likely due to the competing radiative effects of stratospheric water vapor, ozone, and sulfate aerosol changes (27, 52). The tropospheric warming signals approach significance after 2 y, perhaps due to factors unrelated to Hunga Tonga, including the still-uncertain contributions to the exceptional global surface warming in 2023-2024 from factors other than ENSO and solar variability (53, 54).
Fig. 2 also shows S/N results based on HISText and observational noise estimates. Regardless of whether CTL, HISTex) or observed variability is used, signals from the Pinatubo, Australian wildfires, and Hunga Tonga events that are significant relative to CTL-based noise remain significant against noise from both HISText and observations. However, the onset and duration of detectable signals vary depending on the choice of noise estimate. The reasons for these differences are discussed in the next section.
Removing the influence of ENSO, solar variability, and the QBO is essential for volcano and wildfire signal identification. If the same statistical analysis is performed on the raw temperature anomaly data (SI Appendix, Fig. S5), key responses—such as tropospheric cooling from Pinatubo and stratospheric cooling from Hunga Tonga—are either masked or substantially underestimated. Conversely, the post-Pinatubo stratospheric warming signal is in phase with the transition to solar maximum, thus biasing the estimated significance of Pinatubo’s stratospheric warming signal in raw data (SI Appendix, Figs. S3 and S4).
Signals and Internal Variability Distributions. It is instructive to compare the event signals with the modeled and observed variability distributions on which the S/N results in Fig. 2 are based. Fig. 3 shows these signals and variability distributions for each of the three events and for each of the six atmospheric layers of interest, but for a specific timescale only. The timescales chosen are postevent averaging windows of 4 mo for the Australian wildfires, 16 mo for Pinatubo, and 20 mo for Hunga Tonga. These timescales highlight periods of strong signal detectability (Fig. 2)

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Averaging window length (months)

while ensuring adequate sample sizes for estimating probability distributions of internal variability. The distributions from the CMIP6 CTL runs represent “pure” internal variability, while the HISText runs and observations (excluding the three event periods) provide complementary estimates that do not represent internal variability alone (see below).
Stratospheric warming from Pinatubo lies well outside all modeled and observed variability distributions, with the signal magnitude decreasing with increasing altitude in the stratosphere. The Pinatubo-induced tropospheric cooling is also significant at the 5% level. This canonical vertical fingerprint is generally consistent with previous attribution studies (6, 14, 17, 47). The Australian wildfires produce significant warming in TLS and SSU1, but not in the upper stratosphere or troposphere. This suggests that the wildfire smoke, while more localized and vertically confined [with typical altitudes up to ~22 km and an isolated smoke vortex reaching —35 km, compared to the globally extensive 16 to over 30 km sulfate layer from Pinatubo (13, 18)], can still exert a globally detectable stratospheric forcing. Cooling from Hunga Tonga in the mid-to-upper stratosphere exceeds modeled variability, consistent with the global dispersion of high-altitude water vapor (2, 29). In contrast, the tropospheric and lower stratospheric signals from Hunga Tonga remain within modeled and observed variability distributions.
Fig. 3 also indicates that the internal variability of global-mean temperatures in CMIP6 models is much smaller in the

Fig. 2. S/N characteristics and detectability of global-mean atmospheric temperature responses. S/N ratios are shown as a function of averaging window length for the three major events considered. Panels (A-F) display results for SSU3, SSU2, SSU1, MSU TLS, MSU ΤΤΤ, and MSU TLT, respectively. Colored lines represent different events: Pinatubo (orange, labeled PIN), Australian wildfires (red, labeled ANY), and Hunga Tonga (blue, labeled ΗΤ). Solid lines indicate S/N ratios based on noise estimated from CMIP6 CTL runs; dashed lines indicate noise estimated from CMIP6 HISText runs, and dotted lines indicate noise estimated from observations. The gray shading is bounded by horizontal dashed lines at ±1.96 and encompasses results that do not achieve significance at the 5% level. Colored shading indicates the range between the three noise-estimate curves for each event where the signals exceed the 5% significance threshold. For ANY, averaging ends at a window length of 24 mo to avoid overlapping with the ΗΤ period; for HT, averaging is limited to 35 mo due to the end of available observations in December 2024.

stratosphere than in the troposphere, which enhances the event detectability in the stratosphere (43, 47). Across all layers, the variability estimated from HISText runs is greater than that from the CTL simulations. This increase is expected because the HIST x-based variability estimates, calculated by subtracting the multimodel mean time series from each individual realization, reflect not only pure internal variability (as in CTL) but also intermodel differences in response to the imposed external forcings (43). In the stratosphere, observed variability (after removing the three event periods, the linear trend, and solar effects) is also consistently larger than the internal variability estimated from CMIP6 CTL runs. This likely reflects residual externally forced variability in the observations, for example, from nonlinear ozone changes (55) and numerous small volcanic events that were not excluded from the period used to estimate observed internal variability (34). Biases in model simulated variability may also contribute to the differences between simulated and observed variability.
Latitude-Altitude Temperature Patterns. The latitude-altitude structure of temperature responses provides further insight into the processes and climate impacts of each of the three perturbation events. Fig. 4 shows latitude—altitude cross-sections of signal, noise, and S/N ratios for Pinatubo, the Australian wildfires, and Hunga Tonga. Results are for the same postevent averaging windows used in Fig. 3, with hatching in the S/N panels indicating

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Fig. 3. Event temperature signals relative to distributions of modeled and observed internal variability anomalies. Panels in the Left (A-F), Middle (G-L), and Right (M-R) columns correspond to results for 16-mo, 4-mo, and 20-mo averaging windows following the Pinatubo eruption, Australian wildfires, and Hunga Tonga eruption, respectively. Rows show, from top to bottom, results for SSU3, SSU2, SSU1, MSU TLS, MSU ΤΤΤ, and MSU TLT layers. Vertical colored lines indicate the observed residual anomalies (signals) for each event: Pinatubo (orange, labeled PIN), Australian wildfires (red, labeled ANY), and Hunga Tonga (blue, labeled HT). Histograms represent the distributions of variability estimated from CMIP6 CTL runs (gray), the CMIP6 HISText runs (green), and the observations (purple). In the latter case, the event periods are excluded {Materials and Methods). Solid black curves show Gaussian fits to the CTL-derived distributions, and dashed black lines mark the ±1.96 SD thresholds from the CTL runs, corresponding to the 5% significance level.

regions where the signal exceeds the 5% significance level. The Pinatubo-induced stratospheric warming and tropospheric cooling exhibit broad latitudinal symmetry spanning both hemispheres, with significant S/N ratios in the tropics and midlatitudes across all six layers (Fig. 4 A-C). This widespread, coherent response reflects the efficient global dispersion and long residence time of the Pinatubo sulfate aerosols in the stratosphere. Using the 16- mo mean, Fig. 4C shows statistically significant warming in the SSU layers near ~60°N, a feature not previously identified (16).
In contrast, the anomalies following the Australian wildfires (Fig. 4 D-F) are spatially asymmetric. In the Southern Hemisphere (SH), where most of the smoke was concentrated (19, 24), there is prominent TLS warming between 10°S and 60°S and significant SSU1 warming in the tropics. The significant SH tropical warming is consistent with smoke shortwave absorption, which is potentially enhanced by the higher solar illumination at low latitudes, even though smoke concentrations were lower there than at mid-latitudes (24). In the Northern Hemisphere (NH), a “see-saw” pattern between low and high latitudes may be due to large-scale dynamical anomalies (56) linked to an unusually strong Arctic vortex in early 2020 (57) (SIAppendix, Fig. S6). These NH anomalies likely arose independently of the fires (23), although one study suggests that SH smoke may have triggered rapid global circulation adjustments contributing to NH midlatitude stratospheric warming (58), while another finds that NH high-latitude cooling from a strong Arctic vortex could have induced some warming in the SH subtropical lower stratosphere (59). Significant tropospheric warming near 60°S coincides with SH heatwave events in early 2020 (60), while NH midlatitude warming in the troposphere is likely associated with internal variability due to variations in the atmospheric flow (61). Note that although some statistically significant responses in the height-latitude domain (Fig. 4F) may reflect substantial internal variability, the influence of internal variability—particularly in the stratosphere—is greatly reduced in the global-mean temperature signals (Figs. 2 and 3).
The anomalies following Hunga Tonga (Fig. 4 G-I) exhibit a more complex structure. Strong cooling dominates the mid-to-upper stratosphere in the tropics and SH midlatitudes, consistent with radiative cooling from enhanced high-altitude water vapor. In contrast, the lower stratosphere shows significant tropical warming, possibly resulting from a slowdown of the Brewer—Dobson circulation due to dynamical adjustment (8). Tropospheric temperature signals are weak and not statistically significant. Recent modeling work suggests that the Hunga Tonga eruption may influence surface climate indirectly through ENSO and dynamical pathways, though such signals remain difficult to detect observationally (33).

A PIN signal (16-month mean)

D ANY signal (4-month mean)

Ν

G HT signal (20-month mean)

1.75

Ν

Β CTL noise (16-month mean)

E CTL noise (4-month mean)

SSU3 -

SSU2 -

SSU1-

MSU TLS -

MSU TLT-

H CTL noise (20-month mean)

1.50

1.25 U

■1-00 Φ CO
-0.75o

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- 0.50

- 0.25

Fig. 4. Latitude-altitude patterns of signal, noise, and S/N ratios for three perturbation events. (A) Zonal-mean residual temperature anomalies (signals) following the Pinatubo eruption (labeled PIN), averaged over the 16 mo window used in Fig. 3. (S) SD of internal variability ("noise") estimated from CMIP6 CTL runs, using the same averaging window length as in the corresponding signal panel. (C) Corresponding S/N ratios for each layer and latitude band, with hatching indicating regions where the signal exceeds the 5% significance level (|S/N | > 1.96). (D-F) As in A-C, but for the Australian wildfires (labeled ANY), averaged over the 4 mo window. (G-/) As in A-C, but for the Hunga Tonga eruption (labeled ΗΤ), averaged over the 20 mo window.

For all three events, analyses using noise estimates from HISText or observations (SI Appendix, Fig. S7) yield S/N patterns similar to those obtained with CTL noise. The general agreement between zonal-mean noise estimates derived from observations and from model ensembles (Fig. 4 and SI Appendix, Fig. S7) demonstrates that our primary signal detection conclusions are relatively insensitive to the internal variability characterizations used in this study. Together, these latitude—altitude S/N patterns highlight the value of spatially resolved diagnostics and reveal how injection altitude, emission type, and geographic origin influence the atmospheric temperature response to extreme stratospheric aerosol and water vapor perturbations. While stratospheric temperatures exhibit considerable internal variability at individual latitudes due to dynamical processes, much of this variability cancels in the global mean, resulting in comparatively low noise levels when globally averaged.
Conclusions
Our results for Pinatubo confirm the classical fingerprint of volcanic sulfate forcing following large explosive eruptions—abrupt stratospheric warming accompanied by more gradually evolving tropospheric cooling (17, 35). We extend this understanding by quantifying the magnitude and detectability of Pinatubo’s temperature signals relative to internal variability, and by evaluating when and for how long these signals are detectable. We also demonstrate that perturbations from nonsulfate sources—such as the 2019-2020 Australian wildfire smoke and the 2022 Hunga Tonga water vapor injection—are identifiable with high statistical confidence in global atmospheric temperature records.
Despite substantial prior investigation of each of these events, particularly for Pinatubo, no previous study has provided a unified assessment of the statistical significance, duration, and vertical coherence of their temperature signals across the full troposphere and stratosphere at the global scale. Nor have previous studies considered the statistical significance of these signals relative to multiple independent estimates of internal variability. By jointly analyzing these radiatively distinct perturbations within a single detection framework, our study provides the first consistent comparison of how episodic natural forcings have influenced atmospheric temperature structure from the lower troposphere to the upper stratosphere over the past four decades.
Sulfate aerosols from tropical eruptions like Pinatubo generate long-lasting, spatially extensive stratospheric warming and tropospheric cooling due to their broad dispersion. High-altitude smoke plumes from wildfires, while more localized and typically lower in aerosol mass, can still produce globally detectable stratospheric warming because of the strong light-absorbing properties of smoke particles. In contrast, water vapor injections, such as from the 2022 Hunga Tonga eruption, lead to sustained cooling in the mid-to-upper stratosphere and warming in the lower stratosphere, driven by enhanced infrared emission and circulation adjustments. These established mechanisms provide the physical context for interpreting the statistically robust vertical and temporal patterns identified by our detection framework.

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As we show in this study, the reduction of noise by removing ENSO, solar variability, and QBO effects is critical for reliably estimating the temperature signals caused by Pinatubo, the Australian wildfires, and Hunga Tonga. Our findings underscore the need for climate models to realistically represent such perturbations in order to better understand their contributions to observed atmospheric temperature variability. Representation of these perturbations also facilitates direct comparisons between observed variability and the variability in model simulations, which generally neglect the atmospheric forcing from wildfires and Hunga Tonga. In addition, continued high-quality satellite measurements are essential for detecting future volcanic signals, and for monitoring likely increases in wildfire intensity under anthropogenic climate change (62). The detection framework developed here provides a foundation for future efforts to identify and interpret different drivers of atmospheric temperature change in a warming world.
Materials and Methods
Satellite Data. We use monthly-mean satellite temperature observations from the MSU (including both Microwave and Advanced Microwave Sounding Units) and SSU spanning January 1986 to December 2024, as provided by three groups: the Center for Satellite Applications and Research (STAR) (63,64), Remote Sensing Systems (RSS) (65), and the University of Alabama at Huntsville (UAH) (66). SSU temperature data for channels 1, 2, and 3 are taken from the STAR version 3.0 product, which is currently the only source of spatially resolved SSU observations (41). For MSU-derived layers, including TLT, ΤΤΤ, and TLS, we use the ensemble mean of three observational products: STAR version 5.0, RSS version 4.0, and UAH version 6.1. SI Appendix, Fig. S1 shows the close agreement in both high- and low-frequency variability of the three MSU datasets, indicating that event signals and observational estimates of internal variability are relatively insensitive to the choice of dataset. This agreement supports our use of the average of the three datasets in the main analysis. As ΤΤΤ is not directly provided by the groups, we compute it from the reported temperature of the mid-to-upper troposphere (TMT) using a standard regression-based adjustment to account for lower stratospheric cooling influences (67, 68). Raw temperature anomalies are calculated relative to the 1986-2024 monthly climatology.
Model Data. We use synthetic SSU and MSU temperatures from CMIP6 model simulations (39), generated by applying channel-specific vertical weighting functions (48) to model output to enable direct comparison with satellite observations. Three categories of CMIP6 simulations are analyzed: 1) historical runs that incorporate estimated natural and anthropogenic forcings from 1850 to 2014, 2) scenario runs with post-2014 changes in anthropogenic forcings that are specified according to a Shared Socioeconomic Pathway which reaches radiative forcing of 8.5 W/m^2 by 2,100 (SSP5-8.5), and 3) preindustrial control runs with constant external forcings to characterize internal variability (referred to as CTL runs). Historical and scenario simulations are concatenated to form extended historical runs (referred to as HISText runs), covering the 1986-2024 period selected for satellite observations. Following the approach of Santer et al. (43,47), we analyze nine HISText realizations from nine CMIP6 models, along with the CTL runs from the same nine models. Because the length of CTL runs vary, we use the last 450 y of each model's preindustrial control simulation to ensure that models with longer simulations do not have larger influence on multimodel internal variability estimates. Additional details on the construction of synthetic SSU and MSU temperatures can be found in Santer et al. (43, 47).
enso. Solar Cycle, and QBO Removal. We use multiple linear regression to remove the linear trend and the influences of ENSO, solar variability, and the QBO from the observed raw temperature anomalies. To account for temporal autocorrelation in the residuals, we apply generalized least squares regression with first-order autoregressive errors [GLSAR with AR(1)], which reduces bias in both the postevent means (i.e., signals) and the estimated residual variance compared to ordinary least squares.
To isolate forced signals in global-mean temperature anomalies, we test four combinations of widely used ENSO and solar indices—Νino 3 and Nino 3.4 for ENSO, and TSI and the 10.7 cm solar radio flux (F10.7) for solar variability (SI Appendix, Fig. S2). We test lags ranging from O to 12 mo.AII combinations include the QB030 index (zonal winds averaged over 5°N to 5“S at 30 hPa) with zero lag (69). We find that the combination of Nino 3.4 and F10.7, each with its optimal lag-defined as the lag that maximizes the correlation with global-mean temperature anomalies for a given atmospheric layer-yields the highest overall correlation across all six atmospheric layers. This combination is used in all subsequent regression-based analyses, including the zonal-mean analysis. To avoid biasing the regression by sampling the effects of the large event-driven temperature perturbations that are unrelated to changes in ENSO, the QBO, and solar forcing, we first exclude the postevent periods from the regression calculations: the 2.5 y after Pinatubo and Hunga Tonga and the 6 mo after the Australian wildfires. The regression-based estimates of ENSO, solar, and QBO contributions to global-mean temperature signals are shown in SI Appendix, Fig. S3. Detrending the raw data and removing the ENSO, solar, and QBO signals yields the residual anomalies shown in black in Fig. 1.
The same regression model is applied to zonal-mean temperature fields, using the same predictors and the respective optimal lags for each latitude band and atmospheric layer. The resulting zonal-mean residual anomalies are shown in SI Appendix, Fig. S6, and the reconstructed anomalies attributable to ENSO, solar, and QBO signals are shown in SI Appendix, Fig. S4.
Signal, Noise, and Variability Definition and Calculation. We define the signal as the mean of observed residual temperature anomalies over a specific postevent averaging window, with the window length L ranging from 1 to 36 mo for Pinatubo, 1 to 24 mo for the Australian wildfires, and 1 to 35 mo for Hunga Tonga. For each L-month window, the noise is estimated as the SD of the sampling distribution of nonoverlapping window means calculated from the CTL, HISText and observed variability time series.The noise is a measure of the expected spread of temperature anomalies arising from internal variability alone (in the case of the CTL simulations) or from internal variability and other sources (in the case of the observational and HISText variability estimates) (43). Signal-to-noise (S/N) ratios are then calculated for each atmospheric layer at both global-mean and zonal-mean scales. A key feature of the analysis is that S and Ν both vary as a function of the window length L.
The statistical significance of the temperature signal is determined by the S/N ratio. Statistical significance is assessed using a two-tailed test, with |S/N| > 1.96 corresponding to the 5% significance level. We use a two-tailed test to take a conservative approach, avoiding any a priori assumption about the sign of the temperature response to the selected events across different atmospheric layers and regions. While some responses (e.g., stratospheric warming after Pinatubo) are expected from physical reasoning, others may differ in sign depending on altitude, latitude, or event type; a two-tailed test ensures that both positive and negative anomalies are treated symmetrically in the detection framework.
We construct variability time series from three sources:
1.		CMIP6 control simulations (CTL): We use the last 450 y of the preindustrial control run from each of nine CMIP6 models and concatenate them to generate a single dataset representing "pure" internal variability under unforced conditions.
2.		CMIP6 extended historical simulations (HIST_ext): For the 1986-2024 period, we analyze nine realizations from the same nine CMIP6 models. To isolate internal variability, we subtract the multimodel mean from each individual realization. This removes the externally forced component common to all models, although some residual forced variability remains due to intermodel differences in the responses to external forcings (34). We exclude the 2.5 y following the Pinatubo eruption to avoid contamination from divergent model responses to large volcanic forcing. The resulting anomaly fields are concatenated to form a single dataset of internal variability under forced conditions. The CMIP6 HISText simulations were performed before the Australian wildfires and Hunga Tonga eruption occurred. Forcing from these events is not included in the CMIP6 HISText simulations.
3.		SSU and MSU satellite observations: Observed variability is derived from detrended, solar cycle-removed anomalies. We also exclude postevent periods: the 2.5 y after Pinatubo and Hunga Tonga and the 6 mo after the Australian wildfires. We retain natural internal modes such as ENSO and the QBO to maintain consistency with modeled CTL and HISText variability, which also include these modes.

Data, Materials, and Software Availability. All of the observational satellite temperature data (MSU and SSU) used in this study are publicly available at Remote Sensing Systems (https://www.remss.com/measurements/brightness- temperature/) (70), the University of Alabama at Huntsville (https://www.nsstc. uah.edu/data/msu/v6.0/) (71), and the Center for Satellite Applications and Research (https://www.star.nesdis.noaa.gov/smcd/emb/mscat/products.php) (72). The processed observational satellite temperature data and synthetic satellite temperature data from CMIP6 simulation output are available at Zenodo (https://doi.org/10.5281/zenodo.16899833) (73). The code used to generate all the main figures in this analysis is available at Zenodo (https:// doi.org/10.5281/zenodo.16899833)(73).
ACKNOWLEDGMENTS. Y.L. gratefully acknowledges support from the National Oceanic and Atmospheric Administration (NOAA) Climate & Global Change
Postdoctoral Fellowship Program under NOAA Science Collaboration Program Award NA23OAR4310383B.S.S. gratefully acknowledges grant AGS-2316980 from the NSF. Q.F. is supported by NSF grant AGS-2202812. D.W.J.T. is supported by the NASA under grant 80NSSC23K0113 and the NSF Climate and Large-Scale Dynamics Program under grant AGS-2116186. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output.
Author affiliations: Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139; School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, United Kingdom; Department of Atmospheric Sciences, Colorado State University, Fort Collins, CO 80521; and "Department of Atmospheric and Climate Science, University of Washington, Seattle, WA 98195

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